Soil erosion in the Alps : causes and risk assessment

The issue of soil erosion in the Alps has long been neglected due to the low economic value of the agricultural land. However, soil stability is a key parameter which affects ecosystem services like slope stability, water budgets (drinking water reservoirs as well as flood prevention), vegetation productivity, ecosystem biodiversity and nutrient production. In alpine regions, spatial estimates on soil erosion are difficult to derive because the highly heterogeneous biogeophysical structure impedes measurement of soil erosion and the applicability of soil erosion models. However, remote sensing and geographic information system (GIS) methods allow for spatial estimation of soil erosion by direct detection of erosion features and supply of input data for soil erosion models. Thus, the main objective of this work is to address the problem of soil erosion risk assessment in the Alps on catchment scale with remote sensing and GIS tools. Regarding soil erosion processes the focus is on soil erosion by water (here sheet erosion) and gravity (here landslides). For these two processes we address i) the monitoring and mapping of the erosion features and related causal factors ii) soil erosion risk assessment with special emphasis on iii) the validation of existing models for alpine areas. All investigations were accomplished in the Urseren Valley (Central Swiss Alps) where the valley slopes are dramatically affected by sheet erosion and landslides. For landslides, a natural susceptibility of the catchment has been indicated by bivariate and multivariate statistical analysis. Geology, slope and stream density are the most significant static landslide causal factors. Static factors are here defined as factors that do not change their attributes during the considered time span of the study (45 years), e.g. geology, stream network. The occurrence of landslides might be significantly increased by the combined effects of global climate and land use change. Thus, our hypothesis is that more recent changes in land use and climate affected the spatial and temporal occurrence of landslides. The increase of the landslide area of 92% within 45 years in the study site confirmed our hypothesis. In order to identify the cause for the trend in landslide occurrence time-series of landslide causal factors were analysed. The analysis revealed increasing trends in the frequency and intensity of extreme rainfall events and stocking of pasture animals. These developments presumably enhanced landslide hazard. Moreover, changes in land-cover and land use were shown to have affected landslide occurrence. For instance, abandoned areas and areas with recently emerging shrub vegetation show very low landslide densities. Detailed spatial analysis of the land use with GIS and interviews with farmers confirmed the strong influence of the land use management practises on slope stability. The definite identification and quantification of the impact of these non-stationary landslide causal factors (dynamic factors) on the landslide trend was not possible due to the simultaneous change of several factors. The consideration of dynamic factors in statistical landslide susceptibility assessments is still unsolved. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. Thus, we evaluated the effect of dynamic landslide causal factors on the validity of landslide susceptibility maps for spatial and temporal predictions. For this purpose, a logistic regression model based on data of the year 2000 was set up. The resulting landslide susceptibility map was valid for spatial predictions. However, the model failed to predict the landslides that occurred in a subsequent event. In order to handle this weakness of statistic landslide modelling a multitemporal approach was developed. It is based on establishing logistic regression models for two points in time (here 1959 and 2000). Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of dynamic causal factors on the landslide probability. The deviation map explained 85% of new independent landslides occurring after 2000. Thus, we believe it to be a suitable tool to add a time element to a susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions or human interactions. In contrast to landslides that are a direct threat to buildings and infrastructure, sheet erosion attracts less attention because it is often an unseen process. Nonetheless, sheet erosion may account for a major proportion of soil loss. Soil loss by sheet erosion is related to high spatial variability, however, in contrast to arable fields for alpine grasslands erosion damages are long lasting and visible over longer time periods. A crucial erosion triggering parameter that can be derived from satellite imagery is fractional vegetation cover (FVC). Measurements of the radiogenic isotope Cs-137, which is a common tracer for soil erosion, confirm the importance of FVC for soil erosion yield in alpine areas. Linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and the spectral index NDVI are applied for estimating fractional abundance of vegetation and bare soil. To account for the small scale heterogeneity of the alpine landscape very high resolved multispectral QuickBird imagery is used. The performance of LSU and MTMF for estimating percent vegetation cover is good (r²=0.85, r²=0.71 respectively). A poorer performance is achieved for bare soil (r²=0.28, r²=0.39 respectively) because compared to vegetation, bare soil has a less characteristic spectral signature in the wavelength domain detected by the QuickBird sensor. Apart from monitoring erosion controlling factors, quantification of soil erosion by applying soil erosion risk models is done. The performance of the two established models Universal Soil Loss Equation (USLE) and Pan-European Soil Erosion Risk Assessment (PESERA) for their suitability to model erosion for mountain environments is tested. Cs-137 is used to verify the resulting erosion rates from USLE and PESERA. PESERA yields no correlation to measured Cs-137 long term erosion rates and shows lower sensitivity to FVC. Thus, USLE is used to model the entire study site. The LSU-derived FVC map is used to adapt the C factor of the USLE. Compared to the low erosion rates computed with the former available low resolution dataset (1:25000) the satellite supported USLE map shows “hotspots” of soil erosion of up to 16 t ha-1 a-1. In general, Cs-137 in combination with the USLE is a very suitable method to assess soil erosion for larger areas, as both give estimates on long-term soil erosion. Especially for inaccessible alpine areas, GIS and remote sensing proved to be powerful tools that can be used for repetitive measurements of erosion features and causal factors. In times of global change it is of crucial importance to account for temporal developments. However, the evaluation of the applied soil erosion risk models revealed that the implementation of temporal aspects, such as varying climate, land use and vegetation cover is still insufficient. Thus, the proposed validation strategies (spatial, temporal and via Cs-137) are essential. Further case studies in alpine regions are needed to test the methods elaborated for the Urseren Valley. However, the presented approaches are promising with respect to improve the monitoring and identification of soil erosion risk areas in alpine regions

