From 5bb750252e7ef6a7e7bddd852ea2a89475663fec Mon Sep 17 00:00:00 2001 From: crvernon Date: Thu, 21 Jan 2021 10:46:04 -0500 Subject: [PATCH 01/20] adding some refs that may be useful --- paper/paper.bib | 59 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 59 insertions(+) create mode 100644 paper/paper.bib diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 0000000..c4d18b3 --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,59 @@ +@article{voisin2013improved, + title={On an improved sub-regional water resources management representation for integration into earth system models.}, + author={Voisin, Nathalie and Li, Hongyi and Ward, D and Huang, Maoyi and Wigmosta, M and Leung, LR}, + journal={Hydrology \& Earth System Sciences}, + volume={17}, + number={9}, + year={2013} +} + +@article{hejazi201521st, + title={21st century United States emissions mitigation could increase water stress more than the climate change it is mitigating}, + author={Hejazi, Mohamad I and Voisin, Nathalie and Liu, Lu and Bramer, Lisa M and Fortin, Daniel C and Hathaway, John E and Huang, Maoyi and Kyle, Page and Leung, L Ruby and Li, Hong-Yi and others}, + journal={Proceedings of the National Academy of Sciences}, + volume={112}, + number={34}, + pages={10635--10640}, + year={2015}, + publisher={National Acad Sciences} +} + +@article{li2013physically, + title={A physically based runoff routing model for land surface and earth system models}, + author={Li, Hongyi and Wigmosta, Mark S and Wu, Huan and Huang, Maoyi and Ke, Yinghai and Coleman, Andr{\'e} M and Leung, L Ruby}, + journal={Journal of Hydrometeorology}, + volume={14}, + number={3}, + pages={808--828}, + year={2013} +} + +@article{li2015evaluating, + title={Evaluating global streamflow simulations by a physically based routing model coupled with the community land model}, + author={Li, Hong-Yi and Leung, L Ruby and Getirana, Augusto and Huang, Maoyi and Wu, Huan and Xu, Yubin and Guo, Jiali and Voisin, Nathalie}, + journal={Journal of Hydrometeorology}, + volume={16}, + number={2}, + pages={948--971}, + year={2015} +} + +@article{li2017understanding, + title={Understanding Water-Energy-Ecology Nexus from an Integrated Earth-Human System Perspective}, + author={Li, Hong-Yi and Zhang, Xiao and Wan, Wenhua and Zhuang, Yuan and Hejazi, Mohamad Issa and Leung, L Ruby}, + journal={AGUFM}, + volume={2017}, + pages={H41Q--05}, + year={2017} +} + +@article{voisin2013one, + title={One-way coupling of an integrated assessment model and a water resources model: evaluation and implications of future changes over the US Midwest}, + author={Voisin, Nathalie and Liu, Lu and Hejazi, M and Tesfa, T and Li, Hongyi and Huang, Maoyi and Liu, Ying and Leung, LR}, + journal={Hydrology and Earth System Sciences}, + volume={17}, + number={11}, + pages={4555}, + year={2013}, + publisher={Citeseer} +} From 9a44a91b9368b1d92ad2f0a431c67c139df76cec Mon Sep 17 00:00:00 2001 From: crvernon Date: Thu, 21 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zA$Y*M3gK|zZH#mPqu<)#sl<8!z$9CL#-$QmT~yxk{`?tPkDr4TxkMn9QN!i4^hrfNK8HNo7yzifqZY z{{RSe-_wxxz0sR1b-lvuh&vWbjCE%RnxbW${R(&V2ED)23hPvv@Zjt4``(6T4(t}KS3p>)YXWaI@lF@n)>Gnyp-y@nZ&u#m zV%+^wM-S_D5w03%-0!~vn2B8o`PV5kvCu&CTC=47hK=Hr{3qfLqigKRPQ)*iJn?9H zX#dr6_I=0+YN?+b3H2ys$}FpM^^^geglGYL-AL>gesJh%zZ}t4pDk{KC1NdmW`gVy zhDR5u-y;pF6SrD;`I3U6+7iXs0{utQ8UO$Q literal 0 HcmV?d00001 From 3c2ae51a3d8cebf485e6362be000c74ba0b76536 Mon Sep 17 00:00:00 2001 From: crvernon Date: Thu, 21 Jan 2021 10:57:33 -0500 Subject: [PATCH 03/20] initial paper blueprint --- paper/paper.md | 42 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 paper/paper.md diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 0000000..606a28b --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,42 @@ +--- +title: 'wolfgang: A Python implementaion of the MOSART-WM coupled hydrologic routing and water management model' +tags: + - Python + - Water management + - MultiSector Dynamics +authors: + - name: Travis Thurber + orcid: need your ORCID + affiliation: 1 + - name: Chris R. Vernon + orcid: 0000-0002-3406-6214 + affiliation: 1 +affiliations: + - name: Pacific Northwest National Laboratory, Richland, WA., USA + index: 1 +date: 20 January 2021 +bibliography: paper.bib +--- + +# Statement of need +This would describe why you build the Python implementaion of MOSART-WM. + +![This is just a space holder for an image that will represent your new Python package.](figure_1.png) + +# Summary +I just took this content dirctly from several existing repos to prime the pump and demonstrate how references are included in text; it needs to be rewritten for continutity: + +MOSART was developed as a scalable framework for representing and studying riverine dynamics of water, energy, and biogeochemistry cycles across local, regional and global scales from an integrated human-earth system perspective [@li2013physically]. In particular, a dam/reservoir module has been embedded within MOSART (denoted as MOSART-WM) to simulate the effects of dam operations as well as surface-water withdrawal on downstream flow and water temperature across a range of spatial scales [@voisin2013improved]. + +@voisin2013improved introduced MOSART-WM as a large scale spatially distributed reservoir and water management model which is coupled with the river routing MOSART model and represents the spatially distributed withdrawals, as well as withdrawals from river channels and diversion from reservoir releases. River regulation is represented through reservoirs (up to one per grid cell). Each reservoir release is independently calibrated using long term mean monthly inflow into the reservoir, long term mon monthly demand associated with this reservoir and reservoir goals (flood control, irrigation, other, combination of flood control with irrigation). Generic monthly pre-release rules and storage targets are set up for individual reservoirs however those releases are updated annually for inter-annual variability (dry or wet year) and for daily constraints such as environmental flow minimun release, spill and minimum storage. The WM model allows evaluating the impact of water management over multiple river basins at once (global, continental scales) and with consistent representation of human operations over the full domain. + +`wolfgang` was implemented to... + +`wolfgang` is offers the following features... + +`wolfgang` can be accessed on GitHub (https://github.com/IMMM-SFA/wolfgang). We provide an walthrough of some key functionality in a step-by-step tutorial in our website here: [Tutorial](link to the readthedocs.io that will have the tutorial). + +# Acknowledgements +This research was supported in part by the U.S. Department of Energy, Office of Science, as part of research in MultiSector Dynamics, Earth and Environmental System Modeling Program. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. The views and opinions expressed in this paper are those of the authors alone. + +# References From f63d76c7f52cc2b8076936d29f2312c182cb1b82 Mon Sep 17 00:00:00 2001 From: crvernon Date: Thu, 21 Jan 2021 11:00:17 -0500 Subject: [PATCH 04/20] make sample figure smaller --- paper/figure_1.png | Bin 1115127 -> 177453 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/paper/figure_1.png b/paper/figure_1.png index 27c8dd8aa51c31b23739fccb707cc29decd013e2..4beabfccd8dcc35a05e2ba55655bfefafbb96346 100644 GIT binary patch literal 177453 zcmbTdcUV)w*De~0iim;;3IZ02ROwO$6r}_NqzeI}(xikyD3Kb)j?~bUD$=Az=@1}d zp%)=SfB-=ugb*Ma2$(>^&F?$kIp^N{&$-W?o$Ni&li8D5v)6vtTJM@Um_Aqp2w9l} z%>i6o0Ki?&0XX;tNH%*I;0FNM*Z`CO001B05SK83hg0L?900DffJ6UP2LPq?3 zflJ}P+i(K_F9HC(|J}x&bNx@b%lUHd{_iW#JFfrT<2&yE*_!LoJD&elU+1(29Bcse zZa;uVK*JwELoaBlssr>)fi}GV>77<1kCnRiU3!$unty243=`qi@##nI;^32At&M zTIYJi!*vqCeUgjkB-g<=z$7i?whxH?@PE!>`!~dh%|7`w$ydKPR)KdLm79haG 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zA$Y*M3gK|zZH#mPqu<)#sl<8!z$9CL#-$QmT~yxk{`?tPkDr4TxkMn9QN!i4^hrfNK8HNo7yzifqZY z{{RSe-_wxxz0sR1b-lvuh&vWbjCE%RnxbW${R(&V2ED)23hPvv@Zjt4``(6T4(t}KS3p>)YXWaI@lF@n)>Gnyp-y@nZ&u#m zV%+^wM-S_D5w03%-0!~vn2B8o`PV5kvCu&CTC=47hK=Hr{3qfLqigKRPQ)*iJn?9H zX#dr6_I=0+YN?+b3H2ys$}FpM^^^geglGYL-AL>gesJh%zZ}t4pDk{KC1NdmW`gVy zhDR5u-y;pF6SrD;`I3U6+7iXs0{utQ8UO$Q From 31fe009b952d306e13bd8ad5dc0c5e619176c6a2 Mon Sep 17 00:00:00 2001 From: crvernon Date: Thu, 21 Jan 2021 11:04:00 -0500 Subject: [PATCH 05/20] adjust image size in markdown --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 606a28b..e8df22e 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -21,7 +21,7 @@ bibliography: paper.bib # Statement of need This would describe why you build the Python implementaion of MOSART-WM. -![This is just a space holder for an image that will represent your new Python package.](figure_1.png) +![This is just a space holder for an image that will represent your new Python package.](figure_1.png = 4x5.13) # Summary I just took this content dirctly from several existing repos to prime the pump and demonstrate how references are included in text; it needs to be rewritten for continutity: From 4955e2a46c857bfc535d07afb55e36cf4e0f0669 Mon Sep 17 00:00:00 2001 From: crvernon Date: Thu, 21 Jan 2021 11:06:51 -0500 Subject: [PATCH 06/20] adjust image size in markdown --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index e8df22e..082e2ce 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -21,7 +21,7 @@ bibliography: paper.bib # Statement of need This would describe why you build the Python implementaion of MOSART-WM. -![This is just a space holder for an image that will represent your new Python package.](figure_1.png = 4x5.13) +![This is just a space holder for an image that will represent your new Python package.](figure_1.png =4x5.13) # Summary I just took this content dirctly from several existing repos to prime the pump and demonstrate how references are included in text; it needs to be rewritten for continutity: From 19072a7d669ffa4f36d8e4a4cf9fd7c0f23626c2 Mon Sep 17 00:00:00 2001 From: crvernon Date: Thu, 21 Jan 2021 11:08:06 -0500 Subject: [PATCH 07/20] adjust image size in markdown --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 082e2ce..c7662f1 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -21,7 +21,7 @@ bibliography: paper.bib # Statement of need This would describe why you build the Python implementaion of MOSART-WM. -![This is just a space holder for an image that will represent your new Python package.](figure_1.png =4x5.13) +![This is just a space holder for an image that will represent your new Python package.](figure_1.png =100x) # Summary I just took this content dirctly from several existing repos to prime the pump and demonstrate how references are included in text; it needs to be rewritten for continutity: From ccf1a39d0d438dda640206e508ed68242e91547a Mon Sep 17 00:00:00 2001 From: crvernon Date: Thu, 21 Jan 2021 11:10:23 -0500 Subject: [PATCH 08/20] adjust image size in markdown --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index c7662f1..6de7ab0 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -21,7 +21,7 @@ bibliography: paper.bib # Statement of need This would describe why you build the Python implementaion of MOSART-WM. -![This is just a space holder for an image that will represent your new Python package.](figure_1.png =100x) +![This is just a space holder for an image that will represent your new Python package.](figure_1.png){ width=25% } # Summary I just took this content dirctly from several existing repos to prime the pump and demonstrate how references are included in text; it needs to be rewritten for continutity: From ba07eb2fdb01fb326a7ee25ca04fe08efcdbb765 Mon Sep 17 00:00:00 2001 From: travis Date: Tue, 9 Mar 2021 13:42:19 -0800 Subject: [PATCH 09/20] first draft of paper --- README.md | 2 +- docs/index.rst | 4 ++-- paper/figure_1.png | Bin 177453 -> 701068 bytes paper/paper.bib | 49 +++++++++++++++++++++++++++++++++++++++++++++ paper/paper.md | 28 +++++++++++++------------- 5 files changed, 66 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 2bf114a..9781da5 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ ![build](https://github.com/IMMM-SFA/mosartwmpy/workflows/build/badge.svg) [![codecov](https://codecov.io/gh/IMMM-SFA/mosartwmpy/branch/main/graph/badge.svg?token=IPOY8984MB)](https://codecov.io/gh/IMMM-SFA/mosartwmpy) -### mosartwmpy +### getting started `mosartwmpy` is a python translation of Mosart-WM, a model for water routing and reservoir management written in Fortran. The original code can be found at [IWMM](https://github.com/IMMM-SFA/iwmm) and [E3SM](https://github.com/E3SM-Project/E3SM), in which Mosart is the hdyrological component of a larger suite of earth-science models. The motivation for rewriting is largely for developer convenience -- running, debugging, and adding new capabilities were becoming increasingly difficult due to the complexity of the codebase and lack of familiarity with Fortran. This version aims to be intuitive, lightweight, and well documented, while still being highly interoperable. diff --git a/docs/index.rst b/docs/index.rst index 1562aff..d4ba1c0 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,5 +1,5 @@ -Contents -======== +mosartwmpy +========== Introduction ------------ diff --git a/paper/figure_1.png b/paper/figure_1.png index 4beabfccd8dcc35a05e2ba55655bfefafbb96346..82c69439c44b0564f50bebc0834c83ea374a7b13 100644 GIT binary patch literal 701068 zcmeFYWmKHa5-v(0xVr{-cZVSm+}+)RyMzD}2n5%lfdmK^oWUhwu;30sg1ZNIX66jv z_np1>J?GxF?vH!Vug$wyYv``7x4XK!>#3)P7%dG&Yzztv1OxHEGO~!k5G*pLCZT?Eed{Q5u)Etx6|HxkZ2vQI|G}LBPTX4>d?7MvO zExGOc zf661VzP|wvFH%lW4C#|H%>i~{6?^Cr-qHw4RK4VgVgTHY5QH_R;Ic{tgs-?WrkXs- z>}QP8*mk@xql;B?i~E>Vatr#>*>idWae5`5(M|_S8j~RW9@QFW-*S9OeQb)?uJYUl z9YNeK8tIPy3&Ms{wBpvbx~|P@R4V?C^}%~e$38rkq)efRUya`)sMs@9eclK_$Ne8Y z28Z>r`fA_5XHJ#x<8b*9(w;8Br4r+Wcca8_V?#F^b^F68zb=ZbIF=|y+4=2E8qaR$ zmuJkq)rc0aiF!-$qEx+YzY5*Qt9gl%+O)_{(0e@-(cWxVV#QUR@SGhe`Pq-Np++86 zb>_AZmXiGc$A&YHXIa+7YO86(e#z=mG z8h*{lDvAxDfRX;yXp;J1T4cY=k^P(Vt{{*+J)ele`wEO#F)0*ZJ_mnc2QozGL=yN< z(I#n0(*@O%gz*ZhnM?Q^vno%`#Q7;<%5DgC)D*RPQM}D%A4$+UsG=24CuM#eeAbEB zLi_76FusA7Tl+K1my}UT%F;1Z9>mFaNV3l`S=R;-re%cjny&<}IxP>fJv)*3laV7j z@a;FFbxUF8y z?ucfmle`3!xZxnFf{emJGh@ij60l-Dr`qL+wVEi6Kb}C%>vlzVQF+lPT7F^LL!xL* z$o5-JZbA0RrccLy1HwvSM{_TU^;?j@yw?$xKbi|7=Mn2{ym>DU$JqpXP^F3d`^gRc zyDb^&Gr-1~AYc0DwD#F{|MpOwabmQ%v=hb7Z065&gpM&7My|Izq;)L>&;fkT0K)(g zgtS%sE0+Ty-K!9ptX=Q{$qo`;t(TS6+M1zx0}jH&nt;ObV|l&ihZ>}BfcE&BWa4ta zmUCGIy?mrRR$D8a$}ptL{uhDIfOuHlokn<=1TWdM5ZgJ!4~i5Vf4 zcPiA9yP|@_rSj3K5c|8FJfE4N%ypT!1hsYgOkl2rhdzI)g!aOkwl5mlCQg*$Pc$w; zbOha`l4NRZ5rc8`b8XuCHKWo{M*-t*M^YVkHEjwS1 z=*O0bmM<-Ff${SiM~v4gN{qIt0&MSyR5wyEl!}Y|iar+=7I74DPra=pSqMMUz+ov( zVcJ68!Z^n{$3G`N*YyU-XWFQq753^pPa{vWPvc4>8Ddf)EzT=fY%E35A}m{&xtmdO zM!F)pqP}X($jC^~sF@Aq0?kTRsZ=eSI5tu{hve^1=f0-;&G;$2DXl5NKi)s*`jbZK z;^g~%r%S|pouC(x=a6TRAHs5Pi-ZkL^Q8>iP1>~|Vy`h@#3okZm^#L4fHWe zOSKW1OPD8eYX&5`H~x9HoYNK(ge4{N!{~?P52hfA%dvf_X_o!P{iuD+OVvw}!@P+T zJ1Z;~l&CI8PCHlYTpM38L2O_}&M@;YONvxNs`fbCUJ7M5E-aL_l36$3pN}KtLpp_v5E30SW6_ z+c800;c;P0x6t12i{aMzuj!hM+`MKsa5L!QMu+V6>on?uxYW5AxQH`yI2Sn|s*b8s zD@lxw>)Jfj8?QlQ4Hj<8&dV;g)&gS=8R|bqUzo;LB~=ahR~?ft2KZn5v-tyl3oS_1 zi`4D@CS9CcT=mJ9crLajF(Yo~!zg|)x+7X0@ck%4{Ec`_Zuw_!vBEKnF`M11v10}_ zhG0b{g_*>beOVvtcA~bKc1fy1Dsn0Bu7Q#i9)*Q?70O1NnQU!KGbwW)^X8Tnzv%$V z!=}!vpU3I#A0<|Q{Dyo&U1nIatyZk>AD$d5j4Tw1@7NwZrP{qKbgbBQYl{^F$N)0~l(!k7Bh91N_mXdX$%}^WsK$--T}37E?Ztt_+6aZ&1)kuBBaqhBfu(;O366GVDG2X!+6kW1=r(Uh_X^P(pAr(pNk!L%i|sL}(qZT00G4JA$G)f8w9 z>2SsNmA$8cGk?$>71RXk^Rm{eG2fJo){HIq6M1qg|An!E%xrn3yLR$7*%^kN=!#yR zdWL!t2k>yr=t71-a!zVHM^bL)mjIcsJcB8@xen4*uT9!#j7`{&2X2NfKdy4brMjgv zoDZJwAR@I`gTHxdJ>~}*kr^48--ZUAU;dB}l282_Cy?vF`Q@xZ^!sgQ_N^LpWY##N zerG&LG)L;~G?kr3U+!${tdHuB|7cuRbBMHG=-A^bX$!Ye>)L2{S|k^f1Ji|yI57IQ zphK@+&mibR5k@1z-Ax)p+Q@Zn^xgd|lmF$yo%h^%?761ut?C$T>sohP6G>Gdvx)1% zq{&*$tJUrA1QO9&AcHK3mkBeKYs#n0FaeNnK?)6L6B-aB*H!02UT$5p@0i zb4kC0^2p!_y4ifW>V6dSd%A7T(W6ItF+}Kr{9%|PrH?$}hx*r&#GRR)Yb0CThNj!* zBY^1nT9aHxR5>mswS|8~P|?;v=j29VNPkg(P@?0D6W7nbgKl~w2!q5x)_Gz=_b>O>S$&NPjf?(6Tq8k zZ|JO|z);h2)1uu|YA5I+p@_2CU88NmOV#sm;dp70=Y(iwA1rY~y>77(viv}dt41>e z9X_*c7ymAf1HcLiJk#zjZ2$Z?wwtt_1n^6jEC7@RI-fQC$$qGpTV7wzgk{|-&iU+L zK)*}@uAr4~)=zC#ExUl`N1rDauXL`x*M6nW_0=^0AcvGR@>HL6Eq-r?oD8i{f>)tk zyFJ-SY&3b-ny^>*dUp$FKEs*ccy3V6QBaB`$u%2^JJ)0c1fB&pVj~2xBg7AT!g#eq z#&prYO6Qd0-D(m}50&OG0sj)Ec1US@c?Sdrh$PFQJyJ9?3g|554YrIRvcUg%SH<$&6QcoKy zfYQ3r3(qXjJ(Y}o5D-Y1o_?Px>oA@oARr<;=^6PNsjG?Gdbn|0+j-d7a|gP4!le-q zBm>3aAKmPIt!V?@T-|-d1EuKyk`RY~ero2Sr~OOB*F}onNL`Co&coZDR)|}Wo0nc1 zgO-+7(%a5KT<4X-Kf~dFNzuRd_4O3z;Ry%`;0_Sr_V9M(;S&=RU)bq;T$JX1))7Qzvo%TtuwT*|LuM|D~(~JJ~^Y=XM1D*c;CU>8I zmIYrR&r=T%A2%=0|J^WOCx`!sVNX4O5BqCff4@%hNtw8oQ=q-8@hc}cdv_oB)TG74 z_$B{(&HvW(?}`4$NTYv`or|^qN(S>}IE#$Ddie!{D{{TC5 z{daTUo}AoZs!xawdIb3U`#aveb#S;@@fjC;flb9$oXE(Q!uD~*Sqti@%iBGdJ5Gkhm)-X@qr_W^w7S0!o!-R5^fuc z!8b<$8yNlruBpiP{+TAK^slX24g?I>G$0tSL@Y${sdv4wEG`SK?;&X(*bD8M#XjMC z<1rb|OFE?A8~r3QlK7+o-h2D{fW9Opw;%eKf?B_xw^)1BN<~vKzMVc=bYIe3(ncll zNqAA*G;Yf|1jP0`F}86J7;lg|_7MiXt7i{n0fiq^_>@3|TRmi9zrQP6ju0?E|cpb`*^m8&>l z1Y8iWv(Hlfd0$at^RD-v^6)R+m0*2I8%U#{fm~D-UoEl2HY?>}k~QHWkEvo4mQPvI znc%K_uBaw1L@sHCizW7qhZS z%+mA>F6#EufQb20qXA5HL_>>lm6jdc8%cNRC=_wycX7l$BU_SR)`9ClJDM{Hri60u zDuFTP_GaWyfWt(2^ z*ZH9Pc~}gHl@lL0s_`8l{|I5-^yql!+);LVs9qvSbma;%R(8`~ORep#HJ%3wDKfj? 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zgT`@XMM9z-yx}y6I5%;9{r-D+PO#=5CM?=M)cz7xjom;A#B-yb?UVp%oIpFbsvWiZpOGmtQ@H66yH>^!