Soil Conservation Planning Research Articles

Projected climate change impacts on soil erosion over Iran

In the past few decades, there has been increasing interest in studying various environmental phenomena derived from climate change and its social, environmental, and economic serious impacts. It is particularly critical to developing countries where resources are limited, and conditions may worsen in the midst of environmental disasters caused by soil erosion, leading to indispensable costs in resources and human lives. Since no studies have investigated these implications on a regional scale for the case of Iran, here we assessed the impact of climate change on soil erosion across the country. The RUSLE model was employed to estimate potential soil erosion based on historical climate records and projected future data to assess climate change implications. Daily rainfall data (1987–2006) from 103 meteorological stations were used to bias correct the Climate Forecast System Reanalysis (CFSR) rainfall data. Furthermore, CFSR data, as the historical record period data (1987–2006), was used for bias correcting five General Circulation Models (GCMs) to develop rainfall erosivity values for the future climate period (2046–2065) under SSP2-4.5 and SSP5-8.5 scenarios. Based on these data sets, rainfall erosivity maps were developed to compare the variations with projected climate results across Iran. Results show an overall decrease in rainfall erosivity and soil erosion in most provinces. Considering the average outputs of five GCMs and under both SSPs, the most affected provinces with projected soil erosion decrease are P.11 and P.14, whereas the highest increase in soil erosion is likely to occur in P.13 by SSP5-8.5. In the northern provinces, particularly in P.7, the highest increase in soil erosion is expected. It was also noted that the highest range of soil erosion change among the five selected GCMs occurs at P.27, with the maximum increase of above 135% and the minimum decrease of 71% from the historical period. The present study represents the first assessment of soil erosion at a large scale in Iran, providing an overview of soil erosion that may be useful for soil and water conservation planning, hazard mapping, agriculture, and other activities that are subject to soil erosion.

Machine learning applications for water-induced soil erosion modeling and mapping

Assessment of water-induced soil erosion as a crucial part of soil conservation plans is costly and time-consuming when applied to an extensive area. In this study, we propose a methodology based on recording the annual soil erosion in a portion of the study area using erosion pins and assessing the spatial distribution of soil erosion for the entire area using machine learning techniques. First, soil erosion pins were installed, and the amount of soil loss in each pin was recorded. The controlling factors of soil erosion (percentage of vegetation canopy, curvature, slope degree, slope length, percentage of sand, percentage of silt, and percentage of clay) were determined, and the dataset was divided into training (75% of the data) and testing (25% of the data) subsets. Three machine learning algorithms, namely boosted regression trees (BRT), deep learning (DL), and multiple linear regression (MLR), were employed to identify the relationship between soil erosion and its controlling factors. Then, the methods were evaluated by comparison between the predicted and observed values on the testing subset using statistical coefficients including coefficient of determination (R-squared), normalized root mean squared error (NRMSE), and Nash-Sutcliffe efficiency (NSE). Results show that the BRT outperformed the other algorithms in the assessment of the annual soil erosion (R-squared: 0.92, NSE: 0.9, and NRMSE: 0.32). Finally, the optimal algorithm (BRT) was selected to estimate the spatial distribution of soil erosion across the entire study area, and the final erosion map was verified using additional verification pins.

Responses of Hydrological Processes under Different Shared Socioeconomic Pathway Scenarios in the Huaihe River Basin, China

The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides more scenarios and reliable climate change results for improving the accuracy of future hydrological parameter change analysis. This study uses five CMIP6 global climate models (GCMs) to drive the variable infiltration capacity (VIC) model, and then simulates the hydrological response of the upper and middle Huaihe River Basin (UMHRB) under future shared socioeconomic pathway scenarios (SSPs). The results show that the five-GCM ensemble improves the simulation accuracy compared to a single model. The climate over the UMHRB likely becomes warmer. The general trend of future precipitation is projected to increase, and the increased rates are higher in spring and winter than in summer and autumn. Changes in annual evapotranspiration are basically consistent with precipitation, but seasonal evapotranspiration shows different changes (0–18%). The average annual runoff will increase in a wavelike manner, and the change patterns of runoff follow that of seasonal precipitation. Changes in soil moisture are not obvious, and the annual soil moisture increases slightly. In the intrayear process, soil moisture decreases slightly in autumn. The research results will enhance a more realistic understanding of the future hydrological response of the UMHRB and assist decision-makers in developing watershed flood risk-management measures and water and soil conservation plans.

