Soil Water Content In Root Zone Research Articles

Vegetation factors and atmospheric dryness regulate the dynamics of ecosystem water use efficiency in a temperate semiarid shrubland

Ecosystem water use efficiency (WUE), a key indicator of the coupling between carbon and water cycle, has been widely used to quantify ecosystem responses to climate change. However, large uncertainties remain regarding the dynamics and driving factors of WUE in temperate semiarid shrublands. Using eddy-covariance measurements, we investigated the role of vegetation and hydrometeorological factors in affecting the temporal dynamics of WUE (GPP/ET, where GPP and ET represent gross primary production and evapotranspiration, respectively) over a semiarid shrubland of northern China during 2012–2016. Daily WUE was lower (<1.5 g C kg−1 H2O) in early-spring and late-autumn, and higher (2.0–7.0 g C kg−1 H2O) in summer months (June–August). Annual (January–December) WUE (1.43–1.78 g C kg−1 H2O) was higher than most values previously reported for semiarid and arid shrubland ecosystems. Vegetation factors such as surface conductance (gs) and normalized difference vegetation index (NDVI) played a primary role in promoting monthly WUE during the warm and moist growing-season (April–October), while high vapor pressure deficit (VPD) and low root-zone soil water content (SWC) were key hydrometeorological factors that imposed constraints on daily WUE. Using both a machine-learning model and a data-binning algorithm, we showed that NDVI and VPD were the most important factors regulating WUE dynamics. Ongoing climate warming and increasingly frequent extreme events (e.g., droughts and heatwaves) are expected to induce high VPD, which in turn could reduce ecosystem WUE and undermine ecosystem carbon sequestration in semiarid shrublands. In addition, increasing vegetation cover in the studied area owing to continued restoration efforts is expected to enhance ecosystem WUE, and may result in stronger vegetation controls on WUE dynamics.

Tracing Water Recharge and Transport in the Root-Zone Soil of Different Vegetation Types in the Poyang Lake Floodplain Wetland (China) Using Stable Isotopes

Background: root-zone water transport is crucial in the water transformation from precipitation to groundwater, directly influencing soil moisture distribution and resource acquisition for wetland plants. Methods: This study investigated the movement mechanism of root-zone (0–80 cm) soil water in the Poyang Lake wetland, China, during a dry year. Hydrological observation and stable isotopes (δ18O and δD) were utilized. Results: The root-zone soil water content was low (2.9–12.6%) at the high site covered by Artemisia capillaris, while it remained high (25.2–30.2%) at the median and low sites covered by Phragmites australis and Carex cinerascens, respectively. The isotopic values of shallow soil water (0–40 cm) in the A. capillaris site followed the seasonal pattern of rainfall isotopes, indicating predominantly rainfall recharge. Rainfall was primarily transported by piston flow, with an infiltration depth of approximately 60 cm. Conversely, depleted water isotopes measured at certain depths in P. australis and C. cinerascens sites closely resembled those of rainfall, suggesting that preferential flow dominated. The average groundwater contribution proportions in root-zone soil water were 65.5% and 57.4% in P. australis and C. cinerascens sites, respectively, while no contribution was detected in A. capillaris site. Conclusions: Preferential flow and groundwater recharge occurred in the P. australis and C. cinerascens sites. They enhance the hydrological connection at the profile scale and are useful for maintaining a favorable root-zone moisture environment for wetland ecosystems in dry years. However, the hydrological connectivity between root-zone soil and groundwater was found to be obstructed in the A. capillaris site. This might be the main reason for vegetation degradation at high elevations in the Poyang Lake wetland.

