Suchergebnisse

Ongoing land degradation in Ethiopia requires urgent action, and has been addressed at different levels of society, including widespread soil and water conservation activities, and the introduction of technologies which integrate local knowledge and farmer's initiatives. This comment, drawing on extensive research on soil erosion processes in Ethiopia since 1994, in intense cooperation with farmers and local authorities, challenges the conclusions of a paper published in this journal on environmental rehabilitation and rapid agricultural intensification for food self-sufficiency in Ethiopia (Keeley & Scoones 2000). In our view, this paper firstly underestimates the importance of environmental degradation and apparently rejects current conservation techniques and policy, and secondly makes an artificial contradiction between environmental rehabilitation policy and a participatory approach. In our experience, and in line with studies reviewed elsewhere, natural resources conservation in Ethiopia is directed towards an integration of food self-sufficiency with conservation/restoration of the environment, and frequently follows a participatory approach.

Abstract. In this study we present a model for the global Probabilistic Hydrological Estimation of LandSlides (PHELS). PHELS estimates the daily hazard of hydrologically triggered landslides at a coarse spatial resolution of 36 km, by combining landslide susceptibility (LSS) and (percentiles of) hydrological variable(s). The latter include daily rainfall, a 7 d antecedent rainfall index (ARI7) or root-zone soil moisture content (rzmc) as hydrological predictor variables, or the combination of rainfall and rzmc. The hazard estimates with any of these predictor variables have areas under the receiver operating characteristic curve (AUC) above 0.68. The best performance was found with combined rainfall and rzmc predictors (AUC = 0.79), which resulted in the lowest number of missed alarms (especially during spring) and false alarms. Furthermore, PHELS provides hazard uncertainty estimates by generating ensemble simulations based on repeated sampling of LSS and the hydrological predictor variables. The estimated hazard uncertainty follows the behaviour of the input variable uncertainties, is about 13.6 % of the estimated hazard value on average across the globe and in time and is smallest for very low and very high hazard values.

Abstract. This study assesses global landslide susceptibility (LSS) at the coarse 36 km spatial resolution of global satellite soil moisture observations to prepare for a subsequent combination of a global LSS map with dynamic satellite-based soil moisture estimates for landslide modeling. Global LSS estimation contains uncertainty, arising from errors in the underlying data, the spatial mismatch between landslide events and predictor information, and large-scale LSS model generalizations.
For a reliable uncertainty assessment, this study combines methods from the landslide community with common practices in meteorological modeling to create an ensemble of global LSS maps. The predictive LSS models are obtained from a mixed effects logistic regression, associating hydrologically triggered landslide data from the Global Landslide Catalog (GLC) with predictor variables describing the landscape. The latter are taken from the Catchment land surface modeling system (including input parameters of soil (hydrological) properties and resulting climatological statistics of water budget estimates), as well as geomorphological and lithological data. Road network density is introduced as a random effect to mitigate potential landslide inventory bias.
We use a blocked random cross validation to assess the model uncertainty that propagates into the LSS maps. To account for other uncertainty sources, such as input uncertainty, we also perturb the predictor variables and obtain an ensemble of LSS maps. The perturbations are optimized so that the total predicted uncertainty fits the observed discrepancy between the ensemble average LSS and the landslide presence or absence from the GLC. We find that the most reliable total uncertainty estimates are obtained through the inclusion of a topography-dependent perturbation between 15 % and 20 % to the predictor variables. The areas with the largest LSS uncertainty coincide with moderate ensemble average LSS, because of the asymptotic nature of the LSS model. The spatial patterns of the average LSS agree well with previous global studies and yield areas under the receiver operating characteristic between 0.84 and 0.92 for independent regional to continental landslide inventories.

