Model Of Soil Moisture Dynamics Research Articles

Data-Driven Modelling of Soil Moisture Dynamics for Smart Irrigation Scheduling

In the face of increasing water scarcity and uncertainties of climate change, improving crop water use efficiency and productivity, while minimizing negative environmental impacts, is becoming crucial to meet the surging global food demand. Smart irrigation has a potential of improving water use efficiency in precision agriculture especially when efficient irrigation control strategies are adopted. Conventionally, irrigation systems rely on heuristic methods to schedule irrigation which either leads to over-irrigation or under-irrigation. This influences the crop physiological characteristics as well as the water use efficiency. To tackle this menace, model-based irrigation management has been overemphasized. A closed-loop irrigation control strategy relies on a mathematical model of the system for irrigation scheduling decisions. In this study, a data-driven approach was used to learn soil moisture dynamics from a drip irrigated tomato in an open field agricultural system. A total number of 9674 data samples were collected using an ATMOS41 weather station, TERROS 12 soil moisture sensor and a YFS-201 flow sensor for crop evapotranspiration and precipitation, soil moisture and irrigation volumes respectively. Data driven modelling was then performed using the system identification toolbox in a MATLAB environment. The model formulation was a multi-input single-output (MISO) system with reference evapotranspiration, irrigation and rainfall as inputs and soil moisture as the output. Different model structures including transfer functions, state space models, polynomial models and ARX models were evaluated. Model performance was evaluated using the mean square error (MSE), final prediction error (FPE) and estimated fit of the model approaches. Simulation results indicate that the soil moisture dynamics model provides a satisfactory approximation of the process dynamics with a state space model giving an estimated fit of 97.04 %, MSE and FPE of 1.74×10−7 and 1.75×10−7 respectively. This model will be used to design a model predictive controller for smart irrigation scheduling in open field environmental conditions.

Two-Layer numerical model of soil moisture dynamics: Model assessment and Bayesian uncertainty estimation.

A two-layer model based on the integrated form of Richards' equation (RE) was recently developed to simulate the soil water movement in the roots layer and the vadose zone with a relatively shallow and dynamic water table. The model simulates thickness-averaged volumetric water content and matric suction as opposed to point values and was numerically verified for three soil textures using HYDRUS as a benchmark. However, the strengths and limitations of the two-layer model and its performance in stratified soils and under actual field conditions have not been tested. This study further examined the two-layer model using two numerical verification experiments and, most importantly, tested its performance at site-level under actual, highly variable hydroclimate conditions. Moreover, model parameters were estimated and uncertainty and sources of errors were quantified using a Bayesian framework. First, the two-layer model was evaluated for 231 soil textures under varying soil layer thicknesses with a uniform soil profile. Second, the two-layer model was assessed for stratified conditions where the top and bottom soil layers have contrasting hydraulic conductivities. The model was evaluated by comparing soil moisture and flux estimates to those from the HYDRUS model. Last, a case study of model application using data from a Soil Climate Analysis Network (SCAN) site was presented. Bayesian Monte Carlo (BMC) method was implemented for model calibration and quantifying sources of uncertainty under real hydroclimate and soil conditions. For a homogeneous soil profile, the two-layer model generally had excellent performance in estimating volumetric water content and fluxes, while the model performance slightly declined with increasing layer thickness and coarser textured soils. The model configurations regarding layer thicknesses and soil textures that generate accurate soil moisture and flux estimations were further suggested. With the two layers of contrasting permeability, model-simulated soil moisture contents and fluxes agreed well with those computed by HYDRUS, indicating that the two-layer model accurately handles the water flow dynamics around the layer interface. In the field application, given the highly variable hydroclimate conditions, the two-layer model combined with the BMC method showed good agreement with the observed average soil moisture of the root zone and the vadose zone below (RMSE <0.021 during calibration and <0.023 during validation periods). The contribution of parametric uncertainty to the total model uncertainty was too small compared to other sources. The numerical tests and the site level application showed that the two-layer model can reliably simulate thickness-averaged soil moisture and estimate fluxes in the vadose zone under various soil and hydroclimate conditions. Results also indicated that the BMC method could be a robust framework for vadose zone hydraulic parameters identification and model uncertainty estimation.