|Er~hVnX3SWAA{-K zpm?ssB?tHgj_)eliB5SQ_Ix&W{e-ZkTbx`wrNEq%L`+SObpioXc7UEk{#Ade67Qjr zO7NNj?5elbbib5ceR%z)g1^M7aQi-WvawWUv|oE#)AuWx6K$Y|cg!m}%k Date: Thu, 18 Mar 2021 14:04:40 -0700 Subject: [PATCH 10/20] second draft of paper; and updates documentation and variable names --- README.md | 28 +- config_defaults.yaml | 18 +- mosartwmpy/grid/grid.py | 5 +- mosartwmpy/input/demand.py | 16 +- mosartwmpy/input/runoff.py | 1 + mosartwmpy/input_output_variables.py | 107 +++++++ mosartwmpy/main_channel/irrigation.py | 10 +- mosartwmpy/model.py | 109 ++++--- mosartwmpy/reservoirs/regulation.py | 8 +- mosartwmpy/state/state.py | 428 +++++++++++++------------- mosartwmpy/subnetwork/irrigation.py | 8 +- mosartwmpy/update/update.py | 12 +- paper/paper.bib | 68 +++- paper/paper.md | 21 +- validation/validate.py | 4 +- 15 files changed, 534 insertions(+), 309 deletions(-) create mode 100644 mosartwmpy/input_output_variables.py diff --git a/README.md b/README.md index 9781da5..b7e71df 100644 --- a/README.md +++ b/README.md @@ -41,12 +41,21 @@ Alternatively, one can update the settings via code in the driving script: mosart_wm.config['simulation.end_date'] = datetime(1985, 12, 31) ``` -By default, key model variables are output on a monthly basis at a daily averaged resolution to `./output//__.nc`. Support for the [CSDMS standard names](https://github.com/csdms/standard_names) will be added shortly, but for now see configuration file for examples of how to modify the outputs, and the `./src/_initialize_state.py` file for state variable names. +By default, key model variables are output on a monthly basis at a daily averaged resolution to `./output//__.nc`. See the configuration file for examples of how to modify the outputs, and the `./mosartwmpy/state/state.py` file for state variable names. +Alternatively, certain model outputs deemed most important can be accessed using the BMI interface methods. For example: +```python +# get a list of model output variables +mosart_wm.get_output_var_names() + +# get the flattened numpy.ndarray of values for an output variable +mosart_wm.get_value_ptr('supply_water_amount') +``` -### input files -Several input files in NetCDF format are required to successfuly run a simulation, which are not shipped with this repository due to their large size. The grid files, reservoir files, and a small range of runoff and demand input files are available for public download as a zip archive [here](https://zenodo.org/record/4537907/files/mosartwmpy_sample_input_data_1980_1985.zip?download=1). This data can also be obtained using the download utility by running `python download.py` in the repository root and choosing option 1 for "sample_input". Currently, all input files are assumed to be at the same resolution (for the sample files this is 1/8 degree over the CONUS). Below is a summary of the various input files: +### input + +Several input files in NetCDF format are required to successfully run a simulation, which are not shipped with this repository due to their large size. The grid files, reservoir files, and a small range of runoff and demand input files are available for public download as a zip archive [here](https://zenodo.org/record/4537907/files/mosartwmpy_sample_input_data_1980_1985.zip?download=1). This data can also be obtained using the download utility by running `python download.py` in the repository root and choosing option 1 for "sample_input". Currently, all input files are assumed to be at the same resolution (for the sample files this is 1/8 degree over the CONUS). Below is a summary of the various input files: Name | Description | Configuration Path | Notes --- | --- | --- | --- @@ -56,6 +65,19 @@ Reservoirs | Locations of reservoirs (possibly aggregated) and their physical an Runoff | Surface runoff, subsurface runoff, and wetland runoff per grid cell averaged per unit of time; used to drive the river routing | `runoff.path` | Demand | Water demand of grid cells averaged per unit of time; currently assumed to be monthly | `water_management.reservoirs.demand` | there are plans to support other time scales, such as epiweeks +Alternatively, certain model inputs can be set using the BMI interface. This can be useful for coupling `mosartwmpy` with other models. If setting an input that would typically be read from a file, be sure to disable the `read_from_file` configuration value for that input. For example: +```python + # get a list of model input variables + mosart_wm.get_input_var_names() + + # disable the runoff read_from_file + mosart_wm.config['runoff.read_from_file'] = False + + # set the runoff values manually (i.e. from another model's output) + surface_runoff = np.empty(mosart_wm.get_grid_size()) + surface_runoff[:] = + mosart_wm.set_value('surface_runoff_flux', surface_runoff) +``` ### testing and validation diff --git a/config_defaults.yaml b/config_defaults.yaml index d7fd84e..b138830 100644 --- a/config_defaults.yaml +++ b/config_defaults.yaml @@ -63,19 +63,15 @@ simulation: name: WRM_STORAGE long_name: Mosart storage in reservoir units: m3 - - variable: reservoir_supply + - variable: grid_cell_supply name: WRM_SUPPLY long_name: Mosart reservoir supply to grid cell units: m3/s - - variable: reservoir_monthly_demand + - variable: grid_cell_demand_rate name: WRM_DEMAND0 long_name: Mosart demand reqeusted by grid cell units: m3/s - - variable: reservoir_demand - name: WRM_DEMAND - long_name: Mosart unmet demand at grid cell; same as deficit # TODO probably not - units: m3 - - variable: reservoir_deficit + - variable: grid_cell_deficit name: WRM_DEFICIT long_name: Mosart unmet demand at grid cell units: m3 @@ -166,8 +162,9 @@ grid: ### Runoff (forcing input) ### runoff: - # flag to turn this feature on or off - enabled: true + # flag to enable/disable reading runoff from file + # if disabled, runoff variables should be provided by the model driver + read_from_file: true # path to the runoff file; can be absolute or relative to the source code root path: ./input/runoff/Livneh_NLDAS_1980_1985.nc # longitude field name @@ -199,6 +196,9 @@ water_management: extraction_main_channel_enabled: true # Demand demand: + # flag to enable/disable reading demand from file + # if disabled, demand should be provided by the model driver + read_from_file: true # path to the demand file; can be absolute or relative to the source code root path: ./input/demand/RCP8.5_GCAM_water_demand_1980_1985.nc # longitude field name diff --git a/mosartwmpy/grid/grid.py b/mosartwmpy/grid/grid.py index bf815ca..d5bc8ee 100644 --- a/mosartwmpy/grid/grid.py +++ b/mosartwmpy/grid/grid.py @@ -340,7 +340,10 @@ def __init__(self, config: Benedict = None, parameters: Parameters = None, empty if config.get('water_management.enabled', False): logging.debug(' - reservoirs') load_reservoirs(self, config, parameters) - + + def __getitem__(self, item): + return getattr(self, item) + def to_files(self, path: str) -> None: """Builds a dataframe from all the grid values. diff --git a/mosartwmpy/input/demand.py b/mosartwmpy/input/demand.py index e8e2147..169a57f 100644 --- a/mosartwmpy/input/demand.py +++ b/mosartwmpy/input/demand.py @@ -8,7 +8,6 @@ from mosartwmpy.state.state import State from mosartwmpy.utilities.timing import timing -# TODO this currently can only act on the entire domain... need to make more robust so can be handled in parallel # @timing def load_demand(state: State, config: Benedict, current_time: datetime) -> None: @@ -27,15 +26,18 @@ def load_demand(state: State, config: Benedict, current_time: datetime) -> None: path = re.sub('\{d[^}]*}', current_time.strftime('%d'), path) demand = open_dataset(path) - - # TODO still won't work with data on Constance, since there is no time axis - state.reservoir_monthly_demand = np.array(demand[config.get('water_management.demand.demand')].sel({config.get('water_management.demand.time'): current_time}, method='pad')).flatten() + # if the demand file has a time axis, use it; otherwise assume data is just 2d + if config.get('water_management.demand.time', None) in demand: + state.grid_cell_demand_rate = np.array(demand[config.get('water_management.demand.demand')].sel({config.get('water_management.demand.time'): current_time}, method='pad')).flatten() + else: + state.grid_cell_demand_rate = np.array(demand[config.get('water_management.demand.demand')]).flatten() - state.reservoir_monthly_demand = np.where( - np.logical_not(np.isfinite(state.reservoir_monthly_demand)), + # fill missing values with 0 + state.grid_cell_demand_rate = np.where( + np.logical_not(np.isfinite(state.grid_cell_demand_rate)), 0, - state.reservoir_monthly_demand + state.grid_cell_demand_rate ) demand.close() diff --git a/mosartwmpy/input/runoff.py b/mosartwmpy/input/runoff.py index c0c2b02..75d45ef 100644 --- a/mosartwmpy/input/runoff.py +++ b/mosartwmpy/input/runoff.py @@ -7,6 +7,7 @@ from mosartwmpy.state.state import State from mosartwmpy.utilities.timing import timing + # @timing def load_runoff(state: State, grid: Grid, config: Benedict, current_time: datetime) -> None: """Loads runoff from file into the state for each grid cell. diff --git a/mosartwmpy/input_output_variables.py b/mosartwmpy/input_output_variables.py new file mode 100644 index 0000000..c44f0a8 --- /dev/null +++ b/mosartwmpy/input_output_variables.py @@ -0,0 +1,107 @@ +from typing import List + + +class IO: + """Tracks supported model input and output variables.""" + + FLOAT = 'float' + FLOAT64 = 8 + STATE = 'state' + GRID = 'grid' + + class Variable: + """Represents a model input/output variable and associated metadata.""" + + def __init__( + self, + standard_name: str, + variable: str, + variable_type: str, + variable_item_size: int, + variable_class: str, + units: str + ): + self.standard_name: str = standard_name + self.variable: str = variable + self.variable_type: str = variable_type + self.variable_item_size: int = variable_item_size + self.variable_class: str = variable_class + self.units: str = units + + inputs: List[Variable] = [ + Variable( + standard_name='surface_runoff_flux', + variable='hillslope_surface_runoff', + variable_type=FLOAT, + variable_item_size=FLOAT64, + variable_class=STATE, + units='mm s-1', + ), + Variable( + standard_name='subsurface_runoff_flux', + variable='hillslope_subsurface_runoff', + variable_type=FLOAT, + variable_item_size=FLOAT64, + variable_class=STATE, + units='mm s-1', + ), + Variable( + standard_name='demand_flux', + variable='grid_cell_demand_rate', + variable_type=FLOAT, + variable_item_size=FLOAT64, + variable_class=STATE, + units='m3 s-1', + ), + ] + + outputs: List[Variable] = [ + Variable( + standard_name='outgoing_water_volume_transport_along_river_channel', + variable='runoff_land', + variable_type=FLOAT, + variable_item_size=FLOAT64, + variable_class=STATE, + units='m3 s-1' + ), + Variable( + standard_name='incoming_water_volume_transport_along_river_channel', + variable='channel_inflow_upstream', + variable_type=FLOAT, + variable_item_size=FLOAT64, + variable_class=STATE, + units='m3 s-1' + ), + Variable( + standard_name='surface_water_amount', + variable='storage', + variable_type=FLOAT, + variable_item_size=FLOAT64, + variable_class=STATE, + units='m3' + ), + Variable( + standard_name='reservoir_water_amount', + variable='reservoir_storage', + variable_type=FLOAT, + variable_item_size=FLOAT64, + variable_class=STATE, + units='m3' + ), + Variable( + standard_name='supply_water_amount', + variable='grid_cell_supply', + variable_type=FLOAT, + variable_item_size=FLOAT64, + variable_class=STATE, + units='m3 s-1' + ), + Variable( + standard_name='deficit_water_amount', + variable='grid_cell_deficit', + variable_type=FLOAT, + variable_item_size=FLOAT64, + variable_class=STATE, + units='m3 s-1' + ), + ] diff --git a/mosartwmpy/main_channel/irrigation.py b/mosartwmpy/main_channel/irrigation.py index f4d582c..460b06f 100644 --- a/mosartwmpy/main_channel/irrigation.py +++ b/mosartwmpy/main_channel/irrigation.py @@ -19,18 +19,18 @@ def main_channel_irrigation(state: State, grid: Grid, parameters: Parameters) -> depth_condition = calculate_depth_condition(grid.mosart_mask, state.euler_mask, state.tracer, parameters.LIQUID_TRACER, state.channel_depth, parameters.irrigation_extraction_condition) - tiny_condition = calculate_tiny_condition(state.channel_storage, parameters.tinier_value, state.reservoir_demand, grid.channel_length) + tiny_condition = calculate_tiny_condition(state.channel_storage, parameters.tinier_value, state.grid_cell_unmet_demand, grid.channel_length) flow_volume = np.empty_like(state.channel_storage) np.copyto(flow_volume, state.channel_storage) - volume_condition = calculate_volume_condition(parameters.irrigation_extraction_maximum_fraction, flow_volume, state.reservoir_demand) + volume_condition = calculate_volume_condition(parameters.irrigation_extraction_maximum_fraction, flow_volume, state.grid_cell_unmet_demand) - state.reservoir_supply = calculate_reservoir_supply(depth_condition, tiny_condition, volume_condition, state.reservoir_supply, state.reservoir_demand, parameters.irrigation_extraction_maximum_fraction, flow_volume) + state.grid_cell_supply = calculate_reservoir_supply(depth_condition, tiny_condition, volume_condition, state.grid_cell_supply, state.grid_cell_unmet_demand, parameters.irrigation_extraction_maximum_fraction, flow_volume) - flow_volume = calculate_flow_volume(depth_condition, tiny_condition, volume_condition, flow_volume, state.reservoir_demand) + flow_volume = calculate_flow_volume(depth_condition, tiny_condition, volume_condition, flow_volume, state.grid_cell_unmet_demand) - state.reservoir_demand = calculate_reservoir_demand(depth_condition, tiny_condition, volume_condition, state.reservoir_demand, parameters.irrigation_extraction_maximum_fraction, flow_volume) + state.grid_cell_unmet_demand = calculate_reservoir_demand(depth_condition, tiny_condition, volume_condition, state.grid_cell_unmet_demand, parameters.irrigation_extraction_maximum_fraction, flow_volume) flow_volume = update_flow_volume(depth_condition, tiny_condition, volume_condition, parameters.irrigation_extraction_maximum_fraction, flow_volume) diff --git a/mosartwmpy/model.py b/mosartwmpy/model.py index 3a14d64..435e798 100644 --- a/mosartwmpy/model.py +++ b/mosartwmpy/model.py @@ -22,6 +22,7 @@ from mosartwmpy.grid.grid import Grid from mosartwmpy.input.runoff import load_runoff from mosartwmpy.input.demand import load_demand +from mosartwmpy.input_output_variables import IO from mosartwmpy.output.output import initialize_output, update_output, write_restart from mosartwmpy.reservoirs.reservoirs import reservoir_release from mosartwmpy.state.state import State @@ -59,6 +60,9 @@ def __init__(self): self.git_hash = None self.git_untracked = None + def __getitem__(self, item): + return getattr(self, item) + def initialize(self, config_file_path: str, grid: Grid = None, state: State = None) -> None: t = timer() @@ -122,12 +126,11 @@ def initialize(self, config_file_path: str, grid: Grid = None, state: State = No date = date[len(date) - 1].split('_') self.current_time = datetime(int(date[0]), int(date[1]), int(date[2])) else: - logging.warn('Unable to parse date from restart file name, falling back to configured start date.') + logging.warning('Unable to parse date from restart file name, falling back to configured start date.') self.current_time = datetime.combine(self.config.get('simulation.start_date'), time.min) x = open_dataset(path) self.state = State.from_dataframe(x.to_dataframe()) x.close() - # TODO ensure output file writes still workr else: # simulation start time self.current_time = datetime.combine(self.config.get('simulation.start_date'), time.min) @@ -153,23 +156,23 @@ def update(self) -> None: logging.info(f'Begin timestep {step}.') try: # read runoff - if self.config.get('runoff.enabled', False): - logging.debug(f'Reading runoff input.') + if self.config.get('runoff.read_from_file', False): + logging.debug(f'Reading runoff input from file.') load_runoff(self.state, self.grid, self.config, self.current_time) # read demand if self.config.get('water_management.enabled', False): - # only read new demand and compute new release if it's the very start of simulation or new time period - # TODO this currently assumes monthly demand input - if self.current_time == datetime.combine(self.config.get('simulation.start_date'), time.min) or self.current_time == datetime(self.current_time.year, self.current_time.month, 1): - logging.debug(f'Reading demand input.') - # load the demand from file - load_demand(self.state, self.config, self.current_time) - # release water from reservoirs - reservoir_release(self.state, self.grid, self.config, self.parameters, self.current_time) + if self.config.get('water_management.demand.read_from_file', False): + # only read new demand and compute new release if it's the very start of simulation or new time period + # TODO this currently assumes monthly demand input + if self.current_time == datetime.combine(self.config.get('simulation.start_date'), time.min) or self.current_time == datetime(self.current_time.year, self.current_time.month, 1): + logging.debug(f'Reading demand rate input from file.') + # load the demand from file + load_demand(self.state, self.config, self.current_time) + # release water from reservoirs + reservoir_release(self.state, self.grid, self.config, self.parameters, self.current_time) # zero supply and demand - self.state.reservoir_supply[:] = 0 - self.state.reservoir_demand[:] = 0 - self.state.reservoir_deficit[:] = 0 + self.state.grid_cell_supply[:] = 0 + self.state.grid_cell_unmet_demand[:] = 0 # get streamflow for this time period # TODO this is still written assuming monthly, but here's the epiweek for when that is relevant epiweek = Week.fromdate(self.current_time).week @@ -190,6 +193,10 @@ def update(self) -> None: except Exception as e: logging.exception('Failed to write output or restart file; see below for stacktrace.') raise e + # clear runoff input arrays + self.state.hillslope_surface_runoff[:] = 0 + self.state.hillslope_subsurface_runoff[:] = 0 + self.state.hillslope_wetland_runoff[:] = 0 def update_until(self, time: float) -> None: # perform timesteps until time @@ -207,44 +214,36 @@ def download_data(self, *args, **kwargs) -> None: download_data(*args, **kwargs) def get_component_name(self) -> str: - # TODO include version/hash info? return f'mosartwmpy ({self.git_hash})' def get_input_item_count(self) -> int: - # TODO - return 0 + return len(IO.inputs) def get_output_item_count(self) -> int: - # TODO - return 0 + return len(IO.outputs) def get_input_var_names(self) -> Tuple[str]: - # TODO - return [] + return tuple(str(var.standard_name) for var in IO.inputs) def get_output_var_names(self) -> Tuple[str]: - # TODO - return [] + return tuple(str(var.standard_name) for var in IO.outputs) def get_var_grid(self, name: str) -> int: # only one grid used in mosart, so it is the 0th grid return 0 def get_var_type(self, name: str) -> str: - # TODO - return 'TODO' + return next((var.variable_type for var in IO.inputs + IO.outputs if var.standard_name == name), None) def get_var_units(self, name: str) -> str: - # TODO - return 'TODO' + return next((var.units for var in IO.inputs + IO.outputs if var.standard_name == name), None) def get_var_itemsize(self, name: str) -> int: - # TODO - return 0 + return next((var.variable_item_size for var in IO.inputs + IO.outputs if var.standard_name == name), None) def get_var_nbytes(self, name: str) -> int: - # TODO - return 0 + item_size = self.get_var_itemsize(name) + return item_size * self.get_grid_size() def get_var_location(self, name: str) -> str: # node, edge, face @@ -265,25 +264,39 @@ def get_time_units(self) -> str: def get_time_step(self) -> float: return float(self.config.get('simulation.timestep')) - def get_value(self, name: str, dest: np.ndarray) -> np.ndarray: - # TODO copy values into array - return + def get_value(self, name: str, dest: np.ndarray) -> int: + var = next((var for var in IO.inputs + IO.outputs if var.standard_name == name), None) + if var is None: + return 1 + dest[:] = self[var.variable_class][var.variable] + return 0 def get_value_ptr(self, name: str) -> np.ndarray: - # TODO set array to current array pointer - return - - def get_value_at_indices(self, name: str, dest: np.ndarray, inds: np.ndarray) -> np.ndarray: - # TODO copy values from indices into array - return + var = next((var for var in IO.inputs + IO.outputs if var.standard_name == name), None) + if var is None: + raise IOError(f'Variable {name} not found in model input/output definition.') + return self[var.variable_class][var.variable] + + def get_value_at_indices(self, name: str, dest: np.ndarray, inds: np.ndarray) -> int: + var = next((var for var in IO.inputs + IO.outputs if var.standard_name == name), None) + if var is None: + return 1 + dest[:] = self[var.variable_class][var.variable][inds] + return 0 - def set_value(self, name: str, src: np.ndarray) -> None: - # TODO set values of name from array - return + def set_value(self, name: str, src: np.ndarray) -> int: + var = next((var for var in IO.inputs + IO.outputs if var.standard_name == name), None) + if var is None: + return 1 + self[var.variable_class][var.variable][:] = src + return 0 - def set_value_at_indices(self, name: str, inds: np.ndarray, src: np.ndarray) -> None: - # TODO set values of name at indices from array - return + def set_value_at_indices(self, name: str, inds: np.ndarray, src: np.ndarray) -> int: + var = next((var for var in IO.inputs + IO.outputs if var.standard_name == name), None) + if var is None: + return 1 + self[var.variable_class][var.variable][inds] = src + return 0 def get_grid_type(self, grid: int = 0) -> str: return 'uniform_rectilinear' @@ -344,4 +357,4 @@ def get_grid_face_nodes(self, grid: int = 0, face_nodes: np.ndarray = None) -> n raise NotImplementedError def get_grid_nodes_per_face(self, grid: int = 0, nodes_per_face: np.ndarray = None) -> np.ndarray: - raise NotImplementedError \ No newline at end of file + raise NotImplementedError diff --git a/mosartwmpy/reservoirs/regulation.py b/mosartwmpy/reservoirs/regulation.py index 9f72415..fa16f0b 100644 --- a/mosartwmpy/reservoirs/regulation.py +++ b/mosartwmpy/reservoirs/regulation.py @@ -151,11 +151,11 @@ def extraction_regulated_flow(state: State, grid: Grid, parameters: Parameters, state.channel_outflow_downstream = remove_flow(has_reservoir, state.channel_outflow_downstream, flow_volume, delta_t) - cells = pd.DataFrame({'id': grid.id[state.reservoir_demand > 0]}).set_index('id') + cells = pd.DataFrame({'id': grid.id[state.grid_cell_unmet_demand > 0]}).set_index('id') cells['supply'] = 0 # join grid cell demand, then drop where no demand - demand = grid.reservoir_to_grid_mapping.join(pd.DataFrame(state.reservoir_demand, columns=['grid_cell_demand'])) + demand = grid.reservoir_to_grid_mapping.join(pd.DataFrame(state.grid_cell_unmet_demand, columns=['grid_cell_demand'])) demand = demand[demand.grid_cell_demand.gt(0)] # aggregate demand to each reservoir and join to flow volume @@ -214,8 +214,8 @@ def extraction_regulated_flow(state: State, grid: Grid, parameters: Parameters, # merge the supply back in and update demand supplied = pd.DataFrame(grid.id).join(cells).supply.fillna(0).values - state.reservoir_supply = add(state.reservoir_supply, supplied) - state.reservoir_demand = subtract(state.reservoir_demand, supplied) + state.grid_cell_supply = add(state.grid_cell_supply, supplied) + state.grid_cell_unmet_demand = subtract(state.grid_cell_unmet_demand, supplied) # add the residual flow volume back state.channel_outflow_downstream[:] -= pd.DataFrame(grid.reservoir_id, columns=['reservoir_id']).merge(reservoir_demand_flow.flow_volume, how='left', left_on='reservoir_id', right_index=True).flow_volume.fillna(0).values / delta_t diff --git a/mosartwmpy/state/state.py b/mosartwmpy/state/state.py index ee68648..2b6dd2b 100644 --- a/mosartwmpy/state/state.py +++ b/mosartwmpy/state/state.py @@ -11,217 +11,7 @@ from mosartwmpy.reservoirs.state import initialize_reservoir_state class State(): - - # flow [m3/s] - # flow - flow: np.ndarray = np.empty(0) - # outflow into downstream links from previous timestep [m3/s] - # eroup_lagi - outflow_downstream_previous_timestep: np.ndarray = np.empty(0) - # outflow into downstream links from current timestep [m3/s] - # eroup_lagf - outflow_downstream_current_timestep: np.ndarray = np.empty(0) - # initial outflow before dam regulation at current timestep [m3/s] - # erowm_regi - outflow_before_regulation: np.ndarray = np.empty(0) - # final outflow after dam regulation at current timestep [m3/s] - # erowm_regf - outflow_after_regulation: np.ndarray = np.empty(0) - # outflow sum of upstream gridcells, average [m3/s] - # eroutUp_avg - outflow_sum_upstream_average: np.ndarray = np.empty(0) - # lateral flow from hillslope, including surface and subsurface runoff generation components, average [m3/s] - # erlat_avg - lateral_flow_hillslope_average: np.ndarray = np.empty(0) - # routing storage [m3] - # volr - storage: np.ndarray = np.empty(0) - # routing change in storage [m3/s] - # dvolrdt - delta_storage: np.ndarray = np.empty(0) - # routing change in storage masked for land [m3/s] - # dvolrdtlnd - delta_storage_land: np.ndarray = np.empty(0) - # routing change in storage masked for ocean [m3/s] - # dvolrdtocn - delta_storage_ocean: np.ndarray = np.empty(0) - # basin derived flow [m3/s] - # runoff - runoff: np.ndarray = np.empty(0) - # return direct flow [m3/s] - # runofftot - runoff_total: np.ndarray = np.empty(0) - # runoff masked for land [m3/s] - # runofflnd - runoff_land: np.ndarray = np.empty(0) - # runoff masked for ocean [m3/s] - # runoffocn - runoff_ocean: np.ndarray = np.empty(0) - # direct flow [m3/s] - # direct - direct: np.ndarray = np.empty(0) - # direct-to-ocean forcing [m3/s] - # qdto - direct_to_ocean: np.ndarray = np.empty(0) - # flood water [m3/s] - # flood - flood: np.ndarray = np.empty(0) - # hillslope surface water storage [m] - # wh - hillslope_storage: np.ndarray = np.empty(0) - # change of hillslope water storage [m/s] - # dwh - hillslope_delta_storage: np.ndarray = np.empty(0) - # depth of hillslope surface water [m] - # yh - hillslope_depth: np.ndarray = np.empty(0) - # surface runoff from hillslope [m/s] - # qsur - hillslope_surface_runoff: np.ndarray = np.empty(0) - # subsurface runoff from hillslope [m/s] - # qsub - hillslope_subsurface_runoff: np.ndarray = np.empty(0) - # runoff from glacier, wetlands, and lakes [m/s] - # qgwl - hillslope_wetland_runoff: np.ndarray = np.empty(0) - # overland flor from hillslope into subchannel (outflow is negative) [m/s] - # ehout - hillslope_overland_flow: np.ndarray = np.empty(0) - # subnetwork water storage [m3] - # wt - subnetwork_storage: np.ndarray = np.empty(0) - # subnetwork water storage at previous timestep [m3] - # wt_last - subnetwork_storage_previous_timestep: np.ndarray = np.empty(0) - # change of subnetwork water storage [m3] - # dwt - subnetwork_delta_storage: np.ndarray = np.empty(0) - # depth of subnetwork water [m] - # yt - subnetwork_depth: np.ndarray = np.empty(0) - # cross section area of subnetwork [m2] - # mt - subnetwork_cross_section_area: np.ndarray = np.empty(0) - # hydraulic radii of subnetwork [m] - # rt - subnetwork_hydraulic_radii: np.ndarray = np.empty(0) - # wetness perimeter of subnetwork [m] - # pt - subnetwork_wetness_perimeter: np.ndarray = np.empty(0) - # subnetwork flow velocity [m/s] - # vt - subnetwork_flow_velocity: np.ndarray = np.empty(0) - # subnetwork mean travel time of water within travel [s] - # tt - subnetwork_mean_travel_time: np.ndarray = np.empty(0) - # subnetwork evaporation [m/s] - # tevap - subnetwork_evaporation: np.ndarray = np.empty(0) - # subnetwork lateral inflow from hillslope [m3/s] - # etin - subnetwork_lateral_inflow: np.ndarray = np.empty(0) - # subnetwork discharge into main channel (outflow is negative) [m3/s] - # etout - subnetwork_discharge: np.ndarray = np.empty(0) - # main channel storage [m3] - # wr - channel_storage: np.ndarray = np.empty(0) - # change in main channel storage [m3] - # dwr - channel_delta_storage: np.ndarray = np.empty(0) - # main channel storage at last timestep [m3] - # wr_last - channel_storage_previous_timestep: np.ndarray = np.empty(0) - # main channel water depth [m] - # yr - channel_depth: np.ndarray = np.empty(0) - # cross section area of main channel [m2] - # mr - channel_cross_section_area: np.ndarray = np.empty(0) - # hydraulic radii of main channel [m] - # rr - channel_hydraulic_radii: np.ndarray = np.empty(0) - # wetness perimeter of main channel[m] - # pr - channel_wetness_perimeter: np.ndarray = np.empty(0) - # main channel flow velocity [m/s] - # vr - channel_flow_velocity: np.ndarray = np.empty(0) - # main channel evaporation [m/s] - # erlg - channel_evaporation: np.ndarray = np.empty(0) - # lateral flow from hillslope [m3/s] - # erlateral - channel_lateral_flow_hillslope: np.ndarray = np.empty(0) - # inflow from upstream links [m3/s] - # erin - channel_inflow_upstream: np.ndarray = np.empty(0) - # outflow into downstream links [m3/s] - # erout - channel_outflow_downstream: np.ndarray = np.empty(0) - # outflow into downstream links from previous timestep [m3/s] - # TRunoff%eroup_lagi - channel_outflow_downstream_previous_timestep: np.ndarray = np.empty(0) - # outflow into downstream links from current timestep [m3/s] - # TRunoff%eroup_lagf - channel_outflow_downstream_current_timestep: np.ndarray = np.empty(0) - # initial outflow before dam regulation at current timestep [m3/s] - # TRunoff%erowm_regi - channel_outflow_before_regulation: np.ndarray = np.empty(0) - # final outflow after dam regulation at current timestep [m3/s] - # TRunoff%erowm_regf - channel_outflow_after_regulation: np.ndarray = np.empty(0) - # outflow sum of upstream gridcells, instantaneous [m3/s] - # eroutUp - channel_outflow_sum_upstream_instant: np.ndarray = np.empty(0) - # outflow sum of upstream gridcells, average [m3/s] - # TRunoff%eroutUp_avg - channel_outflow_sum_upstream_average: np.ndarray = np.empty(0) - # lateral flow from hillslope, including surface and subsurface runoff generation components, average [m3/s] - # TRunoff%erlat_avg - channel_lateral_flow_hillslope_average: np.ndarray = np.empty(0) - # flux for adjustment of water balance residual in glacier, wetlands, and lakes [m3/s] - # ergwl - channel_wetland_flux: np.ndarray = np.empty(0) - # streamflow from outlet, positive is out [m3/s] - # flow - channel_flow: np.ndarray = np.empty(0) - # tracer, i.e. liquid or ice - TODO ice not implemented yet - tracer: np.ndarray = np.empty(0) - # euler mask - which cells to perform the euler calculation on - euler_mask: np.ndarray = np.empty(0) - # a column of always all zeros, to use as a utility - zeros: np.ndarray = np.empty(0) - - - ## Reservoir related state variables - - # reservoir streamflow schedule - reservoir_streamflow: np.ndarray = np.empty(0) - # StorMthStOp - reservoir_storage_operation_year_start: np.ndarray = np.empty(0) - # storage [m3] - reservoir_storage: np.ndarray = np.empty(0) - # MthStOp, - reservoir_month_start_operations: np.ndarray = np.empty(0) - # MthStFC - reservoir_month_flood_control_start: np.ndarray = np.empty(0) - # MthNdFC - reservoir_month_flood_control_end: np.ndarray = np.empty(0) - # release [m3/s] - reservoir_release: np.ndarray = np.empty(0) - # supply [m3/s] - reservoir_supply: np.ndarray = np.empty(0) - # monthly demand [m3/s] (demand0) - reservoir_monthly_demand: np.ndarray = np.empty(0) - # unmet demand volume within sub timestep (deficit) [m3] - reservoir_demand: np.ndarray = np.empty(0) - # unmet demand over whole timestep [m3] - reservoir_deficit: np.ndarray = np.empty(0) - # potential evaporation [mm/s] # TODO this doesn't appear to be initialized anywhere currently - reservoir_potential_evaporation: np.ndarray = np.empty(0) - - + def __init__(self, grid: Grid = None, config: Benedict = None, parameters: Parameters = None, grid_size: int = None, empty: bool = False): """Initialize the model state. @@ -232,7 +22,216 @@ def __init__(self, grid: Grid = None, config: Benedict = None, parameters: Param grid_size (int): size of the flattened grid empty (bool): if True, return an empty instance """ - + + # flow [m3/s] + # flow + self.flow: np.ndarray = np.empty(0) + # outflow into downstream links from previous timestep [m3/s] + # eroup_lagi + self.outflow_downstream_previous_timestep: np.ndarray = np.empty(0) + # outflow into downstream links from current timestep [m3/s] + # eroup_lagf + self.outflow_downstream_current_timestep: np.ndarray = np.empty(0) + # initial outflow before dam regulation at current timestep [m3/s] + # erowm_regi + self.outflow_before_regulation: np.ndarray = np.empty(0) + # final outflow after dam regulation at current timestep [m3/s] + # erowm_regf + self.outflow_after_regulation: np.ndarray = np.empty(0) + # outflow sum of upstream gridcells, average [m3/s] + # eroutUp_avg + self.outflow_sum_upstream_average: np.ndarray = np.empty(0) + # lateral flow from hillslope, including surface and subsurface runoff generation components, average [m3/s] + # erlat_avg + self.lateral_flow_hillslope_average: np.ndarray = np.empty(0) + # routing storage [m3] + # volr + self.storage: np.ndarray = np.empty(0) + # routing change in storage [m3/s] + # dvolrdt + self.delta_storage: np.ndarray = np.empty(0) + # routing change in storage masked for land [m3/s] + # dvolrdtlnd + self.delta_storage_land: np.ndarray = np.empty(0) + # routing change in storage masked for ocean [m3/s] + # dvolrdtocn + self.delta_storage_ocean: np.ndarray = np.empty(0) + # basin derived flow [m3/s] + # runoff + self.runoff: np.ndarray = np.empty(0) + # return direct flow [m3/s] + # runofftot + self.runoff_total: np.ndarray = np.empty(0) + # runoff masked for land [m3/s] + # runofflnd + self.runoff_land: np.ndarray = np.empty(0) + # runoff masked for ocean [m3/s] + # runoffocn + self.runoff_ocean: np.ndarray = np.empty(0) + # direct flow [m3/s] + # direct + self.direct: np.ndarray = np.empty(0) + # direct-to-ocean forcing [m3/s] + # qdto + self.direct_to_ocean: np.ndarray = np.empty(0) + # flood water [m3/s] + # flood + self.flood: np.ndarray = np.empty(0) + # hillslope surface water storage [m] + # wh + self.hillslope_storage: np.ndarray = np.empty(0) + # change of hillslope water storage [m/s] + # dwh + self.hillslope_delta_storage: np.ndarray = np.empty(0) + # depth of hillslope surface water [m] + # yh + self.hillslope_depth: np.ndarray = np.empty(0) + # surface runoff from hillslope [m/s] + # qsur + self.hillslope_surface_runoff: np.ndarray = np.empty(0) + # subsurface runoff from hillslope [m/s] + # qsub + self.hillslope_subsurface_runoff: np.ndarray = np.empty(0) + # runoff from glacier, wetlands, and lakes [m/s] + # qgwl + self.hillslope_wetland_runoff: np.ndarray = np.empty(0) + # overland flor from hillslope into subchannel (outflow is negative) [m/s] + # ehout + self.hillslope_overland_flow: np.ndarray = np.empty(0) + # subnetwork water storage [m3] + # wt + self.subnetwork_storage: np.ndarray = np.empty(0) + # subnetwork water storage at previous timestep [m3] + # wt_last + self.subnetwork_storage_previous_timestep: np.ndarray = np.empty(0) + # change of subnetwork water storage [m3] + # dwt + self.subnetwork_delta_storage: np.ndarray = np.empty(0) + # depth of subnetwork water [m] + # yt + self.subnetwork_depth: np.ndarray = np.empty(0) + # cross section area of subnetwork [m2] + # mt + self.subnetwork_cross_section_area: np.ndarray = np.empty(0) + # hydraulic radii of subnetwork [m] + # rt + self.subnetwork_hydraulic_radii: np.ndarray = np.empty(0) + # wetness perimeter of subnetwork [m] + # pt + self.subnetwork_wetness_perimeter: np.ndarray = np.empty(0) + # subnetwork flow velocity [m/s] + # vt + self.subnetwork_flow_velocity: np.ndarray = np.empty(0) + # subnetwork mean travel time of water within travel [s] + # tt + self.subnetwork_mean_travel_time: np.ndarray = np.empty(0) + # subnetwork evaporation [m/s] + # tevap + self.subnetwork_evaporation: np.ndarray = np.empty(0) + # subnetwork