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International audience; Managing water is a grand challenge problem and has become one of humanity’s foremost priorities. Surface water resources are typically societally managed and relatively well understood; groundwater resources, however, are often hidden and more difficult to conceptualize. Replenishment rates of groundwater cannot match past and current rates of depletion in many parts of the world. In addition, declining quality of the remaining groundwater commonly cannot support all agricultural, industrial and urban demands and ecosystem functioning, especially in the developed world. In the developing world, it can fail to even meet essential human needs. The issue is: how do we manage this crucial resource in an acceptable way, one that considers the sustainability of the resource for future generations and the socioeconomic and environmental impacts? In many cases this means restoring aquifers of concern to some sustainable equilibrium over a negotiated period of time, and seeking opportunities for better managing groundwater conjunctively with surface water and other resource uses. However, there are many, often-interrelated, dimensions to managing groundwater effectively. Effective groundwater management is underpinned by sound science (biophysical and social) that actively engages the wider community and relevant stakeholders in the decision making process. Generally, an integrated approach will mean “thinking beyond the aquifer”, a view which considers the wider context of surface water links, catchment management and cross-sectoral issues with economics, energy, climate, agriculture and the environment. The aim of the book is to document for the first time the dimensions and requirements of sound integrated groundwater management (IGM). The primary focus is on groundwater management within its system, but integrates linkages beyond the aquifer. The book provides an encompassing synthesis for researchers, practitioners and water resource managers on the concepts and tools required for defensible IGM, including how IGM can be applied to achieve more sustainable socioeconomic and environmental outcomes, and key challenges of IGM. The book is divided into five parts: integration overview and problem settings; governance; socioeconomics; biophysical aspects; and modelling and decision support. However, IGM is integrated by definition, thus these divisions should be considered a convenience for presenting the topics rather than hard and fast demarcations of the topic area.

Given the growing complexity of water-resources management there will be an increasing need for integrated tools to support policy analysis, communication, and research. A key aspect of the design is the combination of process models from different scientific disciplines in an integrated system. In general these models differ in sensitivity and accuracy, while non-linear and qualitative models can be present. The current practice is that the preferences of the designers of a decision-support system, and practical considerations such as data availability guide the selection of models and data. Due to a lack of clear scientific guidelines the design becomes an ad-hoc process, depending on the case study at hand, while selected models can be overly complex or too coarse for their purpose. Ideally, the design should allow for the ranking of selected management measures according to the objectives set by end users, without being more complex than necessary. De Kok and Wind [2003] refer to this approach as appropriate modeling. A good case example is the ongoing pilot project aiming at the design of a decision-support system for the Elbe river basin. Four functions are accounted for: navigability, floodplain ecology, flooding safety, and water quality. This paper concerns the response of floodplain biotope types to river engineering works and changes in the flooding frequency of the floodplains. The HBV-D conceptual rainfall-runoff model is used to simulate the impact of climate and land use change on the discharge statistics. The question was raised how well this rainfall-runoff model should be calibrated as compared to the observed discharge data. Sensitivity analyses indicate that a value of R2 = 0.87 should be sufficient.

Decision support systems (DSSs) are increasingly being used in water management for the evaluation of impacts of policy measures under different scenarios. The exact impacts generally are unknown and surrounded with considerable uncertainties. These uncertainties stem from natural randomness, uncertainty in data, models and parameters, and uncertainty about measures and scenarios. It may therefore be difficult to make a selection of measures relevant for a particular water management problem. In order to support policy makers to make a strategic selection between different measures in a DSS while taking uncertainty into account, a methodology for the ranking of measures has been developed. The methodology has been applied to a pilot DSS for flood control in the Red River basin in Vietnam and China. The decision variable is the total flood damage and possible flood reducing measures are dike heightening, reforestation and the construction of a retention basin. For illustrative purposes, only parameter uncertainty is taken into account. The methodology consists of a Monte Carlo uncertainty analysis employing Latin Hypercube Sampling and a ranking procedure based on the significance of the difference between output distributions for different measures. The significance is determined with the Student test for Gaussian distributions and with the non-parametric Wilcoxon test for non-Gaussian distributions. The results show Gaussian distributions for the flood damage in all situations. The mean flood damage in the base situation is about 2.2 billion US$ for the year 1996 with a standard deviation due to parameter uncertainty of about 1 billion US$. Selected applications of the measures reforestation, dike heightening and the construction of a retention basin reduce the flood damage with about 5, 55 and 300 million US$ respectively. The construction of a retention basin significantly reduces flood damage in the Red River basin, while dike heightening and reforestation reduce flood damage, but not significantly.

