Advancing the Representation of Human Actions in Large‐Scale Hydrological Models: Challenges and Future Research Directions

Characterizing the impact of human actions on terrestrial water fluxes and storages at multi‐basin, continental, and global scales has long been on the agenda of scientists engaged in climate science, hydrology, and water resources systems analysis. This need has resulted in a variety of modeling ef...

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Vydané v:	Water resources research Ročník 61; číslo 7
Hlavní autori:	Galelli, Stefano, Turner, Sean W. D., Pokhrel, Yadu, Yi Ng, Jia, Castelletti, Andrea, Bierkens, Marc F. P., Pianosi, Francesca, Biemans, Hester
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Washington John Wiley & Sons, Inc 01.07.2025 Wiley
Predmet:	Anthropogenic factors catchment hydrology climate Climate and hydrology Climate science Computer applications global hydrology Human influences humans human‐water interactions hydraulic infrastructures Hydrologic models Hydrologic research Hydrology infrastructure large‐scale models Machine learning Modelling Representations satellites Sediment transport socio‐hydrology Systems analysis water Water management water quantity Water resources Water supply Water supply systems water temperature
ISSN:	0043-1397, 1944-7973
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Shrnutí:	Characterizing the impact of human actions on terrestrial water fluxes and storages at multi‐basin, continental, and global scales has long been on the agenda of scientists engaged in climate science, hydrology, and water resources systems analysis. This need has resulted in a variety of modeling efforts focused on the representation of water infrastructure operations. Yet, the representation of human‐water interactions in large‐scale hydrological models is still relatively crude, fragmented across models, and often achieved at coarse resolutions (∼ ${\sim} $10–100 km) that cannot capture local water management decisions. In this commentary, we argue that the concomitance of four drivers and innovations is poised to change the status quo: “hyper‐resolution” hydrological models (∼ ${\sim} $0.1–1 km), multi‐sector modeling, satellite missions able to monitor the outcome of human actions, and machine learning are creating a fertile environment for human‐water research to flourish. We then outline four challenges that chart future research in hydrological modeling: (a) creating hyper‐resolution global data sets of water management practices, (b) improving the characterization of anthropogenic interventions on water quantity, stream temperature, and sediment transport, (c) improving model calibration and diagnostic evaluation, and (d) reducing the computational requirements associated with the successful exploration of these challenges. Overcoming them will require addressing modeling, computational, and data development needs that cut across the hydrology community, thereby requiring a major communal effort. Plain Language Summary Humans have been impacting the hydrological cycle in a variety of manners, such as by using groundwater to support agricultural production or altering river discharge to produce hydropower or reduce flood risks. Hydrological models focusing on large regions can capture some of these actions, but with a level of detail that is still rather crude. We argue that the increasing interest in hyper‐resolution models (∼ ${\sim} $0.1–1 km) and the availability of new remotely‐sensed observations can help change this status quo. This would require addressing four key challenges, namely (a) creating data sets that describe water management practices with an unprecedented level of detail, (b) improving the accuracy with which anthropogenic impacts on water quality and quantity are represented in hydrological models, and ensuring that models continue to be (c) reliable and (d) computationally efficient. Key Points Human actions are still represented crudely in large‐scale hydrological models Hyper‐resolution modeling, new satellite missions, and machine learning tools offer a fertile environment for challenging this status quo Challenges range from creating hyper‐resolution data sets to improving the characterization of human actions on water quantity and quality
Bibliografia:	SourceType-Scholarly Journals-1 ObjectType-Commentary-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	0043-1397 1944-7973
DOI:	10.1029/2024WR039486