Integrating reservoirs and Lakes in the CoSWAT model.

General Description File structure ├── CoSWAT-Framework│ ├── data-preparation│ │ └── resources.zip│ ├── model-data.zip│ └── model-setup│ └── CoSWATv1.5.0-america-bravo.zip└── Scripts ├── res_obs_preprocessing │ └── Reservoir_storage_global_data │ ├── processed │ │ └── data │ │ └── globalReservoirDataAggregated.pkl │ └── raw.zip └── result_analysis └── reservoirs_coswat └── coswat_outputs.zip CoSWAT-Framework data-preparation This directory containts the resources.zip file, which contain simply the vector files for the HydroLAKES (Messager et al. ,2016) and GranD (Lehner et al. ,2011) datasets. Which can also be obtained in their respective official repositories. model-data Contain spatial input data for the CoSWAT model in 9 regions: ├── africa-nile├── africa-orange├── america-bravo├── america-colorado├── america-mississippi├── america-parana├── asia-mekong├── europe-central└── europe-west They further subdivide in: ├── observations: GRDC (https://grdc.bafg.de/) time series for river discharge stations.├── raster: Raster input files (e.g., DEM, Land use map, soil map).├── shapes: Vector input files (e.g., lakes, burn-in rivers).└── tables: SWAT+ formatted lookup tables used to create HRUs. model-setup Model setup contains a .zip file with a fully set-up CoSWAT region (america-bravo). Other regions can be set up similarly using the "model-data" files using the CoSWAT-Framework. Clear instructions on how to set-up these regions, run simulations, process and analyse outputs are provided at: The GitHub repository associated with this dataset. Scripts This folder contains some already processed datasets by the scripts provided in the GitHub repository associated with this dataset. They can be used directly to perform analysis of results of the CoSWAT simulations, without needing to set up the model with the CoSWAT-Framework. res_obs_preprocessing Two files can be found here: globalReservoirDataAggregated.pkl; a pickle file with a Python dictionary with processed reference data including monthly reservoir storage, inflow and outflow from 3 sources, GRS (Li, 2023), ResOpsUs (Steyaert et al., 2022) and Yassin (2018), which can be obtained in their official repositories, but they can also be found in the raw.zip file. The dictionary keys for globalReservoirDataAggregated.pkl 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. coswat_outputs.zip This file has the following structure:├── reservoir_storage│ └── CoSWATv1.5.0└── streamflow ├── CoSWATv1.1.0 └── CoSWATv1.5.0 Each folder contains a pickle with a Python dictionary with processed CoSWAT outputs for reservoirs and rivers. CoSWATv1.1.0: Does not include reservoirs and lakes. CoSWATv1.5.0: Includes reservoirs and lakes. reservoir_storage 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. streamflow 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. Additional information The purpose of this dataset is to be used together with the scripts from the associated GitHub repository. Please download and decompress all files in the same folder structure provided. To use the CoSWAT-Framework, please download its files at the CoSWAT-Framework directory of this dataset. 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.