Estimation of root zone soil moisture at point scale based on soil water measurements from cosmic-ray neutron sensing in a karst catchment

The cosmic-ray neutron sensing (CRNS) is an emerging method for continuously monitoring soil water content (SWC) at an intermediate scale. However, when multiple hydrologic units are present within its footprint, the potential application of CRNS in water resources management is restricted. Here we propose a new strategy to predict point-scale SWC in root zone established on CRNS-based soil moisture and improved relative difference method. A total of 768 days of soil moisture data were collected by CRNS at the intermediate scale and EC-TM sensors at the point scale in a karst catchment. The original and improved mean relative difference methods predicted point-scale SWCs within and without the effective measuring depth, respectively. The mean effective measuring depth was 13.16 cm, ranging from 10.13 to 19.23 cm. Both land use type and soil structure played essential roles in regulating point scale SWC in the soil profile. Point-scale SWC in root zone can be predicted accurately (P < 0.001) based on SWC data derived from the CRNS system. The prediction accuracy of point scale SWC can be improved by increasing the averaging time of the soil moisture values. Our results demonstrated that the proposed strategy was reliable for CRNS to predict SWC beyond the effective measurement depth. This study provides a good perspective for effectively managing of water resources in areas with complex hydrological processes.

Three years of soil moisture observations by a dense cosmic-ray neutron sensing cluster at an agricultural research site in north-east Germany

Abstract. Cosmic-ray neutron sensing (CRNS) allows for the estimation of root-zone soil water content (SWC) at the scale of several hectares. In this paper, we present the data recorded by a dense CRNS network operated from 2019 to 2022 at an agricultural research site in Marquardt, Germany – the first multi-year CRNS cluster. Consisting, at its core, of eight permanently installed CRNS sensors, the cluster was supplemented by a wealth of complementary measurements: data from seven additional temporary CRNS sensors, partly co-located with the permanent ones; 27 SWC profiles (mostly permanent); two groundwater observation wells; meteorological records; and Global Navigation Satellite System reflectometry (GNSS-R). Complementary to these continuous measurements, numerous campaign-based activities provided data by mobile CRNS roving, hyperspectral imagery via UASs, intensive manual sampling of soil properties (SWC, bulk density, organic matter, texture, soil hydraulic properties), and observations of biomass and snow (cover, depth, and density). The unique temporal coverage of 3 years entails a broad spectrum of hydro-meteorological conditions, including exceptional drought periods and extreme rainfall but also episodes of snow coverage, as well as a dedicated irrigation experiment. Apart from serving to advance CRNS-related retrieval methods, this data set is expected to be useful for various disciplines, for example, soil and groundwater hydrology, agriculture, or remote sensing. Hence, we show exemplary features of the data set in order to highlight the potential for such subsequent studies. The data are available at doi.org/10.23728/b2share.551095325d74431881185fba1eb09c95 (Heistermann et al., 2022b).

Empirical equations for estimating field capacity in dryland cropping soils of southeastern Australia

The root zone soil water content at field capacity, θFC, is often crucial for irrigation scheduling and soil-plant-atmosphere modelling. Although θFC is traditionally determined using methodologies based on matric head or when water flux at the bottom of the root-zone equal to a prescribed negligible flux, these approaches can be problematic for some soils and applications. In this study, a novel water storage approach was used to estimate θFC for 11 soil texture classes in southeastern Australia (northwest Victoria) by employing the Soil-Water-Atmosphere-Plant (SWAP) model to simulate gravity drainage from saturation in the active root zone (top 60 cm). Field capacity was specified as the average root zone water content corresponding to a 1% relative change in daily soil water storage. We have also estimated flux-based θFC when prescribed flux at the bottom of the root-zone was 0.1 cm d-1. Three new empirical equations were developed to estimate storage-based θFC as a function of SWAP-simulated drainage flux out of the root zone, the n parameter of the van Genuchten function, and the saturated soil hydraulic conductivity, Ks, or the soil water content at − 100 cm matric head. We have evaluated these three new equations in addition to nine published equations for estimating θFC. The three new equations were found to be better predictors of θFC than most of the nine popular equations reported in the literature when compared to flux-based θFC values. Based on findings from this study the new equations are considered to be effective for irrigation scheduling and crop/climate modelling on the dryland soils of Victoria. Future studies will assess the applicability of the new equations to other parts of Australia.

