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The Lijiang River Basin is a humid, subtropical, karst landform in China and is listed as a World Heritage Site. However, with the rapid development of urbanization and tourism activities in recent years, it faces increasingly severe non-point source pollution. To understand the temporal and spatial variations in nitrogen and phosphorus pollution discharge and the changes in river water quality, the Jingui Small Basin, in the upper reaches of the Lijiang River, was chosen as a representative system. Changes in nitrogen and phosphorus concentrations were continuously monitored in the main river channel and the river water quality was evaluated using the comprehensive water quality identification index method. The results indicated that there were obvious seasonal changes in nitrogen and phosphorus discharge loads in the basin. Both nitrogen and phosphorus discharge loads were higher in the crop-growing season than in the non-growing season. No significant difference in nitrogen and phosphorus discharge load between different scales was found, and the scale was not the key factor affecting the nitrogen and phosphorus discharge load of Jingui River. As the river flowed from the initial water source to the outlet of the basin, water quality was characterized by the spatial pattern of the upper reaches > the middle reaches > the lower reaches. Except for the water quality at the outlet of the basin in November and December, which reached Class V, the comprehensive water quality of each sub-basin reached the target water quality of the water function zoning from May to December. The elucidation of the nitrogen and phosphorus pollution discharge patterns in the Jingui River and the changes in water quality provide a reference for the control and management of agricultural non-point source pollution in the Lijiang River Basin. [ABSTRACT FROM AUTHOR]

The objective of this work was to modify the HEC-HMS flood prediction for the karstic watershed of the Lijiang River, South China, through the quantitative inclusion into the model of the available reservoir capacity of karst (ARCK) as a case study. Due to the complexities caused by hidden drainage networks in karst hydrology, as a new approach, soil moisture accounting loss was used to reflect the ARCK in flood forecasting. The soil moisture loss was analyzed against daily rainfall runoff data across 1.5 years by using an artificial neural network via phyton programming. Through the correlations found among the amounts of soil moisture and river flow fluctuations in response to precipitation and its intervals, coefficients were introduced to the model for output modifications. ARCK analysis revealed that while heavy rainfalls with longer intervals (i.e., 174 mm/2d after 112 days of the dry season) may not cause considerable changes in the river flow magnitude (0.1–0.64 higher owing to high ARCK), relatively small rainfalls with higher frequency (i.e., 83 mm/4d during the wet season) can cause drastic raise of river flow (10–20 times greater at different stations) due to lower ARCK. Soil moisture accounting loss coefficients did enhance the model's simulated hydrographs accuracy (NSE) up to 16% on average as compared to the initial forecasting via real data. However, the modifications were valid for flood events within a few years from the soil moisture observation period. Our result suggested that the inclusion of ARCK in modeling through soil moisture accounting loss can lead to increased prediction accuracy through consistent monitoring. [ABSTRACT FROM AUTHOR]