Spatiotemporal variations of water conservation function based on EOF analysis at multi time scales under different ecosystems of Heihe River Basin

Continuous decline of climate fluctuations in the Kunlun-Pamir Plateau from the perspective of the bioclimatic variables

Understanding the changes in bioclimatic indicators is a prerequisite and foundation for biodiversity simulation and prediction of climate sensitive organisms. However, in high-altitude areas that are extremely sensitive to climate change, the long-term evolution patterns of bioclimatic indicators are still limited in understanding. For this purpose, we selected the Kunlun-Pamir Plateau, an irreplaceable biodiversity hotspot, as the research area, and to analyze the spatiotemporal evolution characteristics of bioclimatic indicators. We employed trend analysis, Empirical Orthogonal Function (EOF), and continuous wavelet analysis to conduct spatiotemporal analysis on 19 bioclimate indicators. We found that (1) Temporally, Mean Diurnal Range, Isothermality, Temperature Seasonality, Temperature Annual Range, Precipitation Seasonality are all trending downward in the time series; Spatially, the trend of change shows spatial variability. Central Kunlun is experiencing the biggest increases in energy factors. A western part of the Kunlun Pamir Plateau exhibits the pronounced rises in water factors. (2) The first two EOFs of 15 bioclimatic variables explained over 50% of the total variance, showing significant variations and spatial heterogeneity. (3) Regarding the periodicity: bioclimatic variables showed no significant periodicity, with the majority of events occurring at irregular intervals of 1-4 years and in different years. These insights enhance our understanding of climate characteristics in the Kunlun-Pamir Plateau, providing valuable input for devising effective biodiversity conservation strategies.

Dynamic evolution trend and driving mechanisms of water conservation in the Yellow River Basin, China

Water conservation (WC) is a critical ecological service function in the Yellow River Basin (YRB). There is currently a lack of detailed exploration of WC development processes and the impact mechanisms of driving factors at spatiotemporal scales in the YRB. By collecting data on DEM, land use, soil, meteorology, reservoirs, and observed discharge, this study established a large-scale WC model using the soil and water assessment tool (SWAT). The abrupt change test, empirical orthogonal function (EOF), wavelet analysis, hierarchical partitioning analysis (HPA), geodetectors, and aridity index were employed to analyze the multi-spatiotemporal characteristics and driving forces of WC calculated using the water balance method. The results are as follows: (1) The average WC among the YRB was 9.11 mm (74.68 × 108 m3) from 1960 to 2020. Pasture and forests contributed to 48.65% and 22.05% of the average annual WC, respectively. (2) WC exhibited four forms: less/more in the YRB, more in the southeast (northwest), and less in the northwest (southeast). (3) Forests and pastures in land use had higher average WC capacity, while Gansu, Shaanxi, and Qinghai ranked in the top three for average WC among the nine provinces. (4) Precipitation was the major driving force affecting WC variations, with the interaction between precipitation and actual evapotranspiration being the most significant. (5) Drought was a significant cause of negative WC. Protecting and managing crucial WC areas was essential for improving the ecological environment. This research elucidates the driving forces of WC in the YRB, providing scientific support for improving regional WC and promoting sustainable development.

Factor analysis of hydrologic services in water-controlled grassland ecosystems by InVEST model and geodetector

Water conservation is highly important for a successful desert grassland ecosystem, but there was no comprehensive view on how to assess influencing factors in managing and addressing water yield and water conservation in desert steppe. The Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, which is specifically used for the assessment of ecosystem services, was combined with geographic detectors to identify the priority areas for water conservation function and analyze the driving factors of water conservation in the Tabu River Basin, Inner Mongolia Autonomous Region, China, using different meteorological data sources. (i) The InVEST model has the advantage of modeling water yield and water conservation at spatial scales by fusion downscaling data. High water yield mainly occurs in the southern hilly mountainous areas, low water yield in the northern desert and grassland areas, and between the two in the central agro-pastoral areas; the multi-year average water conservation and water yield based on the InVEST model are 3.3 and 16 mm, respectively. (ii) Water yield and water conservation roughly show a transitional phenomenon of "high in the south and low in the north." The water yield and water conservation per unit area of the Tabu River Basin are relatively large for construction land, unused land, and cropland, relatively small for grassland and forestland, and basically zero for water bodies. Forest land has the strongest water conservation capacity, followed by grassland and farmland, while the order of water yield capacity is the opposite. (iii) Precipitation shows the strongest explanatory power for water yield (q = 0.427), followed by land use types (q = 0.411). The precipitation ∩ actual evapotranspiration has the strongest explanatory power for water yield (q = 0.87). The explanatory power of water yield on water conservation is the strongest (q = 0.752), followed by precipitation (q = 0.4), and the water yield ∩ soil has the greatest explanatory power on water conservation (q = 0.91). These findings are crucial for promoting regional hydrologic services and can provide a water resources management strategy for decision-makers.