Spatial Sensitivity Of Predicted Soil Erosion And Runoff To Climate Change At Regional Scales Pdf


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02.12.2020 at 14:51
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spatial sensitivity of predicted soil erosion and runoff to climate change at regional scales pdf

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Climate change may be associated with a considerable change in the hydrological cycle in various regions of the world Houghton et al.

Spatial downscaling of climate change scenarios can be a significant source of uncertainty in simulating climatic impacts on soil erosion, hydrology, and crop production.

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Soil Control on Runoff Response to Climate Change in Regional Climate Model Simulations

High levels of water-induced erosion in the transboundary Himalayan river basins are contributing to substantial changes in basin hydrology and inundation.

Basin-wide information on erosion dynamics is needed for conservation planning, but field-based studies are limited. This study used remote sensing RS data and a geographic information system GIS to estimate the spatial distribution of soil erosion across the entire Koshi basin, to identify changes between and , and to develop a conservation priority map.

The revised universal soil loss equation RUSLE was used in an ArcGIS environment with rainfall erosivity, soil erodibility, slope length and steepness, cover-management, and support practice factors as primary parameters. The estimated annual erosion from the basin was around 40 million tonnes 40 million tonnes in and 42 million tonnes in The results were within the range of reported levels derived from isolated plot measurements and model estimates.

Erosion risk was divided into eight classes from very low to extremely high and mapped to show the spatial pattern of soil erosion risk in the basin in and Areas with a high and increasing risk of erosion were identified as priority areas for conservation. The study provides the first assessment of erosion dynamics at the basin level and provides a basis for identifying conservation priorities across the Koshi basin.

The model has a good potential for application in similar river basins in the Himalayan region. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and Supporting Information files. Competing interests: The authors have declared that no competing interests exist. Land degradation, sedimentation, and ecological degradation tend to increase as a result of inappropriate land use and management practices [ 1 ].

Soil erosion is contributing to substantial changes in basin hydrology and inundation [ 2 ] in the transboundary Himalayan river basins, and the problems are compounded by social, economic, and political changes [ 3 ].

Water-induced erosion in the mountain and hill areas of these basins is very high [ 4 , 5 ] as a result of the steep slopes [ 6 ] as well as terrace agricultural practices with poor management.

The rivers in the region transport heavy loads of sediment [ 7 , 8 ] which are deposited downstream, leading among others to the formation of islands in the Ganges and Brahmaputra delta [ 6 , 9 ]. Soil erosion has been reported to affect crop production [ 10 ], and also leads to sedimentation in dams [ 5 , 11 , 12 ]. Information on the spatial distribution patterns and dynamic changes in erosion across the river basins is needed to develop plans and determine priorities for controlling soil erosion at the river basin level.

It has a diverse topography, geology, and geomorphology, and a wide range of different land use practices, and is also strongly affected by soil erosion, sediment transport, and land degradation [ 13 — 15 ]. The land and water resources of the basin are at risk as a result of rapid population growth, deforestation, soil erosion, sediment deposition, and flooding [ 16 , 17 ] and are not used as effectively as they could be to improve the livelihoods and socioeconomic conditions of the local people [ 18 ].

The distinct topography and land cover scenario of the basin means that there are three different erosion regimes: 1 the high mountains with steep to moderate slopes and predominant land cover of grass, snow, and glaciers; 2 the middle mountains with steep to moderate slopes and predominant land cover of forest and agriculture; and 3 the low hills and plains with predominant land cover of agriculture.

Studies based on small-scale erosion assessments using field or model-based methods have reported high erosion rates in the middle mountains of Nepal, which includes the most susceptible part of the Koshi basin [ 19 , 20 ]. High and intense erosion is one of the most distinctive characteristics of the Koshi basin.

