Model Simulated Soil Moisture Research Articles

Optimization of leaching level and alternating drip irrigation start time improved water saving, yield enhancement, and salt leaching

Shortage of water resources and salinization of soil are two important factors restricting the sustainable development of agriculture in Xinjiang. The current drip irrigation strategy cannot meet the high efficiency water saving and salt leaching requirement, seriously limiting sustainable development. Therefore, in 2020 and 2021, a field experiment was conducted with three leaching levels (120, 240 and 360 mm) and four alternate drip irrigation (ADI) start time (no ADI, seedling, budding and flowering). The field experiment was combined with HYDRUS to investigate the effect of water saving and salt leaching by combining ADI with leaching during the reproductive period. The results showed that the HYDRUS-2D model simulated soil moisture and salinity with high accuracy under different irrigation conditions, with R2 ranging from 0.63 to 0.93, MRE from 2.45 % to 11.66 %, and RMSE from 1.88 % to 4.41 % for soil moisture and 0.29 g kg−1 to 0.97 g kg−1 for soil salinity, respectively. Optimization of irrigation method, leaching level and ADI start time all improved water and salinity stress characteristics. Although initially high soil salinity resulted in the conversion of some severe stress to moderate due to the irrigation, these disadvantages were offset by reductions in severe stress duration and area. Water-salt production model constructed based on water and salt stress characteristics demonstrated good yield simulation performance. According to HYDRUS, water-salt production model and comprehensive evaluation, starting ADI at the 3rd irrigation event with a leaching level of 290 mm was found to be optimal considering yield, water use efficiency, soil desalination and desalination efficiency. Considering the practicability, the recommended optimum ADI leaching level was 262–318 mm, with ADI initiated between the 2nd and 4th irrigation event. The study provided a vital theoretical basis for efficient water-salt management, supporting sustainable development in saline-alkali cotton fields in Xinjiang under limited water resources.

WINDS Model Simulation of Guayule Irrigation

The WINDS (Water-Use, Irrigation, Nitrogen, Drainage, and Salinity) model uses the FAO56 dual crop coefficient and a daily time-step soil–water balance to simulate evapotranspiration and water content in the soil profile. This research calibrated the WINDS model for simulation of guayule under full irrigation. Using data from a furrow irrigated two-season guayule experiment in Arizona, this research developed segmented curves for guayule basal crop coefficient, canopy cover, crop height and root growth. The two-season guayule basal crop coefficient (Kcb) curve included first and second season development, midseason, late-season and end-season growth stages. For a fully irrigated guayule crop, the year one midseason Kcb was 1.14. The second year Kcb development phase began after the crop was semi-dormant during the first winter. The second year Kcb value was 1.23. The two-season root growth curve included a growth phase during the first season, no growth during winter, and a second growth phase during the second winter. A table allocated fractions of total transpiration to soil layers as a function of root depth. With the calibrated tables and curves, the WINDS model simulated soil moisture content with a root mean squared error (RMSE) of 1- to 3-% volumetric water content in seven soil layers compared with neutron probe water contents during the two-year growth cycle. Thus, this research developed growth curves and accurately simulated evapotranspiration and water content for a two-season guayule crop.

Toward Improved Regional Hydrological Model Performance Using State‐Of‐The‐Science Data‐Informed Soil Parameters

AbstractAccurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF‐Hydro) over a central California domain. Our results show WRF‐Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling‐Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well‐established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS‐adjusted WRF‐Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF‐Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO‐informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre‐calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

An Automated Data-Driven Irrigation Scheduling Approach Using Model Simulated Soil Moisture and Evapotranspiration

Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making.

