Ahmed Elkouk

Climate change is expected to alter floods in complex ways. Understanding changes in flood frequency and driving processes under changing climatic conditions is urgently needed to develop sustainable adaptation measures to floods in rivers such as the Swiss section of the Rhône, which has been hit by a severe flood in June 2024. The investigation of future flood characteristics and driving processes is, however, challenging because these events exhibit large interannual variability and are, among other processes, caused by localized intense precipitation, which is not optimally represented in global climate model simulations. High-resolution initial condition large ensembles and convection-resolving climate simulations can be used to address these challenges. This work leverages these two types of climate ensembles and process-based hydrologic modeling to quantify long-term changes in peak flows in the Swiss section of the Rhône until the end of the century, and their uncertainty. We will use these simulations to investigate the driving processes underlying future floods and how these differ from those relevant for flood generation today. In doing so, we provide information crucial for decision-making related to future flood protection and adaptation.

Enhanced drought modeling is crucial for realistic prediction and effective management of water resources, especially with climate change anticipated to exacerbate drought frequency and severity. Global water models (GWMs) simulate historical and future terrestrial water storage (TWS) with continuous spatial and temporal coverage. However, a global evaluation of TWS simulations by GWMs focused on drought is lacking. Here we evaluate, for the first time, GWMs' capability to represent TWS droughts by comparing simulations with Gravity Recovery and Climate Experiment satellite data. We find notable underestimation of drought severity and coverage by GWMs, across diverse regions, including North America, South America, Africa, and Northern Asia. When examined without trend removal, the underestimation of TWS droughts is more pronounced in recent years (2016–2019) compared to 2002–2015, especially in northern latitudes. This underrepresentation highlights the necessity to improve GWMs to simulate TWS droughts. Our results imply that previously reported future TWS projections could have underestimated droughts.

Persistent droughts and anthropogenic warming have brought the southwestern United States—one of the world's most highly managed hydrologic regions—to the brink of a water supply crisis. Here, we examine the changes in hydrologic fluxes due to a shift toward aridity and the extent to which water storage in major reservoirs has offset these changes using a novel, high‐resolution (4 km) configuration of a state‐of‐the‐science land model, the Community Land Model. We leverage an emulation‐based parameter optimization approach to improve model hydrologic performance and reservoir parametrizations (generic and data‐driven) to simulate water storage and release. The hydrologic performance is notably improved across 121 basins in the Southwest, which translates into improved simulation of inflow, storage, and release across 133 reservoirs. The data‐driven approach outperforms the generic one, in simulating reservoir storage but not releases due to biases in inflows. Model simulations show a widespread increase in evapotranspiration (especially its soil evaporation component), driven primarily by increased absorption of solar radiation due to snowpack loss, which suppresses runoff. Increases in snowmelt mitigate runoff deficits during spring, which highlights the sensitivity of runoff response to the seasonal distribution of hydroclimatic supply and demand in the Southwest. Up to 40% of inflow deficits are met by surface water storage in large reservoirs (i.e., Mead and Powell). Simulated estimates of this supply‐demand imbalance are sensitive to biases in simulated inflows and releases. Caution is therefore warranted when using land models with reservoir parametrization for future drought risk and water scarcity assessments.

Crucial to the assessment of future water security is how the land model component of Earth System Models partition precipitation into evapotranspiration and runoff, and the sensitivity of this partitioning to climate. This sensitivity is not explicitly constrained in land models nor the model parameters important for this sensitivity identified. Here, we seek to understand parametric controls on runoff sensitivity to precipitation and temperature in a state‐of‐the‐science land model, the Community Land Model version 5 (CLM5). Process‐parameter interactions underlying these two climate sensitivities are investigated using the sophisticated variance‐based sensitivity analysis. This analysis focuses on three snow‐dominated basins in the Colorado River headwaters region, a prominent exemplar where land models display a wide disparity in runoff sensitivities. Runoff sensitivities are dominated by indirect or interaction effects between a few parameters of subsurface, snow, and plant processes. A focus on only one kind of parameters would therefore limit the ability to constrain the others. Surface runoff exhibits strong sensitivity to parameters of snow and subsurface processes. Constraining snow simulations would require explicit representation of the spatial variability across large elevation gradients. Subsurface runoff and soil evaporation exhibit very similar sensitivities. Model calibration against the subsurface runoff flux would therefore constrain soil evaporation. The push toward a mechanistic treatment of processes in CLM5 have dampened the sensitivity of parameters compared to earlier model versions. A focus on the sensitive parameters and processes identified here can help characterize and reduce uncertainty in water resource sensitivity to climate change.

Understanding how land surface models (LSMs) partition precipitation into evapotranspiration and runoff under changing climate is key to improved future hydrologic predictions. This sensitivity is rarely tuned in land models, as evidenced by prevalent biases in the sensitivity of simulated runoff to precipitation and temperature change compared to observational estimates. Here, using the Community Land Model (CLM5) over the Colorado River basin (CRB), we investigate what the informative model parameters for runoff sensitivities are and how their choices affect the sensitivities under changing temperature and precipitation. We focus on the headwater region of the CRB, motivated by inconsistent model estimates of runoff sensitivities in the region and the critical need to better understand runoff changes to address the ongoing water crises in the CRB. In each headwater basin, a set of informative parameters were identified through parameter perturbations using “one at a time” method within an adaptive surrogate-based model optimization scheme (ASMO). Results of perturbations highlight that different parameter sets with similar performance (with respect to water-year discharge) provide very different runoff sensitivities to temperature and precipitation during the 1951-2010 period. Additionally, both precipitation and temperature sensitivities of runoff show sensitivity to similar parameters across the region. The most sensitive parameters control the conductance-photosynthesis relationship, soil surface resistance for direct evaporation, the partitioning of runoff into the surface and the subsurface component, and soil hydraulic properties. We show how the importance of each parameter varies through the parameter space and derive parameter estimates by maximizing the “fit to observed sensitivities” within the ASMO scheme. Our results provide key insights regarding parameters optimization to improve long-term hydrologic sensitivities in LSMs.