DOI: 10.5281/zenodo.18633377
title: Reservoirs and Lakes in the CoSWAT model: Processed simulation results and observations for reservoir storage, inflow, outflow, and streamflow.

description: General Description

The zip file contains pickle files with Python Dictionaries. There are three folders:



Reservoir_simulations corresponds to CoSWAT (Chawanda et al., 2025) monthly storage, inflow and outflow outputs for the period 1970-2015 for reservoirs with available observations.

Reservoir_observations corresponds to reference data gathered from GRS (Li, 2023), ResOpsUs (Steyaert et al., 2022), and selected data by Yassin (2018) that corresponds to simulations.

Streamflow corresponds to both monthly processed simulated and observed (GRDC;https://grdc.bafg.de/data/data_portal/) streamflow time series for selected stations.


Dictionary structure

CoSWAT Reservoir outputs

Each dictionary (per region), has a unique identifier, and per item an additional dicionary is a available with three sub-keys:



"Hylak_id"      : Identification on the HydroLakes dataset (Messager et al. ,2016)

"grand_id"     : Identification on the GranD dataset (Lehner et al. ,2011)

"coswatOut"  : Dataframe with time series.


CoSWAT Streamflow outputs

Each dictionary (per region), has the GRDC station number identifier, per item. an additional dicionary is a available with five sub-keys:



"sim_data"      : Dataframe with simulated monthly streamflow.

"obs_data"      : Dataframe with observed monthly streamflow.

"Channel"       : Identifier in CoSWAT channel network.

"Station"         : Station name (Given by GRDC)

"down_res"     : Boolean indicating if it is downstream of a reservoir/lake represented in the model or not.


Reservoir reference data

There is a unique, global dictionary. The dictionary keys are the GranD dataset identifier. Each item per key is a Dataframe with the average, maximum and minimum storage, inflow, and outflow, across the three sources. If there is not more than one source, maximum and minimum are equal to the average.

References







Chawanda, C. J., Van Griensven, A., Nkwasa, A., Teran Orsini, J. P., Jeong, J., Choi, S.-K., Srinivasan, R., and Arnold, J. G.: CoSWAT Model v1: A high-resolution global SWAT+ hydrological model, Hydrol. Earth Syst. Sci., 29, 6901–6916, https://doi.org/10.5194/hess-29-6901-2025, 2025.



Li, Y.: Global Reservoir Storage (GRS) dataset, https://doi.org/10.5281/ZENODO.7855477, 2023.

Steyaert, J. C., Condon, L. E., W.D. Turner, S., and Voisin, N.: ResOpsUS, a dataset of historical reservoir operations in the contiguous United States, Sci. Data, 9, 34, https://doi.org/10.1038/s41597-022-01134-7, 2022.

Yassin, F.: Reservoir inflow, storage and realease, https://doi.org/10.5281/ZENODO.1492043, 2018.



Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., and Wisser, D.: High‐resolution mapping of the world’s reservoirs and dams for sustainable river‐flow management, Front. Ecol. Environ., 9, 494–502, https://doi.org/10.1890/100125, 2011.




Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.: Estimating the volume and age of water stored in global lakes using a geo-statistical approach, Nat. Commun., 7, 13603, https://doi.org/10.1038/ncomms13603, 2016.