A surrogate modeling method for distributed land surface hydrological models based on deep learning

Surrogate modelling methods have been used to estimate spatially distributed parameters of land surface hydrological models due to their computational efficiency. However, traditional surrogate techniques have trouble in modelling high-dimensional input–output maps. In addition, their applications to a distributed model over large spatial and temporal domains are very unlikely to produce consistent spatial pattern and dynamic relationship of model input and output, leading to unbalanced model performance across different spatiotemporal domains. To address these issues, we employ a deep autoregressive neural network to construct an accurate and reliable surrogate system of distributed dynamic model outputs. The network uses a deep convolutional encoder-decoder architecture to take full advantage of the deep convolutional layers in high-dimensional image processing and spatial feature extraction. Moreover, the autoregressive strategy makes the network good at handling dynamic relationship between the time-varying model input and output. We apply this deep learning (DL) network to building a surrogate model of the Variable Infiltration Capacity (VIC) model simulated soil moisture over the Huaihe river basin of China. The results show that the adopted deep autoregressive neural network can provide an accurate surrogate model of the high-dimensional dynamic relationship between 15 input fields and 1 output field, each covering 408 grids, over multiple years. This surrogate model significantly outperforms the popular long short-term memory (LSTM) model, achieving an average mean squared error (MSE) of 0.26 and an average R2 of 0.76 using the test datasets. The surrogate's MSE and R2 performance represents an average improvement of 43% and 33%, respectively, compared to the LSTM model. In addition, it shows better spatial performance than the LSTM model, demonstrating its superior performance in approximating spatial patterns of VIC simulated soil moisture. The accuracy of this surrogate method and its ability to handle high-dimensional data is promising for advancing parameter uncertainty quantification of distributed land surface hydrological models, which usually have high dimensionality due to the large number of variables involved, including distributed parameters, input forcing variables, and output variables.

Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture

The development of a computationally efficient machine learning-based framework to understand the underlying causes for biases in climate model simulated fields is presented in this study. The framework consists of a two-step approach, with the first step involving the development of a Random Forest (RF) model, trained on observed data of the climate variable of interest and related predictors. The second step involves emulations of the climate variable of interest with the RF model developed in step one by replacing the observed predictors with those from the climate model one at a time. The assumption is that comparing these emulations with that of a reference emulation driven by all observed predictors can shed light on the contribution of respective predictor biases to the biases in the climate model simulation. The proposed framework is used to understand the biases in the Global Environmental Multiscale (GEM) model simulated surface soil moisture (SSM) for the April–September period, over a domain covering part of north-east Canada. The grid cell-based RF model, trained on daily SSM and related climate predictors (water availability, 2 m temperature, relative humidity, snowmelt, maximum snow water equivalent) from the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), demonstrates great skill in emulating SSM, with root mean square error of 0.036. Comparison of the five RF emulations based on GEM predictors with that based on ERA5 predictors suggests that the biases in the mean April–September SSM can be attributed mainly to biases in three predictors: water availability, 2 m temperature and relative humidity. The regions where these predictors contribute to biases in SSM are mostly collocated with the regions where they are shown to be the among the top three influential predictors through the predictor importance analysis, i.e., 2 m temperature in the southern part of the domain, relative humidity in the northern part of the domain and water availability over rest of the domain. The framework, without having to undertake expensive simulations with the climate model, thus successfully identifies the main causes for SSM biases, albeit with slightly reduced skill for heavily perturbed simulations. Furthermore, identification of the causes for biases, by informing targeted climate model improvements, can lead to additional reductions in computational costs.

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

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

Influence of water table dynamics on spatial and temporal patterns of hydroclimate extremes over Lake Victoria Basin, East Africa: Comparison of wet and dry years

