Soil Water Atmosphere Plant Model Research Articles

Optimization of winter irrigation management for salinized farmland using a coupled model of soil water flow and crop growth

Drip irrigation under film mulch (DIUFM) and subsurface pipe drainage (SPD) are important measures to cope with water shortages and soil salinization in arid areas of northwest China. To investigate the coordinated operation mode of DIUFM and SPD, a new H2DSWAP model was developed based on coupling the HYDRUS-2D with a Soil–Water–Atmosphere–Plant model (SWAP). In the H2DSWAP model, real-time evapotranspiration, simple root growth, and the interaction between crops and soil water and salt are considered to improve the simulation accuracy. The model was calibrated and validated using parameter estimation and uncertainty analysis software (PEST) using field experiment datasets in 2019 and 2020 respectively. Compared with the original HYDRUS-2D model, the simulation accuracy of the H2DSWAP for soil water (root mean square error (RMSE) = 0.011 cm3·cm−3; mean absolute error (MAE) = 0.008 cm3·cm−3; determinant coefficient (R2) = 0.869) and soil salt (RMSE = 0.296 g·kg−1; MAE = 0.231 g·kg−1; R2 = 0.959) contents has been greatly improved. In addition, the simulation of the leaf area index (LAI) and yield also fitted well with field observations. The calibrated model was used to predict salt transport at depths of 0–100 cm and the change in maize yield under DIUFM. The results indicated that maize yield decreased yearly, and soil salinity increased yearly under DIUFM without SPD. Based on the obtained results, the management strategies of winter irrigation (WIR) under different drip irrigation water amounts, namely 600 mm (S1), 540 mm (S2), 480 mm (S3), and 420 mm (S4), were further investigated. Water productivity (WP) and yield were used as evaluation indices. The WIR was carried out every 4–5 years when the drip irrigation amount of the maize field was S1 and S2 was recommended. However, it was recommended to carry out WIR every 2 and 3 years under S3 and S4, respectively. Overall, the H2DSWAP model can be used as a useful tool to guide the operation mode of drip irrigation under mulch and subsurface pipe drainage in saline soils.

Modeling effectiveness of two runoff mitigation measures in the Netherlands

Rainfall that exceeds the soil's maximum infiltration rate is prone to runoff, and the excess rainfall will flow toward open water systems. Nutrients, pesticides or other contaminants may be transported along with this overland flow, thus contaminating surface waters. There are various measures that can be implemented to prevent or reduce runoff, which involve either improving the soil's infiltration capacity or temporarily storing more water at the field scale. The aim of this study was to determine the effectiveness of two mitigation measures, i.e., micro-dams and edge-of-field trenches, in reducing the total number of runoff events and the runoff volume for specific rainfall events. For this purpose, numerical simulations were performed with a deterministic soil–water–atmosphere–plant model for reference situations and for situations involving either of the two mitigation measures. The mitigation measures are implemented as a change in the ponding threshold height above which the model predicts runoff. For this purpose, we considered several soil / groundwater level / crop / intrinsic field soil surface storage situations that are common in the Netherlands. For ridge-furrow cropping systems, micro-dams are more effective than edge-of-field trenches. Depending on the soil type (excluding sand), the minimum effectiveness is 70% and may be >90% in specific situations. For the edge-of-field trench, the reduction in runoff events was mostly in the 24–35% range, while the effectiveness for the runoff volume for a rainfall event that typically occurs once per year was in the 13–48% range (excluding sand). Due to the relatively high hydraulic conductivity at saturation for the sandy soils, runoff was simulated in only a few cases for these soils. The effectiveness was evidently dependent on intrinsic field soil surface storage and soil types, varied slightly between crop types and was very similar across the groundwater level classes considered.

