Impact of Soil Texture and Position of Groundwater Level on Evaporation from the Soil Root Zone

2018 ◽  
pp. 167-181
Author(s):  
M. Gomboš ◽  
D. Pavelková ◽  
B. Kandra ◽  
A. Tall
Keyword(s):  
Soil Texture ◽  
Groundwater Level ◽  
Root Zone

scholarly journals Geophysical mapping of soil texture variability in the root zone to improve modelling of the water and nutrient flow.

2020 ◽  
Author(s):  
Kim Schwartz Madsen ◽  
Bo Vangsø Iversen ◽  
Christen Duus Børgesen
Keyword(s):  
Soil Texture ◽  
Hydraulic Properties ◽  
Root Zone ◽  
Clay Fraction ◽  
Clay Content ◽  
Auxiliary Variable ◽  
Pedotransfer Functions ◽  
Soil Hydraulic Properties ◽  
Sampling Density ◽  
Soil Hydraulic

<p>Modelling is often used to acquire information on water and nutrient fluxes within and out of the root zone. The models require detailed information on the spatial variability of soil hydraulic properties derived from soil texture and other soil characteristics using pedotransfer functions (PTFs). Soil texture can vary considerably within a field and is cumbersome and expensive to map in details using traditionally measurements in the laboratory. The electrical conductivity (EC) of the soil have shown to correlate with its textural composition.</p><p>This study investigates the ability of electromagnetic induction (EMI) methods to predict clay content in three soil layers of the root zone. As the clay fraction often is a main predictor in PTFs predicting soil hydraulic properties this parameter is of high interest. EMI and soil textural surveys on four Danish agricultural fields with varying textural composition were used. Sampling density varied between 0.5 and 38 points per hectare. The EMI data was gathered with a Dualem21 instrument with a sampling density 200-3000 points per hectare. The EC values were used together with the measured values of the clay content creating a statistical relationship between the two variables. Co-kriging of the clay content from the textural sampling points with the EC as auxiliary variable produces clay content maps of the fields. Unused (80%) texture points were used for validation. EMI-predicted clay content maps and clay content maps based on the survey were compared. The two sets of soil texture maps are used as predictors for PTF models to predict soil hydraulic properties as input in field-scale root zone modelling.</p><p>The comparisons between EC and clay content show some degree of correlation with an R<sup>2</sup> in the range of 0.55 to 0.80 for the four fields. The field with the highest average clay content showed the best relationship between the two parameters. Co-kriging with EC decreased mean error by 0.016 to 0.52 and RMSE by 0.04 to 1.80 between observed and predicted clay maps.</p>

Methane emissions from an unmanaged degraded tropical peatland: Interacting influences of plant-processes and groundwater level

2021 ◽  
Author(s):  
Chandra Shekhar Deshmukh ◽  
Ankur R. Desai ◽  
Chris D. Evans ◽  
Susan E. Page ◽  
Ari Putra Susanto ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Groundwater Level ◽  
Water Surface ◽  
Root Zone ◽  
Methane Emissions ◽  
Spatiotemporal Variability ◽  
Published Data ◽  
Canopy Conductance ◽  
Methane Formation ◽  
Tropical Peatland