AbstractWRF model coupled to water table dynamics has been adapted to investigate the spatial and temporal evolution of wet and dry conditions over Lake Victoria Basin. Two 2‐year long simulations were conducted using coupled model with water table and the uncoupled (without water table) for wet and dry periods. Influence of water table on land–atmosphere coupling and interconnections among precipitation, soil moisture, evapotranspiration, and surface energy fluxes were examined. Overall, the coupled model simulated significantly higher monthly rainfall amounts during both the short (March‐May) and long (October‐December) rains of the wet year, which was more consistent with observations particularly over the lake surface and immediate hinterlands. Simulated monthly rainfall differences between coupled and uncoupled were pronounced during the peak of long rains, exceeding 100mm over the lake surface. Toward the end of the rainfall season (May) the difference was minimal. During the short rains significant differences occurred mostly in November, especially over the eastern shores. But during the relatively dry year minimal differences were generally witnessed throughout the year except in May, east of Lake Victoria. The coupled model simulated stronger matching among rainfall, soil moisture, and evapotranspiration over areas with shallow water table, for example in Kisumu to the east and Bukoba to the west. In these areas coupled model simulated higher soil moisture corresponding to higher evapotranspiration and precipitation. These interconnections were more pronounced during short and long rains in 1997 (wet) compared to 2010 (dry). Wet conditions over the gulf of Kisumu corresponded with rise in water table especially during October–December 1997 consistent with ENSO‐related flooding over the area. Hence, our study demonstrated that incorporating water table resulted in realistic interconnections between precipitation, soil moisture, ET, and surface energy fluxes, and could improve simulation and prediction of spatial‐temporal evolution of wet and dry conditions over Lake Victoria Basin.

Evaluation of the DNDCv.CAN model for simulating greenhouse gas emissions under crop rotations that include winter cover crops

Context Process-based modelling studies can help inform conservation practices for mitigating soil surface CO2 and N2O fluxes. Aims We evaluated the ability of the DeNitrification-DeComposition (DNDC) model to predict field-measured soil surface CO2 and N2O emissions in crop rotations managed with cover crop (CC) and without cover crop (NC) under the 27-year no-till field experiment in South Dakota, USA. Methods Emissions were measured in a 2-year corn–soybean and a 4-year corn–soybean–oat–winter wheat rotation. The model was calibrated with 2-year NC treatment and evaluated against three treatments (2-year CC, 4-year NC and 4-year CC) during the growing season of corn (2017) and soybean (2018). Key results Across all treatments, the model simulated soil temperature (MBE, −0.73–0.29°C; RMSE, 1.47–4.03°C; NSE, 0.54–0.90; d, 0.89–0.98; R2, 0.64–0.93) and moisture [water-filled porosity (wfps)] (MBE, 0.03–0.06 wfps; RMSE, 0.09–40.13 wfps; NSE, −0.24–0.49; d, 0.78–0.87; R2, 0.45–0.69) that agreed well with field measurements. Predicted daily soil CO2 fluxes (kg C ha−1) provided ‘good’ agreement with MBE (range −0.58−4.67), RMSE (range 2.10−7.36), d (range 0.68–0.93), NSE (range −0.92–0.79), and R2 (range 0.49–0.85). Statistics showed ‘poor’ agreement between the simulated and measured daily N2O emissions because peak emissions events in the measured data were less than predicted. Cumulative CO2 and N2O emissions and crop yields were well estimated by the model. Conclusions DNDCv.CAN simulated the impacts of diverse crop rotations and cover crops on soil moisture, temperature and greenhouse gas emissions in the humid south-east of USA. Implications Nitrogen transformation routines and effect of rainfall interception on soil water content need further investigation to address the variations in daily N2O emissions.

Blending Noah, SMOS and In-Situ Soil Moisture Using Multiple Weighting and Sampling Schemes

