A soil moisture accounting-procedure with a Richards' equation-based soil texture-dependent parameterization

Abstract. Interactions between shallow groundwater and land surface processes play an important role in the ecohydrology of riparian zones. Some recent land surface models (LSMs) incorporate groundwater-land surface interactions using parameterizations at varying levels of detail. In this paper, we examine the sensitivity of land surface evapotranspiration (ET) to water table depth, soil texture, and two commonly used soil hydraulic parameter datasets using four models with varying levels of complexity. The selected models are Hydrus-1D, which solves the pressure-based Richards equation, the Integrated Biosphere Simulator (IBIS), which simulates interactions among multiple soil layers using a (water-content) variant of the Richards equation, and two forms of a steady-state capillary flux model coupled with a single-bucket soil moisture model. These models are first evaluated using field observations of climate, soil moisture, and groundwater levels at a semi-arid site in south-central Nebraska, USA. All four models are found to compare reasonably well with observations, particularly when the effects of groundwater are included. We then examine the sensitivity of modelled ET to water table depth for various model formulations, node spacings, and soil textures (using soil hydraulic parameter values from two different sources, namely Rawls and Clapp-Hornberger). The results indicate a strong influence of soil texture and water table depth on groundwater contributions to ET. Furthermore, differences in texture-specific, class-averaged soil parameters obtained from the two literature sources lead to large differences in the simulated depth and thickness of the "critical zone" (i.e., the zone within which variations in water table depth strongly impact surface ET). Depending on the depth-to-groundwater, this can also lead to large discrepancies in simulated ET (in some cases by more than a factor of two). When the Clapp-Hornberger soil parameter dataset is used, the critical zone becomes significantly deeper, and surface ET rates become much higher, resulting in a stronger influence of deep groundwater on the land surface energy and water balance. In general, we find that the simulated sensitivity of ET to the choice of soil hydraulic parameter dataset is greater than the sensitivity to soil texture defined within each dataset, or even to the choice of model formulation. Thus, our findings underscore the need for future modelling and field-based studies to improve the predictability of groundwater-land surface interactions in numerical models, particularly as it relates to the parameterization of soil hydraulic properties.

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Simulação integrada dos recursos hídricos nos reservatórios Engenheiro Ávidos e São Gonçalo na Paraíba

Revista Verde de Agroecologia e Desenvolvimento Sustentável ◽

10.18378/rvads.v12i5.5443 ◽

2017 ◽

Vol 12 (5) ◽

pp. 892

Author(s):

Erika Lira da Silva ◽

Allan Sarmento Vieira

Keyword(s):

Soil Moisture ◽

Soil Moisture Accounting ◽

Accounting Procedure

Objetivou-se realizar uma simulação integrada nos reservatórios Engenheiro Ávidos e São Gonçalo, localizados, na Sub-bacia do Alto Piranhas no Estado da Paraíba, utilizando o modelo de rede de fluxo Acquanet. Utilizou-se o modelo tipo transformação chuva-vazão SMAP (Soil Moisture Accounting Procedure) para complementar a série de vazões naturais no período de 2010 a 2016. Assim a simulação realizada considerou uma série histórica de 1962 a 2016, com um cenário futuro onde foram adotados valores das demandas projetadas para o ano de 2032. Analisando a parte operacional dos reservatórios foi observado, no período simulado, que os mesmos atingiram seu volume máximo em muitos meses; na maioria dos períodos o volume meta foi respeitado; o volume mínimo foi atingido em alguns momentos devido aos baixos índices pluviométricos, fato que se intensificou nos últimos anos. Quanto às demandas, o abastecimento humano e dessedentação apresentaram confiabilidade acima dos 90%, já para a irrigação e indústria, o número de falhas foi maior. No cenário futuro, os resultados tiveram uma representação diferente, em poucos momentos chegaram ao volume máximo, por muitos meses operaram no volume mínimo, não conseguindo atender ao requerimento operacional do volume meta, e consequentemente o atendimento às demandas também não foi satisfatório. As falhas ocorreram no abastecimento humano e animal nos dois reservatórios, onde a confiabilidade ficou acima de 70%, e as demandas irrigação e indústria apresentaram um grande déficit de atendimento.

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Long-term hydrologic simulation using SCS-CN-based improved soil moisture accounting procedure

Hydrological Processes ◽

10.1002/hyp.7789 ◽

2010 ◽

Vol 25 (4) ◽

pp. 561-579 ◽

Cited By ~ 28

Author(s):

Dilip G. Durbude ◽

Manoj K. Jain ◽

Surendra K. Mishra

Keyword(s):

Soil Moisture ◽

Hydrologic Simulation ◽

Soil Moisture Accounting ◽

Scs Cn ◽

Accounting Procedure

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Comment on Dong and Ochsner (2018): "Soil Texture often Exerts stronger Influence Than Precipitation on Mesoscale Soil Moisture Patterns"

10.1002/essoar.10502967.1 ◽

2020 ◽

Author(s):

Jannis C. Jakobi ◽

Johan A. Huisman ◽

Heye Bogena

Keyword(s):

Soil Moisture ◽

Soil Texture

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A generally applicable pedotransfer function that estimates field capacity and permanent wilting point from soil texture and bulk density

Canadian Journal of Soil Science ◽

10.4141/cjss07120 ◽

2008 ◽

Vol 88 (5) ◽

pp. 761-774 ◽

Cited By ~ 23

Author(s):

J. A. P. Pollacco

Keyword(s):