Watershed Prioritization Based on Morphometric Parameters and PCA Technique: A Case Study of Deonar River Sub Basin, Sidhi Area, Madhya Pradesh, India

Abstract Watershed prioritization is beneficial for soil, groundwater development as well as for the implementation of the artificial recharge schemes. It is a well-known scientific process of drainage network delineation and ranking of different sub-watersheds of a basin. This is the mostly adopted morphometric interpretation and assessment technique and used for prioritization of watershed. In the present study, morphometric analysis of the Deonar river sub basin has been carried out to interpret and assess its hydrological importance. The study was carried out using geospatial approach and it is reflected in morphometric parameters such as linear, areal and relief aspects of the watershed. Detailed morphometric analysis and drainage map has been prepared from ASTER DEM (digital elevation model) having 30m spatial resolution using GIS environment. The delineated and analysed morphometric parameters are helpful for planning of soil and water conservation with the PCA approach. Principal component analysis (PCA) is a well-known technique that reduces the dimensionality of the given input data set which maximizes the amount of variance information. The delineated drainage order comes 1 to 5 and the watershed comprises of 6 sub-watersheds named as SW-I to SW-VI were analyzed. Based on Deonar river sub basin morphometric analysis, the sub-watershed is categorised into three classes as high, medium and low priority zones. Watershed prioritization and correlation of morphometric parameters of the Deonar river basin illustrates and indicates the high priority due to the greater degree of erosion. The analysed result is useful for making better planning and management, to identify the artificial recharge structures of the various sub-watersheds for improving the water potentiality.

Modelling streamflow using the SWAT model and multi-site calibration utilizing SUFI-2 of SWAT-CUP model for high altitude catchments, NW Himalaya's

Modelling streamflow is important in developing effective long-term management, soil conservation planning, and utilization of water resource management strategies. The current study aims to establish a stable hydrological model simulating streamflow with minimal uncertainty amongst the calibration parameters. The SWAT 2012 model is utilized to simulate the Lidder catchment streamflow located in the Southeastern part of the Kashmir valley for the (2007–2015) periods. SWAT- CUP’s algorithm called Sequential Uncertainty Fitting 2 (SUFI-2) is employed in the study for multi-site model calibration and validation for monthly time steps. The calibration results for monthly simulation for the (2009–2012) period displayed an excellent model performance for flow rates with R2 of 0.89, 0.85, and 0.89, and NSE values obtained are 0.73, 0.72, and 0.83. However, the validation results for monthly simulation for the (2013–2015) period also displayed good model performance for flow rates with R2 values obtained as 0.86, 0.81, and 0.80, and NSE values of 0.76, 0.52, and 0.56. Following calibration, the combined impact of each used parameter is ranked by the SWAT-CUP global sensitivity function. A total of twenty parameters are optimized in the calibration. From the study, we find that the most sensitive parameters were Snowfall temperature (SMTMP), Temperature lapse rate (TLAPS), melt factor for snow (SFTMP), and initial SCS curve number value (CN2). Therefore, it is concluded that while developing a reliable hydrologic model, complete knowledge about the hydrological processes occurring within the river basins and awareness of an appropriate and meaningful set of parameters is important.

Geospatial technology for prioritization of Koyna River basin of India based on soil erosion rates using different approaches.

