A soil temperature and energy balance model for integrated assessment of Global Change impacts at the regional scale

The investigation of the impact of Global Change on the basic resources on which life, and man, depends, is the main objective of the environmental science community at the beginning of the 21st century. Advances in information technology, new methods of spatially distributed data retrieval and increased understanding of the physical, chemical and biological processes in the Earth system facilitate integrative models of the dynamic processes under change. Together with the integration of deep actors models from social and economical sciences into a common model framework, scenario runs based on inputs from Regional Climate Models (RCMs) and constrained by prognoses of the future developments in demography, economy and human behaviour are now possible. The objective of the integrative project GLOWA-Danube is the development of such a modelling system and its application on the mesoscale catchment of the Upper Danube river with an area of about 77,000 km2. The decision support system DANUBIA is designed for plausible predictions of the impact of changes in climate, human behaviour and land use on the future of the water and related matter cycles. DANUBIA is able to assist knowledge-based management decisions, by predicting the effects of adaptation and mitigation strategies on the natural resources of the Upper Danube basin. The closure of the water, energy, nitrogen and carbon cycles in the soil-vegetation-atmosphere system relies on the adequate representation of all processes involved and their interaction. To close the energy cycle in the soil-vegetation-atmosphere system and provide valuable input data for biochemical models of soil nitrogen and carbon transformation, this thesis presents the Soil Heat Transfer Module (SHTM) together with an energy balance algorithm of the soil surface for regional scale simulations. SHTM combines simplified physical algorithms for the computation of the actual temperature in the upper soil layers and a dynamic lower boundary condition to represent Climate Change conditions. Changes in soil moisture and soil freezing are explicitly taken into account. The surface ground heat flux as the driving force of the model is provided by an explicit solution of the soil surface energy balance and a snow-soil coupling algorithm, respectively. This thesis shows, that the soil temperature and energy balance modules developed as extensions of PROMET (PROcesses of Matter and Energy Transfer) are ready to bridge the gap between regional scale (up to 100,000 km2) application and the requirement of physical process models in predictive, coupled modelling systems like DANUBIA

Impact of potential climate change on plant available soil water and percolation in the Upper Danube basin

The soil root zone of the land surface provides plants with water for transpiration and therefore biomass production and its excess water percolates downwards and ultimately recharges the groundwater aquifers. Within the project GLOWA-Danube regional scale impacts of climate change on the water cycle are investigated. Potential changes in the water cycle based on climate scenarios for 2011 to 2060 are simulated with the decision support system DANUBIA that integrates models of natural as well as social sciences. This article presents the results of DANUBIA driven by an ensemble of 12 climates scenarios generated with a stochastic climate simulator regarding the future state of soil moisture and groundwater recharge in the Upper Danube basin

Design and implementation of the land surface model NaturalEnvironment within the generic framework OpenDanubia for integrative, distributed environmental modelling

The project GLOWA-Danube (http://www.glowa-danube.de) aimed at investigating the manifold consequences of Global Change on regional water resources in the Upper Danube Basin. In order to achieve this task, an interdisciplinary, university-based network of experts developed the integrative Decision Support System OpenDanubia (OD). The common base for implementing and coupling the various scientific model components is a generic framework, which provides the coordination of the coupled models that run in parallel exchanging iteratively data via their interfaces. The OD framework takes care of technical aspects, such as ordered data exchange between sub-models, data aggregation, data output, model parallelization and data distribution over the network, which means that model developers do not have to be concerned about complexities evolving from coupling their models. Within this framework the sub-model NaturalEnvironment, representing a land surface model, was developed and implemented. The object-oriented design of this sub-model facilitates a plain, logical representation of the actual physical processes simulated by the sub-model. Physical processes to be modelled are organized in naturally ordered, exchangeable lists that are executed on each spatial computation unit for each modelling time step, depending on their land cover. The type of land cover to be simulated on each freely defined spatial unit is distinguished by one of the three types aquatic, terrestrial and glacier. Additionally, the type terrestrial is influenced by dynamic land use changes which can be triggered e.g. by the socio-economic OD sub-model Farming. This paper presents the basic design of the open source (GPL'ed) OD framework and highlights the implementation of the sub-model NaturalEnvironment within this framework, as well as its interactions with other components included in OD

Water models and scenarios inventory for the Danube region

This technical report presents an inventory of existing models currently used in the Danube Region by local, regional, national authorities and scientific institutes for the development of a hydro-economic multi-model ensemble for the Danube with a common database. It also presents a first identification of regional scenarios of policy options relevant for river basin management planning.JRC.H.1-Water Resource

