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Computer-based coupled soilscape-landform evolution models provide an avenue for estimating the erosion potential of a landform over geological timescales. They also provide tools to understand soil-landform relationships in the context of both field and modelled pedogenesis processes. In this study, we used the State Space Soil Production and Assessment Model (SSSPAM) and CAESAR-Lisflood landform evolution model to simulate the evolution of a natural catchment where soil erosion rates have been closely monitored over the last 20 years. Both models predict comparable erosion rates to that of field measurements and produce similar catchment scale geomorphological patterns. Rock content data measured in the field along two transects were compared with SSSPAM simulations at the same representative points. The results showed that SSSPAM produced similar rock content variation along the respective transects. The findings demonstrate 'SSSPAM's ability to predict catchment scale erosion and surface soil distribution. The insights demonstrated here provide confidence in the SSSPAM model for Soilscape predictions for natural, agricultural and post-mining landform assessment. [Display omitted] • SSSPAM and CAESAR-Lisflood models were used to simulate the evolution of a natural catchment. • Both models produced catchment erosion rates comparable with field observations. • SSSPAM reproduced surface rock content variation on 2 transects observed in the field. • SSSPAM also revealed a possible relationship between area, slope and soil grading. • This study demonstrates SSSPAM's ability to predict erosion and surface soil distribution. [ABSTRACT FROM AUTHOR]

An experimental study was conducted to evaluate the effect of drainage water recycling (DWR) on reducing nitrogen (N), phosphorus (P), and sediment losses from agricultural fields to downstream surface water bodies. The two-year study (May 2019-April 2021) was conducted at an agricultural field in eastern North Carolina, U.S.A. A reservoir existed at the site was used to store subsurface drainage and surface runoff water during wet periods and provide supplemental irrigation during dry periods of the crop growing season. On average, the reservoir retained 14% of received inflow, with a higher flow reduction in the dry year (2019–2020; 29%) than the wet year (2020–2021; 8%). The hydraulic retention time (HRT) for the reservoir was 33.8 days for the dry year and 12.4 days for the wet year. The reservoir significantly reduced the loadings of N by 47%, P by 30% and sediment by 87%. Nitrogen load reduction was primarily driven by nitrate assimilation, the dominant form of N in the reservoir. Phosphorus load reduction was attributed to Orthophosphate assimilation as the reservoir released more particulate P than received. Reductions in both water flow and species concentration contributed to nutrient load reductions. Results suggested the removal efficiency of the reservoir would be highest during the summer and early fall months when the reservoir has a smaller water volume (due to irrigation), longer HRT, and warmer temperature. This study clearly demonstrated the potential of DWR for significantly reducing N, P, and sediment losses from agricultural land to receiving surface water. Further research is needed to investigate the physical, chemical, and biological processes that occur in the storage reservoir and affect the fate and transport of nutrients and sediment. The understanding of these processes will enable optimizing the treatment efficiency of DWR, which maximizes the system's benefits and reduces construction cost. • Drainage water recycling (DWR) had significant water quality benefits. • DWR reduced losses of nitrogen by 47%, phosphorus by 30%, and sediment by 87%. • DWR treatment positively correlated with water retention time and temperature. • DWR treatment negatively correlated with reservoir water volume and inflow rate. [ABSTRACT FROM AUTHOR]