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Modeling of the Hudson basin, New York, USA.

Catchment model for the course ENVM1502.

Summary notebook:

All code is summarised in one notebook: 4.0.SummaryNotebook.ipynb found in python files. It can also be viewed here more reliably. Alternatively they can be viewed as pdf via html or latex in the repository.

reproducability

The files requirement_envm1502.txt for pip or requirement_envm1502.yml for conda contain all the packagenames needed to run the jupyter notebooks provided.

Simply run conda env create -f requirement_envm1502.yml in the anaconda promt with the .yml located correctly (recommended), or use pip install -r requirements_envm1502.txt. The MEV package is the only one which needs to be downloaded seperately & moved to the correct install folder with other python packages.

area: The basin is 20 000km^2 in northern part of the hudson basin.

map_hudson_stations

Work overview:
Unit What To dos Who Done? Result
0.1 P data (& T) Precip data, likely also temp Anne Yes loaded in from NOAA
0.1.3 P data reweighing use theissen polygon rather than mean David Yes completed,more promising
0.2 Q data From UGSG David Yes loaded in
0.3 EP data from satelite product? David Yes GLEAM used in the end
0.4 Combining all data take the mentioned data & load in David Yes one dataframe made
---- ---- ---- ---- ---- ----
1.1 Budyko curve Plotting Ea/P vs EP/p & desribing Anne Yes Plots nicely on the curve
1.2 EVA Creating MEV & GEV David Yes clear line obtained
1.3 Vegetation Estimate rootzone storage Anne Yes estimated 73.86mm
1.4 snow Estimate snow storage & melt David Yes modeled nicely on 4 plateaus
1.5 Muskingum Flood routing Anne Yes Completed, takes long, little use
1.6 Mositure recycle Local mositure David Yes Most moisture from sea, little recycling
---- ---- ---- ---- ---- ----
2.1 map reservoirs Use landsat to select surface water David Yes Map showing 371km^2 of reservoirs
2.2 remote P measure analyse P with satilites/microwave obs Anne NA Not done due to enough data
2.3 moisture map soil moisture David Yes Insitu, microwave,SMAP & CCDS loaded in
2.4 DEMS & gravity Use dem & estimate S using grace Anne Yes Grace loaded in, also used in 2.6, decrease water storages
2.5 Evaporation extract evaporation from rs David Yes loaded from era 5, see 0.3
2.6 & 7 Data assimilation tweak data to close waterbalance Both Yes Now use Gleam instead of era5
2.8 Climate predictions Look at monthly precipitation predict David Yes extreme precipitation will increase
---- ---- ---- ---- ---- ----
3.1 Linear reservoir K & alpha values Anne Yes too simple, but shows good
3.2 lumped model Insert values into model Anne Yes shows promising results, gets timing right, mainly summer overshoots
3.3 callibration callibrate values in model Anne Yes see above
3.4 distributed model Use landscape and divide into gridcells Anne Yes underestimates snow
---- ---- ---- ---- ---- ----
4.0 overview nb Combine everything David Yes all combined & run