Enabling a process-oriented hydro-biogeochemical model to simulate soil erosion and nutrient losses

Abstract

Abstract. Water-induced erosion and associated particulate carbon (PC), particulate nitrogen (PN) and particulate phosphorus (PP) nutrient losses are vital parts of biogeochemical cycling. Identifying their intensity and distribution characteristics is of great significance for the control of soil and water loss and nitrogen/phosphorus nonpoint source pollution. This study incorporated modules of physical soil erosion and associated PC, PN and PP losses into a process-oriented hydro-biogeochemical model (Catchment Nutrients Management Model coupled with DeNitrification–DeComposition, CNMM-DNDC) to enable it to predict soil and water loss. The results indicated that the upgraded CNMM-DNDC (i) performed well in simulating the observed temporal dynamics and magnitudes of surface runoff, sediment and PN/PP yields in the lysimetric plot of the Jieliu catchment in Sichuan Province and (ii) successfully predicted the observed monthly dynamics and magnitudes of stream flow, sediment yield and PN yields at the catchment outlet, with significant univariate linear regressions and acceptable Nash–Sutcliffe indices higher than 0.74. The upgraded CNMM-DNDC demonstrated that a greater proportion of PN to total nitrogen (TN) during the period with large precipitation events and amounts than that during the drought period (16.2 %–26.6 % versus 2.3 %–12.4 %). The intensities of soil erosion and particulate nutrient yields in the Jieliu catchment were closely related to land use type in the following order: sloping cultivated upland (SU) > residential areas (RA) > forest land (FL). The scenario analysis demonstrated that high greenhouse gas (GHG) emissions scenarios provided a greater risk of soil erosion than did low GHG emissions scenarios and that land use change (i.e., from SU to FL) could help to mitigate soil and water loss accelerated by climate change in the future. The upgraded model was demonstrated to have the ability of predicting ecosystem productivity, hydrologic nitrogen loads, emissions of GHGs and pollutant gases, soil erosion and particulate nutrient yields, which renders it a potential decision support tool for soil erosion and nonpoint source pollution control coordinated with increasing production and reducing GHG and pollutant gases emissions in a catchment.