Upscaling point-scale soil hydraulic properties for application in a catchment model using Bayesian calibration: An application in two agricultural regions of New Zealand

Hydrological modeling for landscape and catchment scale applications requires upscaling of soil hydraulic parameters which are generally only available at point scale. We present a case study where hourly root zone soil water content and drainage observations from nine flat, pastoral sites (Waikato and Canterbury regions in New Zealand) were used to develop an upscaling approach to parameterize the soil water balance module of the TopNet catchment model, based on scaling multi-layer soil profile information from the national soil data base, S-map, to the single-layer soil profile used in TopNet. Using a Bayesian calibration approach, the hydraulic behavioral parameters of TopNet's soil water balance module were identified. Of the eleven calibration parameters considered three were found to be insensitive to data (stress point, unsaturated hydraulic conductivity and infiltration rate); three were correlated and could be determined from specific soil water content observations (wilting point, field capacity and drainable water); and five were correlated and could be determined from combined specific soil water content and drainage observations (drainage rate, saturated hydraulic conductivity profile, effective soil depth, soil water holding capacity and wetting front suction). Based on the eight correlated parameters, upscaling functions were then developed to derive suitable model parameters from S-map-hydro for each site. The validity of the upscaling functions was verified at each site. The approach used in this research can be used to parameterize the TopNet model at other similar locations, and also provides a transferable framework to parameterize other catchment-scale hydrology models where point-scale soil hydraulic data available.

An analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts

Irrigation water is an expensive and limited resource and optimal scheduling can boost water efficiency. Scheduling decisions often need to be made several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs including initial soil water status, crop conditions and weather. Since each input is subject to uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions. This study aims to develop an uncertainty-based analysis framework for evaluating irrigation scheduling decisions under uncertainty, with a focus on the uncertainty arising from short-term rainfall forecasts. To achieve this, a biophysical process-based crop model, APSIM (The Agricultural Production Systems sIMulator), was used to simulate root-zone soil water content for a study field in south-eastern Australia. Through the simulation, we evaluated different irrigation scheduling decisions using ensemble short-term rainfall forecasts. This modelling produced an ensemble of simulations of soil water content, as well as ensemble simulations of irrigation runoff and drainage. This enabled quantification of risks of over- and under-irrigation. These ensemble estimates were interpreted to inform the timing of the next irrigation event to minimize both the risks of stressing the crop and/or wasting water under uncertain future weather. With extension to include other sources of uncertainty (e.g., evapotranspiration forecasts, crop coefficient), we plan to build a comprehensive uncertainty framework to support on-farm irrigation decision-making.

Snow Albedo Feedbacks Enhance Snow Impurity‐Induced Radiative Forcing in the Sierra Nevada

This study employs a fully coupled meteorology‐chemistry‐snow model to investigate the impacts of light‐absorbing particles (LAPs) on snow darkening in the Sierra Nevada. After comprehensive evaluation with spatially and temporally complete satellite retrievals, the model shows that LAPs in snow reduce snow albedo by 0.013 (0–0.045) in the Sierra Nevada during the ablation season (April‐July), producing a midday mean radiative forcing of 4.5 W m−2 which increases to 15–22 W m−2 in July. LAPs in snow accelerate snow aging processes and reduce snow cover fraction, which doubles the albedo change and radiative forcing caused by LAPs. The impurity‐induced snow darkening effects decrease snow water equivalent and snow depth by 20 and 70 mm in June in the Sierra Nevada bighorn sheep habitat. The earlier snowmelt reduces root‐zone soil water content by 20%, deteriorating the forage productivity and playing a negative role in the survival of bighorn sheep.