Effect of hardpan on the vertical distribution of water stress in a converted paddy field

Upland fields converted from paddy fields often have a hardpan layer that can cause excessive wet and dry conditions. In this study, the water stress affected by this hardpan was evaluated. Data were obtained from a converted maize field that was divided into plots with and without hardpan. A soil-water-atmosphere-plant (SWAP) model was used to estimate the water stress conditions, such as the reduction in the daily transpiration rate and the vertical distribution of reduction in root water uptake for both plots. When hardpan was present, stress due to wet and dry conditions was more intense in the layer above the hardpan, and dry condition persisted after the hardpan dried. Without hardpan, the wet stress decreased more quickly than when the hardpan was present. The vertical distributions of reduction in root water uptake revealed that dry and wet conditions occasionally occurred at the same time in both plots. In this situation, without hardpan, the shallow layer dried and the deeper layer was wet. With hardpan, the same condition occurred, or the hardpan dried and other shallower and deeper layers were wet. It was considered that the latter condition was a unique characteristic of plots with hardpan—converted fields. The vertical distributions of reduction in root water uptake with the SWAP model can be used as an index to examine the relationship between water stress and tillage practices.

Sensitivity analysis of the SWAP (Soil-Water-Atmosphere-Plant) model under different nitrogen applications and root distributions in saline soils

Sensitivity analysis is important for determining the parameters in the model calibration process. In our study, a variance-based global sensitivity analysis (extended Fourier amplitude sensitivity test, EFAST) was applied to an agro-hydrological model (the SWAP (Soil-Water-Atmosphere-Plant model) model). The sensitivities of 20 parameters belonging to 4 categories (soil hydraulics, solute transport, root water uptake, and environmental stresses) for the simulated accumulated transpiration, dry matter (DM), and yield of sunflowers were analyzed under three nitrogen application rates (N1, N2, and N3), four salinity levels (S1, S2, S3, and S4), and three root distributions (R1, R2, and R3). The results indicated that for predominantly loamy soils, the high-impact parameters for accumulated transpiration, DM, and yield were the soil hydraulic parameters (α and n), critical stress index for compensatory root water uptake (ωc), the salt level at which salt stress starts (Pi), the decline of root water uptake above Pi (SSF), residual water content (θr), saturated water content (θs), and relative uptake of solutes by roots (TSCF). We also found that nitrogen application did not change the order of the parameter impacts on accumulated transpiration, DM, and yield. However, TSCF replaced α as the highest-impact parameter for the accumulated transpiration, DM, and yield at high salinity levels (S2 and S3). Furthermore, α was also the highest-impact parameter for DM and yield under different root distributions, but the highest-impact parameters for transpiration were ωc, α, and θs under R1, R2, and R3, respectively. Nitrogen application could be neglected when considering the interactive effects of nitrogen application, salinity level, and root distribution on the transpiration, DM, and yield. Additionally, the mean values and uncertainties of the transpiration, DM, and yield were similar in all scenarios, except S3, which showed a sharp decrease in the mean values. We suggest determining the above eight parameters (α, n, ωc, Pi, SSF, θr, θs, and TSCF) and the saturated vertical hydraulic conductivity (KS) based on rigorous calibrations with direct or indirect local measurements using economical methods (e.g., a literature review), with limited observations for other parameters when using the SWAP model and other similar agro-hydrological models.

Role of Short-Term Weather Forecast Horizon in Irrigation Scheduling and Crop Water Productivity of Rice

Crop simulation models in conjunction with weather forecasts help in understanding soil-water-plant interactions in real time, and thus in effective management of irrigation water. However, selection of the optimal forecast horizon for use with irrigation scheduling is challenging in the context of uncertainties associated with weather forecasts. This study is aimed at evaluating the effectiveness of the Indian Meteorological Department (IMD) short-term weather forecasts with different forecast horizons (1, 3, and 5 days) in simulating crop water and yield dynamics of rice. Rule-based irrigation is triggered by specifying thresholds on the current day’s ponding depth (do), precipitation (Po), and evapotranspiration (ETo) as well as their forecast values (∑i=1nPi; ∑i=1nETi; n=1,3,5). The IMD forecasts of temperature, humidity, wind speed, and cloud cover were translated into ETo forecasts using the Penman-Monteith equation and region-specific crop coefficients. The Soil–Water–Atmosphere–Plant (SWAP) model was modified to simulate soil–water and plant growth conditions by considering the three forecast scenarios along with conventional irrigation (ignoring weather forecast), and a hypothetical perfect 5-day forecast (reference). Experiments were conducted in four paddy fields in a command area of south India for two monsoon seasons (2018 and 2019) to calibrate and parameterize the SWAP model. The accuracy of forecast variables is increased with increase in length of forecast horizon (root-mean-square error = 12.5±0.98, skill score = 0.6%±0.03% at 1-day lead to 5.9±2.5, 22.4%±7.8% at 5-day lead) due to averaging. Conventional irrigation has resulted in the highest use of irrigation water (912±12.5 mm), percolation losses (1,245±19.5 mm), and electricity (806.5±100.5 kW), achieving a low yield (2,331±138 kg ha−1). Irrigation scheduling with 5-day forecast horizon outperformed other scenarios (69% water saving and 23% higher yield), and is slightly inferior to the hypothetical perfect forecast. Our results conclude that IMD forecasts, though moderately reliable at multiple lead times, can serve as a valuable tool in scheduling irrigation activities for sustainable management.

