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Regularly updated soil moisture maps are needed that combine wide scale for regional and national studies, whilstcapturing the field-scale variability that is needed by many applications in agriculture, hydrology and meteorology.C-band SAR satellites, such as Sentinel-1, offer the high spatial and temporal resolution required, but the estimationof soil moisture from SAR requires correction for many contributing factors including vegetation, soil roughness,soil texture and temperature. This paper reviews and predicts the significance of soil texture and organic mattercontent to the errors that may be present in any estimation that is made using default assumptions. We showthat each factor may contribute to a 10% error if an incorrect assumption is made. Soil moisture retrieval overagricultural fields in northern latitudes requires any algorithm to account for rapid and large changes in SARbackscatter due to crop growth and harvesting, tillage operations and freezing of the soil surface. This has particularsignificance for the extending the use of change detection approaches into arable farming areas. We discuss theprospect for developing a model to guide the setting of soil roughness parameters based on land use, soil textureand tillage, and for automatic correction for frozen soil. Successful implementation will improve the accuracy andvalidity of estimating soil moisture from C-band SAR satellite data, at the field scale.

The need to provide accurate estimates of precipitation over catchments in the Hindu Kush, Karakoram, and Himalaya mountain ranges for hydrological and water resource systems assessments is widely recognized, as is identifying precipitation extremes for assessing hydro-meteorological hazards. Here, we investigate the ability of bias-corrected Weather Research and Forecasting model output at 5-km grid spacing to reproduce the spatiotemporal variability of precipitation for the Beas and Sutlej river basins in the Himalaya, measured by 44 stations spread over the period 1980 to 2012. For the Sutlej basin, we find that the raw (uncorrected) model output generally underestimated annual, monthly, and (particularly low-intensity) daily precipitation amounts. For the Beas basin, the model performance was better, although biases still existed. It is speculated that the cause of the dry bias over the Sutlej basin is a failure of the model to represent an early-morning maximum in precipitation during the monsoon period, which is related to excessive precipitation falling upwind. However, applying a nonlinear bias-correction method to the model output resulted in much better results, which were superior to precipitation estimates from reanalysis and two gridded datasets. These findings highlight the difficulty in using current gridded datasets as input for hydrological modeling in Himalayan catchments, suggesting that bias-corrected high-resolution regional climate model output is in fact necessary. Moreover, precipitation extremes over the Beas and Sutlej basins were considerably underrepresented in the gridded datasets, suggesting that bias-corrected regional climate model output is also necessary for hydro-meteorological risk assessments in Himalayan catchments. © 2019. The Authors.

Regularly updated soil moisture maps are needed that combine wide scale for regional and national studies, whilstcapturing the field-scale variability that is needed by many applications in agriculture, hydrology and meteorology.C-band SAR satellites, such as Sentinel-1, offer the high spatial and temporal resolution required, but the estimationof soil moisture from SAR requires correction for many contributing factors including vegetation, soil roughness,soil texture and temperature. This paper reviews and predicts the significance of soil texture and organic mattercontent to the errors that may be present in any estimation that is made using default assumptions. We showthat each factor may contribute to a 10% error if an incorrect assumption is made. Soil moisture retrieval overagricultural fields in northern latitudes requires any algorithm to account for rapid and large changes in SARbackscatter due to crop growth and harvesting, tillage operations and freezing of the soil surface. This has particularsignificance for the extending the use of change detection approaches into arable farming areas. We discuss theprospect for developing a model to guide the setting of soil roughness parameters based on land use, soil textureand tillage, and for automatic correction for frozen soil. Successful implementation will improve the accuracy andvalidity of estimating soil moisture from C-band SAR satellite data, at the field scale.

The need to provide accurate estimates of precipitation over catchments in the Hindu Kush, Karakoram, and Himalaya mountain ranges for hydrological and water resource systems assessments is widely recognized, as is identifying precipitation extremes for assessing hydro-meteorological hazards. Here, we investigate the ability of bias-corrected Weather Research and Forecasting model output at 5-km grid spacing to reproduce the spatiotemporal variability of precipitation for the Beas and Sutlej river basins in the Himalaya, measured by 44 stations spread over the period 1980 to 2012. For the Sutlej basin, we find that the raw (uncorrected) model output generally underestimated annual, monthly, and (particularly low-intensity) daily precipitation amounts. For the Beas basin, the model performance was better, although biases still existed. It is speculated that the cause of the dry bias over the Sutlej basin is a failure of the model to represent an early-morning maximum in precipitation during the monsoon period, which is related to excessive precipitation falling upwind. However, applying a nonlinear bias-correction method to the model output resulted in much better results, which were superior to precipitation estimates from reanalysis and two gridded datasets. These findings highlight the difficulty in using current gridded datasets as input for hydrological modeling in Himalayan catchments, suggesting that bias-corrected high-resolution regional climate model output is in fact necessary. Moreover, precipitation extremes over the Beas and Sutlej basins were considerably underrepresented in the gridded datasets, suggesting that bias-corrected regional climate model output is also necessary for hydro-meteorological risk assessments in Himalayan catchments. © 2019. The Authors.