Simulated Soil Moisture Research Articles

Evaluation of SWAT Model for Simulation of Runoff and Soil Moisture in Doiraj Basin of Ilam Province

Evaluation of SWAT Model for Simulation of Runoff and Soil Moisture in Doiraj Basin of Ilam Province

Numerical Analysis of the Effect of Subgrid Variability in a Physically Based Hydrological Model on Runoff, Soil Moisture, and Slope Stability

AbstractIn coarse resolution hydrological modeling, we face the problem of subgrid variability, the effects of which are difficult to express and are often hidden in the parameterization and calibration. We present a numerical experiment with the physically based hydrological model ParFlow‐CLM with which we quantify the effect of subgrid heterogeneities in headwater catchments within the cell size typically used for regional hydrological applications. We simulate homogeneous domains and domains with subgrid heterogeneities in topography or soil thickness for two climates and soil types. The presence of side slope is the main error source, leading to large underestimation of runoff, and marginally also of evapotranspiration. The spatial distribution of soil saturation in the presence of subgrid variability in topography also leads to underestimation of landslide risk. Soil thickness is the second influential subgrid property, affecting soil moisture distribution and surface runoff formation. Results are consistent for the climates and the soil types considered. The topographic wetness index approach is tested as a way to downscale soil moisture simulations within the domain. Although this method is successful in reproducing some spatial variability and patterns, it fails when the coarse grid mean soil saturation is inaccurate or subgrid topography does not represent subsurface flow paths accurately. We conclude that ignoring subgrid variability in topography and soil thickness in coarse‐scale hydrological models may lead locally to underestimation of runoff and slope instability. Users of such models should be aware of these biases and consider ways to include subgrid effects in coarse‐scale hydrological predictions.

Application of a root water uptake model and numerical simulation to walnut trees in arid areas of northwest China

Water shortages restrict the sustainable development of agriculture in arid areas of northwest China. Quantitative studies of root water uptake (RWU) and soil water dynamics can guide the optimal regulation of irrigation. We studied walnut trees under drip irrigation in Hongqopo Farm Experimental Base in Aksu Prefecture, Xinjiang. Our aim was to parameterize the root distribution of walnut trees, establish a flexible and practical macroscopic two-dimensional RWU model, and numerically simulate soil moisture. Multiple regression analysis was applied to establish the root length density (RLD) distribution function and RWU model. The finite difference method was applied to the RWU rate inversion solution, and the alternating direction implicit difference (ADI) method was applied to numerically simulate soil moisture. The RLD distribution function accurately described the location of maximum root density and zero root density using four spatial distribution parameters (R2 = 0.86). The RWU model comprised three parameters: transpiration intensity, RLD distribution, and soil moisture distribution. There was a close agreement between simulated and measured RWU rates. The RWU was concentrated at depths of 10–60 cm. The mean absolute error of soil moisture numerical simulation for 16 days was 0.027 cm3·cm-3, which indicated that the proposed RLD distribution function, RWU model, and numerical simulation method were reliable. These approaches have the advantages of simple parameter measurement and flexible consideration of the spatial variability of the RWU rate and are applicable to other fruit trees. They provide a theoretical basis for water-saving irrigation and scientific management of orchards in arid areas.

Development of the Hydrus-1D freezing module and its application in simulating the coupled movement of water, vapor, and heat

In cold regions, freeze-thaw cycles play a critical role in many engineering and agricultural applications and cause soil water flow and heat transport studies to be much more complicated due to phase changes involved. A fully coupled numerical module for simulating the simultaneous movement of water, vapor, and heat during freezing-thawing periods was developed and incorporated in the Hydrus-1D software in this study. To avoid numerical instabilities caused by a sudden increase in the apparent heat capacity during a phase change, a new approach based on the available energy concept was adopted to adjust soil temperature when the freezing temperature is reached. The proposed freezing module's performance was then validated using experimental data collected at three field sites with typical seasonal freezing/thawing processes. Results showed that the model could efficiently obtain a convergent solution and that simulated soil moisture and temperature variations captured the observed data well. Driven by soil matric potential and temperature gradients, both liquid water and water vapor flowed towards the freezing front. The isothermal liquid flux was the most significant component of overall flow in most soil depths except in the frozen layer, where it decreased by 1–5 orders of magnitude from values before freezing. Instead, the thermal vapor flux was the dominant moisture transfer mechanism in the frozen layer and contributed about 10% to the ice formation. These results indicate that the model, which considers the coupled movement of water, vapor, and heat, can better describe the physical mechanisms of the hydrological cycle in the vadose zone during the freezing-thawing periods.