AbstractSoil moisture can be obtained from in-situ measurements, satellite observations, and model simulations. This study evaluates the importance of in-situ observations in soil moisture blending, and compares different weighting and sampling methods for combining model, satellite, and in-situ soil moisture data to generate an accurate and spatially-continuous soil moisture product at 4-km resolution. Four different datasets are used: Antecedent Precipitation Index (API), KAPI, which incorporates in-situ soil moisture observations with the API using regression kriging, SMOS L3 soil moisture, and model-simulated soil moisture from the Noah model as part of the North American Land Data Assimilation System (NLDAS). Triple collocation, least square weighting, and equal weighting are used to generate blended soil moisture products. An enumerated weighting scheme is designed to investigate the impact of different weighting schemes. The sensitivity of the blended soil moisture products to sampling schemes, station density and data formats (absolute, anomalies and percentiles) are also investigated. The results reveal KAPI outperforms API. This indicates that incorporating in-situ soil moisture improves the accuracy of the blended soil moisture products. There are no statistically significant (p&gt;0.05) differences between blended soil moisture using triple collocation and equal weighting approaches, and both methods provide sub-optimal weighting. Optimal weighting is achieved by assigning larger weights to KAPI and smaller weights to SMOS. Using multiple sources of soil moisture is helpful for reducing uncertainty and improving accuracy, especially when the sampling density is low, or the sampling stations are less representative. These results are consistent regardless of how soil moisture is represented (absolute, anomalies or percentiles).

Evaluation of simulated soil moisture from China Land Data Assimilation System (CLDAS) land surface models

ABSTRACT This study evaluated the land surface model-simulated soil moisture (SM) product from the China Land Data Assimilation System (CLDAS). This was achieved using three densely instrumented in situ observation networks in China that have different environmental conditions, i.e., the Hebi, Naqu and Heihe sites. The European Space Agency Climate Change Initiative multi-satellite-retrieved SM product (CCI-SM) was also included for inter-comparison purposes. Standard validation scores indicated that the CLDAS-SM product has high correlation and low uncertainty with both surface and root-zone soil moisture observations. The target accuracy (0.04 m3 m−3) was achieved over all three sites. Compared with the CCI-SM product, the CLDAS-SM product showed higher accuracy for the Hebi and Heihe sites but slightly lower accuracy for the Naqu site located at the centre of the Tibetan Plateau. Regionally, the unbiased root mean square difference between the CLDAS-SM and CCI-SM products was noticeably smaller within China than in neighbouring countries. Given that the performance of the CCI-SM product should be unaffected by country boundaries, the better performance of the CLDAS-SM product in China can be attributed primarily to the high-quality meteorological forcing data.

Agro-hydrologic modelling for simulating soil moisture dynamics in the root zone of Potato based on crop coefficient approach under limited climatic data

ABSTRACT Field lysimetric experiments were conducted on Potato crop grown in a humid sub-tropical agro-climate during 2014–2017 cropping seasons. Reference evapotranspiration (ET0) methods based on limited climatic data were evaluated with the FAO-56 Penman-Monteith (P-M) method. The performance of temperature-based and radiation-based methods was reliable, but evaporation-based methods performed poorly. The best methods in each category viz. Hargreaves-Samani, FAO-24 Radiation, and Snyder along with FAO-56 P-M were considered to estimate the empirical crop evapotranspiration (ETc) using the crop coefficient approach. The empirical ETc was compared with the field ETc obtained through water balance approach. A root water uptake-based numerical model was utilized to simulate the soil moisture dynamics in the crop root zone, wherein ETc is an important input parameter. The model simulated soil moisture was compared with the field observed values. Results indicate that FAO-56 P-M and Hargreaves-Samani-based simulated values presented close agreement with field observed values. FAO-24 Radiation and Snyder-based simulations underestimated and overestimated the field observed soil moisture, respectively. The field soil moisture depletion of 30% corresponds to simulated depletion of 15% (Snyder), 25% (Hargreaves-Samani), 28% (FAO-56 P-M) and 40% (FAO-24 Radiation).