Optimizing agricultural water use through simulation of soil water content and water uptake under soil moisture and irrigation water salinity stresses: Case study of corn roots using SWAP model

In order to evaluate soil water content and water uptake by corn plant roots (Single cross 260) under different soil moisture and water salinity stresses using the agro-hydrological Soil–Water–Atmosphere–Plant (SWAP) model, an experiment was conducted in 2015 at the green house of the Agriculture Research Center of Shahrood, Iran. The statistical model employed was a split plot based on a randomized complete block design with three replications. The main plots consisted of three levels of irrigation; 50 (I1) (full irrigation as the control), 75 (I2) and 100% (I3) of Total Available Water (TAW) depletion while the sub-plots consisted of three levels of salinity of irrigation water; 2 (S1), 4 (S2) and 6 (S3) ds.m-1. The results showed a good correspondence between the simulated soil moisture, water uptake and measured values. The normalized root mean square error (nRMSE) and the root mean square error (RMSE) values of the predicted soil moisture were 4.58 and 24.96 and for the water uptake by the roots were 23.37 and 35.48, respectively. The R2 of coefficient of simulation for water uptake by roots in different treatments were 0.38 to 0.8. The dataset of the predicted and measured values were close to the 1:1 scale line for both soil moisture and water uptake. This study indicated that the SWAP model can be used as a powerful tool to simulate field water cycle and evaluate irrigation practices. Accordingly, taking into account the existing conditions of the region such as weather and soil type and preparing scenarios based on possible management options, management strategies can be optimized according to the results achieved for the SWAP model simulation.

Estimating Soil Water Content and Evapotranspiration of Winter Wheat under Deficit Irrigation Based on SWAP Model

Deficit irrigation strategy is essential for sustainable agricultural development in arid regions. A two−year deficit irrigation field experiment was conducted to study the water dynamics of winter wheat under deficit irrigation in Guanzhong Plain in Northwest China. Three irrigation levels were implemented during four growth stages of winter wheat: 100%, 80% and 60% of actual evapotranspiration (ET) measured by the lysimeter with sufficient irrigation treatment (CK). The agro−hydrological model soil−water−atmosphere−plant (SWAP) was used to simulate the components of the farmland water budget. Sensitivity analysis for parameters of SWAP indicated that the saturated water content and water content shape factor n were more sensitive than the other parameters. The verification results showed that the SWAP model accurately simulated soil water content (average relative error (MRE) < 21.66%, root mean square error (RMSE) < 0.07 cm3 cm−3) and ET (R2 = 0.975, p < 0.01). Irrigation had an important impact on actual plant transpiration, but the actual soil evaporation had little change among different treatments. The average deep percolation was 14.54 mm and positively correlated with the total irrigation amount. The model established using path analysis and regression methods for estimating ET performed well (R2 = 0.727, p < 0.01). This study provided effective guidance for SWAP model parameter calibration and a convenient way to accurately estimate ET with fewer variables.

