Land surface temperature (LST) is a crucial input parameter in the study of land surface water and energy budgets at local and global scales. Because of cloud obstruction, there are many gaps in thermal infrared remote sensing LST products. To fill these gaps, an improved LST reconstruction method for cloud-covered pixels was proposed by building a linking model for the moderate resolution imaging spectroradiometer (MODIS) LST with other surface variables with a random forest regression method. The accumulated solar radiation from sunrise to satellite overpass collected from the surface solar irradiance product of the Feng Yun-4A geostationary satellite was used to represent the impact of cloud cover on LST. With the proposed method, time-series gap-free LST products were generated for Chongqing City as an example. The visual assessment indicated that the reconstructed gap-free LST images can sufficiently capture the LST spatial pattern associated with surface topography and land cover conditions. Additionally, the validation with in situ observations revealed that the reconstructed cloud-covered LSTs have similar performance as the LSTs on clear-sky days, with the correlation coefficients of 0.92 and 0.89, respectively. The unbiased root mean squared error was 2.63 K. In general, the validation work confirmed the good performance of this approach and its good potential for regional application.

The identification of the water level time lag (WLTL) under the regulation processes is of great significance for environmental impact, flood control, and sediment transport of huge reservoirs. The traditional hydrodynamic method can calculate the flood inflow process and the water level change process along the river channel, but it is difficult to estimate the time difference of the reservoir water level fluctuation to the dispatching process. To quantitatively evaluate the reservoir regulation effect on the WLTL in the Three Gorges Reservoir (TGR), the daily water level data from 2011 to 2017 of five stations in the TGR are analyzed in this paper. The results revealed that there is a significant water level difference along the reservoir from April 1 to October 31. The gap between the end of the reservoir and the Three Gorges Dam (TGD) is the largest, reaching 23.67 m on July 2. The longer the distance from the TGD, the longer the time lag. Furthermore, the WLTL is also different at the four different operating periods of the reservoir in a year. During the low water level operation period and high water level operation period, the time lag is 3 days which is the greatest, while in the water level decline period and water level rise period, the time lag is within 2 days.

Abstract. As a key component in the water and energy cycle, estimates of precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. However, current satellite-based precipitation products have a coarse spatial resolution (from 10 to 50 km) not meeting the needs of several applications (e.g., flash floods and landslides). The implementation of spatial downscaling methods can be a suitable approach to overcome this shortcoming. In this study, we developed a soil moisture-based precipitation downscaling (SMPD) method for spatially downscaling the integrated multisatellite retrievals for global precipitation measurement (IMERG) V06B daily precipitation product over a complex topographic and climatic area in southwestern Europe (Iberian Peninsula) in the period 2016–2018. By exploiting the soil-water balance equation, high-resolution surface soil moisture (SSM) and normalized difference vegetation index (NDVI) products were used as auxiliary variables. The spatial resolution of the IMERG daily precipitation product was downscaled from 10 to 1 km. An evaluation using 1027 rain gauge stations highlighted the good performance of the downscaled 1 km IMERG product compared to the original 10 km product, with a correlation coefficient of 0.61, root mean square error (RMSE) of 4.83 mm and a relative bias of 5 %. Meanwhile, the 1 km downscaled results can also capture the typical temporal and spatial variation behaviors of precipitation in the study area during dry and wet seasons. Overall, the SMPD method greatly improves the spatial details of the original 10 km IMERG product also with a slight enhancement of accuracy. It shows good potential to be applied for the development of high-quality and high-resolution precipitation products in any region of interest.