A novel regional irrigation water productivity model coupling irrigation- and drainage-driven soil hydrology and salinity dynamics and shallow groundwater movement in arid regions in China

Abstract. The temporal and spatial distributions of regional irrigation water productivity (RIWP) are crucial for making decisions related to agriculture, especially in arid irrigated areas with complex cropping patterns. Thus, in this study, we developed a new RIWP model for an irrigated agricultural area with complex cropping patterns. The model couples the irrigation- and drainage-driven soil water and salinity dynamics and shallow groundwater movement in order to quantify the temporal and spatial distributions of the target hydrological and biophysical variables. We divided the study area into 1 km × 1 km hydrological response units (HRUs). In each HRU, we considered four land use types: sunflower fields, wheat fields, maize fields, and uncultivated lands (bare soil). We coupled the regional soil hydrological processes and groundwater flow by taking a weighted average of the water exchange between unsaturated soil and groundwater under different land use types. The RIWP model was calibrated and validated using 8 years of hydrological variables obtained from regional observation sites in a typical arid irrigation area in North China, the Hetao Irrigation District. The model simulated soil moisture and salinity reasonably well as well as groundwater table depths and salinity. However, overestimations of groundwater discharge were detected in both the calibration and validation due to the assumption of well-operated drainage ditch conditions; regional evapotranspiration (ET) was reasonably estimated, whereas ET in the uncultivated area was slightly underestimated in the RIWP model. A sensitivity analysis indicated that the soil evaporation coefficient and the specific yield were the key parameters for the RIWP simulation. The results showed that the RIWP decreased from maize to sunflower to wheat from 2006 to 2013. It was also found that the maximum RIWP was reached when the groundwater table depth was between 2 and 4 m, regardless of the irrigation water depth applied. This implies the importance of groundwater table control on the RIWP. Overall, our distributed RIWP model can effectively simulate the temporal and spatial distribution of the RIWP and provide critical water allocation suggestions for decision-makers.

Comparison of soil hydraulic properties estimated from steady-state experiments and transient field observations through simulating soil moisture in regenerated Sphagnum moss

Growing Sphagnum moss for peatland restoration and fibre farming requires the proper moisture regime be maintained; thus, there is a desire to optimize growth by creating ideal hydrological conditions. However, it is uncertain which parameterization method is most suitable to describe field-scale processes and which soil water retention model and hydraulic conductivity curve (approach) is the most acceptable to use. Parameterizations of the van Genuchten – Mualem (VGM) equation were done using RETC, curve fitting to direct measurements of the water retention and unsaturated hydraulic conductivity curves; and Hydrus-1D, inverse modelling to field observations of soil moisture. The acceptability of each parameterization was tested by comparing soil moisture estimates based on forward simulations to observed soil moisture in two regenerated moss profiles, established in 1970 and 2006 cases, respectively. The transient field model simulated soil moisture well, and had an RMSE of 0.05 and 0.06 for 1970 and 2006, respectively. The most error occurred during the wettest and driest periods of the simulations. Simulated soil moisture was consistently drier than the observed soil moisture, in the steady-state laboratory simulation, and had markedly higher RMSE, 0.14 and 0.27 for the 1970 and 2006 profiles, respectively. The estimate of the VGM α parameter, an approximately the inverse of the air-entry pressure, fit to direct measurements of the retention and unsaturated hydraulic conductivity curves was an order of magnitude higher than that fit to field observation. The results of the simulation suggest that inverse modelling to field soil moisture should be used to estimate VGM parameters to more accurately represent field-scale soil moisture dynamics.

Role of precipitation forcing on the uncertainty of land surface model simulated soil moisture estimates