Organic Matter ◽

Soil Moisture ◽

Bulk Density ◽

Soil Texture ◽

The United States ◽

Pedotransfer Functions ◽

Pedotransfer Function ◽

Data Set ◽

Wide Range ◽

Fitting Parameters

Hydrological models require the determination of fitting parameters that are tedious and time consuming to acquire. A rapid alternative method of estimating the fitting parameters is to use pedotransfer functions. This paper proposes a reliable method to estimate soil moisture at -33 and -1500 kPa from soil texture and bulk density. This method reduces the saturated moisture content by multiplying it with two non-linear functions depending on sand and clay contents. The novel pedotransfer function has no restrictions on the range of the texture predictors and gives reasonable predictions for soils with bulk density that varies from 0.25 to 2.16 g cm-3. These pedotransfer functions require only five parameters for each pressure head. It is generally accepted that the introduction of organic matter as a predictor improves the outcomes; however it was found by using a porosity based pedotransfer model, using organic matter as a predictor only modestly improves the accuracy. The model was developed employing 18 559 samples from the IGBP-DIS soil data set for pedotransfer function development (Data and Information System of the International Geosphere Biosphere Programme) database that embodies all major soils across the United States of America. The function is reliable and performs well for a wide range of soils occurring in very dry to very wet climates. Climatical grouping of the IGBP-DIS soils was proposed (aquic, tropical, cryic, aridic), but the results show that only tropical soils require specific grouping. Among many other different non-climatical soil groups tested, only humic and vitric soils were found to require specific grouping. The reliability of the pedotransfer function was further demonstrated with an independent database from Northern Italy having heterogeneous soils, and was found to be comparable or better than the accuracy of other pedotransfer functions found in the literature. Key words: Pedotransfer functions, soil moisture, soil texture, bulk density, organic matter, grouping

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Modified Richards’ Equation to Improve Estimates of Soil Moisture in Two-Layered Soils after Infiltration

Water ◽

10.3390/w10091174 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1174 ◽

Cited By ~ 5

Author(s):

Honglin Zhu ◽

Tingxi Liu ◽

Baolin Xue ◽

Yinglan A. ◽

Guoqiang Wang

Keyword(s):

Soil Moisture ◽

Overland Flow ◽

Hydrological Cycle ◽

Soil Depth ◽

Controlling Factors ◽

Moisture Distribution ◽

Richards Equation ◽

Infiltration Process ◽

Soil Moisture Distribution

Soil moisture distribution plays a significant role in soil erosion, evapotranspiration, and overland flow. Infiltration is a main component of the hydrological cycle, and simulations of soil moisture can improve infiltration process modeling. Different environmental factors affect soil moisture distribution in different soil layers. Soil moisture distribution is influenced mainly by soil properties (e.g., porosity) in the upper layer (10 cm), but by gravity-related factors (e.g., slope) in the deeper layer (50 cm). Richards’ equation is a widely used infiltration equation in hydrological models, but its homogeneous assumptions simplify the pattern of soil moisture distribution, leading to overestimates. Here, we present a modified Richards’ equation to predict soil moisture distribution in different layers along vertical infiltration. Two formulae considering different controlling factors were used to estimate soil moisture distribution at a given time and depth. Data for factors including slope, soil depth, porosity, and hydraulic conductivity were obtained from the literature and in situ measurements and used as prior information. Simulations were compared between the modified and the original Richards’ equations and with measurements taken at different times and depths. Comparisons with soil moisture data measured in situ indicated that the modified Richards’ equation still had limitations in terms of reproducing soil moisture in different slope positions and rainfall periods. However, compared with the original Richards’ equation, the modified equation estimated soil moisture with spatial diversity in the infiltration process more accurately. The equation may benefit from further solutions that consider various controlling factors in layers. Our results show that the proposed modified Richards’ equation provides a more effective approach to predict soil moisture in the vertical infiltration process.

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Multi-source data assimilation for physically-based hydrological modeling of an experimental hillslope

10.5194/hess-2018-18 ◽

2018 ◽

Author(s):

Anna Botto ◽

Enrica Belluco ◽

Matteo Camporese

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Hydrological Modeling ◽

Nonlinear Models ◽

Pressure Head ◽

Richards Equation ◽

Hydrological Models ◽

Filter Performance ◽

Trade Offs ◽

Source Data

Abstract. Data assimilation has been recently the focus of much attention for integrated surface-subsurface hydrological models, whereby joint assimilation of water table, soil moisture, and river discharge measurements with the ensemble Kalman filter (EnKF) have been extensively applied. Although the EnKF has been specifically developed to deal with nonlinear models, integrated hydrological models based on the Richards equation still represent a challenge, due to strong nonlinearities that may significantly affect the filter performance. Thus, more studies are needed to investigate the capabilities of the EnKF to correct the system state and identify parameters in cases where the unsaturated zone dynamics are dominant, as well as to quantify possible tradeoffs associated with assimilation of multi-source data. Here, the model CATHY (CATchment HYdrology) is applied to reproduce the hydrological dynamics observed in an experimental two-layered hillslope, equipped with tensiometers, water content reflectometer probes, and tipping bucket flow gages to monitor the hillslope response to a series of artificial rainfall events. Pressure head, soil moisture, and subsurface outflow are assimilated with the EnKF in a number of scenarios and the challenges and issues arising from the assimilation of multi-source data in this real-world test case are discussed. Our results demonstrate that the EnKF is able to effectively correct states and parameters even in a real application characterized by strong nonlinearities. However, multi-source data assimilation may lead to significant trade-offs: the assimilation of additional variables can lead to degradation of model predictions for other variables that were otherwise well reproduced. Furthermore, we show that integrated observations such as outflow discharge cannot compensate for the lack of well-distributed data in heterogeneous hillslopes.

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