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

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

10.5194/hess-2020-602 ◽

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

Environmental Research Letters ◽

10.1088/1748-9326/abb10b ◽

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

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A hydrological model for root zone water storage simulation on a global scale

10.5194/gmd-2019-52 ◽

2019 ◽

Author(s):

Ganquan Mao ◽

Junguo Liu

Keyword(s):

Soil Water ◽

Hydrological Model ◽

Storage Capacity ◽

Root Zone ◽

Water Storage ◽

Global Scale ◽

Distribution Model ◽

Lumped Model ◽

Zone Water ◽

Strong Capacity

Abstract. The soil water stored in the root zone is a critical variable for many applications as it plays key role in several hydrological and atmospheric processes. Many studies have been done to obtain reliable soil water information in the root zone layer. However, most of them are mainly focused on the soil moisture in a certain depth rather than the water stored in the entire rooting system. In this work, a hydrological model is developed to simulate the root zone water storage (RZWS) on a global scale. The model is based on a well validated lumped model and has been extended now to a distribution model. To reflect the natural spatial heterogeneity of the plant rooting system across the world, a key variable that influencing the RZWS, i.e. root zone storage capacity (RZSC), is integrated into the model. The newly developed model is evaluated on runoff and RZWS simulation across ten major basins. The evaluation of runoff indicates the strong capacity of the model for monthly simulation with a good performance on time series and distribution depiction. Results also show the ability of the model for RZWS dynamics mimicing in most of the regions. This model may offer benefits for many applications due to its ability for RZWS simulation. However, attentions need to also be paid for application as the high latitude regions are not investigated by this work due to the incomplete latitudinal coverage of the RZSC. Therefore, the performance of the model in such regions are not justified.

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

10.1101/2021.09.17.460332 ◽

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.

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

Hydrology and Earth System Sciences ◽

10.5194/hess-22-4097-2018 ◽

2018 ◽

Vol 22 (7) ◽

pp. 4097-4124 ◽

Cited By ~ 3

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.

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

10.5194/hess-2017-723 ◽

2017 ◽

Cited By ~ 2

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.

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Identification of hydrological model parameters variation using ensemble Kalman filter

10.5194/hess-2015-407 ◽

2016 ◽

Cited By ~ 1

Author(s):

Chao Deng ◽

Pan Liu ◽

Shenglian Guo ◽

Zejun Li ◽

Dingbao Wang

Keyword(s):

Kalman Filter ◽

Temporal Variation ◽

Ensemble Kalman Filter ◽

Water Conservation ◽

Hydrological Modeling ◽

Hydrological Model ◽

Storage Capacity ◽

Water Storage ◽

Model Parameters ◽

Water Storage Capacity

Abstract. Hydrological model parameters play an important role in the ability of model prediction. In a stationary content, parameters of hydrological models are treated as constants. However, model parameters may vary dynamically with time under climate change and human activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model by assimilating the runoff observations, where one of state equations is that the model parameters should not change much within a short time period. Through a synthetic experiment, the proposed method is evaluated with various types of parameter variations including trend, abrupt change, and periodicity. The application of the method to the Wudinghe basin shows that the water storage capacity, a parameter in the model, has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of water storage capacity is explained by land use and land cover changes due to soil and water conservation measurements. Whereas, the application to the Tongtianhe basin demonstrates that the parameter of water storage capacity has no significant variation during the simulation of 1982–2013, corresponding to the relatively stationary catchment characteristics. Additionally, the proposed method improves the performance of hydrological modeling, and provides an effective tool for quantifying temporal variation of model parameters.

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