Simulated Soil Water Content Research Articles

Modelling soil water and nutrient dynamics under different irrigation techniques of onion production

ABSTRACT Irrigation remains important to meet the needs of the people by increasing agricultural productivity. However, its water productivity in developing countries is low due to inefficient irrigation water management. The main objective of this study was to evaluate soil water, crop growth and nutrient dynamics under different irrigation techniques. Hydrus-1D model was used to simulate soil water content (SWC), actual evapotranspiration (ETa) and soil leachates. The model performance was evaluated by comparing the measured and simulated treatment variables. The Hydrus-1D performance showed good agreement between the observed and simulated SWC (R2 = 0.55–0.81; NRMSE = 0.01-0.09; d-index = 0.95-0.99) and with a very good agreement for nitrate-nitrogen (NO3–N) leaching (R2 = 0.97, d index = 0.99 and NRMSE = 0.02). Similarly, performance in simulating PO4-P were acceptable (R2 = 0.88; d index = 0.99; NRMSE = 0.04). The value of SWC (cm3 cm−3) ranged from 0.30 to 0.38 at 10 cm and from 0.27 to 0.37 at 20 cm soil depths. The seasonal drainage water was reduced to 86%, 87%, and 54 %, respectively for AFI, FFI, and OHI treatments compared with CFI treatment. The NO3–N leaching was reduced by 41%, 71%, and 83% under AFI, FFI, and OHI compared with CFI while phosphate – P (PO4-P) leaching was 60%, 66%, and 79.6%, respectively, lower in AFI, FFI, and OHI than CFI. The highest seasonal ETa (421.95 mm) was found in CFI while the lowest (335.22 mm) was found in AFI treatment. Besides, the highest IWP (9.11 kg/m3) was obtained from AFI technique indicating that that AFI was the most efficient irrigation technique in saving both nutrient and water under onion production. Hydrus-1D could be a successful tool for predicting water and nutrient transport management decisions to improve water and nutrient management.

Modeling the response of sesame (Sesamum indicum L.) to different soil fertility levels under rain-fed conditions in the semi-arid areas of western Tigray, Ethiopia

Sesame, a crucial oilseed crop in Ethiopia, ranks second only to coffee in its importance as an exported agricultural commodity. However, inadequate soil fertility management has hampered its productivity despite its substantial international market demand. Hence, this study was conducted to model the response of sesame to different nitrogen fertilizer levels using the AquaCrop model, and to assess the capability of the model as a decision-support tool for optimizing soil fertility management strategies in the study area. The experiment was laid out in a randomized complete block design, consisting of four nitrogen fertilizer rates (0, 23, 46, and 69 kg/ha nitrogen) and three distinct sesame varieties (Setit-1, Setit-2, and Humera-1). Over the course of the three cropping seasons, data on soil physical and chemical properties, crop growth, yield and yield components were collected for each treatment. Evaluation of model performance relied upon established metrics of coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (N-RMSE), model efficiency (E), and degree of agreement (D). Analysis of results revealed the AquaCrop model appropriately calibrated for simulation of soil water content, showing R2 values ranging from 0.92 to 0.98, RMSE values varying from 6.5 to 13.9 mm, E values from 0.78 to 0.94, and D values from 0.95 to 0.99. Similarly, simulation outputs for aboveground biomass (AB) demonstrated good accuracy of the model, with R2 values varying from 0.92 to 0.98, RMSE values ranging from 0.33 to 0.54 tons/ha, and D values from 0.9 to 0.98. Notable accuracy was also observed in the simulation of canopy cover (CC), revealing R2 values between 0.95 and 0.99, and RMSE values ranging from 5.3 to 8.6 %. In conclusion, this study substantiates the successful calibration and validation of the AquaCrop model for predicting sesame response to diverse nitrogen fertilizer levels. The performance of the model in predicting soil water content, CC, AB, and yield highlights its potential as a valuable tool for optimizing soil fertility management and enhancing sesame cultivation practices in Ethiopia.