The information about different morphometric parameters of any watershed is necessary for better watershed management and planning. This study aimed to investigate morphometric characteristics, to assess the soil erosion risk, and to prioritize different sub-watersheds of the Koyna River basin, India, with two different approaches using geospatial technology. Different linear, shape, and relief parameters of the basin were estimated and analyzed. The linear and shape parameters indicated that the basin has less flood hazard. The relief parameters indicated that the basin has moderate roughness and unevenness. The parallel drainage pattern is dominant inside the basin due to the highly elongated nature of the basin. The bifurcation ratio (Rb) indicated lithological and geological variations inside the basin. Two different approaches namely morphometric analysis and empirical Revised Universal Soil Loss Equation (RUSLE) method were applied for prioritization of different sub-watersheds. Rainfall, soil, digital elevation model (DEM), and normalized difference vegetation index (NDVI) data were used for identifying erosion-prone zones with RUSLE analysis. Based on RUSLE analysis, the entire study area was divided into five soil erosion risk classes namely very slight (80.43 %), slight (14.94 %), moderate (3.21 %), severe (0.79 %), and very severe (0.63%), respectively. Most of the study area was found to be under a very slight soil erosion vulnerability class based on the RUSLE approach. The conservation practices should be carried out as per the priority ranking of different sub-watershed based on soil erosion rates. The results found in this study can surely assist in the implementation of soil conservation planning and management practices to reduce soil loss in the Koyna River basin of India.

Spatial-temporal variations of soil erosion in Southern Yunnan Mountainous Area using GIS and RUSLE: A case study in Yuanyang County, Yunnan Province, China.

It is of great significance to understand the spatial-temporal change of soil erosion in the Southern Yunnan Mountainous Area, especially for soil and water conservation planning and ecolo-gical protection in the world cultural heritage area. Based on the RUSLE model and GIS/RS space information technology, we examined the spatial-temporal variation of soil erosion and its relationship with environmental factors in Yuanyang County from 2005 to 2015. The results showed that, during the study period, the spatial differentiation of soil erosion in Yuanyang County was substantial. The soil erosion area was concentrated on the southeast and southwest region. Erosion widely distributed among both sides of Tengtiao River. Extreme and severe erosion presented a point distribution pattern. The average soil erosion modulus was 11.06 t·hm-2·a-1 from 2005 to 2015. The erosion level was basically slight and mild, accounting 80% of the total. Mild and moderate erosion contributed nearly 50% of total annual soil erosion, which was the key level of soil erosion control. During the study period, soil erosion had been slowed down with the improvement of forest cover. The terrain was complicated and changeable in Yuanyang County, and soil erosion distribution had a prominent correlation with altitude and slope. The formation of this distribution pattern was driven by numerous natural and human factors. The key area of soil and water protective governance were below 500 m, above 1500 m and slope located at 25°-45°.

Evaluation of Rainfall Erosivity Factor Estimation Using Machine and Deep Learning Models

Rainfall erosivity factor (R-factor) is one of the Universal Soil Loss Equation (USLE) input parameters that account for impacts of rainfall intensity in estimating soil loss. Although many studies have calculated the R-factor using various empirical methods or the USLE method, these methods are time-consuming and require specialized knowledge for the user. The purpose of this study is to develop machine learning models to predict the R-factor faster and more accurately than the previous methods. For this, this study calculated R-factor using 1-min interval rainfall data for improved accuracy of the target value. First, the monthly R-factors were calculated using the USLE calculation method to identify the characteristics of monthly rainfall-runoff induced erosion. In turn, machine learning models were developed to predict the R-factor using the monthly R-factors calculated at 50 sites in Korea as target values. The machine learning algorithms used for this study were Decision Tree, K-Nearest Neighbors, Multilayer Perceptron, Random Forest, Gradient Boosting, eXtreme Gradient Boost, and Deep Neural Network. As a result of the validation with 20% randomly selected data, the Deep Neural Network (DNN), among seven models, showed the greatest prediction accuracy results. The DNN developed in this study was tested for six sites in Korea to demonstrate trained model performance with Nash–Sutcliffe Efficiency (NSE) and the coefficient of determination (R2) of 0.87. This means that our findings show that DNN can be efficiently used to estimate monthly R-factor at the desired site with much less effort and time with total monthly precipitation, maximum daily precipitation, and maximum hourly precipitation data. It will be used not only to calculate soil erosion risk but also to establish soil conservation plans and identify areas at risk of soil disasters by calculating rainfall erosivity factors.

