Enhancing the representation of water management in global hydrological models

Abeshu, Guta Wakbulcho; Tian, Fuqiang; Wild, Thomas; Zhao, Mengqi; Turner, Sean; Chowdhury, A. F. M. Kamal; Vernon, Chris R.; Hu, Hongchang; Zhuang, Yuan; Hejazi, Mohamad; Li, Hong-Yi

doi:10.5194/gmd-16-5449-2023

Title: Enhancing the representation of water management in global hydrological models

Journal Article · Fri Sep 22 00:00:00 EDT 2023 · Geoscientific Model Development (Online)

DOI:https://doi.org/10.5194/gmd-16-5449-2023· OSTI ID:2004626

Abeshu, Guta Wakbulcho;

; Wild, Thomas;

;

; Vernon, Chris R.; Hu, Hongchang; Zhuang, Yuan; Hejazi, Mohamad;

Abstract. This study enhances an existing global hydrological model (GHM), Xanthos, by adding a new water management module that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We remapped reservoirs in the Global Reservoir and Dam (GRanD) database to the 0.5∘ spatial resolution in Xanthos so that a single lumped reservoir exists per grid cell, which yielded 3790 large reservoirs. We implemented unique operation rules for each reservoir type, based on their primary purposes. In particular, hydropower reservoirs have been treated as flood control reservoirs in previous GHM studies, while here, we determined the operation rules for hydropower reservoirs via optimization that maximizes long-term hydropower production. We conducted global simulations using the enhanced Xanthos and validated monthly streamflow for 91 large river basins, where high-quality observed streamflow data were available. A total of 1878 (296 hydropower, 486 irrigation, and 1096 flood control and others) out of the 3790 reservoirs are located in the 91 basins and are part of our reported results. The Kling–Gupta efficiency (KGE) value (after adding the new water management) is ≥ 0.5 and ≥ 0.0 in 39 and 81 basins, respectively. After adding the new water management module, model performance improved for 75 out of 91 basins and worsened for only 7. To measure the relative difference between explicitly representing hydropower reservoirs and representing hydropower reservoirs as flood control reservoirs (as is commonly done in other GHMs), we use the normalized root mean square error (NRMSE) and the coefficient of determination (R2). Out of the 296 hydropower reservoirs, the NRMSE is > 0.25 (i.e., considering 0.25 to represent a moderate difference) for over 44 % of the 296 reservoirs when comparing both the simulated reservoir releases and storage time series between the two simulations. We suggest that correctly representing hydropower reservoirs in GHMs could have important implications for our understanding and management of freshwater resource challenges at regional-to-global scales. This enhanced global water management modeling framework will allow the analysis of future global reservoir development and management from a coupled human–earth system perspective.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Science Foundation (NSF)

Grant/Contract Number:: 551981; AC05-76RL01830; 1804560

OSTI ID:: 2004626

Alternate ID(s):: OSTI ID: 2328506

Report Number(s):: PNNL-SA-189980

Journal Information:: Geoscientific Model Development (Online), Journal Name: Geoscientific Model Development (Online) Vol. 16 Journal Issue: 18; ISSN 1991-9603

Publisher:: Copernicus GmbHCopyright Statement

Country of Publication:: Germany

Language:: English

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