A modified SWAP model for soil water and heat dynamics and seed–maize growth under film mulching

Soil–Water–Atmosphere–Plant (SWAP) model fails to consider the impacts of plastic film mulching on soil moisture and heat regimes, and crop growth and yield. In this study, SWAP model was modified in the modules of precipitation interception, soil evaporation, soil temperature, and crop growth to accommodate the changes of soil moisture and soil temperature caused by film mulching and the consequent variation of crop growth. The modified SWAP model was compared with original SWAP model. Here, the original model showed the case where there was no film mulching effect under the same input conditions as the modified model. The models were calibrated and validated by a field experiment of seed–maize conducted in the Shiyang River Basin of Northwest China in 2017, 2018 and 2019. The experiment included three drip irrigation treatments under film mulching conditions, i.e., WF (full irrigation), WM (medium irrigation, 70%WF), WL (low irrigation, 40%WF). Results showed the normalized root mean square errors (NRMSEs) for soil water storage (SWS), soil temperature at 0, 5, 10, 20 cm soil depths, leaf area index (LAI), aboveground dry biomass (ADB) and yield under different irrigation treatments for the three seasons by the modified model were 14.8%, 43.5% (an averaged value for the 4 soil depths), 70.5%, 56.4% and 82.1% lower than those of the original model, respectively, which demonstrated higher simulation accuracy of the modified model for mulched field. The modified model could more accurately depict the changes of the SWS and LAI caused by the film mulching at various growth stages. It could also reveal the enhanced soil temperature by film mulching especially at the early stage (0–60 day after sowing (DAS)). We analyzed the film mulching effects by comparing the simulation results of the modified model and the original model which could represent the difference between film mulching and no mulching. Results showed that soil evaporation and evapotranspiration under film mulching conditions were 60.7% and 10.1% lower while crop transpiration was 20.2% higher compared with no mulching. The yield and water use efficiency (WUE) under film mulching conditions improved by 38.9% and 54.3% compared with no mulching. Film mulching had more significant influence on the SWS, LAI, ADB, yield, crop transpiration and WUE under WL treatment than under WF and WM treatments.

Evaluating Maize Drought and Wet Stress in a Converted Japanese Paddy Field Using a SWAP Model

Japanese government recommend farmers to cultivate upland crops in paddy fields (“converted fields”) to suppress the overproduction of rice. Converted fields are subject to excessively wet and dry conditions that reduce the yield of non-rice crops. Drought and wet stresses are critical to crop growth within specific growth periods. To provide data for use in mitigating crop yield reduction, we evaluated drought and wet stresses in maize (Zea mays L.). A SWAP (soil–water–atmosphere–plant) model was applied to a converted maize field. Observations were carried out in 2019 and 2018 for model calibration and validation. Thereafter, we evaluated the water stress of maize in 2019 (actual conditions) and at a tillage depth 11 cm deeper (scenario conditions). We found that (1) drought and wet stresses occurred within the relevant critical growth periods under actual conditions; (2) in the critical periods, the drought and wet stresses under scenario conditions were 33%–75% and 10%–82%, respectively, of those under actual conditions; (3) water stress at depths of 10 and 20 cm was lower under the scenario conditions than under the actual conditions. These results indicate that deeper tillage may mitigate both drought and wet stresses and can be used to reduce water stress damage in converted fields.

Evaluating the Impact of Climate Change on Water Productivity of Maize in the Semi-Arid Environment of Punjab, Pakistan

Impact assessments on climate change are essential for the evaluation and management of irrigation water in farming practices in semi-arid environments. This study was conducted to evaluate climate change impacts on water productivity of maize in farming practices in the Lower Chenab Canal (LCC) system. Two fields of maize were selected and monitored to calibrate and validate the model. A water productivity analysis was performed using the Soil–Water–Atmosphere–Plant (SWAP) model. Baseline climate data (1980–2010) for the study site were acquired from the weather observatory of the Pakistan Meteorological Department (PMD). Future climate change data were acquired from the Hadley Climate model version 3 (HadCM3). Statistical downscaling was performed using the Statistical Downscaling Model (SDSM) for the A2 and B2 scenarios of HadCM3. The water productivity assessment was performed for the midcentury (2040–2069) scenario. The maximum increase in the average maximum temperature (Tmax) and minimum temperature (Tmin) was found in the month of July under the A2 and B2 scenarios. The scenarios show a projected increase of 2.8 °C for Tmax and 3.2 °C for Tmin under A2 as well as 2.7 °C for Tmax and 3.2 °C for Tmin under B2 for the midcentury. Similarly, climate change scenarios showed that temperature is projected to decrease, with the average minimum and maximum temperatures of 7.4 and 6.4 °C under the A2 scenario and 7.7 and 6.8 °C under the B2 scenario in the middle of the century, respectively. However, the highest precipitation will decrease by 56 mm under the A2 and B2 scenarios in the middle of the century for the month of September. The input and output data of the SWAP model were processed in R programming for the easy working of the model. The negative impact of climate change was found under the A2 and B2 scenarios during the midcentury. The maximum decreases in Potential Water Productivity (WPET) and Actual Water Productivity (WPAI) from the baseline period to the midcentury scenario of 1.1 to 0.85 kgm−3 and 0.7 to 0.56 kgm−3 were found under the B2 scenario. Evaluation of irrigation practices directs the water managers in making suitable water management decisions for the improvement of water productivity in the changing climate.

