Capability of a regional climate model to simulate climate variables requested for water balance computation: a case study over northeastern France

Abstract

WRF ET 0 is in better agreement with observations . In order to evaluate WRF's capability to simulate a reliable ET 0 , the water balance of thirty Douglas-fir stands was computed using a process-based model. Three soil water deficit indexes corresponding to the sum of the daily deviations between the relative extractible water and a critical value of 40 % below which the low soil water content affects tree growth, were calculated using the nearest weather station, SAFRAN analyses weather data, or by merging observation and WRF weather variables. Correlations between Douglasfir growth and the three estimated soil water deficit indexes show similar results. These results showed through the ET 0 estimation and the relation between mean annual SWDI and Douglas-fir growth index that the main difficulties of the WRF model to simulate soil water deficit is mainly attributable to its precipitation biases. In contrast, the low discrepancies between WRF and observations for air temperature, wind speed, relative humidity and solar radiation make then usable for the water balance and ET 0 computation.

Frost stress evolution and winter pea ideotype in the context of climate warming at a regional scale

“…Note that for further studies it matches with the grid size of regional climate simulation (e.g. Boulard et al, 2015). For each grid the longitude, latitude and height from Digital Elevation Model (DEM) were used as the first order external drift predictors (Fig.…”

Bias correction of dynamically downscaled precipitation to compute soil water deficit for explaining year-to-year variation of tree growth over northeastern France

“…Quintana-Seguí et al (2008) and Vidal et al (2010) already assessed the quality of SAFRAN analyses over France and highlighted that the increasing number of ground observations improves its robustness over time, especially for 2-m air temperature and precipitation. Relative humidity, solar radiation and wind speed are the variables most affected by the scarcity of observations at the regional scale (Boulard et al, 2015). A detailed analysis of WRF climatic variables against observed or high-resolution reanalysed data (e.g., SAFRAN) was performed in Boulard et al (2015).…”

“…Biased representation of precipitation intensities and associated temporal and spatial variability often prevent RCM precipitation outputs to be directly used for climate change impact assessment (Fowler et al, 2007;Maraun et al, 2010). A previous analysis documenting the capability of the WRF model to regionalize near-surface atmospheric variables over Burgundy concluded on good skills for simulating the first four aforementioned variables (Boulard et al, 2015), but a clear tendency to over-estimate precipitation amounts, especially those of convective nature (Marteau et al, 2014).…”

“…These indices could be better correlated with radial growth than monthly climatic variables because soil water availability is a major limiting factor for tree growth (Michelot et al, 2012). Furthermore, using impact models as an additional source of information for evaluating climate models or post-correction methods can provide many benefits, but the use of impact indicators to test them is infrequent (Stéfanon et al, 2015;Boulard et al, 2015).…”

“…The model quantifies the intensity, the duration and the starting date of drought experienced by the stand. It has already been successfully applied to different stands (Bréda et al, 2006;Gandois et al, 2010;Van der Heijden et al, 2011;Michelot et al, 2012) and over parts of Burgundy (Sergent et al, 2012;Van der Heijden et al, 2013;Boulard et al, 2015). Requested climatic data to compute the water balance include 2-m air temperature and relative humidity, 2-m wind speed, solar radiation and precipitation.…”