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 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 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 Characterizing the Water Storage Capacity and Hydrological Role of Mountain Peatlands in the Arid Andes of North-Central Chile

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1071 ◽  
Author(s):  
Remi Valois ◽  
Nicole Schaffer ◽  
Ronny Figueroa ◽  
Antonio Maldonado ◽  
Eduardo Yáñez ◽  
...  
Keyword(s):  
High Altitude ◽  
Storage Capacity ◽  
Water Storage ◽  
Catchment Scale ◽  
North Central ◽  
Central Chile ◽  
Water Storage Capacity ◽  
Order Of Magnitude ◽  
Local Water

High-altitude peatlands in the Andes, i.e., bofedales, play an essential role in alpine ecosystems, regulating the local water balance and supporting biodiversity. This is particularly true in semiarid Chile, where bofedales develop near the altitudinal and hydrological limits of plant life. The subterranean geometry and stratigraphy of one peatland was characterized in north-central Chile using Electrical Resistivity Tomography (ERT), Ground Penetrating Radar (GPR) and core extraction. Two sounding locations, two transversal and one longitudinal profile allowed a 3D interpretation of the bofedal’s internal structure. A conceptual model of the current bofedal system is proposed. Geophysical results combined with porosity measurements were used to estimate the bofedal water storage capacity. Using hydrological data at the watershed scale, implications regarding the hydrological role of bofedales in the semiarid Andes were then briefly assessed. At the catchment scale, bofedal water storage capacity, evapotranspiration losses and annual streamflow are on the same order of magnitude. High-altitude peatlands are therefore storing a significant amount of water and their impact on basin hydrology should be investigated further.

scholarly journals Evaluating the Drought Code Using In Situ Drying Timelags of Feathermoss Duff in Interior Alaska

Fire ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 25
Author(s):  
Eric Miller ◽  
Brenda Wilmore
Keyword(s):  
Water Balance ◽  
Evaporation Rate ◽  
Storage Capacity ◽  
Water Storage ◽  
Water Balance Model ◽  
Balance Model ◽  
Potential Evaporation ◽  
Water Storage Capacity ◽  
Interior Alaska

The Drought Code (DC) is a moisture code of the Canadian Forest Fire Weather Index System underlain by a hydrological water balance model in which drying occurs in a negative exponential pattern with a relatively long timelag. The model derives from measurements from an evaporimeter and no soil parameters are specified, leaving its physical nature uncertain. One way to approximate the attributes of a “DC equivalent soil” is to compare its drying timelag with measurements of known soils. In situ measurements of timelag were made over the course of a fire season in a black spruce-feathermoss forest floor underlain by permafrost in Interior Alaska, USA. On a seasonally averaged basis, timelag was 28 d. The corresponding timelag of the DC water balance model was 60 d. Water storage capacity in a whole duff column 200 mm deep was 31 mm. Using these figures and a relationship between timelag, water storage capacity, and the potential evaporation rate, a “DC equivalent soil” was determined to be capable of storing 66 mm of water. This amount of water would require a soil 366 mm deep, suggesting a revision of the way fire managers in Alaska regard the correspondence between soil and the moisture codes of the FWI. Nearly half of the soil depth would be mineral rather than organic. Much of the soil water necessary to maintain a 60 d timelag characteristic of a “DC equivalent soil” is frozen until after the solstice. Unavailability of frozen water, coupled with a June peak in the potential evaporation rate, appears to shorten in situ timelags early in the season.

scholarly journals Diagnosis toward predicting mean annual runoff in ungauged basins

2021 ◽  
Vol 25 (2) ◽  
pp. 945-956
Author(s):  
Yuan Gao ◽  
Lili Yao ◽  
Ni-Bin Chang ◽  
Dingbao Wang
Keyword(s):  
Water Balance ◽  
Soil Water ◽  
Storage Capacity ◽  
Water Storage ◽  
Annual Runoff ◽  
Balance Model ◽  
Soil Water Storage ◽  
Ungauged Basins ◽  
Water Storage Capacity ◽  
Soil Water Storage Capacity

Abstract. Prediction of mean annual runoff is of great interest but still poses a challenge in ungauged basins. The present work diagnoses the prediction in mean annual runoff affected by the uncertainty in estimated distribution of soil water storage capacity. Based on a distribution function, a water balance model for estimating mean annual runoff is developed, in which the effects of climate variability and the distribution of soil water storage capacity are explicitly represented. As such, the two parameters in the model have explicit physical meanings, and relationships between the parameters and controlling factors on mean annual runoff are established. The estimated parameters from the existing data of watershed characteristics are applied to 35 watersheds. The results showed that the model could capture 88.2 % of the actual mean annual runoff on average across the study watersheds, indicating that the proposed new water balance model is promising for estimating mean annual runoff in ungauged watersheds. The underestimation of mean annual runoff is mainly caused by the underestimation of the area percentage of low soil water storage capacity due to neglecting the effect of land surface and bedrock topography. Higher spatial variability of soil water storage capacity estimated through the height above the nearest drainage (HAND) and topographic wetness index (TWI) indicated that topography plays a crucial role in determining the actual soil water storage capacity. The performance of mean annual runoff prediction in ungauged basins can be improved by employing better estimation of soil water storage capacity including the effects of soil, topography, and bedrock. It leads to better diagnosis of the data requirement for predicting mean annual runoff in ungauged basins based on a newly developed process-based model finally.

scholarly journals Diagnosis toward predicting mean annual runoff in ungauged basins

2020 ◽  
Author(s):  
Yuan Gao ◽  
Lili Yao ◽  
Ni-Bin Chang ◽  
Dingbao Wang
Keyword(s):  
Soil Water ◽  
Storage Capacity ◽  
Water Storage ◽  
Annual Runoff ◽  
Balance Model ◽  
Soil Water Storage ◽  
Ungauged Basins ◽  
Water Storage Capacity ◽  
Soil Water Storage Capacity ◽  
Soil Storage Capacity

Abstract. The present work diagnoses the prediction in mean annual runoff affected by the uncertainty in estimated distribution of soil water storage capacity. Based on a distribution function, a water balance model for estimating mean annual runoff is developed, in which the effects of climate variability and the distribution of soil water storage capacity are explicitly represented. As such, the two parameters in the model have explicit physical meanings, and relationships between the parameters and controlling factors on mean annual runoff are established. The estimated parameters from the existing data of watershed characteristics are applied to 35 watersheds. The results showed that the model could capture 88.2 % of the actual runoff on average, indicating that the proposed new water balance model is promising for estimating mean annual runoff in ungauged watersheds. The underestimation of runoff is mainly caused by the underestimation of the spatial heterogeneity of soil storage capacity due to neglecting the effect of land surface and bedrock topography. A higher spatial variability of soil storage capacity estimated through the Height Above the Nearest Drainage (HAND) indicated that topography plays a crucial role in determining the actual soil water storage capacity. The performance of mean annual runoff prediction in ungauged basins can be improved by employing better estimation of soil water storage capacity including the effects of soil, topography and bedrock. The purpose of this study is to diagnose the data requirement for predicting mean annual runoff in ungauged basins based on a newly developed process-based model.

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.

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