<p>Tropical peatlands are a complex ecosystem with poorly understood biogeochemical regimes. An immense peat carbon stock and waterlogged-anaerobic conditions may possibly favor methane formation in this ecosystem. Methane is released to the atmosphere either from soil/water surface or through vegetation (both herbaceous plant and tree). Using the conventional flux chamber method, assessing spatiotemporal variability and vegetation-mediated methane emissions remains a practical challenge for scientists. Consequently, research related to ecosystem-scale methane exchange remains limited. Yet, published data display a large range of methane emission estimates and, hence, highlight a knowledge gap in our science on tropical peatland methane cycling.</p><p>In this context, we set out to measure the net ecosystem methane exchange (NEE-CH<sub>4</sub>) from an unmanaged degraded peatland in the east coast of Sumatra, Indonesia. The measurements were conducted using the eddy covariance system, composed of a 3D sonic anemometer coupled with a LI-7700 open-path methane analyzer, above the vegetation canopy at 41 m tall tower for over 4 years period (October 2016-September 2020). Therefore, the measurements incorporated all existing methane sources and sinks within the flux footprint, i.e. soil surface, trunk of living tree, vascular plant, and water surface.</p><p>Our measurements indicate that unmanaged degraded tropical peatland emitted 54±12 kg CH<sub>4</sub> ha<sup>-1</sup> year<sup>-1</sup> to the atmosphere. The magnitude of daytime NEE-CH<sub>4</sub> were up to six times larger than those during the nighttime. This cautions that sampling bias (e.g. only daytime measurements) can overestimate the daily NEE-CH<sub>4</sub>. The diurnal variation in NEE-CH<sub>4</sub> was correlated with associated changes in the canopy conductance to water vapor. Therefore, it was attributed to the vegetative transport of dissolved methane via transpiration. There was no clear relationship between NEE-CH<sub>4</sub> and soil temperature, while it decreased exponentially with declining groundwater level. Low groundwater level enhances methane oxidation in the upper oxic peat layer. Further, low groundwater level might relocate methane production below the root zone, resulting in insufficient methane in the root zone to be taken and transported to the atmosphere.</p><p>Our results, which are among the first eddy covariance exchange data reported for any tropical peatland, should help to reduce uncertainty in the estimation of methane emissions from a globally important ecosystem, and to better understand how land-use changes affect methane emissions.</p>

scholarly journals Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3109
Author(s):  
Roïya Souissi ◽  
Ahmad Al Bitar ◽  
Mehrez Zribi
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Soil Texture ◽  
Surface Soil ◽  
Root Zone ◽  
Data Filtering ◽  
Surface Soil Moisture ◽  
The Mean ◽  
Artificial Neural

This paper explores the accuracy in using an artificial neural network (ANN) to estimate root-zone soil moisture (RZSM) at multiple worldwide locations using only in situ surface soil moisture (SSM) as a training dataset. The paper also addresses the transferability of the trained ANN across climatic and soil texture conditions. Data from the International Soil Moisture Network (ISMN) were collected for several networks with variable soil texture and climate classes. Several scaling, feature extraction, and training approaches were tested. An artificial neural network employing rolling averages (ANNRAV) of SSM over 10, 30, and 90 days was developed. The results show that applying a standard scaling (SSCA) to the ANN input features improves the correlation, Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) for 52%, 91%, and 87%, respectively, of the tested stations, compared to MinMax scaling (MMSCA). Different training sets are suggested, namely, training on data from all networks, data from one network, or data of all networks excluding one. Based on these trainings, new transferability (TranI) and contribution (ContI) indices are defined. The results show that one network cannot provide the best prediction accuracy if used alone to train the ANN. They also show that the removal of the less contributing networks enhances performance. For example, elimination of the densest network (SCAN) from the training enhances the mean correlation by 20.5% and the mean NSE by 42.5%. This motivates the implementation of a data filtering technique based on the ANN’s performance. A median, max, and min correlation of 0.77, 0.96, and 0.65, respectively, are obtained by the model after data filtering. The performances are also analyzed with respect to the covered climatic regions and soil texture, providing insights into the robustness and limitations of the approach, namely, the need for complementary information in highly evaporative regions. In fact, the ANN using only SSM to predict RZSM has low performance when decoupling between the surface and root zones is observed. The application of ANN to obtain spatialized RZSM will require integrating remote sensing-based surface soil moisture in the future.

scholarly journals Quantification of the impact of hydrology on agricultural production as a result of too dry, too wet or too saline conditions

2016 ◽  
Author(s):  
M. J. D. Hack-ten Broeke ◽  
J. G. Kroes ◽  
R. P. Bartholomeus ◽  
J. C. van Dam ◽  
A. J. W. de Wit ◽  
...  
Keyword(s):  
The Netherlands ◽  
Crop Yield ◽  
Groundwater Level ◽  
Agricultural Production ◽  
Root Zone ◽  
Crop Growth ◽  
Extreme Weather Events ◽  
Yield Reduction ◽  
Soil Units ◽  
The Impact