Land surface processes considerably influence the global weather and climate patterns which makes its quantification significant to scientists, hydrologists as well as policymakers alike. Considering the lack of available in-situ measurement, retrieval of the land surface fluxes mostly relies on remotely sensed satellite retrieval and through simulations from land surface models (LSMs). Hence, it is essential to quantify the uncertainties present in the output of these land surface models which are mainly due to errors in forcing data, model parameters and model structure. Precipitation is one of the key input forcing data used in LSMs. With the advancement of remote sensing techniques, multiple sources of precipitation products are made available to the user community. This study examines the effect of precipitation uncertainty in LSM simulated soil moisture. For this study, four precipitation products are used namely, Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RT v7, Global Data Assimilation System (GDAS), Climate Hazards Infrared Precipitation with Stations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP). These data products are used as meteorological forcing in Noah 3.6 LSM for the simulation of soil moisture. The uncertainty inherent in the precipitation products are examined using two approaches a) By evaluating the precipitation products against the gridded India Meteorological Department (IMD) precipitation dataset and b) By using the precipitation products for simulating soil moisture outputs. These were validated over the Indian subcontinent using validation data from the European Space Agency-Climate Change Initiative (ESA-CCI) soil moisture and Central Tibetan Plateau Soil Moisture and Temperature Monitoring Network (CTP-SMTMN) dataset for the years 2010 to 2012. The study utilizes various graphical as well as quantitative evaluation methods to determine the best performing precipitation product. Our study indicates that the simulated soil moisture forced with GDAS and MSWEP precipitation product performed consistently superior among all the other simulation outputs over India.

Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM land surface schemes for landslide hazard application

Abstract. This study assesses the usability of Weather Research and Forecasting (WRF) model simulated soil moisture for landslide monitoring in the Emilia Romagna region, northern Italy, during the 10-year period between 2006 and 2015. In particular, three advanced land surface model (LSM) schemes (i.e. Noah, Noah-MP, and CLM4) integrated with the WRF are used to provide detailed multi-layer soil moisture information. Through the temporal evaluation with the single-point in situ soil moisture observations, Noah-MP is the only scheme that is able to simulate the large soil drying phenomenon close to the observations during the dry season, and it also has the highest correlation coefficient and the lowest RMSE at most soil layers. It is also demonstrated that a single soil moisture sensor located in a plain area has a high correlation with a significant proportion of the study area (even in the mountainous region 141 km away, based on the WRF-simulated spatial soil moisture information). The evaluation of the WRF rainfall estimation shows there is no distinct difference among the three LSMs, and their performances are in line with a published study for the central USA. Each simulated soil moisture product from the three LSM schemes is then used to build a landslide prediction model, and within each model, 17 different exceedance probability levels from 1 % to 50 % are adopted to determine the optimal threshold scenario (in total there are 612 scenarios). Slope degree information is also used to separate the study region into different groups. The threshold evaluation performance is based on the landslide forecasting accuracy using 45 selected rainfall events between 2014 and 2015. Contingency tables, statistical indicators, and receiver operating characteristic analysis for different threshold scenarios are explored. The results have shown that, for landslide monitoring, Noah-MP at the surface soil layer with 30 % exceedance probability provides the best landslide monitoring performance, with its hit rate at 0.769 and its false alarm rate at 0.289.

Simulation of Phosphorus Chemistry, Uptake and Utilisation by Winter Wheat.

The phosphorus (P) supply from soils is crucial to crop production. Given the complexity involved in P-cycling, a model that can simulate the major P-cycling processes and link with other nutrients and environmental factors, e.g., soil temperature and moisture, would be a useful tool. The aim of this study was to describe a process-based P module added to the SPACSYS (Soil Plant and Atmosphere Continuum System) model and to evaluate its predictive capability on the dynamics of P content in crops and the impact of soil P status on crop growth. A P-cycling module was developed and linked to other modules included in the SPACSYS model. We used a winter wheat (Triticum aestivum, cv Xi-19) field experiment at Rothamsted Research in Harpenden to calibrate and validate the model. Model performance statistics show that the model simulated aboveground dry matter, P accumulation and soil moisture dynamics reasonably well. Simulated dynamics of soil nitrate and ammonium were close to the observed data when P fertiliser was applied. However, there are large discrepancies in fields without P fertiliser. This study demonstrated that the SPACSYS model was able to investigate the interactions between carbon, nitrogen, P and water in a single process-based model after the tested P module was implemented.

