Abstract: Vertically resolved soil moisture improves the understanding of large-scale vegetation productivity. 11 Extended water-related control on vegetation productivity emerges when considering multi-layer soil moisture versus total soil moisture. Sub-surface soil moisture is particularly important for vegetation productivity in semiarid regions.
“…There are, of course, exceptions where certain plants have adapted to frequent water stress by extending roots into far deeper soil layers and approaching underground aquifers (Fan et al., 2017; Gao et al., 2014). Nonetheless, for most ecosystems, more than 75% of vegetation roots are located in the top 50 cm of the soil column (Zeng, 2001), and soil moisture availability above 30 cm is most responsible for vegetation dynamics (Li et al., 2021). Therefore, θ v is expected to be representative of the general vegetation root water stress conditions.…”
Section: Methodsmentioning
confidence: 99%
“…However, plant roots can reach to soil water storages at several tens of centimeters and for certain limited species, to tens of meters (Fan et al., 2017; Gao et al., 2014; Yang et al., 2016). Therefore, evapotranspiration can draw water from soil layers that are considerably deeper than the θ s sampling depth (Buitink et al., 2020; Green et al., 2019; Humphrey et al., 2021; Li et al., 2021). Based on this line of reasoning, the expectation is that θ s is likely biased in its representation of evapotranspiration regime transitions or overall land surface energy partitioning (Hirschi et al., 2014; Mueller & Seneviratne, 2012).…”
The transition of evapotranspiration between energy‐ and water‐limitation regimes also denotes a nonlinear change in surface water and energy coupling strength. The regime transitions are primarily dominated by available moisture in the soil, although other micro‐meteorological factors also play a role. Remotely sensed soil moisture is frequently used for detecting evapotranspiration regime transitions during inter storm dry downs. However, its sampling depth does not include the entire soil profile, over which water uptake is dominated by plant root distribution. We use flux tower, surface (θs; observations at 5 cm), and vertically integrated in situ soil moisture (θv ${\theta }_{v}$; 0–50 cm) observations to address the question: Can surface soil moisture robustly identify evapotranspiration regime transitions? Results demonstrate that θs and θv are hydraulically linked and have synchronized evapotranspiration regime transitions. As such, θs and θv capture comparable statistics of evapotranspiration regime prevalence, which supports the utility of remote‐sensing θs for large‐scale land‐atmosphere exchange analysis.
“…There are, of course, exceptions where certain plants have adapted to frequent water stress by extending roots into far deeper soil layers and approaching underground aquifers (Fan et al., 2017; Gao et al., 2014). Nonetheless, for most ecosystems, more than 75% of vegetation roots are located in the top 50 cm of the soil column (Zeng, 2001), and soil moisture availability above 30 cm is most responsible for vegetation dynamics (Li et al., 2021). Therefore, θ v is expected to be representative of the general vegetation root water stress conditions.…”
Section: Methodsmentioning
confidence: 99%
“…However, plant roots can reach to soil water storages at several tens of centimeters and for certain limited species, to tens of meters (Fan et al., 2017; Gao et al., 2014; Yang et al., 2016). Therefore, evapotranspiration can draw water from soil layers that are considerably deeper than the θ s sampling depth (Buitink et al., 2020; Green et al., 2019; Humphrey et al., 2021; Li et al., 2021). Based on this line of reasoning, the expectation is that θ s is likely biased in its representation of evapotranspiration regime transitions or overall land surface energy partitioning (Hirschi et al., 2014; Mueller & Seneviratne, 2012).…”
The transition of evapotranspiration between energy‐ and water‐limitation regimes also denotes a nonlinear change in surface water and energy coupling strength. The regime transitions are primarily dominated by available moisture in the soil, although other micro‐meteorological factors also play a role. Remotely sensed soil moisture is frequently used for detecting evapotranspiration regime transitions during inter storm dry downs. However, its sampling depth does not include the entire soil profile, over which water uptake is dominated by plant root distribution. We use flux tower, surface (θs; observations at 5 cm), and vertically integrated in situ soil moisture (θv ${\theta }_{v}$; 0–50 cm) observations to address the question: Can surface soil moisture robustly identify evapotranspiration regime transitions? Results demonstrate that θs and θv are hydraulically linked and have synchronized evapotranspiration regime transitions. As such, θs and θv capture comparable statistics of evapotranspiration regime prevalence, which supports the utility of remote‐sensing θs for large‐scale land‐atmosphere exchange analysis.