Abstract. For calculating the effects of hydrological measures on agricultural production in the Netherlands a new comprehensive and climate proof method is being developed: WaterVision Agriculture (in Dutch: Waterwijzer Landbouw). End users have asked for a method that considers current and future climate, that can quantify the differences between years and also the effects of extreme weather events. Furthermore they would like a method that considers current farm management and that can distinguish three different causes of crop yield reduction: drought, saline conditions or too wet conditions causing oxygen shortage in the root zone. WaterVision Agriculture is based on the hydrological simulation model SWAP and the crop growth model WOFOST. SWAP simulates water transport in the unsaturated zone using meteorological data, boundary conditions (like groundwater level or drainage) and soil parameters. WOFOST simulates crop growth as a function of meteorological conditions and crop parameters. Using the combination of these process-based models we have derived a meta-model, i.e. a set of easily applicable simplified relations for assessing crop growth as a function of soil type and groundwater level. These relations are based on multiple model runs for at least 72 soil units and the possible groundwater regimes in the Netherlands. So far, we parameterized the model for the crops silage maize and grassland. For the assessment, the soil characteristics (soil water retention and hydraulic conductivity) are very important input parameters for all soil layers of these 72 soil units. These 72 soil units cover all soils in the Netherlands. This paper describes (i) the setup and examples of application of the process-based model SWAP-WOFOST, (ii) the development of the simplified relations based on this model and (iii) how WaterVision Agriculture can be used by farmers, regional government, water boards and others to assess crop yield reduction as a function of groundwater characteristics or as a function of the salt concentration in the root zone for the various soil types.

scholarly journals The manifestation of VIS-NIRS spectroscopy data to predict and map soil texture in the Triffa plain (Morocco)

2020 ◽  
Vol 48 (1) ◽  
Author(s):  
Ayoub Lazaar ◽  
◽  
Kamal El Hammouti ◽  
Zakariae Naiji ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Soil Texture ◽  
Near Infrared ◽  
Prediction Models ◽  
Root Zone ◽  
Clay Fraction ◽  
Coefficient Of Determination ◽  
Good Prediction ◽  
Sand Fraction ◽  
Silt Fraction ◽  
North East

The use of standard laboratory methods to estimate the soil texture is complicated, expensive, and time-consuming and needs considerable effort. The reflectance spectroscopy represents an alternative method for predicting a large range of soil physical properties and provides an inexpensive, rapid, and reproducible analytical method. This study aimed to assess the feasibility of Visible (VIS: 350-700 nm) and Near-Infrared and Short-Wave-Infrared (NIRS: 701-2500 nm) spectroscopy for predicting and mapping the clay, silt, and sand fractions of the soils of Triffa plain (north-east of Morocco). A total of 100 soil samples were collected from the non-root zone of soil (0-20 cm) and then analyzed for texture using the VIS-NIRS spectroscopy and the traditional laboratory method. The partial least squares regression (PLSR) technique was used to assess the ability of spectral data to predict soil texture. The results of prediction models showed excellent performance for the VIS-NIRS spectroscopy to predict the sand fraction with a coefficient of determination R2 = 0.93 and Root Mean Squares Error (RMSE) =3.72, good prediction for the silt fraction (R2=0.87; RMSE = 4.55), and acceptable prediction for the clay fraction (R2 = 0.53; RMSE = 3.72). Moreover, the range situated between 2150 and 2450 nm is the most significant for predicting the sand and silt fractions, while the spectral range between 2200 and 2440 nm is the optimal to predict the clay fraction. However, the maps of predicted and measured soil texture showed an excellent spatial similarity for the sand fraction, a certain difference in the variability of clay fraction, while the maps of silt fraction show a lower difference.

