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Snow and glacier melt (SGM) estimation plays an important role in water resources management. Although melting process can be modelled by energy balance methods, such studies require detailed data, which is rarely available. Hence, new and simpler approaches are needed for SGM estimations. The present study aims at developing an artificial neural networks (ANN) based technique for estimating the energy available for melt (EAM) and SGM rates using available and easy to obtain data such as temperature, short-wave radiation and relative humidity. Several ANN and multiple linear regression models (MLR) were developed to represent the energy fluxes and estimate the EAM. The models were trained using measured data from the Zongo glacier located in the outer tropics and validated against measured data from the Antizana glacier located in the inner tropics. It was found that ANN models provide a better generalisation when applied to other data sets. The performance of the models was improved by including Antizana data into the training set, as it was proved to provide better results than other techniques like the use of a prior logarithmic transformation. The final model was validated against measured data from the Alpine glaciers Argentière and Saint-Sorlin. Then, the models were applied for the estimation of SGM at Condoriri glacier. The estimated SGM was compared with SGM estimated by an enhanced temperature method and proved to have the same behaviour considering temperature sensibility. Moreover, the ANN models have the advantage of direct application, while the temperature method requires calibration of empirical coefficients

Snow and glacier melt (SGM) estimation plays an important role in water resources management. Although melting process can be modelled by energy balance methods, such studies require detailed data which is rarely available. Hence, new and simpler approaches are needed for SGM estimations. Artificial Neural Networks (ANN) is a modelling paradigm able to reproduce complex non-linear processes without the need of an explicit representation. The present study aims at developing an ANN based technique for estimating SGM rates using available and easy to obtain data such as Temperature and short wave radiation. Several ANN models were developed to represent the SGM process of a tropical glacier in the Bolivian Andes. The main data consisted on short wave radiation and temperature. It was found that accuracy may be increased by considering relative humidity and melting from previous time steps. The model represents the daily pattern showing variation of the melting rates throughout the day, with highest rate at noon. The melting rate in October (1.35 mm h^−1) is nearly three times higher than July's melting rate (0.50 mm h^−1). Results indicate that the exposure time to melting in October is 12 h, while in July is 9 h. This new methodology allows estimation of SGM at different hours throughout the day, reflecting its daily variation which is very important for tropical glaciers where the daily variation is greater than the yearly one. This methodology will provide useful data for better understanding the glacier retreat process and for analysing future water scenarios

Glacier retreat will increase sea level and decrease fresh water availability. Glacier retreat will also induce morphologic and hydrologic changes due to the formation of glacial lakes. Hence, it is important not only to estimate glacier volume, but also to understand the spatial distribution of ice thickness. There are several approaches for estimating glacier volume and glacier thickness. However, it is not possible to select an optimal approach that works for all locations. It is important to analyse the relation between the different glacier volume estimations and to provide confidence intervals of a given solution. The present study presents a probabilistic approach for estimating glacier volume and its confidence interval. Glacier volume of the Andean glacier Huayna West was estimated according to different scaling relations. Besides, glacier volume and glacier thickness were estimated assuming plastic behaviour. The present study also analysed the influence of considering a variable glacier density due to ice firn densification. It was found that the different estimations are described by a lognormal probability distribution. Considering a confidence level of 90%, the estimated glacier volume is 0.0275 km³ ± 0.0052 km³. Considering a confidence level of 90%, the estimated glacier thickness is 24.98 m with a confidence of ±4.67 m. The mean basal shear stress considering plastic behaviour is 82.5 kPa. The reconstruction of glacier bed topography showed the future formation of a glacier lake with a maximum depth of 32 m

Glaciers from the West side of the Royal Andes are an important source of fresh water for some of the most important Bolivian cities like El Alto. Temperature is an important datum for hydrological modelling and for glacier melt estimation. All temperature measurement devices have some degree of uncertainty due to systematic errors; thus, any temperature measurement has some errors. It is important to estimate the influence of such errors on the results from hydrological models and the estimation of melt water. The present study estimates the melt water contribution from the glaciers Tuni and Huayna West as a source of water supply for human consumption of El Alto considering the errors from temperature measurements. The hydrologic response of the basins was simulated with a hydrologic model. The glacier melt contribution was estimated as the difference between the discharge from the current scenario (with glaciers) and the discharge from a scenario without glaciers. Several volumes of melt water were estimated considering the temperature measurement and its possible errors. The uncertainty of such melt water volume was addressed by performing a Monte Carlo analysis of the possible melt water. The melt water contribution from glacier Tuni and Huayna West during the hydrologic year 2011-2012 was between 1.37 × 10 6 m 3 and 1.72 × 10 6 m 3. Such water volume is enough to meet the yearly water demand of between 6.81% and 8.55% of El Alto.