Physiological Responses of Typical Wetland Plants Following Flooding Process—From an Eco-Hydrological Model Perspective

Anaerobics increase resistance to gas transport and microbial activity in flooded soils. This may result in the presence of aerenchyma in the roots of some wetland plants. Increased aerenchyma airspaces enable oxygen to be transported from the above-ground plant parts to the submerged roots and rhizosphere. Nevertheless, there is still a lack of studies linking field experiments and eco-hydrological modeling to the parameterization of the physiological responses of typical wetland plant species to natural flooding events. Furthermore, from the modeling perspective, the contribution of aerenchyma was not sufficiently considered. The goal of this study was to develop and apply an eco-hydrological model capable of simulating various patterns of plant physiological responses to natural flooding events based on key processes of root oxygen diffusion and aerenchyma functioning in a variably-saturated wetland soil environment. Eco-hydrological experiments were conducted accordingly, with surface water level, root-zone soil water content, soil temperature, leaf net photosynthesis rate and root morphology monitored simultaneously in situ at a site dominated by meadow species Deyeuxia angustifolia (Kom.) Y. L. Chang and invaded shrub species Salix rosmarinifolia Linn. var. brachypoda (Trautv.et Mey.) Y.L. Chou in a typical natural floodplain wetland. The results are as follows: (1) Root oxygen respiration rates are strongly correlated with leaf net photosynthesis rates of the two plant types, particularly under flooding conditions during the growing season; (2) Meadow species with a preference for wet microhabitats has a competitive advantage over first-year invading shrub species during flooding events; and (3) an aerenchyma sub-model could improve the eco-hydrological model’s accuracy in capturing plant physiological responses. These findings have the potential to contribute to the management of wetland and its restorations.

Assessing the Effects of Surface Conditions on Potential Evapotranspiration in a Humid Subtropical Region of China

The ability of the atmosphere to remove water from land surface is measured by potential evapotranspiration (ETp), which is often inferred by the reference evapotranspiration (ETo). Therefore, ETp is often considered only influenced by the above-ground meteorological conditions. Based on its concept, ETp should also link with surface conditions that influence the surface resistance. Such linkages differ in dry and wet regions with different surface covers. Here, we calculated ETo and especially analyzed the effects of surface conditions including vegetation cover indicated by NDVI (Normalized Difference Vegetation Index) and root-zone SWC (soil water content) in a humid subtropical province of China. Results show that ETp, NDVI, wind speed, temperatures have increased significantly during 1982–2015 and relative humidity (RH) has decreased significantly. Linear trends of these variables varied across seasons, but similarities were found between spring and winter and between summer and autumn. Summer saw the greatest changes in ETp per unit of environmental variable change. Solar radiation, RH, and precipitation exerted overall stronger influence on ETp (R2 &amp;gt; 0.50) than other factors. NDVI and SWC were found positively and negatively affecting ETp at all time scales. Partial correlation analysis showed significant influence of NDVI and SWC at the monthly scale; moreover, SWC influenced ETp more significantly in summer than other seasons (p &amp;lt; 0.05). Since actual evapotranspiration is often deducted from ETp by multiplicative stress functions in many hydrologic models, understanding the relationships between ETp and environmental changes can help improve the formulation and estimation of actual evapotranspiration.

Projected soil organic carbon loss in response to climate warming and soil water content in a loess watershed