scholarly journals Partitioning snowmelt and rainfall in the critical zone: effects of climate type and soil properties

2019 ◽  
Author(s):  
John C. Hammond ◽  
Adrian A. Harpold ◽  
Sydney Weiss ◽  
Stephanie K. Kampf
Keyword(s):  
Soil Properties ◽  
Sierra Nevada ◽  
Soil Texture ◽  
Root Zone ◽  
Soil Depth ◽  
Deep Drainage ◽  
Event Scale ◽  
Streamflow Generation ◽  
Dry Climates ◽  
Annual Scale

Abstract. Streamflow generation and deep groundwater recharge in high elevation and high latitude locations may be vulnerable to loss of snow, making it important to quantify how snowmelt is partitioned between soil storage, deep drainage, evapotranspiration, and runoff. Based on previous findings, we hypothesize that snowmelt produces greater streamflow and deep drainage than rainfall and that this effect is greatest in dry climates. To test this hypothesis we examine how snowmelt and rainfall partitioning vary with climate and soil properties using a physically based variably saturated subsurface flow model, HYDRUS-1D. To represent climate variability we use historical inputs from five SNOTEL sites in each of three mountain regions with humid to semiarid climates: Northern Cascades, Sierra Nevada, and Uinta. Each input scenario is run with three soil profiles of varying hydraulic conductivity, soil texture, and bulk density. We also create artificial input scenarios to test how the concentration of input in time, conversion of snow to rain input, and soil profile depth affect partitioning of input into deep drainage and runoff. Results indicate that event-scale runoff is higher for snowmelt than for rainfall due to higher antecedent moisture and input rates in both wet and dry climates. At the annual scale, surface runoff also increases with snowmelt fraction, whereas deep drainage is not correlated with snowmelt fraction. Deep drainage is less affected by changes from snowmelt to rainfall because it is controlled by deep soil moisture changes over longer time scales. However, extreme scenarios with input highly concentrated in time, such as during melt of a deep snowpack, yield greater deep drainage below the root zone than intermittent input. Soil texture modifies daily wetting and drying patterns but has limited effect on annual scale partitioning of rain and snowmelt, whereas increases in soil depth decrease runoff and increase deep drainage. Overall these results indicate that runoff may be substantially reduced with seasonal snowpack decline in all climates. These mechanisms help explain recent observations of streamflow sensitivity to changing snowpack and emphasize the need to develop strategies to mitigate impacts of reduced streamflow generation in places most at risk for shifts from snow to rain.

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.

scholarly journals Land suitability assesment for Coffea arabica on the land overgrowed by Uncaria gambir

2021 ◽  
Vol 886 (1) ◽  
pp. 012121
Author(s):  
Rahmawaty ◽  
Y A Ginting ◽  
R Batubara ◽  
G Carenina ◽  
C F Ginting ◽  
...  
Keyword(s):  
Soil Texture ◽  
Coffea Arabica ◽  
Root Zone ◽  
Laboratory Data ◽  
Land Suitability ◽  
Land Evaluation ◽  
Limiting Factor ◽  
Matching Method ◽  
North Sumatra

Abstract Aornakan I and Kuta Tinggi villages are villages located in Pak-pak Bharat Regency, North Sumatra Province. Currently, the villagers are planting Uncaria gambir and Coffea arabica. This study aimed to evaluate land for cofffe plantations on land overgrown with gambier in Pak-pak Bharat Regency. Sampling was carried out purposively on land overgrown with gambier plants in Aornakan I Village, Pargetteng-getteng Sengkut Sub-district and Kuta Tinggi Village, Salak Sub-district, Pakpak Bharat Regency. The evaluation of land suitability for coffee uses the matching method, namely by analysing laboratory data and data measured in the field with the characteristics of the land for cofffe. The results showed that the land evaluation for Coffea arabica was marginally suitable (S3) with the limiting factor was the root zone media (rc) in terms of soil texture.

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

2008 ◽  
Vol 5 (2) ◽  
pp. 579-648
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 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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