Desertification

IPCC SPECIAL REPORT ON CLIMATE CHANGE AND LAND (SRCCL) Chapter 3: Climate Change and Land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystem

Increased nitrogen export from eastern North America to the Atlantic Ocean due to climatic and anthropogenic changes during 1901-2008

We used a process-based land model, Dynamic Land Ecosystem Model 2.0, to examine how climatic and anthropogenic changes affected riverine fluxes of ammonium (NH4+), nitrate (NO3-), dissolved organic nitrogen (DON), and particulate organic nitrogen (PON) from eastern North America, especially the drainage areas of the Gulf of Maine (GOM), Mid-Atlantic Bight (MAB), and South Atlantic Bight (SAB) during 1901-2008. Model simulations indicated that annual fluxes of NH4+, NO3-, DON, and PON from the study area during 1980-2008 were 0.0190.003 (mean1 standard deviation) TgNyr(-1), 0.180.035TgNyr(-1), 0.100.016TgNyr(-1), and 0.043 +/- 0.008TgNyr(-1), respectively. NH4+, NO3-, and DON exports increased while PON export decreased from 1901 to 2008. Nitrogen export demonstrated substantial spatial variability across the study area. Increased NH4+ export mainly occurred around major cities in the MAB. NO3- export increased in most parts of the MAB but decreased in parts of the GOM. Enhanced DON export was mainly distributed in the GOM and the SAB. PON export increased in coastal areas of the SAB and northern parts of the GOM but decreased in the Piedmont areas and the eastern parts of the MAB. Climate was the primary reason for interannual variability in nitrogen export; fertilizer use and nitrogen deposition tended to enhance the export of all nitrogen species; livestock farming and sewage discharge were also responsible for the increases in NH4+ and NO3- fluxes; and land cover change (especially reforestation of former agricultural land) reduced the export of the four nitrogen species

Analysis of soil erosion characteristics in small watershed of the loess tableland Plateau of China