SIMULATION OF SOIL HUMIDITY CHANGES IN THE PROCESS OF THE FRIGATE IRRIGATION MACHINE OPERATION

In this article, the authors analyze the modern methods and means of soil moistening, as well as existing mathematical models of soil moisture dynamics during the operation of the "Frigate" sprinkler. The necessity of developing a model of moisture distribution in the root and near-surface layers of the soil is substantiated. A mathematical model of soil moisture dynamics in the space of states has been developed, which takes into account the volumes of water consumed by plants and fed to the fields by sprinklers, and also comes naturally in the form of random precipitation and groundwater.

Modelling of soil moisture change during the operation of the “Frigate” sprinkler

The irrigation process is one of the most important elements of technologies for growing crops on irrigated soils. The tasks of ensuring optimal soil water regime, eliminating gravita-tional water losses outside the zone of active moisture exchange and promoting the fullest use of plants of bioclimatic potential of the region require modeling of soil moisture dynamics in the process of wetting, as well as structures and technologies related to irrigation facilities. Soil moistening in the south of Ukraine is carried out mainly by sprinkling with the use of a Frigate sprinkler, which is largely close to the natural processes of moisture in soil structures, especially with the use of the latest Xi-Wob sprinklers, which provide uniform irrigation and uniform droplet size. The aim of the article is to develop a model of soil moisture change, which will determine the moisture content in both the root and near-surface layers of the soil and prognoses the movement parameters of the "Frigate" sprinkler elements. The designs of sprinklers currently available allow to provide uniform intensity of spraying of drops on all working area of "Frigate" sprinkler, therefore it is possible to choose a separate point for modeling. The data obtained for one point can be extended to the entire work area. The Darcy equation is used to mathematically describe the movement of water in the soil. The finite difference method was used to solve the Darcy equation based on the initial and boundary conditions. Assuming that within each soil element the humidity changes linearly, and replacing the partial derivatives of moisture in depth and time with the analogues, the discretized model of soil moisture dynamics in the final differences was obtained, which allows you to predict moisture at any time due to hydro-soil characteristics in previous time and boundary conditions. Discretization of the Darcy equation made it possible to obtain a model of the dynamics of the humidity of the root layer in the space of states. It turned out to be nonlinear due to the hydrophysical characteristics of the soil and was linearized by transformation into the Taylor series. The developed mathematical model of soil moisture change in the space of states allows to determine the moisture content in both the root and near-surface soil layer depending on its hydrological characteristics, type of cultivated plants, volume of water entering the fields from irrigation systems or in the form of random precipitation used to predict the movement parameters of the "Frigate" sprinkler elements.

Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting

Abstract. Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ETo) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ETo forecasting provides valuable information for effective water resource management and planning. Climate forecasts from general circulation models (GCMs) have been increasingly used to produce seasonal ETo forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in GCM-based ETo forecasts. However, time-dependent errors resulting from GCM misrepresentations of climate trends have not been explicitly corrected in ETo forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ETo forecasts. To test this hypothesis, we calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian joint probability trend-aware (BJP-ti) model. Raw ETo forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 (%) in regions where climate trends are misrepresented by raw forecasts. Skillful ETo forecasts produced in this study could be used for streamflow forecasting, modeling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ETo forecasting and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ETo forecasting will benefit from correcting time-dependent errors through trend reconstruction.

