Surface Erosion and Sedimentation Associated with Forest Land Use in Interior Alaska

Completion reportThe magnitude of sheet-rill erosion associated with various landscape manipulations is presented. The Universal Soil Loss Equation's usefulness for predicting annual sheet-rill erosion within interior Alaska is confirmed. Investigations of sheet-rill erosion indicate that removing the trees from forested areas with only minor ground cover disturbance did not increase erosion. Removing the ground cover, however, increased erosion 18 times above that on forested areas. Erosion is substantially reduced when disturbed areas are covered with straw mulch and fertilizer. Comparison of the actual erosion and the quantity of erosion predicted with the Universal Soil Loss Equation indicates that the equation overestimates annual erosion by an average of 21 percent. It overestimates individual storm erosion by an average of 174 percent. Data are also presented concerning sheet-rill erosion in a permafrost trail, distribution of the rainfall erosion index, and suggested cover and management factor values.This work was supported by the Institute of Northern Forestry, Pacific Northwest Forest and Range Experiment Station, USDA. The Institute of Water Resources, University of Alaska, provided facilities for this research

Development of a framework for the evaluation of the environmental benefits of controlled traffic farming

Although controlled traffic farming (CTF) is an environmentally friendly soil management system, no quantitative evaluation of environmental benefits is available. This paper aims at establishing a framework for quantitative evaluation of the environmental benefits of CTF, considering a list of environmental benefits, namely, reducing soil compaction, runoff/erosion, energy requirement and greenhouse gas emission (GHG), conserving organic matter, enhancing soil biodiversity and fertiliser use efficiency. Based on a comprehensive literature review and the European Commission Soil Framework Directive, the choice of and the weighting of the impact of each of the environmental benefits were made. The framework was validated using data from three selected farms. For Colworth farm (Unilever, UK), the framework predicted the largest overall environmental benefit of 59.3% of the theoretically maximum achievable benefits (100%), as compared to the other two farms in Scotland (52%) and Australia (47.3%). This overall benefit could be broken down into: reducing soil compaction (24%), tillage energy requirement (10%) and GHG emissions (3%), enhancing soil biodiversity (7%) and erosion control (6%), conserving organic matter (6%), and improving fertiliser use efficiency (3%). Similar evaluation can be performed for any farm worldwide, providing that data on soil properties, topography, machinery, and weather are available

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

Addressing Uncertainty in TMDLS: Short Course at Arkansas Water Resources Center 2001 Annual Conference

Management of a critical natural resource like water requires information on the status of that resource. The US Environmental Protection Agency (EPA) reported in the 1998 National Water Quality Inventory that more than 291,000 miles of assessed rivers and streams and 5 million acres of lakes do not meet State water quality standards. This inventory represents a compilation of State assessments of 840,000 miles of rivers and 17.4 million acres of lakes; a 22 percent increase in river miles and 4 percent increase in lake acres over their 1996 reports. Siltation, bacteria, nutrients and metals were the leading pollutants of impaired waters, according to EPA. The sources of these pollutants were presumed to be runoff from agricultural lands and urban areas. EPA suggests that the majority of Americans-over 218 million-live within ten miles of a polluted waterbody. This seems to contradict the recent proclamations of the success of the Clean Water Act, the Nation\u27s water pollution control law. EPA also claims that, while water quality is still threatened in the US, the amount of water safe for fishing and swimming has doubled since 1972, and that the number of people served by sewage treatment plants has more than doubled

REGIONAL ECONOMIC IMPACTS OF A WATERSHED PLANNING PROCESS TO REDUCE EROSION AND STREAM SEDIMENTATION

Farm-level and watershed-wide land-use changes resulting from policy initiatives are linked to a regional input/output model. As a result not only can the direct economic impacts at the farm and watershed levels be determined, so too can the direct and induced economic impacts at the regional level.Resource /Energy Economics and Policy,

On the stable recovery of the sparsest overcomplete representations in presence of noise

Let x be a signal to be sparsely decomposed over a redundant dictionary A, i.e., a sparse coefficient vector s has to be found such that x=As. It is known that this problem is inherently unstable against noise, and to overcome this instability, the authors of [Stable Recovery; Donoho et.al., 2006] have proposed to use an "approximate" decomposition, that is, a decomposition satisfying ||x - A s|| < \delta, rather than satisfying the exact equality x = As. Then, they have shown that if there is a decomposition with ||s||_0 < (1+M^{-1})/2, where M denotes the coherence of the dictionary, this decomposition would be stable against noise. On the other hand, it is known that a sparse decomposition with ||s||_0 < spark(A)/2 is unique. In other words, although a decomposition with ||s||_0 < spark(A)/2 is unique, its stability against noise has been proved only for highly more restrictive decompositions satisfying ||s||_0 < (1+M^{-1})/2, because usually (1+M^{-1})/2 << spark(A)/2. This limitation maybe had not been very important before, because ||s||_0 < (1+M^{-1})/2 is also the bound which guaranties that the sparse decomposition can be found via minimizing the L1 norm, a classic approach for sparse decomposition. However, with the availability of new algorithms for sparse decomposition, namely SL0 and Robust-SL0, it would be important to know whether or not unique sparse decompositions with (1+M^{-1})/2 < ||s||_0 < spark(A)/2 are stable. In this paper, we show that such decompositions are indeed stable. In other words, we extend the stability bound from ||s||_0 < (1+M^{-1})/2 to the whole uniqueness range ||s||_0 < spark(A)/2. In summary, we show that "all unique sparse decompositions are stably recoverable". Moreover, we see that sparser decompositions are "more stable".Comment: Accepted in IEEE Trans on SP on 4 May 2010. (c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other work

Effectiveness of exclosures to control soil erosion and local communities perception on soil erosion

The study investigated how effective exclosures are in the fight against soil erosion and how they are perceived as a means to control soil erosion by the local community (farmers and local experts). The universal soil loss equation (USLE) used to estimate potential soil erosion. Data on local community perception obtained from a survey of 62 farm households and five local experts. In-depth interview, group discussion and non-participant field observation also carried out to obtain additional information. The USLE results agreed with the farmers' (67%) and local experts' opinion that erosion at study area is severe and affect the quality of lives of residents. Insignificant difference (p > 0.05) was observed in the estimated soil loss among treatments. However, the estimated soil loss from free grazing lands was higher by 47% than soil loss from exclosures which illustrated that exclosures are effective to control soil erosion. The majority of farmers (70%) also rated exclosures effectiveness to control soil erosion as high. Local communities were optimistic about the chances to rehabilitate degraded lands and make them productive. The majority of farmers (60%) did not consider population growth as a cause of soil erosion. For the majority of interviewed farmers, poor land management is more important. Efforts to create awareness within the rural communities should focus on the link between high population growth, environmental degradation and poverty. The optimistic view of local communities can be considered as an asset for the planning and development of degraded lands rehabilitation efforts

Numerical modelling of soil erosion susceptibility in the Maltese Islands using geographic information systems and the Revised Uni- versal Soil Loss Equation (RUSLE)

The Mediterranean region is subject to various factors that exacerbate soil erosion pressures. Such factors include agricultural land fragmentation and abandonment, unsustainable agricultural practices and rapid urbanisation. Soil erosion in the Maltese Islands has been identi ed as a predominating land degradation process and a major threat to the sustainability of the agricultural sector. The small scale of the Maltese Islands facilitates an in detail national study of soil erosion processes and contributing socio-economic dynamics. The research methods, erosion rate values and controlling dynamics discussed in this work have a particular relevance to the Mediterranean area.peer-reviewe