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.

A spatial optimal allocation method considering multi-attribute decision making and multiple BMPs random combination in sub-watersheds.

Best management practices (BMPs) have been extensively employed in effective watershed management for non-point source pollution. The weights of objective functions and the restrictive conditions of combined BMPs are the vital requirements for BMPs allocation. Therefore, it is more beneficial to explore that a spatial optimal allocation method considering multi-attribute decision making and multiple BMPs random combination. Here is the novel framework based on Soil and Water Assessment Tool (SWAT) and the Non-dominated Sorting Genetic Algorithm II (NSGA-Ⅱ), which considers multiple objectives in deriving watershed-scale pollution control practices by considering BMPs cost and combined reduction rates of total nitrogen (TN) and total phosphorus (TP). The framework also integrates combined Entropy Weight method (EWM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the weights of TN and TP, and considers the attributes of the sub-basin itself, which is more local suitability. Four categories of BMPs, tillage management, nutrient management, vegetative filter strips, and landscape management, were evaluated in the Jing River Basin (JRB) and resulted in reduction rates of 9.77%, 10.53%, 16.40%, and 14.27% averagely, respectively. BMP allocation schemes, derived from multi-objective optimization, are stratified into three financial scenarios. Low-cost scenario, costing up to 2 billion RMB, primarily targets the grain for green program in 28.81% of sub-basins. Medium-cost scenario, between 2 and 6 billion RMB, predominantly utilizes the grain for green in areas with a slope greater than 15°, accounting for 20.00% of sub-basins. High-cost scenario exceeds 6 billion RMB, mainly due to the implementation of multiple combination measures. The three configuration scenarios can provide decision-makers with a trade-off between measure costs and reduction efficiency. Overall, the innovative framework not only facilitates cost-effective implementation but provides a beneficial methodology for selecting cost-effective conservation practices in other regions.