A near-global, high resolution land surface parameter dataset for the variable infiltration capacity model

Schaperow, J.; Li, Dongyue; Margulis, S. A.; Lettenmaier, Dennis P.

doi:10.1038/s41597-021-00999-4

Cited by 11 publications

(2 citation statements)

References 43 publications

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“…The Reservoir Assessment Tool (RAT) 3.0 (Minocha et al, 2023) was used to obtain satellite estimates of unregulated streamflow estimates using its default options of models and datasets (Bennett et al, 2020;Hamman et al, 2018, p. 5;Liang et al, 1994;Schaperow et al, 2021) for reservoirs in both Cumberland and Kerala regions. The RAT 3.0 software with all documentation and tutorials is available at http://ratdocs.io and https://www.satellitedams.net.…”

Section: Open Research Data Availability Statementmentioning

confidence: 99%

ResORR: A Globally Scalable and Satellite Data-driven Algorithm for River Flow Regulation due to Reservoir Operations

Das,

Hossain,

Minocha

et al. 2023

Preprint

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Storage and release of surface water by reservoirs can alter the natural streamflow pattern of rivers with negative impacts on the environment. Such reservoir-driven river regulation is poorly understood at a global scale due to a lack of publicly available in-situ data on reservoir operations. However, with rapid advancements in satellite remote sensing-based tracking of reservoir state, this gap in data availability can be bridged. In this study, we modeled regulated flow of rivers using only satellite-observed reservoir state and hydrological modeling forced also with satellite precipitation data. We propose a globally scalable algorithm, ResORR (Reservoir Operations driven River Regulation), to predict regulated river flow and tested it over the heavily regulated basin of the Cumberland River in the US. ResORR was found able to model regulated river flow due to upstream reservoir operations of the Cumberland River. Over a mountainous basin dominated by high rainfall, ResORR was effective in capturing extreme flooding modified by upstream hydropower dam operations. ResORR successfully captured the peak of the regulated river flow altered by hydropower dam and flood control operations during the devastating floods of 2018 in the South Indian state of Kerala. On average, ResORR improved regulation river flow simulation by more than 50% across all performance metrics when compared to a hydrologic model without a regulation module. ResORR is a timely algorithm for understanding human regulation of surface water as satellite-estimated reservoir state is expected to improve globally with the recently launched Surface Water and Ocean Topography (SWOT) mission.

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Section: Open Research Data Availability Statementmentioning

confidence: 99%

ResORR: A Globally Scalable and Satellite Data-driven Algorithm for River Flow Regulation due to Reservoir Operations

Das,

Hossain,

Minocha

et al. 2023

Preprint

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“…The hydrologic model's performance is improved through better estimation and prediction of weather and land cover characteristics [12][13][14]. The hydrologic model's prediction is improved by appropriate geographical and temporal resolution of the used land cover and the better weather data [15,16]. Several easy-to-use and widely accessible spatial and weather datasets have been generated because of the increased availability of spatial information in watershed modeling [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Impact of Satellite-Derived Land Cover Resolution Using Machine Learning and Hydrological Simulations

Hanif,

Kanae,

Farooq

et al. 2023

Remote Sensing

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This study carefully assesses the capability of supervised machine learning classification algorithms in identifying land cover (LC) in the context of the Jhelum River basin in Kashmir. Sentinel 2 and Landsat 8 high-resolution data from two satellite sources were used. Through preprocessing techniques, we removed any potential noise inherent to satellite imagery and assured data consistency. The study then utilized and compared the skills of the supervised algorithms random forest (RF) and support vector machine (SVM). A hybrid approach, amalgamating classifications from both methods, was also tested for potential synergistic enhancements in accuracy. Using a stratified random sampling approach for validation, the SVM algorithm emerged with a commendable accuracy rate of 82.5%. Using simulations from 2000 to 2015, the soil and water assessment tool (SWAT) model was used to further explore the hydrological effects of LC alterations. Between 2009 and 2019, there were discernible changes in the land cover, with a greater emphasis on ranges, forests, and agricultural plains. When these changes were combined with the results of the hydrologic simulation, a resultant fall in average annual runoff—from above 700 mm to below 600 mm—was seen. With runoff values possibly ranging between 547 mm and 747 mm, the statistics emphasize the direct effects of urban communities encroaching upon forest, agricultural, and barren lands. This study concludes by highlighting the crucial role that technical pipelines play in enhancing LC classifications and by providing suggestions for future water resource estimation and hydrological impact evaluations.

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Developing stage-specific drought vulnerability curves for maize: The case study of the Po River basin

Monteleone

Borzì

Bonaccorso

et al. 2022

Agricultural Water Management

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A near-global, high resolution land surface parameter dataset for the variable infiltration capacity model

Cited by 11 publications

References 43 publications

ResORR: A Globally Scalable and Satellite Data-driven Algorithm for River Flow Regulation due to Reservoir Operations

ResORR: A Globally Scalable and Satellite Data-driven Algorithm for River Flow Regulation due to Reservoir Operations

Impact of Satellite-Derived Land Cover Resolution Using Machine Learning and Hydrological Simulations

Developing stage-specific drought vulnerability curves for maize: The case study of the Po River basin

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