The combination of runoff-generating areas (saturated soils) and overland flow concentration in features such as drainage ditches makes sloping farmland vulnerable to soil erosion. The establishment of drainage ditches aims at draining the excess of water from the farmland, particularly in areas where soils are saturated in the rainy season. The hydrogeomorphic impacts on the farmland itself and on downstream areas need however also to be studied. Off site, downstream problems comprise higher peak discharges, leading to gully initiation, an increase in sediment load, and flooding problems. On-site problems such as the development of the drainage ditches into (ephemeral) gullies are less documented, although they may be important, as illustrated in the Lake Tana Basin (Ethiopia). The similarities and interactions between ephemeral gully channels and drainage ditches have to be considered to better understand all effects of drainage. Drainage ditches are a potential source of conflict between farmers with different interests and power, as well as between upstream and downstream users. A case study on drainage ditches on sloping farmlands in the Lake Tana Basin showed that nine out of ten catchments had drainage densities by ditches ranging from 53 to 510 m ha−1. Drainage ditches were constructed with an average top width of 27 (±9) cm. A significant correlation was found between stone bund density (physical conservation structures) and ditch drainage density (R = −0·72), in line with the Ethiopian government's ban on drainage ditches in farmlands where stone bunds have been constructed. ; Peer reviewed

Disaster risk zonation is often proposed as a long-term disaster risk reduction strategy by international treaties and academic research. This strategy has been implemented in the city of Limbe, which is known to be a disaster-prone one. Citizens are forced to settle in unsafe terrains, ranging from wetlands to unstable hillslopes due to the city's geographical location and economic attraction. Following the fatal landslides and floods in 2001, a local crisis committee identified affected areas and declared them 'risk zones' to prevent further exposure. Empirically, this study narrates the production and implementation of risk zonation policy in the city of Limbe. Theoretically, it uses an urban political ecology perspective, which incorporates science and technology studies, post-political theory and disaster research to interpret the drivers and implications of the mismatch between research, policy and action. In this case study, we investigate the implications for disaster risk reduction by describing three underlying socio-political drivers of the risk zonation policy: (i) authoritarian science regime, (ii) post-political discourse, and (iii) blame diversion. We argue that authorities from national to local level use a post-political discourse to promote and implement disaster risk reduction in the city of Limbe through the development and the application of risk zonation policy. As a consequence, risk zonation leads to poor enforcement of the law and corruption, ultimately leading to risk accumulation in this case. This analysis allows us to draw broader conclusions on drivers and implications of the implementation of disaster risk zonation policy in urban areas that are primarily governed hierarchically and prone to corruption.

Abstract. The inhabited zone of the Ugandan Rwenzori Mountains is affected by landslides, frequently causing loss of life, damage to infrastructure and loss of livelihood. This area of ca. 1230 km2 is characterized by contrasting geomorphologic, climatic and lithological patterns, resulting in different landslide types. In this study, the spatial pattern of landslide susceptibility is investigated based on an extensive field inventory constructed for five representative areas within the region (153 km2) and containing over 450 landslides. To achieve a reliable susceptibility assessment, the effects of (1) using different topographic data sources and spatial resolutions and (2) changing the scale of assessment by comparing local and regional susceptibility models on the susceptibility model performances are investigated using a pixel-based logistic regression approach. Topographic data are extracted from different digital elevation models (DEMs) based on radar interferometry (SRTM and TanDEM-X) and optical stereophotogrammetry (ASTER DEM). Susceptibility models using the radar-based DEMs tend to outperform the ones using the ASTER DEM. The model spatial resolution is varied between 10, 20, 30 and 90 m. The optimal resolution depends on the location of the investigated area within the region but the lowest model resolution (90 m) rarely yields the best model performances while the highest model resolution (10 m) never results in significant increases in performance compared to the 20 m resolution. Models built for the local case studies generally have similar or better performances than the regional model and better reflect site-specific controlling factors. At the regional level the effect of distinguishing landslide types between shallow and deep-seated landslides is investigated. The separation of landslide types allows us to improve model performances for the prediction of deep-seated landslides and to better understand factors influencing the occurrence of shallow landslides such as tangent curvature and total rainfall. Finally, the landslide susceptibility assessment is overlaid with a population density map in order to identify potential landslide risk hotspots, which could direct research and policy action towards reduced landslide risk in this under-researched, landslide-prone region.