lateral inflow from hillslope [m3/s] + # etin + self.subnetwork_lateral_inflow: np.ndarray = np.empty(0) + # subnetwork discharge into main channel (outflow is negative) [m3/s] + # etout + self.subnetwork_discharge: np.ndarray = np.empty(0) + # main channel storage [m3] + # wr + self.channel_storage: np.ndarray = np.empty(0) + # change in main channel storage [m3] + # dwr + self.channel_delta_storage: np.ndarray = np.empty(0) + # main channel storage at last timestep [m3] + # wr_last + self.channel_storage_previous_timestep: np.ndarray = np.empty(0) + # main channel water depth [m] + # yr + self.channel_depth: np.ndarray = np.empty(0) + # cross section area of main channel [m2] + # mr + self.channel_cross_section_area: np.ndarray = np.empty(0) + # hydraulic radii of main channel [m] + # rr + self.channel_hydraulic_radii: np.ndarray = np.empty(0) + # wetness perimeter of main channel[m] + # pr + self.channel_wetness_perimeter: np.ndarray = np.empty(0) + # main channel flow velocity [m/s] + # vr + self.channel_flow_velocity: np.ndarray = np.empty(0) + # main channel evaporation [m/s] + # erlg + self.channel_evaporation: np.ndarray = np.empty(0) + # lateral flow from hillslope [m3/s] + # erlateral + self.channel_lateral_flow_hillslope: np.ndarray = np.empty(0) + # inflow from upstream links [m3/s] + # erin + self.channel_inflow_upstream: np.ndarray = np.empty(0) + # outflow into downstream links [m3/s] + # erout + self.channel_outflow_downstream: np.ndarray = np.empty(0) + # outflow into downstream links from previous timestep [m3/s] + # TRunoff%eroup_lagi + self.channel_outflow_downstream_previous_timestep: np.ndarray = np.empty(0) + # outflow into downstream links from current timestep [m3/s] + # TRunoff%eroup_lagf + self.channel_outflow_downstream_current_timestep: np.ndarray = np.empty(0) + # initial outflow before dam regulation at current timestep [m3/s] + # TRunoff%erowm_regi + self.channel_outflow_before_regulation: np.ndarray = np.empty(0) + # final outflow after dam regulation at current timestep [m3/s] + # TRunoff%erowm_regf + self.channel_outflow_after_regulation: np.ndarray = np.empty(0) + # outflow sum of upstream gridcells, instantaneous [m3/s] + # eroutUp + self.channel_outflow_sum_upstream_instant: np.ndarray = np.empty(0) + # outflow sum of upstream gridcells, average [m3/s] + # TRunoff%eroutUp_avg + self.channel_outflow_sum_upstream_average: np.ndarray = np.empty(0) + # lateral flow from hillslope, including surface and subsurface runoff generation components, average [m3/s] + # TRunoff%erlat_avg + self.channel_lateral_flow_hillslope_average: np.ndarray = np.empty(0) + # flux for adjustment of water balance residual in glacier, wetlands, and lakes [m3/s] + # ergwl + self.channel_wetland_flux: np.ndarray = np.empty(0) + # streamflow from outlet, positive is out [m3/s] + # flow + self.channel_flow: np.ndarray = np.empty(0) + # tracer, i.e. liquid or ice - TODO ice not implemented yet + self.tracer: np.ndarray = np.empty(0) + # euler mask - which cells to perform the euler calculation on + self.euler_mask: np.ndarray = np.empty(0) + # a column of always all zeros, to use as a utility + self.zeros: np.ndarray = np.empty(0) + + + ## Reservoir related state variables + + # reservoir streamflow schedule + self.reservoir_streamflow: np.ndarray = np.empty(0) + # StorMthStOp + self.reservoir_storage_operation_year_start: np.ndarray = np.empty(0) + # storage [m3] + self.reservoir_storage: np.ndarray = np.empty(0) + # MthStOp, + self.reservoir_month_start_operations: np.ndarray = np.empty(0) + # MthStFC + self.reservoir_month_flood_control_start: np.ndarray = np.empty(0) + # MthNdFC + self.reservoir_month_flood_control_end: np.ndarray = np.empty(0) + # release [m3/s] + self.reservoir_release: np.ndarray = np.empty(0) + # supply [m3/s] + self.grid_cell_supply: np.ndarray = np.empty(0) + # demand rate [m3/s] (demand0) + self.grid_cell_demand_rate: np.ndarray = np.empty(0) + # unmet demand volume within sub timestep [m3] + self.grid_cell_unmet_demand: np.ndarray = np.empty(0) + # unmet demand over whole timestep [m3] + self.grid_cell_deficit: np.ndarray = np.empty(0) + # potential evaporation [mm/s] # TODO this doesn't appear to be initialized anywhere currently + self.reservoir_potential_evaporation: np.ndarray = np.empty(0) + # shortcut to get an empty state instance if empty: return @@ -255,7 +254,10 @@ def __init__(self, grid: Grid = None, config: Benedict = None, parameters: Param if config.get('water_management.enabled', False): logging.debug(' - reservoirs') initialize_reservoir_state(self, grid, config, parameters) - + + def __getitem__(self, item): + return getattr(self, item) + def to_dataframe(self) -> pd.DataFrame: """Builds a dataframe from all the state values. diff --git a/mosartwmpy/subnetwork/irrigation.py b/mosartwmpy/subnetwork/irrigation.py index ad39be9..1e4cfd1 100644 --- a/mosartwmpy/subnetwork/irrigation.py +++ b/mosartwmpy/subnetwork/irrigation.py @@ -22,13 +22,13 @@ def subnetwork_irrigation(state: State, grid: Grid, parameters: Parameters) -> N flow_volume = np.empty_like(state.subnetwork_storage) np.copyto(flow_volume, state.subnetwork_storage) - volume_condition = calculate_volume_condition(flow_volume, state.reservoir_demand) + volume_condition = calculate_volume_condition(flow_volume, state.grid_cell_unmet_demand) - state.reservoir_supply = calculate_reservoir_supply(depth_condition, volume_condition, state.reservoir_supply, state.reservoir_demand, flow_volume) + state.grid_cell_supply = calculate_reservoir_supply(depth_condition, volume_condition, state.grid_cell_supply, state.grid_cell_unmet_demand, flow_volume) - flow_volume = calculate_flow_volume(depth_condition, volume_condition, flow_volume, state.reservoir_demand) + flow_volume = calculate_flow_volume(depth_condition, volume_condition, flow_volume, state.grid_cell_unmet_demand) - state.reservoir_demand = calculate_reservoir_demand(depth_condition, volume_condition, state.reservoir_demand, flow_volume) + state.grid_cell_unmet_demand = calculate_reservoir_demand(depth_condition, volume_condition, state.grid_cell_unmet_demand, flow_volume) flow_volume = update_flow_volume(depth_condition, volume_condition, flow_volume) diff --git a/mosartwmpy/update/update.py b/mosartwmpy/update/update.py index 45f3d20..7f79380 100644 --- a/mosartwmpy/update/update.py +++ b/mosartwmpy/update/update.py @@ -23,7 +23,7 @@ # filter pandas chained assignment warnings -- pretty sure handling this correctly pd.options.mode.chained_assignment = None -# TODO add docstrings to each method/class + def update(state: State, grid: Grid, parameters: Parameters, config: Benedict) -> None: """Advance the simulation one timestamp. @@ -56,6 +56,8 @@ def update(state: State, grid: Grid, parameters: Parameters, config: Benedict) - state.delta_storage[:] = 0 state.delta_storage_land[:] = 0 state.delta_storage_ocean[:] = 0 + if config.get('water_management.enabled', False): + state.grid_cell_deficit[:] = 0 # flood flood(state, grid, parameters, config) @@ -90,8 +92,8 @@ def update(state: State, grid: Grid, parameters: Parameters, config: Benedict) - state.channel_lateral_flow_hillslope_average[:] = 0 # get the demand volume for this substep - if config.get('water_management.enabled'): - state.reservoir_demand = state.reservoir_monthly_demand * delta_t + if config.get('water_management.enabled', False): + state.grid_cell_unmet_demand = state.grid_cell_demand_rate * delta_t # iterate substeps for remaining routing for __ in np.arange(config.get('simulation.routing_iterations')): @@ -169,6 +171,8 @@ def update(state: State, grid: Grid, parameters: Parameters, config: Benedict) - extraction_regulated_flow(state, grid, parameters, config, delta_t) state.outflow_after_regulation = -state.channel_outflow_downstream state.channel_flow = state.channel_flow - state.channel_outflow_downstream + # aggregate deficit + state.grid_cell_deficit = state.grid_cell_deficit + state.grid_cell_unmet_demand # accumulate local flow field state.flow = state.flow + state.channel_flow @@ -181,7 +185,7 @@ def update(state: State, grid: Grid, parameters: Parameters, config: Benedict) - if config.get('water_management.enabled'): # convert supply to flux - state.reservoir_supply = state.reservoir_supply / config.get('simulation.timestep') + state.grid_cell_supply = state.grid_cell_supply / config.get('simulation.timestep') # convert runoff back to m3/s for output state.hillslope_surface_runoff = state.hillslope_surface_runoff * grid.area diff --git a/paper/paper.bib b/paper/paper.bib index 51f3f7d..f3f3eb2 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -99,10 +99,74 @@ @article{https://doi.org/10.1029/2020WR027902 number = {10}, pages = {e2020WR027902}, keywords = {Large-scale hydrological and water resource model, reservoir, dam, simulation, drought, water supply}, - doi = {https://doi.org/10.1029/2020WR027902}, + doi = {10.1029/2020WR027902}, url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020WR027902}, eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR027902}, note = {e2020WR027902 2020WR027902}, abstract = {Abstract Large-scale hydrological and water resource models (LHMs) are used increasingly to study the vulnerability of human systems to water scarcity. These models rely on generic reservoir release schemes that often fail to capture the nuances of operations at individual dams. Here we assess wheth er empirically derived release-availability functions tailored to individual dams could improve the simulation performance of an LHM. Seasonally varying, linear piecewise relations that specify water release as a function of prevailing storage levels and forecasted future inflow are compared to a common generic scheme for 36 key reservoirs of the Columbia River Basin. When forced with observed inflows, the empirical approach captures observed release decisions better than the generic scheme—including under conditions of drought. The inclusion of seasonally varying inflow forecasts used by reservoir operators adds fur ther improvement. When exposed to biases and errors inherent in the LHM, data-driven policies fail to offer a robust improvement; inclusion of forecasts deteriorates LHM reservoir simulation performance in some cases. We perform sensitivity analysis to explain this result, finding that the bias inherent in LHM streamflow is amplified by a reservoir model that relies on forecasts. To harness the potential of interpretable, data-driven reservoir operating schemes, research must address LHM flow biases arising from inaccuracies in climate input, runoff generation, flow routing, and water withdrawal and consumption data. }, year = {2020} -} \ No newline at end of file +} + +@article{devex, + author = {Fagerholm, Fabian and Münch, Jürgen}, + year = {2013}, + month = {11}, + pages = {}, + title = {Developer experience: Concept and definition}, + doi = {10.1109/ICSSP.2012.6225984}, + url = {https://ieeexplore.ieee.org/document/6225984} +} + +@article{prechelt, + author = {L. {Prechelt}}, + journal = {Computer}, + title = {An empirical comparison of seven programming languages}, + year = {2000}, + volume = {33}, + number = {10}, + pages = {23-29}, + doi = {10.1109/2.876288}, + url = {https://ieeexplore.ieee.org/document/876288} +} + +@INPROCEEDINGS{Nanz, + author = {S. {Nanz} and C. A. {Furia}}, + booktitle = {2015 IEEE/ACM 37th IEEE International Conference on Software Engineering}, + title = {A Comparative Study of Programming Languages in Rosetta Code}, + year = {2015}, + volume = {1}, + number = {}, + pages = {778-788}, + doi = {10.1109/ICSE.2015.90}, + url = {https://ieeexplore.ieee.org/document/7194625} +} + + @misc{octoverse_2020, + url = {https://octoverse.github.com/}, + journal = {The State of the Octoverse}, + publisher = {GitHub}, + year = {2020}, + month = {Dec} + } + +@article{abm, + author = {Hyun, Jin-Young and Huang, Shih-Yu and Yang, Yi-Chen and Tidwell, Vincent and Macknick, Jordan}, + year = {2019}, + month = {05}, + pages = {2261-2278}, + title = {Using a coupled agent-based modeling approach to analyze the role of risk perception in water management decisions}, + volume = {23}, + journal = {Hydrology and Earth System Sciences}, + doi = {10.5194/hess-23-2261-2019} +} + +@article{reservoirs, + author = {Turner, Sean and Xu, Wenwei and Voisin, Nathalie}, + year = {2020}, + month = {03}, + pages = {1275-1291}, + title = {Inferred inflow forecast horizons guiding reservoir release decisions across the United States}, + volume = {24}, + journal = {Hydrology and Earth System Sciences}, + doi = {10.5194/hess-24-1275-2020} +} diff --git a/paper/paper.md b/paper/paper.md index a141012..beafa5a 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -21,20 +21,27 @@ bibliography: paper.bib --- # Statement of need -Though often overlooked, the developer experience can have a large downstream influence on the quality, legibility, and extendability of a codebase. Scientists and data wranglers alike have come to expect the ease and portability of working with frameworks like `numpy` and `pandas`, while dreading the need to fiddle with the intricacies of compilers and interpreters on instituitonal computing systems. This dread can often translate to hours lost in scanning cryptic log messages, tracking down an esoteric version of a system library, or just silently seething at the scientists that came before for their arcane and indiscernable code. `mosartwmpy` is a Python reimplementation of `MOSART-WM` [@voisin2013improved] -- a hydrological model for water routing and reservoir management written in Fortran -- that focuses on the developer experience by being intuitive, lightweight, well-documented, and interoperable. +Developer experience [@devex] can have a large downstream influence on the quality, usability, and extensibility of a codebase [@prechelt; @nanz]. Scientists and data wranglers alike have come to expect the ease and portability of working with libraries such as NumPy and Pandas, without the need to struggle with the intricacies of compilers and interpreters on institutional computing systems. This added effort can often translate to hours lost in scanning cryptic log messages or tracking down an esoteric version of a system library when hoping to add or improve a software feature. `mosartwmpy` is a Python reimplementation of MOSART-WM [@voisin2013improved] -- a hydrological model for water routing and reservoir management written in Fortran -- that focuses on the developer experience by being intuitive, lightweight, well-documented, extensible, and interoperable. -![River basin flow as output from `mosartwmpy`.](figure_1.png){ width=50% } +![River basin flow as output from `mosartwmpy`.\label{fig:1}](figure_1.png){ width=50% } # Summary -MOSART (MOdel for Scale Apaptive River Transport) was developed as a scalable framework for representing and studying riverine dynamics of water, energy, and biogeochemistry cycles across local, regional and global scales from an integrated human-earth system perspective [@li2013physically]. In particular, a water management module for dams/reservoirs has been embedded within MOSART (denoted as MOSART-WM) to simulate the effects of dam operations as well as surface-water withdrawal on downstream flow and water temperature across a range of spatial scales [@voisin2013improved]. +MOSART (MOdel for Scale Apaptive River Transport) was developed as a scalable framework for representing and studying riverine dynamics of water, energy, and biogeochemistry cycles across local, regional and global scales from an integrated human-earth system perspective [@li2013physically]. Additionally, @voisin2013improved introduced a component to MOSART named WM (Water Management) as a large scale spatially distributed reservoir and water management model which is tightly-coupled with the river routing MOSART model and represents the spatially distributed withdrawals, as well as withdrawals from river channels and diversion from reservoir releases. River regulation is represented through reservoirs (up to one per grid cell). Each reservoir release is independently calibrated using long term mean monthly inflow into the reservoir, long term monthly demand associated with this reservoir, and reservoir goals (flood control, irrigation, recreation, etc.). Generic monthly pre-release rules and storage targets are set up for individual reservoirs however those releases are updated annually for inter-annual variability (dry or wet year) and for daily constraints such as environmental flow minimum release, spill and minimum storage. The WM model allows an evaluation of the impact of water management over multiple river basins at once (global, continental scales) and with consistent representation of human operations over the full domain. -@voisin2013improved introduced MOSART-WM as a large scale spatially distributed reservoir and water management model which is coupled with the river routing MOSART model and represents the spatially distributed withdrawals, as well as withdrawals from river channels and diversion from reservoir releases. River regulation is represented through reservoirs (up to one per grid cell). Each reservoir release is independently calibrated using long term mean monthly inflow into the reservoir, long term monthly demand associated with this reservoir, and reservoir goals (flood control, irrigation, recreation, etc.). Generic monthly pre-release rules and storage targets are set up for individual reservoirs however those releases are updated annually for inter-annual variability (dry or wet year) and for daily constraints such as environmental flow minimun release, spill and minimum storage. The WM model allows evaluating the impact of water management over multiple river basins at once (global, continental scales) and with