Data assimilation of in situ soil moisture measurements in hydrological models: third annual doctoral progress report, work plan and achievements

Efficient water utilization and optimal water supply/distribution to increase food and fodder productivity are of utmost importance in confronting worldwide water scarcity, climate change, growing populations and increasing water demands. In this respect, irrigation efficiency, which is influenced by the type of irrigation and irrigation scheduling, is an essential issue for achieving higher productivity. To improve irrigation strategies in precision agriculture, soil water status can be more accurately described using a combination of advanced monitoring and modeling. Our study focuses on the combination of high resolution hydrological data with hydrological models that predict water flow and solute (pollutants and salts) transport and water redistribution in agricultural soils under irrigation. Field plots of a potato farmer in a sandy region in Belgium were instrumented to continuously monitor soil moisture and water potential before, during and after irrigation in dry summer periods. The aim is to optimize the irrigation process by assimilating online sensor field data into process based models. This research is part of Activity 305 ‘Precision agriculture and remote sensing’ of the VITO GWO and is also part of the strategic cooperation with UGent within the platform ‘Managing Natural Resources’. Over the past 2 years, we applied a combination of in-situ monitoring and numerical modeling -Hydrus 1D- to estimate water content fluctuations in a heterogeneous sandy grassland soil under irrigation with water table fluctuating between 80 and 155 cm. Over the last year, more sampling and analyses were carried out to further characterize the hydraulic properties over the entire field. Modeling results for the field demonstrated clearly the profound effect of the position of the GWL, and to a lesser extent, the effect of spatially variable soil hydraulic properties (Ks, n and α) on the estimated water content in the sandy two-layered soil under grass. Our results show that currently applied uniform water distribution using sprinkler irrigation seems not to be efficient since at locations with shallow groundwater, the amount of water applied will be excessive as compared to the plant requirements while in locations with a deeper GWL, requirements will not be met. To derive the optimal parameter set best describing the measured soil moisture content, 37 optimization scenarios were conducted with two to six parameters using various parameter combinations for the two soil layers. The best performing parameter optimization scenario was a 2-parameter scenario with Ks optimized for each layer. The results showed a better identifiability of the parameters (less correlations among parameters) with equal performance as compared to three, four or six parameter optimization. Model predictions using the calibrated model (with data from 2012) for an independent data set of soil moisture data in the validation period (2013) showed satisfactory performance of the model in view of irrigation management purposes. Comparing the degree of water stress for different optimization scenarios of groundwater depth, showed that grass was exposed to water stress in summer in 2013 but not for such a long period as compared to the 2012 growing season. The degree of water stress simulated with Hydrus 1D suggested to increase the irrigation amount in 2012 and 2013 and at least one or two times in the summer (June and July) and further distributing the amount of irrigation during the growing season, instead of using a huge amount of irrigation later in the season, as is common practice by the farmer. A second part of the study focused on finding a relation between measured soil hydraulic properties and apparent electrical conductivity ECa. Our measurements of hydraulic properties of the field clearly confirm that there is considerable spatial variability in the field and that this has an impact on the simulation of soil moisture content. Therefore this should be taken into account when upscaling soil hydraulic properties to the field scale in order to in understand and model flow, solute and energy fluxes in the field and develop strategies for efficient irrigation. Upscaling soil hydraulic properties to the field scale can be done by linking them to apparent electrical conductivity (ECa), which can be measured efficiently and inexpensively so a spatially dense dataset for describing within-field spatial soil variability can be generated. In this study relations between the spatial variation of soil hydraulic properties and apparent soil electrical conductivity ECa measured with EM38 and DUALEM-21S sensors at two depths of explorations (DOE) 0-50 and 0-100 cm were investigated. Two predictive modelling approaches, i.e. i) a simple regression and ii) applying Archie’s laws for saturated and unsaturated conditions in combination with MVG equations, were developed and it was compared how they were able to explain the observed values of hydraulic parameters. Results demonstrated the spatial variability and heterogeneity of ECa and soil hydraulic properties Ks, α and n. We derived a regression relationship between log Ks and ECa measured with DUALEM (r2≥0.70) and with EM38 (r2>0.46) sensors. The predicted results were tested vs measured data and confirmed that the performance of DUALEMp,100-Ks model is relatively better than that of the same sensor with lower DOE and of the EM38 sensor (RMSE = 1.31 cmh-1, R2 = 0.55). The relationships between MVG shape parameters and ECa datasets were generally poor (0.05<R2<0.26). In the second approach, we showed that the water retention curve can be translated to ECa-(h) and ECa-Se relations by combining the MVG equations and Archie’s law. Results also show that reformulating the MVG equations based on ECa-Se relationships can help to estimate unsaturated hydraulic conductivity at the field scale. In the third year, a second study site has been set up in a nearby field where potatoes are grown and has been instrumented with soil moisture sensors, tensiometers, groundwater level loggers and a weather station. Field hydraulic properties for the field will be derived using the equations developed for the first study site and the modeling approach developed for the first field will be tested here. Also quasi 3D-modelling of water flow at the field scale will be conducted. In this modeling set-up, the field will be modeled as a collection of 1D-columns representing the different field conditions (combination of soil properties, GWL, root zone depth). Combining this model with crop based models such as LINGRA-N or Aquacrop gives a direct simulation of the impact of irrigation strategies on crop yield at the field scale

