Towards an Indian land data assimilation system (ILDAS): A coupled hydrologic-hydraulic system for water balance assessments

被引:4
|
作者
Magotra, Bhanu [1 ]
Prakash, Ved [1 ]
Saharia, Manabendra [1 ,2 ]
Getirana, Augusto [3 ,4 ]
Kumar, Sujay [3 ]
Pradhan, Rohit [8 ]
Dhanya, C. T. [1 ]
Rajagopalan, Balaji [6 ,7 ]
Singh, Raghavendra P. [5 ]
Pandey, Ayush [1 ]
Mohapatra, Mrutyunjay [9 ]
机构
[1] Indian Inst Technol Delhi, Dept Civil Engn, Hauz Khas, New Delhi 110016, India
[2] Indian Inst Technol Delhi, Yardi Sch Artificial Intelligence, Hauz Khas, New Delhi 110016, India
[3] NASA Goddard Space Flight Ctr, Hydrol Sci Lab, Greenbelt, MD USA
[4] Sci Applicat Int Corp, Greenbelt, MD USA
[5] Indian Space Res Org, Indian Inst Remote Sensing, Dehra Dun 248001, India
[6] Univ Colorado, Dept Civil Environm & Architectural Engn, Boulder, CO USA
[7] Univ Colorado, CIRES, Boulder, CO USA
[8] Indian Space Res Org, Space Applicat Ctr, Ahmadabad 380015, India
[9] Indian Meteorol Dept, New Delhi 110003, India
关键词
Indian Land Data Assimilation System (ILDAS); Water balance assessments; Streamflow; south Asia; SOIL-MOISTURE; PERFORMANCE EVALUATION; SURFACE MODELS; CALIBRATION; ACCURACY; PRODUCTS; ERROR;
D O I
10.1016/j.jhydrol.2023.130604
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Effective management of water resources requires reliable estimates of land surface states and fluxes, including water balance components. But most land surface models run in uncoupled mode and do not produce river discharge at catchment scales to be useful for water resources management applications. Such integrated systems are also rare over India where hydrometeorological extremes have wreaked havoc on the economy and people. So, an Indian Land Data Assimilation System (ILDAS) with a coupled land surface and a hydrodynamic model has been developed and driven by multiple meteorological forcings (0.1 degrees, daily) to estimate land surface states, channel discharge, and floodplain inundation. ILDAS benefits from an integrated framework as well as the largest suite of observation records collected over India and has been used to produce a reanalysis product for 1981-2021 using four forcing datasets, namely, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ECMWF's ERA-5, and Indian Meteorological Department (IMD) gridded precipitation. We assessed the uncertainty and bias in these precipitation datasets and validated all major components of the terrestrial water balance, i.e., surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and streamflow, against a combination of satellite and in situ observation datasets. Our assessment shows that ILDAS can represent the hydrological processes reasonably well over the Indian landmass with IMD precipitation showing the best relative performance. Evaluation against ESA-CCI soil moisture shows that MERRA-2 based estimates outperform the others, whereas ERA-5 performs best in simulating evapotranspiration when evaluated against MODIS ET. Evaluations against observed records show that CHIRPS-based estimates have the highest performance in reconstructing surface runoff and streamflow. Once operational, this system will be useful for supporting transboundary water management decision making in the region.
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页数:21
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