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The South Asian summer monsoon brings abundant precipitation and associated latent heat release to the south of central Himalaya, and alters hydrothermal conditions of this region. This study explored the impact of South Asian summer monsoon on the elevation-dependence of meteorological variables along the south slope of Mt. Everest in the central Himalaya, which is crucial to modelling the glacio-hydrological processes in this elevated region. The data were collected at five stations deployed at 2660-5600 m above sea level (asl) along the slope during 2007-2011. Major findings are the following: (1) The amount of precipitation during the monsoon season usually decreases with elevation but it is relatively uniform between 3600 and 5000 m asl. This uniform profile may be attributed to the monsoon-terrain-land interactions, particularly to the retard effect of glacier cooling on daytime upvalley wind; (2) Cloud shielding effects cause lower solar radiation and higher downward longwave radiation in the monsoon than in the other seasons. In particular, higher elevations have more clouds in the afternoon, resulting in an abnormal elevation-dependence of solar radiation (i.e. higher elevations receive less solar radiation); (3) Strong daytime upvalley wind and moist convection homogenizes the vertical distributions of air mass along the slope, causing a constant lapse rate of both surface air temperature and dew-point temperature (representing humidity) during typical monsoon months, but this phenomenon is not found in the other seasons. These findings provide critical guidance for extrapolating the meteorological variables from lower to higher elevations in this region.

This contribution aims at linking temperature and precipitation trends detected with ground stations at high elevations of south slopes of Mt. Everest in the last twenty years, extended back with gridded and reanalysis data, with changes observed for discharges of Dudh Koshi River (3715 km2), Nepal. The study is carried out though: a) the daily temperature and precipitation reconstruction of the last twenty years (1994-2013) at 5050 m a.s.l. and 25 AWSs located at lower elevation and on the Tibetan Plateau (Salerno et al., on TCD); d) all available gridded and reanalysis data set both for temperature and precipitation for extending our analysis back to '60s. b) glacier surfaces (about 400 km2) changes since 60s using all available satellite imagery and glacier mass losses since the beginning of 2000s (Thakuri et al., 2014); c) a stochastic hydrological model for detecting changes observed for discharges of Dudh Koshi river since 60s; c) a degree day model for simulating the glacier melt since 60s. In the last years physically-based hydrological models are started to be adopted for Himalayan glacierized catchments. However, such models present several limitations due to lack of data for the calibration/validation and to an over-parameterization. In this paper, a simple statistical method to simulate the river discharge of a glacierized (14%) Himalayan basin, based on Standardized Precipitation Index (SPI), is proposed. The SPI approach is ground on these assumptions: a) as a first approximation discharges can be assessed at monthly time scale; b) in monsoon regimes the discharge is mainly dependent on precipitation taken into account at different time scales and with different "weights"; and c) the parameters linking the precipitation regime to discharge are considered constant over time. On the base of such assumptions, to seek for relationships between the precipitation regime and discharge (Q), a multi-linear regression model (called SPI-Q) is calibrated and validated at monthly scale using the least-square method. We observed an increasing temperature trend occurred mainly in winter months, but during the summer ones we noted a slight decreasing of maximum temperature. We confirm for these high elevations the generalized weakening of the monsoon, already observed in literature, accounting here over 50% of reduction in the last twenty years! In the previous period (70s to 90s) gridded and reanalysis data revealed for our reference site a slight increasing mean temperature and weak increasing precipitation. As a result, glaciers experienced an overall surface area loss of 13% and an upward shift of the snowline altitude (SLA) by 182 m.. Moreover, since early '90s, we found a significant upward shift of SLA which increased almost three times. The accelerated shrinkage in recent decades has only affected glaciers with the largest sizes (>10 km2), presenting accumulation zones at higher elevations and along the preferable south-north direction of the monsoons. This finding leads to the hypothesis that Mt. Everest glaciers are shrinking, not only due to warming temperatures, but also as a result of weakening Asian monsoons (Thakuri et al., 2014). Our hypothesis are corroborated by coupling of the stochastic hydrological and the degree-day model. The SPI-Q model implies that (i) the Dudh Koshi river discharge is mainly dependent on precipitation from 1960s to 2000s. In this period, the monsoon alone is able to describe over 90% of annual river discharge; (ii) however since the beginning of 2000s, we observed a non-stationarity in the river discharge and the model fails to predict correctly the increased summer discharge, not justified by the observed weakening monsoon; (iii) coupling SPI-Q with the degree-day model underline as in the last decades the summer discharges are affected by an accelerated glacier melting, even observed with the glaciological analysis.

