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Abstract:Spatiotemporal trends in precipitation may influence vegetation restoration, and extreme precipitation events profoundly affect soil erosion processes on the Loess Plateau. Daily data collected at 89 meteorological stations in the area between 1957 and 2009 were used to analyze the spatiotemporal trends of precipitation on the Loess Plateau and the return periods of different types of precipitation events classified in the study. Nonparametric methods were employed for temporal analysis, and the Kriging interpolation method was employed for spatial analysis. The results indicate a small decrease in precipitation over the Loess Plateau in last 53 years (although a Mann-Kendall test did not show this decrease to be significant), a southward shift in precipitation isohyets, a slightly delayed rainy season, and prolonged return periods, especially for rainstorm and heavy rainstorm events. Regional responses to global climate change have varied greatly. A slightly increasing trend in precipitation in annual and sub-annual series, with no obvious shift of isohyets, and an evident decreasing trend in extreme precipitation events were detected in the northwest. In the southeast, correspondingly, a more seriously decreasing trend occurred, with clear shifts of isohyets and a slightly decreasing trend in extreme precipitation events. The result suggests that a negative trend in annual precipitation may have led to decreased soil erosion but an increase in sediment yield during several extreme events. These changes in the precipitation over the Loess Plateau should be noted, and countermeasures should be taken to reduce their adverse impacts on the sustainable development of the region.

Abstract:Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. However, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolution of satellite data are available. The objective of the study is to present a satellite and rain gauge data-merging framework adapting for coarse resolution and data-sparse designs. In the framework, a statistical spatial downscaling method based on the relationships among precipitation, topographical features, and weather conditions was used to downscale the 0.25˝daily rainfall field derived from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation product version 7. The nonparametric merging technique of double kernel smoothing, adapting for data-sparse design, was combined with the global optimization method of shuffled complex evolution, to merge the downscaled TRMM and gauged rainfall with minimum cross-validation error. An indicator field representing the presence and absence of rainfall was generated using the indicator kriging technique and applied to the previously merged result to consider the spatial intermittency of daily rainfall. The framework was applied to estimate daily precipitation at a 1 km resolution in the Qinghai Lake Basin, a data-scarce area in the northeast of the Qinghai-Tibet Plateau. The final estimates not only captured the spatial pattern of daily and annual precipitation with a relatively small estimation error, but also performed very well in stream flow simulation when applied to force the geomorphology-based hydrological model (GBHM). The proposed framework thus appears feasible for rainfall estimation at high spatiotemporal resolution in data-scarce areas.