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.

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