A two-layer numerical model of soil moisture dynamics: Model development

Simulating water moisture flow in variably saturated soils with a relatively shallow water table is challenging due to the high nonlinear behavior of Richards' equation (RE). A two-layer approximation of RE was derived in this paper, which describes vertically-averaged soil moisture content and flow dynamics in the root zone and the unsaturated soil below. To this end, the partial differential equation (PDE) describing RE was converted into two-coupled ordinary differential equations (ODEs) describing dynamic vertically-averaged soil moisture variations in the two soil zones subject to a deep or shallow water table in addition to variable soil moisture flux and pressure conditions at the surface. The coupled ODEs were solved numerically using the iterative Huen's method for a variety of flux and pressure-controlled top and bottom boundary conditions (BCs). The numerical model was evaluated for three typical soil textures with free-drainage and mixed flux-pressure head at the bottom boundary under various atmospheric conditions. The results of soil water contents and fluxes were validated using HYDRUS-1D as a benchmark. Simulated values showed that the new model is numerically stable and generally accurate in simulating vertically-averaged soil moisture in the two layers under various flux and prescribed pressure BCs. A hypothetical simulation scenario involving desaturation of initially saturated soil profile caused by exponentially declining water table demonstrated the robustness of the numerical model in tracking vertically-averaged moisture contents in the roots layer and the lower vadose soil as the water table continued to fall. The two-layer model can be used by researchers to simulate variably saturated soils in wetlands and by water resources planners for efficient coupling of land-surface systems to groundwater and management of conjunctive use of surface and groundwater.

ПОЧВЕННО-ГИДРОФИЗИЧЕСКОЕ ИНФОРМАЦИОННОЕ ОБЕСПЕЧЕНИЕ ПРЕЦИЗИОННОГО ИРРИГАЦИОННОГО ЗЕМЛЕДЕЛИЯ

The relevance of the study is determined by the demand for a physically adequate mathematical description of the interactions of water in the soil to develop a model of soil moisture dynamics as the intellectual core of resource-saving technologies for precise irrigation farming. The aim of the work is theoretical substantiation and mathematical formulation of the hydrophysical functions of the soil, taking into account hysteresis. A description of three systems of soil hydrophysical functions is given. To verify and compare the systems, computational experiments were carried out using both the package of original software and “3305 Ida silt loam (&gt; 15 cm)” soil data from the authoritative literary source – the Mualem catalogue. The parameters of the functions were identified by the method of point approximation of the experimental data on the main branches of the hysteretic water-retention capacity. Using these parameters, we calculated (i) predictive estimates for the values of the function of relative hydraulic conductivity; (ii) scanning branches of the hysteretic water-retention capacity; (iii) precise irrigation rate. The hysteresis phenomenon is not typical for the hydraulic conductivity as a function of the volumetric water content in the soil. The original functions of System 3 are recommended for use. The advantages of the proposed method for calculating the precise irrigation rate are shown. The benefit of each system is that the functions forming this system, namely the water-retention capacity and the relative hydraulic conductivity of the soil, have a common set of parameters. For the type of soil considered, in case of using the identical value of pre-irrigation soil moisture (179 [cm3 · cm-3]), both for calculating the precision irrigation rate and according to the “traditional” method, when moistening 50 cm soil layer, the total unproductive water consumption at irrigation rate 555 [m3 · ha-1] can reach 0.029 [cm3 · cm-3] or 140 [m3 · ha-1] in the calculated layer. At the same time, when applying precision standards, an excess of free moisture is not formed. It shows additional opportunities not only to save water during irrigation, especially in arid regions, but also to reduce the leaching of nutrients and agrochemicals outside the calculated soil layer and, accordingly, to reduce the additional environmental load on the surrounding area.