The high levels of erosion result in high levels of sedimentation which affect storage infrastructure filling of dammed lakes , can destroy agricultural land, and contribute to downstream fluvial hazards. This poses challenges for planning, especially planning of water infrastructure such as hydropower and irrigation schemes, where knowledge of the potential sedimentation risk is paramount, and planning to reduce erosion risk. The main approach used in sustainable sediment management is to reduce levels of erosion, although directing sedimentation can also play a role.

But in order to be able to control erosion effectively, it is first necessary to have information about its spatial and temporal distribution.

Erosion control also has an important role to play in reducing flood risk in the flood plains of Nepal and Bihar, where siltation following floods is one of the major causes of loss of useful agricultural land.

Small scale field studies can help in planning erosion control measures for small catchments, but spatial information on erosion dynamics and quantity at the river basin scale is needed to plan effective soil conservation and erosion control measures that address the problems of siltation along the major rivers and downstream in the flood plain areas. It is important to identify the most sensitive areas for soil erosion in the Koshi basin, so that priority areas can be determined for conservation measures, but this is methodologically challenging.

Soil erosion management strategies in the Koshi basin are constrained by the scarcity and fragmented nature of the available data. Few field measurements have been carried out using standardised protocols, and none over the whole basin, and there have been very few studies that analyse the spatial trends in erosion and the relationship to land use practices and rainfall regimes.

Most studies on erosion in the Koshi basin have focused on individual plots or catchments in the middle mountains of the Nepal Himalayas because the topography, land use dynamics, and high spatial and temporal variability in rainfall lead to higher levels of erosion [ 5 , 7 ]. Although a number of researchers have attempted to fill the gap in erosion data at various scales [ 5 , 8 , 21 , 22 ], none have presented information on erosion patterns and dynamics for the entire basin.

This paper aims to help fill this gap by describing a relatively simple method for estimating the spatial distribution and total value of soil erosion across the whole basin.

Soil erosion can be estimated using empirical or physically-based models. In theory, physically-based models have an advantage over empirical models because they can be combined with physically-based hydrological models. Complete listings and descriptions of different soil erosion models can be found in [ 29 ]. The empirical RUSLE model remains the most popular tool for assessing water erosion hazards due to its modest data demands and easily comprehensible model structure, especially in developing countries where the possibilities for applying more complex models are often limited by a lack of adequate input data.

In recent decades, RUSLE and its adapted versions [ 26 , 30 ] have been applied worldwide in different regions and at different spatial scales. The RUSLE-GIS interface has several advantages in terms of easy updating, integration of spatially referenced data, and the facility to present the mapping results in different forms. There have been a number of model-based studies of soil erosion in small individual watersheds in the Nepal Himalayas.

Quincey and others Quincey et al. In the present study, we used the RUSLE model together with remote sensing RS data and GIS to make a basin-wide assessment of erosion dynamics in the Koshi river basin and determine priority areas for soil conservation and erosion prevention.

The study did not require any specific permission for field sites because most of the analysis was carried out using remotely-sensed data. The Koshi river basin lies between The basin contains a rich biodiversity and is a source of valuable ecosystem services that sustain the lives and livelihoods of millions of people in China, India, and Nepal [ 41 ] The regulating and support services include ground water recharge, flood control, and carbon sequestration, and contribute to both regional and global climate regulation.

The basin has five distinct landscapes: the Tibetan plateau, high mountains, middle mountains, low mountains and hills, and plains or Terai. The climate in the northern and southern parts is different. The maximum average annual precipitation in the basin is mm and the minimum mm [ 44 ]. The average outflow of the Koshi river is estimated to be Many families live in fear of the river bursting its banks, and flooding their homes and land.

The equations use a combination of geophysical and land cover factors to estimate the likely annual soil loss from a unit of land. RUSLE was used to assess the spatial patterns of erosion risk in the study area. Recent advances in GIS and remote sensing technology have enabled a more accurate estimation of the factors used in the calculation [ 46 , 47 , 48 , 49 ]. Each of the factors was derived separately in raster data format and the erosion calculated using the map algebra functions.