BackgroundSoil organic carbon (SOC) plays a crucial role in the global carbon cycle and terrestrial ecosystem functions. It is widely known that climate change and soil water content (SWC) could influence the SOC dynamics; however, there are still debates about how climate change, especially climate warming, and SWC impact SOC. We investigated the spatiotemporal changes in SOC and its responses to climate warming and root-zone SWC change using the coupled hydro-biogeochemical model (SWAT-DayCent) and climate scenarios data derived under the three Representative Concentration Pathways (RCPs2.6, 4.5, and 8.5) from five downscaled Global Climate Models (GCMs) in a typical loess watershed––the Jinghe River Basin (JRB) on the Chinese Loess Plateau.ResultsThe air temperature would increase significantly during the future period (2017–2099), while the annual precipitation would increase by 2.0–13.1% relative to the baseline period (1976–2016), indicating a warmer and wetter future in the JRB. Driven by the precipitation variation, the root-zone SWC would also increase (by up to 27.9% relative to the baseline under RCP4.5); however, the SOC was projected to decrease significantly under the future warming climate. The combined effects of climate warming and SWC change could more reasonably explain the SOC loss, and this formed hump-shaped response surfaces between SOC loss and warming-SWC interactions under both RCP2.6 and 8.5, which can help explain diverse warming effects on SOC with changing SWC.ConclusionsThe study showed a significant potential carbon source under the future warmer and wetter climate in the JRB, and the SOC loss was largely controlled by future climate warming and the root-zone SWC as well. The hump-shaped responses of the SOC loss to climate warming and SWC change demonstrated that the SWC could mediate the warming effects on SOC loss, but this mediation largely depended on the SWC changing magnitude (drier or wetter soil conditions). This mediation mechanism about the effect of SWC on SOC would be valuable for enhancing soil carbon sequestration in a warming climate on the Loess Plateau.

Effects of environment and canopy structure on stem sap flow in a Larix principis-rupprechtii plantation.

Accurately quantifying the impacts of environmental factors and canopy structure on stem sap flow is of great significance for deeply understanding water use strategies of trees in changing environment. The stem sap flow of Larix principis-rupprechtii plantation was observed using thermal diffusion probes from June to September of 2019 in the Xiangshuihe small watershed of Liupan Mountains, with the meteorological conditions, root-zone soil water content and canopy structure being simultaneously recorded. We first analyzed the relationships of sap flow rate (Jc) to potential evapotranspiration (PET), relative extract water (REW) and canopy leaf area index (LAI), and then quantified their relative contribution to Jc. The results showed that the response of Jc to PET, LAI, and REW conformed to binomial, linearly increase and saturated exponential function, respectively. The Jc model coupling multiple factors was established as a continuous multiplication of the response functions of Jc to PET, REW and LAI, which had good simulation precision. PET was the main factor leading to the difference of Jc in different weather conditions. The average contribution rate of PET had obvious difference in sunny (with a contribution rate of 40.3%), cloudy (4.3%), and rainy days (-26.3%). PET and LAI were the leading factors affecting the Jc variation among months. The ranges of the contribution rates of PET and LAI were from -23.1% to 16.8% and from -12.3% to 11.0%, respectively. The Jc model coupling the multi-factor effect developed in this study could be used to predict Jc, and quantify the impacts of each leading factor, which had the potential to be an effective tool to analyze the water use of trees in the changing environment.

Agriculture in Iraq

The climate of Iraq is of the subtropical semi-dry type; however, the country was rich in water resources until a few decades ago. The climate change, the construction of many dams on the Tigris and Euphrates Rivers in the neighboring countries, wasting water, and mismanagement caused water shortages. Now, there is a need to decrease consumption, good management of water resources and determining the water requirements and water footprints of the major crops because agriculture is the first consumer of water. The FAO CROPWAT 8.0 simulation model, the CLIMWAT 2.0 tool, and the Aqua-Crop model can be used in Iraq to find the crop water requirements (CWR), irrigation schedules and water footprint (WF) for major crops, the Aqua-Crop software can predict the effects of water deficits on crop productivity or yield to improve irrigation under limited water conditions. All of that is to improve the management of water resources to cope with drought. The objectives of this proposal study beside the calculating of wheat water footprint are to assess the capability of the AquaCrop model to simulate wheat (Triticum aestivum L.) performance in hot dry conditions under full and deficient water conditions in south of Iraq, to study the effect of various irrigation scenarios (stages of crop growth and water depth applied) on wheat yield. The AquaCrop model will be evaluated with experimental data during the field experiment. The AquaCrop model can accurately simulate root zone, crop biomass and grain yield soil water content, with less than 10 percent standardized root mean square error (RMSE). Keywords: Water, Agriculture, AquaCrop, Iraq.

