Satellite precipitation products have been one of the most important technologies with wide coverage more suitable for areas without data. However, their performance cannot fully meet the harsh requirement of the high resolution and precision precipitation data in recent years. It is very necessary to downscale and then integrate the satellite products with the ground observation data for better data quality. In this study, the Lantsang River Basin in the southern Tibetan Plateau was taken as an example, particularly considering topographic, geographic and vegetational elements. The geographically weighted regression (GWR) model was established with the spatial downscaling data of the tropical rainfall measurement mission (TRMM) satellite and the precipitation estimation from remotely sensed information using artificial neural networks-climate data record (PERSIANN-CDR). The GWR downscaling model was supposed to improve the correlation accuracy between satellite products and ground observation precipitation data. After that, the ensemble Kalman filter was used to take the inverse distance weighted (IDW) interpolated data of the ground meteorological station as the observed values of the fusion and then fused the downscaling TRMM and PERSIANN-CDR data to further improve the accuracy of precipitation data. The results show that: 1) The mean value of determination coefficient（R²） of PERSIANN-CDR monthly precipitation increased from 0.35 to 0.75 after GWR downscaling. At the same time, the root mean square error (RMSE) and mean absolute error (MAE) decreased by 13.98 and 10.13 mm, respectively. There was a significant increase in the correlation degree of the PERSIANN-CDR satellite precipitation product in all months after GWR downscaling. Meanwhile, the R² of TRMM monthly precipitation increased from 0.53 to 0.85, and the RMSE and MAE decreased by 11.49 and 15.50 mm, respectively. A significant improvement was achieved in the months with the low correlation degree for the surface meteorological stations before downscaling, such as May, June, and December, where the R² reached 0.67 or above after downscaling. In addition, the two types of products presented the more significant effects on the accuracy evaluation in the dry season (from November to April), compared with the rainy season (from May to October). It infers that the GWR greatly improved the monitoring performance of these two types of satellite precipitation products on precipitation in the dry season. 2) The accuracy was improved better than before after the data integration and downscaling from the ground stations. Furthermore, the ensemble Kalman filter was used for the data fusion of down-scaled products. The overestimation of precipitation was enhanced at ground meteorological stations by satellite products, especially with the less uncertainty of the data after fusion, indicating the high precision fusion. In summary, the downscaling and fusion can be expected to increase the spatial resolution and accuracy of data. The high spatial resolution of satellite products was also achieved in the high correlation with the precipitation data observed on the ground.

The Grain for Green Program (GGP) is an effective way to control soil erosion and improve the eco-environment in China. How to develop the most cost-effective GGP scheme, which can balance the contradiction among ecology, economy, and food security, is the key point for the sustainable development of the GGP. In this study, Xixian watershed located in the upper reaches of the Huaihe River is taken as the study area. Based on the current land use situation, the distributed hydrological model SWAT (Soil and Water Assessment Tool) was used to simulate the runoff and sediment processes. Then sediment reduction resultants per unit area of GGP, which defined as the sediment reduction coefficients, were obtained by simulating the successive GGP operations in each sub-basin based on the validated SWAT model. Meanwhile, considering the spatial correspondence between GDP (gross domestic product) and land use type, GDP loss coefficients at sub-basin scale were obtained by overlapping the GDP map and current land use map. On this basis, the ecological benefit and economic benefit of the GGP operation was expressed by sediment reduction coefficients and GDP loss coefficients respectively. Finally, the multi-objective genetic algorithm NSGA-II was used to optimize the GGP scheme at sub-basin scale. The results showed that 1) The SWAT model performed high simulation accuracy for runoff and sediment modeling. The Nash–Sutcliffe coefficients were above 0.90 and 0.70, the deterministic coefficients were both greater than 0.80, and the percentage deviation of the total amount is controlled within −20% to 20%, respectively. It can be conclude that the SWAT model can be used to evaluate the impact of the GGP on sediment reduction. 2) The sediment reduction coefficients ranged from 26.70 to 2 675.85 t/km², decreasing gradually from the upper reaches to the lower reaches, which indicated that implementation of GGP per unit area can reduce sediment more effectively in the upstream river source area. 3) The GDP loss coefficient presented spatial differences significantly with the range from −5 756.83 yuan/km² to 136.26 yuan/km², showing that both increased GDP (i.e GDP loss coefficient values were greater than 0) and decreased GDP (i.e GDP loss coefficient values were less than 0) could be observed among sub-basins. Notably, sub-basins where the values of the GDP loss coefficient appeared to be the smallest were mainly concentrated in the main residential areas of cities and towns. That is, the GGP in these sub-basins would prove more costly. 4) The GGP schemes obtained by multi-objective optimization maintained the per capita cultivated land area between 1.04×10⁻³ and 1.54×10⁻³ km², which was significantly higher than the warning level of food security. Meanwhile, the Pareto-optimal set was able to reduce sediment yield by 53.54% to 69.86% of the initial value and still achieve the sustainable soil erosion level, while only losing 30.13% to 37.67% of the initial economic output. The GGP optimization method proposed in this study based on ecological sediment reduction benefits and economic benefits can provide reference and guidance for scientific planning GGP and other soil and water conservation measures.

Non-point source pollution can often be caused by nitrogen loss from fertilization in the water system. It is very necessary to identify and evaluate the environmental risks of nitrogen application for water pollutant prevention. In this study, an improved model was proposed to minimize the cumulative resistance, according to the "source-sink" theory in landscape ecology. The watershed was also selected above Hongze Lake of the Huaihe River. The source risk of nitrogen non-point source pollution was evaluated by the fertilization environmental risk index when the amount of nitrogen was applied. The key resistance factors were selected as the elevation, slope, land use, terrain moisture index, rainfall erosivity, and soil erodibility. The migration of non-point source pollution was used to construct a comprehensive resistance base. An innovative approach was proposed to construct the sink risk of nitrogen non-point source pollution using flow concentration routing. Finally, the comprehensive risk index of nitrogen non-point source pollution was formed to combine the source and sink risk. The risk level of non-point source pollution was also classified in the study area. The results show the following. 1) The source risk value of nitrogen non-point source pollution was 0-0.81, with an average value of 0.55. There was a widespread situation of excessive fertilization, especially in Shangqiu, Zhoukou, Zhumadian, and Xinyang City within Henan Province. 2) The resistance base shared a spatial trend of gradually decreasing from the southwest to the northeast under various resistance factors. A decreasing trend of sink risk was observed around the main stream. Moreover, the sink risk in the north part of the main stream was significantly lower than that in the southern region, due to the longer flow routing. 3) The comprehensive risk index demonstrated that 71.16% of the entire study area was above the middle-risk level. There was serious nitrogen non-point source pollution in the study area. 4) Extremely high-risk areas were mainly concentrated in the upper reaches of the main stream, such as Xinyang City and the northern part of Zhumadian City. There were large areas of high-risk areas in Zhoukou, Shangqiu, Fuyang, Suzhou, and Bozhou cities, which were located in the north part of the main stream. Low-risk areas were distributed mainly in the mountainous areas of the headwaters of the main and tributary rivers. Specific prevention and control measures were proposed, according to the comprehensive risk of nitrogen non-point source pollution at different levels. The finding can also provide the decision-making basis for the scientific prevention and effective management of agricultural non-point source pollution at the watershed scale.