“…The latter is critical for mitigating climate change by absorbing human-emitted CO 2 4 . Vegetation requires sufficient energy and nutrients, and also soil moisture availability is essential, particularly in semi-arid regions 5 , 6 . As a result of ongoing climate change, soil moisture is declining in many regions as a consequence of decreased precipitation and higher evaporative water demand due to increased temperatures 7 .…”
Section: Introductionmentioning
confidence: 99%
“…In fact, leaf area index (LAI) products and other vegetation indices related to vegetation greenness and productivity can represent long-term global vegetation growth dynamics 8 , 16 – 19 . They are routinely employed to study land-atmosphere interactions as they are sensitive to soil moisture dynamics, and can diagnose temporal sensitivity to environmental drivers thanks to their relatively higher signal-to-noise ratio than photosynthesis-related indicators such as sun-induced fluorescence 5 . Furthermore, LAI is readily available as a key prognostic variable from land surface models (LSMs) 20 .…”
Section: Introductionmentioning
confidence: 99%
“…Furthermore, LAI is readily available as a key prognostic variable from land surface models (LSMs) 20 . From a modeling perspective, a more accurate representation of LAI response to soil water stress requires the differentiation between soil layers, as near-surface soil moisture primarily controls soil evaporation and precipitation infiltration and co-varies more with atmospheric conditions 21 , while sub-surface soil moisture is a more relevant plant water source 5 , 22 . State-of-the-art soil moisture reanalyses cover multiple layers and allow for comprehensive analyses of the vegetation-water interplay by benefiting from satellite-observed surface soil moisture and in-situ multi-depth soil moisture measurements 23 , 24 .…”
Global vegetation and associated ecosystem services critically depend on soil moisture availability which has decreased in many regions during the last three decades. While spatial patterns of vegetation sensitivity to global soil water have been recently investigated, long-term changes in vegetation sensitivity to soil water availability are still unclear. Here we assess global vegetation sensitivity to soil moisture during 1982-2017 by applying explainable machine learning with observation-based leaf area index (LAI) and hydro-climate anomaly data. We show that LAI sensitivity to soil moisture significantly increases in many semi-arid and arid regions. LAI sensitivity trends are associated with multiple hydro-climate and ecological variables, and strongest increasing trends occur in the most water-sensitive regions which additionally experience declining precipitation. State-of-the-art land surface models do not reproduce this increasing sensitivity as they misrepresent water-sensitive regions and sensitivity strength. Our sensitivity results imply an increasing ecosystem vulnerability to water availability which can lead to exacerbated reductions in vegetation carbon uptake under future intensified drought, consequently amplifying climate change.
Recent studies have highlighted the importance of understanding ecohydrological drought feedbacks to secure water resources under a changing climate and increasing anthropogenic impacts. In this study, we monitored and modelled feedbacks in the soil-plant-atmosphere continuum to the European drought summer 2018 and the following 2 years. The physically based, isotope-aided model EcH 2 O-iso was applied to generic vegetation plots (forest and grassland) in the lowland, groundwaterdominated research catchment Demnitzer Millcreek (NE Germany; 66 km 2 ). We included, inter alia, soil water isotope data in the model calibration and quantified changing "blue" (groundwater recharge) and "green" (evapotranspiration) water fluxes and ages under each land use as the drought progressed. Novel plant xylem isotope data were excluded from calibration but were compared with simulated root uptake signatures in model validation. Results indicated inter-site differences in the dynamics of soil water storage and fluxes with contrasting water age both during the drought and the subsequent 2 years. Forest vegetation consistently showed a greater moisture stress, more rapid recovery and higher variability in root water uptake depths from a generally younger soil water storage. In contrast, the grassland site, which had more water-retentive soils, showed higher and older soil water storage and groundwater recharge fluxes. The damped storage and flux dynamics under grassland led to a slower return to younger water ages at depth. Such evidencebased and quantitative differences in ecohydrological feedbacks to drought stress in contrasting soil-vegetation units provide important insights into Critical Zone water cycling. This can help inform future progress in the monitoring, modelling and development of climate mitigation strategies in drought-sensitive lowlands.
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