Data assimilation of in situ soil moisture measurements in hydrological models: first annual doctoral progress report, work plan and achievements

Water scarcity and the presence of water of good quality is a serious public concern since it determines the availability of water to society. Water scarcity especially in arid climates and due to extreme droughts related to climate change drive water use technologies such as irrigation to become more efficient and sustainable. Plant root water and nutrient uptake is one of the most important processes in subsurface unsaturated flow and transport modeling, as root uptake controls actual plant evapotranspiration, water recharge and nutrient leaching to the groundwater, and exerts a major influence on predictions of global climate models. To improve irrigation strategies, water flow needs to be accurately described using advanced monitoring and modeling. Our study focuses on the assimilation of hydrological data in hydrological models that predict water flow and solute (pollutants and salts) transport and water redistribution in agricultural soils under irrigation. Field plots of a potato farmer in a sandy region in Belgium were instrumented to continuously monitor soil moisture and water potential before, during and after irrigation in dry summer periods. The aim is to optimize the irrigation process by assimilating online sensor field data into process based models. Over the past year, we demonstrated the calibration and optimization of the Hydrus 1D model for an irrigated grassland on sandy soil. Direct and inverse calibration and optimization for both heterogeneous and homogeneous conceptualizations was applied. Results show that Hydrus 1D closely simulated soil water content at five depths as compared to water content measurements from soil moisture probes, by stepwise calibration and local sensivity analysis and optimization the Ks, n and α value in the calibration and optimization analysis. The errors of the model, expressed by deviations between observed and modeled soil water content were, however, different for each individual depth. The smallest differences between the observed value and soil-water content were attained when using an automated inverse optimization method. The choice of the initial parameter value can be optimized using a stepwise approach. Our results show that statistical evaluation coefficients (R2, Ce and RMSE) are suitable benchmarks to evaluate the performance of the model in reproducing the data. The degree of water stress simulated with Hydrus 1D suggested to increase irrigation at least one time, i.e. at the beginning of the simulation period and further distribute the amount of irrigation during the growing season, instead of using a huge amount of irrigation later in the season. In the next year, we will further look for to the best method (using soft data and methods for instance PTFs, EMI, Penetrometer) to derive and predict the spatial variability of soil hydraulic properties (saturated hydraulic conductivity) of the soil and link to crop yield at the field scale. Linear and non-linear pedotransfer functions (PTFs) have been assessed to predict penetrometer resistance of soils from their water status (matric potential, ψ and degree of saturation, S) and bulk density, ρb, and some other soil properties such as sand content, Ks etc. The geophysical EMI (electromagnetic induction) technique provides a versatile and robust field instrument for determining apparent soil electrical conductivity (ECa). ECa, a quick and reliable measurement, is one of ancillary properties (secondary information) of soil, can improve the spatial and temporal estimation of soil characteristics e.g., salinity, water content, texture, prosity and bulk density at different scales and depths. According to previous literature on penetrometer measurements, we determined the effective stress and used some models to find the relationships between soil properties, especially Ks, and penetrometer resistance as one of the prediction methods for Ks. The initial results obtained in the first yearshowed that a new data set would be necessary to validate the results of this part. In the third year, quasi 3D-modelling of water flow at the field scale will be conducted. In this modeling set -up, the field will be modeled as a collection of 1D-columns representing the different field conditions (combination of soil properties, groundwater depth, root zone depth). The measured soil properties are extrapolated over the entire field by linking them to the available spatially distributed data (such as the EMI-images). The data set of predicted Ks and other soil properties for the whole field constructed in the previous steps will be used for parameterising the model. Sensitivity analysis ‘SA’ is essential to the model optimization or parametrization process. To avoid overparameterization, the use of global sensitivity analysis (SA) will be investigated. In order to include multiple objectives (irrigation management parameters, costs, …) in the parameter optimization strategy, multi-objective techniques such as AMALGAM have been introduced. We will investigate multi-objective strategies in the irrigation optimization