consistent representation of human operations over the full domain. +MOSART-WM is often utilized as the hydrological component in a larger suite of earth-science models, such as in @doecode_10475. In this context, MOSART-WM is quite efficient and streamlined when running on a supported High-Performance Computing (HPC) cluster. However, learning how to use, extend, and test a tightly-coupled codebase written with domain knowledge implied in a lower-level programming language may greatly reduce the userbase. Broadening the code’s accessibility using a programming language such as Python, which in 2020 was the second most utilized language on GitHub [@octoverse_2020], provides a more accessible option to learn and contribute to the science on most computational platforms. -Today, MOSART-WM is often utilised as the hydrological component in a larger suite of earth-science models, such as in @doecode_10475. In this incarnation, MOSART-WM is quite efficient and streamlined when running on a supported HPC cluster. But, learning to use this complex codebase can take quite a while, especially with the common need to wait in a queue in order to run the code on a supported machine. This slow turnaround time further complicates the ability to add new features to the existing codebase. Imagine waiting half a day or even just half an hour between writing new code and being able to test it. Furthermore, early career scientists and engineers rarely know Fortran or C, or the intricacies of working with the various compilers and architectures. +`mosartwmpy` was designed to bridge the gap between domain scientist who want a performant software that can still be extended for future research needs and the new user who may not have expertise within the hydrologic sciences but wishes to either 1) integrate the process into their own workflow, or 2) wants to quickly become capable in conducting hands-on experimentation for educational purposes. A refactor of MOSART-WM in Python ameliorates the steep learning curve of the FORTRAN version by providing an easy to learn, use, and modify interface. `mosartwmpy` was also built with interoperability in mind by implementing the Community Surface Dynamics Modeling System (CSDMS) Basic Model Interface (BMI) [@peckham2013component; @hutton2020basic], which offers a familiar set of controls for operating and coupling the model. `mosartwmpy` can be accessed on GitHub (https://github.com/IMMM-SFA/mosartwmpy), and a walkthrough of key functionality and use can be found here: [tutorial](https://mosartwmpy.readthedocs.io/en/latest/). Model results have been validated against historical simulations [@https://doi.org/10.1029/2020WR027902], and a validation utility is included with the code. -Enter `mosartwmpy`, a refactor of MOSART-WM in Python which aims to eliminate the noise by being easy to learn, use, and modify. Python has the advantages of being widely known by early career scientists and engineers, and of being able to run on nearly any platform without much fuss. `mosartwmpy` implements the Community Surface Dynamics Modeling System (CSDMS) Basic Model Interface (BMI) [@peckham2013component] [@hutton2020basic], which offers a familiar set of controls for operating and coupling the model. `mosartwmpy` can be accessed on GitHub (https://github.com/IMMM-SFA/mosartwmpy), and a walkthrough of key functionality and use can be found here [Tutorial](https://mosartwmpy.readthedocs.io/en/latest/). Model results have been validated against historical simulations [@https://doi.org/10.1029/2020WR027902], and a validation utility is included with the code. +As an open-source software, `mosartwmpy` promotes collaborative and community model development to meet scientific objectives. Two examples of the potential to extend this codebase are currently underway as a part of the U.S. DOE’s Department of Energy’s Integrated MultiSector MultiScale MultiModel (IM3) basic research project: an Agent Based Model (ABM) for more accurately simulating irrigation demand based on the economics of various crop types [@abm], and an improved reservoir operations module for more accurately simulating reservoir release based on data-driven harmonic functions [@reservoirs]. -Two new model features (and associated scientific papers) are already under development in the new paradigm: an Agent Based Model (ABM) for more accurately simulating irrigation demand based on the economics of various crop types led by Jim Yoon, and an improved reservoir operations module for more accurately simulating reservoir release based on data-driven harmonic functions led by Sean Turner. The turnaround time for implementing these new features in the codebase is proving to be much quicker than past work. +# Functionality and limitations +`mosartwmpy` replicates the entirety of the physics found in MOSART-WM. Input consists of surface, subsurface, and wetland runoff (i.e. rain and snow melt that flows downhill over or under the land). The flow of runoff populates the river channels and tributaries, which generally flow toward the oceans [@fig:1]. In many locations along the main channels, reservoirs collect and store water from the channels for various uses by humanity (i.e. hydroelectricity, drinking water), and release portions of the storage downstream over time. Water is also consumed directly from the channels and tributaries for irrigation or industry. + +In `mosartwmpy`, these interactions are considered using multiple, dynamic timescales. On a 1/8 degree grid of the United States for instance, the overland flow is usually considered at a one-hour resolution, the reservoir extraction at a 20-minute resolution, and the channel flow at resolutions ranging from 12 minutes to half a minute based on the complexity of the grid cell. + +Any spatial or simulated variable can be written to an output file in the NetCDF format at predetermined intervals, typically at a daily averaged resolution. Similarly, the model can write out the entirety of its current state as a compressed restart file, allowing the simulation to be regularly checkpointed. + +Currently, the simulation can efficiently scale across ten or so CPUs by using C-bindings to parallelize the vectorized calculations. More work needs to be done in this area to measure up to the speeds obtainable in the Fortran version of MOSART-WM, which can effectively scale across any number of CPUs by distributing cell-by-cell calculations. For this reason, `mosartwmpy` has the advantage of being able to run on any system that supports Python, but the disadvantage of being significantly slower for large scale simulations. This disparity will be addressed in ongoing work. # Acknowledgements This research was supported in part by the U.S. Department of Energy, Office of Science, as part of research in MultiSector Dynamics, Earth and Environmental System Modeling Program. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. The views and opinions expressed in this paper are those of the authors alone. diff --git a/validation/validate.py b/validation/validate.py index cb18235..0cb953f 100644 --- a/validation/validate.py +++ b/validation/validate.py @@ -22,7 +22,7 @@ print("https://github.com/IMMM-SFA/mosartwmpy") print() -data_path = input("Where are your output files located? Enter the absolute path or path relative to the wolfgang root directory: ") +data_path = input("Where are your output files located? Enter the absolute path or path relative to the mosartwmpy root directory: ") print() assert os.path.exists(data_path), f"Unable to find this path, {str(data_path)} - please double check and try again." @@ -43,7 +43,7 @@ baseline_data = baseline_data[variables_of_interest] except: print(f"Either your data or the baseline data does not contain the expected variables: {variables_of_interest}.") - print("Please double check and try again or update this code to look for your vairables of interest.") + print("Please double check and try again or update this code to look for your variables of interest.") quit() # normalize the time indexes to prevent alignment errors From e320d7b20c39e0eb046e83b9c3933a78a84bc266 Mon Sep 17 00:00:00 2001 From: travis Date: Fri, 19 Mar 2021 08:01:13 -0700 Subject: [PATCH 11/20] update figure reference --- paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index beafa5a..a02fae6 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -23,7 +23,7 @@ bibliography: paper.bib # Statement of need Developer experience [@devex] can have a large downstream influence on the quality, usability, and extensibility of a codebase [@prechelt; @nanz]. Scientists and data wranglers alike have come to expect the ease and portability of working with libraries such as NumPy and Pandas, without the need to struggle with the intricacies of compilers and interpreters on institutional computing systems. This added effort can often translate to hours lost in scanning cryptic log messages or tracking down an esoteric version of a system library when hoping to add or improve a software feature. `mosartwmpy` is a Python reimplementation of MOSART-WM [@voisin2013improved] -- a hydrological model for water routing and reservoir management written in Fortran -- that focuses on the developer experience by being intuitive, lightweight, well-documented, extensible, and interoperable. -![River basin flow as output from `mosartwmpy`.\label{fig:1}](figure_1.png){ width=50% } +![River basin flow as output from `mosartwmpy`.\label{fig:one}](figure_1.png){ width=75% } # Summary MOSART (MOdel for Scale Apaptive River Transport) was developed as a scalable framework for representing and studying riverine dynamics of water, energy, and biogeochemistry cycles across local, regional and global scales from an integrated human-earth system perspective [@li2013physically]. Additionally, @voisin2013improved introduced a component to MOSART named WM (Water Management) as a large scale spatially distributed reservoir and water management model which is tightly-coupled with the river routing MOSART model and represents the spatially distributed withdrawals, as well as withdrawals from river channels and diversion from reservoir releases. River regulation is represented through reservoirs (up to one per grid cell). Each reservoir release is independently calibrated using long term mean monthly inflow into the reservoir, long term monthly demand associated with this reservoir, and reservoir goals (flood control, irrigation, recreation, etc.). Generic monthly pre-release rules and storage targets are set up for individual reservoirs however those releases are updated annually for inter-annual variability (dry or wet year) and for daily constraints such as environmental flow minimum release, spill and minimum storage. The WM model allows an evaluation of the impact of water management over multiple river basins at once (global, continental scales) and with consistent representation of human operations over the full domain. @@ -35,7 +35,7 @@ MOSART-WM is often utilized as the hydrological component in a larger suite of e As an open-source software, `mosartwmpy` promotes collaborative and community model development to meet scientific objectives. Two examples of the potential to extend this codebase are currently underway as a part of the U.S. DOE’s Department of Energy’s Integrated MultiSector MultiScale MultiModel (IM3) basic research project: an Agent Based Model (ABM) for more accurately simulating irrigation demand based on the economics of various crop types [@abm], and an improved reservoir operations module for more accurately simulating reservoir release based on data-driven harmonic functions [@reservoirs]. # Functionality and limitations -`mosartwmpy` replicates the entirety of the physics found in MOSART-WM. Input consists of surface, subsurface, and wetland runoff (i.e. rain and snow melt that flows downhill over or under the land). The flow of runoff populates the river channels and tributaries, which generally flow toward the oceans [@fig:1]. In many locations along the main channels, reservoirs collect and store water from the channels for various uses by humanity (i.e. hydroelectricity, drinking water), and release portions of the storage downstream over time. Water is also consumed directly from the channels and tributaries for irrigation or industry. +`mosartwmpy` replicates the entirety of the physics found in MOSART-WM. Input consists of surface, subsurface, and wetland runoff (i.e. rain and snow melt that flows downhill over or under the land). The flow of runoff populates the river channels and tributaries, which generally flow toward the oceans (as in Figure \autoref{@fig:one}). In many locations along the main channels, reservoirs collect and store water from the channels for various uses by humanity (i.e. hydroelectricity, drinking water), and release portions of the storage downstream over time. Water is also consumed directly from the channels and tributaries for irrigation or industry. In `mosartwmpy`, these interactions are considered using multiple, dynamic timescales. On a 1/8 degree grid of the United States for instance, the overland flow is usually considered at a one-hour resolution, the reservoir extraction at a 20-minute resolution, and the channel flow at resolutions ranging from 12 minutes to half a minute based on the complexity of the grid cell. From 3d246db1d9de68b2dc9e4c4689c2396971878df7 Mon Sep 17 00:00:00 2001 From: crvernon Date: Mon, 22 Mar 2021 12:26:18 -0400 Subject: [PATCH 12/20] edits for draft 2 --- paper/paper.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index a02fae6..0fd8449 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,5 +1,5 @@ --- -title: 'mosartwmpy: A Python implementaion of the MOSART-WM coupled hydrologic routing and water management model' +title: 'mosartwmpy: A Python implementation of the MOSART-WM coupled hydrologic routing and water management model' tags: - Python - hydrology @@ -16,32 +16,32 @@ authors: affiliations: - name: Pacific Northwest National Laboratory, Richland, WA., USA index: 1 -date: 21 March 2021 +date: 22 March 2021 bibliography: paper.bib --- # Statement of need -Developer experience [@devex] can have a large downstream influence on the quality, usability, and extensibility of a codebase [@prechelt; @nanz]. Scientists and data wranglers alike have come to expect the ease and portability of working with libraries such as NumPy and Pandas, without the need to struggle with the intricacies of compilers and interpreters on institutional computing systems. This added effort can often translate to hours lost in scanning cryptic log messages or tracking down an esoteric version of a system library when hoping to add or improve a software feature. `mosartwmpy` is a Python reimplementation of MOSART-WM [@voisin2013improved] -- a hydrological model for water routing and reservoir management written in Fortran -- that focuses on the developer experience by being intuitive, lightweight, well-documented, extensible, and interoperable. +Developer experience [@devex] can have a large downstream influence on the quality, usability, and extensibility of a codebase [@prechelt; @nanz]. Scientific codebases that have been modified throughout their development life-cycle to include new features that represent different systems in a tightly-coupled manner are often more difficult for new collaborators to utilize [@inproceedings]. Scientists and data wranglers alike have also come to expect the ease and portability of working with libraries such as NumPy and Pandas, without the need to struggle with the intricacies of compilers and interpreters on institutional computing systems. These challenges can often translate to hours lost in scanning cryptic log messages for traceability or tracking down an esoteric version of a system library when hoping to add or improve a software feature. `mosartwmpy` is a Python reimplementation of MOSART-WM [@voisin2013improved] -- a hydrological model for water routing and reservoir management written in Fortran -- that focuses on the developer experience by being intuitive, lightweight, well-documented, extensible, and interoperable. ![River basin flow as output from `mosartwmpy`.