Experimental evaluation of different subsurface water movement models in an agricultural field with soil heterogeneity

Spatial heterogeneity in soil hydraulic properties poses severe challenges to the modeling of soil moisture dynamics in agricultural fields. For simplicity, 1-D water flow models that ignore soil heterogeneity are commonly used to simulate water movement in agricultural fields. As soil hydraulic properties are one of the most crucial input data, it is particularly desirable to investigate how the simulations of these water flow models compare with experimental observations. This study aims to examine the performances of streamtube, 1-D, and 3-D Richards equation based water flow models with experimental observations. Experiments were conducted at an agricultural plot of 20 m × 30 m located in IIT Kanpur, India, for 1 year under wheat (Triticum aestivum L.) and rice (Oryza sativa L.) crop covers. The site was heavily instrumented to monitor soil moisture variation, weather conditions, root depth, and LAI. Further, 54 undisturbed soil samples were analyzed to determine the heterogeneity in soil hydraulic parameters. The results indicate that the performance of the models varies with land cover, atmospheric boundary condition, and crop growth stages. Besides, the difference between model estimates varies with soil moisture wetness and a maximum relative error of 13% was observed under wetting conditions. The streamtube model showed the best performance under wheat crop cover for all boundary conditions and growth stages, as it considers spatial variability in soil hydraulic properties and irrigation application at each subplot. The numerical-1D model was the best predictor under rice crop cover. Further, the results suggest that an increase in model complexity does not lead to an improvement in water simulation, and accurate representation of soil hydraulic properties plays a more important role.

Investigating the effect of limited climatic data on evapotranspiration-based numerical modeling of soil moisture dynamics in the unsaturated root zone: a case study for potato crop

Investigating the effect of limited climatic data on evapotranspiration-based numerical modeling of soil moisture dynamics in the unsaturated root zone: a case study for potato crop

Accounting for landscape heterogeneity improves spatial predictions of tree vulnerability to drought.

As climate change continues, forest vulnerability to droughts and heatwaves is increasing, but vulnerability varies regionally and locally through landscape position. Also, most models used in forecasting forest responses to heat and drought do not incorporate relevant spatial processes. In order to improve spatial predictions of tree vulnerability, we employed a nonlinear stochastic model of soil moisture dynamics accounting for landscape differences in aspect, topography and soils. Across a watershed in central Texas we modeled dynamic water stress for a dominant tree species, Juniperus ashei, and projected future dynamic water stress through the 21st century. Modeled dynamic water stress tracked spatial patterns of remotely sensed drought-induced canopy loss. Accuracy in predicting drought-impacted stands increased from 60%, accounting for spatially variable soil conditions, to 72% when also including lateral redistribution of water and radiation/temperature effects attributable to aspect. Our analysis also suggests that dynamic water stress will increase through the 21st century, with trees persisting at only selected microsites. Favorable microsites/refugia may exist across a landscape where trees can persist; however, if future droughts are too severe, the buffering capacity of an heterogeneous landscape could be overwhelmed. Incorporating spatial data will improve projections of future tree water stress and identification of potential resilient refugia.

The IoT as a tool to combine the scheduling of the irrigation with the geostatistics of the soils

Persistent droughts, population growth, and consequences of the climate change put severeconstraints on agriculture in many Regions. This emergence can be ameliorated by: (i) either increasing the water supply, or (ii) reducing the water demand. One viable avenue is to enhance the irrigation’s efficiency by increasing on one hand the water supply (via the use of recycled waters), and on the other by decreasing the water consumption (via the use of drip irrigation, autonomous network of sensors and predictive models). To this aim, we propose an IoT-framework to assess and control the environmental risks associated with the use of recycled water(s). The IoT-framework is organized along the following main streams:(i) design an autonomous network of sensors that collect data about soil moisture and concentration of dissolved contaminants; (ii) assimilate these data together with precipitation forecast into predictive models of soil moisture dynamics and contaminant migration; (iii) use these data-driven models to optimize the irrigation practices while minimizing their environmental impact. The fundamental pre-requisite common to (i)–(iii) is the proper processing of the soil data. The present paper will focus on such a topic by showing how it works within the IoT-framework.