An improved canopy interception scheme into biogeochemical model for precise simulation of carbon and water fluxes in subtropical coniferous forest

The process-based Biome-BGC (Biome Biogeochemical Cycles) model is widely used to simulate the fluxes of carbon and water of terrestrial ecosystems. While exhibiting excellent performance in simulating carbon cycle processes, this model provides a relatively simple simulation of the water cycle processes, particularly in canopy interception, which may result in significant errors in evapotranspiration (ET) and soil moisture estimations. This study initially refined the temporal scale of the original Biome-BGC model from a daily scale to an hourly scale, and then incorporated a theoretical interception module into the hourly-scale model for correcting canopy interception algorithm, to enhance the accuracy of hydrological processes simulations. The modified model was tested using the half-hourly observed meteorological and eddy covariance flux data at the Qianyanzhou subtropical coniferous forest site. In comparison with the original daily-scale model, simulations from the hourly-scale model showed better agreement with measurements at Qianyanzhou forest site, with 30.61 % and 40.92 % lower annual average gross primary productivity (GPP) and ET simulations. Although the accuracy of GPP and ET simulations did not show a significant improvement after canopy interception correction, it notably enhanced the accuracy of canopy interception and soil moisture simulations. The canopy interception rates were 55.8 % and 44.1 % for the original daily-scale and hourly-scale model, obviously overstimated compared with canopy correction simulation (31.1 %), leading to a significant underestimation of runoff. The canopy interception and runoff simulations after interception correction were proven relatively more reasonable. Additionally, the coefficient of determination (R2) for measured vs simulated soil water content (SWC) were 0.38, 0.51 and 0.60 for the original daily-scale, hourly-scale, and canopy interception correction simulations respectively, with a notable improvement in accuracy. This study suggested that improvements in temporal scale and canopy interception for process-based biogeochemical model can provide a better understanding of the carbon and water exchanges in response to instantaneous meteorological conditions and support improved water resource management.

Water conservation in tomato production using deficit irrigation and SALTMED model under greenhouse conditions

The use of modeling for water conservation during irrigation is still in the research stage. There are many models can be used for this purpose, including Saltmed model. The SALTMED model is a holistic model can be used for different irrigation water and soil qualities, variety of crops, and water irrigation systems. This research aimed to interpret the tomato yield using different water quantities and qualities under greenhouse experiments. The trial included deficit irrigation with 3 treatments (80 %, 60 %, 40 % along with 100 % crop water requirements (ETc), with three water qualities (ECw 1.0, 2.3 and 3.6 dSm−1). The outcomes of the SALTMED model indicate the potential of the model to interpret soil-water content, salinity and tomato yield in regions having semi-arid climates under deficit irrigation strategies. Results of the SALTMED model revealed that data was underestimated. Furthermore, the values range of the CRM coefficient of the residual mass were 0.005–0.055, -0.01–0.02, and -0.02–0.04 for 0.9, 2.3 and 3.6 dSm−1, respectively. Most salinity showed the CRM values 0.18 to -0.02, 0.2 to -0.41, and -0.05 to -0.38 respectively, for the three irrigation water qualities. The values of observed and simulated data predicted a good relationship obtained. For soil water content and salinity, R2 values ranged between 0.80–0.99 and 0.74–0.99 respectively. Model efficiently simulates tomato yield outcomes under saline and freshwater. The correlation between the simulated and observed yield were 0.92, 0.94, and 0.79 for water quality of 1.0, 2.3 and 3.6 dSm−1, respectively. Finally, the SALTMED model showed good simulations of soil water content, soil salinity, and final tomato yield.

Positive asymmetric responses indicate larger carbon sink with increase in precipitation variability in global terrestrial ecosystems

<p>Climate changes have caused high inter-annual variability in precipitation. However, how the terrestrial ecosystem responds to precipitation variability remains unclear. Using global remote sensing data and a meta-analysis by synthesizing 800 pairwise observations of experimental manipulations worldwide, we quantified the responses of the terrestrial ecosystem net carbon productivity (NEP) to precipitation variability. The results indicate that NEP displays a positive asymmetry in response to precipitation change, e.g., the magnitude of the increase in NEP (33.4%) under water-addition treatments is larger than that of the decline in NEP (-24.62%) under water-reduction treatments. The positive asymmetry of NEP in arid regions (< 500 mm) is larger than that in humid regions (> 500 mm). The former is mainly due to the positive asymmetry in vegetation productivity, while the latter results from the respiration process, i.e., the decrease in soil respiration in water-reduction treatments is stronger than in water-addition treatments. Furthermore, land models reproduce a positive NEP asymmetry in response to precipitation change, but display poor performance in ecosystem respiration (ER) responses owing to uncertainties in simulating soil water content (SWC). The positive asymmetry of NEP in this study implies that the increase in precipitation variability (except extreme anomalies) is conducive to high carbon sink in the global terrestrial ecosystem. Meanwhile, the performance of the models when simulating SWC in response to precipitation in humid regions needs to be further improved to better predict the carbon sink in the terrestrial ecosystem.</p>

Soil moisture forecasting for precision irrigation management using real-time electricity consumption records

