The analysis of climate change impact on the hydrology of high altitude glacierized catchments in the Himalayas is complex due to the high variability in climate, lack of data, large uncertainties in climate change projection and uncertainty about the response of glaciers. Therefore a high resolution combined cryospheric hydrological model was developed and calibrated that explicitly simulates glacier evolution and all major hydrological processes. The model was used to assess the future development of the glaciers and the runoff using an ensemble of downscaled climate model data in the Climatic Change (2012) Langtang catchment in Nepal. The analysis shows that both temperature and precipitation are projected to increase which results in a steady decline of the glacier area. The river flow is projected to increase significantly due to the increased precipitation and ice melt and the transition towards a rain river. Rain runoff and base flow will increase at the expense of glacier runoff. However, as the melt water peak coincides with the monsoon peak, no shifts in the hydrograph are expected.

[1] Physically based hydrological models describe natural processes more accurately than conceptual models but require extensive data sets to produce accurate results. To identify the value of different data sets for improving the performance of the distributed hydrological model TOPKAPI we combine a multivariable validation technique with Monte Carlo simulations. The study is carried out in the snow and ice-dominated Rhonegletscher basin, as these types of mountainous basins are generally the most critical with respect to data availability and sensitivity to climate fluctuations. Each observational data set is used individually and in combination with the other data sets to determine a subset of best parameter combinations out of 10,000 Monte Carlos runs performed with randomly generated parameter sets. We validate model results against discharge, glacier mass balance, and satellite snow cover images for a 14 year time period (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007). While the use of all data sets combined provides the best overall model performance (defined by the concurrent best agreement of simulated discharge, snow cover and mass balance with their respective measurements), the use of one or two variables for constraining the model results in poorer performance. Using only one data set for constraining the model glacier mass balance proved to be the most efficient observation leading to the best overall model performance. Our main result is that a combination of discharge and satellite snow cover images is best for improving model performance, since the volumetric information of discharge data and the spatial information of snow cover images are complementary.Citation: Finger, D., F. Pellicciotti, M. Konz, S. Rimkus, and P. Burlando (2011), The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model, Water Resour.

The objective of this study is to investigate the suitability of remote sensing data to calibrate a distributed hydrological model of a Nepalese Himalayan headwater catchment. Snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite are used to calibrate the snow and glacier routine of the conceptual hydrological model TAC D . The snow cover information is useful to constrain the hydrological model in the calibration phase, especially in data-scarce regions. Simulation results using the MODIS calibrated parameter sets are evaluated against independent observations measured discharge data and glacier mass balance measurements of the Langtang Khola catchment. The constraint posed in the calibration phase by the spatially distributed observations allows a reduction in the equifinality problem, thus producing a better balance among the modelled processes and a more plausible partitioning of the different runoff components. Accordingly, we demonstrate that the use of a remote sensing-based additional source of information allows enhancing streamflow predictions for data scarce areas, thus also allowing water resources assessment in remotely located regions.

a b s t r a c tHydrological modelling of glacierized catchments is challenging because internal inconsistencies might be hidden due to ice melt which represents an additional source of water. This is even more significant if there are no data available to evaluate model simulations, as is often the case in remote areas. On the other hand, these glacierized catchments are important source regions for water, and detailed knowledge of water availability is a prerequisite for good resource management strategies. An important question is how useful a limited amount of data might be for model applications. Therefore, in this study the predictive power of limited discharge measurements, mass balance observations and the combination of both was analyzed by means of Monte Carlo analyses with multi-criteria model performance evaluation. Ensembles of 100 parameter sets were selected by evaluating the simulations based on a limited number of discharge measurements, glacier mass balance, and the combination of discharge and mass balance observations. Then the ensemble simulation of runoff was evaluated for the entire runoff series. The result indicated that a single annual glacier mass balance observation contained useful information to constrain hydrological models. Combining mass balance observations with a few discharge data improved the internal consistency and significantly reduced the uncertainties compared to parameter set selections based on discharge measurements alone. To obtain good ensemble predictions, information on discharge was required for at least 3 days during the melting season. This demonstrated that the timing of runoff measurements is important for the information contained in these data.Ó 2010 Elsevier B.V. All rights reserved. IntroductionGlacierized headwaters are source regions for many important rivers in the world. These 'water towers' supply water to surrounding lowlands and are even more important in arid climates (Viviroli et al., 2007). However, due to the remoteness and the harsh climate, there is often a shortage of continuous discharge measurements, which hinders the assessment of water resources of high mountain catchments. Modelling ungauged catchments is always challenging, but even more so for glacierized catchments because here the catchment water balance is not constrained. The issue of ungauged catchments recently has received increased attention due to the PUB (prediction in ungauged basins) initiative (Sivapalan et al., 2003). While the predictive capabilities of runoff models have increased significantly, calibration of parameters is usually still important and requires adequate data. While, it is difficult to make predictions in totally ungauged basins, it has been demonstrated that a limited number of gaugings can help to achieve acceptable model simulations (Seibert and Beven, 2009). This raises the question of which and what amount of data are needed to find reliable parameter sets for the application of conceptual models in high mountain areas.Contributions in the literature...

Abstract. The paper presents a catchment modeling approach for remote glacierized Himalayan catchments. The distributed catchment model TAC D , which is widely based on the HBV model, was further developed for the application in highly glacierized catchments on a daily timestep and applied to the Nepalese Himalayan headwater Langtang Khola (360 km 2 ). Low laying reference stations are taken for temperature extrapolation applying a second order polynomial function. Probability based statistical methods enable bridging data gaps in daily precipitation time series and the redistribution of cumulated precipitation sums over the previous days. Snow and ice melt was calculated in a distributed way based on the temperature-index method employing calculated daily potential sunshine durations. Different melting conditions of snow and ice and melting of ice under debris layers were considered. The spatial delineation of hydrological response units was achieved by taking topographic and physiographic information from maps and satellite images into account, and enabled to incorporate process knowledge into the model. Simulation results demonstrated that the model is able to simulate daily discharge for a period of 10 years and point glacier mass balances observed in the research area with an adequate reliability. The simple but robust data pre-processing and modeling approach enables the determination of the components of the water balance of a remote, data scarce catchment with a minimum of input data.