Water use efficiency and TN/TP concentrations as indicators for watershed land-use management: A case study in Miyun District, north China

Under dual influences of climate change and human disturbances, it is an important measure for sustainable watershed development to conduct land use planning with considerations of water saving and pollution control. In this research, the following tasks has been accomplished: (1) identifying runoff variations due to the land use changes, (2) investigating concentration variations in total nitrogen (TN) and total phosphorus (TP) in overland flow of different vegetation, (3) based on the rules found by the analyses above, determining the optimal land use demand of water, grass, forest, farm and construction lands through the adoption of multi-objective linear programming and a hydrological model (i.e., MIKE-SHE), (4) taking the relationship between land use changes and hydrological factors at the watershed scale as a corrected input of CLUE-S model to visually allocate land use under three scenarios (i.e., the most optimal situation, adjustment planning after the check point and the government planning for 2020), and further making the detailed crops distribution map for agricultural management with aim of improving water use efficiency and controlling TN/TP concentrations. Thus the “top-down” land use optimal allocation combining the macro scale and the field scale was implemented. The results can provide useful decision alternatives for the land use management of the watershed. Pine, chestnut and walnut were distributed on relatively steep hillside above 400 m. Crops like corn, millet and sweet potato were allocated to the flat areas with the slope less than 3° and the altitude below 400 m. The combination of CLUE-S model and MIKE-SHE model improved the accuracy of land use demand and specific vegetation distributions for farmers. The “top-down” land use optimal allocation considering both water saving and pollution control would support decision makers with feasible suggestions of optimizing land management at different scales.