\label{fig:one}](figure_1.png){ width=75% } # Summary -MOSART (MOdel for Scale Apaptive River Transport) was developed as a scalable framework for representing and studying riverine dynamics of water, energy, and biogeochemistry cycles across local, regional and global scales from an integrated human-earth system perspective [@li2013physically]. Additionally, @voisin2013improved introduced a component to MOSART named WM (Water Management) as a large scale spatially distributed reservoir and water management model which is tightly-coupled with the river routing MOSART model and represents the spatially distributed withdrawals, as well as withdrawals from river channels and diversion from reservoir releases. River regulation is represented through reservoirs (up to one per grid cell). Each reservoir release is independently calibrated using long term mean monthly inflow into the reservoir, long term monthly demand associated with this reservoir, and reservoir goals (flood control, irrigation, recreation, etc.). Generic monthly pre-release rules and storage targets are set up for individual reservoirs however those releases are updated annually for inter-annual variability (dry or wet year) and for daily constraints such as environmental flow minimum release, spill and minimum storage. The WM model allows an evaluation of the impact of water management over multiple river basins at once (global, continental scales) and with consistent representation of human operations over the full domain. +MOSART (MOdel for Scale Adaptive River Transport) was developed as a scalable framework for representing and studying riverine dynamics of water, energy, and biogeochemistry cycles across local, regional and global scales from an integrated human-Earth system perspective [@li2013physically]. Additionally, @voisin2013improved introduced a component to MOSART named WM (Water Management) as a large scale spatially distributed reservoir and water management model which is tightly-coupled with the river routing MOSART model and represents the spatially distributed withdrawals, as well as withdrawals from river channels and diversion from reservoir releases. River regulation is represented through reservoirs (up to one per grid cell). Each reservoir release is independently calibrated using long-term mean monthly inflow into the reservoir, long-term monthly demand associated with this reservoir, and reservoir goals (flood control, irrigation, recreation, etc.). Generic monthly pre-release rules and storage targets are set up for individual reservoirs; however, those releases are updated annually for inter-annual variability (dry or wet year) and for daily constraints such as environmental flow minimum release, spill and minimum storage. The WM model allows an evaluation of the impact of water management over multiple river basins at once (global, continental scales) and with consistent representation of human operations over the full domain. MOSART-WM is often utilized as the hydrological component in a larger suite of earth-science models, such as in @doecode_10475. In this context, MOSART-WM is quite efficient and streamlined when running on a supported High-Performance Computing (HPC) cluster. However, learning how to use, extend, and test a tightly-coupled codebase written with domain knowledge implied in a lower-level programming language may greatly reduce the userbase. Broadening the code’s accessibility using a programming language such as Python, which in 2020 was the second most utilized language on GitHub [@octoverse_2020], provides a more accessible option to learn and contribute to the science on most computational platforms. -`mosartwmpy` was designed to bridge the gap between domain scientist who want a performant software that can still be extended for future research needs and the new user who may not have expertise within the hydrologic sciences but wishes to either 1) integrate the process into their own workflow, or 2) wants to quickly become capable in conducting hands-on experimentation for educational purposes. A refactor of MOSART-WM in Python ameliorates the steep learning curve of the FORTRAN version by providing an easy to learn, use, and modify interface. `mosartwmpy` was also built with interoperability in mind by implementing the Community Surface Dynamics Modeling System (CSDMS) Basic Model Interface (BMI) [@peckham2013component; @hutton2020basic], which offers a familiar set of controls for operating and coupling the model. `mosartwmpy` can be accessed on GitHub (https://github.com/IMMM-SFA/mosartwmpy), and a walkthrough of key functionality and use can be found here: [tutorial](https://mosartwmpy.readthedocs.io/en/latest/). Model results have been validated against historical simulations [@https://doi.org/10.1029/2020WR027902], and a validation utility is included with the code. +`mosartwmpy` was designed to bridge the gap between the domain scientist who wants a performant software that can still be extended for future research needs and the new user who may not have expertise within the hydrologic sciences but wishes to either 1) integrate the process into their own workflow, or 2) wants to quickly become capable in conducting hands-on experimentation for educational purposes. A refactor of MOSART-WM in Python ameliorates the steep learning curve of the FORTRAN version by providing an easy to learn, use, and modify interface. `mosartwmpy` was also built with interoperability in mind by implementing the Community Surface Dynamics Modeling System (CSDMS) Basic Model Interface (BMI) [@peckham2013component; @hutton2020basic], which offers a familiar set of controls for operating and coupling the model. `mosartwmpy` can be accessed on GitHub (https://github.com/IMMM-SFA/mosartwmpy), and a walkthrough of key functionality and use can be found here: [tutorial](https://mosartwmpy.readthedocs.io/en/latest/). Model results have been validated against historical simulations [@https://doi.org/10.1029/2020WR027902], and a validation utility is included with the code. As an open-source software, `mosartwmpy` promotes collaborative and community model development to meet scientific objectives. Two examples of the potential to extend this codebase are currently underway as a part of the U.S. DOE’s Department of Energy’s Integrated MultiSector MultiScale MultiModel (IM3) basic research project: an Agent Based Model (ABM) for more accurately simulating irrigation demand based on the economics of various crop types [@abm], and an improved reservoir operations module for more accurately simulating reservoir release based on data-driven harmonic functions [@reservoirs]. # Functionality and limitations -`mosartwmpy` replicates the entirety of the physics found in MOSART-WM. Input consists of surface, subsurface, and wetland runoff (i.e. rain and snow melt that flows downhill over or under the land). The flow of runoff populates the river channels and tributaries, which generally flow toward the oceans (as in Figure \autoref{@fig:one}). In many locations along the main channels, reservoirs collect and store water from the channels for various uses by humanity (i.e. hydroelectricity, drinking water), and release portions of the storage downstream over time. Water is also consumed directly from the channels and tributaries for irrigation or industry. +`mosartwmpy` replicates the entirety of the physics found in MOSART-WM. Inputs consists of surface, subsurface, and wetland runoff (i.e. rain and snow melt that flows downhill over or under the land). The flow of runoff populates the river channels and tributaries, which generally flow toward the oceans (as in Figure \autoref{@fig:one}). In many locations along the main channels, reservoirs collect and store water from the channels for various societal uses (i.e. hydroelectricity, drinking water), and release portions of the storage downstream over time. Water is also consumed directly from the channels and tributaries for irrigation or industry. In `mosartwmpy`, these interactions are considered using multiple, dynamic timescales. On a 1/8 degree grid of the United States for instance, the overland flow is usually considered at a one-hour resolution, the reservoir extraction at a 20-minute resolution, and the channel flow at resolutions ranging from 12 minutes to half a minute based on the complexity of the grid cell. -Any spatial or simulated variable can be written to an output file in the NetCDF format at predetermined intervals, typically at a daily averaged resolution. Similarly, the model can write out the entirety of its current state as a compressed restart file, allowing the simulation to be regularly checkpointed. +Any spatial or simulated variable can be written to an output file in NetCDF format at predetermined intervals, typically at a daily averaged resolution. Similarly, the model can write out the entirety of its current state as a compressed restart file, allowing the simulation to be regularly checkpointed. -Currently, the simulation can efficiently scale across ten or so CPUs by using C-bindings to parallelize the vectorized calculations. More work needs to be done in this area to measure up to the speeds obtainable in the Fortran version of MOSART-WM, which can effectively scale across any number of CPUs by distributing cell-by-cell calculations. For this reason, `mosartwmpy` has the advantage of being able to run on any system that supports Python, but the disadvantage of being significantly slower for large scale simulations. This disparity will be addressed in ongoing work. +Currently, the simulation can efficiently scale across ten or so CPUs using C-bindings to parallelize the vectorized calculations. More work needs to be done in this area to measure up to the speeds obtainable in the Fortran version of MOSART-WM, which can effectively scale across any number of CPUs by distributing cell-by-cell calculations. For this reason, `mosartwmpy` has the advantage of being able to run on any system that supports Python, but the disadvantage of being significantly slower for large scale simulations. This disparity will be addressed in ongoing work. # Acknowledgements This research was supported in part by the U.S. Department of Energy, Office of Science, as part of research in MultiSector Dynamics, Earth and Environmental System Modeling Program. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. The views and opinions expressed in this paper are those of the authors alone. From a551e5768283d400313837f37f981a829da9ed17 Mon Sep 17 00:00:00 2001 From: crvernon Date: Mon, 22 Mar 2021 12:26:40 -0400 Subject: [PATCH 13/20] add ref for modularity --- paper/paper.bib | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/paper/paper.bib b/paper/paper.bib index f3f3eb2..d42c999 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -91,7 +91,7 @@ @article{hutton2020basic journal = {Journal of Open Source Software} } -@article{https://doi.org/10.1029/2020WR027902, +@article{https://doi.org/10.1029/2020WR027902, author = {Turner, Sean W. D. and Doering, Kenji and Voisin, Nathalie}, title = {Data-Driven Reservoir Simulation in a Large-Scale Hydrological and Water Resource Model}, journal = {Water Resources Research}, @@ -170,3 +170,12 @@ @article{reservoirs journal = {Hydrology and Earth System Sciences}, doi = {10.5194/hess-24-1275-2020} } + +@inproceedings{schwanke1991intelligent, + title={An intelligent tool for re-engineering software modularity}, + author={Schwanke, Robert W}, + booktitle={Proceedings-13th International Conference on Software Engineering}, + pages={83--84}, + year={1991}, + organization={IEEE Computer Society} +} From a00b7041b1ed11d1cb14803cd8747cc7f86b3d0d Mon Sep 17 00:00:00 2001 From: travis Date: Mon, 22 Mar 2021 10:31:49 -0700 Subject: [PATCH 14/20] update docs --- README.md | 130 ++++++++++++++++++++++++++++++++++++++++++---------- config.yaml | 10 ++-- launch.py | 2 +- 3 files changed, 110 insertions(+), 32 deletions(-) diff --git a/README.md b/README.md index b7e71df..5d23121 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,11 @@ ![build](https://github.com/IMMM-SFA/mosartwmpy/workflows/build/badge.svg) [![codecov](https://codecov.io/gh/IMMM-SFA/mosartwmpy/branch/main/graph/badge.svg?token=IPOY8984MB)](https://codecov.io/gh/IMMM-SFA/mosartwmpy) - -### getting started +## mosartwmpy `mosartwmpy` is a python translation of Mosart-WM, a model for water routing and reservoir management written in Fortran. The original code can be found at [IWMM](https://github.com/IMMM-SFA/iwmm) and [E3SM](https://github.com/E3SM-Project/E3SM), in which Mosart is the hdyrological component of a larger suite of earth-science models. The motivation for rewriting is largely for developer convenience -- running, debugging, and adding new capabilities were becoming increasingly difficult due to the complexity of the codebase and lack of familiarity with Fortran. This version aims to be intuitive, lightweight, and well documented, while still being highly interoperable. +## getting started + Install requirements with `pip install -r requirements.txt`. `mosartwmpy` implements the [Basic Model Interface](https://csdms.colorado.edu/wiki/BMI) defined by the CSDMS, so driving it should be familiar to those accustomed to the BMI: @@ -41,29 +42,95 @@ Alternatively, one can update the settings via code in the driving script: mosart_wm.config['simulation.end_date'] = datetime(1985, 12, 31) ``` -By default, key model variables are output on a monthly basis at a daily averaged resolution to `./output//__.nc`. See the configuration file for examples of how to modify the outputs, and the `./mosartwmpy/state/state.py` file for state variable names. - -Alternatively, certain model outputs deemed most important can be accessed using the BMI interface methods. For example: -```python -# get a list of model output variables -mosart_wm.get_output_var_names() - -# get the flattened numpy.ndarray of values for an output variable -mosart_wm.get_value_ptr('supply_water_amount') -``` - - -### input -Several input files in NetCDF format are required to successfully run a simulation, which are not shipped with this repository due to their large size. The grid files, reservoir files, and a small range of runoff and demand input files are available for public download as a zip archive [here](https://zenodo.org/record/4537907/files/mosartwmpy_sample_input_data_1980_1985.zip?download=1). This data can also be obtained using the download utility by running `python download.py` in the repository root and choosing option 1 for "sample_input". Currently, all input files are assumed to be at the same resolution (for the sample files this is 1/8 degree over the CONUS). Below is a summary of the various input files: - -Name | Description | Configuration Path | Notes ---- | --- | --- | --- -Grid | Spatial constants dimensioned by latitude and longitude relating to the physical properties of the river channels | `grid.path` | -Land Fraction | Fraction of grid cell that is land (as opposed to i.e. ocean water) dimensioned by latitude and longitude | `grid.land.path` | as a TODO item, this variable should be merged into the grid file (historically it was separate for the coupled land model) -Reservoirs | Locations of reservoirs (possibly aggregated) and their physical and political properties | `water_management.reservoirs.path` | -Runoff | Surface runoff, subsurface runoff, and wetland runoff per grid cell averaged per unit of time; used to drive the river routing | `runoff.path` | -Demand | Water demand of grid cells averaged per unit of time; currently assumed to be monthly | `water_management.reservoirs.demand` | there are plans to support other time scales, such as epiweeks +## model input + +Several input files in NetCDF format are required to successfully run a simulation, which are not shipped with this repository due to their large size. The grid files, reservoir files, and a small range of runoff and demand input files can be obtained using the download utility by running `python download.py` in the repository root and choosing option 1 for "sample_input". Currently, all input files are assumed to be at the same resolution (for the sample files this is 1/8 degree over the CONUS). Below is a summary of the various input files: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Name + + Description + + Configuration Path + + Notes +
+ Grid + + Spatial constants dimensioned by latitude and longitude relating to the physical properties of the river channels + + grid.path + +
+ Land Fraction + + Fraction of grid cell that is land (as opposed to i.e. ocean water) dimensioned by latitude and longitude + + grid.land.path + + As a TODO item, this variable should be merged into the grid file (historically it was separate for the coupled land model) +
+ Reservoirs + + Locations of reservoirs (possibly aggregated) and their physical and political properties + + water_management.reservoirs.path + +
+ Runoff + + Surface runoff, subsurface runoff, and wetland runoff per grid cell averaged per unit of time; used to drive the river routing + + runoff.path + +
+ Demand + + Water demand of grid cells averaged per unit of time; currently assumed to be monthly + + water_management.reservoirs.demand + + There are plans to support other time scales, such as epiweeks +