The installation of the smart electricity meters (SEMs) to the high-density pumping wells in the North China Plain (NCP) provided a method to control the groundwater withdraw. The record in real-time electricity consumption from SEMs also provided an alternative method to forecast soil moisture to make irrigation decisions to improve irrigation efficiency. The irrigation timing and the irrigation amount could be obtained on-line from the SEMs based on the coefficients obtained for electricity-to-water conversion (E-Wc). The real-time recordings in the irrigation timing and amounts were further used to forecast the next irrigation timing and amount based on the water rights per area of land, the lower limit of soil water contents for different crops at their different growing stages and online meteorological data. A smartphone app was developed based on this framework. The running of the app to simulate soil water contents based on the soil water balance equation was supported by databases on soil texture, crop coefficients, water rights, E-Wc and threshold soil water content values as well as the real-time collection of electricity consumption and meteorological data. The simulated real-time soil water contents were shown on the mobile phone screen and could be compared with the threshold values for making irrigation decisions for users. The accuracy in forecasting the soil water contents using the methods developed in this framework was tested under different irrigation schedules for winter wheat and summer maize during 2018–2019 and 2021–2022, and the app was tested for three locations starting from sowing winter wheat in October 2022 to April in 2023. The results showed that the app could be easily run, and precise real-time soil water contents were simulated. With the application of the pumping well renovation project in the NCP, most of the pumping wells were installed with the SEMs, and using SEMs for groundwater pumping control and irrigation decisions should be possible.

Optimizing crop production water footprints in the face of water scarcity: a combined experimental and simulation study of wheat in Zimbabwe

Abstract The optimization of water footprints in crop production is critical, given that agroecosystems currently account for more than 70% of global freshwater use. To achieve this, crop growth models provide insights into the impact of various crop and irrigation management strategies on both crop productivity and water use. This study evaluated the capability of the AquaCrop model in simulating wheat yields, crop water use, and water footprints in the Middle-Manyame Sub-Catchment, Zimbabwe. The model was calibrated and validated using experimental data collected from field experiments. Simulation experiments were conducted to assess the impact of early and late planting, drip and sprinkler irrigation techniques, and no mulch, organic mulch, and synthetic mulch options on the water footprint (WF). The AquaCrop model accurately simulated soil water content, crop water use, crop biomass, and grain yield. Simulation runs showed that early planting reduced WFblue and WFgreen by 25 and 4%, respectively. The lowest consumptive WF was observed with drip irrigation and synthetic mulching. The greatest decline in WFblue and WFgreen (52 and 11%) was simulated under early planting, using drip irrigation and synthetic mulching. Overall, the study highlights the importance of efficient crop and irrigation management practices to reduce water footprints in agroecosystems.

Sensitivity of simulated soil water content, evapotranspiration, gross primary production and biomass to climate change factors in Euro-Mediterranean grasslands

Grassland models often yield more uncertain outputs than arable crop models due to more complex interactions and the largely undocumented sensitivity of grassland models to environmental factors. The aim of the present study was to assess the impact of single-factor changes in temperature, precipitation, and atmospheric [CO2] on simulated soil water content (SWC), actual evapotranspiration (ET), gross primary production (GPP) and yield biomass, and also to link the sensitivity analysis with experimental results. We employed an unprecedented multi-model framework consisting of seven grassland models at nine sites with different environmental characteristics in Europe and Israel, with two management options at three sites. For warming/cooling and wetting/drying, models showed general consistency in the direction of SWC and ET changes, but less agreement regarding GPP and biomass changes. The simulated responses consistently revealed an overall positive effect of CO2 enrichment on GPP and biomass, while the direction of change differed for SWC and ET. Comparing with single-factor experimental manipulations, SWC simulations slightly underestimated the observed effect of warming, while the overall mean model sensitivity for biomass (+7.5%) closely matched the mean response observed with 1–2 °C warming (+6.6%). The models exhibited lower sensitivity of SWC to wetting or drying compared to the experiments. The overall mean sensitivity of biomass to drying was -4.3%, contrasting with the mean experimental effect size of -9.6%, which proved to be more realistic than the mean wetting effect (+3.2%, against +38.9% in the field trials). The simulated sensitivity of SWC to CO2 enrichment was markedly underestimated, while the biomass response (+12.0%) closely matched the observations (+17.5%). Although the multi-model averaging did not manifestly improve the realism of the simulations, it ensured a realistic response in the direction of change to varying conditions. The results suggest a paradigm shift in grassland modelling meaning that the usual practice of model optimisation/validation needs to be complemented by a sensitivity analysis following the approach presented. The results also highlight the importance of model improvements, especially in terms of soil hydrology representation, a key environmental driver of grassland functioning.

