scholarly journals Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

2018 ◽  
Vol 22 (6) ◽  
pp. 3229-3243 ◽  
Author(s):  
Maoya Bassiouni ◽  
Chad W. Higgins ◽  
Christopher J. Still ◽  
Stephen P. Good
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Dry Season ◽  
Soil Water Balance ◽  
Rainfall Pattern ◽  
Site Specific ◽  
Soil Moisture Dynamics ◽  
Soil Saturation ◽  
Moisture Dynamics

Abstract. Vegetation controls on soil moisture dynamics are challenging to measure and translate into scale- and site-specific ecohydrological parameters for simple soil water balance models. We hypothesize that empirical probability density functions (pdfs) of relative soil moisture or soil saturation encode sufficient information to determine these ecohydrological parameters. Further, these parameters can be estimated through inverse modeling of the analytical equation for soil saturation pdfs, derived from the commonly used stochastic soil water balance framework. We developed a generalizable Bayesian inference framework to estimate ecohydrological parameters consistent with empirical soil saturation pdfs derived from observations at point, footprint, and satellite scales. We applied the inference method to four sites with different land cover and climate assuming (i) an annual rainfall pattern and (ii) a wet season rainfall pattern with a dry season of negligible rainfall. The Nash–Sutcliffe efficiencies of the analytical model's fit to soil observations ranged from 0.89 to 0.99. The coefficient of variation of posterior parameter distributions ranged from < 1 to 15 %. The parameter identifiability was not significantly improved in the more complex seasonal model; however, small differences in parameter values indicate that the annual model may have absorbed dry season dynamics. Parameter estimates were most constrained for scales and locations at which soil water dynamics are more sensitive to the fitted ecohydrological parameters of interest. In these cases, model inversion converged more slowly but ultimately provided better goodness of fit and lower uncertainty. Results were robust using as few as 100 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series to estimate ecohydrological parameters from sparse records. Our work combines modeling and empirical approaches in ecohydrology and provides a simple framework to obtain scale- and site-specific analytical descriptions of soil moisture dynamics consistent with soil moisture observations.

Soil water balance and evapotranspiration of irrigated cotton

1967 ◽  
Vol 69 (1) ◽  
pp. 95-101 ◽  
Author(s):  
W. R. Stern
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Balance Equation ◽  
Dry Season ◽  
Soil Water Balance ◽  
Wet Season ◽  
Water Balance Equation ◽  
Soil Moisture Storage ◽  
Moisture Storage

In a series of five irrigated cotton sowings (T2, T7, T9, T11, T14) evapotranspiration (Et) was determined for the period between October 1961 and October 1962 by observing frequently the changes in soil moisture storage, calculating through drainage, and solving for evapotranspiration in the water balance equation. Thus a water balance was obtained for each sowing extending over the entire crop.The average evapotranspiration in wet season sowings was of the order of 6·5 mm day−1 and in dry season sowings of the order of 4·5 mm day−1. The highest evapotranspiration values ranged between 10 and 12 mm day−1 in T2, T7 and T9 and between 7 and 9·5 mm day−1 in T11 and T14.

scholarly journals Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

2017 ◽  
Author(s):  
Maoya Bassiouni ◽  
Chad W. Higgins ◽  
Christopher J. Still ◽  
Stephen P. Good
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Dynamics ◽  
Control Soil ◽  
Soil Water Dynamics ◽  
Water Losses ◽  
Near Surface ◽  
Soil Moisture Dynamics ◽  
Soil Saturation ◽  
Moisture Dynamics

Abstract. Ecohydrological parameters that describe vegetation controls on soil moisture dynamics are not easy to measure at hydrologically meaningful scales and site-specific values are rarely available. We hypothesize that sufficient information required to determine these ecohydrological parameters is encoded in empirical probability density functions (pdfs) of soil saturation, and that this information can be extracted through inverse modeling of the commonly used stochastic soil water balance. We developed a generalizable Bayesian inference approach to estimate soil saturation thresholds at which plants control soil water losses, based only on soil texture, rainfall and soil moisture data at point, footprint, and satellite scales. The optimal analytical soil saturation pdfs were statistically consistent with empirical pdfs and parameter uncertainties were on average under 10 %. Parameter estimates were most constrained for scales and locations at which soil water dynamics are more sensitive to the fitted ecohydrological parameters of interest. The algorithm convergence was most successful and the best goodness-of-fit statistics were obtained at the satellite scale. Robust and accurate results were obtained with as little as 75 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series. A sensitivity analysis showed that estimates of soil saturation thresholds at which plants control soil water losses were not sensitive to soil depth and near-surface observations are valuable to characterize ecohydrological factors driving soil water dynamics at ecosystem scales. This work combined modeling and empirical approaches in ecohydrology and provided a simple framework to obtain analytical descriptions of soil moisture dynamics at a range of spatial scales that are consistent with soil moisture observations.