Alternatively, certain model inputs can be set using the BMI interface. This can be useful for coupling `mosartwmpy` with other models. If setting an input that would typically be read from a file, be sure to disable the `read_from_file` configuration value for that input. For example: ```python @@ -79,7 +146,20 @@ Alternatively, certain model inputs can be set using the BMI interface. This can mosart_wm.set_value('surface_runoff_flux', surface_runoff) ``` -### testing and validation +## model output + +By default, key model variables are output on a monthly basis at a daily averaged resolution to `./output//__.nc`. See the configuration file for examples of how to modify the outputs, and the `./mosartwmpy/state/state.py` file for state variable names. + +Alternatively, certain model outputs deemed most important can be accessed using the BMI interface methods. For example: +```python +# get a list of model output variables +mosart_wm.get_output_var_names() + +# get the flattened numpy.ndarray of values for an output variable +mosart_wm.get_value_ptr('supply_water_amount') +``` + +## testing and validation Before running the tests or validation, make sure to download the "sample_input" and "validation" datasets using the download utility `python download.py`. diff --git a/config.yaml b/config.yaml index 6754bb1..e425e34 100644 --- a/config.yaml +++ b/config.yaml @@ -4,9 +4,9 @@ simulation: # name this simulation name: mosartwmpy demo # date to begin the simulation - start_date: 1980-01-01 + start_date: 1981-01-01 # date to end the simulation - end_date: 1980-01-31 + end_date: 1981-01-31 # For example -- to change the input files, update the paths below: @@ -17,8 +17,8 @@ simulation: # # path to the land grid domain file; can be absolute or relative to the source code root # path: /pic/projects/im3/iwmm/share/domains/domain.clm/domain.lnd.nldas2_0224x0464_c110415.nc # runoff: -# # flag to turn this feature on or off -# enabled: true +# # whether or not to read runoff input from file +# read_from_file: true # # path to the runoff file; can be absolute or relative to the source code root # path: /pic/projects/im3/iwmm/lnd/dlnd7/NLDAS/Livneh_NLDAS_1980_2011.nc # water_management: @@ -30,5 +30,3 @@ simulation: # reservoirs: # # path to the reservoir parameter file; can be absolute or relative to the source code root # path: /pic/projects/im3/iwmm/input_data/runoff/US_reservoir_8th_NLDAS3_updated_20200421.nc -# multiprocessing: -# enabled: false diff --git a/launch.py b/launch.py index 07f2a2c..bbe60df 100644 --- a/launch.py +++ b/launch.py @@ -9,4 +9,4 @@ # launch simulation mosart_wm = Model() mosart_wm.initialize('./config.yaml') - mosart_wm.update_until(mosart_wm.get_end_time()) \ No newline at end of file + mosart_wm.update_until(mosart_wm.get_end_time()) From 36d2361b4a07b83150b4ae12cfe5aec8f0108861 Mon Sep 17 00:00:00 2001 From: travis Date: Tue, 23 Mar 2021 08:20:27 -0700 Subject: [PATCH 15/20] remove numba dependency; allow mid month starts (fixes #26); update documenation --- config_defaults.yaml | 7 +++---- mosartwmpy/output/output.py | 9 +++++---- mosartwmpy/reservoirs/regulation.py | 25 ++----------------------- requirements.txt | 3 +-- 4 files changed, 11 insertions(+), 33 deletions(-) diff --git a/config_defaults.yaml b/config_defaults.yaml index b138830..f091f32 100644 --- a/config_defaults.yaml +++ b/config_defaults.yaml @@ -23,12 +23,11 @@ simulation: routing_iterations: 5 # routing method to use # TODO only 1 is implemented so far - # 1 --> variable storage method from SWAT model; 2 --> Muskingum method? ( 1 -- Kinematic wave method; 4 -- Diffusion wave method. --Inund. ) + # 1 --> variable storage method from SWAT model; 2 --> Muskingum method? routing_method: 1 # log level, one of DEBUG, INFO, WARNING, ERROR, CRITICAL log_level: INFO # resume simulation from restart file - # TODO not yet supported restart_file: ~ # how often to write a restart file; one of 'daily', 'monthly', 'yearly' -- a restart file is also always written at the end of the simulation restart_file_frequency: yearly @@ -151,7 +150,7 @@ grid: # bankfull depth of main channel field name [m] grid_channel_depth: rdep # land grid information - # TODO this is just needed for land_frac, so should just add it to mosart grid + # TODO this is only needed for land_frac variable, so should just add it to mosart grid # TODO assumed to be the same grid as the main grid land: # path to the land grid domain file; can be absolute or relative to the source code root @@ -209,7 +208,7 @@ water_management: time: time # demand field name # TODO support the different demand types using the flags demand: totalDemand - # TODO these aren't available yet + # TODO these modules aren't available yet # flag to enable or disable groundwater groundwater_enabled: false # flag to enable or disable return flow diff --git a/mosartwmpy/output/output.py b/mosartwmpy/output/output.py index 61a788e..5143b00 100644 --- a/mosartwmpy/output/output.py +++ b/mosartwmpy/output/output.py @@ -2,7 +2,8 @@ import numpy as np import pandas as pd -from datetime import datetime, timezone, timedelta +from datetime import timezone, timedelta +from pathlib import Path from xarray import concat, open_dataset from mosartwmpy.utilities.timing import timing @@ -66,7 +67,7 @@ def write_output(self): if self.current_time.month == 1 and self.current_time.day == 2 and self.current_time.hour == 0 and self.current_time.second == 0: is_new_period = True else: - logging.warn(f'Configuration value for `simulation.output_file_frequency: {period}` is not recognized.') + logging.warning(f'Configuration value for `simulation.output_file_frequency: {period}` is not recognized.') return filename += '.nc' @@ -100,8 +101,8 @@ def write_output(self): if output.get('units'): frame[output.get('name')].attrs['units'] = output.get('units') - # if new period, write to new file and include grid variables, otherwise update file - if not is_new_period: + # if file exists and it's not a new period, update existing file else write to new file and include grid variables + if not is_new_period and Path(filename).is_file(): nc = open_dataset(filename).load() # slice the existing data to account for restarts nc = nc.sel(time=slice(None, pd.to_datetime(self.current_time) - pd.Timedelta('1ms'))) diff --git a/mosartwmpy/reservoirs/regulation.py b/mosartwmpy/reservoirs/regulation.py index fa16f0b..f936c1c 100644 --- a/mosartwmpy/reservoirs/regulation.py +++ b/mosartwmpy/reservoirs/regulation.py @@ -3,7 +3,6 @@ import pandas as pd from benedict.dicts import benedict as Benedict -from numba import jit, prange from mosartwmpy.config.parameters import Parameters from mosartwmpy.grid.grid import Grid @@ -167,7 +166,7 @@ def extraction_regulated_flow(state: State, grid: Grid, parameters: Parameters, case = reservoir_demand_flow else: # subset reservoir list to speed up calculation - case = reservoir_demand_flow[parallel_is_in(reservoir_demand_flow.index.astype(int).values, demand.reservoir_id.unique())] + case = reservoir_demand_flow[np.isin(reservoir_demand_flow.index.astype(int).values, demand.reservoir_id.unique())] case.loc[:, 'reservoir_demand'] = case.join(demand.groupby('reservoir_id')[['grid_cell_demand']].sum()).grid_cell_demand.fillna(0) # ratio of flow to total demand @@ -175,7 +174,7 @@ def extraction_regulated_flow(state: State, grid: Grid, parameters: Parameters, # case 1 if case.demand_fraction.gt(1).any(): - case = demand[parallel_is_in(demand.reservoir_id.values, case[case.demand_fraction.gt(1)].index.astype(int).values)] + case = demand[np.isin(demand.reservoir_id.values, case[case.demand_fraction.gt(1)].index.astype(int).values)] case.loc[:, 'condition_count'] = case.groupby(case.index)['reservoir_id'].transform('count') case.loc[:, 'supply'] = divide(case.grid_cell_demand, case.condition_count) taken_from_reservoir = reservoir_demand_flow.join(case.groupby('reservoir_id').supply.sum()).supply.fillna(0).values @@ -220,26 +219,6 @@ def extraction_regulated_flow(state: State, grid: Grid, parameters: Parameters, # add the residual flow volume back state.channel_outflow_downstream[:] -= pd.DataFrame(grid.reservoir_id, columns=['reservoir_id']).merge(reservoir_demand_flow.flow_volume, how='left', left_on='reservoir_id', right_index=True).flow_volume.fillna(0).values / delta_t -@jit(nopython=True, parallel=True, fastmath=True, cache=True) -def parallel_is_in(a: np.ndarray, b: np.ndarray) -> np.ndarray: - """Just-in-time compiled parallel C code for determing the boolean mask on array a values that exist in array b. This is slightly faster than pandas/numpy. - - Args: - a (np.ndarray): the array of values to mask - b (np.ndarray): the array of values for comparison - - Returns: - np.ndarray: a boolean mask of a in b. - """ - s = set(b) - l = len(a) - result = np.full(l, False) - for i in prange(l): - for j in range(len(s)): - if a[i] == b[j]: - result[i] = True - break - return result calculate_flow_volume = ne.NumExpr( 'where(' diff --git a/requirements.txt b/requirements.txt index be1ae57..2391b4d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,13 +6,12 @@ h5netcdf==0.10.0 matplotlib==3.3.3 nc-time-axis==1.2.0 netCDF4==1.5.4 -numba==0.51.2 numexpr==2.7.2 numpy==1.19.4 pandas==1.1.4 pathvalidate==2.3.0 psutil==5.7.3 -pyarrow==3.0.0 +pyarrow==3.0.0ß python-benedict==0.22.0 recommonmark==0.7.1 sphinx==3.5.2 From 7c15b43b2cc2cd05efc40e2dcfb2f24c05696e10 Mon Sep 17 00:00:00 2001 From: travis Date: Tue, 23 Mar 2021 08:36:26 -0700 Subject: [PATCH 16/20] check for out of order start and end dates; fixes #25 --- mosartwmpy/model.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/mosartwmpy/model.py b/mosartwmpy/model.py index 435e798..50f1f5b 100644 --- a/mosartwmpy/model.py +++ b/mosartwmpy/model.py @@ -43,11 +43,11 @@ class Model(Bmi): """ def __init__(self): - self.name = None + self.name: str = None self.config = benedict() self.grid = None self.restart = None - self.current_time = None + self.current_time: datetime = None self.parameters = None self.state = None self.output_buffer = None @@ -93,6 +93,9 @@ def initialize(self, config_file_path: str, grid: Grid = None, state: State = No logging.info(f' * {u}') except: pass + # ensure that end date is after start date + if self.config.get('simulation.end_date') < self.config.get('simulation.start_date'): + raise ValueError(f"Configured `end_date` {self.config.get('simulation.end_date')} is prior to configured `start_date` {self.config.get('simulation.start_date')}; please update and try again.") # detect available physical cores self.cores = psutil.cpu_count(logical=False) logging.info(f'Cores: {self.cores}.') @@ -199,6 +202,10 @@ def update(self) -> None: self.state.hillslope_wetland_runoff[:] = 0 def update_until(self, time: float) -> None: + # make sure that requested end time is after now + if time < self.current_time.timestamp(): + logging.error('`time` is prior to current model time. Please choose a new `time` and try again.') + return # perform timesteps until time t = timer() while self.get_current_time() < time: From eb25c4e6d514cfac0cc7a99bcb852de7a722dfff Mon Sep 17 00:00:00 2001 From: travis Date: Tue, 23 Mar 2021 08:52:50 -0700 Subject: [PATCH 17/20] log to stdout and file by default --- config_defaults.yaml | 2 ++ mosartwmpy/model.py | 8 ++++++-- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/config_defaults.yaml b/config_defaults.yaml index f091f32..6499c69 100644 --- a/config_defaults.yaml +++ b/config_defaults.yaml @@ -27,6 +27,8 @@ simulation: routing_method: 1 # log level, one of DEBUG, INFO, WARNING, ERROR, CRITICAL log_level: INFO + # whether or not to log to console; slightly more efficient to disable this when running on a cluster + log_to_std_out: true # resume simulation from restart file restart_file: ~ # how often to write a restart file; one of 'daily', 'monthly', 'yearly' -- a restart file is also always written at the end of the simulation diff --git a/mosartwmpy/model.py b/mosartwmpy/model.py index 50f1f5b..b16e15b 100644 --- a/mosartwmpy/model.py +++ b/mosartwmpy/model.py @@ -76,11 +76,15 @@ def initialize(self, config_file_path: str, grid: Grid = None, state: State = No self.name = sanitize_filename(self.config.get('simulation.name')).replace(" ", "_") # setup logging and output directories Path(f'./output/{self.name}/restart_files').mkdir(parents=True, exist_ok=True) + handlers = [logging.FileHandler(f'./output/{self.name}/mosartwmpy.log')] + if self.config.get('simulation.log_to_std_out'): + handlers.append(logging.StreamHandler()) logging.basicConfig( - filename=f'./output/{self.name}/mosartwmpy.log', level=self.config.get('simulation.log_level', 'INFO'), format='%(asctime)s - mosartwmpy: %(message)s', - datefmt='%m/%d/%Y %I:%M:%S %p') + datefmt='%m/%d/%Y %I:%M:%S %p', + handlers=handlers + ) logging.info('Initalizing model.') logging.info(self.config.dump()) try: From 6f76243c613983228ee743bc95dd3e9935500773 Mon Sep 17 00:00:00 2001 From: travis Date: Tue, 23 Mar 2021 09:02:47 -0700 Subject: [PATCH 18/20] require python 3.9 in setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index d860ce9..7d739cf 100644 --- a/setup.py +++ b/setup.py @@ -23,6 +23,6 @@ def get_requirements(): author_email='thurber', description='Python implementation of MOSART-WM: A water routing and management model', long_description=readme(), - python_requires='>=3.6.*, <4', + python_requires='>=3.9.*, <4', install_requires=get_requirements() ) From b2ab6b7ea503413b98cf939664bf5baa22015c91 Mon Sep 17 00:00:00 2001 From: travis Date: Tue, 23 Mar 2021 09:08:00 -0700 Subject: [PATCH 19/20] force python 3.9 in github actions --- .github/workflows/build.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index ce479a5..251bcea 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -12,7 +12,7 @@ jobs: env: OS: ${{ matrix.os }} - PYTHON: '3.8' + PYTHON: '3.9' steps: @@ -21,7 +21,7 @@ jobs: - name: Set up Python uses: actions/setup-python@master with: - python-version: 3.8 + python-version: 3.9 - name: Install dependencies run: | From f783b7fa9499b31567ac6feb2963a9bf4b9b66b9 Mon Sep 17 00:00:00 2001 From: travis Date: Wed, 31 Mar 2021 13:01:21 -0700 Subject: [PATCH 20/20] use pkg_resources for local files, fixes #27; update code structure; update Readme, adding a full example --- MANIFEST.in | 4 + README.md | 144 ++++++++++++++---- mosartwmpy/__init__.py | 2 +- mosartwmpy/config/__init__.py | 0 mosartwmpy/config/config.py | 11 +- .../config_defaults.yaml | 0 mosartwmpy/direct_to_ocean/__init__.py | 0 download.py => mosartwmpy/download.py | 10 +- mosartwmpy/flood/__init__.py | 0 mosartwmpy/grid/__init__.py | 0 mosartwmpy/grid/grid.py | 3 +- mosartwmpy/hillslope/__init__.py | 0 mosartwmpy/input/__init__.py | 0 mosartwmpy/main_channel/__init__.py | 0 mosartwmpy/model.py | 2 +- mosartwmpy/output/__init__.py | 0 mosartwmpy/output/output.py | 4 +- mosartwmpy/reservoirs/__init__.py | 0 mosartwmpy/state/__init__.py | 0 mosartwmpy/subnetwork/__init__.py | 0 mosartwmpy/tests/test_model.py | 10 +- mosartwmpy/update/__init__.py | 0 mosartwmpy/utilities/__init__.py | 0 mosartwmpy/utilities/download_data.py | 15 +- {validation => mosartwmpy}/validate.py | 7 +- requirements.txt | 5 +- setup.py | 1 + validate.sh | 1 - 28 files changed, 156 insertions(+), 63 deletions(-) create mode 100644 MANIFEST.in create mode 100644 mosartwmpy/config/__init__.py rename config_defaults.yaml => mosartwmpy/config_defaults.yaml (100%) create mode 100644 mosartwmpy/direct_to_ocean/__init__.py rename download.py => mosartwmpy/download.py (87%) create mode 100644 mosartwmpy/flood/__init__.py create mode 100644 mosartwmpy/grid/__init__.py create mode 100644 mosartwmpy/hillslope/__init__.py create mode 100644 mosartwmpy/input/__init__.py create mode 100644 mosartwmpy/main_channel/__init__.py create mode 100644 mosartwmpy/output/__init__.py create mode 100644 mosartwmpy/reservoirs/__init__.py create mode 100644 mosartwmpy/state/__init__.py create mode 100644 mosartwmpy/subnetwork/__init__.py create mode 100644 mosartwmpy/update/__init__.py create mode 100644 mosartwmpy/utilities/__init__.py rename {validation => mosartwmpy}/validate.py (94%) delete mode 100755 validate.sh diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000..dda27d3 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,4 @@ +graft mosartwmpy/tests +include mosartwmpy/*.yaml +global-exclude *.py[cod] +prune input output validation paper docs dask-worker-space **/.mypy_cache .github \ No newline at end of file diff --git a/README.md b/README.md index 5d23121..013de99 100644 --- a/README.md +++ b/README.md @@ -6,46 +6,111 @@ ## getting started -Install requirements with `pip install -r requirements.txt`. +Install `mosartwmpy` with: +```shell +pip install mosartwmpy +``` + +Download a sample input dataset spanning 1980-1985 by running the following and selecting option `1`. This will download and unpack the inputs to your current directory. Note that this data is about 1.5GB in size. -`mosartwmpy` implements the [Basic Model Interface](https://csdms.colorado.edu/wiki/BMI) defined by the CSDMS, so driving it should be familiar to those accustomed to the BMI: +```shell +python -m mosartwmpy.download +``` + +Settings are defined by the merger of the `mosartwmpy/config_defaults.yaml` and a user specified file which can override any of the default settings. Create a `config.yaml` file that defines your simulation: + +> `config.yaml` +> ```yaml +> simulation: +> name: tutorial +> start_date: 1981-05-24 +> end_date: 1981-05-26 +> +> grid: +> path: ./input/domains/MOSART_NLDAS_8th_20160426.nc +> land: +> path: ./input/domains/domain.lnd.nldas2_0224x0464_c110415.nc +> +> runoff: +> read_from_file: true +> path: ./input/runoff/Livneh_NLDAS_1980_1985.nc +> +> water_management: +> enabled: true +> demand: +> read_from_file: true +> path: ./input/demand/RCP8.5_GCAM_water_demand_1980_1985.nc +> reservoirs: +> path: ./input/reservoirs/US_reservoir_8th_NLDAS3_updated_20200421.nc +> ``` + +`mosartwmpy` implements the [Basic Model Interface](https://csdms.colorado.edu/wiki/BMI) defined by the CSDMS, so driving it should be familiar to those accustomed to the BMI. To launch the simulation, open a python shell and run the following: ```python -from datetime import datetime, time -from mosartwmpy.mosartwmpy import Model +from mosartwmpy import Model + +# path to the configuration yaml file +config_file = "config.yaml" # initialize the model mosart_wm = Model() -mosart_wm.initialize() +mosart_wm.initialize(config_file) # advance the model one timestep mosart_wm.update() -# advance until a specificed timestamp -mosart_wm.update_until(datetime.combine(datetime(2030, 12, 31), time.max).timestamp()) +# advance until the `simulation.end_date` specified in config.yaml +mosart_wm.update_until(mosart_wm.get_end_time()) ``` -Settings are defined by the merger of the `config_defaults.yaml` and an optional user specified file which can override any of the default settings: +Alternatively, one can update the settings via code in the driving script using dot notation: ```python -mosart_wm = Model('path/to/config/file.yaml') +from mosartwmpy import Model +from datetime import datetime + +mosart_wm = Model() +mosart_wm.initialize() + +mosart_wm.config['simulation.name'] = 'Tutorial' +mosart_wm.config['simulation.start_date'] = datetime(1981, 5, 24) +mosart_wm.config['simulation.end_date'] = datetime(1985, 5, 26) +# etc... ``` -Alternatively, one can update the settings via code in the driving script: +One can use the usual python plotting libraries to visualize data. Model state and output are stored as one-dimensional numpy ndarrays, so they must be reshaped to visualize two-dimensionally: ```python - mosart_wm = Model() - mosart_wm.initialize() - - mosart_wm.config['simulation.name'] = 'Water Management' - mosart_wm.config['simulation.start_date'] = datetime(1981, 