RUSLE is expressed as given in [ 23 ]: 1 where, A is estimated average soil loss in t ha -1 yr -1 , R is the rainfall-erosivity factor, K is the soil erodibility index, L is the slope length factor dimensionless , S is the slope steepness factor dimensionless , C is the cover-management factor dimensionless , and P is the supporting practices factor dimensionless.

The input data, their sources, and the equations used are listed in Table 1. The equations available in the literature for calculating the factors were tested iteratively and the optimal equations chosen based on their suitability for use with the data available and ability to produce estimates comparable to published field-based erosion measurements.

The calculation of the individual factors is described in more detail in the next sections. Annual rainfall erosivity is the total rainfall erosivity within a year. The rainfall erosivity factor R describes the erosivity of rainfall at a particular location based on the rainfall amount and intensity. This is an important parameter for soil erosion risk assessment under future land use and climate change [ 55 ].

Fig 3A shows the rainfall erosivity factor map derived for the study area. Spatial distribution of four of the factors used in RUSLE: a rainfall-erosivity factor, b soil erodibility factor, c slope length factor, and d slope steepness factor. The soil erodibility factor K is a quantitative description of the inherent erodibility of a particular soil type; it is a measure of the susceptibility of soil particles to detachment and transport by rainfall and runoff [ 57 ].

The main soil properties influencing the K factor are soil texture, organic matter, soil structure, and permeability of the soil profile. For a particular soil, the soil erodibility factor is the rate of erosion per unit erosion index from a standard plot. In this study, K values at soil order level were computed from the published literature on mountain areas [ 5 , 7 ]. The erodibility of various soil types in the Koshi basin is given in Table 2.

Fig 3B shows the spatial distribution of the soil erodibility factor in the study area. The slope-length factor L represents the effect of slope length on erosion.

It is the ratio of field soil loss to the corresponding soil loss from a Wischmeier and Smith [ 23 ] have described various ways of determining m for different slopes and these have been applied in the Indian subcontinent [ 7 , 51 ]. In the present study, the value taken for m was based on the slope gradient and determined using the slope map as input Table 3.

Fig 3C shows the spatial distribution of the slope length factor in the study area. The slope-steepness factor S represents the effect of slope steepness on erosion. Soil loss increases more rapidly with slope steepness than it does with slope length. The relationship of soil loss to gradient is influenced by the density of vegetation cover and soil particle size. The S factor is calculated using Eq 5 as described in [ 23 ]: 5 where s is the slope in per cent.

Fig 3D shows the spatial distribution of the slope steepness factor in the study area. The cover-management factor C is used to reflect the effect of cropping and other management practices on erosion rates. Vegetation cover is the second most important factor next to topography controlling soil erosion risk [ 58 ].

The land cover intercepts rainfall, increases infiltration, and reduces rainfall energy. The C factor reflects the effect of surface cover, and practices that change the amount of surface cover, on erosion. In areas where land uses other than cropping dominate, as in the Himalayas, the C factor is normally assigned based on a simple assessment of vegetation cover, rather than close analysis of agricultural cropping patterns.

Fig 4A and 4B show the spatial distribution of the cover-management factor in the study area in and Spatial distribution of the cover-management factor: a , b The support practice factor P reflects the impact of support practices such as contouring or strip cropping on the erosion rate. By definition, it is the ratio of soil loss with a specific practice to the corresponding loss with straight row ploughing up and down slope [ 53 , 60 ].

Practices include all the different ways of using land, not simply agricultural practices, thus essentially the factor relates a particular type of land cover use to its erosion potential. The detailed methodology used to prepare the land cover maps is described in [ 54 ]. Briefly, eCognition Developer software was used to divide the image into segments that are similar in terms of selected attributes using indices like the Normalized Difference Vegetation Index NDVI and Normalized Difference Snow Index NDSII derived from spectral values of the image, together with a land water mask, and slope and texture information.

The land cover maps for and are shown in Fig 5A and 5B. Land cover map of the Koshi basin: a , b ; spatial distribution of the support practice factor: c , d Values for the support practice factor for particular types of land cover were taken from published sources [ 7 , 53 , 56 , 63 , 64 ] and linked with the land cover maps to generate maps of the spatial distribution of the support practice factor in the study area for and Fig 5C and 5D.

The results are shown in Fig 6A and 6b The study area was divided into eight erosion risk classes, from very low to extremely high, based on the estimated erosion rates.

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Sensitivity of runoff and soil erosion to climate change in two Mediterranean watersheds Part II assessing impacts from changes in storm rainfall, soil moisture and vegetation cover. Hydrological Processes 23 8 : , Sensitivity of runoff and soil erosion to climate change in two Mediterranean watersheds; Part I, Model parameterization and evaluation. Hydrological Processes Soil control on runoff response to climate change in regional climate model simulations. Journal of Climate 18 17 : , Impact of climate change on soil erosion, runoff, and wheat productivity in central Oklahoma.

Effective soil erosion prediction models and proper conservation practices are important tools to mitigate soil erosion in hillside agricultural areas. We calibrated both the models in maize monocropping and simultaneously validated them in maize-chili intercropping with Leucaena hedgerow for nine rainfall events in , with the aim to evaluate their performances in runoff and sediment prediction on a skeleton soil in a hillslope, Western Thailand. In contrast, the calibrated WEPP model had a better performance in runoff prediction in the validation. Thus, the WEPP model was more suitable for runoff prediction than sediment prediction in the monocropping system, whereas the WaNuLCAS model was better suited for sediment yield prediction than runoff prediction, especially in complex intercropping systems. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Sensitivity of Grapevine Soil–Water Balance to Rainfall Spatial Variability at Local Scale Level

High levels of water-induced erosion in the transboundary Himalayan river basins are contributing to substantial changes in basin hydrology and inundation. Basin-wide information on erosion dynamics is needed for conservation planning, but field-based studies are limited. This study used remote sensing RS data and a geographic information system GIS to estimate the spatial distribution of soil erosion across the entire Koshi basin, to identify changes between and , and to develop a conservation priority map. The revised universal soil loss equation RUSLE was used in an ArcGIS environment with rainfall erosivity, soil erodibility, slope length and steepness, cover-management, and support practice factors as primary parameters. The estimated annual erosion from the basin was around 40 million tonnes 40 million tonnes in and 42 million tonnes in

Impact of land use and land cover change on soil erosion is still imperfectly understood, especially in northeastern China where severe soil erosion has occurred since the s. It is important to identify temporal changes of soil erosion for the black soil region at different spatial scales.

Spatial sensitivity of predicted soil erosion and runoff to climate change at regional scales

Human activity and related land use change are the primary cause of accelerated soil erosion, which has substantial implications for nutrient and carbon cycling, land productivity and in turn, worldwide socio-economic conditions. We challenge the previous annual soil erosion reference values as our estimate, of Moreover, we estimate the spatial and temporal effects of land use change between and and the potential offset of the global application of conservation practices. Our findings indicate a potential overall increase in global soil erosion driven by cropland expansion. The least developed economies have been found to experience the highest estimates of soil erosion rates.

Citation: Binoy Kumar Barman, K. Prasad, Uttam Kumar Sahoo. Soil erosion assessment using revised universal soil loss equation model and geo-spatial technology: A case study of upper Tuirial river basin, Mizoram, India[J]. AIMS Geosciences, , 6 4 : Article views PDF downloads Cited by 0. Binoy Kumar Barman, K.


Spatial sensitivity of predicted soil erosion and runoff to climate change at regional scales Article · References · Info & Metrics · PDF to climate change at large scales, and to simulate the “regional” impacts of climate change on soil erosion.


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