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• The NPPRI, MCR and SWAT models are superimposed in space to identify the critical source areas. • On the basis of analyzing the influence of BMPs on the landscape characteristics of watershed, the response relationship between NPS and landscape pattern are further revealed. • Based on landscape pattern theory and information entropy method, the best combination management measures for watersheds are proposed and evaluated. The deterioration of ecological environment caused by non-point source pollution (NPS) has seriously threatened human health and socioeconomic development. Effective identification of critical source areas and selecting appropriate best management practices (BMPs) for watersheds are the keys to controlling NPS. This paper, using Hanjiang River basin, the vital water source for the South-to-North Water Division, China, as a case study, presents a method for accurate critical source areas dentification according to the spatial distribution characteristics of underlying surface and pollution load by coupling NPS risk index (NPPRI), Minimum Cumulative Resistance (MCR) Model, and Soil and Water Assessment Tool (SWAT). In this research, ten single and three combined scenarios are set up in the critical source areas, including three non-engineering management practices, two engineering management practices, and five ecological buffer zones. Finally, the effect of BMPs for non-point source pollution is assessed by using landscape pattern theory and information entropy method. Furthermore, the influences of the five ecological buffer zones on the characteristics of landscape in watershed and the response relationship between the landscape pattern and NPS are analyzed. The following findings are obtained: (1) The critical source areas were mainly located in the south-central and southeastern part of the study area, accounting for 35.9 % of the total watershed area, and yielding 54.2 % and 60.7 % of the total nitrogen(TN) and total phosphorus(TP) loads in the watershed; (2) All ecological buffer zones play a positive role in NPS control with an average reduction rate of 32.1 % for TN and 46.7 % for TP, and the control effectiveness for TP are better than that for TN; (3) The deployment of the five ecological buffer zones have a significant impact on the interspersion juxtaposition index (IJI), the connectivity of the "sink" landscape is enhanced, the diversity of the "source" landscape is reduced and the "sink" landscape's ability of interception is boosted; (4) The calculation result of information entropy shows the highest attribute value of practices in the ecological buffer zones is Buffer 200 m. Overall, the findings serve as a reference for NPS control and water environment management in the watershed and strengthen the feasibility of implementing of BMPs in the watershed. [ABSTRACT FROM AUTHOR]

• Effects of interpolation methods on rainfall data scarcity were compared. • Impacts of of data scarcity on model uncertainty were investigated. • The effects of the different imputed data sets on H/NPS results were quantified. • The EMB algorithm was proved to be superior to the traditional methods. • The threshold effect and monitoring during high-flow periods were highlighted. Rainfall data scarcity has caused enormous problems in hydrologic and non-point pollution (H/NPS) predictions, as rainfall data represent the key input to watershed models. In this study, the effects of different imputation methods such as the data augmentation (DA) and the expectation maximization with bootstrap (EMB) algorithms on rainfall data scarcity were compared. The effects of different data scarcity rates and periods on model performance and prediction uncertainty were then quantified. Finally, the effects of different imputed data sets on H/NPS results were evaluated with the soil and water assessment tool (SWAT). A real case study in the Daning River watershed, Three Gorges Reservoir Region, China, was evaluated. The results indicated that rainfall data scarcity during low flow periods would result in poorer model performance and larger prediction uncertainty, especially to some minimum values, and the time when the maximum values are more susceptible is the rainfall data scarcity during high flow periods. The repair of rainfall data and the H/NPS model performance obtained by the EMB algorithm are superior to the traditional DA and weather generator performances. This advantage of the EMB algorithm would be more definitive if a specific threshold of data scarcity is reached. It is noted that even if the best algorithm is used, the imputed value is always lower than the peak observed value. This paper reports important implications for the choice of imputation methods and the use of H/NPS models for solving data scarcity problems for watershed studies. [ABSTRACT FROM AUTHOR]

• A new hydrological-water quality model for humid and semi-humid catchments in China is presented. • Both horizontal and vertical heterogeneity are considered in the hydrological process and the water quality process modelling. • Model uncertainty and the sensitivity of model parameters are investigated. Non-point source (NPS) pollutants may cause water quality deterioration and eutrophication, posing a significant threat to aquatic systems. Understanding and modelling surface and sub-surface hydrological processes and nutrient cycles are therefore essential for water resources management and pollution control. This paper presents a new hydrological-water quality model for application in humid and semi-humid catchments in China, considering both hydrological processes, and nutrient transport and transformation. The model divides the soil into three layers, and each layer is implemented with individual computational procedures for soil wetness and nutrient process. Nutrient transport is driven by hydrological processes and follows the same pathways as water flow in the model: surface runoff, infiltration, and outflow from individual soil layers. River channels are described separately with routines to account for the turnover of nutrients. Model parameters are selected according to literature/open-sourced data or estimated from soil texture or land use types. The model is applied to simulate the hydrological processes and phosphorus transport in the Tongyang River Basin in China and the model performance is confirmed by comparing the predicted discharge and total phosphorus concentrations with measured data at the catchment outlets of Tonghe River and Yanghe River over 3 years. Uncertainty analysis has been further carried out using the GLUE method to demonstrate sensitivity of the simulation results to the selection of model parameters. After model calibration, the predicted results are found to compare well with field observations in terms of flow discharge and total phosphorus concentration. From sensitivity analysis, it is found that the recession coefficient of channel system (CS) and the pollutant recession coefficient of surface runoff (p KS) are the most influential hydrological and water quality parameters that control the arrival time and duration of flood peak and nutrient concentration peak, respectively. [ABSTRACT FROM AUTHOR]

Hydrological and non-point source pollution (H/NPS) predictions in ungagged basins have become the key problem for watershed studies, especially for those large-scale catchments. However, few studies have explored the comprehensive impacts of rainfall data scarcity on H/NPS predictions. This study focused on: 1) the effects of rainfall spatial scarcity (by removing 11%–67% of stations based on their locations) on the H/NPS results; and 2) the impacts of rainfall temporal scarcity (10%–60% data scarcity in time series); and 3) the development of a new evaluation method that incorporates information entropy. A case study was undertaken using the Soil and Water Assessment Tool (SWAT) in a typical watershed in China. The results of this study highlighted the importance of critical-site rainfall stations that often showed greater influences and cross-tributary impacts on the H/NPS simulations. Higher missing rates above a certain threshold as well as missing locations during the wet periods resulted in poorer simulation results. Compared to traditional indicators, information entropy could serve as a good substitute because it reflects the distribution of spatial variability and the development of temporal heterogeneity. This paper reports important implications for the application of Distributed Hydrological Models and Semi-distributed Hydrological Models, as well as for the optimal design of rainfall gauges among large basins. [ABSTRACT FROM AUTHOR]