Climate-analogy mapping as a tool to develop a temporally-adaptive hydrological model of the Meuse basin for more reliable predictions under change

Abstract

The future hydrological response of a river system is often predicted using hydrological models that are calibrated on past observations. By using static model parameters it is assumed that hydrological systems do not change in time. For long-term predictions, this is in clear contrast with the notion that vegetation actively adapts its root-system. Moreover, it neglects the possible impact that climate change might have on vegetation species and land-use. The root-zone storage capacity (S_r) is a key component in hydrological models as it has a critical function in the partitioning of water fluxes. This research (1) proposes a regression relationship using climate analogy to estimate the time-dynamic root-zone storage capacity for the Meuse basin, and (2) quantifies the impact of this time-dynamic root-zone storage capacity on the change in hydrological response under future climate change.

A selection of catchments from large sample data sets have been used in the analysis. For each of these catchments we calculated 27 catchment descriptors and the S_r using the water balance method. The regression relationship that is developed based on climate analogy and Multi-Linear Regression analysis showed that the S_r value can be estimated based on 4 catchment descriptors, namely Holdridge Aridity Index, phase shift of precipitation, seasonal amplitude for the potential evaporation, and sand fraction. This regression relationship has an adjusted R² value of approximately 0.70.

We considered three model scenarios. Each scenario consists of a part that models the historical hydrological response which is forced using the simulated historical climate data, and a part that models the future hydrological response which is forced using the simulated 2K climate data. The differences between these model parts indicate how the hydrological response of a system will change in the future. The benchmark scenarios use a static S_r value which is estimated for the simulated historical climate data using different methods. We considered the benchmark scenario for the water balance method and for the regression relationship. The third model scenario is called the dynamic regression scenario. For this scenario the regression relationship is used to estimate the S_r of the simulated historical and simulated 2K climate data. These values are then used for the historical and future part of the model, respectively.

Comparing the benchmark scenarios shows that the regression relationship only has a minimal impact on the change in hydrological response. The impact of the time-dynamic S_r on the change in hydrological response of the system is quantified by comparing the benchmark scenario for the regression relationship with the dynamic regression scenario. The comparison indicates that a time-dynamic S_r results in an increase of absolute evaporation during the summer months (+6.6%), while at the same time resulting in lower values for the streamflow (-8.6%) and groundwater storage (-4.8%) during the winter months. The root-zone storage capacity shows an increase throughout the whole year (maximum +23.6%). In other words, the time-dynamic root-zone storage capacity has a significant impact on the seasonality of the change in the hydrological response. The results also show that the regression relationship is promising and suggests that it might be a good way to estimate the root-zone storage capacity for climate projections in temperate climates.