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[1] This paper investigates the actual extrapolation capacity of three hydrological models in differing climate conditions. We propose a general testing framework, in which we perform series of split-sample tests, testing all possible combinations of calibration-validation periods using a 10 year sliding window. This methodology, which we have called the generalized split-sample test (GSST), provides insights into the model's transposability over time under various climatic conditions. The three conceptual rainfall-runoff models yielded similar results over a set of 216 catchments in southeast Australia. First, we assessed the model's efficiency in validation using a criterion combining the root-mean-square error and bias. A relation was found between this efficiency and the changes in mean rainfall (P) but not with changes in mean potential evapotranspiration (PE) or air temperature (T). Second, we focused on average runoff volumes and found that simulation biases are greatly affected by changes in P. Calibration over a wetter (drier) climate than the validation climate leads to an overestimation (underestimation) of the mean simulated runoff. We observed different magnitudes of these models deficiencies depending on the catchment considered. Results indicate that the transfer of model parameters in time may introduce a significant level of errors in simulations, meaning increased uncertainty in the various practical applications of these models (flow simulation, forecasting, design, reservoir management, climate change impact assessments, etc.). Testing model robustness with respect to this issue should help better quantify these uncertainties.

Evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981–2012, and its three components: transpiration from vegetation (Et), direct evaporation from the soil (Es) and vaporization of intercepted rainfall from vegetation (Ei). During this period, ET over land has increased significantly (p < 0.01), caused by increases in Et and Ei, which are partially counteracted by Es decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in Et over land is about twofold of the decrease in Es. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle.

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[1] This paper describes the modeling of climate change impact on runoff across southeast Australia using a conceptual rainfall-runoff model SIMHYD and presents the results and assesses the robustness of the modeling approach. The future climate series is obtained by scaling the historical series, informed by 15 global climate models (GCMs), to reflect a 0.9°C increase in global average surface air temperature, using a daily scaling method that considers changes in the future mean seasonal rainfall and potential evapotranspiration as well as in the daily rainfall distribution. The majority of the modeling results indicate that there will be less runoff in southeast Australia in the future. However, there is considerable uncertainty, with the results ranging from a 17% decrease to a 7% increase in the mean annual runoff averaged across the study area for the 0.9°C global warming. The model assessments indicate that the modeling approach is generally robust and can be used to estimate the climate impact on runoff. The modeled mean annual runoff is generally within 10-20% of the observed runoff. The modeling results for an independent test period are only slightly poorer than the calibration period, indicating that a satisfactorily calibrated rainfall-runoff model can be used to estimate runoff for another climate period. The modeled impact on various runoff characteristics as estimated by two rainfall-runoff models investigated here differ by less than 10%, which is relatively small compared to the range of modeled runoff results using rainfall projections from different GCMs.

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