HS6.9

Accurate measurements of various hydrological cycle components (e.g. precipitation, evapotranspiration, soil moisture and water storage changes) are essential for understanding the hydrological processes and further for sustainable water resources management. Hydrological cycle components are characterized by significant variability in time and space. The conventional in-situ measurements from gauges are generally considered to be the most accurate measurements, but scientific communities are often encountered with the limited availability and capability of in-situ measurements. Specifically, the network of gauge stations is often sparse and overall the number of stations is still on decreasing trend over the globe. The point-based feature makes gauge measurements insufficient to capture spatial and temporal variability of hydrological cycle components. Therefore, alternative data sources should be investigated to fill the data gaps.

Satellite remote sensing has been shown great capability of estimating various hydrological cycle components at different temporal and spatial scales. Various communities have recognized the importance of satellite remote sensing, but they have been stressing the need for improvements in accuracy and particularly the spatial resolution because the spatial resolution of remotely sensed products is still often too coarse for many applications. To this regard, a specific topic “spatial downscaling” has emerged; over last decades, considerable efforts have been made to develop various spatial downscaling algorithms to improve the spatial resolution of remotely sensed estimates.

Machine learning and geostatistical methods have been innovatively utilized to advance the spatial downscaling in satellite remote sensing community. Together with the algorithms development in spatial downscaling, further pertinent research question arise: how to accurately evaluate the skill of downscaled remote sensing products? All current approaches for evaluation contain known limitations and, hence, there is a clear need for the development of novel procedures for fair evaluation particularly considering the limitations (e.g. representativeness and availability) of ground measurements form gauge stations.

The aim of this session is to present and discuss novel procedures in spatial downscaling of remotely sensed hydrological cycle components with emphasis on algorithms development, innovative evaluation and application of downscaled estimates.

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Convener: Zheng DuanECSECS | Co-conveners: Jianzhi DongECSECS, Jian PengECSECS, Hongkai Gao
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| Attendance Tue, 05 May, 16:15–18:00 (CEST)

Accurate measurements of various hydrological cycle components (e.g. precipitation, evapotranspiration, soil moisture and water storage changes) are essential for understanding the hydrological processes and further for sustainable water resources management. Hydrological cycle components are characterized by significant variability in time and space. The conventional in-situ measurements from gauges are generally considered to be the most accurate measurements, but scientific communities are often encountered with the limited availability and capability of in-situ measurements. Specifically, the network of gauge stations is often sparse and overall the number of stations is still on decreasing trend over the globe. The point-based feature makes gauge measurements insufficient to capture spatial and temporal variability of hydrological cycle components. Therefore, alternative data sources should be investigated to fill the data gaps.

Satellite remote sensing has been shown great capability of estimating various hydrological cycle components at different temporal and spatial scales. Various communities have recognized the importance of satellite remote sensing, but they have been stressing the need for improvements in accuracy and particularly the spatial resolution because the spatial resolution of remotely sensed products is still often too coarse for many applications. To this regard, a specific topic “spatial downscaling” has emerged; over last decades, considerable efforts have been made to develop various spatial downscaling algorithms to improve the spatial resolution of remotely sensed estimates.

Machine learning and geostatistical methods have been innovatively utilized to advance the spatial downscaling in satellite remote sensing community. Together with the algorithms development in spatial downscaling, further pertinent research question arise: how to accurately evaluate the skill of downscaled remote sensing products? All current approaches for evaluation contain known limitations and, hence, there is a clear need for the development of novel procedures for fair evaluation particularly considering the limitations (e.g. representativeness and availability) of ground measurements form gauge stations.

The aim of this session is to present and discuss novel procedures in spatial downscaling of remotely sensed hydrological cycle components with emphasis on algorithms development, innovative evaluation and application of downscaled estimates.

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