Neural network soil moisture model for irrigation scheduling

Real-time irrigation scheduling systems attempt to eliminate crop water stress and achieve a high yield at harvest through the control of soil moisture. Artificial intelligence algorithms are possibly to learn the soil moisture dynamics in the soil-plant-atmosphere system and then are embedded into a low-cost controller to generate appropriate irrigation schedules. In this study, a neural network (NN) model was proposed to learn from a process-based agricultural systems model, the Root Zone Water Quality Model (RZWQM2) in predicting the root zone soil moisture during the crop growing season. Climatic data, rooting depth and hesternal soil moisture are set as inputs of the NN model to predict intraday soil moisture in different layers. Conditions with and without water supply are modeled separately to achieve a higher accuracy. The NN-based irrigation scheduling method (NN method), triggers irrigation when the predicted soil moisture drops to a level defined by the product of management-allowed depletion multiplied by the depth of available water to plant. Irrigation quantity was set to replenish the root zone soil water content to field capacity. NN method was compared with the reported water stress (WS) method based on RZWQM2. The results reveal that though the constructed NN model well-predicted soil moisture changes during the main crop season with minor errors, but the error was larger at lower soil moisture thereby decreased the scheduling efficiency. An NN ensemble model was tested and shown to improve the precision and robustness of soil moisture prediction as well as the scheduling performance in view of water conservation and yield maintenance. Combined with the NN ensemble model and adjusted lowest soil moisture for triggering irrigations, the NN ensemble-based irrigation scheduling method achieved a performance not better than that of the RZWQM2-WS method but exceeded the method based on evapotranspiration and water balance by up to 20%. The constructed NN ensemble model and NN ensemble-based irrigation scheduling method could act as an alternative to predicting soil moisture and obtaining efficient irrigation scheduling.

Testing and Application of the AquaCrop Model for Wheat Production Under Different Field Management Conditions in South-Eastern Australia

A field experiment involving two spring wheat varieties (EGA Gregory and Livingston) was conducted for 2 years (2013 and 2014), late sown in the first year and early sown in the second year, under two soil water regimes (rainfed and supplemental irrigation) at Wagga Wagga, Australia. The FAO’s AquaCrop model version 4.0 was calibrated and validated for crop canopy cover, dry aboveground biomass, soil water content and grain yield. The root-mean-square error (RMSE) for grain yield and dry aboveground biomass was 0.293 and 2.2 t ha−1, respectively. The RMSE for the rootzone soil water content was 25 mm. The validated model was used to analyse the effect of in-season and off-season conditions on grain yield and water productivity. Grain yield and water productivity decreased (50% for Gregory and 43% for Livingston) with the delay in sowing date. Applying four irrigations to the mid-May sown wheat resulted in a higher (6.5, 5.7 and 5.2 t ha−1, respectively, at 80% exceedance probability) yield relative to the mid-April and mid-June sowing dates. Applying supplemental irrigation both in September and October resulted in a better yield (6.7 vs. 6.0 t ha−1) and water productivity than applying irrigation only in October. The effect of off-season managements such as mulch and pre-irrigation on yield is 68% higher in low-rainfall years that that of in the wet years.