Simulation of saffron growth using AquaCrop model with high-resolution measured data

Saffron is one of the most expensive and worth-to-cultivate crops in countries struggling with water scarcity due to its low water requirement and compatibility with arid climates. This study was conducted to calibrate and validate the AquaCrop model for saffron at the research farm of the Ferdowsi University of Mashhad (FUM), Iran, in two growing seasons of 2021–2022 (two-year-old saffron) and 2022–2023 (three-year-old saffron). The soil water content, canopy cover, and biomass values were measured continuously during both growing seasons. The field measurements of the first and second growing seasons were used to calibrate and validate the AquaCrop model, respectively. Furthermore, using the Particle Swarm Optimization (PSO) algorithm, a new module for the inverse solution of the AquaCrop model has been presented. The module was successfully employed to fine-tune sensitive soil parameters of the field. The results showed that the AquaCrop model could accurately simulate soil water content, biomass, and canopy cover in both growing seasons. The root mean square error (RMSE) of simulations for soil water content, biomass, and canopy cover comparing to in-situ measured data was 8.3 mm, 0.3 ton ha−1 and 3.4% for the first year and 5.5 mm, 0.4 ton ha−1 and 2.7% for the second year, respectively. In these simulations, the saffron daughter corm production was considered as the crop yield, because the stigma yield is affected by the climatic conditions of the past year while corms are produced at the end of the prevailing season. To investigate the effect of cold and heat on saffron yield, the hottest and coldest growing seasons in the past 30 years were determined in 6 regions besides Mashhad, and the biomass in these seasons was simulated using the model. It was concluded that the cold growing season had a more significant effect than the hot season in reducing the saffron corm yield.

Simulation of Soil Water and Nitrogen Dynamics for Tomato Crop using EU-Rotate_N Model at Different Nitrogen Levels in the Greenhouse

To pursue high yields, the excessive application of nitrogen (N) fertilizer has been reported in high-residual soil nitrate levels, excessive nitrate leaching, and nitrate contamination of groundwater. In this study, tomato crops (Lycopersicon esculentum Mill.) were subjected to various nitrogen treatments, and the nitrate nitrogen content, soil water content at different soil layers, dry matter, and yield were measured. A mechanistic model, EU-Rotate_N, was used to simulate the aforementioned indexes in a region of Jiangsu province with a relatively higher water table. The predicted values of soil moisture and soil nitrate content at various soil depths agree well with the measured values during tomato growth. The statistical index of soil water content ranged from 0.367 to 0.749, 0.856 to 0.947, and the statistical index of soil nitrate nitrogen content ranged from 0.365 to 0.698, and 0.869 to 0.932, for Autumn-Winter (AW) and Spring-Summer (SS) crops, respectively. Moreover, the dry weight and yield simulation effects of the tomato are also in good agreement with the actual measured values. The results show that the EU-Rotate_N model is effective in simulating soil water content, nitrate nitrogen content, dry matter quality, and yield in Jiangsu province, with little underestimation in soil water content at a soil depth of 20–30 cm during SS, which might be improved further considering the high water table of the region.

Quantifying irrigation uptake in olive trees: a proof-of-concept approach combining isotope tracing and Hydrus-1D

ABSTRACT An isotope-enabled module of Hydrus-1D was applied to a potted olive tree to trace water parcels originating from 26 irrigation events in a glasshouse experiment. The soil hydraulic parameters were optimized via inverse modelling by minimizing the discrepancies between observed and simulated soil water content and soil water isotope (18O) values at three soil depths. The model’s performance was validated with observed sap flow z-scores and xylem water 18O. We quantified the source and transit time of irrigation water by analysing the mass breakthrough curves derived from a virtual tracer injection experiment. On average, 26% of irrigation water was removed by plant transpiration with a mean transit time of 94 hours. Our proof of concept work suggests that transit time may represent a functional indicator for the uptake of irrigation water in agricultural ecosystems.