A multi-sourced assessment of the spatiotemporal dynamics of soil moisture in the MARINE flash flood model

Abstract. The MARINE (Model of Anticipation of Runoff and INundations for Extreme events) hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are explored in this work. On one hand, transfers of water through the subsurface, formerly relying on water height, now take place in a homogeneous soil column based on the soil saturation degree (SSF model). On the other hand, the soil column is divided into two layers, which represent, respectively, the upper soil layer and the deep weathered rocks (SSF–DWF model). The aim of the present work is to assess the accuracy of these new representations for the simulation of soil moisture during flash flood events. An exploration of the various products available in the literature for soil moisture estimation is performed. The efficiency of the models for soil saturation degree simulation is estimated with respect to several products either at the local scale or spatially distributed: (i) the gridded soil moisture product provided by the operational modeling chain SAFRAN-ISBA-MODCOU; (ii) the gridded soil moisture product provided by the LDAS-Monde assimilation chain, which is based on the ISBA-A-gs land surface model and assimilating satellite derived data; (iii) the upper soil water content hourly measurements taken from the SMOSMANIA observation network; and (iv) the Soil Water Index provided by the Copernicus Global Land Service (CGLS), which is derived from Sentinel-1 C-SAR and ASCAT satellite data. The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017–2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data, lead to the following conclusion: both the dynamics and the amplitudes of the soil saturation degree simulated with the SSF and SSF–DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. Finally, the soil saturation degree simulated by the two-layers model for the deep layer is compared to the soil saturation degree provided by the LDAS-Monde product at corresponding depths. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil saturation degree simulation during flash floods with respect to both gridded data and local soil moisture measurements.

The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model

Abstract. Recent advances in soil moisture remote sensing have produced satellite data sets with improved soil moisture mapping under vegetation and with higher spatial and temporal resolutions. In this study, we evaluate the potential of a new, experimental version of the Advanced Scatterometer (ASCAT) soil water index data set for multiple objective calibrations of a conceptual hydrologic model. The analysis is performed in 213 catchments in Austria for the period 2000–2014. An HBV (Hydrologiska Byråns Vattenbalansavdelning)-type hydrologic model is calibrated based on runoff data, ASCAT soil moisture data, and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data for various calibration variants. Results show that the inclusion of soil moisture data in the calibration mainly improves the soil moisture simulations, the inclusion of snow data mainly improves the snow simulations, and the inclusion of both of them improves both soil moisture and snow simulations to almost the same extent. The snow data are more efficient at improving snow simulations than the soil moisture data are at improving soil moisture simulations. The improvements of both runoff and soil moisture model efficiencies are larger in low elevation and agricultural catchments than in others. The calibrated snow-related parameters are strongly affected by including snow data and, to a lesser extent, by soil moisture data. In contrast, the soil-related parameters are only affected by the inclusion of soil moisture data. The results indicate that the use of multiple remote sensing products in hydrological modeling can improve the representation of hydrological fluxes and prediction of runoff hydrographs at the catchment scale.

SHETRAN and HEC HMS Model Evaluation for Runoff and Soil Moisture Simulation in the Jičinka River Catchment (Czech Republic)

Due to the improvement of computation power, in recent decades considerable progress has been made in the development of complex hydrological models. On the other hand, simple conceptual models have also been advanced. Previous studies on rainfall–runoff models have shown that model performance depends very much on the model structure. The purpose of this study is to determine whether the use of a complex hydrological model leads to more accurate results or not and to analyze whether some model structures are more efficient than others. Different configurations of the two models of different complexity, the Système Hydrologique Européen TRANsport (SHETRAN) and Hydrologic Modeling System (HEC-HMS), were compared and evaluated in simulating flash flood runoff for the small (75.9 km2) Jičinka River catchment in the Czech Republic. The two models were compared with respect to runoff simulations at the catchment outlet and soil moisture simulations within the catchment. The results indicate that the more complex SHETRAN model outperforms the simpler HEC HMS model in case of runoff, but not for soil moisture. It can be concluded that the models with higher complexity do not necessarily provide better model performance, and that the reliability of hydrological model simulations can vary depending on the hydrological variable under consideration.