New sensing methods for scheduling variable rate irrigation to improve water use efficiency and reduce the environmental footprint : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, Palmerston North, New Zealand

Figures are re-used under an Attribution 4.0 International (CC BY 4.0) license, or are not copyrighted.Irrigation is the largest user of allocated freshwater, so conservation of water use should begin with improving the efficiency of crop irrigation. Improved irrigation management is necessary for humid areas such as New Zealand in order to produce greater yields, overcome excessive irrigation and eliminate nitrogen losses due to accelerated leaching and/or denitrification. The impact of two different climatic regimes (Hawkes Bay, Manawatū) and soils (free and imperfect drainage) on irrigated pea (Pisum sativum., cv. ‘Ashton’) and barley (Hordeum vulgare., cv. ‘Carfields CKS1’) production was investigated. These experiments were conducted to determine whether variable-rate irrigation (VRI) was warranted. The results showed that both weather conditions and within-field soil variability had a significant effect on the irrigated pea and barley crops (pea yield - 4.15 and 1.75 t/ha; barley yield - 4.0 and 10.3 t/ha for freely and imperfectly drained soils, respectively). Given these results, soil spatial variability was characterised at precision scales using proximal sensor survey systems: to inform precision irrigation practice. Apparent soil electrical conductivity (ECa) data were collected by a Dualem-421S electromagnetic (EM) survey, and the data were kriged into a map and modelled to predict ECa to depth. The ECa depth models were related to soil moisture (θv), and the intrinsic soil differences. The method was used to guide the placement of soil moisture sensors. After quantifying precision irrigation management zones using EM technology, dynamic irrigation scheduling for a VRI system was used to efficiently irrigate a pea crop (Pisum sativum., cv. ‘Massey’) and a French bean crop (Phaseolus vulgaris., cv. ‘Contender’) over one season at the Manawatū site. The effects of two VRI scheduling methods using (i) a soil water balance model and (ii) sensors, were compared. The sensor-based technique irrigated 23–45% less water because the model-based approach overestimated drainage for the slower draining soil. There were no significant crop growth and yield differences between the two approaches, and water use efficiency (WUE) was higher under the scheduling regime based on sensors. ii To further investigate the use of sensor-based scheduling, a new method was developed to assess crop height and biomass for pea, bean and barley crops at high field resolution (0.01 m) using ground-based LiDAR (Light Detection and Ranging) data. The LiDAR multi-temporal, crop height maps can usefully improve crop coefficient estimates in soil water balance models. The results were validated against manually measured plant parameters. A critical component of soil water balance models, and of major importance for irrigation scheduling, is the estimation of crop evapotranspiration (ETc) which traditionally relies on regional climate data and default crop factors based on the day of planting. Therefore, the potential of a simpler, site-specific method for estimation of ETc using in-field crop sensors was investigated. Crop indices (NDVI, and canopy surface temperature, Tc) together with site-specific climate data were used to estimate daily crop water use at the Manawatū and Hawkes Bay sites (2017-2019). These site-specific estimates of daily crop water use were then used to evaluate a calibrated FAO-56 Penman-Monteith algorithm to estimate ETc from barley, pea and bean crops. The modified ETc–model showed a high linear correlation between measured and modelled daily ETc for barley, pea, and bean crops. This indicates the potential value of in-field crop sensing for estimating site-specific values of ETc. A model-based, decision support software system (VRI–DSS) that automates irrigation scheduling to variable soils and multiple crops was then tested at both the Manawatū and Hawkes Bay farm sites. The results showed that the virtual climate forecast