none9siSoil is an essentially limited natural resource that natural and human-induced processes have both generated and damaged. Soil degradation has become one of the most crucial socio-economic and environmental problems since it produces deterioration in productivity and quality of soil resources. Soil erosion, a natural phenomenon that causes degradation of soil and, curves the soil surface away from natural physical forces. To reveal the main factors influencing the spatial distribution of soil erosion in the small watershed of the Loess Plateau, the present study has investigated the synergistic as well as the independent influence of land use, vegetation coverage, and slope on the spatial distribution characteristics of soil erosion in the Wangdonggou watershed in 2015. Soil samples have been collected and analyzed in the laboratory together with high-resolution satellite imagery and meteorological data and derived data from digital elevation model (DEM). The results have shown that soil erosion in Wangdonggou watershed in 2015 has been characterized by a slight erosion, highlighting a gradually increased intensity from North to South. Among different land-uses, woodland and grassland have caused more than 50% soil erosion in the study area, and the areas with vegetation coverage of ≥ 50% have been the main source of soil erosion, and they have been all affected by slope. Furthermore, the practice of expanding vege- tation presence on the lower coverage of woodland and grassland, particularly where the slope is between 15◦ ~45◦ , and converting sloping woodland and grassland to the terrace have seemed to be effective strategies for controlling soil erosion in the Wangdonggou watershed. Finally, the current study has revealed that the RUSLE- GIS integrated model could be a useful tool to quantitatively and spatially map soil erosion at the watershed scale in the Loess Plateau, taking into account the provision of landscape services.openJing Wan, Pingda Lu, Donatella Valente, Irene Petrosillo, Subhash Babu, Shiying Xu, Changcheng Li, Donglin Huang, Mengyun LiuWan, Jing; Lu, Pingda; Valente, Donatella; Petrosillo, Irene; Babu, Subhash; Xu, Shiying; Li, Changcheng; Huang, Donglin; Liu, Mengyu

Spatio-temporal appraisal of water-borne erosion using optical remote sensing and GIS in the Umzintlava catchement (T32E), Eastern Cape, South Africa.

Globally, soil erosion by water is often reported as the worst form of land degradation owing to its adverse effects, cutting across the ecological and socio-economic spectrum. In general, soil erosion negatively affects the soil fertility, effectively rendering the soil unproductive. This poses a serious threat to food security especially in the developing world including South Africa where about 6 million households derive their income from agriculture, and yet more than 70% of the country’s land is subject to erosion of varying intensities. The Eastern Cape in particular is often considered the most hard-hit province in South Africa due to meteorological and geomorphological factors. It is on this premise the present study is aimed at assessing the spatial and temporal patterns of water-borne erosion in the Umzintlava Catchment, Eastern Cape, using the Revised Universal Soil Loss Equation (RUSLE) model together with geospatial technologies, namely Geographic Information System (GIS) and remote sensing. Specific objectives were to: (1) review recent developments on the use of GIS and remote sensing technologies in assessing and deriving soil erosion factors as represented by RUSLE parameters, (2) assess soil erosion vulnerability of the Umzintlava Catchment using geospatial driven RUSLE model, and (3) assess the impact of landuse/landcover (LULC) change dynamics on soil erosion in the study area during the period 1989-2017. To gain an understanding of recent developments including related successes and challenges on the use of geospatial technologies in deriving individual RUSLE parameters, extensive literature survey was conducted. An integrative methodology, spatially combining the RUSLE model with Systeme Pour l’Obsevation de la Terre (SPOT7) imagery within a digital GIS environment was used to generate relevant information on erosion vulnerability of the Umzintlava Catchment. The results indicated that the catchment suffered from unprecedented rates of soil loss during the study period recording the mean annual soil loss as high as 11 752 t ha−1yr−1. Topography as represented by the LS-factor was the most sensitive parameter to soil loss occurring in hillslopes, whereas in gully-dominated areas, soil type (K-factor) was the overriding factor. In an attempt to understand the impact of LULC change dynamics on soil erosion in the Umzintlava Catchment from the period 1989-2017 (28 years), multi-temporal Landsat data together with RUSLE was used. A post-classification change detection comparison showed that water bodies, agriculture, and grassland decreased by 0.038%, 1.796%, and 13.417%, respectively, whereas areas covered by forest, badlands, and bare soil and built-up area increased by 3.733%, 1.778%, and 9.741% respectively, during the study period. The mean annual soil loss declined from 1027.36 t ha−1yr−1 in 1989 to 138.71 t ha−1yr−1 in 2017. Though soil loss decreased during the observed period, there were however apparent indications of consistent increase in soil loss intensity (risk), most notably, in the elevated parts of the catchment. The proportion of the catchment area with high (25 – 60 t ha−1yr−1) to extremely high (>150 t ha−1yr−1) soil loss risk increased from 0.006% in 1989 to 0.362% in 2017. Further analysis of soil loss results by different LULC classes revealed that some LULC classes, i.e. bare soil and built-up area, agriculture, grassland, and forest, experienced increased soil loss rates during the 28 years study period. Overall, the study concluded that the methodology integrating the RUSLE model with GIS and remote sensing is not only accurate and time-efficient in identifying erosion prone areas in both spatial and temporal terms, but is also a cost-effective alternative to traditional field-based methods. Although successful, few issues were encountered in this study. The estimated soil loss rates in Chapter 3 are above tolerable limits, whereas in Chapter 4, soil loss rates are within tolerable limits. The discrepancy in these results could be explained by the differences in the spatial resolution of SPOT (5m * 5m) and Landsat (30m * 30m) images used in chapters 3 and 4, respectively. Further research should therefore investigate the impact of spatial resolution on RUSLE-estimated soil loss in which case optical sensors including Landsat, Sentinel, and SPOT images may be compared

