Contribution of deep soil layers to the transpiration of a temperate deciduous forest: Implications for the modelling of productivity

Maysonnave, Jean; Delpierre, Nicolas; François, Christophe; Jourdan, Marion; Cornut, Ivan; Bazot, Stéphane; Vincent, Gaëlle; Morfin, Alexandre; Berveiller, Daniel

doi:10.1016/j.scitotenv.2022.155981

Cited by 9 publications

(5 citation statements)

References 66 publications

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“…Data obtained by automatic imaging tools in crops revealed diurnal patterns as well, with dynamics differing between monocot and dicot crops (Kronenberg et al, 2021; Nagelmüller et al, 2018; Yates et al, 2019). Under field conditions, it remains mostly unknown if and when plants have access to soil moisture deep in the rooted profile (e.g., Maysonnave et al, 2022). Therefore, it is difficult to delineate the local, moisture‐driven seasonal plant activity by meteorological thresholds.…”

Section: A Meteorological Definition Of Seasonmentioning

confidence: 99%

Four ways to define the growing season

2023

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What is addressed as growing season in terrestrial ecosystems is one of the main determinants of annual plant biomass production globally. However, there is no well‐defined concept behind. Here, we show different facets of what might be termed growing season, each with a distinct meaning: (1) the time period during which a plant or a part of it actually grows and produces new tissue, irrespective of net carbon gain (growing season sensu stricto). (2) The period defined by developmental, that is, phenological markers (phenological season). (3) The period during which vegetation as a whole achieves its annual net primary production (NPP) or a net ecosystem production (NEP), expressed as net carbon gain (productive season) and (4) the period during which plants could potentially grow based on meteorological criteria (meteorological season). We hypothesize that the duration of such a ‘window of opportunity’ is a strong predictor for NPP at a global scale, especially for forests. These different definitions have implications for the understanding and modelling of plant growth and biomass production. The common view that variation in phenology is a proxy for variation in productivity is misleading, often resulting in unfounded statements on potential consequences of climatic warming such as carbon sequestration.

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Section: A Meteorological Definition Of Seasonmentioning

confidence: 99%

Four ways to define the growing season

2023

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“…A more complex root distribution than the exponential one assumed in this study and related temporally varying transpiration water losses from different depths adds for complexity that is not captured by the SMAR model. Maysonnave et al (2022), for instance, found that root water uptake in forests can vary with time and depth depending on the water availability in different layers. These features can neither be reproduced by the original nor modified SMAR and makes forest sites generally challenging, in particular for simplified models.…”

Section: Discussionmentioning

confidence: 99%

“…Deep roots play a significant role for the water supply of plant ecosystems especially during dry conditions (Canadell et al, 1996) i.e. through hydraulic redistribution (see e.g., Neumann and Cardon, 2012) or increased root water uptake from deeper soil layers under drought conditions (Maysonnave et al, 2022). Furthermore, plant species influence infiltration and vertical soil moisture patterns through species dependent root distributions (e.g.…”

Section: Introductionmentioning

confidence: 99%

Depth-extrapolation of field-scale soil moisture time series derived with cosmic-ray neutron sensing using the SMAR model

Rasche,

Blume,

Güntner

2024

Preprint

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Abstract. Soil moisture measurements at the field scale are highly beneficial for different hydrological applications including the validation of space-borne soil moisture products, landscape water budgeting or multi-criteria calibration of rainfall-runoff models from field to catchment scale. Many of these applications require information on soil water dynamics in deeper soil layers. Cosmic-ray neutron sensing (CRNS) allows for non-invasive monitoring of field-scale soil moisture across several hectares around the instrument but only for the first few tens of centimeters of the soil. Simple depth-extrapolation approaches often used in remote sensing applications may be used to estimate soil moisture in deeper layers based on the near-surface soil moisture information. However, most approaches require a site-specific calibration using depth-profiles of in-situ soil moisture data, which are often not available. The physically-based soil moisture analytical relationship SMAR is usually also calibrated to sensor data, but could be applied without calibration if all its parameters were known. However, in particular its water loss parameter is difficult to estimate. In this paper, we introduce and test a simple modification of the SMAR model to estimate the water loss in the second layer based on soil physical parameters and the surface soil moisture time series. We apply the model at a forest site with sandy soils with and without calibration. Comparing the model results against in-situ reference measurements down to depths of 450 cm shows that the SMAR models both with and without modification do not capture the observed soil moisture dynamics well. The performance of the SMAR models nevertheless meets a previously used benchmark RMSE of ≤ 0.06 cm3 cm−3 in both, calibrated and uncalibrated scenarios. Only with effective parameters in a non-physical range, a better model performance could be achieved. Different transfer functions to derive surface soil moisture from CRNS do not translate into markedly different results of the depth-extrapolated soil moisture time series simulated with SMAR. However, a more accurate estimation of the sensitive measurement depth of the CRNS improved the soil moisture estimates in the second layer. Despite the fact that the soil moisture dynamics are not well represented at our study site using physically reasonable parameters, the modified SMAR model may provide valuable first estimates of soil moisture in a deeper soil layer derived from surface measurements based on stationary and roving CRNS as well as remote sensing products where in-situ data for calibration are not available.