Multidimensional hydrological modeling of a forested catchment in a German low mountain range using a modular runoff and water balance model

Sufficient plant-available water is one of the most important requirements for vital, stable, and well-growing forest stands. In the face of climate change, there are various approaches to derive recommendations considering tree species selection based on plant-available water provided by measurements or simulations. Owing to the small-parcel management of Central European forests as well as small-spatial variation of soil and stand properties, in situ data collection for individual forest stands of large areas is not feasible, considering time and cost effort. This problem can be addressed using physically based modeling, aiming to numerically simulate the water balance. In this study, we parameterized, calibrated, and verified the hydrological multidimensional WaSiM-ETH model to assess the water balance at a spatial resolution of 30 m in a German forested catchment area (136.4 km2) for the period 2000–2021 using selected in situ data, remote sensing products, and total runoff. Based on the model output, drought-sensitive parameters, such as the difference between potential and effective stand transpiration (Tdiff) and the water balance, were deduced from the model, analyzed, and evaluated. Results show that the modeled evapotranspiration (ET) correlated significantly (R2 = 0.80) with the estimated ET using MODIS data (MOD16A2GFv006). Compared with observed daily, monthly, and annual runoff data, the model shows a good performance (R2: 0.70|0.77|0.73; Kling–Gupta efficiency: 0.59|0.62|0.83; volumetric efficiency: 0.52|0.60|0.83). The comparison with in situ data from a forest monitoring plot, established at the end of 2020, indicated good agreement between observed and simulated interception and soil water content. According to our results, WaSiM-ETH is a potential supplement for forest management, owing to its multidimensionality and the ability to model soil water balance for large areas at comparable high spatial resolution. The outputs offer, compared to non-distributed models (like LWF-Brook90), spatial differentiability, which is important for small-scale parceled forests, regarding stand structure and soil properties. Due to the spatial component offered, additional verification possibilities are feasible allowing a reliable and profound verification of the model and its parameterization.

Representation of soil hydrology in permafrost regions may explain large part of inter-model spread in simulated Arctic and subarctic climate

Abstract. The current generation of Earth system models exhibits large inter-model differences in the simulated climate of the Arctic and subarctic zone, with differences in model structure and parametrizations being one of the main sources of uncertainty. One particularly challenging aspect in modelling is the representation of terrestrial processes in permafrost-affected regions, which are often governed by spatial heterogeneity far below the resolution of the models' land surface components. Here, we use the Max Planck Institute (MPI) Earth System Model to investigate how different plausible assumptions for the representation of permafrost hydrology modulate land–atmosphere interactions and how the resulting feedbacks affect not only the regional and global climate, but also our ability to predict whether the high latitudes will become wetter or drier in a warmer future. Focusing on two idealized setups that induce comparatively “wet” or “dry” conditions in regions that are presently affected by permafrost, we find that the parameter settings determine the direction of the 21st-century trend in the simulated soil water content and result in substantial differences in the land–atmosphere exchange of energy and moisture. The latter leads to differences in the simulated cloud cover during spring and summer and thus in the planetary energy uptake. The respective effects are so pronounced that uncertainties in the representation of the Arctic hydrological cycle can help to explain a large fraction of the inter-model spread in regional surface temperatures and precipitation. Furthermore, they affect a range of components of the Earth system as far to the south as the tropics. With both setups being similarly plausible, our findings highlight the need for more observational constraints on the permafrost hydrology to reduce the inter-model spread in Arctic climate projections.

Simulating Long-Term Effects of Sowing Date on the Yield of Dryland and Irrigated Winter Wheat

Highlights This study employed AquaCrop model to simulate winter wheat growth in Oklahoma. Different sowing dates and water availability at the time of sowing were investigated. 5 October was found to be the most optimum sowing date by producing overall greater wheat yields. 25% decrease in dryland crop yield was noted when soil water content at sowing was decreased from 100% to 40% of total available water. Abstract. Dryland winter wheat is the most widely grown crop in Oklahoma, where water availability and temperature variations are the major factors affecting the yield of this crop. These effects were assessed using the AquaCrop model. Model calibration and validation were performed using field measurements collected from 11 site-years. The validated model satisfactorily simulated soil water content, maturity biomass, and yield, with overall normalized root mean square errors of 11%, 5%, and 9%, respectively. The model was then used in long-term (1994-2019) simulations of winter wheat at three locations in Oklahoma’s wheat belt under five sowing dates (15 Sept., 25 Sept., 5 Oct., 15 Oct., and 25 Oct.) and three soil water contents (SWC) of 40%, 70%, and 100% of the total available water at sowing. The highest simulated average dryland yields (6.4 to 6.6 Mg ha-1) were obtained for early to mid-October sowing dates under the largest SWC at sowing. The lowest average yields (2.67 to 4.15 Mg ha-1) belonged to earlier sowing (mid-September) under the smallest SWC at sowing. Irrigation had a positive impact on the yield of winter wheat and this impact increased for later sowing. The yield of winter wheat sowed in mid-September increased by 0.28 Mg ha-1 under irrigation when averaged for all years and sites, while late-October average yield was 0.96 Mg ha-1 higher than dryland average yield. Irrigation water productivities also increased with later sowing dates. Keywords: AquaCrop, Irrigation water productivity, Oklahoma, Soil water content.