Early prediction of wheat grain yield production from root-zone soil water content at heading using Crop RS-Met

Wheat production in drylands is determined greatly by the available water at the critical growth stages. In dry years, farmers usually face the dilemma of whether to harvest at an early stage for hay or silage, with reduced profit, or leave the crop for grain production with the risk of a major economic loss. Thus, an early prediction of potential wheat grain yield production is essential for agricultural decision making, particularly in water-limited areas.Here, we test whether using a proximal-based biophysical model of actual evapotranspiration (water use) and root-zone soil water content (SWC) – Crop RS-Met – may assist in providing early grain yield predictions in dryland wheat fields. Crop RS-Met was examined in eight experimental fields comprising a variety of spring wheat (Triticum aestivum L.) cultivars exposed to different treatments and amounts of water supply (185 mm - 450 mm). Crop RS-Met was first validated against SWC measurements at the root-zone profile. Then, modeled SWC at heading (SWCHeading) was regressed against end-of-season grain yields (GYEOS), which ranged from 1.30 tons ha−1 to 7.12 tons ha−1, for a total of 56 treatment blocks in 4 seasonal years (2014–2017).Results show that Crop RS-Met accurately reproduce seasonal changes in SWC with an average R2 of 0.89 ± 0.05 and RMSE and bias of 0.014 ± 0.004 m3 m−3 and -0.002 ± 0.004 m3 m−3, respectively. Modeled SWCHeading showed high and significant positive linear relationship with GYEOS (GYEOS[tons ha-1] = 0.080×SWCHeading[mm] - 5.387; R2 = 0.90; P < 0.001; N=56). Moreover, Crop RS-Met showed to be capable of accurately predicting GYEOS even in cases where water supply and grain yield had adverse relationships. Aggregating results to the field-scale level and classifying fields per water supply conditions resulted in an even stronger linear relationship (R2 = 0.94; P < 0.001; N=9). We conclude that Crop RS-Met may be used to predict GYEOS at heading in dryland fields for possible use by farmers in decision making at critical wheat growth stages.

Explicit wheat production model adjusted for semi-arid environments

Current literature suggests that wheat production models are limited either to wide-scale or plot-based predictions ignoring pattern of habitat conditions and surficial hydrological processes. We present here a high-spatial resolution (50 m) non-calibrated GIS-based wheat production model for predictions of aboveground wheat biomass (AGB) and grain yield (GY). The model is an integration of three sub-models, each simulating elemental processes relevant for wheat growth dynamics in water-limited environments: (1) HYDRUS-1D, a finite element model that simulates one-dimensional movement of water in the soil profile; (2) a two-dimensional GIS-based surface runoff model; and (3) a one-dimensional process-driven mechanistic wheat growth model. By integrating the three sub-models, we aimed to achieve a more accurate spatially continuous water balance simulation with a better representation of root zone soil water content (SWC) impacts on plant development. High-resolution grid-based rainfall data from a meteorological radar system were used as input to HYDRUS-1D. Twenty-two commercial wheat fields in Israel were used to validate the model in two seasons (2010/11 and 2011/12). Results show that root zone SWC was accurately simulated by HYDRUS-1D in both seasons, particularly at the top 10-cm soil layer. Observed vs simulated AGB and GY were highly correlated with R2 = 0.93 and 0.72 (RMSE = 171 g m−2 and 70 g m−2) having low biases of -41 g m−2 (8%) and 52 g m−2 (10%), respectively. Model sensitivity test showed that HYDRUS-1D was mainly driven by spatial variability in the input soil characteristics while the integrated wheat production model was mostly affected by rainfall spatial variability indicating the importance of using accurate high-resolution rainfall data as model input. Using the integrated model, we predict decreases in AGB and GY of c. 10.5% and c. 12%, respectively, for 1 °C of warming and c. 7.7% and c. 7.3% for 5% reduction in rainfall amount in our study sites. The suggested model could be used by scientists to better understand the causes of spatial and temporal variability in wheat production and the consequences of future scenarios such as climate change.

