<p>Australia has scarce freshwater resources and is already becoming drier under the impacts of climate change. Climate change impacts and other important hydrological processes occur on multiple temporal and spatial scales, prompting the need for large-scale, high-resolution, multidecadal hydrological models. Large-scale hydrological models rely on accurate process descriptions and inputs to be able to simulate realistic multi-scale processes, however parameterization is required to account for limitations in observational inputs and sub-grid scale processes. For example, defining the soil hydraulic boundary conditions at multiple depths using soil input maps at high-resolution across an entire continent is subject to uncertainty. A common way to reduce uncertainty associated with static inputs and parameterization, thereby improving model accuracy and reliability, is to optimize the model parameters toward a long record of historical data, namely calibration. The Australian Bureau of Meteorology&#8217;s operational hydrological model (The Australian Water Resources Assessment model: AWRA-L, www.bom.gov.au/water/landscape), which provides real-time monitoring of the continental water balance, is calibrated to a combined performance metric. This metric optimizes model performance against catchment based streamflow and satellite based evaporation and soil moisture observations for 295 sites across the country, where 21 separate parameters are calibrated continentally. Using this approach, AWRA-L has been shown to reproduce independent, historical in-situ data accurately across the water balance.</p><p>Additionally, the AWRA-L model is being used to project future hydrological fluxes and states using bias corrected meteorological inputs from multiple global climate models. Towards improving AWRA-L&#8217;s performance and stability for use in hydrological projections, we aim to generate a set of model parameters that perform well under conditions of climate variability as well as under historical conditions, with a two-stage approach. Firstly, a variance based sensitivity analysis for water balance components (e.g. low/mean/high flow, soil moisture and evapotranspiration) is performed, to rank the most influential parameters affecting the water balance components and to subsequently decrease the number of calibratable parameters, thus decreasing dimensionality and uncertainty in the calibration process. Secondly, the reduced parameter set is put through a multi-objective evolutionary algorithm (Borg MOEA, www.borgmoea.org), to capture the tradeoffs between the water balance component performance objectives. The tradeoffs between the water balance component objective functions and in-situ validation data are examined, including evaluation of performance in: a) Climate zones, b) Seasons, c) Wet and dry periods, and d) Trend reproduction. This comprehensive evaluation was undertaken to choose a model parameterization (or set thereof) which produces reasonable hydrological responses under future climate variability across the water balance. The outcome is a suite of parameter sets with improved performance across varying and non-stationary climate conditions. We propose this approach to improve confidence in hydrological models used to simulate future impacts of climate change.</p>
A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty
