Spatial-Temporal Pattern Analysis of Land Use and Water Yield in Water Source Region of Middle Route of South-to-North Water Transfer Project Based on Google Earth Engine

Abstract

The water source area of the middle route of the South-to-North Water Diversion Project is an important water conservation and ecological protection area in China. Based on remote sensing data, this paper analyzed the evolution process of land use/cover change in water source region in the past 35 years. Then, based on the InVEST model, the spatial-temporal patterns of water yield in the water source region were calculated with land use cover, meteorology and soil data as inputs. The impacts of climate factors such as precipitation and temperature and land use change on water yield were discussed, and the responses of water yield to these two changes were also discussed. The results show that from 1985 to 2020, the average water yield depth in the middle route of the South-to-North Water Diversion Project increases first and then decreases, from 615 mm in 1985 to 738 mm in 2000, and then decreases to 521 mm in 2020. The spatial heterogeneity of the water-producing capacity is obvious. The high value of the water-producing capacity is concentrated in the Daba Mountain area in the south, while the low values are concentrated in the Hanzhong Basin, Ankang Basin and the eastern plain area. The spatial pattern of water producing depth has no obvious change. The average water yield depth of forest, grassland and shrub in the region was the largest, and forest and cultivated land were the main contributors to the total water yield of the region, providing 82% and 14% of the total water yield in 2020. Precipitation has a significant effect on water yield, while land use/cover change has a small effect on water yield.

Comprehensive Benefit Assessment of the Middle Route of South-to-North Water Diversion Project Based on Markowitz Theory

“…Fig. 4 Effective frontier calculation result graph After the calculation of the effective frontier, the global minimum variance combination, which is the leftmost vertex of the effective frontier curve and also the theoretically optimal weight allocation combination, can be solved according to Formulas (11) and (12). The results are shown in Table 3.…”

Google Earth Engine for analysis of land cover change and streamflow trends in the Ribeirão da Caveira River Basin, Brazil from 1988-2019

“…Cloud computing platforms, such as Google Earth Engine (GEE), allows the processing of large amounts of data for the understanding of climate and LULC changes on river basins, especially those located in arid and semi-arid regions (Souza et al 2020;Niu et al 2022). With the adequate analysis, remote sensing imagery techniques give important insights into how climate and land use changes affect, at different spatial and temporal resolutions, a basin and local communities (Alam et al 2020) and have been used to detect soil loss, deforestation rates, and water use at a regional level (Uddin et al 2018).…”

“…Limitations to ground monitoring systems include slow, point-based measurements, which are often di cult to obtain and subjected to failures (Oularé et al 2023). In contrast, remote sensing technology provides comprehensible information that, when coupled with climatic and ground-based data, can become a powerful monitoring tool (Mekonnen et al 2017;Rodgers et al 2020;Niu et al 2022;Jiang et al 2023). However, processing satellite imagery demands high computing power, which has been a major limiting factor (Kang et al…”

Spatial-Temporal Changes in Water Supply and Demand in the Citarum Watershed, West Java, Indonesia Using a Geospatial Approach

“…This pattern is similar to the pattern of annual rainfall in the study area (Figure 2b), in which the high rainfall was found in the middle and the southwest of the CW. Previous studies reported that the spatial pattern of WY within the watershed is mainly influenced by precipitation [55,56]. Using a correlation analysis, ref.…”