Skills in the representation of the propagation of the meteorological droughts through the eco-hydrological system by a land surface model across two Mediterranean catchments

Mediterranean mountainous basins provide critical water supply and ecosystem services, yet these environments are increasingly at risk due to anthropogenic stressors and competition for water across urban, agricultural and environmental demands. On the top of this, future climate projections suggest a drier and warmer Mediterranean with large increases in the frequency, duration, and severity of hydrological droughts with serious consequences for the management of water resources and natural ecosystems. So due to its vulnerability, it is crucial that land surface models (LSMs) correctly characterise these phenomena, as a first step to the provision of reliable projections of future water availability across the Mediterranean region.

As hydrological systems are intrinsically intertwined with climatological and ecological systems, the propagation of meteorological droughts (i.e., precipitation below than normal and higher temperatures) through them is modulated by a variety of mechanisms which are linked to carbon and water cycle interactions and specifically to how well LSMs represent evaporation fluxes and water storage.

The aim of this study is to analyse how Noah-MP LSM represents agricultural and hydrological droughts and in particular the propagation of the precipitation and evaporative demand anomalies to the soil moisture and streamflow anomalies in two typically and eco-hydrologically different Mediterranean catchments in the Upper Tiber River in Central Italy.

The analysis is carried out with the NASA Land Information System’s Noah-Multi Parameterization (Noah-MP) model configured with four soil layers with the layer thicknesses, varying from 0.1, 0.3, 0.6, and 1 m, from the surface and using a simple groundwater reservoir beneath the soil layer allowing for soil moisture–groundwater interaction and related runoff production. Noah-MP allows for the prognostic representation of vegetation growth in combination with a Ball–Berry photosynthesis-based stomatal resistance. The LAI is calculated from leaf carbon mass by multiplying by the specific leaf area. The model is validated with ground-based soil moisture and streamflow observations as well as with remote sensing-based products of evaporation, vegetation and soil moisture.

Results show a sub-optimal representation of runoff (magnitude) and LAI (with phase shift and magnitude) especially during some important drought events that have hit the region in 2012 and in 2022 (specifically over the more mountainous catchment) suggesting that improvements could be obtained from a better model parameterization of the vegetation and runoff schemes via calibration and assimilation techniques.