Nepal's quake-driven landslide hazards Large earthquakes can trigger dangerous landslides across a wide geographic region. The 2015 M w 7.8 Gorhka earthquake near Kathmandu, Nepal, was no exception. Kargal et al. used remote observations to compile a massive catalog of triggered debris flows. The satellite-based observations came from a rapid response team assisting the disaster relief effort. Schwanghart et al. show that Kathmandu escaped the historically catastrophic landslides associated with earthquakes in 1100, 1255, and 1344 C.E. near Nepal's second largest city, Pokhara. These two studies underscore the importance of determining slope stability in mountainous, earthquake-prone regions. Science , this issue p. 10.1126/science.aac8353 ; see also p. 147

In this paper we present a partitioning interwell tracer test (PITT) technique for the detection, estimation, and remediation performance assessment of the subsurface contaminated by nonaqueous phase liquids (NAPLs). We demonstrate the effectiveness of this technique by examples of experimental and simulation results. The experimental results are from partitioning tracer experiments in columns packed with Ottawa sand. Both the method of moments and inverse modeling techniques for estimating NAPL saturation in the sand packs are demonstrated. In the simulation examples we use UTCHEM, a comprehensive three‐dimensional, chemical flood compositional simulator developed at the University of Texas, to simulate a hypothetical two‐dimensional aquifer with properties similar to the Borden site contaminated by tetrachloroethylene (PCE), and we show how partitioning interwell tracer tests can be used to estimate the amount of PCE contaminant before remedial action and as the remediation process proceeds. Tracer tests results from different stages of remediation are compared to determine the quantity of PCE removed and the amount remaining. Both the experimental (small‐scale) and simulation (large‐scale) results demonstrate that PITT can be used as an innovative and effective technique to detect and estimate the amount of residual NAPL and for remediation performance assessment in subsurface formations.

Groundwater simulation models have been incorporated into a genetic algorithm to solve three groundwater management problems: maximum pumping from an aquifer; minimum cost water supply development; and minimum cost aquifer remediation. The results show that genetic algorithms can effectively and efficiently be used to obtain globally (or, at least near globally) optimal solutions to these groundwater management problems. The formulation of the method is straightforward and provides solutions which are as good as or better than those obtained by linear and nonlinear programming. Constraints can be incorporated into the formulation and do not require derivatives with respect to decision variables as in nonlinear programming. More complicated problems, such as transient pumping and multiphase remediation, can be formulated and solved using this method. The computational time required for the solution of genetic algorithm groundwater management models increases with the complexity of the problem. The speedup attainable by solving genetic algorithm problems on massively parallel computers is significant for problems where the simulation time required to complete each generation is high. have included linear programming [Aguado et al., 1974; Molz and Bell, 1977; Willis, 1979], nonlinear programming Paper number 94WR00554. ß 0043-1397/94/94 WR-00554505.00 [Gorelick et al., 1979, 1984; Wanakule et al., 1986; Ahlfeld et al., 1988; McKinney and Lin, 1992b], dynamic programming [Andricevic, 1990; Lee and Kitanidis, 1991; Culver and Shoemaker, 1992], and optimal control [Willis and Newman, 1977; Jones et al., 1987]. Extensive literature reviews on this topic can be found in the works by Gorelick [ 1983], Ahlfeld [1986], and Willis and Yeh [1987].Nonlinear programming techniques have been used to solve groundwater management problems for the past decade. These methods employ gradient-based algorithms to adjust decision variables so as to optimize the objective function of a management model. These algorithms require the computation of sensitivities of state variables, e.g., head or concentration, at certain locations to decision variables, e.g., pumping rates, at other locations. Sensitivities can be obtained by either the adjoint sensitivity or perturbation methods [Yeh, 1986]. These sensitivities are difficult to program, in the case of the adjoint sensitivity method, or computationally expensive to generate, in the case of perturbation methods, and in general, are not robust. Furthermore, the cost functions of typical groundwater system components may be either discontinuous, e.g., well field capital costs, or highly complicated, e.g., treatment process costs, making it difficult to calculate or estimate the derivatives of these functions with respect to the decision variables. Groundwater management problems tend to be highly nonlinear and nonconvex mathematical programming problems, especially in the case of aquifer remediation design with mass transport constraints. As such, there is no guarantee that a global optimum o...

This paper presents a water resources sustainability index that makes it possible to evaluate and compare different water management policies with respect to their sustainability. The sustainability index identifies policies that preserve or improve the desired water management characteristics of the basin in the future. This index is based on a previous sustainability index with improvements in its structure, scale, and content to make it more flexible and adjustable to the requirements of each water user, type of use, and basin. The Rio Grande transboundary basin is used as a case study demonstrating the use of the index. Tailor-made sustainability indexes are defined for water users in Mexico, the United States, the environment, and for meeting system requirements (international treaty obligations). Group sustainability indexes are calculated to summarize the results for groups of water users of each country, the environment, and the basin as a whole. Sustainability indexes by subbasins are calculated to identify areas of potential improvement and regions at risk.

Abstract. Debris thickness is an important characteristic of debris-covered glaciers in the Everest region of the Himalayas. The debris thickness controls the melt rates of the glaciers, which has large implications for hydrologic models, the glaciers' response to climate change, and the development of glacial lakes. Despite its importance, there is little knowledge of how the debris thickness varies over these glaciers. This paper uses an energy balance model in conjunction with Landsat7 Enhanced Thematic Mapper Plus (ETM+) satellite imagery to derive thermal resistances, which are the debris thickness divided by the thermal conductivity. Model results are reported in terms of debris thickness using an effective thermal conductivity derived from field data. The developed model accounts for the nonlinear temperature gradient in the debris cover to derive reasonable debris thicknesses. Fieldwork performed on ImjaLhotse Shar Glacier in September 2013 was used to compare to the modeled debris thicknesses. Results indicate that accounting for the nonlinear temperature gradient is crucial. Furthermore, correcting the incoming shortwave radiation term for the effects of topography and resampling to the resolution of the thermal band's pixel is imperative to deriving reasonable debris thicknesses. Since the topographic correction is important, the model will improve with the quality of the digital elevation model (DEM). The main limitation of this work is the poor resolution (60 m) of the satellite's thermal band. The derived debris thicknesses are reasonable at this resolution, but trends related to slope and aspect are unable to be modeled on a finer scale. Nonetheless, the study finds this model derives reasonable debris thicknesses on this scale and was applied to other debris-covered glaciers in the Everest region.