A Primer for Monitoring Water Funds

This document is intended to assist people working on Water Funds to understand their information needs and become familiar with the strengths and weaknesses of various monitoring approaches. This primer is not intended to make people monitoring experts, but rather to help them become familiar with and conversant in the major issues so they can communicate effectively with experts to design a scientifically defensible monitoring program.The document highlights the critical information needs common to Water Fund projects and summarizes issues and steps to address in developing a Water Fund monitoring program. It explains key concepts and challenges; suggests monitoring parameters and an array of sampling designs to consider as a starting-point; and provides suggestions for further reading, links to helpful resources,and an annotated bibliography of studies on the impacts that result from activities commonly implemented in Water Fund projects

The Effect of Land Use Land Cover Change on Land Degradation in the Highlands of Ethiopia

Land use and land cover change through inappropriate agricultural practices and high human and livestock population pressure have led to severe land degradation in the Ethiopian highlands. This has led to further degradation such as biodiversity loss, deforestation, soil erosion and soil quality. Agricultural and economic growth in Ethiopia is constrained by the deteriorating natural resource base, especially in the highlands where 80% of the population lives. This threat stems from the depletion and degradation of the vegetation cover of the country. Loss of biodiversity is associated with land use/land cover changes that are related to a range of biophysical and socio-economic drivers. The implications of these changes suggest that the land use/cover changes have skewed to the rampant conversion of areas once covered with vegetation to cultivation without adequate use of soil and water conservation and rehabilitation practices. Understanding of the driving forces of land use and land cover change (LULC C) is essential for effective sustainable land resource management. Change in LULC can also negatively affect the potential use of an area and may ultimately lead to land degradation.  Improving the understanding of land use and land cover dynamics can help in projecting future changes in land use and land cover and to instigate more appropriate policy interventions for achieving better land management. Keywords: Deforestation, Ethiopia, land degradation, land use, soil quality

Vegetation change detection and soil erosion risk assessment modelling in the Man River basin, Central India

