scholarly journals Prediction of Surface Runoff and Soil Erosion at Watershed Scale: Analysis of the AnnAGNPS Model in Different Environmental Conditions

Author(s):  
Demetrio Antonio ◽  
Giuseppe Bombino ◽  
Pietro Denisi ◽  
Feliciana Licciardello ◽  
Santo Marcello
2020 ◽  
Author(s):  
Jinshi Jian ◽  
Xuan Du ◽  
Ryan D. Stewart ◽  
Zeli Tan ◽  
Ben Bond-Lamberty

Abstract. Soil erosion is a major threat to soil resources, continuing to cause environmental degradation and social poverty in many parts of the world. Many field and laboratory experiments have been performed over the past century to study spatio-temporal patterns of soil erosion caused by surface runoff under different environmental conditions. However, these historical data have never been integrated together in a way that can inform current and future efforts to understand and model soil erosion at different scales. Here, we designed a database (SoilErosionDB) to compile field and laboratory measurements of soil erosion caused by surface runoff, with data coming from sites across the globe. The SoilErosionDB includes 18 columns for soil erosion related indicators and 73 columns for background information that describe factors such as latitude, longitude, climate, elevation, and soil type. Currently, measurements from 99 geographic sites and 22 countries around the world have been compiled into SoilErosionDB. We provide examples of linking SoilErosionDB with an external climate dataset and using annual precipitation to explain annual soil erosion variability under different environmental conditions. All data and code to reproduce the results in this study can be found at: Jian, J., Du, X., Stewart, R., Tan, Z. and Bond-Lamberty, B.: jinshijian/SoilErosionDB: First release of SoilErosionDB, Zenodo, https://doi.org/10.5281/zenodo.4030875, 2020b. All data are also available through GitHub: https://github.com/jinshijian/SoilErosionDB.


2011 ◽  
Vol 8 (10) ◽  
pp. 2999-3009 ◽  
Author(s):  
J. Deng ◽  
Z. Zhou ◽  
B. Zhu ◽  
X. Zheng ◽  
C. Li ◽  
...  

Abstract. The degradation of water quality has been observed worldwide, and inputs of nitrogen (N), along with other nutrients, play a key role in the process of contamination. The quantification of N loading from non-point sources at a watershed scale has long been a challenge. Process-based models have been developed to address this problem. Because N loading from non-point sources result from interactions between biogeochemical and hydrological processes, a model framework must include both types of processes if it is to be useful. This paper reports the results of a study in which we integrated two fundamental hydrologic features, the SCS (Soil Conservation Service) curve function and the MUSLE (Modified Universal Soil Loss), into a biogeochemical model, the DNDC. The SCS curve equation and the MUSLE are widely used in hydrological models for calculating surface runoff and soil erosion. Equipped with the new added hydrologic features, DNDC was substantially enhanced with the new capacity of simulating both vertical and horizontal movements of water and N at a watershed scale. A long-term experimental watershed in Southwest China was selected to test the new version of the DNDC. The target watershed's 35.1 ha of territory encompass 19.3 ha of croplands, 11.0 ha of forest lands, 1.1 ha of grassplots, and 3.7 ha of residential areas. An input database containing topographic data, meteorological conditions, soil properties, vegetation information, and management applications was established and linked to the enhanced DNDC. Driven by the input database, the DNDC simulated the surface runoff flow, the subsurface leaching flow, the soil erosion, and the N loadings from the target watershed. The modeled water flow, sediment yield, and N loading from the entire watershed were compared with observations from the watershed and yielded encouraging results. The sources of N loading were identified by using the results of the model. In 2008, the modeled runoff-induced loss of total N from the watershed was 904 kg N yr−1, of which approximately 67 % came from the croplands. The enhanced DNDC model also estimated the watershed-scale N losses (1391 kg N yr−1) from the emissions of the N-containing gases (ammonia, nitrous oxide, nitric oxide, and dinitrogen). Ammonia volatilization (1299 kg N yr−1) dominated the gaseous N losses. The study indicated that process-based biogeochemical models such as the DNDC could contribute more effectively to watershed N loading studies if the hydrological components of the models were appropriately enhanced.


2011 ◽  
Vol 8 (4) ◽  
pp. 6383-6413 ◽  
Author(s):  
J. Deng ◽  
Z. Zhou ◽  
B. Zhu ◽  
X. Zheng ◽  
C. Li ◽  
...  

