Identification of watershed priority management areas based on landscape positions: An implementation using SWAT+

Abstract

The priority management areas (PMAs) of a watershed are areas with high contributions to the pollutant load to the assessment outlet, such as the watershed outlet, and, thus, have high priority in the decision making for comprehensive watershed management. Existing spatial units used to identify PMAs are commonly based on three concepts including subbasins, artificial geographic entities, and grid cells. However, these identification units cannot balance general applicability to diverse geographic environments and the representation degree of spatial heterogeneity, which impacts the effectiveness of the PMAs. This study proposes utilizing landscape positions along the hillslope as identification units of PMAs, which can be represented by slope position units (e.g., upland, backslope, and valley). Landscape position units inherently have upstream–downstream relations with each other and with channels. Therefore, their contributions to the assessment outlet can be quantified based on the propagation effects of hillslope and channel routing processes. The proposed method was implemented using a restructured and enhanced version of the Soil and Water Assessment Tool (SWAT+) to quantify the pollutants released, and a Markov chain-based surrogate model to distinguish the source contribution with the improvement of the transition matrix in representing both landscape position and channel units. Two watersheds, one in China and one in the USA, with different geographic characteristics were selected to separately conduct comparative experiments to identify PMAs at the landscape position and subbasin levels. The results show that PMAs based on landscape positions have more accurate spatial distribution and require less area for the future configuration of management practices to achieve the same management goal as PMAs based on subbasins. The better effectiveness of landscape position units in identifying PMAs is mainly due to their better ability to represent hillslope processes and the spatial heterogeneity of underlying surface environments within subbasins. The proposed method can be implemented using other watershed models that support landscape position units or different types of spatial units with explicit upstream–downstream relations within subbasins.