scholarly journals Evaluation of DSSAT soil-water balance module under cropped and bare soil conditions

2003 ◽  
Vol 46 (4) ◽  
pp. 489-498 ◽  
Author(s):  
Rogério Teixeira de Faria ◽  
Walter Truman Bowen
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Water Flux ◽  
Soil Water Potential ◽  
Bare Soil ◽  
Root Water Uptake ◽  
Soil Water Balance ◽  
Soil Conditions ◽  
Soil Water Flux

The performance of the soil water balance module (SWBM) in the models of DSSAT v3.5 was evaluated against soil moisture data measured in bare soil and dry bean plots, in Paraná, southern Brazil. Under bare soil, the SWBM showed a low performance to simulate soil moisture profiles due to inadequacies of the method used to calculate unsaturated soil water flux. Improved estimates were achieved by modifying the SWBM with the use of Darcy's equation to simulate soil water flux as a function of soil water potential gradient between consecutive soil layers. When used to simulate water balance for the bean crop, the modified SWBM improved soil moisture estimation but underpredicted crop yield. Root water uptake data indicated that assumptions on the original method limited plant water extraction for the soil in the study area. This was corrected by replacing empirical coefficients with measured values of soil hydraulic conductivity at different depths.

scholarly journals Ecohydrologic controls on vegetation density and evapotranspiration partitioning across the climatic gradients of the central United States

2008 ◽  
Vol 5 (2) ◽  
pp. 649-700 ◽  
Author(s):  
J. P. Kochendorfer ◽  
J. A. Ramírez
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Water Use ◽  
Soil Surface ◽  
Soil Water Balance ◽  
Vegetation Density ◽  
Study Region ◽  
Central United States

Abstract. The soil-water balance and plant water use are investigated over a domain encompassing the central United States using the Statistical-Dynamical Ecohydrology Model (SDEM). The seasonality in the model and its use of the two-component Shuttleworth-Wallace canopy model allow for application of an ecological optimality hypothesis in which vegetation density, in the form of peak green leaf area index (LAI), is maximized, within upper and lower bounds, such that, in a typical season, soil moisture in the latter half of the growing season just reaches the point at which water stress is experienced. Another key feature of the SDEM is that it partitions evapotranspiration into transpiration, evaporation from canopy interception, and evaporation from the soil surface. That partitioning is significant for the soil-water balance because the dynamics of the three processes are very different. The partitioning and the model-determined peak in green LAI are validated based on observations in the literature, as well as through the calculation of water-use efficiencies with modeled transpiration and large-scale estimates of grassland productivity. Modeled-determined LAI are seen to be at least as accurate as the unaltered satellite-based observations on which they are based. Surprising little dependence on climate and vegetation type is found for the percentage of total evapotranspiration that is soil evaporation, with most of the variation across the study region attributable to soil texture and the resultant differences in vegetation density. While empirical evidence suggests that soil evaporation in the forested regions of the most humid part of the study region is somewhat overestimated, model results are in excellent agreement with observations from croplands and grasslands. The implication of model results for water-limited vegetation is that the higher (lower) soil moisture content in wetter (drier) climates is more-or-less completely offset by the greater (lesser) amount of energy available at the soil surface. This contrasts with other modeling studies which show a strong dependence of evapotranspiration partitioning on climate.

Groundwater recharge estimates combining soil isotope profiles and classical soil water monitoring techniques

2021 ◽  
Author(s):  
Nina Krüger ◽  
Christoph Külls ◽  
Marcel Kock
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Groundwater Recharge ◽  
Soil Water ◽  
Soil Profile ◽  
Tunable Diode Laser ◽  
Soil Water Balance ◽  
Advection Dispersion ◽  
Flow Processes ◽  
Percolation Rate