Snowpack melting can represent a major contribution to the seasonal variability of the surface and ground water budget at basin scale: the snow pack, acting as a natural reservoir, stores water during the winter season and releases it during spring and summer. The radiative budget driving the melting process depends on numerous variables that may be affected by the ongoing climatic changes. As a result, a shift in time during the spring and summer discharge may significantly affect surface water management at basin scale. For this reason, a reliable model able to quantitatively describe the snow melting processes is very important also for management purposes. The estimation of snow melt rate requires a full energy (mass) balance snowpack assessment. The limited availability of necessary data often does not allow implementing a radiative (mass) balance model. As an alternative we propose here a simple methodology to reconstruct the daily snowmelt and associated melt rate function based only on solid precipitation, air mean temperature and snowpack depth measurements, while snow density observations are often missing. The model differentiates between the melting and the compaction processes based on a daily mean temperature threshold (i.e. above or below the freezing point) and the snowpack state. The snow pack is described as two-layer model, each of them considers its own depth and density. The first one is a fresh snow surface layer whose density is a constant parameter. It is modulated by the daily snowfall/melting budget or it can be compacted and embedded within the second layer. The second one is the ripe snow, whose density is a weighted average with depths of antecedent snowpack and possible first layer contribution. The two snow layers allow starting the fusion by the snowpack's top where the density is lower, while much water is released when the ripe snow starts melting during the late melting season. Finally, we estimate the associated degree-day and antecedent temperature index to derive the local melt rate function. The proposed methodology has been applied on the snowpack height observations located in the Southern Alps.

Conditions of shortage in a water supply system occur when available resources are not able to satisfy the related demands. Concerning surface reservoirs, three main elements should be taken into account in transient conditions: 1) the inflow; 2) the actual amount of water stored in the reservoir; 3) and the water demand. To assess the risk of water shortage (i.e. reliability, resiliency and vulnerability), a simple model able to translate the randomness of climate into reliable scenarios of inflow to the reservoir is extremely useful. In this contest physically-based water balance models (i.e. models based on hydrological processes) often present several limitations due to lack of observations for the calibration/validation procedure and to an over-parameterization. In this paper a simple statistical method to simulate the inflow to a surface reservoir based on Standardized Precipitation Indices is proposed. It is based on some basic assumptions: a) for management purposes the inflow to the reservoir and the connected water demand, can be assessed at monthly time scale; b) the monthly inflow is determined by the climatic forcing averaged in space over the watershed; c) as a first approximation the discharge is mainly dependent on precipitation taken into account at different time scales and with different "weights"; d) the parameters linking the precipitation regime to the inflow are considered constant over time. On the base of such assumptions, to seek for relationships between the precipitation regime and the inflow a multilinear regression model (called SPI-Q) is calibrated and validated at monthly scale using the least-square method: Q(m,i) = a_SPI1(m)SPI1(m,i)+ a_SPI3(m)SPI3(m,i) + a_SPI6(m)SPI6(m,i) + a0(m), where Q(m,i) is the inflow for the month m, year i; SPI1(m,i), SPI3(m,i) SPI6(m,i) are the Standardized Precipitation Indices computed for the month m, year i on the precipitations cumulated over 1, 3 and 6 months; a_SPI1(m), a_SPI3(m), a_SPI6(m) and a0(m) are the coefficients from the multilinear regression of SPI1, SPI3, SPI6 for the month m. It is worth to note that to suitably calibrate the SPI-Q model a statistically significant dataset (both for inflow and precipitation) is mandatory. The SPI-Q model has been applied to three basins in Italy, quite different in terms of climate conditions and hydrological features: the Lake Maggiore basin (Switzerland and North Italy), the Ridracoli basin (Central Italy) and the Occhito Basin (South Italy). Simulations resulted in good agreement with observations, mostly for low inflow regime; moreover, the values of the multilinear regression coefficients appeared to be representative of the different hydrological processes that affect the total monthly discharge to the reservoirs: for example, for the case study of the Lake Maggiore, during spring the inflow is mostly affected by the SPI6 that takes into account the snow melting of the cumulated winter precipitations, whereas the inflow to the Occhito reservoir is mostly related to the SPI1. The physical meaning of the coefficients a_SPI1, a_SPI3 and a_SPI6 are widely discussed for the three case studies.

The problem of supporting decision- and policy-makers in managing issues related to solid waste and water quality was addressed within the context of a participatory modeling framework in the Sagarmatha National Park and Buffer Zone in Nepal. We present the main findings of management-oriented research projects conducted within this framework, thus providing an overview of the current situation in the park regarding solid waste and water quality issues. We found that most of the solid waste generated in the park is composed of organic matter, paper, and minor reused waste that is mainly reused for cattle feeding and manure, while disposal of other nondegradable categories of collected waste (glass, metal, and plastic) is not properly managed. Particularly, burning or disposal in open dumps poses a great hazard to environmental, human, and animal health, as most dump sites situated close to water courses are prone to regular flooding during the rainy season, thereby directly contaminating river water. Pollutants and microbiological contamination in water bodies were found and anthropogenic activities and hazardous practices such as solid waste dump sites, open defecation, and poor conditions of existing septic tanks are suggested as possibly affecting water quality. Collection of these data on solid waste and water quality and compilation of management information on the targeted social-ecological system allowed us to develop consensus-building models to be used as management supporting tools. By implementing such models, we were able to simulate scenarios identifying and evaluating possible management solutions and interventions in the park. This work reveals insights into general dynamics that can support the quest for solutions to waste and water quality management problems in other protected areas and mountain landscapes where traditional livelihood and land use patterns are changing under the influence of a growing population, changing consumption patterns, and international tourism.