Evaluation of AquaCrop model for estimating of changes process of soil moisture, evapotranspiration and yield of maize under salinity and fertility stresses

IntroductionIn the presence of environmental stresses such as salinity and fertility stresses, water and nutrients less absorbed by the plant. Despite the stresses of salinity and nitrogen deficiency (fertility stress), the determination of the actual water requirement of plants with the AquaCrop model, will be important. Therefore, the aim of this research was to evaluate the AquaCrop model for estimating the soil moisture, evapotranspiration and yield of maize, under the salinity and fertility stresses.Materials and methodsIn this research, two treatments of water salinity and nitrogen deficity in four levels and three replications, with a control plot, were implemented as a factorial experiment in a randomized complete of block design. The studied plant was maize with cultivar of 704 Sc and was planted in plots with dimensions of 3 × 3 meters and 1.5 meters apart. In this plan, fertility stress was in the form of nitrogen fertilizer consumption and at four levels. Treatments of N_0، N_1، N_2 and N_3consisted of consumption of 100, 75, 50 and 25% of nitrogen fertilizer based on fertilizer recommendation, respectively. Salinity stress has been applied by irrigation of the plant with saline water. Water salinity treatments were selected based on yield potential of maize at four levels of 100, 90, 75 and 50% (3). According to the above four performance levels, treatments of S_0، S_1، S_2 and S_3 were included; irrigation water with electric conductivity of 0.5, 1.2, 3.5 and 7.5 (ds/m) respectively. Determining the irrigation time, was the same as the moisture content reached the RAW (Readily Available Water) level. Between the two irrigation intervals, the stomatal resistance of maize leaf, was measured by the AP4 prometer device. At the same time as increasing stomatal resistance, RAW was calculated and plots were irrigated. In the days of between two irrigation, was measured the soil moisture content of the plots at the depth of root development. The daily evapotranspiration of the plant, was calculated based on the amount of daily water depletion. For optimal calibration of parameters in the AquaCrop model, was used the method of Generalized Likelihood Uncertainty Estimation (GLUE). Among 16 treatments, 8 treatments were randomly selected for calibration and the rest were selected for validation. Results and discussionResults were obtained for evaluating the AquaCrop model at the validation stage. The root mean square error (RMSE) of soil moisture simulation, varied from 1.43 to 2.6%. The normalized error value (NRMSE) ranged from 4 to 6 percent. The AquaCrop model showed a similar trend in the evapotranspiration simulation. The root mean square error (RMSE) of evapotranspiration simulation, varied from 1.85 to 2.35 mm. The normalized error value (NRMSE) ranged from 3.5 to 4.5 percent. For yield simulation, RMSE was 0.34 ton. ha-1 and NRMSE was 0.65%. The value of the R^2, EF, and d statistics showed a good correlation between the data and the optimal efficiency of the modeling. Therefore, the results showed that the performance of the model was good in estimating the parameters. ConclusionsEvaluating the capability of the models is a great help to the agricultural sector planners, in estimation the parameters. In this research, evaluated the estimation of soil moisture content, evapotranspiration and yield of maize, under salinity and fertility stresses with AquaCrop model. The purpose of the model calibration was to nearing the simulated data to the real data (measured in the region). The obtained amounts for NRMSE and R^2 were less than 10% and greater than 0.9, respectively, which indicated the optimal performance of the model in this regard.

Numerical simulation of hydraulic properties of soil in the GSPAC system under variable water-levels

The effects of spatial variability and human factors on soil lead to complex soil hydraulic properties. In areas with highly variable water levels, it is impossible to use analytic expressions to accurately quantify and describe the process of moisture transport. In this study, the HYDRUS software package was used to calculate and simulate soil moisture transport under variable water levels by analyzing soil and underground water as an integrated system, and conducting in-situ monitoring tests on a large indoor soil trough. The results showed that: (1) the initial model verification achieved by adjusting the groundwater level revealed that when the groundwater dropped, the soil moisture in the vadose zone transitioned from balanced to unbalanced to balanced again; (2) the values and development trends of the negative pressure and water content of the soil as calculated by the model using adjusted parameters were consistent with those of the measured result, which indicated that the moisture transport model established in the GSPAC system was credible; and (3) the model established by the HYDRUS software package was able to effectively predict the soil moisture transport and infiltration dynamics.

