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

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

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Climate change, land degradation and land use are linked in a complex web of causality. One important impact of climate change on land degradation is that increasing global temperatures intensify the hydrological cycle, resulting in more intense rainfall, which is an important driver of soil erosion. This means that sustainable land management SLM becomes even more important with climate change.

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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.

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

Climate change may be associated with a considerable change in the hydrological cycle in various regions of the world Houghton et al. In many applications aimed at the assessment of climate-induced changes in the hydrology of large river basins, use is made of a chain of deterministic models: general circulation models GCMs providing global projections of present and future weather and climate, statistical or dynamical downscaling tools to enhance spatial and temporal detail of relevant meteorological forcings, hydrological models focusing on the partitioning of precipitation over evaporation, soil storage and runoff generation, and hydraulic models of the organized water transport via a river network.

The downscaling step is considered to add information by explicit use of local parameters that generate meteorological variability orography, land—sea masks, land use, and soil information, etc. Dynamical downscaling via regional climate models RCMs is explored widely, as to some extent it avoids assumptions of static relations between large-scale meteorological dynamics and local weather variables, as used in many statistical downscaling techniques Murphy Obviously, the assessment of climate change impacts on the hydrological cycle depends on the ability of the GCM and RCM systems to accurately simulate this cycle and the feedback processes acting on it.

A problem often reported in GCM and RCM studies is the systematic existence of excessive continental summer drying in the simulations. Hagemann et al. In many cases models overemphasize the positive land—atmosphere feedback that leads to a dry soil, strong evaporation stress, and reduced precipitation see Seneviratne et al. This poses severe problems in the interpretation of hydrological aspects of climate change in future greenhouse gas emission scenarios. If models are not successful in reproducing the regional hydrological cycle to a sufficient accuracy, their sensitivity to a changed climate forcing is likewise questionable.

A well-recognized important but sensitive component in the hydrological cycle is the land—atmosphere exchange. Like GCMs, RCMs acknowledge the role of the land surface component of the hydrological cycle by carrying a land surface parameterization LSP scheme that simulates the essential processes of precipitation partitioning over evaporation, storage and discharge, and the controls of both the atmospheric evaporative demand and soil water availability on the partitioning of radiant energy over sensible and latent heat fluxes.

As such, they simulate the process of runoff generation and evaporation conceptually similar to hydrological models used for river discharge calculations Giorgi et al. In some cases routing schemes are included in the RCM to directly simulate river discharge. The land component is important since the terrestrial hydrological memory soil water and accumulated snow amount represents a long time scale subjective to accumulation of systematic errors and drift Viterbo ; Betts et al.

It is sensitive as apparently small changes to the formulation of transpiration or runoff generation may have a strong impact on the simulated hydrological cycle. Land—atmosphere feedback is shown to have a strong control on the intensity and the spatial and temporal variability of the hydrological cycle on sub continental spatial scales Beljaars et al. At least part of the problem is related to the difficulty in specifying the spatial distribution of the effective soil hydrological memory.

This memory is represented by the combination of the depth of the soil water reservoir that may interact with the atmosphere via evaporation and transpiration, the temporal dynamics of precipitation, and the formulation of the dependence of evaporation and runoff on soil water content, which highly control the dynamics of the soil moisture evolution.

This dynamic range is a result of choices of the shape of the hydraulic conductivity and diffusivity curve: near saturation, additional water storage is limited by rapid losses due to percolation, and for dry soils, additional water loss is confined by the rapid decrease of vertical water motion at low moisture contents. In addition, the shape of the canopy stress function affects the timing of the water losses by transpiration throughout the year Lenderink et al.

The definition of effective soil hydrological memory is different from the effective soil hydrological capacity, which is usually indicated by a difference between field capacity and wilting point multiplied by the total soil depth.

The storage range is determined by a convolution of this effective storage capacity, the temporal dynamics of precipitation, and the dependence of evaporation and runoff on soil water content. Together these variables determine the degree to which the maximum storage capacity is used throughout the annual cycle. In principle, independent information of this soil memory parameter may be derived from a combination of available information on precipitation, evaporation, and runoff.

Interpretation in terms of soil hydrological capacity remains difficult as long as these regional- or continental-scale hydrological studies fail to close the water budget on an interannual time frame, but various analysis studies are reporting increasing success Seneviratne et al. The Rhine basin upstream of the Netherlands entry point near Lobith has an area of approximately km 2.

In addition, differences in response of the regional hydrological cycle to a given change in greenhouse gas concentrations are related to this terrestrial water storage capacity. The analyses in this study focus at the hydrological budget terms of the Rhine basin as a whole. Seasonal cycles are represented by processing the data in monthly averaged quantities.

