GLOBAL 5-ARCMIN RESOLUTION WATER RESOURCE ASSESSMENT USING THE H08 GLOBAL HYDROLOGICAL MODEL

2020 ◽  
Vol 76 (2) ◽  
pp. I_109-I_114
Author(s):  
Keisuke DOI ◽  
Menaka REVEL ◽  
Naota HANASAKI ◽  
Shinjiro KANAE
Keyword(s):  
Water Resource ◽  
Hydrological Model ◽  
Resource Assessment ◽  
Water Resource Assessment

scholarly journals Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

2021 ◽  
Vol 14 (6) ◽  
Author(s):  
Jinming Yang ◽  
Chengzhi Li
Keyword(s):  
Remote Sensing ◽  
Kalman Filter ◽  
Water Resource ◽  
Ensemble Kalman Filter ◽  
Snow Depth ◽  
Resource Assessment ◽  
Measured Data ◽  
Hydrological Process ◽  
Depth Data ◽  
Water Resource Assessment

AbstractSnow depth mirrors regional climate change and is a vital parameter for medium- and long-term numerical climate prediction, numerical simulation of land-surface hydrological process, and water resource assessment. However, the quality of the available snow depth products retrieved from remote sensing is inevitably affected by cloud and mountain shadow, and the spatiotemporal resolution of the snow depth data cannot meet the need of hydrological research and decision-making assistance. Therefore, a method to enhance the accuracy of snow depth data is urgently required. In the present study, three kinds of snow depth data which included the D-InSAR data retrieved from the remote sensing images of Sentinel-1 synthetic aperture radar, the automatically measured data using ultrasonic snow depth detectors, and the manually measured data were assimilated based on ensemble Kalman filter. The assimilated snow depth data were spatiotemporally consecutive and integrated. Under the constraint of the measured data, the accuracy of the assimilated snow depth data was higher and met the need of subsequent research. The development of ultrasonic snow depth detector and the application of D-InSAR technology in snow depth inversion had greatly alleviated the insufficiency of snow depth data in types and quantity. At the same time, the assimilation of multi-source snow depth data by ensemble Kalman filter also provides high-precision data to support remote sensing hydrological research, water resource assessment, and snow disaster prevention and control program.

scholarly journals Water resource assessment, gaps, and constraints of vegetable production in Robit and Dangishta watersheds, Upper Blue Nile Basin, Ethiopia

2019 ◽  
Vol 226 ◽  
pp. 105767 ◽  
Author(s):  
Abeyou W. Worqlul ◽  
Yihun T. Dile ◽  
Petra Schmitter ◽  
Jaehak Jeong ◽  
Manyowa N. Meki ◽  
...  
Keyword(s):  
Water Resource ◽  
Resource Assessment ◽  
Vegetable Production ◽  
Nile Basin ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Water Resource Assessment ◽  
Upper Blue Nile Basin

scholarly journals GLOBAL WATER RESOURCE ASSESSMENT INCLUDING GLACIER MELTWATER FROM MOUNTAINOUS REGIONS

2015 ◽  
Vol 71 (4) ◽  
pp. I_451-I_456
Author(s):  
Masahiro TANOUE ◽  
Satoshi WATANABE ◽  
Takahiro TSUNEKAWA ◽  
Naota HANASAKI ◽  
Yukiko HIRABAYASHI
Keyword(s):  
Water Resource ◽  
Resource Assessment ◽  
Mountainous Regions ◽  
Glacier Meltwater ◽  
Water Resource Assessment ◽  
Global Water

scholarly journals Optimization of the Multi-Start Strategy of a Direct-Search Algorithm for the Calibration of Rainfall–Runoff Models for Water-Resource Assessment

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1876 ◽  
Author(s):  
García-Romero ◽  
Paredes-Arquiola ◽  
Solera ◽  
Belda ◽  
Andreu ◽  
...  
Keyword(s):  
Water Resource ◽  
Search Algorithm ◽  
Resource Assessment ◽  
Rainfall Runoff ◽  
Soil Moisture Accounting ◽  
Shuffled Complex Evolution ◽  
Efficient Performance ◽  
Direct Search Algorithm ◽  
Calibration Techniques ◽  
Water Resource Assessment

Calibration of conceptual rainfall–runoff models (CRRM) for water-resource assessment (WRA) is a complicated task that contributes to the reliability of results obtained from catchments. In recent decades, the application of automatic calibration techniques has been frequently used because of the increasing complexity of models and the considerable time savings gained at this phase. In this work, the traditional Rosenbrock (RNB) algorithm is combined with a random sampling method and the Latin hypercube (LH) to optimize a multi-start strategy and test the efficiency in the calibration of CRRMs. Three models (the French rural-engineering-with-four-daily-parameters (GR4J) model, the Swedish Hydrological Office Water-balance Department (HBV) model and the Sacramento Soil Moisture Accounting (SAC-SMA) model) are selected for WRA at nine headwaters in Spain in zones prone to long and severe droughts. To assess the results, the University of Arizona’s shuffled complex evolution (SCE-UA) algorithm was selected as a benchmark, because, until now, it has been one of the most robust techniques used to solve calibration problems with rainfall–runoff models. This comparison shows that the traditional algorithm can find optimal solutions at least as good as the SCE-UA algorithm. In fact, with the calibration of the SAC-SMA model, the results are significantly different: The RNB algorithm found better solutions than the SCE-UA for all basins. Finally, the combination created between the LH and RNB methods is detailed thoroughly, and a sensitivity analysis of its parameters is used to define the set of optimal values for its efficient performance.

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