&lt;p&gt;To improve knowledge of hydrological and hydrogeological flow processes and their dependency on climate conditions it is becoming increasingly important to integrate sensors technology, independent observation methods, and new modeling techniques. Established isotope methods are usually regarded as a supplement and extension to classical hydrological investigation methods but are rarely included in soil water balance models. However, the combination could close knowledge gaps and thus lead to more precise and realistic predictions and therefore to better water management. Within the Wasserpfad project, a project of the Department of Civil Engineering at the TH L&amp;#252;beck, soil moisture has been measured since May 2018. SMT100 soil moisture sensors from TRUEBNER GmbH are used at depths of 20, 40, 60, and 80 cm. Next to the station a 2m deep soil profile was taken in 2020, to estimate groundwater recharge using stable isotope equilibration methods and cryogenic extraction combined with soil water balance modeling. Vertical profiles of stable isotopes have been determined with a 10-cm resolution and measured with Tunable Diode Laser spectrometry. Percolation through the soil profile has been estimated based on the convolution of a seasonal input function using advection-dispersion transport models. Percolation rate estimate based on environmental isotope profiles results in 230 mm per year. Fitting of the advection-dispersion equation using a sinusoidal isotope input fitted to available time series provides an estimate of 255 mm per year. This difference is due to the dispersion effect on the isotope minima and maxima. The result of modeling the soil moisture data with a soil water balance model integrating the Richards equation for water transport and Penmen-Monteith based calculation of actual evaporation is used to verify the percolation rates. The analysis of soil moisture and isotope data by modeling provides a direct and efficient way to estimate the percolation rate. The combination of isotope methods with classical hydrological measuring techniques offers the possibility to verify results, to calibrate models, or to investigate the limits of isotope methods. Thus, flow processes can be predicted more reliably in the future.&lt;/p&gt;

Estimation of Soil Water Balance Components Based on Continuous Soil Moisture Measurement and Inversed Richards Method in an Irrigated Agricultural Field of a Desert Oasis

2020 ◽  
Author(s):  
Hu Liu ◽  
Yang Yu ◽  
Zhongkai Li ◽  
Wenzhi Zhao ◽  
Qiyue Yang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Estimation Method ◽  
Growing Season ◽  
Soil Water Balance ◽  
Richards Equation ◽  
Water Balance Components ◽  
Sustainable Water Resources Management ◽  
Irrigation Drainage

&lt;p&gt;An accurate assessment of soil water balance components (&lt;em&gt;SWBCs&lt;/em&gt;) is necessary for improving irrigation strategies in any water-limited environment. However, quantitative information of &lt;em&gt;SWBCs&lt;/em&gt; is usually challenging to obtain, because none of the components (i.e., irrigation, drainage, and evapotranspiration) can be easily measured under actual conditions. Soil moisture is a variable that integrates the water balance components of land surface hydrology, and the evolution of soil moisture is assumed to contain the memory of antecedent hydrologic fluxes, and thus can be used to determine &lt;em&gt;SWBCs&lt;/em&gt; from a hydrologic balance. A database of&amp;#160;soil&amp;#160;moisture&amp;#160;measurements from six experimental plots with different treatments in the middle Heihe River Basin of China was used to test the potential of a soil moisture database in estimating the &lt;em&gt;SWBCs&lt;/em&gt;. We first compared the hydrophysical properties of the soils in these plots, such as vertical saturated hydraulic conductivity (&lt;em&gt;K&lt;/em&gt;&lt;sub&gt;s&lt;/sub&gt;) and soil water retention features, for supporting the &lt;em&gt;SWBC&lt;/em&gt; estimations. Then we determined evapotranspiration and other SWBCs through a method that combined the soil water balance method and the inverse Richards equation (a model of unsaturated soil water flow based on the Richards equation). To test the accuracy of our estimation, we used both indirect methods (such as power consumption of the pumping irrigation well, and published SWBCs values at nearby sites), and the water balance equation technique to verify the estimated &lt;em&gt;SWBCs&lt;/em&gt; values, all of which showed a good reliability of our estimation method. Finally, the uncertainties of the proposed methods were analyzed to evaluate the systematic error of the &lt;em&gt;SWBC&lt;/em&gt; estimation and any restrictions on its application. The results showed significant variances among the film-mulched plots in both the cumulative irrigation volumes (652.1~ 867.3 mm) and deep drainages (170.7~364.7 mm). Moreover, the unmulched plot had remarkably higher values in both cumulative irrigation volumes (1186.5 mm) and deep drainages (651.8 mm) compared with the mulched plots. Obvious correlation existed between the volume of irrigation and that of drained water. However, the ET demands for all the plots behaved pretty much the same, with the cumulative ET values ranging between 489.1 and 561.9 mm for the different treatments in 2016, suggesting that the superfluous irrigation amounts had limited influence on the accumulated ET throughout the growing season because of the poor water-holding capacity of the sandy soil. This work confirmed that relatively reasonable estimations of the &lt;em&gt;SWBCs&lt;/em&gt; in coarse-textured sandy soils can be derived by using soil moisture measurements; the proposed methods provided a reliable solution over the entire growing season and showed a great potential for identifying appropriate irrigation amounts and frequencies, and thus a move toward sustainable water resources management, even under traditional surface irrigation conditions.&lt;/p&gt;

scholarly journals An optimality-based model of the coupled soil moisture and root dynamics

