Rangelands, conflicts, and society in the Upper Mustang Region, Nepal

Rangelands are considered critical ecosystems in the Nepal Himalayas and provide multiple ecosystem services that support local livelihoods. However, these rangelands are under threat from various anthropogenic stresses. This study analyzes an example of conflict over the use of rangeland, involving two villages in the Mustang district of Nepal. This prolonged conflict over the use of rangeland rests on how use rights are defined by the parties, that is, whether they are based on traditional use or property ownership. Traditionally, such conflicts in remote areas were managed under the Mukhiya (village chief) system, but this became dysfunctional after the political change of 1990. The continuing conflict suggests that excessive demand for limited rangelands motivates local villagers to gain absolute control of the resources. In such contexts, external support should focus on enhancing the management and production of forage resources locally, which requires the establishment of local common property institutions to facilitate sustainable rangeland management.<br /

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling-Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75 %) were randomly selected for building landslide susceptibility models, while the remaining 80 (25 %) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16 %. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57 % of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80 % accuracy (i.e. 89.15 % for IOE model, 89.10 % for LR model and 87.21 % for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling-Narayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.ArticleNATURAL HAZARDS. 65(1):135-165 (2013)journal articl

Search for Tensor, Vector, and Scalar Polarizations in the Stochastic Gravitational-Wave Background

The detection of gravitational waves with Advanced LIGO and Advanced Virgo has enabled novel tests of general relativity, including direct study of the polarization of gravitational waves. While general relativity allows for only two tensor gravitational-wave polarizations, general metric theories can additionally predict two vector and two scalar polarizations. The polarization of gravitational waves is encoded in the spectral shape of the stochastic gravitational-wave background, formed by the superposition of cosmological and individually unresolved astrophysical sources. Using data recorded by Advanced LIGO during its first observing run, we search for a stochastic background of genetically polarized gravitational waves. We find no evidence for a background of any polarization, and place the first direct bounds on the contributions of vector and scalar polarizations to the stochastic background. Under log-uniform priors for the energy in each polarization, we limit the energy densities of tensor, vector, and scalar modes at 95% credibility to Omega(T)(0) < 5.58 x 10(-8), Omega(V)(0) < 6.35 x 10(-8), and Omega(S)(0) < 1.08 x 10(-7) at a reference frequency f(0) = 25 Hz