Hydrological Response To Parameter Spatial Aggregation In AnnAGNPS

With the influence of global climate change and the intensification of human activities, soil erosion has caused a series of environmental and ecological negative consequences, such as soil degradation and water pollution. Distributed hydrological model has become an effective approach to study the change characteristics of runoff and sediment yield under the changed environment.Distributed hydrological model divided the watershed into a large number of sub-basins to reflect the characteristics of the spatial variation of watershed. Distributed hydrological model solve these cells equations to simulate hydrological processes. The resolution of sub-basins is usually larger than the topography, soil, land use and other input data spatial resolution. So the parameter of each sub-basin will show some degree of aggregate, named parameter spatial aggregation. The parameter spatial aggregate have significant impact on watershed model results Therefore, it is of great significance to evaluate the impacts of parameter space aggregation on runoff and sediment in different time scales, and the related results will help to better understand the model uncertainty and constructed a high precision distributed hydrological model.Shanmei reservoir basin is located within the southeast coast of China, Quanzhou, and is the drinking water source for Quanzhou city. Construction a daily runoff and sediment distributed hydrological model with high precision is of great significance to protect water resources and promote regional social and economic sustainable development.The Shanmei reservoir basin was selected as the study area. Based on the sensitivity analysis of parameter by differential sensitivity analysis (DSA) method, the daily time step Annualized Agricultural Non-point Source Pollution model (AnnAGNPS) was constructed. Then nine discretization scenarios based on the values of critical source area (CSA) were carried out to evaluate the impact of cell division on the parameter aggregation, and the impact the input parameter spatial aggregation on runoff and sediment yield was quantitative analyzed respectively at annual, monthly and daily time scale. The results show as follow:(1) AnnAGNPS model can simulate well for annual, monthly and daily time scale runoff and sediment process in the study area. (2) With increasing of the value of CSA, the topography, soil, land use and cover of the research area show a significant aggregation effects. The topography aggregation effects were reflected by the apparent reducing of the river network density, and the slope of channel and cell; the land use cover aggregation effects were reflected by a significant increasing of woodland area and a decreasing of area of garden, cultivated and construction land; while the soil aggregation effects were various. (3) The spatial aggregation of SL parameters reduced the annual runoff, while the T parameters aggregation had not significant influence on the annual runoff. Both T parameters and SL parameters aggregation reduced annual sediment yield, and predicated sediment yield decreased at a greater rate relative to annual runoff response to the increase in values of CSA. Moreover, aggregation effects of SL parameters on reducing sediment load were significantly greater than that of T parameters. (4) Aggregation of T parameters was the main factor affecting the daily runoff process, resulting in the increase of peak runoff and decrease of the time to the peak runoff. And the influence on the annual maximum 1-day runoff was greater than the annual maximum 5-day. (5) The spatial aggregation of parameters have great effects on annual maximum 1-day sediment load, the annual maximum 1-day sediment load decreased significantly with the increasing degree of aggregation. The change ratio of the annual maximum 5-day sediment load was larger than that of annual maximum 1-day. Additionally, the T parameters aggregation result in the decrease of the time to peak runoff.