Modeling Soil Water–Heat Dynamic Changes in Seed-Maize Fields under Film Mulching and Deficit Irrigation Conditions

The Soil–Water–Atmosphere–Plant (SWAP) model does not have a mulching module to simulate the effect of film mulching on soil water, heat dynamics and crop growth. In this study, SWAP model parameters were selected to simulate the soil water–heat process and crop growth, taking into account the effect of film mulching on soil evaporation, temperature, and crop growth, in order to predict the influence of future climate change on crop growth and evapotranspiration (ET). A most suitable scheme for high yield and water use efficiency (WUE) was studied by an experiment conducted in the Shiyang River Basin of Northwest China during 2017 and 2018. The experiment included mulching (M1) and non-mulching (M0) under three drip irrigation treatments, including full (WF), medium (WM), low (WL) water irrigation. Results demonstrated that SWAP simulated soil water storage (SWS) well, soil temperature at various depths, leaf area index (LAI) and aboveground dry biomass (ADB) with the normalized root mean square error (NRMSE) of 16.2%, 7.5%, 16.1% and 16.4%, respectively; and yield, ET, and WUE with the mean relative error (MRE) of 10.5%, 12.4% and 14.8%, respectively, under different treatments on average. The measured and simulated results showed film mulching could increase soil temperature, promote LAI during the early growth period, and ultimately improve ADB, yield and WUE. Among the treatments, M1WM treatment with moderate water deficit and film mulching could achieve the target of more WUE, higher yield, less irrigation water. Changes in atmospheric temperature, precipitation, and CO2 concentration are of worldwide concern. Three Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP8.5) showed a negative effect on LAI, ADB and yield of seed-maize. The yield of seed-maize on an average decreased by 33.2%, 13.9% under the three RCPs scenarios for film mulching and non-mulching, respectively. Predicted yields under film mulching were lower than that under non-mulching for the next 30 years demonstrating that current film mulching management might not be suitable for this area to improve crop production under the future climate scenarios.

Radiative behavior and canopy light extinction coefficient in a savanna urban area

The knowledge of the radiative characteristics of an area is essential to understanding the flows of matter and energy. The value of the Light Extinction Coefficient (K) is a parameter that describes the efficiency of the interception of light in a given canopy, being required, as input, for several SWAP (Soil-Water-Atmosphere-Plant) models, which allow the characterization of the interactive properties among soil, plant and atmosphere concerning these exchanges of matter and energy. This study aimed to obtain the light extinction coefficient (K) for a savanna fragment located in the urban area of Cuiabá. The used data correspond to one measurement each month, totaling twelve measurements in 30 points during the period from October 2014 to September 2015. The measured variables were the LAI (Leaf Area Index), the photosynthetically active incident radiation (PARinc) and the transmitted radiation (PARtrans), and the calculated ones were the zenith angle (Zh) and the extinction coefficient (K). Was observed an annual variability for the light extinction coefficient between 0.49 and 0.69. There are seasonal changes that interfere with the canopy geometry and the position of the study area in relation to the solar radiation incidence, concluding that the K variability is predominantly temporal.