Abstract. The degradation of water quality has been observed worldwide, and inputs of nitrogen (N), along with other nutrients, play a key role in the process of contamination. The quantification of N loading from non-point sources at a watershed scale has long been a challenge. Process-based models have been developed to address this problem. Because N loading from non-point sources result from interactions between biogeochemical and hydrological processes, a model framework must include both types of processes if it is to be useful. This paper reports the results of a study in which we integrated two fundamental hydrologic features, the SCS (Soil Conservation Service) curve function and the MUSLE (Modified Universal Soil Loss), into a biogeochemical model, the DNDC. The SCS curve equation and the MUSLE are widely used in hydrological models for calculating surface runoff and soil erosion. Equipped with the new added hydrologic features, DNDC was substantially enhanced with the new capacity of simulating both vertical and horizontal movements of water and N at a watershed scale. A long-term experimental watershed in Southwest China was selected to test the new version of the DNDC. The target watershed's 35.1 ha of territory encompass 19.3 ha of croplands, 11.0 ha of forest lands, 1.1 ha of grassplots, and 3.7 ha of residential areas. An input database containing topographic data, meteorological conditions, soil properties, vegetation information, and management applications was established and linked to the enhanced DNDC. Driven by the input database, the DNDC simulated the surface runoff flow, the subsurface leaching flow, the soil erosion, and the N loadings from the target watershed. The modeled water flow, sediment yield, and N loading from the entire watershed were compared with observations from the watershed and yielded encouraging results. The sources of N loading were identified by using the results of the model. In 2008, the modeled runoff-induced loss of total N from the watershed was 904 kg N yr−1, of which approximately 67 % came from the croplands. The enhanced DNDC model also estimated the watershed-scale N losses (1391 kg N yr−1) from the emissions of the N-containing gases (ammonia, nitrous oxide, nitric oxide, and dinitrogen). Ammonia volatilization (1299 kg N yr−1) dominated the gaseous N losses. The study indicated that process-based biogeochemical models such as the DNDC could contribute more effectively to watershed N loading studies if the hydrological components of the models were appropriately enhanced.


2011 ◽  
Vol 14 (3) ◽  
pp. 469-484 ◽  
Author(s):  
Matthew T. Gallagher ◽  
Joel W. Snodgrass ◽  
David R. Ownby ◽  
Adrianne B. Brand ◽  
Ryan E. Casey ◽  
...  

2017 ◽  
Vol 28 ◽  
pp. 273-282 ◽  
Author(s):  
Andreas Dittrich ◽  
Ralf Seppelt ◽  
Tomáš Václavík ◽  
Anna F. Cord

2016 ◽  
Vol 41 (8) ◽  
pp. 1018-1026 ◽  
Author(s):  
Arnold Thompson ◽  
Jerry D. Davis ◽  
Andrew J. Oliphant
Keyword(s):  

CATENA ◽  
2018 ◽  
Vol 166 ◽  
pp. 147-157 ◽  
Author(s):  
Torsten Starkloff ◽  
Jannes Stolte ◽  
Rudi Hessel ◽  
Coen Ritsema ◽  
Victor Jetten

2016 ◽  
Author(s):  
Ammar Rafiei Emam ◽  
Martin Kappas ◽  
Linh Hoang Khanh Nguyen ◽  
Tsolmon Renchin

Abstract. Hydrological modeling of ungauged basins which have a high risk of natural hazards (e.g., flooding, droughts) is always imperative for policymakers and stakeholders. The Aluoi district in Hue province is a representative case study in Central Vietnam, as it is under extreme pressure of natural and anthropogenic factors. Flooding, soil erosion and sedimentation are the main hazards in this area, which threaten socio-economic activities not only in this district but also those of the area downstream. To evaluate the water resources and risk of natural hazards, we used Soil and Water Assessment Tools (SWAT) to set up a hydrological model in the ungauged basin of Aluoi district. A regionalization approach was used to predict the river discharge at the outlet of the basin. The model was calibrated in three time scales: daily, monthly and yearly by river discharge, actual evapotranspiration (ETa) and crop yield, respectively. The model was calibrated with Nash-Sutcliff and an R2 coefficients greater than 0.7, in daily and monthly scales, respectively. In the yearly scale, the crop yield inside the model was calibrated and validated with RMSE less than 2.4 ton/ha, which showed the high performance of the model. The water resource components were mapped temporally and spatially. The outcomes showed that the highest mean monthly surface runoff, 700 to 765 mm, between September and November, resulted in extreme soil erosion and sedimentation. The monthly average of actual evapotranspiration was the highest in May and lowest in December. Furthermore, installing "Best Management Practice" (BMPs) reduced surface runoff and soil erosion in agricultural lands. However, using event-based hydrological and hydraulically models in the prediction and simulation of flooding events is recommended in further studies.


2021 ◽  
Author(s):  
Suresh Kumar ◽  
Ravinder Pal Singh ◽  
Justin George Kalambukattu

Abstract Daily surface runoff, sediment and nutrient loss data collected from a watershed located in Uttarakhand state of Indian Himalayan region, in year 2010-2011 and of which half of the events data were used for calibration and remaining for validation. Model was calibrated for surface runoff, sediment loss and nutrient loss to optimize the input given to the model to predict the sediment loss, erosion and nutrient loss. The calibration was done by changing the sensitive parameters. Analysis showed that SCS CN number was found most sensitive to runoff, followed by saturated hydraulic conductivity, available water-holding capacity, CN retention parameter and C factor whereas erosion control practice (P) factor was found to be most sensitive, followed by C factor, sediment routing coefficient, average upland slope and soil erodibility (K) factor for the sediment and nutrient loss. APEX model calibrated for the watershed and it predicted quite well for the surface runoff (r=0.92, NSE=0.50), sediment loss (r=0.88, NSE=0.61 and nutrients of total carbon (r=0.78, NSE=0.59) and fairly for total nitrogen (r=0.77, NSE=0.19). Surface runoff was predicted well for low and medium rainfall; however, it was over predicted for high rainfall events. Over prediction may be attributed to the unaccountable conservation measures and practices which were not accounted by the model. Similarly, sediment loss was estimated on daily basis at the watershed scale and was well predicted for low and medium rainfalls but under-estimated for high rainfall events. The area is prone to landslips occurred at high rainfall events was not accounted by the model that may be a reason for under prediction of sediment loss by the model.


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