Managing stand density to enhance the adaptability of Scots pine stands to climate change: A modelling approach

In the Mediterranean region most climatic forecasts predict longer and more intense drought periods that can affect tree growth and mortality over broad geographic regions. One of the silvicultural treatments that has gained currency to lessen the impacts of climatic change is the reduction of stand density by thinning. However, we lack information on how the response of forest stands to different thinning treatments will be affected by climate change, and on the post-thinning temporal dynamics of water balance, specifically blue and green water. We adopted a modelling approach to explore the long-term effects of different thinning intensities on forest dynamics and water balance under climate change scenarios, coupling an individual-based model of forest dynamics (SORTIE-ND) with a mechanistic model of soil moisture dynamics and plant drought stress. We used as a case study three Scots pine plots across a gradient of climatic conditions, and we assessed the effect of site, three climatic scenarios and eight thinning intensities on tree growth, stand productivity, tree drought stress and blue water. The best thinning intensity in terms of stand productivity was obtained when between 20 and 40% of the basal area was removed, whereas the final stand stock rapidly decreased at higher thinning intensities. Moreover, the decrease in final basal area occurred at lower thinning intensities the drier the site conditions. Moderate and heavy thinnings (>30%) doubled basal area increment (BAI) of the following years in all the plots, although the effect vanished after 30–40 years, independently of the site and climate scenario. As expected, thinning was simulated to have an overall positive effect on the blue water yield and tree water status, which increased and also tended to last longer for higher thinning intensities. However, the magnitude of this effect on tree water status was most dependent on the site and climatic scenario, as drier conditions generally raised stronger and longer lasting reductions in drought stress for a given thinning intensity. Furthermore, our results highlight the existence of a site- and climate-dependent trade-off between the gain in stand productivity and the improvement in tree water status obtained by thinning, particularly for moderate or heavy thinning intensities. Our simulations suggest that thinning is a useful management tool to mitigate climate change but strongly argue against the application of general recipes across sites and appeals for carefully taking into consideration local climatic trajectories for management planning.

Time series modeling of soil moisture dynamics on a steep mountainous hillside

Summary The response of soil moisture to rainfall events along hillslope transects is an important hydrologic process and a critical component of interactions between soil vegetation and the atmosphere. In this context, the research described in this article addresses the spatial distribution of soil moisture as a function of topography. In order to characterize the temporal variation in soil moisture on a steep mountainous hillside, a transfer function, including a model for noise, was introduced. Soil moisture time series with similar rainfall amounts, but different wetness gradients were measured in the spring and fall. Water flux near the soil moisture sensors was modeled and mathematical expressions were developed to provide a basis for input–output modeling of rainfall and soil moisture using hydrological processes such as infiltration, exfiltration and downslope lateral flow. The characteristics of soil moisture response can be expressed in terms of model structure. A seasonal comparison of models reveals differences in soil moisture response to rainfall, possibly associated with eco-hydrological process and evapotranspiration. Modeling results along the hillslope indicate that the spatial structure of the soil moisture response patterns mainly appears in deeper layers. Similarities between topographic attributes and stochastic model structures are spatially organized. The impact of temporal and spatial discretization scales on parameter expression is addressed in the context of modeling results that link rainfall events and soil moisture.

A minimal probabilistic model for soil moisture in seasonally dry climates

AbstractIn seasonally dry climates, a distinct rainy season is followed by a pronounced dry season during which rainfall often makes a negligible contribution to soil moisture. Using stochastic analytical models of soil moisture to represent the effects of this seasonal change has been hindered by the need to mathematically represent the stochastic influence of wet season climate on dry season soil water dynamics. This study presents a simple process‐based stochastic model for soil moisture dynamics, which explicitly models interseasonal transient dynamics while accounting for carry over soil moisture storage between the wet and dry seasons, and allows a derivation of an analytical expression for the dry season mean first passage time below a soil moisture threshold. Such crossing times pose controls on both vegetation productivity and water stress during dry summers. The new model, along with an existing model that incorporates nonzero dry season rainfall but not variability in the soil moisture condition at the start of the dry season, are tested against data from the Tonzi Ranch Ameriflux site. Both models predict first passage times well for high soil moisture thresholds, but the new model improves prediction at lower thresholds. The annual soil moisture probability distribution function (PDF) from the new model also compares well with observations.