Geographic information system (GIS) based soil loss estimation using RUSLE model for soil and water conservation planning in anka_shashara watershed, southern Ethiopia

Water induced soil erosion has been continued to threaten the land resources in sub humid northwestern highlands of Ethiopia. Human-induced land cover (LC) changes due to improper land management practices are contributing factors in deteriorating soil quality. Soil and water conservation measures have been implemented without site-specific scientifically quantified soil erosion data and priority bases in this regard; this study was conducted with a view to quantifying soil erosion in Anka-Shashara watershed. To do this, we have opted to use the RUSLE model based on geographic information systems. By collecting data on rainfall, soils, vegetation, slopes and conservation practices separately as a layer and determining the pixel values for each of these factors, a quantified assessment of erosion in the basin is obtained. The result reveals that the mean annual soil loss (15.22t/ha/yr) of the most parts of study area falls in tolerable levels and almost 66% (1594 ha), moderate level about 23% and 10% at high level of watershed. The soil loss of watershed is classified in to four main classes. None to Slight rates of soil erosion (0-10t/ha) is cover the almost 66% of watershed moderate about 23% and high about 10% of erosion rate as seen from the erosion rate distribution map. The upstream of catchment needs management and protection of those existing resources and the fundamental attention for SWC as recommended bases. Therefore, to reduce sedimentation problem and ensue sustainability of the watershed need to special attention for implementing recommended SWC intervention at the earlier.

A wrapper feature selection approach for efficient modelling of gully erosion susceptibility mapping

Identifying the vulnerability level of an area to soil erosion, particularly gully erosion, is key to the development of an efficient management strategy for policymakers. While efforts into susceptibility mapping of natural disasters have grown in recent years, understanding the most relevant predictive causal factors is still a challenge. As the selection of these factors, among many potentially relevant factors, is an important part of the model selection process, we propose a hybrid intelligent approach for the optimal selection of a set of relevant factors based on logistic regression (LR) and genetic algorithms. In order to verify the effectiveness of the proposed approach, this study also identified areas that were highly susceptible to gully erosion using three different classifiers – namely, the LR, support vector machine (SVM) and k-nearest neighbours (k-NN) techniques. We tested the approach in the Yeli Bedrag watershed in north-eastern Golestan province, Iran. The results showed that the elevation, distance to fault, slope and the index of connectivity were the most important causal factors affecting the successful prediction of gully occurrence. Comparison of maximum True Skill Statistic values showed that increased model sophistication did not necessarily result in a higher level of model performance. In terms of performance, k-NN was superior to the SVM and LR methods. This method can be effectively used for gully erosion susceptibility (GES) zonation in the study area, which is very important to support spatial planning to initiate designing mitigation strategies and conservation needs over a large area, or to plan additional conservation efforts and relocate soil conservation plans. In conclusion, our findings indicate that by incorporating the proposed hybrid intelligent approach, the number of relevant factors for GES mapping was reduced, while classification accuracy was increased.

Review of Soil Erosion Risk For Soil and Water Conservation Planning Under Ethiopian Condition

Soil erosion is one of the major factors affecting sustainability of agricultural production in Ethiopia. This paper reviews Ethiopia’s experience and research progress in soil erosion risk for Soil and Water Conservation planning. In most findings conducted at Ethiopian watershed possibilities of high soil lose and risk was reported. Their findings revealed that the study areas are facing different problems such that forest transformation, which is very likely intensified by the demand for subsistence agricultural land. Most of the parts of these watersheds have experienced intensive soil erosion behavior, which is beyond the tolerable soil loss level. This threatens the annual crop production and the productivity of the land impacting the local farmers’ food security. Analysis of a compilation of soil loss rates due to sheet and rill erosion at plot and catchment scales indicates that this soil degradation process varies strongly spatially, with a mean soil loss 29.9 t ha −1 yr −1.The highest rates were observed in Anjeni (110 t ha −1 yr −1) and Chemoga (102 t ha −1 yr −1) of the Upper Blue Nile Basin. Studies in the northern Ethiopian highlands and the Central Rift Valley of Ethiopia reported larger erosive power of rainfall as compared to elsewhere in the world.FAO (1986) estimated of gross annual soil loss nationwide of 1.9 × 10 9 t of which 80% originates from croplands. Hurni (1988) estimated a nationwide annual gross soil loss of 1.5 × 109 t, extrapolating data obtained from six SCRP research stations in which the highest loss is from croplands (42 t ha −1 yr −1 ). Sonneveld et al. (2011) provided a tentative nationwide mean annual soil loss map combining the results of different model estimates. They stated that soil loss varies remarkably from 0 t ha −1 yr −1 in the eastern and southeastern parts of Ethiopia to more than 100 t ha −1 yr −1 in the northwestern part of the country. Hence, review on identifying and prioritizing erosion susceptible areas for soil conservation measures are quite essential. Reviewing or quantifying the effects of the soil loss helps to substantiate investment in sustainable land management for the benefits to land users. Appropriate soil conservation measures bring economic advantages to the land users. Review on amount of soil loss and the status of the existing soil conservation measures can be realistic for farmers and policy makers if expressed in terms of understandable value.

