Model performance of downscaling 1999-2004 hydrometeorological fields to the upper Rio Grande basin using different forcing datasets

This study downscaled more than five years of data (1999-2004) for hydrometeorological fields over the upper Rio Grande basin (URGB) to a 4-km resolution using a regional model [fifth-generation Pennsylvania State University-National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5, version 3)] and two forcing datasets that include National Centers for Environmental Prediction (NCEP)-NCAR reanalysis-1 (R1) and North America Regional Reanalysis (NARR) data. The long-term high-resolution simulation results show detailed patterns of hydroclimatological fields that are highly related to the characteristics of the regional terrain; the most important of these patterns are precipitation localization features caused by the complex topography. In comparison with station observational data, the downscaling processing, on whichever forcing field is used, generated more accurate surface temperature and humidity fields than the Eta Model and NARR data, although it still included marked errors, such as a negative (positive) bias toward the daily maximum (minimum) temperature and overestimated precipitation, especially in the cold season. Comparing the downscaling results forced by the NARR and R1 with both the gridded and station observational data shows that under the NARR forcing, the MM5 model produced generally better results for precipitation, temperature, and humidity than it did under the R1 forcing. These improvements were more apparent in winter and spring. During the warm season, although the use of NARR improved the precipitation estimates statistically at the regional (basin) scale, it substantially underestimated them over the southern upper Rio Grande basin, partly because the NARR forcing data exhibited warm and dry biases in the monsoon-active region during the simulation period and improper domain selection. Analyses also indicate that over mountainous regions, both the Climate Prediction Center's (CPC's) gridded (0.25°) and NARR forcings underestimate precipitation in comparison with station gauge data. © 2008 American Meteorological Society

Deliverable D8b: Development of the physical/statistical downscaling methodology and application to climate model CLIMBER for BIOCLIM Workpackage 3. Work Package 3, Simulation of the future evolution of the biosphere system using the hierarchical strategy. Modelling Sequential Biosphere Systems under Climate Change for Radioactive Waste Disposal (BIOCLIM)

The overall aim of BIOCLIM is to assess the possible long term impacts due to climate change on the safety of radioactive waste repositories in deep formations. This aim is addressed through the following specific objectives: • Development of practical and innovative strategies for representing sequential climatic changes to the geosphere-biosphere system for existing sites over central Europe, addressing the timescale of one million years, which is relevant to the geological disposal of radioactive waste. • Exploration and evaluation of the potential effects of climate change on the nature of the biosphere systems used to assess the environmental impact. • Dissemination of information on the new methodologies and the results obtained from the project among the international waste management community for use in performance assessments of potential or planned radioactive waste repositories. This deliverable has the following specific motivations and objectives: Its main aim is to provide time series of climatic variables at the high resolution as needed by performance assessment (PA) of radioactive waste repositories, on the basis of coarse output from the CLIMBER-GREMLINS climate model

Improving the Physical Processes and Model Integration Functionality of an Energy Balance Model for Snow and Glacier Melt

The Hindu-Kush Himalayan region possesses a large resource of snow and ice, which acts as a freshwater reservoir for irrigation, domestic water consumption or hydroelectric power for billions of people in South Asia. Monitoring hydrologic resources in this region is challenging because of the difficulty of installing and maintaining a climate and hydrologic monitoring network, limited transportation and communication infrastructure and difficult access to glaciers. As a result of the high, rugged topographic relief, ground observations in the region are extremely sparse. Reanalysis data offer the potential to compensate for the data scarcity, which is a barrier in hydrological modeling and analysis for improving water resources management. Reanalysis weather data products integrate observations with atmospheric model physics to produce a spatially and temporally complete weather record in the post-satellite era. This dissertation creates an integrated hydrologic modeling system that tests whether streamflow prediction can be improved by taking advantage of the National Aeronautics and Space Administration (NASA) remote sensing and reanalysis weather data products in physically based energy balance snow melt and hydrologic models. This study also enhances the energy balance snowmelt model by adding capability to quantify glacier melt. The novelty of this integrated modeling tool resides in allowing the user to isolate various components of surface water inputs (rainfall, snow and glacier ice melt) in a cost-free, open source graphical-user interface-based system that can be used for government and institutional decision-making. Direct, physically based validation of this system is challenging due to the data scarcity in this region, but, to the extent possible, the model was validated through comparison to observed streamflow and to point measurements at locations in the United States having available dat

Regional climate downscaling with prior statistical correction of the global climate forcing

International audienceA novel climate downscaling methodology that attempts to correct climate simulation biases is proposed. By combining an advanced statistical bias correction method with a dynamical downscaling it constitutes a hybrid technique that yields nearly unbiased, high-resolution, physically consistent, three-dimensional fields that can be used for climate impact studies. The method is based on a prior statistical distribution correction of large-scale global climate model (GCM) 3-dimensional output fields to be taken as boundary forcing of a dynamical regional climate model (RCM). GCM fields are corrected using meteorological reanalyses. We evaluate this methodology over a decadal experiment. The improvement in terms of spatial and temporal variability is discussed against observations for a past period. The biases of the downscaled fields are much lower using this hybrid technique, up to a factor 4 for the mean temperature bias compared to the dynamical downscaling alone without prior bias correction. Precipitation biases are subsequently improved hence offering optimistic perspectives for climate impact studies

Preliminary Water Assessment Reports of The Test Basins of The Watch Project

This report presents the initial plans of the case studies how they link to rest of the Watch project and on which water resources they will focus. This report will function as the basis for further discussions on how to improve the integration of the case studies within the project and to develop a more general protocol for each of the case studies. Currently 5 catchments are used within the Watch project, they differ in climatic and hydro-geological features and expected climate changes: the Glomma River basin (Eastern Norway), the upper Guadiana basin (Central Spanish Plateau), the Nitra River basin (central Slovakia), the Upper-Elbe basin (part of the Elbe River) and the island of Crete. Also the water resources issues vary over these cases. Agricultural (and domestic) water use is under pressure in the Mediterranean catchments probably aggravating with the expected increase in drought frequency under future climate. The Norwegian catchment provides hydropower services under threat of precipitation increase rather than decrease. The central European catchments are threatened mainly by increased variability, i.e. increased frequencies of extremes in a densely populated environment, and river flow may need additional buffers (reservoirs) to reduce floodrisk and store water for dry period