2008 ◽  
Vol 12 (3) ◽  
pp. 913-932 ◽  
Author(s):  
S. J. Schymanski ◽  
M. Sivapalan ◽  
M. L. Roderick ◽  
J. Beringer ◽  
L. B. Hutley
Keyword(s):  
Soil Moisture ◽  
Water Uptake ◽  
Root Distribution ◽  
Root Respiration ◽  
Root Water Uptake ◽  
Dry Season ◽  
Soil Moisture Dynamics ◽  
Model Based ◽  
Vertical Root Distribution ◽  
Moisture Dynamics

Abstract. The main processes determining soil moisture dynamics are infiltration, percolation, evaporation and root water uptake. Modelling soil moisture dynamics therefore requires an interdisciplinary approach that links hydrological, atmospheric and biological processes. Previous approaches treat either root water uptake rates or root distributions and transpiration rates as given, and calculate the soil moisture dynamics based on the theory of flow in unsaturated media. The present study introduces a different approach to linking soil water and vegetation dynamics, based on vegetation optimality. Assuming that plants have evolved mechanisms that minimise costs related to the maintenance of the root system while meeting their demand for water, we develop a model that dynamically adjusts the vertical root distribution in the soil profile to meet this objective. The model was used to compute the soil moisture dynamics, root water uptake and fine root respiration in a tropical savanna over 12 months, and the results were compared with observations at the site and with a model based on a fixed root distribution. The optimality-based model reproduced the main features of the observations such as a shift of roots from the shallow soil in the wet season to the deeper soil in the dry season and substantial root water uptake during the dry season. At the same time, simulated fine root respiration rates never exceeded the upper envelope determined by the observed soil respiration. The model based on a fixed root distribution, in contrast, failed to explain the magnitude of water use during parts of the dry season and largely over-estimated root respiration rates. The observed surface soil moisture dynamics were also better reproduced by the optimality-based model than the model based on a prescribed root distribution. The optimality-based approach has the potential to reduce the number of unknowns in a model (e.g. the vertical root distribution), which makes it a valuable alternative to more empirically-based approaches, especially for simulating possible responses to environmental change.

scholarly journals Modeling the monthly mean soil-water balance with a statistical-dynamical ecohydrology model as coupled to a two-component canopy model

2010 ◽  
Vol 14 (10) ◽  
pp. 2099-2120 ◽  
Author(s):  
J. P. Kochendorfer ◽  
J. A. Ramírez
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Water Balance ◽  
Soil Water ◽  
Soil Texture ◽  
Root Zone ◽  
Soil Water Balance ◽  
Area Index ◽  
Two Component ◽  
Canopy Model

Abstract. The statistical-dynamical annual water balance model of Eagleson (1978) is a pioneering work in the analysis of climate, soil and vegetation interactions. This paper describes several enhancements and modifications to the model that improve its physical realism at the expense of its mathematical elegance and analytical tractability. In particular, the analytical solutions for the root zone fluxes are re-derived using separate potential rates of transpiration and bare-soil evaporation. Those potential rates, along with the rate of evaporation from canopy interception, are calculated using the two-component Shuttleworth-Wallace (1985) canopy model. In addition, the soil column is divided into two layers, with the upper layer representing the dynamic root zone. The resulting ability to account for changes in root-zone water storage allows for implementation at the monthly timescale. This new version of the Eagleson model is coined the Statistical-Dynamical Ecohydrology Model (SDEM). The ability of the SDEM to capture the seasonal dynamics of the local-scale soil-water balance is demonstrated for two grassland sites in the US Great Plains. Sensitivity of the results to variations in peak green leaf area index (LAI) suggests that the mean peak green LAI is determined by some minimum in root zone soil moisture during the growing season. That minimum appears to be close to the soil matric potential at which the dominant grass species begins to experience water stress and well above the wilting point, thereby suggesting an ecological optimality hypothesis in which the need to avoid water-stress-induced leaf abscission is balanced by the maximization of carbon assimilation (and associated transpiration). Finally, analysis of the sensitivity of model-determined peak green LAI to soil texture shows that the coupled model is able to reproduce the so-called "inverse texture effect", which consists of the observation that natural vegetation in dry climates tends to be most productive in sandier soils despite their lower water holding capacity. Although the determination of LAI based on complete or near-complete utilization of soil moisture is not a new approach in ecohydrology, this paper demonstrates its use for the first time with a new monthly statistical-dynamical model of the water balance. Accordingly, the SDEM provides a new framework for studying the controls of soil texture and climate on vegetation density and evapotranspiration.

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