Conference Agenda

This paper presents a new water and energy budget-based glacier mass balance model. Enthalpy, rather than temperature, is used in the energy balance equations to simplify the computation of the energy transfers through the water phase change and the movement of liquid water in the snow. A new parameterization for albedo estimation and state-of-the-art parameterization schemes for rainfall/snowfall type identification and surface turbulent heat flux calculations are implemented in the model. This model was driven with meteorological data and evaluated using mass balance and turbulent flux data collected during a field experiment implemented in the ablation zone of the Parlung No. 4 Glacier on the Southeast Tibetan Plateau during 2009 and 2015–2016. The evaluation shows that the model can reproduce the observed glacier ablation depth, surface albedo, surface temperature, sensible heat flux, and latent heat flux with high accuracy. Comparing with a traditional energy budget-based glacier mass balance model, this enthalpy-based model shows a superior capacity in simulation accuracy. Therefore, this model can reasonably simulate the energy budget and mass balance of glacier melting in this region and be used as a component of land surface models and hydrological models.

Most estimates of melt and surface sublimation rates for the glaciers of the Tibetan Plateau have been obtained at the point scale, and it is not entirely established how ablation components vary across the entire extent of a glacier in this environment. Energy balance models can provide accurate simulations of energy fluxes and ablation components, thus fostering understanding of the main processes at the glacier-atmosphere interface, but need a high number of meteorological and surface input variables that can be available at the point scale of Automatic Weather Stations but are difficult to extrapolate across the distributed domain of an entire glacier.

Here, we simulate the distributed energy and mass balance of Parlung No. 4 Glacier, for the ablation season 2016, using a distributed energy balance model. We use input data at one AWS and two novel methods, which combine in-situ and reanalysis data, to generate fields of near-surface air temperature and wind speed that are needed to force the model. We calculate the spatial distribution of energy fluxes and ablation rates over the glacier surface at a high spatial resolution (50 m), which allows the inclusion of small-scale processes, such as katabatic and valley winds, refreezing and topographic shading, and knowledge of the local variability of topographic parameters, such as sky-view factors and local slopes.

We also compare our model results with the simulations of a point-scale energy-balance model based on enthalpy calculations which includes advanced schemes for calculation of albedo and turbulent fluxes; and with large-scale, regional energy balance simulations driven by satellite input data. The model is validated with in-situ and satellite observations of ablation, surface temperature and turbulent fluxes.

Our main objectives are to: i) advance our understanding of glacier ablation in the Tibetan Plateau by providing one of the first spatially-distributed quantifications of energy fluxes and ablation rates on a glacier in this region, including understanding the role that surface sublimation plays; ii) explore the use of two relatively new methods to generate fields of air temperature and wind speed, which provide alternatives to solve some of the interactions between large-scale meteorological forcing and the atmospheric boundary layer over a mountain glacier during the summer period; and iii) understand the level of model complexity that is needed for accurate simulations of energy fluxes and ablation components at different spatial scales.

The project is focusing on monitoring water resources in the Red River basin using microwave remote sensing. The Red River, also known as the Yuan River in Chinese, is a trans-boundary river basin with its total area of 169,000km2 shared by Vietnam (51%), China (48%) and Laos (1%). The Red River basin has a tropical or subtropical climate, dominated by the southwest monsoon from May to September and the northeast monsoon from October to April. The monsoon results in massive flow volume fluctuations. Flooding is a significant problem during the rainy season, particularly in July and August. At the same time, large area of karst landform causes the river flow loss and water shortage. One of the greatest challenges for flood prediction and integrated water management in the Red River basin is a lack of information on reservoir management as a consequence it is not easy to estimate the water resources. Since it is a trans-boundary river, there are difficulties to manage the area as a whole, and the information might not be in time for flood and drought early warning.

Hydrological data on major rivers, lakes and wetlands can often be difficult to obtain due to a region's inaccessibility, sparse distribution of gauge stations or the slow dissemination of data. Remote sensing technologies can be used to overcome such shortcomings.

The main objective of this project is to develop the algorithms and synergies between different Microwave Remote Sensing sensors to be able to monitoring water resources in the Red River Basin. For that purpose, the water elevation information, precipitation, soil moisture and evapotranspiration by remote sensing will be integrated in a hydrological model to improve its accuracy.

In this presentation we will be showing the first results of the variables monitored during this first year.