Abstract
The prevention and control of non-point source pollution is an important link in managing basin water quality and is an important factor governing the environmental protection of watershed water in China over the next few decades. The control of non-point source pollution relies on the recognition of the amount, location, and influencing factors. The watershed nonpoint source pollution mechanism model is an effective method to address the issue. However, due to the complexity and randomness of non-point source pollution, both the development and application of the watershed water environment model have always focused on the accuracy and rationality of model parameters. In this pursuit, the present study envisaged the temporal and spatial heterogeneity of non-point source pollution caused by the complex underlying surface conditions of the watershed, and the insufficient coverage of hydrological and water quality monitoring stations. A refined watershed non-point source pollution simulation method, combining the Monte Carlo analytic hierarchy process (MCAHP) and the sub-watershed parameter transplantation method (SWPT), was established on the basis of the migration and transformation theory of the non-point source pollution, considering the index selection, watershed division, sub-watershed simulation, and parameter migration. Taking the Erhai Lake, a typical plateau lake in China, as the representative research object, the MCAHP method effectively reduced the uncertainty of the weights of the watershed division indexes compared to the traditional AHP method. Furthermore, compared to the traditional all watershed parameter simulation (AWPS) approach, the simulation accuracy was improved by 40% using the SWPT method, which is important for the prevention and control of non-point source pollution in large-scale watersheds with significant differences in climatic and topographic conditions. Based on the simulation results, the key factors affecting the load of the non-point source pollution in the Erhai watershed were identified. The results showed that the agricultural land in Erhai Lake contributed a majority of the load for several reasons, including the application of nitro phosphor complex fertilizer. Among the different soil types, paddy soil was responsible for the largest pollution load of total nitrogen and total phosphorus discharge into the lake. The zones with slopes of 0–18° were found to be the appropriate area for farming. Our study presents technical methods for the assessment, prevention, and control of non-point source pollution load in complex watersheds.
Highlights
Non-point source pollution has become a major factor leading to the deterioration of water quality in rivers and lakes [1,2], and is an important challenge for the protection of water quality worldwide [3]
In China, agricultural non-point source pollution contributes to about 40–60% of the watershed pollution load [4], and similar situations occurred in the United States in the last decade [5]
Considering the characteristic spatial formula climate of the Erhai Lake watershed and the obvious spatial difference in the underlying surface conditions such as soil type, slope, and land use type, initially, the Erhai Lake watershed was divided into north, west, south, and east regions using the Monte Carlo analytic hierarchy process (MCAHP) method
Summary
Non-point source pollution has become a major factor leading to the deterioration of water quality in rivers and lakes [1,2], and is an important challenge for the protection of water quality worldwide [3]. Affected by the land use type and distribution [6], rainfall conditions [7], agricultural planting structure [8], and watershed hydrological characteristics [9], the generation and emission of non-point source pollutants are intermittent and random [10], resulting in strong heterogeneity in the temporal and spatial distribution [11]. The accurate description of the temporal and spatial variation characteristics of watershed non-point source pollution, identification of the primary influencing factors, and implementation of targeted treatment measures are the most effective approaches to watershed water environment treatment in China [13].
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