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“…Woody plant use of water stored beneath soils in weathered bedrock has been documented as early as the beginning of the 20th century (Cannon, 1911). 'Rock moisture', or water derived from the unsaturated weathered bedrock layer, is now understood to be an essential plant water reservoir, particularly in seasonally dry regions where it sustains transpiration later into the dry season (e.g., Schwinning, 2010;Rempe and Dietrich, 2018;Rose, 2003;McCormick et al, 2021;Hahm et al, 2022Hahm et al, , 2020Ruiz et al, 2010;Maysonnave et al, 2022). It has been difficult to incorporate deeper water storage into dynamic vegetation models and Earth system models because weathered bedrock storage capacity has been historically challenging to quantify except at intensively monitored study sites.…”

Section: Introductionmentioning

confidence: 99%

Inclusion of bedrock vadose zone in dynamic global vegetation models is key for simulating vegetation structure and functioning

Lapides,

Hahm,

Forrest

et al. 2023

Preprint

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Abstract. Across many upland environments, soils are thin and plant roots extend into fractured and weathered bedrock where moisture and nutrients can be obtained. Root water extraction from unsaturated weathered bedrock is widespread and, in many environments, can explain gradients in vegetation community composition, transpiration, and plant sensitivity to climate. Despite increasing recognition of its importance, the "rock moisture" reservoir is rarely incorporated into vegetation and Earth system models. Here, we address this weakness in a widely used dynamic global vegetation model (DGVM, LPJ-GUESS). First, we use a water flux-tracking deficit approach to more accurately parameterize plant-accessible water storage capacity across the contiguous United States, which critically includes the water in bedrock below depths typically prescribed by soils databases. Secondly, we exploit field-based knowledge of contrasting plant-available water storage capacity in weathered bedrock across two bedrock types in the Northern California Coast Ranges as a detailed case-study. For the case study in Northern California, climate and soil water storage capacity are similar at the two study areas, but the site with thick weathered bedrock and ample rock moisture supports a mixed evergreen temperate broadleaf-needleleaf forest whereas the site with thin weathered bedrock and limited rock moisture supports an oak savanna. The distinct biomes, seasonality and magnitude of transpiration and primary productivity, and baseflow magnitudes only emerge from the DGVM when a new and simple subsurface storage structure and hydrology scheme is parameterized with storage capacities extending beyond the soil into the bedrock. Across the contiguous United States, the updated hydrology and subsurface storage improve annual evapotranspiration estimates as compared to satellite-derived products, particularly in seasonally dry regions. Specifically, the updated hydrology and subsurface storage allow for enhanced evapotranspiration through the dry season that better matches actual evapotranspiration patterns. While we made changes to both the subsurface water storage capacity and the hydrology, the most important impacts on model performance derive from changes to the subsurface water storage capacity. Our findings highlight the importance of rock moisture in explaining and predicting vegetation structure and function, particularly in seasonally dry climates. These findings motivate efforts to better incorporate the rock moisture reservoir into vegetation, climate, and landscape evolution models.

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Contribution of deep soil layers to the transpiration of a temperate deciduous forest: Implications for the modelling of productivity

Cited by 9 publications

References 66 publications

Four ways to define the growing season

Four ways to define the growing season

Depth-extrapolation of field-scale soil moisture time series derived with cosmic-ray neutron sensing using the SMAR model

Inclusion of bedrock vadose zone in dynamic global vegetation models is key for simulating vegetation structure and functioning

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