Modelling root water uptake under deficit irrigation and rewetting in Northwest China

AbstractThe spatial and temporal distribution of root water uptake (RWU) under deficit irrigation are critical factors for crop growth. The SWAP (soil–water–atmosphere–plant) model was applied to analyze the pattern of RWU for winter wheat (Triticum aestivum L.) under three irrigation levels: no water deficit (100% evapotranspiration [ET]), moderate water deficit (80% ET) and severe water deficit (60% ET). The 2–yr experiments indicated that SWAP was highly accurate (mean relative error [MRE] <21.7%, root mean square error [RMSE] <0.07 cm3 cm−3) in simulating the soil water content (SWC). Root water uptake was significantly (P < 0.01) different in the 0‐ to 60‐cm soil layer. The 0‐ to 60‐cm soil layer was the main source of RWU, and the average value accounted for 89.4% of the total root zone. Water stress had the greatest adverse effect on heading to grain filling, reducing RWU by 0.0026 cm3 cm−3 d−1. The critical SWC was 67.9% of the field capacity, when the RWU dropped to 95% of the control treatment. After rewetting, compensation and hysteresis effects on RWU were observed. The ranking of RWU recovery ability after rewetting was: emergence to jointing > jointing to heading > grain filling to maturity > heading to grain filling. Recovery time of RWU was 2 to 11 d and gradually increased with growth stage. The simplified RWU model established using path analysis and regression performed well (R2 = 0.836; P < 0.01) for RWU. This provided a more convenient way to accurately estimate RWU with fewer variables.

Simulation of Saline Water Irrigation for Seed Maize in Arid Northwest China Based on SWAP Model

Water resource shortages restrict the economic and societal development of China’s arid northwest. Drawing on groundwater resources for irrigation, field experiments growing seed maize (Zea mays L.) were conducted in 2013 and 2014 in the region’s Shiyang River Basin. The Soil–Water–Atmosphere–Plant (SWAP) model simulated soil water content, salinity, and water–salt transport, along with seed maize yield, in close agreement with measured values after calibration and validation. The model could accordingly serve to simulate different saline water irrigation scenarios for maize production in the study area. Waters with a salinity exceeding 6.0 mg/cm3 were not suitable for irrigation, whereas those between 3.0 and 5.0 mg/cm3 could be acceptable over a short period of time. Brackish water (0.71–2.0 mg/cm3) could be used with few restrictions. Long-term (five years) simulation of irrigation with saline water (3.0–5.0 mg/cm3) showed soil salinity to increase by over 9.5 mg/cm3 compared to initial levels, while seed maize yield declined by 25.0% compared with irrigation with brackish water (0.71 mg/cm3). An irrigation water salinity of 3.0–5.0 mg/cm3 was, therefore, not suitable for long-term irrigation in the study area. This study addressed significance issues related to saline water irrigation and serves as a guide for future agricultural production practices.

A dynamic agricultural prediction system for large-scale drought assessment on the Sunway TaihuLight supercomputer

Crop models are widely used to evaluate the response of crop growth to drought. However, over large geographic regions, the most advanced models are often restricted by available computing resource. This limits capacity to undertake uncertainty analysis and prohibits the use of models in real-time ensemble forecasting systems. This study addresses these concerns by presenting an integrated system for the dynamic prediction and assessment of agricultural yield using the top-ranked Sunway TaihuLight supercomputer platform. This system enables parallelization and acceleration for the existing AquaCrop, DNDC (DeNitrification and DeComposition) and SWAP (Soil Water Atmosphere Plant) models, thus facilitating multi-model ensemble and parameter optimization and subsequent drought risk analysis in multiple regions and at multiple scales. The high computing capability also opens up the possibility of real-time simulation during droughts, providing the basis for more effective drought management. Initial testing with varying core group numbers shows that computation time can be reduced by between 2.6 and 3.6 times. Based on the powerful computing capacity, a county-level model parameter optimization (2043 counties for 1996–2007) by Bayesian inference and multi-model ensemble using BMA (Bayesian Model Average) method were performed, demonstrating the enhancements in predictive accuracy that can be achieved. An application of this system is presented predicting the impacts of the drought of May–July 2017 on maize yield in North and Northeast China. The spatial variability in yield losses is presented demonstrating new capability to provide high resolution information with associated uncertainty estimates.