A unique vadose zone model for shallow aquifers: the Hetao irrigation district, China

Abstract. Rapid population growth is increasing pressure on the world water resources. Agriculture will require crops to be grown with less water. This is especially the case for the closed Yellow River basin, necessitating a better understanding of the fate of irrigation water in the soil. In this paper, we report on a field experiment and develop a physically based model for the shallow groundwater in the Hetao irrigation district in Inner Mongolia, in the arid middle reaches of the Yellow River. Unlike other approaches, this model recognizes that field capacity is reached when the matric potential is equal to the height above the groundwater table and not by a limiting soil conductivity. The field experiment was carried out in 2016 and 2017. Daily moisture contents at five depths in the top 90 cm and groundwater table depths were measured in two fields with a corn crop. The data collected were used for model calibration and validation. The calibration and validation results show that the model-simulated soil moisture and groundwater depth fitted well. The model can be used in areas with shallow groundwater to optimize irrigation water use and minimize tailwater losses.

Estimation of root water uptake and soil hydraulic parameters from root zone soil moisture and deep percolation

For efficient irrigation management practices, an accurate prediction of water uptake in the root zone and soil information are foremost important. The present study deals with the identification and estimation of root water uptake (RWU) and soil hydraulic parameters using inverse modeling. These parameters were estimated by minimizing the difference between observed and model simulated soil moisture and deep percolation during the crop growth period. The linked simulation optimization model is tested for three different objective functions using hypothetically generated observed data. Results indicate that the optimizer with objective function defined by soil moisture, failed to provide unique estimate of RWU and soil hydraulic parameters. Further, it has been observed that with the objective function defined by deep percolation, soil hydraulic parameters were uniquely estimated but RWU parameter was not estimated accurately. However, with the objective function, that includes both soil moisture and deep percolation, these parameters were uniquely estimated. A Lysimeter experiments were conducted with four crops i.e. berseem (Trifolium alexandrinum), wheat (Triticum aestivum), maize (Zea mays) and pearl millet (Pennisetum glaucum). Daily monitoring of soil moisture and deep percolation along with soil and crop parameter measurements were done for model validation. Inversely estimated soil hydraulic parameters were found to be in close agreement with laboratory obtained values. The results indicate that specifically for soils with high hydraulic conductivity, the information about deep percolation along with soil moisture is necessary for inverse estimation of root and soil parameters simultaneously. The moisture depletion pattern and deep percolation corresponding to optimized parameters for these crops were found to be in close agreement with observed values.

Numerical simulation of water- and heat-flow regimes of mulched soil in rain-fed soybean field in central Japan

Numerical simulations for predicting soil-water and heat-flow regimes beneath mulched soils are important to increase the opportunity for efficient use of mulching in agriculture. This study simulated water and heat flows by HYDRUS-1D model under rice-straw mulching and bare soil treatments for rain-fed soybean (Glycine max) cultivation at Gifu Prefecture in central Japan. Soil hydraulic and thermal parameters with the straw mulching were optimized by adding a hypothetical 3-cm straw layer. The optimized parameters were validated with field-measured soil-moisture and temperature regimes of two consecutive soybean-growing seasons of 2015 and 2016. HYDRUS-1D model performed fairly well in simulating heat and water movements at 5, 15 and 25 cm soil depths under the two treatments. The model simulated soil moisture and temperature with small Root-Mean Square Error (RMSE): 0.016–0.044 cm3 cm–3 for soil moisture and 0.94–1.63 °C for soil temperature. The simulation results revealed that straw mulching increased soil moisture but reduced soil temperature at the three soil depths compared to the bare soil. The moisture content of the additional straw layer (3 cm) and heat flow through it showed a decreasing pattern compared to the soil layers. The straw mulching contributed 16–96% vapor to the total water flow and stored/released 0.0322 to 0.0295 MJ m–2 heat per day at 0–3 cm straw layer. Water balance in the soybean field revealed that the straw layer enhanced rain water infiltration by limiting evaporation from the soil surface. The simulation results can be useful for better management of soil hydro-thermal regimes under straw-mulched fields in dryland cultivations.