Land use change directly increased soil erosion risk, which is a very sensitive environmental issue in Central India. To evaluate the response of land use changes on soil erosion risk, research was implemented using remote sensing techniques, coupled with ground information, to develop an integrated modelling approach to study the factors driving land use changes in the Man River basin, Central India. Results were used to assess the impact of land use change on soil erosion risk. First, a series of sub methods were applied to monitor and verify land use land cover change in the study area which included pre-processing, classification and assessment of land use transaction from 1971 to 2013 using Landsat time series imagery. Additionally, an independent spatial assessment of deforestation, forest degradation and responsible drivers for the period 2009-2013 was conducted to enable a deeper analysis of forestry activates using the GIS based direct interpretation approach. The research also developed a robust accuracy assessment method to check the quality of the 2009 and 2013 classification maps using good quality Google Earth TM imagery and a field measured GPS dataset. These approaches were largely based on the GOFC- GOLD (2010) and IPCC good recommendations for land use land cover mapping and verification. The information obtained from an accuracy assessment was also used to estimate deforestation area and construct confidence intervals that reflect the uncertainty of the area estimates obtained. Such analysis is rarely applied in current published verification assessments. In the second phase of the study, a Geo-spatial interface for process-based Water Erosion Prediction Project (GeoWEPP) was implemented, to estimate the response of land use and land cover change on soil erosion risk in several scenarios derived from both ground and satellite based precipitation, DEMs and vegetation change. GeoWEPP was used at the hillslope scale in three selected watersheds within the Man River basin using Landsat, LISSIII, Cartosat-1, ASTER, SRTM, TRMM and ground based datasets. The results highlight that the study developed a realistic approach using remote sensing techniques to understand the pattern and process of landscape change in the Man River basin and its response on soil erosion risk. Over the last four decades, forest and agriculture areas were found to be the most dynamic land use /land cover categories. During the last four decades, around 54200 ha (33.7 %) forest area has been decreased due to the expansion of agriculture, forest harvesting and infrastructure development. The direct interpretation approach estimated similar patterns of deforestation and forest degradation associated with iii drivers for the 2009 to 2013 time period, but this approach also provided more accurate and location specific information than automatic analysis. The overall correspondence between the map and reference data are a good measure for 2009 and 2013; 94.03 % and 92.8 % respectively. User‘s and producer‘s accuracies of individual classes range from 75 % to 99 %. Using the accuracy assessment data and a simple set of equations, an error-adjusted estimate of the area of deforestation was obtained (± 95% confidence interval) of 23382 ± 550 ha. The estimated average annual soil loss for all three watersheds is 21 T/ha which was found to be comparable to similar studies carried out in the study region. The highest soil loss rates occurred in areas of agriculture (301 T. /ha /yr) and fallow land (158 T/ha/yr), while the lowest rates were recorded in forest land (33.45 T/ha/yr). Agriculture extension (316.5 ha) due to forest harvesting (234 ha) in the last four decades is one of the significant drivers to speed up soil erosion (7.37 T/ha/yr.) in all three watersheds. The spatial pattern of erosion risk indicates that areas with forest cover have minimum rates of soil erosion, while areas with extensive human intervention such as agriculture and fallow land, have high estimated rates of soil erosion. The different DEMs generated varied topographic and hydrologic attributes, which in turn led to significantly different erosion simulations. GeoWEPP using Cartosat-1 (30 m) and SRTM (90 m) produced the most accurate estimation of soil loss which was close to similar already published studies in the area. TRMM rainfall data has good to use as a rainfall parameter for soil erosion risk mapping in study area. Overall, the integrated approach using remote sensing and GIS allowed a clear understanding of the factors that drive land use/land cover change to be developed and enabled the impact of this change on soil erosion risk in the Man River basin, Central India to be assessed

INTEGRATED MODELING OF LAND USE AND CLIMATE CHANGE IMPACTS ON MULTISCALE ECOSYSTEMS OF CENTRAL AFRICAN WATERSHEDS

Assessment and management of ecosystem services demands diverse knowledge of the system components. Land use change occurring mainly through deforestation, expansion of agriculture and unregulated extraction of natural resources are the greatest challenges of the Congo basin and yet is central to supporting over 100 million people. This study undertook to implement an integrated modeling of multiscale ecosystems of central African watersheds and model the impact of anthropogenic factors on elephant population in Greater Virunga landscape. The study was conducted at varied scales, regional, landscape, and community. Regional study included watershed analysis and hydrological assessment using remotely sensed data implemented in Geographical information System. Species distribution modeling using generalized linear models using presence only and pseudo absence data was also done at regional scale. The major findings were that ecosystem services for the Congo basin are spatially varied and there are significant differences in their distribution of ecosystem services at a subwatershed level three and six and the distribution of endangered and threatened species is more concentrated in the central part of the basin and most of these species occur mainly inside protected areas. Poaching and sustained civil wars were the biggest threat to elephant conservation in the region. Improvement in law enforcement, monitoring, and increasing household incomes for communities living adjacent to protected areas would help to reduce the impact of poaching on elephant population dynamics. Climate change did not show immediate direct impacts on the elephant population, but thermal and latent heat effects could be occurring. A change in habitat, however, showed resulted in an upward trend in elephant population for all age classes. The local communities heavily depend on these ecosystem services for their livelihoods. It is therefore recommended that reasonable financing to strengthen law enforcement and monitoring is done, multiple policy options should be considered in order to maximize ecosystem benefits. Creation of partnerships and research networks need to be promoted. Existing transboundary collaboration initiatives should continue and be supported by the donor community in order to build peace and dram support for wildlife and wild habitat protection