Relating soil-root hydraulic resistance variation to stomatal regulation in soil-plant water transport modeling

Soil-root hydraulic resistance variation and stomatal regulation are two critical hydrophysiological responses of plants to drought stress; however, few studies have been developed to quantify their interactions. To fill this gap, we developed a soil-plant hydraulic model (SR-HRV) that attempts to characterize the effects of stomatal regulation and three universal soil-root hydraulic resistance variations, i.e., root aquaporins promotion (AQU), apoplastic path damage (APD), and root-soil contact loosening (CONTACT). The sensitive parameters of the SR-HRV model were analyzed and optimized based on a field experiment with sunflower plants (Helianthus annuus L.). Several simulation scenarios were designed to clarify the individual and interactive effects of soil-root hydraulic resistance variations for plants with different stomatal sensitivities. Results show that the sensitivity of simulated stomatal conductance and soil water content response to stomatal regulation parameters, especially to abscisic acid-related parameters, are more active than to soil-root hydraulic resistance variation parameters. But as the soil dries, the sensitivities to APD and CONTACT parameters are rapidly increased. The simulation demonstrates that AQP alleviates the leaf water potential drop-down and maintains relatively high root water absorption of the plant when it is in mild drought conditions, while CONTACT and APD respectively restrict the water flux and drought signal responses with continuous soil dehydration. Moreover, the AQP effects are more pronounced but the effects of APD and CONTACT would be restricted for plants with higher stomatal sensitivity to drought signals. These simulation results imply the diverse response strategies of plants to drought, the collaborations between stomatal regulation and soil-root hydraulic resistance variations should be considered in soil-plant water transport modeling.

Bayesian Calibration and Uncertainty Assessment of HYDRUS-1D Model Using GLUE Algorithm for Simulating Corn Root Zone Salinity under Linear Move Sprinkle Irrigation System

Soil salinization is one of the significant concerns regarding irrigation with saline waters as an alternative resource for limited freshwater resources in arid and semi-arid regions. Thus, the investigation of proper management methods to control soil salinity for irrigation with saline waters is inevitable. The HYDRUS-1D model is a well-known numerical model that can facilitate the exploration of management scenarios to mitigate the consequences of irrigation with saline waters, especially soil salinization. However, before using the model as a decision support system, it is crucial to calibrate the model and analyze the model’s parameters and outputs’ uncertainty. Therefore, the generalized likelihood uncertainty estimation (GLUE) algorithm was implemented for the HYDRUS-1D model in the R environment to calibrate the model and assess the uncertainty aspects for simulating soil salinity of corn root zone under saline irrigation with linear move sprinkle irrigation system. The results of the study have detected a lower level of uncertainty in the α, n, and θs (saturated soil water content) parameters of water flow simulations, dispersivity (λ), and adsorption isotherm coefficient (Kd) parameters of solute transport simulations comparing to the other parameters. A higher level of uncertainty was found for the diffusion coefficient as its corresponding posterior distribution was not considerably changed from its prior distribution. The reason for this phenomenon could be the minor contribution of diffusion to the solute transport process in the soil compared with advection and hydrodynamic dispersion under saline water irrigation conditions. Predictive uncertainty results revealed a lower level of uncertainty in the model outputs for the initial growth stages of corn. The analysis of the predictive uncertainty band also declared that the uncertainty in the model parameters was the predominant source of uncertainty in the model outputs. In addition, the excellent performance of the calibrated model based on 50% quantiles of the posterior distributions of the model parameters was observed in terms of simulating soil water content (SWC) and electrical conductivity of soil water (ECsw) at the corn root zone. The ranges of NRMSE for SWC and ECsw simulations at different soil depths were 0.003 to 0.01 and 0.09 to 0.11, respectively. The results of this study have demonstrated the authenticity of the GLUE algorithm to seek uncertainty aspects and calibration of the HYDRUS-1D model to simulate the soil salinity at the corn root zone at field scale under a linear move irrigation system.