Modeling the response of soil moisture to climate variability in the Mediterranean region

Abstract. Future climate scenarios for the Mediterranean region indicate a possible decrease in annual precipitation associated with an intensification of extreme rainfall events in the coming years. A major challenge in this region is to evaluate the impacts of changing precipitation patterns on extreme hydrological events such as droughts and floods. For this, it is important to understand the impact of climate change on soil moisture since it is a proxy for agricultural droughts, and the antecedent soil moisture condition plays a key role on runoff generation. This study focuses on 10 sites, located in southern France, with available soil moisture, temperature, and precipitation observations for a 10-year time period. Soil moisture is simulated at each site at the hourly time step using a model of soil water content. The sensitivity of the simulated soil moisture to different changes in precipitation and temperature is evaluated by simulating the soil moisture response to temperature and precipitation scenarios generated using a delta change method for temperature and a stochastic model (the Neyman–Scott rectangular pulse model) for precipitation. Results show that soil moisture is more impacted by changes in precipitation intermittence than precipitation intensity and temperature. Overall, increased temperature and precipitation intensity associated with more intermittent precipitation leads to decreased soil moisture and an increase in the annual number of days with dry soil moisture conditions. In particular, a temperature increase of +4 ∘C combined with a decrease of annual rainfall between 10 % and 20 %, corresponding to the current available climate scenarios for the Mediterranean, lead to a lengthening of the drought period from June to October with an average of +28 d of soil moisture drought per year.

On the occurrence of the worst drought in South Asia in the observed and future climate

Droughts in South Asia pose substantial challenges to water resources, agricultural, and socio-economic growth of more than two billion people. However, the most detrimental soil moisture droughts, their impacts, and occurrence in the observed and projected climate in South Asia remain unidentified. We conducted soil moisture simulations using the variable infiltration capacity model and meteorological forcing based on the four global observational products (Climate Research Unit, WATCH-Forcing-Data-ERA-Interim, University of Delaware, and Princeton) to identify the worst soil moisture drought during 1951–2016. The worst soil moisture drought during the observed record occurred in 2002 and affected more than 65% of South Asia and resulted in a decline in rice yield (∼400 kg ha−1). A large uncertainty based on the four observational products was found in the number of drought spells and their characteristics. We estimated the frequency of droughts that exceeded the overall severity score of the worst drought during 1951–2016 under the future climate using 40 simulations from Community Earth System Model-Large Ensemble. The frequency of the worst droughts is projected to increase by about 1.5 times in the future (2035–2100) in comparison to the historic (1951–2016) climate. The worst droughts in the future are likely to be more intense and widespread in South Asia. A considerable influence of industrial aerosols was found on the occurrence of the worst droughts in South Asia. Our findings have implications for food and water security in one of the most densely populated regions in the world.

Validation of SMAP L2 passive-only soil moisture products using upscaled in situ measurements collected in Twente, the Netherlands

Abstract. The Twente region in the east of the Netherlands has a network with 20 soil monitoring stations that has been utilized for validation of the Soil Moisture Active/Passive (SMAP) passive-only soil moisture products. Over the period from April 2015 until December 2018, seven stations covered by the SMAP reference pixels have fairly complete data records. Spatially distributed soil moisture simulations with the Dutch National Hydrological Model have been utilized for the development of upscaling functions to translate the spatial mean of point measurements to the domain of the SMAP reference pixels. The native and upscaled spatial soil moisture means computed using the in situ measurements have been adopted as references to assess the performance of the SMAP (i) Single Channel Algorithm at Horizontal Polarization (SCA-H), (ii) Single Channel Algorithm at Vertical Polarization (SCA-V), and (iii) Dual Channel Algorithm (DCA) soil moisture estimates. In the case of the Twente network it was found that the SCA-V SMAP soil moisture observations collected in the afternoon had the best agreement with the native spatial mean, leading to an unbiased root mean squared error (uRMSE) of 0.059 m3 m−3, whereas for the upscaled in situ references primarily larger biases were found. These error levels are larger than the mission's target accuracy of 0.04 m3 m−3, which can be attributed to large over- and under-estimation errors (>0.08 m3 m−3), in particular at the end of dry spells and during freezing, respectively. The strong vertical dielectric gradients associated with rapid soil freezing and wetting cause the disparity in soil depth characterized by SMAP and in situ that leads to the large mismatches. Once filtered for frozen conditions and antecedent rainfall, the uRMSE improves to 0.043 m3 m−3.

