scholarly journals Global distribution of the rooting zone water storage capacity reflects plant adaptation to the environment

2021 ◽  
Author(s):  
Benjamin D. Stocker ◽  
Shersingh Joseph Tumber-Dávila ◽  
Alexandra G. Konings ◽  
Martha B. Anderson ◽  
Christopher Hain ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Storage Capacity ◽  
Water Storage ◽  
Spatial Variations ◽  
Rooting Depth ◽  
Water Storage Capacity ◽  
Rooting Zone ◽  
Vegetation Activity ◽  
Zone Water

AbstractThe rooting zone water storage capacity (S0) extends from the soil surface to the weathered bedrock (the Critical Zone) and determines land-atmosphere exchange during dry periods. Despite its importance to land-surface modeling, variations of S0 across space are largely unknown as they cannot be observed directly. We developed a method to diagnose global variations of S0 from the relationship between vegetation activity (measured by sun-induced fluorescence and by the evaporative fraction) and the cumulative water deficit (CWD). We then show that spatial variations in S0 can be predicted from the assumption that plants are adapted to sustain CWD extremes occurring with a return period that is related to the life form of dominant plants and the large-scale topographical setting. Predicted biome-level S0 distributions, translated to an apparent rooting depth (zr) by accounting for soil texture, are consistent with observations from a comprehensive zr dataset. Large spatial variations in S0 across the globe reflect adaptation of zr to the hydroclimate and topography and implies large heterogeneity in the sensitivity of vegetation activity to drought. The magnitude of S0 inferred for most of the Earth’s vegetated regions and particularly for those with a large seasonality in their hydroclimate indicates an important role for plant access to water stored at depth - beyond the soil layers commonly considered in land-surface models.

scholarly journals Testing an optimality-based model of rooting zone water storage capacity in temperate forests

2018 ◽  
Vol 22 (7) ◽  
pp. 4097-4124 ◽  
Author(s):  
Matthias J. R. Speich ◽  
Heike Lischke ◽  
Massimiliano Zappa
Keyword(s):  
Water Balance ◽  
Storage Capacity ◽  
Water Storage ◽  
Water Balance Model ◽  
Balance Model ◽  
Rooting Depth ◽  
Water Storage Capacity ◽  
Rooting Zone ◽  
Local Water ◽  
Zone Water

Abstract. Rooting zone water storage capacity Sr is a crucial parameter for modeling hydrology, ecosystem gas exchange and vegetation dynamics. Despite its importance, this parameter is still poorly constrained and subject to high uncertainty. We tested the analytical, optimality-based model of effective rooting depth proposed by Guswa (2008, 2010) with regard to its applicability for parameterizing Sr in temperate forests. The model assumes that plants dimension their rooting systems to maximize net carbon gain. Results from this model were compared against values obtained by calibrating a local water balance model against latent heat flux and soil moisture observations from 15 eddy covariance sites. Then, the effect of optimality-based Sr estimates on the performance of local water balance predictions was assessed during model validation. The agreement between calibrated and optimality-based Sr varied greatly across climates and forest types. At a majority of cold and temperate sites, the Sr estimates were similar for both methods, and the water balance model performed equally well when parameterized with calibrated and with optimality-based Sr. At spruce-dominated sites, optimality-based Sr were much larger than calibrated values. However, this did not affect the performance of the water balance model. On the other hand, at the Mediterranean sites considered in this study, optimality-based Sr were consistently much smaller than calibrated values. The same was the case at pine-dominated sites on sandy soils. Accordingly, performance of the water balance model was much worse at these sites when optimality-based Sr were used. This rooting depth parameterization might be used in dynamic (eco)hydrological models under cold and temperate conditions, either to estimate Sr without calibration or as a model component. This could greatly increase the reliability of transient climate-impact assessment studies. On the other hand, the results from this study do not warrant the application of this model to Mediterranean climates or on very coarse soils. While the cause of these mismatches cannot be determined with certainty, it is possible that trees under these conditions follow rooting strategies that differ from the carbon budget optimization assumed by the model.

scholarly journals Testing an optimality-based model of rooting zone water storage capacity in temperate forests

2017 ◽  
Author(s):  
Matthias J. R. Speich ◽  
Heike Lischke ◽  
Massimiliano Zappa
Keyword(s):  
Soil Moisture ◽  
Storage Capacity ◽  
Water Storage ◽  
Balance Model ◽  
Rooting Depth ◽  
Water Storage Capacity ◽  
Rooting Zone ◽  
Local Water ◽  
Zone Water ◽  
Good Agreement

