A framework for testing large-scale distributed soil erosion and sediment delivery models: Dealing with uncertainty in models and the observational data

Forestation as part of the Returning Farmland to Forest Project was implemented to mitigate soil erosion in southwestern China. However, whether forestation has effectively reduced soil erosion in southwestern China remains unclear, mostly because of the lack of monitoring forest cover change and soil erosion at watershed scales. We interpreted forest cover change from satellite images and simulated soil erosion changes for the period of 1986–2018 in the Chong’an River Basin with the Water and Tillage Erosion Model and Sediment Delivery Model. Our results show that the change in forest cover has the highest correlation coefficient with the sediment yield in the watershed, with an obvious inverse phase relationship between them for all the simulated years. From 2002 to 2014, large-scale forestation and frequent droughts caused the forest cover to vary, resulting in significant changes in the annual soil erosion amount. Because crevices favoring tree growth are more developed in limestone than in dolomite, the forest cover reduction on dolomite is significantly higher than that on limestone under severe droughts in karst areas. Our study implied that the function of forestation in preventing soil erosion depends on lithology in karst areas.

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Testing distributed soil erosion and sediment delivery models using137Cs measurements

Hydrological Processes ◽

10.1002/hyp.1169 ◽

2003 ◽

Vol 17 (5) ◽

pp. 901-916 ◽

Cited By ~ 15

Author(s):

Q. He ◽

D. E. Walling

Keyword(s):

Soil Erosion ◽

Sediment Delivery ◽

Delivery Models

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An uncertainty-based framework for incorporating sediment source fingerprinting into spatially-distributed soil erosion model testing

10.5194/egusphere-egu2020-20211 ◽

2020 ◽

Author(s):

Pedro Velloso Gomes Batista ◽

J Patrick Laceby ◽

Jessica Davies ◽

Teotônio Soares de Carvalho ◽

Diego Tassinari ◽

...

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Soil Erosion ◽

Model Testing ◽

Sediment Source ◽

Sediment Delivery ◽

Sediment Loads ◽

Spatially Distributed ◽

Delivery Models ◽

Grid Based

Evaluating the usefulness of spatially-distributed soil erosion and sediment delivery models is inherently difficult. Complications stem from the uncertainty in models and measurements of system responses, as well as from the scarcity of commensurable spatial data for model testing. Here, we present an approach for evaluating distributed soil erosion and sediment delivery models, which incorporates sediment source fingerprinting into model testing within a stochastic framework. We applied the Generalized Likelihood Uncertainty Estimation (GLUE) methodology to the Sediment Delivery Distributed (SEDD) model for the Mortes River catchment (~6600 km&#178;) in Southeast Brazil. Sediment concentration measurements were used to estimate long-term sediment loads with a sediment rating curve. Regression uncertainty was propagated with posterior simulations of model coefficients. A Monte Carlo simulation was used to generate SEDD model realizations, which were compared against limits of acceptability of model errors derived from the uncertainty in the curve-estimated sediment loads. The models usefulness for identifying the sediment sources in the catchment was assessed by evaluating behavioral model realizations against sediment fingerprinting source apportionments. Accordingly, we developed a hierarchical tributary sampling design, in which sink sediments were sampled from multiple nodes in the main river channel. The relative contributions of the main sub-catchments in the basin were subsequently estimated by solving the fingerprinting un-mixing model with a Monte Carlo simulation. Results indicated that gauging station measurements of sediment loads were fairly uncertain (average annual specific sediment yields = 0.47 &#8211; 11.95 ton ha-1 yr-1). This led to 23.4 % of SEDD model realizations being considered behavioral system representations. Spatially-distributed estimates of sediment delivery to water courses were also highly uncertain, as grid-based absolute errors of SEDD results were hundredfold the median of the predictions. A comparison of SEDD outputs and fingerprinting source apportionments revealed an overall agreement between modeled contributions from individual sub-catchments to sediment loads, although some large discrepancies were found in a specific tributary. From a falsificationist perspective, the SEDD model could not be rejected, as many model realizations were behavioral. The partial agreement between fingerprinting and SEDD results provide some conditional corroboration of the models capability to identify the sources of sediments in the catchment, at least with some degree of spatial aggregation. However, the uncertainty in the grid-based outputs might dispute the models usefulness for actually quantifying sediment dynamics under the testing conditions. For management purposes, both SEDD and fingerprinting results indicated that most of the sediments reaching the hydroelectric power plant reservoir located at the outlet of the Mortes River originated from mid and upper catchment tributaries. The convergence of model results therefore evince that reducing reservoir sedimentation rates requires widespread soil conservation efforts throughout the catchment, instead of local/proximal interventions. Ultimately, we have shown how sediment source fingerprinting can be incorporated into the evaluation of spatially-distributed soil erosion and sediment delivery models while considering the uncertainty in both models and observational data.