An ecohydrological approach to conserving urban water through optimized landscape irrigation schedules

Despite the high percentage of residential water used outdoors, particularly in arid climates, there is a poor understanding of the fate of water used to maintain urban landscapes. Furthermore, opportunities exist for water conservation through improved irrigation schedules that take advantage of seasonal weather patterns and the natural ability of plants to resist water stress. This study uses a calibrated numerical model of soil moisture dynamics with meteorological data to determine irrigation schedules that minimize outdoor water use while maintaining specified levels of plant water stress. Results suggest annual irrigation well below local municipal recommendations, and substantial modifications to recommended schedules. Differences between xeric (low water use, drip irrigators, and gravel cover) and mesic (high water use, sprinklers, and turfgrass) landscaping, with respect to irrigation scheduling, soil water losses, plant water stress, and potential water savings, are presented to improve planning and maintenance of urban landscapes, particularly in water-scarce regions.

Modelling soil moisture, water partitioning, and plant water stress under irrigated conditions in desert urban areas

ABSTRACTThis study conducts a comparative analysis of the effects of irrigation scheduling at two urban residential sites in the Phoenix, Arizona, metropolitan area: a xeriscaped site (gravel base with low water use plants) and a mesiscaped site (turf grass and shade trees). A model of soil moisture dynamics was calibrated to observed soil moisture data from irrigated and non‐irrigated sensors at the xeric site and an irrigated sensor at the mesic site. Model simulations were conducted at both irrigated sites to investigate effects of irrigation scheduling; plant stress parameters; and precipitation variability on soil moisture dynamics, water balance partitioning, and plant water stress. Results indicated a substantial difference in soil water storage capacity at the two sites. At the xeric site, seasonal variation of irrigation input was shown to be critical in avoiding losses from the rooting zone, while reducing plant water stress. At the mesic site, sensitivity to irrigation scenarios was lower, though small amounts of water savings were achieved with seasonal applications. The model was then used to determine the minimum annual irrigation required to achieve specified levels of plant stress using long‐term meteorological records. A bimodal schedule consisting of low winter and summer irrigation was identified as a means to conserve water while keeping moderate levels of dynamic water stress. Results from the quantitative model can potentially assist water and landscape managers in desert urban areas by identifying opportunities for water savings, while providing a deeper understanding of the ecohydrological differences between the two types of landscape designs. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Revisiting the Two-Layer Hypothesis: Coexistence of Alternative Functional Rooting Strategies in Savannas

The two-layer hypothesis of tree-grass coexistence posits that trees and grasses differ in rooting depth, with grasses exploiting soil moisture in shallow layers while trees have exclusive access to deep water. The lack of clear differences in maximum rooting depth between these two functional groups, however, has caused this model to fall out of favor. The alternative model, the demographic bottleneck hypothesis, suggests that trees and grasses occupy overlapping rooting niches, and that stochastic events such as fires and droughts result in episodic tree mortality at various life stages, thus preventing trees from otherwise displacing grasses, at least in mesic savannas. Two potential problems with this view are: 1) we lack data on functional rooting profiles in trees and grasses, and these profiles are not necessarily reflected by differences in maximum or physical rooting depth, and 2) subtle, difficult-to-detect differences in rooting profiles between the two functional groups may be sufficient to result in coexistence in many situations. To tackle this question, I coupled a plant uptake model with a soil moisture dynamics model to explore the environmental conditions under which functional rooting profiles with equal rooting depth but different depth distributions (i.e., shapes) can coexist when competing for water. I show that, as long as rainfall inputs are stochastic, coexistence based on rooting differences is viable under a wide range of conditions, even when these differences are subtle. The results also indicate that coexistence mechanisms based on rooting niche differentiation are more viable under some climatic and edaphic conditions than others. This suggests that the two-layer model is both viable and stochastic in nature, and that a full understanding of tree-grass coexistence and dynamics may require incorporating fine-scale rooting differences between these functional groups and realistic stochastic climate drivers into future models.

