Rivers in Earth System Modelling: CaMa-Flood supporting land surface model development at the km-scale

Global streamflow modelling is crucial, as it underlies our capacity to forecast riverine floods able to devastate infrastructure and ecosystems, and adversely affect human lives. To that end, the hydrodynamic Catchment-based Macro-scale Floodplain model (CaMa-Flood) has been included in ECMWF’s Land Surface Modelling System (ecLand), and consequently in the Integrated Forecasting System (IFS). Precipitation, which is partitioned into infiltration and runoff by ecLand’s land surface processes, is eventually converted into streamflow through CaMa-Flood. This means streamflow carries an imprint of both meteorology and land surface processes. This is particularly relevant, as runoff is not available as an observation, while streamflow is, making the latter a key variable for the aggregated evaluation of modelled land surface processes. As this analysis is done under the auspices of the Destination Earth project, it presents the additional possibility to evaluate land surface processes across all operational spatial scales up to the km-scale and temporal scales from daily to hourly. For example, through running daily CaMa-Flood simulations driven with runoff forcing from the control ensemble member and from the Continuous-Extremes Digital Twin (C-EDT), the land surface’s hydrological processes can be evaluated at the spatial resolutions of ~9km and ~4.4km, respectively. Comparing results from these daily simulations with streamflow observations, we found that the C-EDT generates insufficient surface runoff in orographic regions. This stems from the sub-grid runoff parameterization in ecLand, which generates less surface runoff at higher resolutions for the same amount of precipitation, and is therefore not scale-adaptive.

Beyond providing hydrological simulations, CaMa-Flood is used in this study as a diagnostic tool for hydrological processes to guide future development of ecLand. More specifically, we have implemented scale-specific orographic parameters in the model’s runoff-generating algorithm, aiming to provide consistent orographic surface runoff generation across spatial scales. Runoff partitioning is important for flood extremes on timescales of a few days, because it directly modulates the magnitude of the flood peak. In addition, it affects the soil moisture, and consequently sub-surface runoff and streamflow on timescales of months to years. Therefore, the efficacy of these adaptations is tested with both long-term land surface experiments with ecLand/CaMa-Flood and fully-coupled 5-day meteorological forecasts with IFS/CaMa-Flood at spatial resolutions of ~29km, ~9km and ~4.4km. For the forecasts on shorter time scales, we assess the flood peak magnitude, timing and durations errors. For the long-term integrations from 1990 – 2025, streamflow time series allow a robust evaluation of the simulations against the observations, and its components as a measure of goodness-of-fit. Moving beyond the KGE, a cross-spectral analysis is applied to evaluate the time signature of the hydrological processes undelrying streamflow and occurring at different time scales, which is especially useful considering the partitioning between surface (fast) and sub-surface (slow) runoff. Through addressing these scale-dependent issues, ample surface runoff generation is ensured, allowing the river hydrology simulated by CaMa-Flood to benefit fully from running meteorology and the land surface at the km-scale.