a School of Civil, Environmental and Mining Engineering, University of Adelaide, SA, Australia b Hydrology and Atmospheric Sciences, University of Arizona, Arizona, USAFenner School of Environment, Australian National University, Canberra, SA, Australia d College of Science and Engineering, Flinders University, SA, Australia e Bren School of Environmental Science & Management, UC Santa Barbara, CA, USA Virtual Hydrological Laboratories: Developing the next generation of conceptual models to support decision-making under change Mark Thyer (a), Hoshin Gupta (b), Seth Westra(a), David McInerney(a), Holger Maier(a), Dmitri Kavetski(a), Tony Jakeman(c), Barry Croke(c), Craig Simmons(d), Margaret Shanafield(d) , Daniel Partington(d), Christina Tague(e) Lead Author Email: mark.thyer@adelaide.edu.au Presenting Author Email: Tony.Jakeman@anu.edu.au 1. Conceptual models commonly used in practice, but may provide poor predictions of future hydrological change J4. Hydrologic non-stationarity and extrapolating models to predict the future • Conceptual models (see Figure 1) are the most common type of surface hydrological model used for decision-making (Peel and McMahon, 2020) • Due to suitable predictive performance, ease of use and computational speed which facilitates scenario/sensitivity/uncertainty analysis • They have significant limitations in their ability to capture the wide range of hydrological changes (see Figure 2) (Partington et al, 2022, Fowler, et al, 2022) Figure 1: ‘End-Member’ types on the spectrum of hydrological models 2. Developing next generation of conceputal models: Hydrological Lines of Evidence (HLE) Figure 2: Range of Drivers of Hydrological Change Robustness in terms of (1) Predictive Ability: accurate predictions of observations (2) Hydrological Fidelity: Capture key processes under change 3. Emerging HLE: Virtual Hydrological Laboratory 4. Evaluation of hydrological lines of evidence to provide conceptual models robust to future change Key Question(s): Can the HLE evaluate for “robustness” for 1. Catchments of practically relevant size (100’s to 10,000’s km2) 2. Catchments with diverse range of different properties 3. Wide range of process/predictions (not just streamflow, but also ET, soil moisture etc) 4. Using real observed data 5. Robustness to “Significant” Change – see Table 1 (below) for different approaches to evaluating change 5. Unique Strengths of Virtual Hydrological Laboratory to Accelerate Development of Conceptual Models 1. Conceptual model development informed by wide range of processes In a virtual catchment all hydrological components can be observed (albeit virtually), and conceptual models can be subjected to a higher level of scrutiny than current observational datasets allow. 2. Enables the use of controlled hydrological change experiments Ability to systematically change climate and land cover/use characteristics provides opportunity to undertake hydrological change experiments and pro-actively evaluate CMs on a wide range of future hydrological change scenarios that are outside the envelope of observations. 3. Integrate Multiple Hydrological Lines of Evidence A VHL provides the opportunity to integrate multiple lines of evidence (Figure 4) thereby overcoming their respective strengths and weaknesses to build the next generation of conceptual models 6. Call to Action Predictive skill in hydrology is confronted by rapid and multi-faceted change. Our current suite of conceptual models have limited ability to deal with change. New thinking will be needed as hydrological systems are being pushed outside the envelope of historical experience “Virtual Hydrological Laboratories” provide a unique opportunity to 1. Undertake “controlled experiments” to evaluate robustness to future change 2. Complement (and integrate) existing “Hydrological Lines of Evidence” 3. Lead to new generation of conceptual models that are (i) Robust to change (ii) Embody best science (iii) Workhorse for practical decisio