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Abstract. Reviews of field studies of groundwater recharge have attempted to investigate how climate characteristics control recharge, but due to a lack of data have not been able to draw any strong conclusions beyond that rainfall is the major determinant. This study has used numerical modelling for a range of Köppen-Geiger climate types (tropical, arid and temperate) to investigate the effect of climate variables on recharge for different soil and vegetation types. For the majority of climate types, the correlation between the modelled recharge and total annual rainfall is weaker than the correlation between recharge and the annual rainfall parameters reflecting rainfall intensity. Under similar soil and vegetation conditions for the same annual rainfall, annual recharge in regions with winter-dominated rainfall is greater than in regions with summer-dominated rainfall. The importance of climate parameters other than rainfall in recharge estimation is highest in the tropical climate type. Mean annual values of solar radiation and vapour pressure deficit show a greater importance in recharge estimation than mean annual values of the daily mean temperature. Climate parameters have the lowest relative importance in recharge estimation in the arid climate type (with cold winters) and the temperate climate type. For 75 % of all soil, vegetation and climate types investigated, recharge elasticity varies between 2 and 4 indicating a 20 % to 40 % change in recharge for a 10 % change in annual rainfall. Understanding how climate controls recharge under the observed historical climate allows more informed choices of analogue sites if they are to be used for climate change impact assessments.

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Reviews of field studies of groundwater recharge have attempted to investigate how climate characteristics control recharge, but due to a lack of data have not been able to draw any strong conclusions beyond that rainfall is the major determinant. This study has used numerical modeling for a range of Köppen-Geiger climate types (tropical, arid and temperate) to investigate the effect of climate variables on recharge for different soil and vegetation types. For the majority of climate types the total annual rainfall had a weaker correlation with recharge than the rainfall parameters reflecting rainfall intensity. In regions with winter-dominated rainfall, annual recharge under the same annual rainfall, soils and vegetation conditions is greater than in regions with summer-dominated rainfall. The relative importance of climate parameters other than rainfall is higher for recharge under annual vegetation, but overall is highest in the tropical climate type. Solar radiation and vapour pressure deficit show a greater relative importance than mean annual daily mean temperature. Climate parameters have lowest relative importance in the arid climate type (with cold winters) and the temperate climate type. For 75% of all considered cases of soil, vegetation and climate types recharge elasticity varies between 2 and 4, indicating a 20% to 40% change in recharge for a 10% change in annual rainfall Understanding how climate controls recharge under the observed historical climate allows more informed choices of analogue sites if they are to be used for climate change impact assessments

Abstract. The simulation of routing and distribution of water through a regulated river system with a river management model will quickly result in complex and nonlinear model behaviour. A robust sensitivity analysis increases the transparency of the model and provides both the modeller and the system manager with a better understanding and insight on how the model simulates reality and management operations.In this study, a robust, density-based sensitivity analysis, developed by Plischke et al. (2013), is applied to an eWater Source river management model. This sensitivity analysis methodology is extended to not only account for main effects but also for interaction effects. The combination of sensitivity indices and scatter plots enables the identification of major linear effects as well as subtle minor and nonlinear effects.The case study is an idealized river management model representing typical conditions of the southern MurrayDarling Basin in Australia for which the sensitivity of a variety of model outcomes to variations in the driving forces, inflow to the system, rainfall and potential evapotranspiration, is examined. The model outcomes are most sensitive to the inflow to the system, but the sensitivity analysis identified minor effects of potential evapotranspiration and nonlinear interaction effects between inflow and potential evapotranspiration.

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In this study, we developed a workflow that applies a complex groundwater model for purpose-driven groundwater monitoring network design and uses linear uncertainty analysis to explore the predictive dependencies and provide insights into the veracity of the monitoring design. A numerical groundwater flow model was used in a probabilistic modelling framework for obtaining the spatial distribution of predicted drawdown for a wide range of plausible combination of uncertain parameters pertaining to the deep sedimentary basin and groundwater flow processes. Reduced rank spatial prediction was used to characterize dominant trends in these spatial drawdown patterns using empirical orthogonal functions (EOF). A differential evolution algorithm was used to identify optimal locations for multi-level piezometers for collecting groundwater pressure data to minimize predictive uncertainty in groundwater drawdown. Data-worth analysis helps to explore the veracity of the design by using only the sensitivities of the observations to predictions independent of the absolute values of predictions. A 10-bore monitoring network that collects drawdown data from multiple depths at each location was designed. The data-worth analysis revealed that the design honours sensitivities of the predictions of interest to parameters. The designed network provided relatively high data-worth for minimizing uncertainty in the drawdown prediction at locations of interest.

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