Coupled stochastic soil moisture simulation‐optimization model of deficit irrigation

This study presents an explicit stochastic optimization‐simulation model of short‐term deficit irrigation management for large‐scale irrigation districts. The model which is a nonlinear nonconvex program with an economic objective function is built on an agrohydrological simulation component. The simulation component integrates (1) an explicit stochastic model of soil moisture dynamics of the crop‐root zone considering interaction of stochastic rainfall and irrigation with shallow water table effects, (2) a conceptual root zone salt balance model, and 3) the FAO crop yield model. Particle Swarm Optimization algorithm, linked to the simulation component, solves the resulting nonconvex program with a significantly better computational performance compared to a Monte Carlo‐based implicit stochastic optimization model. The model has been tested first by applying it in single‐crop irrigation problems through which the effects of the severity of water deficit on the objective function (net benefit), root‐zone water balance, and irrigation water needs have been assessed. Then, the model has been applied in Dasht‐e‐Abbas and Ein‐khosh Fakkeh Irrigation Districts (DAID and EFID) of the Karkheh Basin in southwest of Iran. While the maximum net benefit has been obtained for a stress‐avoidance (SA) irrigation policy, the highest water profitability has been resulted when only about 60% of the water used in the SA policy is applied. The DAID with respectively 33% of total cultivated area and 37% of total applied water has produced only 14% of the total net benefit due to low‐valued crops and adverse soil and shallow water table conditions.

Monitoring and Modeling Farmland Productivity Along the Venice Coastland, Italy

The southern portion of the Venice coastland is a very precarious environment and salt contamination of land and groundwater is a severe problem that is seriously impacting the farmland productivity. Geophysical surveys, lab testing and continuous monitoring of hydrological parameters together with crop yield distribution were performed and acquired from 2010 to 2012 in a 21ha basin cultivated with maize crop and representative of the area. The dataset is here used to set-up a numerical model of soil moisture dynamics coupled with plant transpiration, photosynthesis and growth. The hydraulic model is linked to the atmosphere by the calculation of the stomatal conductance which is optimized for maximum carbon gain. The model is applied to the field site to understand the impact of land elevation, soil heterogeneities, and seawater contamination on land productivity.

Probabilistic modeling of soil moisture dynamics in a revegetated desert area

Soil moisture is the key link between land hydrological and ecological processes which plays an important role in the terrestrial water cycle. As extreme weather events have increased in recent years, the stochastic simulation of soil moisture has gradually become the focus of ecohydrology research. Based on continuous monitoring of soil moisture data from 2008 to 2011, and historical precipitation data from 1991 to 2011, combined with the Rodriguez-Iturbe soil moisture dynamic stochastic model, soil moisture dynamics and its probability density function in a revegetated desert area was simulated. Results show that annual soil moisture dynamic changes of the revegetated desert area during the growing season complied with rainfall distribution; soil moisture probability presents a single-peak distribution in the plant rhizosphere layer (0–60 cm). The peak width in the 20 cm topsoil was wider than in other soils, and the distribution presented the strong fluctuations and multiple aggregates. The peak widths of 40 cm and 60 cm soil moisture probability distribution were small, which are in accordance with simulated results of the Rodriguez-Iturbe model. This confirms that the Rodriguez-Iturbe model has good applicability and can well simulate the statistical characteristics of soil moisture in an arid revegetated desert area.