Data assimilation of soil water flow by considering multiple uncertainty sources and spatial–temporal features: a field-scale real case study

Accurate estimates of soil moisture and soil hydraulic parameters via data assimilation largely depend on the quality of the model structure, input data and observations. Often, however, all of this information is subject to uncertainty under real circumstances. This real-case study seeks to understand the effects of different uncertainty sources and observation scales on data assimilation performance. Ensemble Kalman filter method based on the soil water-flow model, a sub-module of soil–water–atmosphere–plant model, is established to simultaneously estimate the model states and parameters. The soil hydraulic parameters are extensively measured or calibrated to examine the parameter estimation accuracy. Furthermore, considering the high spatial and temporal variability of soil moisture observation in the field-scale problem, an analysis of spatiotemporal characteristics is combined with data assimilation. Results indicated that simultaneously considering parameter and initial conditions uncertainty leads to a better soil moisture and parameter estimation than that ignoring initial uncertainty in realistic practice. Unlike the other error sources, an inadequate description to the meteorological forcing has a negative influence on surface soil moisture estimation, which might be attributed to the persistent disturbances of evaporation uncertainty and the lack of observations at shallow soil depth. Moreover, a prior knowledge of spatiotemporal features of soil moisture observation is beneficial to efficiently improve data assimilation performance. It is possible to implement field-scale data assimilation with a few representative points, instead of using the spatial average of all observations at a high cost. The assimilation results highlight the possibly positive outcomes of accounting for the multi-source of uncertainties and emphasize the significant importance of characterizing the spatial–temporal feature of soil moisture for a field-scale application.

Improving crop yield estimation by assimilating LAI and inputting satellite-based surface incoming solar radiation into SWAP model

Precise crop yield forecast at regional scales would increase global food security, especially in strategic crops such as wheat and barley. Soil Water Atmosphere Plant (SWAP) is an agro-hydrological model based on a crop growth detailed module that could properly estimate crop yield using satellite observations as input data. In this study, in order to reduce crop yield estimation errors in wheat and barley, MODIS-based leaf area index (LAI) was assimilated using a sequential update algorithm into SWAP, and GLDAS/Noah-derived surface incoming solar radiation (SISR) was used as an alternative to measured SISR. The assimilation of remotely sensed LAI and using SISR as input was examined in nine different cases. Results showed that soil adjusted vegetation index (SAVI) was the best VI for LAI estimation with coefficient of determination (R2) of 0.72 and root mean square error (RMSE) of 0.87 m2 m−2. Also noise equivalent variations indicated an appropriate sensitivity of SAVI along the entire range of LAI variability. GLDAS/Noah-derived SISR showed good agreement with measured SISR; therefore LAI and SISR were jointly used in the model. Simulation results showed that the lowest percent absolute error (PAE) for aboveground dry biomass and grain yield was obtained in case 7 (the assimilation of the peak LAI in addition to ten days after and before the peak LAI is reached) with 1.59% and case 5 (the daily assimilation of LAI until twenty days after the peak LAI is reached) with 6.06%, respectively. Crop yield estimates were improved by 26.25 and 14.4% compared with no LAI assimilation case. Overall, LAI assimilation into SWAP associated with the most efficient cases in this study would result in an accurate crop yield forecast in wheat and barley.

Estimating Soil Hydraulic Parameters by Inverse Modeling with PEST

Core Ideas Coupling SWAP with PEST, soil moisture and ET are included in inverse modeling of soil water flow. Multi‐objective optimization could improve model predictions and reduce parameter uncertainty. Observation errors and frequency and spatial arrangement impact predictions and uncertainty. The accurate estimation of soil hydraulic properties is an important part of the application of hydrological models for quantifying water transport in the vadose zone. Various inverse models have been developed to solve hydraulic properties optimization problems, and the accuracy of the results obtained by different algorithms is still debated because of the inherent ill‐posedness of such problems. In this study, we coupled an agrohydrological Soil–Water–Atmosphere–Plant (SWAP) model with an independent parameter estimation program (PEST) to calibrate the soil hydraulic parameters and investigate the uncertainty in the parameter estimations. The objectives of this study were to assess to what extent the SWAP model can be calibrated from a single observation (only soil moisture, θ) and further to investigate whether involving additional evapotranspiration fluxes including actual evapotranspiration (ETa), actual evaporation (Ea), or actual transpiration (Ta) can lead to an improvement in model predictions and a reduction in parameter uncertainty. Extensive synthetic experiments were conducted to achieve the objectives. Both double (winter wheat [Triticum aestivum L.]–summer maize [Zea mays L.]) and single (only winter wheat or only summer maize) cropping systems were considered in the multi‐objective optimizations. The results indicate that although the addition of evapotranspiration fluxes does not necessarily improve the accuracy of the soil moisture prediction, it can reduce the parameter uncertainty for both single‐ and double‐cropping systems. Moreover, the parameter estimation of the topsoil layer could greatly benefit from the addition of Ea, whereas the addition of Ta could help reduce the parameter uncertainty of the lower layers.