Prioritization of potential soil erosion susceptibility region using fuzzy logic and analytical hierarchy process, upper Blue Nile Basin, Ethiopia

Soil erosion poses a global threat to soil loss, agricultural land deterioration, and crop yield decreases. This paper accounts for the establishment of sustainable land planning, and methods for evaluating soil erosion rates. The study was conducted based on the system complex analysis to prioritize soil erosion in Ribb watershed to improve soil and water conservation planning. Several geographical-environmental factors affecting soil erosion (land use, soil, slope, SPI, TWI, river Proximity, curvature, aspect) considered to assess potential soil erosion risk. The information levels were focused on expert experience, and the dynamic decision-making process built in a network framework. The weights for each layer were computed by R Studio fuzzy AHP package. The map of soil erosion susceptibility was developed by combining layers in a platform based on their weights and means of erosion-related incidents in regional surveys. Hence, the soil erosion map's overall results showed very high, high, moderate, low, and very low vulnerability to erosion with areal coverage of 13.4 %, 27.93 %, 32.74 %, 17.42 %, and 8.5 %, respectively. The susceptibility map demonstrates that high soil erosion sensitive areas with Fuzzy and AHP respectively cover 11.05 % and 15.75 %. The study indicates that priority should be given to high and very high erosion-prone areas in Rib watershed to adapt to climate change and control sedimentation problems in the Rib reservoir and Lake Tana.

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

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.

Improvement of the Water Erosion Prediction Project (WEPP) model for quantifying field scale subsurface drainage discharge

In the poorly drained regions of the world, subsurface drainage systems are required to remove excess water for crop growth. Plastic drains alter a field’s hydrology by lowering the water table, reducing surface ponding, and reducing surface runoff. One significant concern with the use of subsurface drainage systems is adverse environmental effects because of the modification of the soil water dynamics. Some effects include the reduction of ecological services since wetlands change to croplands, water quality concerns, particularly sediment, nitrogen, and phosphorus losses in agricultural subsurface discharge water, as well as changes in the volume and timing of off-site discharges. Hydrological simulation models predict surface and artificial subsurface flow at different scales. Often in these models, Hooghoudt-based expressions are adapted in their internal algorithms. In this study, the Water Erosion Prediction Project (WEPP) model, developed by the United States Department of Agriculture - Agricultural Research Service (USDA-ARS) for soil and water conservation planning activities, was tested and improved to simulate surface and subsurface discharges. The modified WEPP model was tested and validated on an extensive dataset collected at four experimental sites managed by USDA-ARS within the Lake Erie Watershed. Predicted drainage discharges show Nash-Sutcliffe Efficiency (NSE) values ranging from 0.50 to 0.70, and Percent Bias ranging from −30% to +15% at daily and monthly resolutions. Evidence suggests that the WEPP model can be used to produce reliable estimates of subsurface flow with minimum calibration. Future work includes the extension of the model for quantifying subsurface drainage under controlled water table and watershed-scale simulations.

Monitoring sediment yield for soil and water conservation planning in rural catchments.

Sediment yield in river catchments can cause siltation of reservoirs and channels, carries contaminants adhered to sediment particles, and represents water erosion at the catchment scale, leading to decreased agricultural productivity. Hydrological monitoring enables the understanding of overland flow and soil erosion dynamics. In this study, we analyzed whether the relationship between precipitation (P), water flow (Q), and suspended sediment concentration (SSC) during rainfall events shows the usefulness of hydrological and sedimentological monitoring in soil and water conservation projects to river catchments. We conducted a study in the Lajeado Ferreira catchment in southern Brazil. This catchment is characterized by high soil fragility and erosion rates because of relief and intense tobacco cultivation. The small size of the catchment (120ha) allowed a better understanding of the processes that occurred between hillslope and watercourses. We analyzed 43 rainfall-runoff events (P-Q), and we selected characteristic variables of each event and related them to independent variables (climate, land use, and soil management) and their seasonality using regression techniques. We also conducted a hysteresis analysis to understand the behavior of SSC in relation to runoff. The results showed a high relation of sediment yield (SY) with maximum water flow of the event (Qmax), and linear regression models showed the best performance between characteristic variables. In addition, the seasonal variability of the land coverage presented greater influence on the SY than the precipitation itself.

