Quantifying and modeling water availability in temperate forests: a review of drought and aridity indices

Abstract

Biogeosciences and Forestry Biogeosciences and Forestry Quantifying and modeling water availability in temperate forests: a review of drought and aridity indices Matthias JR Speich (1-2-3) Climatic water availability is a major determinant of forest structure and composition, while drought events may severely impact forest dynamics. In recent decades, an increasing number of severe drought events has been reported in forests around the world. In the future, climate models project increasingly dry conditions in many temperate regions. Various tools have been applied to better understand the effects of drought on forests, such as dendrochronological analyses, climatic trend analyses and dynamic models. With these approaches, water availability is often summarized as a single scalar, termed a drought or aridity index. As droughts are complex phenomena, such indices are always associated with a loss of information. Many different such indices exist, and have been developed for various purposes. This review asks whether some of these indices are more suitable than others to quantify water availability in temperate forests. In a first step, the rationale and theoretical background of different drought indices are spelled out and compared among each other. Then, evaluations and intercomparisons of drought indices from the literature are reviewed. The implementation of drought indices in dynamic forest models is also discussed. Finally, two current research questions are identified: the role of dry air for physiological drought, and the suitability of various drought indices under climate change. It appears from this review that indices accounting for evaporative demand generally perform better than indices based on precipitation alone. When comparing sites with different edaphic conditions, indices accounting for soil moisture storage are more suitable. Nevertheless, results from intercomparisons show considerable divergence, and it is not possible to clearly favor one index. Furthermore, a differential response of tree species to different drought indices is often observed, although no clear pattern emerges from this comparison. More intercomparisons of indices, across climates and species, might provide valuable knowledge. Another key finding is that the properties of indices also depend on choices regarding, e.g., the calculation of evaporative demand, or the underlying water balance model. Reporting such methodological details could greatly increase the value of future evaluations of drought indices.

Advancing simulations of water fluxes, soil moisture and drought stress by using the LWF-Brook90 hydrological model in R

“…Cumulative droughts and extreme drought events severely affected forest functioning in Central Europe over 2018 and 2019 (Buras et al, 2019;Trotsiuk et al, 2020), and the intensity of such drought events is expected to increase in the coming decades (Teuling, 2018). SVAT and similar models in particular allow for the quantification of cumulative droughts that cannot be determined from bare soil and climatic data, such as the site water balance (Speich, 2019). In order to develop mitigation and adaptation strategies, we therefore need to be able to upscale these models for assessing the impacts of climate change on forest functions at a larger scale, for which the presented LWFBrook90R package provides convenient solutions.…”

“…At the national to regional scale, SVAT models have been connected to climate projections, soil grids and forest inventory data, to determine recent and future trends of water availability and drought stress in climate impact assessment studies (De Cáceres et al, 2015;Ruffault et al, 2014Ruffault et al, , 2013Schmidt-Walter et al, 2019;Schwärzel et al, 2009;Thiele et al, 2017), providing information on future ecosystem services, forest growth conditions, and species suitability to design and implement mitigation and adaptation strategies (Lindner et al, 2010). At the local scale, SVAT models are applied to derive drought indicators (for an overview see Speich, 2019) to understand vitality patterns and growth depressions of forests (Granier et al, 2007;Lebourgeois et al, 2005;Manrique-Alba et al, 2017;Michelot et al, 2012), tree mortality (Allen et al, 2010;Seidl et al, 2017), to estimate crop water requirements (Fischer et al, 2018), and to explain secondary droughtrelated impacts on forests, such as insect infestations (Netherer et al, 2019). Apart from such explanatory applications, SVAT models also offer estimates of water balance components that are difficult to obtain from field measurements (e.g.…”

On the impact of increasing drought on the relationship between soil water content and evapotranspiration of a grassland

“…To characterize the occurrence of water stress, we used the ratio between ET a and ET crop as the water stress index (WSI) (Eden, 2012; Speich, 2019). Water stress index values <1 reflect conditions when the plant can no longer transpire optimally and reduces its transpiration rate.…”

Climate and Aridity Measures Relationships with Spectral Vegetation Indices across Desert Fringe Shrublands in the South-Eastern Mediterranean Basin

“…Under current global climate change trends, extensive semi-arid and desert fringe ecosystems experience declining water resources and increasing atmospheric temperatures (e.g., Tuel and Eltahir, 2020;Pugnaire, et al, 2019;Polade et al, 2017;Maestre et al, 2012;Blondel et al, 2010;Giorgi and Lionello, 2008;Mariotti, 2008;Aussenac, 2002). Since its introduced by De Martonne (1926), the Aridity Index (AI) with its Thornthwaite (Huschke,1959) , Budyko (1974) and UNEP (Barrow, 1992;Greve et al, 2019) versions is widely used to monitor the spread of aridity across these ecosystems (e.g., Pellicone, 2019;Speich, 2019;Traore et al, 2020;Derdous et al, 2021;;and Zomer, 2022). Other aridity and dryness indicators were calculated based on the difference between precipitation and potential evapotranspiration with Bussay et al, (2012) and Vincente-Serrano et al, (2012) relating to it as Water Deficit (WD) while Stephenson (1990), Saha et al, (2020), Kuang-Yu et al (2018), Abatzoglou et al, (2018),…”