Irrigation management for spring maize grown on saline soil based on SWAP model

Soil salinization and water quality deterioration severely limit the development of irrigation agriculture in arid areas. Transient numerical models such as the agro-hydrological Soil-Water-Atmosphere-Plant (SWAP) model have played an important role in agricultural irrigation management. In order to find the optimal irrigation practices for spring maize grown on saline soil in Shiyang River Basin, the SWAP model was calibrated and validated to simulate water-salt transport and relative yield based on field experiments in 2008 and 2009. The average annual precipitation in this area is about 164mm. Experimental observations of soil water content, soil salinity and maize growth were made in lysimeters filled with disturbed loam soil having saturation extract water salinity (ECe) around 5.76dSm−1. Six irrigation treatments altogether including three levels of irrigation water amount: 100%, 67% and 50% ETc (evapotranspiration, 510mm) and two levels of irrigation water salinity: 0.65 and 3.2dSm−1 were arranged, each treatment with 3 replications for each treatment. The acceptable root mean square error (RMSE), mean relative error (MRE), the Nash and Sutcliffe model efficiency (NSE), the coefficient of determination (R2) between simulation and observation of soil water content, salt concentration and relative yield demonstrated that the calibrated SWAP model was applicable to irrigation management evaluation for maize grown on saline soil. Based on the analysis of simulated water and salt balance, relative yield and relative water use efficiency, the optimal irrigation schedule of maize grown on the saline soil for 50% hydrologic year was irrigation amount of 460mm with four times, for both fresh water 0.65dSm−1 (80Fr) and slight saline water 3.2dSm−1 (80Sal3). Compared to fresh irrigation treatment, higher soil water content and salt concentration, and lower relative yield and relative water use efficiency were obtained by saline irrigation treatment. However, for the maize grown on saline soil, the effect of irrigation amount at high level on relative yield and water use efficiency was unobvious with the difference less than 4%. Relative yield with 0.74 and 0.63 can be obtained by 80Fr and 80Sal3 every year after soil water-salt reached balance.

Water balance implications of switching from continuous submergence to flush irrigation in a rice-growing district

Studies conducted at the field scale report significant reductions in the irrigation requirements of rice when continuous submergence (CS) is replaced by less water-demanding regimes such as flush-irrigation (FI, i.e. intermittent irrigations of rice growing in non-submerged soils). However, the effects of their extensive application in paddy areas with shallow groundwater is much less studied. We present a scenario analysis investigating the impacts on irrigation requirements induced by a shift from CS to FI in an irrigation district of Northern Italy where rice is the main crop, followed by maize and poplar. The area is characterised by a shallow water Table whose depth fluctuates between two meters (in winter) and less than 1m (in summer). We applied a three-stage procedure, where we first analysed present state conditions using the SWAP (Soil, Water, Atmosphere, Plant) model to simulate irrigation deliveries and percolation fluxes. Then, we calibrated an empirical relationship between estimated percolation fluxes and measured depths to groundwater. Finally, we applied this relationship, in combination with the SWAP model, to predict the variation of district irrigation requirements due to a widespread shift from CS to FI. Results show that neglecting the feedback between groundwater recharge due to irrigation and groundwater depth led to overestimating the reduction of irrigation requirements of rice, which decreased from around 80% when no feedback was considered to around 60% when it was accounted for. Moreover, increased groundwater depths resulted in higher irrigation requirements for maize with an estimated growth of more than 50% due to the need of shortening the irrigation turn. These results demonstrate the importance of considering the impacts on the hydrological processes at larger scales when planning the conversion of CS into more efficient field irrigation methods.