Soil erosion modeling using erosion pins and artificial neural networks

Assessment of soil erosion is crucial for any long-term soil conservation plan. Traditional in-situ measurements provide a precise amount of erosion rate; however, the procedure is costly and time-consuming when applied over an extensive area. This study aimed to investigate the use of erosion pins and artificial neural networks (ANNs) to assess the spatial distribution of annual soil erosion rates in the mountainous areas of the north of Iran. First, annual surface erosion and splash erosion were measured using two types of erosion pins. Next, the variables affecting soil erosion (vegetation canopy, the shape of slope, slope gradient, slope length, and soil properties) were identified and estimated through field studies and analysis of a digital elevation model (DEM) and the data set were divided into three subsets of training, cross-validation, and testing. Seven artificial neural network algorithms were used and evaluated to estimate the annual soil erosion rates for the areas without recorded erosion data. Finally, the modeled values were mapped in GIS, and the longitudinal profiles of soil erosion were extracted. Findings showed that (1) Consideration should be given to the generalized feed forward (GFF) network, given the high accuracy rate (NMSE:0.1; R-sqr:0.9) compared to other tested ANN algorithms. (2) Vegetation canopy was found to be the most significant variable in annual soil erosion rate (R: −0.75 to −0.85) compared to other input variables. And (3) Annual measurements of erosion pins revealed that the splash erosion is higher (contributing 62 percent to total erosion) compared to surface runoff erosion (contributing 38 percent to total erosion).

Impact of Land Use-Land Cover and Climatic Pattern on Sediment Yield of Two Contrasting Sub-Catchments in Upper Tapi Basin, India

Abstract Long-term and short-term assessments of sediment yield from river basins are required in planning soil conservation measures, water storage in reservoirs and prediction of morphological responses in rivers. In present study, detailed quantification of land-use land-cover and climate variabilityin two contrasting subcatchments of Upper Tapi basin, India, are reported for period 1980-2010. The pattern of measured sediment concentration, decadal and intra-annual variations of sediment yield at the outlets of boththe sub-catchments are presented and attributed with land-use land-cover and climate variability of the relevant periods. Higher sediment and water yields observed at the outlet of Burhanpur sub-catchment are due to steep and hilly topography, excessive deforestation and relatively high simple daily monsoonal intensity index (SDMII) within the sub-catchment versus Purna sub-catchment. In both the sub-catchments, maximum and minimum sediment concentrations are in July and September months respectively. Higher sediment concentration at the outlet of Purna sub-catchment is due to excessive alluvial deposits, extensive agricultural activities and generation of correspondingly less runoff within the sub-catchment. The observed reduction in sediment yield pattern in recent years is due to development of minor hydraulic structures (water area), decrease in scrub lands, increased planned agricultural land and decrease in the SDMII within both sub-catchments. The correlation analyses have indicated that total monsoonal basin average rainfall (TMBAR), rainy days (RD) and SDMII have been found to be significantly correlated with sediment yield for Burhanpur sub-catchment visà-vis SDMII and TMBAR for the Purna sub-catchment. Also, the sediment yield from the sub-catchments in the past have been found to be excessively higher than planned reservoir sedimentation of Hatnur reservoir, located at the outlet of the Upper Tapi basin.

