Abstract. In the Everest region, Nepal, ground-based monitoring programmes were started on the debris-free Mera Glacier (27.7 ∼ 5520 m a.s.l. confirm that the mean state of this glacier over the last one or two decades corresponds to a limited mass loss, in agreement with remotely-sensed regionwide mass balances of the Everest area. Seasonal mass balance measurements show that ablation and accumulation are concomitant in summer which in turn is the key season controlling the annual glacier-wide mass balance. Unexpectedly, ablation occurs at all elevations in winter due to wind erosion and sublimation, with remobilised snow potentially being sublimated in the atmosphere. Between 2009 and 2012, the small Pokalde Glacier lost mass more rapidly than Mera Glacier with respective mean glacier-wide mass balances of −0.72 and −0.23 ± 0.28 m w.e. yr −1 . Low-elevation glaciers, such as Pokalde Glacier, have been usually preferred for in-situ observations in Nepal and more generally in the Himalayas, which may explain why compilations of ground-based mass balances are biased toward negative values compared with the regional mean under the present-day climate.

In the Everest region, Nepal, ground-based monitoring programs were started on the debris-free Mera Glacier (27.7° N, 86.9° E; 5.1 km², 6420 to 4940 m a.s.l.) in 2007 and on the small Pokalde Glacier (27.9° N, 86.8° E; 0.1 km², 5690 to 5430 m a.s.l., ∼ 25 km North of Mera Glacier) in 2009. These glaciers lie on the southern flank of the central Himalaya under the direct influence of the Indian monsoon and receive more than 80% of their annual precipitation in summer (June to September). Despite a large inter-annual variability with glacier-wide mass balances ranging from −0.77± 0.40 m w.e. in 2011–2012 (Equilibrium-line altitude (ELA) at ∼ 6055 m a.s.l.) to + 0.46 ± 0.40 m w.e. in 2010–2011 (ELA at ∼ 5340 m a.s.l.), Mera Glacier has been shrinking at a moderate mass balance rate of −0.10± 0.40 m w.e. yr⁻¹ since 2007. Ice fluxes measured at two distinct transverse cross sections at ∼ 5350 m a.s.l. and ∼ 5520 m a.s.l. confirm that the mean state of this glacier over the last one or two decades corresponds to a limited mass loss, in agreement with remotely-sensed region-wide mass balances of the Everest area. Seasonal mass balance measurements show that ablation and accumulation are concomitant in summer which in turn is the key season controlling the annual glacier-wide mass balance. Unexpectedly, ablation occurs at all elevations in winter due to wind erosion and sublimation, with remobilized snow likely being sublimated in the atmosphere. Between 2009 and 2012, the small Pokalde Glacier lost mass more rapidly than Mera Glacier with respective mean glacier-wide mass balances of −0.72 and −0.26 ± 0.40 m w.e. yr⁻¹. Low-elevation glaciers, such as Pokalde Glacier, have been usually preferred for in-situ observations in Nepal and more generally in the Himalayas, which may explain why compilations of ground-based mass balances are biased toward negative values compared with the regional mean under the present-day climate

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Land use changes are a key factor for altering hydrological response, and understanding its impacts can help to develop a sustainable and pragmatic strategy in order to preserve a watershed. The objective of this research is to estimate the impact of land use changes on Bagmati river discharge and sediment yield at the Khokana gauging station of the Kathmandu valley outlet. This study analyzes the impact of land use changes from the year 2000 to 2010 using a semi-distributed hydrological, Soil Water Assessment Tool (SWAT) model. The Load Estimator (LOADEST) simulates sediment loads on limited available sediment data. Sensitivity analysis is performed using the ParaSole (Parameter Solution) method within SWAT Calibration and Uncertainty Procedure (SWAT-CUP), which shows that Linear parameters for calculating the maximum amount of sediment that can be re-entrained during channel sediment routing is a most sensitive parameter that affect the hydrological response of the watershed. Monthly discharge and sediment data from 1995 to 2002 are used for calibration and remaining monthly discharge and sediment data from 2003 to 2010 are used for validation. Four statistical parameters including the Coefficient of Determination (R 2 ), Nash-Sutcliffe Efficiency (NSE), RMSE-observations' standard deviation ratio (RSR), and Percentage Bias (PBIAS) are estimated in order to evaluate the model performance. The results show a very good agreement between monthly measured and simulated discharge data as indicated by R 2 = 0.88, NSE = 0.90, RSR = 0.34, and PBIAS = 0.03. The model shows nearly the same performance also with sediment data. The land use change data shows about a 6% increase in built-up areas from the years 2000 to 2010, whereas the remaining areas such as Forest, Shrub, Grass, Agriculture, Open Field, and Rivers/Lakes are decreased. The surface runoff contribution to stream flow and sediment yields are increased by 27% and 5% respectively. In the contrary, lateral flow contribution to stream flow and groundwater contribution to stream flow are decreased by 25% and 21%, respectively.

Abstract:We use the 2013 cotton precision farming survey data to study the adoption of irrigation technologies by cotton farmers in 14 states of the United States. We find that farmers with a higher irrigated yield, and who are from the Southern Plains (Texas and Oklahoma), adopt water-efficient irrigation technologies, such as sub-surface drip and trickle irrigation technologies. There are 10 irrigation technologies that farmers can adopt for cotton production in these 14 cotton-growing states. The intensity of the irrigation technologies, as measured by the number of irrigation technologies adopted in cotton production, is affected by the irrigated cotton yield realized, land holding (total land owned), education, computer use, and the origin of the cotton farmer being from the Southern Plains. We use a multivariate fractional regression model to identify land allocation by the different irrigation technologies used. Our results indicate that significant variables affecting land allocation with different irrigation technologies are the age of the operator, the cover crop, the information sources used, the per acre irrigated yield, the education, and the cotton farmer being from the Southern Plains.