How good should a river basin model be to assess the impact of climate change on river flooding for a specific geographical area? The determination of such an appropriate model should reveal which physical processes should be incorporated and which data and mathematical process descriptions should be used at which spatial and temporal scales. A procedure for determining an appropriate model has been developed and applied to the above mentioned specific problem for the Meuse river in France, Belgium and the Netherlands. The model appropriateness procedure consists of three steps. First, the dominant processes and associated key variables are identified. Second, statistical analyses with respect to the key variables are performed, which result in appropriate spatial and temporal scales for each key variable and relationships between key variable scales and the output variable. These latter relationships are used to combine the appropriate scales to one appropriate model scale. In the third step, mathematical process descriptions consistent with these model scales are selected. The resulting appropriate components have been implemented in an existing modelling framework to obtain the appropriate model. Two additional models were constructed to assess the sensitivity of the results to model complexity. The appropriate spatial model scale turned out to be around 10 km with a daily time step. The model results became somewhat better with increasing model complexity. The general trend with climate change (doubling of the CO2 concentration) is a small decrease (5 %) of the average discharge and a small increase (5-10 %) of discharge variability and extreme discharges. It was found that the uncertainties in extreme discharges with climate change are large and that those due to precipitation and extrapolation errors are the most important ones.

It is increasingly the case that models are being developed as “evolving” products rather than one-off application tools, such that auditable modelling versus ad hoc treatment of models becomes a pivotal issue. Auditable modelling is particularly vital to “parsimonious modelling” aimed at meeting specific modelling requirements. This paper outlines various contributory factors and aims to seed proactively a research topic by inextricably linking value/risk management to parsimonious modelling. Value management in modelling may be implemented in terms of incorporating “enough detail” into a model so that the synergy among the constituent units of the model captures that of the real system. It is a problem of diminishing returns, since further reductions in the constituent units will create an unacceptable difference between the model and the real system; conversely, any further detail will add to the cost of modelling without returning any significant benefit. The paper also defines risk management in relation to modelling. It presents a qualitative framework for value/risk management towards parsimonious modelling by the categorisation of “modelling techniques” in terms of “control volume.”

International audience; Groundwater resources are part of larger social-ecological systems. In this chapter, we review the various dimensions of these complex systems in order to uncover the diversity of elements at stake in the evolution of an aquifer and the loci for possible actions to control its dynamics. Two case studies illustrate how the state of an aquifer is embedded in a web of biophysical and sociopolitical processes. We propose here a holistic view through an IGM-scape that describes the various possible pathways of evolution for a groundwater related social-ecological system. Then we describe the elements of this IGM-scape starting with physical entities and processes, including relations with surface water and quality issues. Interactions with society bring an additional layer of considerations, including decisions on groundwater abstraction, land use changes and even energy related choices. Finally we point out the policy levers for groundwater management and their possible consequences for an aquifer, taking into account the complexity of pathways opened by these levers.

To evaluate the future state of river water in view of actual loading or different management options, water quality models are a useful tool. However, the uncertainty on the model predictions is sometimes too high to draw proper conclusions. It is of high importance to modellers to minimise the uncertainty of the model predictions. Therefor different research is needed according to the origin of the uncertainty. If the uncertainty stems from input data uncertainty or from parameter uncertainty, more reliable results can be obtained by performing specific measurement campaigns. To guide these measurement campaigns, an uncertainty analysis can give important information. In this article an overview of different techniques that give valuable information for the reduction of input and parameter uncertainty is given. The practical case study is the river Dender in Flanders, Belgium. First a global sensitivity analysis shows the importance of the different uncertainty sources. Here it is seenthat the parameters influence the model results more than the input data. Further an analysis in time and space of the uncertainty bands is performed to find differences in uncertainty between certain periods or places. More measurements are needed during periods or on places with high uncertainty. This research also shows that finding a link between periods with high uncertainty and specific circumstances (climatological, eco-regional, etc) can help in gathering data for the calibration of submodels (eg. diffuse pollution vs. point pollution). The methods can be used for every variable under study and for all kind of rivers but the conclusions made for the practical case study are only applicable for the Dender.