GEOSPATIAL TECHNOLOGY BASED SOIL LOSS ESTIMATION FOR SUSTAINABLE URBAN DEVELOPMENT OF BUTWAL SUBMETROPOLITAN CITY, NEPAL

Abstract. Geo-spatial technology was attempted to estimate the potential and actual soil loss and its correlative interpretation with physiographic soil units and land use and cover types in Butwal sub-metropolitan city, Central Region of Nepal. Among several empirical and physically based soil erosion models, widely used RKLS and RKLSCP, Revised Universal Soil Loss Equation (RUSLE) were employed to estimate the potential and actual soil loss in the present investigation, respectively. Five years of rainfall, topographic contour-spot height and soil map were basically used as source of information for in-depth investigation. Butwal sub-metropolitan located at foothill of Chure/Siwalik range was found highly sensitive or prone to soil erosion. A total of 32.68 and 1.83 million tons soil was potentially and actually estimated annually being lost from the city. Erosion rates were found highly correlated with the slope of physiographic soil unit. 60.93% of the total potential soil loss was mainly contributed only by physiographic-soil unit 12 with the spatial extent of 34.10% of the city area. This unit was characterized by steeply to very steeply sloping mountainous terrain having dominant slope greater than 30° and loamy skeletal as dominant soil texture. Significant difference was found in the estimation of RKLS and RKLSCP indicating the substantial reduction contribution of soil loss by land use/cover types predominated by forest. after agriculture. Thus physiographic-soil unit 12 having soil loss highest must be given higher priorities for soil conservation and optimum urban land use planning required for sustainable urban development. Lower percentage of actual soil to the potential loss indicated the fact of contribution of cover management and erosional control practice factor in reducing soil erosion in existing situation.

Spatial modeling of erosion hotspots using GIS-RUSLE interface in Omo-Gibe river basin, Southern Ethiopia: implication for soil and water conservation planning

Soil degradation due to soil erosion is one of the major environmental threats in developing countries. In resource limited conditions, computing the spatial distribution of soil erosion risk has become an essential and practical mechanism to implement soil conservation measures. This study aimed to assess the spatial distribution of soil loss in Omo-Gibe river basin using the integration of computer-based RUSLE and ArcGIS 10.7.1 to identify areas that require erosion prevention priority. Once raster layer of the input parameters was created, overlay analysis was carried to assess the spatial distribution of soil loss. The estimated annual soil loss varies from 0–279 t ha−1 yr−1 with a mean annual soil loss of 69 t ha−1 yr−1. The empirical analysis also confirmed that the basin losses a total of about 89.6 Mt of soil annually. Out of the total area; 7% was in very sever class, 4.8% was found in the sever and 8.7% was categorized in very high range. The remaining area were ranging from low to high erosion risk class. The influence of the combined LS factor for soil loss is significant. It was observed that small area of the Omo-Gibe basin contributed for the significant amount of soil loss. The finding of this study is in a good agreement with previous studies. Compared to the country permissible soil loss rate, 26% of the entire basin significantly exceeds the country threshold value (TSL = 18 t ha−1 yr−1). As a result, precedence and immediate attention should be given to those erosion prone areas. The study output could deliver watershed management experts and policy makers for better management implementation and resource allocation based on the local context.

Mapping soil erosion rates using self-organizing map (SOM) and geographic information system (GIS) on hillslopes

Assessment of soil erosion is necessary for any long-term soil conservation plan, yet, the procedure is costly and time-consuming when applied to a large domain. Using a portion of the Alborz mountains in the north of Iran as a test site, a methodology was developed and tested based on a self-organizing map. First, annual soil erosion rates were measured on a hillslope in the study area through the installation of 120 erosion pins. Soil erosion controlling factors (slope gradient, slope length, slope shape, vegetation canopy, and the percentage of clay, silt, and sand) were determined through analysis of a digital elevation model (DEM) and field studies. Then, the data were normalized and divided into three subsets of training, cross-validation, and testing subsets. Then, a self-organizing map (SOM) was constructed to establish a relationship between soil erosion and its controlling factors. The SOM network was trained using the training and cross-validation subset and was evaluated on the testing subset using statistical coefficients (NMSE and R-squared). The evaluation of the SOM on the testing subset showed its high performance in the soil erosion modeling (NMSE = 0.1 test R-squared = 0.9). Next, the tested SOM was fit to the input variables to model the annual soil erosion rate across the study area. Finally, the modeled values were exported to the geographic information system (GIS) to generate the final map. The generated soil erosion map was verified by comparing the estimated soil erosion rates on the map with the recorded values of 11 erosion pins.