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Abstract The rooting-zone water-storage capacity—the amount of water accessible to plants—controls the sensitivity of land–atmosphere exchange of water and carbon during dry periods. How the rooting-zone water-storage capacity varies spatially is largely unknown and not directly observable. Here we estimate rooting-zone water-storage capacity globally from the relationship between remotely sensed vegetation activity, measured by combining evapotranspiration, sun-induced fluorescence and radiation estimates, and the cumulative water deficit calculated from daily time series of precipitation and evapotranspiration. Our findings indicate plant-available water stores that exceed the storage capacity of 2-m-deep soils across 37% of Earth’s vegetated surface. We find that biome-level variations of rooting-zone water-storage capacities correlate with observed rooting-zone depth distributions and reflect the influence of hydroclimate, as measured by the magnitude of annual cumulative water-deficit extremes. Smaller-scale variations are linked to topography and land use. Our findings document large spatial variations in the effective root-zone water-storage capacity and illustrate a tight link among the climatology of water deficits, rooting depth of vegetation and its sensitivity to water stress.
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Plants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climates
Abstract In Mediterranean-type climates, asynchronicity between energy and water availability means that ecosystems rely heavily on the water-storing capacity of the subsurface to sustain plant water use over the summer dry season. The root-zone water storage capacity ( S m a x [L]) defines the maximum volume of water that can be stored in plant accessible locations in the subsurface, but is poorly characterized and difficult to measure at large scales. Here, we develop an ecohydrological modeling framework to describe how S m a x mediates root zone water storage ( S [L]), and thus dry season plant water use. The model reveals that where S m a x is high relative to mean annual rainfall, S is not fully replenished in all years, and root-zone water storage and therefore plant water use are sensitive to annual rainfall. Conversely, where S m a x is low, S is replenished in most years but can be depleted rapidly between storm events, increasing plant sensitivity to rainfall patterns at the end of the wet season. In contrast to both the high and low S m a x cases, landscapes with intermediate S m a x values are predicted to minimize variability in dry season evapotranspiration. These diverse plant behaviors enable a mapping between time variations in precipitation, evapotranspiration and S m a x , which makes it possible to estimate S m a x using remotely sensed vegetation data − that is, using plants as sensors. We test the model using observations of S m a x in soils and weathered bedrock at two sites in the Northern California Coast Ranges. Accurate model performance at these sites, which exhibit strongly contrasting weathering profiles, demonstrates the method is robust across diverse plant communities, and modes of storage and runoff generation.
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- Award ID(s):
- 1331940
- PAR ID:
- 10401627
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 15
- Issue:
- 10
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- 104074
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract. A common parameter in hydrological modeling frameworks is root zone water storage capacity (SR[L]), which mediates plant water availability during dry periods as well as the partitioning of rainfall between runoff and evapotranspiration. Recently, a simple flux-tracking-based approach was introduced to estimate the value of SR (Wang-Erlandsson et al., 2016). Here, we build upon this original method, which we argue may overestimate SR in snow-dominated catchments due to snow melt and evaporation processes. We propose a simple extension to the method presented by Wang-Erlandsson et al. (2016) and show that the approach provides a lower estimate of SR in snow-dominated watersheds. This SR dataset is available at a 1 km resolution for the continental USA, along with the full analysis code, on the Google Colab and Earth Engine platforms. We highlight differences between the original and new methods across the rain–snow transition in the Southern Sierra Nevada, California, USA. As climate warms and precipitation increasingly arrives as rain instead of snow, the subsurface may be an increasingly important reservoir for storing plant-available water between wet and dry seasons; therefore, improved estimates of SR will better clarify the future role of the subsurface as a storage reservoir that can sustain forests during seasonal dry periods and episodic drought.more » « less
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In forested, seasonally dry watersheds, winter rains commonly replenish moisture deficits in the vadose zone before recharging underlying hillslope groundwater systems that sustain streamflow. However, the relative inaccessibility of the subsurface has hindered efforts to include the role of storage deficits, primarily generated by plant-water uptake, in moderating groundwater recharge. Here, we compare groundwater recharge inferred from the storage-discharge relationship with independent, distributed estimates of vadose zone storage deficits across 12 undisturbed California watersheds, thereby tracking the evolution of the deficit-recharge relationship without intensive field instrumentation. We find accrued deficits during the dry season alone insufficiently explain differences in the wet season partitioning of rainfall due to the non-monotonic behavior of the deficit during the subsequent wet season. Tracking the deficit at the storm event-scale within the wet season, however, reveals a characteristic response in groundwater to increasing rainfall not captured in the seasonal analysis, and may improve estimates of the rainfall required to generate recharge and streamflow on a per-storm basis. Our findings demonstrate the potential for existing public datasets to better capture water partitioning within the subsurface using a combined deficit-recharge approach, though our analysis is currently limited to basins with select characteristics. CODE AVAILABLE ON GITHUB: https://github.com/noah-beniteznelson/recharge_deficitmore » « less
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Abstract Quantifying evapotranspiration (ET) is critical to accurately predict vegetation health, groundwater recharge, and streamflow generation. Hillslope aspect, the direction a hillslope faces, results in variable incoming solar radiation and subsequent vegetation water use that drive ET. Previous work in watersheds with a single dominant vegetation type (e.g., trees) have shown that equator‐facing slopes (EFS) have higher ET compared to pole‐facing slopes (PFS) due to higher evaporative demand. However, it remains unclear how differences in vegetation type (i.e., grasses and trees) influence ET and water partitioning between hillslopes with opposing aspects. Here, we quantified ET and root‐zone water storage deficits between a PFS and EFS with contrasting vegetation types within central coastal California. Our results suggest that the cooler PFS with oak trees has higher ET than the warmer EFS with grasses, which is counter to previous work in landscapes with a singule dominant vegetation type. Our root‐zone water storage deficit calculations indicate that the PFS has a higher subsurface storage deficit and a larger seasonal dry down than the EFS. This aspect difference in subsurface water storage deficits may influence the subsequent replenishment of dynamic water storage, groundwater recharge and streamflow generation. In addition, larger subsurface water deficits on PFS may reduce their ability to serve as hydrologic refugia for oaks during multi‐year droughts. This research provides a novel integration of field‐based and remotely‐sensed estimates of ET required to properly quantify hillslope‐scale water balances. These findings emphasize the importance of resolving hillslope‐scale vegetation structure within Earth system models, especially in landscapes with diverse vegetation types.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1088/1748-9326/abb10b