Performance evaluation of AquaCrop and DSSAT-SUBSTOR-Potato models in simulating potato growth, yield and water productivity under various drip fertigation regimes

Water and fertilizer are two important factors affecting crop growth and yield. Crop growth models are powerful tools for making irrigation and fertilization scheduling and predicting crop yield. The performance of crop models on potato under different irrigation amounts along with different N, P and K rates has been rarely evaluated, especially under drip fertigation. In this study, the accuracy of AquaCrop and DSSAT-SUBSTOR-Potato models in simulating potato growth, yield and water productivity under different drip fertigation regimes was compared. For model calibration and validation, a two-year field experiment with three irrigation levels (W1, 60% ETC; W2, 80% ETC and W3, 100% ETC, where ETC was the crop evapotranspiration) and four fertigation (N-P2O5-K2O) rates (F1: 100–40–150 kg ha−1, F2: 150–60–225 kg ha−1, F3: 200–80–300 kg ha−1 and F4: 250–100–375 kg ha−1) was carried out in a sandy region of northwest China in 2018 and 2019. The statistical indicators showed that the AquaCrop model gave satisfactory predictions of canopy cover (R2≥ 0.92, NRMSE ≤ 14.7%, d ≥ 0.96 and RMSE ≤ 10.8%), total dry matter (R2≥ 0.92, NRMSE ≤ 25.0%, d ≥ 0.94 and RMSE ≤ 2.2 t ha−1), final dry tuber yield (R2≥ 0.57, NRMSE ≤ 12.21%, d ≥ 0.82 and RMSE ≤ 0.83 t ha−1) and fresh tuber yield (R2≥ 0.53, NRMSE ≤ 12.02%, d ≥ 0.81 and RMSE ≤ 4.91 t ha−1) in the four fertigation treatments (F1-F4) under W3 and W2 during the two potato growing seasons. The final total dry matter and tuber yield under W1 were over-estimated. The simulated water productivity and soil water content also agreed well with the observations (R2≥ 0.91, NRMSE ≤ 5.51%, d ≥ 0.94 and RMSE ≤ 0.48 kg m−3, and R2≥ 0.40, NRMSE ≤ 14.8%, d ≥ 0.77 and RMSE ≤ 11.5 mm). The simulation accuracy of DSSAT-SUBSTOR-Potato model was lower than that of AquaCrop model, which only showed a goodness of fit between simulated total dry matter, soil water content, tuber yield, water productivity and observations under high irrigation amount with high fertilization rate (W3F4). Since the observed tuber yield increased with the increase of irrigation level (60–100% ETC) under the same fertigation rate, 120%ETC and 40%ETC were further simulated using the AquaCrop model to test the changes in tuber yield with further increased or decreased irrigation levels. The scenarios simulation showed that the tuber yield was only increased by 0.19%−1.52% under 120%ETC compared with that under 100% ETC, but 18.0% more irrigation water was consumed. Comprehensively considering water resources and tuber yield, the irrigation level of 100% ETC (W3) along with fertigation (N-P2O5-K2O) rate of 200–80–300 kg ha−1 (F3) was recommended for the sustainable potato production in the study region. The results of this study can provide guidance for the application of crop models in drip-fertigated potato.

Simulating the Effects of Different Textural Soils and N Management on Maize Yield, N Fates, and Water and N Use Efficiencies in Northeast China.

Determining the best management practices (BMPs) for farmland under different soil textures can provide technical support for improving maize yield, water- and nitrogen-use efficiencies (WUE and NUE), and reducing environmental N losses. In this study, a two-year (2013-2014) maize cultivation experiment was conducted on two pieces of farmland with different textural soils (loamy clay and sandy loam) in the Phaeozems zone of Northeast China. Three N fertilizer treatments were designed for each farmland: N168, N240, and N312, with N rates of 168, 240, and 312 kg ha-1, respectively. The WHCNS (soil Water Heat Carbon Nitrogen Simulator) model was calibrated and validated using the observed soil water content, soil nitrate concentration, and crop biological indicators. Then, the effects of soil texture combined with different N rates on maize yield, water consumption, and N fates were simulated. The integrated index considering the agronomic, economic, and environmental impacts was used to determine the BMPs for two textural soils. Results indicated that simulated soil water content and nitrate concentration at different soil depths, leaf area index, dry matter, and grain yield all agreed well with the measured values. Both soil texture and N rates significantly affected maize yield, N fates, WUE, and NUE. The annual average grain yield, WUE, and NUE under three N rates in sandy loam soil were 8257 kg ha-1, 1.9 kg m-3, and 41.2 kg kg-1, respectively, which were lower than those of loam clay, 11440 kg ha-1, 2.7 kg m-3, and 46.7 kg kg-1. The order of annual average yield and WUE under two textural soils was N240 > N312 > N168. The average evapotranspiration of sandy loam (447.3 mm) was higher than that of loamy clay (404.9 mm). The annual average N-leaching amount of different N treatments for sandy loam ranged from 5.1 to 13.2 kg ha-1, which was higher than that of loamy clay soil, with a range of 1.8-5.0 kg ha-1. The gaseous N loss in sandy loam soil accounted for 14.7% of the fertilizer N application rate, while it was 11.1%in loamy clay soil. The order of the NUEs of two textural soils was: N168 > N240 > N312. The recommended N fertilizer rates for sandy loam and loamy clay soils determined by the integrated index were 180 and 200 kg ha-1, respectively.

