Remapping annual precipitation in mountainous areas based on vegetation patterns: a case study in the Nu River basin

Abstract

Abstract. Accurate high-resolution estimates of precipitation are vital to improving the understanding of basin-scale hydrology in mountainous areas. The traditional interpolation methods or satellite-based remote sensing products are known to have limitations in capturing the spatial variability of precipitation in mountainous areas. In this study, we develop a fusion framework to improve the annual precipitation estimation in mountainous areas by jointly utilizing the satellite-based precipitation, gauge measured precipitation, and vegetation index. The development consists of vegetation data merging, vegetation response establishment, and precipitation remapping. The framework is then applied to the mountainous areas of the Nu River basin for precipitation estimation. The results demonstrate the reliability of the framework in reproducing the high-resolution precipitation regime and capturing its high spatial variability in the Nu River basin. In addition, the framework can significantly reduce the errors in precipitation estimates as compared with the inverse distance weighted (IDW) method and the TRMM (Tropical Rainfall Measuring Mission) precipitation product.

Spatial Downscaling of the Tropical Rainfall Measuring Mission Precipitation Using Geographically Weighted Regression Kriging over the Lancang River Basin, China

“…We identified anomalous NDVI pixels simply by land use type: pixels categorized as water, wetland, urban, cropland, snow/ice, and barren were identified as anomalies. The detected anomalous pixels were excluded from the original NDVI dataset and then replaced with interpolated values using the IDW method to generate an optimized NDVI dataset (Zhou et al, 2017). The MODIS LST products (MOD11A2) at 1 km resolution from January 2001 to December 2015 were used in this study.…”

“…Accurate estimates of pre-cipitation are crucial for a wide range of applications, from hydrology to climate studies (Prakash et al, 2018). However, obtaining accurate precipitation data in mountainous areas remains challenging due to the sparsity of gauge networks and a remarkable spatio-temporal variability in precipitation (Zhou et al, 2017). Conventional gauge observations could provide a relatively accurate point-based measurement of precipitation, but measurements are susceptible to uncertainties, such as evaporative loss, wind effects, and gauge placement (Derin et al, 2016).…”

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

“…Additionally, due to the strong influence of farming activities (e.g., irrigation, fertilization, and harvest) on the crop growth, vegetation data of farmland were also excluded [56]. As such, we identified anomalous pixels simply by land use type: pixels categorized as water, wetland, urban, cropland, snow/ice, and barren were identified as anomalies.…”

Characteristics and Influencing Factors of Spatiotemporal Distribution of Rural Houses Construction Development in Mountainous Villages of China (1980–2019): A Case Study of Qingyuan Town

“…Settlements refer to the concentrated living spaces within specific geographic areas where social and life organizations form, involving issues discussed by disciplines such as geography, anthropology, economics, sociology, planning, and architecture. The spatial configuration of mountainous rural settlements is heavily influenced by natural geographical factors like terrain, climate, and water resources, which collectively dictate their location, size, and form [36][37][38]. Moreover, the spatial distribution of these settlements often follows certain patterns, such as river alignments and mountain adaptations [39], with the architecture within these settlements seamlessly integrating with the terrain to exploit mountain slopes and create harmonious, livable spaces [40,41].…”