ALBERT

Description: 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.

Description: 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.

Description: 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.

Description: Runoff from catchments with partial glacier cover is an integrated process of glacier melt, snowmelt, and surface and subsurface runoff of meltwater and rain from glacierized and non-glacierized areas. Additionally, inherent characteristics of the tropical Andes such as large meteorological variability, high elevation and steep slopes, hydrological effects of wetlands and lakes, and rapid glacier retreat make it difficult to model glacio-hydrological responses under changing climate. In this study, we developed a semi-distributed conceptual model applicable to partially glacierized catchments in the tropical Andes that considers all of these aspects, and we applied the model to the Huayna Potosi West headwater catchment in the Cordillera Real, Bolivia. Based on the latest 2 yr dataset of meteorological and hydrological monitoring, we showed the spatial and temporal variability of air temperature and precipitation in the region, and the dataset was used to calibrate model parameters and validate the performance of the daily runoff simulation. Variations in the simulated streamflow agreed well with the observed seasonal and temporal variations, and the result also showed that uncertainty pertaining to the spatial and temporal variations in air temperature and precipitation as well as the retarding effect of a wetland and lake strongly affected the runoff hydrograph. The simulated runoff components indicated that runoff from glacier melt occurs mainly in the initial period of the wet season, from October to early December, and in the late period of the wet season, March and April, although the runoff is relatively small in the latter period. Between these two periods in the wet season, major runoff components were estimated to be subsurface runoff in the non-glacierized area and surface runoff due to snowmelt. Given the future meteorological conditions based on the observational data and a predictive general circulation model output, the model quantified the long-term changes in runoff, glacierized area, and cumulative glacier and snow mass balance. The glacier retreat is estimated to continue to 2050, with the magnitude of area decrease and negative cumulative mass balance depending on the increasing temperature trend used. For higher temperature trends, in particular, greater seasonal variation in runoff and larger contributions from subsurface runoff and surface runoff by rainfall were simulated to occur in the wet season, but the change in annual total runoff between the present and 2050 was not significant. These results suggest that it is important to consider how to best adapt to greater seasonal runoff variations in terms of water availability in the downstream region.