Abstract
Abstract. The root zone storage capacity (Sr) is the maximum volume of water in the subsurface that can potentially be accessed by vegetation for transpiration. It influences the seasonality of transpiration as well as fast and slow runoff processes. Many studies have shown that Sr is heterogeneous as controlled by local climate conditions, which affect vegetation strategies in sizing their root system able to support plant growth and to prevent water shortages. Root zone parameterization in most land surface models does not account for this climate control on root development and is based on lookup tables that prescribe the same root zone parameters worldwide for each vegetation class. These lookup tables are obtained from measurements of rooting structure that are scarce and hardly representative of the ecosystem scale. The objective of this research is to quantify and evaluate the effects of a climate-controlled representation of Sr on the water fluxes modeled by the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model. Climate-controlled Sr is estimated here with the “memory method” (MM) in which Sr is derived from the vegetation's memory of past root zone water storage deficits. Sr,MM is estimated for 15 river catchments over Australia across three contrasting climate regions: tropical, temperate and Mediterranean. Suitable representations of Sr,MM are implemented in an improved version of HTESSEL (Moisture Depth – MD) by accordingly modifying the soil depths to obtain a model Sr,MD that matches Sr,MM in the 15 catchments. In the control version of HTESSEL (CTR), Sr,CTR is larger than Sr,MM in 14 out of 15 catchments. Furthermore, the variability among the individual catchments of Sr,MM (117–722 mm) is considerably larger than of Sr,CTR (491–725 mm). The climate-controlled representation of Sr in the MD version results in a significant and consistent improvement of the modeled monthly seasonal climatology (1975–2010) and interannual anomalies of river discharge compared with observations. However, the effects on biases in long-term annual mean river discharge are small and mixed. The modeled monthly seasonal climatology of the catchment discharge improved in MD compared to CTR: the correlation with observations increased significantly from 0.84 to 0.90 in tropical catchments, from 0.74 to 0.86 in temperate catchments and from 0.86 to 0.96 in Mediterranean catchments. Correspondingly, the correlations of the interannual discharge anomalies improve significantly in MD from 0.74 to 0.78 in tropical catchments, from 0.80 to 0.85 in temperate catchments and from 0.71 to 0.79 in Mediterranean catchments. The results indicate that the use of climate-controlled Sr,MM can significantly improve the timing of modeled discharge and, by extension, also evaporation fluxes in land surface models. On the other hand, the method has not been shown to significantly reduce long-term climatological model biases over the catchments considered for this study.
Highlights
Vegetation controls the partitioning of precipitation into evaporation and runoff by transporting water through its roots to the atmosphere and is thereby key in the representation of land surface–atmosphere interactions (Milly, 1994; Seneviratne et al, 2010)
(2016), who found similar ranges of storage capacity (Sr) over Australia by using gridded products of Sr based on rooting depths from observations and optimized inverse modeling, and they found similar ranges of global Sr,memory method” (MM) estimated based on satellite evaporation products
We looked at the HTESSEL land surface model and found that the root zone storage capacity Sr is only a function of soil texture and soil depth, the latter being kept constant over the modeled global domain, while from the state-of-the-art literature (e.g., Collins and Bras, 2007; Guswa, 2008; Gentine et al, 2012; Gao et al, 2014) it is indicated that Sr is, to a large extent, climate-controlled
Summary
Vegetation controls the partitioning of precipitation into evaporation and runoff by transporting water through its roots to the atmosphere and is thereby key in the representation of land surface–atmosphere interactions (Milly, 1994; Seneviratne et al, 2010). The moisture flow from the land surface to the atmosphere through vegetation root water uptake is defined as transpiration and is globally the largest water flux from terrestrial ecosystems (Schlesinger and Jasechko, 2014). The contribution of transpiration to total land evaporation is regulated by the interplay between the atmospheric water demand and the soil moisture within the reach of vegetation’s roots. The parameterization of the root zone in state-of-the-art land surface models (LSMs) is a possible cause for the large uncertainties in water flux representations in these models (Gharari et al, 2019), which is true for land evaporation simulations (Pitman, 2003; Seneviratne et al, 2006; Wartenburger et al, 2018)
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