The importance of ecosystem adaptation on hydrological model predictions in response to climate change

Abstract

Abstract. To predict future hydrological behavior in a changing world, often use is made of models calibrated on past observations, disregarding that hydrological systems, hence model parameters, will change as well. Yet, ecosystems likely adjust their root-zone storage capacity, which is the key parameter of any hydrological system, in response to climate change. In addition, other species might become dominant, both under natural and anthropogenic influence. In this study, we propose a top-down approach, which directly uses projected climate data to estimate how vegetation adapts its root-zone storage capacity at the catchment scale in response to changes in magnitude and seasonality of hydro-climatic variables. Additionally, the Budyko characteristics of different dominant ecosystems in sub-catchments are used to simulate the hydrological behavior of potential future land-use change, in a space-for-time exchange. We hypothesize that changes in the predicted hydrological response as a result of 2 K global warming are more pronounced when explicitly considering changes in the sub-surface system properties induced by vegetation adaptation to changing environmental conditions. We test our hypothesis in the Meuse basin in four scenarios designed to predict the hydrological response to 2 K global warming in comparison to current-day conditions using a process-based hydrological model with (a) a stationary system, i.e. no changes in the root-zone storage capacity of vegetation and historical land use, (b) an adapted root-zone storage capacity in response to a changing climate but with historical land use, and (c, d) an adapted root-zone storage capacity considering two hypothetical changes in land use from coniferous plantations/agriculture towards broadleaved forest and vice-versa. We found that the larger root-zone storage capacities (+34 %) in response to a more pronounced seasonality with drier summers under 2 K global warming strongly alter seasonal patterns of the hydrological response, with an overall increase in mean annual evaporation (+4 %), a decrease in recharge (−6 %) and a decrease in streamflow (−7 %), compared to predictions with a stationary system. By integrating a time-dynamic representation of changing vegetation properties in hydrological models, we make a potential step towards more reliable hydrological predictions under change.

Multicriteria analysis on rock moisture and streamflow in a rainfall‐runoff model improves accuracy of model results

“…Rakovec, Kumar, Mai, et al (2016) found ‘that constraining the model against streamflow only may be necessary but not sufficient to warrant the model fidelity for other complementary variables’. This so‐called equifinality problem is undesirable when models are used to make predictions under scenarios in which baseline drivers have shifted (non‐stationary behaviour, due to, for example, climate change or land use, as detailed by Bouaziz et al (2021)), and when accurate inferences of internal parameters or state variables are important (Anderton et al, 2002; Bouaziz et al, 2020; Heppner et al, 2007; Hrachowitz & Clark, 2017). Multiple criteria analysis, or MCA, has emerged as a useful tool which can help to reduce parameter uncertainty in hydrologic models and potentially improve accuracy of multiple model results.…”