The RCMs differed with respect to the physical and dynamical formulations, land-use characteristics, and the grid and domain in which the models were integrated, although in all cases the models covered the major part of Europe at approximately km resolution. Table 1 gives a brief overview of the specific properties of each of the RCMs. The depth over which S is calculated varies across the RCMs. Some models like MPI have a relatively deep soil of which the lower part is usually saturated and which could be considered to represent the saturated groundwater zone.

Other models like UCM have a shallow soil that is unsaturated most of the time. In all cases, however, the water budget represented by 1 is closed. Runoff is defined as the sum of the water flux that does not infiltrate into the soil surface runoff plus the net flux of water leaving the soil volume via its lower boundary the drainage component. Owing to the differences in the grid orientation and resolution of the models the number of grid points used to calculate a domain-averaged quantity varies between the models see Table 1 , but this difference is considered to be of minor importance in the analysis.

This model has a fairly high spatial resolution of approximately km and includes various changes compared to its parent coupled GCM, the Third Hadley Centre Coupled Model HadCM3 , which improves its simulation of the surface climate over many land areas including Europe.

It the context of the experiments reported here, it was used to simulate the climate of the recent past driven by observed sea surface temperatures SSTs and sea ice SI for the period —90 and observed concentrations of greenhouse gases and emissions of sulphate aerosols.

Some RCM groups used the first year of the HadAM3H runs to spin up their model and reported only the last 30 yr, whereas others simply discarded the first simulation year. Hirschi et al. The average annual cycle reported by Kwadijk has been used to correct the observed Rhine discharge data prior to calculating the terrestrial storage from the budget analysis. As pointed out by Seneviratne et al. Especially in steep orographic terrain, lack of overlap of the atmospheric convergence domain and the basin producing the discharge may lead to errors in the budget calculation owing to the relatively coarse resolution of the atmospheric analysis.

Also, the atmospheric assimilation system that provides estimates of the atmospheric water convergence does not conserve water, which may also affect the water balance closure. Therefore, at least part of the interdecadal variability is likely to be artificial, and is removed by subtracting a running mean with a 3-yr window.

The selection of this time scale is somewhat arbitrary, but it is situated in between the seasonal time scale of soil moisture evolution and the decadal time scale at which soil moisture ranges are considered to be relatively stationary. Use of a 5-yr window instead did not have a noticeable effect on the results reported in this study. Also using only data from onward did not significantly affect the mean annual cycles of the budget terms Fig.

Subtracting a running mean can be considered as a practical means to removing apparent artificial components of the interdecadal variability while preserving as many data as possible.

The integration of the filtered time series in Fig. The position on the vertical scale is arbitrary, and defined by setting the initial soil water content at 0 mm. An average annual cycle of the flux terms in Eq. In spite of a clear annual cycle of the atmospheric moisture convergence, river discharge from the Rhine shows a fairly gradual evolution. The timing of the springtime snowmelt peak varies between years, and this peak is smoothed in the yr average shown in Fig.

The major part of the average annual cycle in moisture convergence is buffered in the terrestrial storage reservoir mainly the soil and groundwater reservoirs , which as a consequence displays a pronounced cycle of drying and wetting. Also shown in Fig. This bias is at least partly related to systematic overestimation of the frequency of blocked circulation patterns van Ulden et al.

This is well justified, given the fact that all simulations conserve water mass. These comparisons should not be considered as direct verification since the control simulations of the RCMs were not driven by ERA as plotted in Fig. However, the intermodel variability is illustrative and can be used to highlight some basic model properties.

The available precipitation database contains daily values for the period —95 for subbasins in the Rhine catchment area. The daily values were spatially averaged weighted by the area of each of the subbasins. Figure 4 shows a comparison between the modeled and observed average annual cycle of precipitation over the Rhine basin for each of the RCMs involved. Most models show a considerably larger than observed range in the annual cycle, with either a peak that is too pronounced in winter or early summer precipitation or a value that is too low by the end of the summer.

This variability of RCM results driven by equal lateral boundaries is consistent with findings of other studies e. This bias is also present in most RCM simulations, and results in an increased eastward advection of water vapor, that probably is partly responsible for the positive wintertime precipitation bias.

On the other hand, the interannual variability in the observations shown by the error bars representing one standard deviation of the monthly means is significant, and the average RCM results generally fall within this range.