1, 1) - mosart_wm.config['simulation.end_date'] = datetime(1985, 12, 31) -``` +import xarray as xr +import matplotlib.pyplot as plt +from mosartwmpy import Model + +mosart_wm = Model() +mosart_wm.initialize('./config.yaml') + +mosart_wm.update_until(mosart_wm.get_end_time()) + +surface_water = mosart_wm.get_value_ptr('surface_water_amount') + +# create an xarray from the data, which has some convenience wrappers for matplotlib methods +data_array = xr.DataArray( + surface_water.reshape(mosart_wm.get_grid_shape()), + dims=['latitude', 'longitude'], + coords={'latitude': mosart_wm.get_grid_x(), 'longitude': mosart_wm.get_grid_y()}, + name='Surface Water Amount', + attrs={'units': mosart_wm.get_var_units('surface_water_amount')} +) +# plot as a pcolormesh +data_array.plot(robust=True, levels=32, cmap='winter_r') + +plt.show() + +``` ## model input -Several input files in NetCDF format are required to successfully run a simulation, which are not shipped with this repository due to their large size. The grid files, reservoir files, and a small range of runoff and demand input files can be obtained using the download utility by running `python download.py` in the repository root and choosing option 1 for "sample_input". Currently, all input files are assumed to be at the same resolution (for the sample files this is 1/8 degree over the CONUS). Below is a summary of the various input files: +Several input files in NetCDF format are required to successfully run a simulation, which are not shipped with this repository due to their large size. The grid files, reservoir files, and a small range of runoff and demand input files can be obtained using the download utility by running `python -m mosartwmpy.download` and choosing option 1 for "sample_input". Currently, all input files are assumed to be at the same resolution (for the sample files this is 1/8 degree over the CONUS). Below is a summary of the various input files: @@ -134,16 +199,27 @@ Several input files in NetCDF format are required to successfully run a simulati Alternatively, certain model inputs can be set using the BMI interface. This can be useful for coupling `mosartwmpy` with other models. If setting an input that would typically be read from a file, be sure to disable the `read_from_file` configuration value for that input. For example: ```python - # get a list of model input variables - mosart_wm.get_input_var_names() - - # disable the runoff read_from_file - mosart_wm.config['runoff.read_from_file'] = False - - # set the runoff values manually (i.e. from another model's output) - surface_runoff = np.empty(mosart_wm.get_grid_size()) - surface_runoff[:] = - mosart_wm.set_value('surface_runoff_flux', surface_runoff) +import numpy as np +from mosartwmpy import Model + +mosart_wm = Model() +mosart_wm.initialize() + +# get a list of model input variables +mosart_wm.get_input_var_names() + +# disable the runoff read_from_file +mosart_wm.config['runoff.read_from_file'] = False + +# set the runoff values manually (i.e. from another model's output) +surface_runoff = np.empty(mosart_wm.get_grid_size()) +surface_runoff[:] = # +mosart_wm.set_value('surface_runoff_flux', surface_runoff) + +# advance one timestep +mosart_wm.update() + +# continue coupling... ``` ## model output @@ -152,19 +228,25 @@ By default, key model variables are output on a monthly basis at a daily average Alternatively, certain model outputs deemed most important can be accessed using the BMI interface methods. For example: ```python +import numpy as np +from mosartwmpy import Model + +mosart_wm = Model() +mosart_wm.initialize() + # get a list of model output variables mosart_wm.get_output_var_names() # get the flattened numpy.ndarray of values for an output variable -mosart_wm.get_value_ptr('supply_water_amount') +supply = mosart_wm.get_value_ptr('supply_water_amount') ``` ## testing and validation -Before running the tests or validation, make sure to download the "sample_input" and "validation" datasets using the download utility `python download.py`. +Before running the tests or validation, make sure to download the "sample_input" and "validation" datasets using the download utility `python -m mosartwmpy.download`. To execute the tests, run `./test.sh` or `python -m unittest discover mosartwmpy/tests` from the repository root. -To execute the validation, run a model simulation that includes the years 1981 - 1982, note your output directory, and then run `./validation.sh` or `python validation/validate.py` from the repository root. This will ask you for the simulation output directory, think for a moment, and then open a figure with several plots representing the NMAE (Normalized Mean Absolute Error) as a percentage and the spatial sums of several key variables compared between your simulation and the validation scenario. Use these plots to assist you in determining if the changes you have made to the code have caused unintended deviation from the validation scenario. The NMAE should be 0% across time if you have caused no deviations. A non-zero NMAE indicates numerical difference between your simulation and the validation scenario. This might be caused by changes you have made to the code, or alternatively by running a simulation with different configuration or parameters (i.e. larger timestep, fewer iterations, etc). The plots of the spatial sums can assist you in determining what changed and the overall magnitude of the changes. +To execute the validation, run a model simulation that includes the years 1981 - 1982, note your output directory, and then run `python -m mosartwmpy.validate` from the repository root. This will ask you for the simulation output directory, think for a moment, and then open a figure with several plots representing the NMAE (Normalized Mean Absolute Error) as a percentage and the spatial sums of several key variables compared between your simulation and the validation scenario. Use these plots to assist you in determining if the changes you have made to the code have caused unintended deviation from the validation scenario. The NMAE should be 0% across time if you have caused no deviations. A non-zero NMAE indicates numerical difference between your simulation and the validation scenario. This might be caused by changes you have made to the code, or alternatively by running a simulation with different configuration or parameters (i.e. larger timestep, fewer iterations, etc). The plots of the spatial sums can assist you in determining what changed and the overall magnitude of the changes. If you wish to merge code changes that intentionally cause significant deviation from the validation scenario, please work with the maintainers to create a new validation dataset. \ No newline at end of file diff --git a/mosartwmpy/__init__.py b/mosartwmpy/__init__.py index 7040c63..3b4d86e 100644 --- a/mosartwmpy/__init__.py +++ b/mosartwmpy/__init__.py @@ -1 +1 @@ -from .model import Model \ No newline at end of file +from .model import Model diff --git a/mosartwmpy/config/__init__.py b/mosartwmpy/config/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mosartwmpy/config/config.py b/mosartwmpy/config/config.py index f0f56e8..73b161e 100644 --- a/mosartwmpy/config/config.py +++ b/mosartwmpy/config/config.py @@ -1,6 +1,8 @@ +import pkg_resources from benedict import benedict from benedict.dicts import benedict as Benedict + def get_config(config_file_path: str) -> Benedict: """Configuration object for the model, using the Benedict type. @@ -10,9 +12,8 @@ def get_config(config_file_path: str) -> Benedict: Returns: Benedict: A Benedict instance containing the merged configuration """ + config = benedict(pkg_resources.resource_filename('mosartwmpy', 'config_defaults.yaml'), format='yaml') + if config_file_path is not None and config_file_path != '': + config.merge(benedict(str(config_file_path), format='yaml'), overwrite=True) - config = benedict('./config_defaults.yaml', format='yaml') - if config_file_path and config_file_path != '': - config.merge(benedict(config_file_path, format='yaml'), overwrite=True) - - return config \ No newline at end of file + return config diff --git a/config_defaults.yaml b/mosartwmpy/config_defaults.yaml similarity index 100% rename from config_defaults.yaml rename to mosartwmpy/config_defaults.yaml diff --git a/mosartwmpy/direct_to_ocean/__init__.py b/mosartwmpy/direct_to_ocean/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/download.py b/mosartwmpy/download.py similarity index 87% rename from download.py rename to mosartwmpy/download.py index ef915c8..6f777f3 100644 --- a/download.py +++ b/mosartwmpy/download.py @@ -1,11 +1,8 @@ -import enum -import os - from benedict import benedict - from mosartwmpy.utilities.download_data import download_data +import pkg_resources -available_data = benedict.from_yaml('./mosartwmpy/data_manifest.yaml') +available_data = benedict.from_yaml(pkg_resources.resource_filename('mosartwmpy', 'data_manifest.yaml')) data_list = [] data = [] @@ -33,7 +30,7 @@ 0) exit """) -try: +try: user_input = int(input(""" Please select a number and press enter: """)) except: @@ -50,4 +47,3 @@ print("") print("") download_data(data_list[user_input - 1]) - diff --git a/mosartwmpy/flood/__init__.py b/mosartwmpy/flood/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mosartwmpy/grid/__init__.py b/mosartwmpy/grid/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mosartwmpy/grid/grid.py b/mosartwmpy/grid/grid.py index d5bc8ee..ecfa39a 100644 --- a/mosartwmpy/grid/grid.py +++ b/mosartwmpy/grid/grid.py @@ -1,6 +1,7 @@ import logging import numpy as np import pandas as pd +from pathlib import Path import pickle import tempfile import xarray as xr @@ -421,7 +422,7 @@ def from_files(path: str) -> 'Grid': Returns: Grid: a Grid instance populated with the columns from the dataframe """ - if not path.endswith('.zip'): + if not Path(path).suffix == '.zip': path += '.zip' grid = Grid(empty=True) diff --git a/mosartwmpy/hillslope/__init__.py b/mosartwmpy/hillslope/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mosartwmpy/input/__init__.py b/mosartwmpy/input/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mosartwmpy/main_channel/__init__.py b/mosartwmpy/main_channel/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mosartwmpy/model.py b/mosartwmpy/model.py index b16e15b..bfa2444 100644 --- a/mosartwmpy/model.py +++ b/mosartwmpy/model.py @@ -76,7 +76,7 @@ def initialize(self, config_file_path: str, grid: Grid = None, state: State = No self.name = sanitize_filename(self.config.get('simulation.name')).replace(" ", "_") # setup logging and output directories Path(f'./output/{self.name}/restart_files').mkdir(parents=True, exist_ok=True) - handlers = [logging.FileHandler(f'./output/{self.name}/mosartwmpy.log')] + handlers = [logging.FileHandler(Path(f'./output/{self.name}/mosartwmpy.log'))] if self.config.get('simulation.log_to_std_out'): handlers.append(logging.StreamHandler()) logging.basicConfig( diff --git a/mosartwmpy/output/__init__.py b/mosartwmpy/output/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mosartwmpy/output/output.py b/mosartwmpy/output/output.py index 5143b00..37c11ff 100644 --- a/mosartwmpy/output/output.py +++ b/mosartwmpy/output/output.py @@ -103,7 +103,7 @@ def write_output(self): # if file exists and it's not a new period, update existing file else write to new file and include grid variables if not is_new_period and Path(filename).is_file(): - nc = open_dataset(filename).load() + nc = open_dataset(Path(filename)).load() # slice the existing data to account for restarts nc = nc.sel(time=slice(None, pd.to_datetime(self.current_time) - pd.Timedelta('1ms'))) frame = concat([nc, frame], dim='time', data_vars='minimal') @@ -158,5 +158,5 @@ def write_restart(self): logging.info('Writing restart file.') x = self.state.to_dataframe().to_xarray() - filename = f'./output/{self.name}/restart_files/{self.name}_restart_{self.current_time.year}_{self.current_time.strftime("%m")}_{self.current_time.strftime("%d")}.nc' + filename = Path(f'./output/{self.name}/restart_files/{self.name}_restart_{self.current_time.year}_{self.current_time.strftime("%m")}_{self.current_time.strftime("%d")}.nc') x.to_netcdf(filename) diff --git a/mosartwmpy/reservoirs/__init__.py b/mosartwmpy/reservoirs/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mosartwmpy/state/__init__.py b/mosartwmpy/state/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mosartwmpy/subnetwork/__init__.py b/mosartwmpy/subnetwork/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mosartwmpy/tests/test_model.py b/mosartwmpy/tests/test_model.py index 2b8f82e..fe13450 100644 --- a/mosartwmpy/tests/test_model.py +++ b/mosartwmpy/tests/test_model.py @@ -1,9 +1,8 @@ -import numpy as np +from pathlib import Path import unittest from mosartwmpy import Model from mosartwmpy.grid.grid import Grid -from mosartwmpy.state.state import State class ModelTest(unittest.TestCase): @@ -11,12 +10,13 @@ class ModelTest(unittest.TestCase): def setUp(self): self.model = Model() - self.grid = Grid.from_files('./mosartwmpy/tests/grid.zip') + self.grid = Grid.from_files(Path('./mosartwmpy/tests/grid.zip')) def test_can_initialize_and_run(self): - self.model.initialize('./mosartwmpy/tests/test_config.yaml', grid = self.grid) + self.model.initialize(Path('./mosartwmpy/tests/test_config.yaml'), grid=self.grid) self.model.update() self.assertTrue(True, "model initializes and updates") + if __name__ == '__main__': - unittest.main() \ No newline at end of file + unittest.main() diff --git a/mosartwmpy/update/__init__.py b/mosartwmpy/update/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mosartwmpy/utilities/__init__.py b/mosartwmpy/utilities/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mosartwmpy/utilities/download_data.py b/mosartwmpy/utilities/download_data.py index bbea51d..e035661 100644 --- a/mosartwmpy/utilities/download_data.py +++ b/mosartwmpy/utilities/download_data.py @@ -1,14 +1,16 @@ -import os import io -import requests -import zipfile import logging +import os +from pathlib import Path +import pkg_resources +import requests import sys +import zipfile from benedict import benedict -def download_data(dataset: str, destination: str = None, manifest: str = './mosartwmpy/data_manifest.yaml') -> None: +def download_data(dataset: str, destination: str = None, manifest: str = pkg_resources.resource_filename('mosartwmpy', 'data_manifest.yaml')) -> None: """Convenience wrapper for the InstallSupplement class. Download and unpack example data supplement from Zenodo that matches the current installed @@ -25,7 +27,10 @@ def download_data(dataset: str, destination: str = None, manifest: str = './mosa if not data_dictionary.get(dataset, None): raise Exception(f'Dataset "{dataset}" not found in the manifest ({manifest}).') - get = InstallSupplement(url = data_dictionary.get(f'{dataset}.url'), destination = destination if destination is not None else data_dictionary.get(f'{dataset}.destination', './')) + get = InstallSupplement( + url=data_dictionary.get(f'{dataset}.url'), + destination=destination if destination is not None else Path(data_dictionary.get(f'{dataset}.destination', './')) + ) get.fetch_zenodo() diff --git a/validation/validate.py b/mosartwmpy/validate.py similarity index 94% rename from validation/validate.py rename to mosartwmpy/validate.py index 0cb953f..08068f7 100644 --- a/validation/validate.py +++ b/mosartwmpy/validate.py @@ -1,6 +1,7 @@ import matplotlib.pyplot as plt import numpy as np import os +from pathlib import Path from xarray import open_mfdataset # TODO accept command line path input as alternative @@ -8,7 +9,7 @@ # TODO allow easily toggling between scenarios for variables of interest (no-wm, wm, heat, etc) years = [1981, 1982] -baseline_data_path = 'validation/mosartwmpy_validation_wm_1981_1982.nc' +baseline_data_path = Path('validation/mosartwmpy_validation_wm_1981_1982.nc') variables_of_interest = ['STORAGE_LIQ', 'RIVER_DISCHARGE_OVER_LAND_LIQ', 'WRM_STORAGE', 'WRM_SUPPLY'] physical_dimensions = ['lat', 'lon'] temporal_dimension = 'time' @@ -29,7 +30,7 @@ data = open_mfdataset(f"{data_path}/*.nc" if data_path[-3:] != '.nc' else data_path) try: - data = data.sel({ temporal_dimension: slice(f"{years[0]}", f"{years[-1]}") }) + data = data.sel({temporal_dimension: slice(f"{years[0]}", f"{years[-1]}")}) timeslice = slice(data[temporal_dimension].values[0], data[temporal_dimension].values[len(data[temporal_dimension].values) - 1]) baseline_data = open_mfdataset(baseline_data_path) baseline_data = baseline_data.sel({ temporal_dimension: timeslice }) @@ -75,4 +76,4 @@ figure.text(0.5, 0.04, 'time', ha='center') figure.text(0.04, 0.5, 'NMAE (%)', va='center', rotation='vertical') figure.tight_layout() -plt.show() \ No newline at end of file +plt.show() diff --git a/requirements.txt b/requirements.txt index 2391b4d..3489137 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,9 +11,12 @@ numpy==1.19.4 pandas==1.1.4 pathvalidate==2.3.0 psutil==5.7.3 -pyarrow==3.0.0ß +pyarrow==3.0.0 python-benedict==0.22.0 recommonmark==0.7.1 +regex==2020.11.13 +requests==2.25.1 +setuptools==54.1.2 sphinx==3.5.2 sphinx-rtd-theme==0.5.1 xarray==0.16.1 \ No newline at end of file diff --git a/setup.py b/setup.py index 7d739cf..b2b8d0c 100644 --- a/setup.py +++ b/setup.py @@ -24,5 +24,6 @@ def get_requirements(): description='Python implementation of MOSART-WM: A water routing and management model', long_description=readme(), python_requires='>=3.9.*, <4', + include_package_data=True, install_requires=get_requirements() ) diff --git a/validate.sh b/validate.sh deleted file mode 100755 index 7b6d274..0000000 --- a/validate.sh +++ /dev/null @@ -1 +0,0 @@ -python validation/validate.py \ No newline at end of file

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