models used for this study provided an adequate prediction of evapotranspiration but over predicted rainfall. However, when local data was used with the VRI–DSS system to simulate results, the soil moisture deficit showed good agreement with weekly neutron probe readings. The use of model system-based irrigation scheduling allowed two-thirds of the irrigation water to be saved for the high available water content (AWC) soil. During the season 2018 – 2019, the VRI–DSS was again used to evaluate the level of available soil water (threshold) at which irrigation should be applied to increase WUE and crop water productivity (WP) for spring wheat (Triticum aestivum L., cv. ‘Sensas’) on the sandy loam and silt loam soil zones at the Manawatū site. Two irrigation thresholds (40% and 60% AWC), were investigated in each soil zone along with a rainfed control. Soil water uptake pattern was affected mainly by the soil type rather than irrigation. The soil iii water uptake decreased with soil depth for the sandy loam whereas water was taken up uniformly from all depths of the silt loam. The 60% AWC treatments had greater irrigation water use efficiency (IWUE) than the 40% AWC treatments, indicating that irrigation scheduling using a 60% AWC trigger could be recommended for this soil-crop scenario. Overall, in this study, we have developed new sensor-based methods that can support improved spatial irrigation water management. The findings from this study led to a more beneficial use of agricultural water

Fusion of multi soil data for the delineation of management zones for variable rate irrigation

Up until now, there have been no multi-sensor approaches used to estimate available water content (AWC) in order to determine variable rate irrigation. This has been a major problem for growers adopting precision farming technologies. The aim of this project is to implement an on-line multi-sensor platform and data fusion approach for the delineation of management zones for site specific irrigation in vegetable crop production systems. This is performed by simultaneous measurement of soil moisture content (MC), organic carbon (OC), clay content (CC), plasticity index (PI) and bulk density (BD) with an on-line visible and near infrared (vis-NIR) spectroscopy sensor and a load cell attached to a subsoiler and frame, which was linked to a three-point linkage of a tractor. The soil apparent Electrical Conductivity (ECa) was separately measured with an Electromagnetic Induction (EMI) device. Measurements were carried out in three fields in Lincolnshire and one in Cambridgeshire. Vis-NIR calibration models of soil properties were developed using partial least squares (PLS) regression. A multiple linear regression analysis (MLR) and an Artificial Neural Network (ANN) was used to derive zones of water holding capacity (WHC), based on correlation between on-line measured OC, CC, PI, BD and ECa with MC. The AWC was calculated with empirical equations, as a function of clay and sand fractions. Result showed that the on-line measurement accuracy for OC and MC were good to excellent (R2=0.71-0.83 and R2=0.75-0.85, RPD=2.00-2.57 and RPD=1.94-2.10 for OC and MC, respectively). For CC and PI, the measurement accuracy (R2=0.64-0.69 and RPD=0.55-0.66 for clay content and PI) was evaluated as moderate. It was observed in the study fields, that the ECa results had a minor response to MC distribution. Furthermore, the fusion of multi-soil data to derive a WHC index with MLR and ANN resulted in successful delineation of homogeneous zones. These were divided into four different normalisation categories of low (0 – 0.25), medium (0.25 – 0.5), high (0.5 – 0.75) and very high (0.75 – 1) of WHC. Spatial similarity between WHC maps with those of CC, IP and MC was documented, and found to be in line with the literature. AWC maps calculated as a function of soil texture classes, showed spatial similarity with WHC maps. Low values of AWC were observed at zones with low WHC index and vice versa. This supports the final conclusion of this work that multi-sensor and data fusion is a useful approach to guide positions of moisture sensor and optimise the amount of water used for irrigation