Large-scale observations of evaporation are not available. The model-only evaporation is plotted in the right panel in Fig. The wintertime overestimation of convergence is closely related to an overestimation of precipitation Fig. For some models the fairly good match with the observations is a consequence of compensating errors. For instance, the high summertime precipitation in MPI Fig. The lack of divergence in summertime in the UCM model is associated to fairly low evaporation amounts.

The runoff generated by the RCMs is linked to the net water flux into the soil P — E and the amount of this flux that is buffered by the soil water range. The processes affecting the soil hydrological balance are mutually coupled since, in general, runoff generation depends on the soil water content. The partitioning of the net water influx over the storage change and runoff is an important property of the hydrological system, as it describes the fate of the water entering the soil: the water put into runoff is lost and cannot be re-evaporated locally, while the soil water content is a storage buffer for later evaporation or runoff generation.

Figure 6 shows the average annual cycle of the runoff generation in the Rhine basin. Also shown is the observed discharge at Lobith the gauging station downstream of the catchment for which the modeled runoff is displayed. Similar to the results shown in Fig. The RCMs produce a fairly good wintertime runoff.

Except for UCM late summertime runoff is too low and for some models well outside one standard deviation of the interannual variability. Also, the rate at which the runoff recovers from the summer dryness is generally much faster in the RCMs than in the observations. Many RCMs have systematic deficiencies in the parameterization of summertime convective precipitation over mountains. Also, the representation of glacier melt is not included. Comparison between the observed change of the river discharge between Lobith and Basel and model generated runoff in the same catchment area indeed shows that in general the shape of the annual runoff cycle of the models is improved results not shown.

This issue will be addressed further later on. The terrestrial storage capacity in a climate model is—apart from the storage as snow—determined by the amount of water that can be stored in the soil. Many of the model parameters that have a strong influence on the actual evolution of the terrestrial water storage are difficult to specify from objective ancillary information, owing to large spatial variability, strong interaction between parameters, and the local climate dependence of the model sensitivity to the parameter values.

Yet, their impact on the simulated hydrological cycle is large, and an independent evaluation of the effective soil storage capacity is valuable. The interannual variability of the estimated value is considerable, and all models except UCM fall within this variability and show more or less a similar behavior. Over Europe, the soil water assimilation damps the annual cycle of soil water considerably van den Hurk et al. In this figure each symbol represents an anomaly value averaged per hydrological summer [July—August—September JAS ] or winter [January—February—March JFM ] season, by subtracting the average annual cycle from each monthly data point.

In contrast to monthly values, anomaly correlations of summertime averages are not strongly mutually affected by the persistence of droughts or wet seasons and may be considered statistically uncorrelated. Given the fairly small average annual cycle in the discharge observations Fig.

In summertime the storage range has on average a large uptake capacity, and anomalies in convergence are rapidly absorbed in the soil. In wintertime, this buffer capacity is less and a stronger preference for discharge is displayed. A similar analysis was carried out for the area downstream of Basel, where complex precipitation or glacier processes in mountainous areas are less significant.

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

Climate change may be associated with a considerable change in the hydrological cycle in various regions of the world Houghton et al. In many applications aimed at the assessment of climate-induced changes in the hydrology of large river basins, use is made of a chain of deterministic models: general circulation models GCMs providing global projections of present and future weather and climate, statistical or dynamical downscaling tools to enhance spatial and temporal detail of relevant meteorological forcings, hydrological models focusing on the partitioning of precipitation over evaporation, soil storage and runoff generation, and hydraulic models of the organized water transport via a river network. The downscaling step is considered to add information by explicit use of local parameters that generate meteorological variability orography, land—sea masks, land use, and soil information, etc. Dynamical downscaling via regional climate models RCMs is explored widely, as to some extent it avoids assumptions of static relations between large-scale meteorological dynamics and local weather variables, as used in many statistical downscaling techniques Murphy Obviously, the assessment of climate change impacts on the hydrological cycle depends on the ability of the GCM and RCM systems to accurately simulate this cycle and the feedback processes acting on it. A problem often reported in GCM and RCM studies is the systematic existence of excessive continental summer drying in the simulations. Hagemann et al.

An assessment of the global impact of 21st century land use change on soil erosion

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 :

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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.

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. In the present study, potential soil erosion in northeastern China was estimated based on the Revised Universal Loss Equation by integrating satellite images, and the variability of soil erosion at different spatial scales following land use changes in , , , , and was analyzed. Regionally, soil erosion rates increased from to and decreased from to , ranging from 3.

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.

Why is it necessary and even vital to maintain the global temperature increase below 1. Adaptation will be less difficult. Our world will suffer less negative impacts on intensity and frequency of extreme events, on resources, ecosystems, biodiversity, food security, cities, tourism, and carbon removal.

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