Anthropogenic warming and intraseasonal summer monsoon variability amplify the risk of future flash droughts in India

Flash droughts cause rapid depletion in root-zone soil moisture and severely affect crop health and irrigation water demands. However, their occurrence and impacts in the current and future climate in India remain unknown. Here we use observations and model simulations from the large ensemble of Community Earth System Model to quantify the risk of flash droughts in India. Root-zone soil moisture simulations conducted using Variable Infiltration Capacity model show that flash droughts predominantly occur during the summer monsoon season (June–September) and driven by the intraseasonal variability of monsoon rainfall. Positive temperature anomalies during the monsoon break rapidly deplete soil moisture, which is further exacerbated by the land-atmospheric feedback. The worst flash drought in the observed (1951–2016) climate occurred in 1979, affecting more than 40% of the country. The frequency of concurrent hot and dry extremes is projected to rise by about five-fold, causing approximately seven-fold increase in flash droughts like 1979 by the end of the 21st century. The increased risk of flash droughts in the future is attributed to intraseasonal variability of the summer monsoon rainfall and anthropogenic warming, which can have deleterious implications for crop production, irrigation demands, and groundwater abstraction in India.

Historical Changes in Surface Soil Moisture Over the Contiguous United States: An Assessment of CMIP6

AbstractThe newly released simulations from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) provide an opportunity to re‐examine long‐term soil moisture trends. In this study, we assessed the historical simulation (1850–2014) of surface soil moisture from 18 general circulation models that are part of CMIP6 and compared them with model simulations from CMIP5 and NLDAS‐2, and in situ observations over CONUS. Our results show that for CONUS as a whole, there is no significant long‐term trend in soil moisture, but significant drying trends exist over medium‐term, short‐term, and near‐present periods based on the CMIP6. Our comparison of CMIP6 to NLDAS‐2 (1980–2014) and in situ observations (2004–2014) demonstrates that it more accurately captures soil moisture trends in CONUS than CMIP5. CMIP6 shows a significant wetting trend in the Northwest, Midwest, Northeast, and Southeast. There is greater agreement amongst the individual models in CMIP6 than CMIP5, but intermodel variability still exists.

Towards hyper-resolution land-surface modeling of surface and root zone soil moisture

The goal of this work is to estimate surface and root zone soil moisture at resolutions that are useful for decision making and water resources management. A 500-m atmospheric forcing dataset is developed from the 12.5-km NLDAS-2 (North America Land Data Assimilation System) products across Oklahoma, where high-quality observations are available for validation purposes. A land surface model is then forced with three combinations of input variables to simulate surface and root zone soil moisture: 1) NLDAS-2 atmospheric forcings at their original resolution; 2) downscaled NLDAS-2 atmospheric variables (i.e., near-surface air temperature and humidity, wind speed and direction, incident longwave and shortwave radiation, pressure) and original resolution NLDAS-2 precipitation; and 3) downscaled NLDAS-2 atmospheric variables and precipitation. Results show that the third simulation is able to bring modeled standard-normal deviates of both surface and root zone soil moisture closer to in-situ observations, whereas the second simulation only shows slight improvements with respect to one forced with original resolution NLDAS-2 data. This is particularly evident for negative values of standard-normal deviates, which correspond to drier than usual cases, due to the improved ability of the downscaled precipitation to detect missed events and no-rain cases. In summary, finer resolution forcings have the potential to improve simulations of soil moisture and the resolution of precipitation plays a critical role in improving time series of soil moisture standard-normal deviates.

RZWQM Simulation of Soil Moisture and Maize Yield in Newly Cultivated Land with Different Soil Bulk Density

ABSTRACT The aim of this study is to determine the optimal soil bulk density in newly added cultivated land for maintaining soil moisture and crops growing. In this work, a field experiment was conducted to study the effect of 5 different soil’s bulk density (BD) on soil moisture and crop yield in newly created land on gravel: T23 (BD = 1.3 g/cm3), T24 (BD = 1.4 g/cm3), T25 (BD = 1.5 g/cm3), T26 (BD = 1.6 g/cm3), T27 (BD = 1.7 g/cm3). The root zone water quality model (RZWQM) was calibrated and verified with experimental data, and then applied to simulate the soil moisture and crop yield under different bulk density from 2004 to 2016. Simulation prediction shows that the average yield of the soil bulk density of T25 treatment is highest with 7551 kg/ha, and the CV of multi-year crop yield is the minimum with 31.9%, meaning that the yield of crops of this bulk density is the most stable. The relationship between soil water storage and growth period yield is approximately linear, with R2 of 0.46. Comprehensive analysis indicates that the soil bulk density of that 0–40 cm – soil bulk density = 1.2 g/cm3; 40–50 cm soil bulk density = 1.5 g/cm3 is more conducive to water conservation and crop growth.

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.

