Ground-water resource assessment of the Montauk area, Long Island, New York

Hater resource managers in semi-arid regions are faced with some unique problems. The wide variations in precipitation and stream flows in semi-arid regions increase man's dependence on the ground water resource for an ample and reliable supply of water. Proper management of the ground water resource is absolutely essential to the economic well being of semi-arid regions. Historians have discovered the remains of vanished advanced civilizations based on irrigated agriculture which were ignorant of the importance of proper ground water resource management. In the United States a great deal of effort is presently being expended in the study and control of toxic discharges to the ground water resource. What many public policy makers fail to understand is that the potential loss to society resulting from the mineralization of the ground water resource is potentially much greater than the loss caused by toxic wastes discharges, particularly in developing countries. Appropriations for ground water resource management studies in developed countries such as the United States are presently much less than those for toxic wastes management and should be increased. It is the reponsibility of the water resource professional to emphasize to public policy makers the importance of ground water resource management. Applications of ground water resource management models in the semi-arid Central Valley of California are presented. The results demonstrate the need for proper ground water resource management practices in semi-arid regions and the use of ground water management models as a valuable tool for the water resource manager.

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

Arabian Journal of Geosciences ◽

10.1007/s12517-021-06699-y ◽

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.

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