Uncertainty Analysis of HYDRUS-1D Model to Simulate Soil Salinity Dynamics under Saline Irrigation Water Conditions Using Markov Chain Monte Carlo Algorithm

Utilizing degraded quality waters such as saline water as irrigation water with proper management methods such as leaching application is a potential answer to water scarcity in agricultural systems. Leaching application requires understanding the relationship between the amount of irrigation water and its quality with the dynamic of salts in the soil. The HYDRUS-1D model can simulate the dynamic of soil salinity under saline water irrigation conditions. However, these simulations are subject to uncertainty. A study was conducted to assess the uncertainty of the HYDRUS-1D model parameters and outputs to simulate the dynamic of salts under saline water irrigation conditions using the Markov Chain Monte Carlo (MCMC) based Metropolis-Hastings algorithm in the R-Studio environment. Results indicated a low level of uncertainty in parameters related to the advection term (water movement simulation) and water stress reduction function for root water uptake in the solute transport process. However, a higher level of uncertainty was detected for dispersivity and diffusivity parameters, possibly because of the study’s scale or some error in initial or boundary conditions. The model output (predictive) uncertainty showed a high uncertainty in dry periods compared to wet periods (under irrigation or rainfall). The uncertainty in model parameters was the primary source of total uncertainty in model predictions. The implementation of the Metropolis-Hastings algorithm for the HYDRUS-1D was able to conveniently estimate the residual water content (θr) value for the water simulation processes. The model’s performance in simulating soil water content and soil water electrical conductivity (ECsw) was good when tested with the 50% quantile of the posterior distribution of the parameters. Uncertainty assessment in this study revealed the effectiveness of the Metropolis-Hastings algorithm in exploring uncertainty aspects of the HYDRUS-1D model for reproducing soil salinity dynamics under saline water irrigation at a field scale.

Modeling heat-water-salt transport, crop growth and water use in arid seasonally frozen regions with an improved coupled SPAC model

The soil–plant-atmosphere continuum (SPAC) that is a dynamic, interactive and coupled system is particularly complicated in arid seasonally frozen regions (ASF regions) with the effects of soil freeze–thaw process and salinity. A quantitative understanding of heat-water-salt transport and crop growth in the SPAC over an entire agricultural year (agr-year) is an importance of promoting best practices for water use and crop production. In this paper, an improved coupled SPAC model (SHAW-SC) is presented and applied for continuously simulating the SPAC system of ASF regions over an agr-year. The model is developed based on modifying the SHAW model (version 3.0) and incorporating the EPIC crop growth module. The main new features of the SHAW-SC include extending the model capability for canopy growth and yield formation, improving the description of soil solute transport in the shallow groundwater systems, and providing functions to describe the effects of surface mulching on soil evaporation and salinity stress on crop growth and transpiration. Model testing and application were conducted with two agr-year data at the sunflower experimental field in irrigated areas of the upper Yellow River basin, northwest China (with an arid and cold climate). Simulations of soil water content, temperature, and crop growth indicators fitted well with the observations both in calibration and validation (with NSE > 0.64 and sufficiently small RMSE); meanwhile, simulation of salinity concentration was also satisfactory (NSE: 0.32 and 0.83; RMSE: 1.09 and 2.44 g L−1), despite the complexity of salt transport and the measurement difficulty. In addition, the comparison with previous experimental results also showed the reasonability of modeling results. Then, a comprehensive and quantitative analysis of the SPAC dynamics was conducted over an agr-year, with identifying five characteristic periods (i.e. pre-freezing, soil-freezing, soil-thawing, pre-planting and crop growth periods). Results provided a fuller understanding of the characteristics of water-salt dynamics and balance components, and the related issues of crop stress, water use and salinity control on an entire agr-year scale. Model testing and application proved that the proposed SHAW-SC was an efficient tool to simulate coupled processes of heat-water-salt transport and crop growth for ASF regions, and as well an enhancement of current SPAC models for practical field applications.