Abstract. Rooting zone water storage capacity Sr is a crucial parameter in models of hydrology, ecosystem gas exchange and vegetation dynamics. Despite its importance, this parameter is still poorly constrained and subject to high uncertainty. We tested the analytical, optimality-based model of effective rooting depth proposed by Guswa (2010) with regard to its applicability for parameterizing Sr in temperate forests. The model assumes that plants dimension their rooting systems in order to maximize net carbon gain. Results from this model were compared against values obtained by calibrating a local water balance model against latent heat flux and soil moisture observations from 15 eddy covariance sites. To increase the applicability of the rooting depth model, we provide a numerical approximation of its underlying probabilistic soil moisture model. The calibration and validation of the local water balance model show that the concept of a single rooting zone storage capacity was appropriate at most temperate and cold sites, but not at Mediterranean sites and for very coarse soils. At a majority of sites, the estimates of Sr are generally in good agreement. However, mismatches were found in stands dominated by Norway spruce, especially at high elevations. These mismatches were attributed to the fact that the model does not consider rooting depth limitations due to oxygen stress and low soil temperature. Also, it is not clear whether the rooting behavior of pines on coarse soils is captured properly. Nevertheless, the overall good agreement suggests that this model may be useful for generating estimates of rooting zone storage capacity for both hydrological and ecological applications. Another potential use is the dynamic parameterization of the rooting zone in process-based models, which greatly increases the reliability of transient climate-impact assessment studies.

scholarly journals Derivation of the Spatial Distribution of Free Water Storage Capacity Based on Topographic Index

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1407
Author(s):  
Bingxing Tong ◽  
Zhijia Li ◽  
Cheng Yao ◽  
Jingfeng Wang ◽  
Yingchun Huang
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Land Surface ◽  
Storage Capacity ◽  
Water Storage ◽  
Free Water ◽  
Flood Events ◽  
Runoff Generation ◽  
Topographic Index ◽  
Water Storage Capacity

Free water storage capacity, an important characteristic of land surface related to runoff process, has a significant influence on runoff generation and separation. It is thus necessary to derive reasonable spatial distribution of free water storage capacity for rainfall-runoff simulation, especially in distributed modeling. In this paper, a topographic index based approach is proposed for the derivation of free water storage capacity spatial distribution. The topographic index, which can be obtained from digital elevation model (DEM), are used to establish a functional relationship with free water storage capacity in the proposed approach. In this case, the spatial variability of free water storage capacity can be directly estimated from the characteristics of watershed topography. This approach was tested at two medium sized watersheds, including Changhua and Chenhe, with the drainage areas of 905 km2 and 1395 km2, respectively. The results show that locations with larger values of free water storage capacity generally correspond to locations with higher topographic index values, such as riparian region. The estimated spatial distribution of free water storage capacity is also used in a distributed, grid-based Xinanjiang model to simulate 10 flood events for Chenhe Watershed and 17 flood events for Changhua Watershed. Our analysis indicates that the proposed approach based on topographic index can produce reasonable spatial variability of free water storage capacity and is more suitable for flood simulation.

scholarly journals Plants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climates

2020 ◽  
Vol 15 (10) ◽  
pp. 104074
Author(s):  
David N Dralle ◽  
W Jesse Hahm ◽  
Daniella M Rempe ◽  
Nathaniel Karst ◽  
Leander D L Anderegg ◽  
...  
Keyword(s):  
Storage Capacity ◽  
Root Zone ◽  
Water Storage ◽  
Vegetation Response ◽  
Water Storage Capacity ◽  
Zone Water

scholarly journals Technical note: Accounting for snow in the estimation of root-zone water storage capacity from precipitation and evapotranspiration fluxes

2020 ◽  
Author(s):  
David N. Dralle ◽  
W. Jesse Hahm ◽  
K. Dana Chadwick ◽  
Erica McCormick ◽  
Daniella M. Rempe
Keyword(s):  
Sierra Nevada ◽  
Hydrological Modeling ◽  
Storage Capacity ◽  
Root Zone ◽  
Water Storage ◽  
Original Method ◽  
Technical Note ◽  
Simple Extension ◽  
Water Storage Capacity ◽  
Zone Water

Abstract. A common parameter in hydrological modeling frameworks is root-zone water storage capacity (SR[L]), which mediates plant-water availability during dry periods and 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 more conservative minimum estimate of SR in snow-dominated watersheds. This SR dataset is available at 1 km resolution for the continental United States, along with the full analysis code, on Google Colaboratory 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; improved estimates of SR will therefore better clarify the future role of the subsurface as a storage reservoir that can sustain forests during seasonal dry periods and episodic drought.

scholarly journals Technical note: Accounting for snow in the estimation of root zone water storage capacity from precipitation and evapotranspiration fluxes

2021 ◽  
Vol 25 (5) ◽  
pp. 2861-2867
Author(s):  
David N. Dralle ◽  
W. Jesse Hahm ◽  
K. Dana Chadwick ◽  
Erica McCormick ◽  
Daniella M. Rempe
Keyword(s):  
Sierra Nevada ◽  
Hydrological Modeling ◽  
Storage Capacity ◽  
Root Zone ◽  
Water Storage ◽  
Original Method ◽  
Technical Note ◽  
Simple Extension ◽  
Water Storage Capacity ◽  
Zone Water

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.

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