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Effects of Vegetation Restoration on Soil Erosion on the Loess Plateau: A Case Study in the Ansai Watershed

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18126266 ◽

2021 ◽

Vol 18 (12) ◽

pp. 6266

Author(s):

Hui Wei ◽

Wenwu Zhao ◽

Han Wang

Keyword(s):

Land Use ◽

Soil Erosion ◽

Loess Plateau ◽

Water Conservation ◽

Large Scale ◽

Land Use Changes ◽

Vegetation Restoration ◽

The Loess Plateau ◽

Local Soil ◽

Erosion Environment

Large-scale vegetation restoration greatly changed the soil erosion environment in the Loess Plateau since the implementation of the “Grain for Green Project” (GGP) in 1999. Evaluating the effects of vegetation restoration on soil erosion is significant to local soil and water conservation and vegetation construction. Taking the Ansai Watershed as the case area, this study calculated the soil erosion modulus from 2000 to 2015 under the initial and current scenarios of vegetation restoration, using the Chinese Soil Loess Equation (CSLE), based on rainfall and soil data, remote sensing images and socio-economic data. The effect of vegetation restoration on soil erosion was evaluated by comparing the average annual soil erosion modulus under two scenarios among 16 years. The results showed: (1) vegetation restoration significantly changed the local land use, characterized by the conversion of farmland to grassland, arboreal land, and shrub land. From 2000 to 2015, the area of arboreal land, shrub land, and grassland increased from 19.46 km2, 19.43 km2, and 719.49 km2 to 99.26 km2, 75.97 km2, and 1084.24 km2; while the farmland area decreased from 547.90 km2 to 34.35 km2; (2) the average annual soil erosion modulus from 2000 to 2015 under the initial and current scenarios of vegetation restoration was 114.44 t/(hm²·a) and 78.42 t/(hm²·a), respectively, with an average annual reduction of 4.81 × 106 t of soil erosion amount thanks to the vegetation restoration; (3) the dominant soil erosion intensity changed from “severe and light erosion” to “moderate and light erosion”, vegetation restoration greatly improved the soil erosion environment in the study area; (4) areas with increased erosion and decreased erosion were alternately distributed, accounting for 48% and 52% of the total land area, and mainly distributed in the northwest and southeast of the watershed, respectively. Irrational land use changes in local areas (such as the conversion of farmland and grassland into construction land, etc.) and the ineffective implementation of vegetation restoration are the main reasons leading to the existence of areas with increased erosion.

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Assessing the large-scale impacts of environmental change using a coupled hydrology and soil erosion model

Earth Surface Dynamics ◽

10.5194/esurf-6-687-2018 ◽

2018 ◽

Vol 6 (3) ◽

pp. 687-703 ◽

Cited By ~ 6

Author(s):

Joris P. C. Eekhout ◽

Wilco Terink ◽

Joris de Vente

Keyword(s):

Soil Erosion ◽

Environmental Change ◽

Sediment Yield ◽

Large Scale ◽

Catchment Scale ◽

Time Step ◽

Vegetation Development ◽

Erosion Model ◽

Complete Representation ◽

Large Catchment

Abstract. Assessing the impacts of environmental change on soil erosion and sediment yield at the large catchment scale remains one of the main challenges in soil erosion modelling studies. Here, we present a process-based soil erosion model, based on the integration of the Morgan–Morgan–Finney erosion model in a daily based hydrological model. The model overcomes many of the limitations of previous large-scale soil erosion models, as it includes a more complete representation of crucial processes like surface runoff generation, dynamic vegetation development, and sediment deposition, and runs at the catchment scale with a daily time step. This makes the model especially suited for the evaluation of the impacts of environmental change on soil erosion and sediment yield at regional scales and over decadal periods. The model was successfully applied in a large catchment in southeastern Spain. We demonstrate the model's capacity to perform impact assessments of environmental change scenarios, specifically simulating the scenario impacts of intra- and inter-annual variations in climate, land management, and vegetation development on soil erosion and sediment yield.

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Estimation of rainfall erosivity based on WRF-derived raindrop size distributions

10.5194/hess-2020-187 ◽

2020 ◽

Author(s):

Qiang Dai ◽

Jingxuan Zhu ◽

Shuliang Zhang ◽

Shaonan Zhu ◽

Dawei Han ◽

...

Keyword(s):

Soil Erosion ◽

Water Conservation ◽

Large Scale ◽

Weather Prediction ◽

Wrf Model ◽

Analysis Data ◽

Potential Effect ◽

Rainfall Erosivity ◽

Size Distributions ◽

Raindrop Size

Abstract. Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driving force for soil erosion and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy (KE). As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops KE. With the development of global atmospheric re-analysis data, numerical weather prediction (NWP) techniques become a promising way to estimate rainfall KE directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware [TAA]) to obtain raindrop size distributions, rainfall KE and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, TAA had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.

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Large Scale Distribution of Galaxies in the Field HS 47.5–22. II. Observational Data Analysis

Astrophysical Bulletin ◽

10.1134/s1990341320030062 ◽

2020 ◽

Vol 75 (3) ◽

pp. 219-233

Author(s):

A. A. Grokhovskaya ◽

S. N. Dodonov ◽

T. A. Movsesyan

Keyword(s):

Data Analysis ◽

Observational Data ◽

Large Scale

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Estimation of rainfall erosivity based on WRF-derived raindrop size distributions

Hydrology and Earth System Sciences ◽

10.5194/hess-24-5407-2020 ◽

2020 ◽

Vol 24 (11) ◽

pp. 5407-5422

Author(s):

Qiang Dai ◽

Jingxuan Zhu ◽

Shuliang Zhang ◽

Shaonan Zhu ◽

Dawei Han ◽

...

Keyword(s):

Soil Erosion ◽

Kinetic Energy ◽

Water Conservation ◽

Large Scale ◽

Weather Prediction ◽

Wrf Model ◽

Analysis Data ◽

Rainfall Erosivity ◽

Size Distributions ◽

Raindrop Size

Abstract. Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driven force for soil erosion, and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy. As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops' kinetic energy. With the development of global atmospheric re-analysis data, numerical weather prediction techniques become a promising way to estimate rainfall kinetic energy directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware) to obtain raindrop size distributions, rainfall kinetic energy, and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, Thompson aerosol-aware had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.

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