Water balance of an Atlantic forest remnant under a prolonged drought period

ABSTRACT Since prolonged droughts have impacted Atlantic forests in Southeastern Brazil, further investigations to understand the effects of such stressful conditions in their hydrological behavior are required. This study aimed to assess the changes in the water balance of a semi-deciduous Atlantic forest remnant and how the forest responds to droughts. The Standardized Precipitation-Evapotranspiration Index was applied to identify droughts (from 1961 to 2019) and their severity in both the hydrological year and summer scales. Drought impacts on actual evapotranspiration, potential percolation, and soil water storage were assessed using Hydrus-1D, having net precipitation and potential evapotranspiration as inputs. Error analyses (< 10%) confirmed Hydrus-1D suitability for simulating soil moisture. Uncertainties regarding the soil saturated hydraulic conductivity are due to preferential flows, which are not accounted for in Richards’ equation. Drought intensification changed forest hydrology and triggered physiological responses to deal with it. Semi-deciduous Atlantic forests are adapted to dry conditions because of the existence of dry-affiliated species and the activation of physiological mechanisms. However, such adaptations responded differently regarding the drought scales. The intensification of summer droughts increased evapotranspiration and decreased the potential percolation. Leaf shedding changed the canopy structure in a two-year time lag as a response to the intensification of hydrological year droughts. Changes in forest hydrology are sudden and trigger physiological responses, such as leaf shedding, in a delayed process after droughts take place.

Spatial‐Temporal Simulations of Soil Moisture Content in a Large Basin of the Loess Plateau, China

Soil moisture is a crucial factor limiting the growth and survival of plants on the Loess Plateau. Its level has a severe impact on plants’ growth and development and the type and distribution characteristics of communities. This study area is the Jihe Basin in the Loess Plateau, China. Multiple linear regression models with different environmental variables (land use, topographic and meteorological factors, etc.) were developed to simulate soil moisture’s spatial and temporal changes by integrating field experiments, indoor analysis, and GIS spatial analysis. The model performances were evaluated in the Jihe Basin, with soil moisture content measurements. The result shows that soil moisture content is positively correlated with soil bulk density, monthly rainfall, topographic wetness index, land use coefficient, and slope aspect coefficient but negatively correlated with the monthly‐averaged temperature and the relative elevation coefficient. The selected variables are all related to the soil moisture content and can account for 75% of the variations of soil moisture content, and the remaining 25% of the variations are related to other factors. Comparing the simulated and measured values at all sampling points shows that the average error of all the simulated values is 0.09, indicating that the simulation has high accuracy. The spatial distribution of soil moisture content is significantly affected by land use and topographic factors, and seasonal variation is remarkable in the year. Seasonal variation of soil moisture content is determined by the seasonal variation of rainfall and the air temperature (determining evaporation) and vegetation growth cycle. Therefore, the proposed model can simulate the spatial and temporal variation of soil moisture content and support developing the soil and water loss model on a basin scale.

Coupled hydrology-crop growth model incorporating an improved evapotranspiration module

Some hydrologic models oversimplify crop growth simulation. Although some studies coupled hydrologic models with crop models, there are several hypotheses and unconsidered factors in these studies, such as no irrigation. To improve simulation of discharge, soil moisture, and evapotranspiration, the Variable Infiltration Capacity (VIC) model was coupled with a crop growth model, incorporating an improved evapotranspiration module based on the soil water stress method. The new evapotranspiration module improved two water stress errors of the Environmental Policy Integrated Climate (EPIC) model. Considering the impact of agricultural irrigation and crop rotation, regional crop growth and hydrology were simulated in the upstream watershed of the Qingkou River in the east of China. Results of simulation by the Coupled Hydrology-Crop growth (CHC) model were compared with observed discharge data, measured soil moisture data, and evapotranspiration data obtained from remote sensing inversion. The daily Nash efficiency coefficient was 0.78 for the calibration period from 1978 to 2008 and was 0.76 for the verification period from 2009 to 2016. The simulated soil moisture was found to be significantly correlated with observed soil moisture, better than the simulation by the VIC model alone. The daily efficiency coefficient of the CHC model simulation increased by 0.13 for the verification period compared with that of the VIC model; and the transpiration module of the CHC model had the best simulation accuracy among the VIC, EPIC, and CHC transpiration modules. Combined with observed data, the reason for the water stress error of the EPIC model, reported by some references, was found: A parameter needs to be further adjusted combined with the actual condition instead of being constant. Thus, the coupled hydrology and crop growth model improved the simulation of hydrological elements and can help guide the efficient use of water resources and adjustment of agricultural cropping practices.