Crop RS-Met: A biophysical evapotranspiration and root-zone soil water content model for crops based on proximal sensing and meteorological data

Assessing crops water use is essential for agricultural water management and planning, particularly in water-limited regions. Here, we present a biophysical model to estimate crop actual evapotranspiration and root-zone soil water content using proximal sensing and meteorological data (Crop RS-Met). The model, which is based on the dual FAO56 formulation, uses a water deficit factor calculated from rainfall and atmospheric demand information to constrain actual evapotranspiration and soil water content in crops growing under dry conditions. We tested the Crop RS-Met model in a dryland experimental field comprising a variety of wheat (Triticum aestivum L. and T. durum) cultivars with diverse phenology. Crop RS-Met was shown to accurately capture seasonal changes in wheat water use during the growing season. The average R2 of modeled vs. observed soil water content for all cultivars (N = 11) was 0.92 ± 0.02 with average relative RMSE and bias of 9.29 ± 1.30% and 0.13 ± 0.03%, respectively. We found that changing the integration time period of the water deficit factor in Crop RS-Met affects the accuracy of the model implying that this factor has a vital role in modeling crop water use under dry conditions. Currently, Crop RS-Met has a simple representation of surface runoff and does not take into consideration heterogeneity in the soil profile. Thus, efforts to combine numerical models that simulate soil water dynamics with a Crop RS-Met model driven by high-resolution remote sensing data may be needed for a spatially continuous assessment of crop water use in fields with more complex edaphic characteristics.

Prediction of profile soil moisture for one land use using measurements at a soil depth of other land uses in a karst depression

Information on root-zone soil water content (SWC) is essential for vegetation restoration, irrigation scheduling, and hydrological modeling. However, measurements of SWC within a variety of land uses may be time-consuming and labor-costing. This study tested whether SWC at a depth of a land use can be used to predict profile SWC of other land uses in terms of temporal stability analysis at a karst depression area in southwest China. A total of 30 datasets of root-zone SWC from 0.1- to 0.5-m depths were collected by time domain reflectometry probes for three typical land uses from March 12 to November 8, 2015. Results showed that the profile mean SWC and its associated standard deviation (SDP) and coefficient of variation (CVP) differed significantly (P < 0.05) among the grassland, farmland, and forestland. The profile SWC was more temporally stable according to the apparently lower CVT in comparison with CVP. The similarities of the vertical patterns of SWC were strong for the same land uses, while were relatively weak between the different land uses. The SWC measurements of the most temporally stable depth can be used to accurately predict profile SWC for both the same land use and other land uses. This study further expands the application of the temporal stability analysis and can aid water resource management in areas with diverse land uses.

Pond and Irrigation Model (PIM): a Tool for Simultaneously Evaluating Pond Water Availability and Crop Irrigation Demand

Agricultural ponds are an important alternative source of water for crop irrigation to conserve surface and ground water resources. In recent years more such ponds have been constructed in Mississippi and around the world. There is currently, however, a lack of a tool to simultaneously estimate crop irrigation demand and pond water availability. In this study, a Pond-Irrigation Model (PIM) was developed to meet this need using STELLA (Structural Thinking, Experiential Learning Laboratory with Animation) software. PIM simulated crop land and agricultural pond hydrological processes such as surface runoff, soil drainage, and evapotranspiration as well as crop irrigation demand and pond water availability. More importantly, PIM was able to decide when to conduct crop irrigation based on management allowable depletion (MAD) root zone soil water content and to determine optimal ratios of agricultural pond size to crop land with sufficient pond water available for crop irrigation. As a case demonstration, the model was applied to concomitantly estimate row crops (i.e., corn, cotton, and soybeans) water irrigation demand and pond water availability in a farm located at East-central Mississippi. Simulations revealed that corn used more soil water for growth than soybeans, whereas soybeans needed more irrigation water than corn and occurred due to less rainwater available for soybeans growth. We also found that there was one time for corn, zero time for cotton, and two times for soybeans when the pond water level was drawn to near zero for irrigation from 2005 to 2014. PIM developed in this study is a useful tool for estimating crop irrigation demand and pond water availability simultaneously.