Evaluation of different sources of uncertainty in climate change impact research using a hydro-climatic model ensemble

The international research project QBic3 (Quebec-Bavarian Collaboration on Climate Change) aims at investigating the potential impacts of climate change on the hydrology of regional scale catchments in Southern Quebec (Canada) and Bavaria (Germany). Yet, the actual change in river runoff characteristics during the next 70 years is highly uncertain due to a multitude of uncertainty sources. The so-called hydro-climatic ensemble that is constructed to describe the uncertainties of this complex model chain consists of four different global climate models, downscaled by three different regional climate models, an exchangeable bias correction algorithm, a separate method to scale RCM outputs to the hydrological model scale and several hydrological models of differing complexity to assess the impact of different hydro model concepts. This choice of models and scenarios allows for the inter-comparison of the uncertainty ranges of climate and hydrological models, of the natural variability of the climate system as well as of the impact of scaling and correction of climate data on mean, high and low flow conditions. A methodology to display the relative importance of each source of uncertainty is proposed and results for past runoff and potential future changes are presented

An ensemble approach to assess hydrological models’ contribution to uncertainties in the analysis of climate change impact on water resources

Over the recent years, several research efforts investigated the impact of climate change on water resources for different regions of the world. The projection of future river flows is affected by different sources of uncertainty in the hydro-climatic modelling chain. One of the aims of the QBic3 5 project (Que´bec-Bavarian International Collaboration on Climate Change) is to assess the contribution to uncertainty of hydrological models by using an ensemble of hydrological models presenting a diversity of structural complexity (i.e. lumped, semi distributed and distributed models). The study investigates two humid, mid-latitude catchments with natural flow conditions; one located in 10 Southern Que´bec (Canada) and one in Southern Bavaria (Germany). Daily flow is simulated with four different hydrological models, forced by outputs from regional climate models driven by a given number of GCMs’ members over a reference (1971–2000) and a future (2041–2070) periods. The results show that the choice of the hydrological model does strongly affect the climate change response of selected hydrological indicators, especially those related to low flows. Indicators related to high flows seem less sensitive on the choice of the hydrological model. Therefore, the computationally less demanding models (usually simple, lumped and conceptual) give a significant level of trust for high and overall mean flows

On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff

In climate change impact research, the assessment of future river runoff as well as the catchment scale water balance is impeded by different sources of modeling uncertainty. Some research has already been done in order to quantify the uncertainty of climate 5 projections originating from the climate models and the downscaling techniques as well as from the internal variability evaluated from climate model member ensembles. Yet, the use of hydrological models adds another layer of incertitude. Within the QBic3 project (Qu´ebec-Bavaria International Collaboration on Climate Change) the relative contributions to the overall uncertainty from the whole model chain (from global climate 10 models to water management models) are investigated using an ensemble of multiple climate and hydrological models. Although there are many options to downscale global climate projections to the regional scale, recent impact studies tend to use Regional Climate Models (RCMs). One reason for that is that the physical coherence between atmospheric and land-surface 15 variables is preserved. The coherence between temperature and precipitation is of particular interest in hydrology. However, the regional climate model outputs often are biased compared to the observed climatology of a given region. Therefore, biases in those outputs are often corrected to reproduce historic runoff conditions from hydrological models using them, even if those corrections alter the relationship between temperature and precipitation. So, as bias correction may affect the consistency between RCM output variables, the use of correction techniques and even the use of (biased) climate model data itself is sometimes disputed among scientists. For those reasons, the effect of bias correction on simulated runoff regimes and the relative change in selected runoff indicators is explored. If it affects the conclusion of climate change analysis in 25 hydrology, we should consider it as a source of uncertainty. If not, the application of bias correction methods is either unnecessary in hydro-climatic projections, or safe to use as it does not alter the change signal of river runoff. The results of the present paper highlight the analysis of daily runoff simulated with four different hydrological models in two natural-flow catchments, driven by different regional climate models for a reference and a future period. As expected, bias correction of climate model outputs is important for the reproduction of the runoff regime of the 5 past regardless of the hydrological model used. Then again, its impact on the relative change of flow indicators between reference and future period is weak for most indicators with the exception of the timing of the spring flood peak. Still, our results indicate that the impact of bias correction on runoff indicators increases with bias in the climate simulations