STATUS OF SOIL ELECTRICAL CONDUCTIVITY STUDIES BY CENTRAL STATE RESEARCHERS

Practical tools are needed to identify and advance sustainable management practices to optimize economic return, conserve soil, and minimize negative off-site environmental effects. The objective of this article is to review current research in non-saline soils of the central U.S. to consider bulk soil electrical conductivity (ECa) as an assessment tool for: (1) tracking N dynamics, (2) identifying management zones, (3) monitoring soil quality trends, and (4) designing and evaluating field-scale experiments. The interpretation and utility of ECa are highly location and soil specific; soil properties contributing to measured ECa must be clearly understood. In soils where ECa is driven by NO3-N, ECa has been used to track spatial and temporal variations in crop-available N (manure, compost, commercial fertilizer, and cover crop treatments) and rapidly assess N mineralization early in the growing season to calculate fertilizer rates for site-specific management (SSM). Selection of appropriate ECa sensors (direct contact, electromagnetic induction, or time domain reflectometry) may improve sensitivity to N fluctuations at specific soil depths. In a dryland cropping system where clay content dominates measured ECa, ECa -based management zones delineated soil productivity characteristics and crop yields. These results provided a framework effective for SSM, monitoring management-induced trends in soil quality, and appraising and statistically evaluating field-scale experiments. Use of ECa may foster a large-scale systems approach to research that encourages farmer involvement. Additional research is needed to investigate the interactive effects of soil, weather, and management on ECa as an assessment tool, and the geographic extent to which specific applications of this technology can be applied

Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management

Precision agriculture offers the technologies to manage for infield variability and incorporate variability into irrigation management decisions. The major limitation of this technology often lies in the reconciliation of disparate data sources and the generation of irrigation prescription maps. Here the authors explore the utility of the cosmic-ray neutron probe (CRNP) which measures volumetric soil water content (SWC) in the top ~ 30 cm of the soil profile. The key advantages of CRNP is that the sensor is passive, non-invasive, mobile and soil temperature-invariant, making data collection more compatible with existing farm operations and extending the mapping period. The objectives of this study were to: (1) improve the delineation of irrigation management zones within a field and (2) estimate spatial soil hydraulic properties to make effective irrigation prescriptions. Ten CRNP SWC surveys were collected in a 53-ha field in Nebraska. The SWC surveys were analyzed using Empirical Orthogonal Functions (EOFs) to isolate the underlying spatial structure. A statistical bootstrapping analysis confirmed the CRNP + EOF provided superior soil hydraulic property estimates, compared to other hydrogeophysical datasets, when linearly correlated to laboratory measured soil hydraulic properties (field capacity estimates reduced 20–25% in root mean square error). The authors propose a soil sampling strategy for better quantifying soil hydraulic properties using CRNP + EOF methods. Here, five CRNP surveys and 6–8 sample locations for laboratory analysis were sufficient to describe the spatial distribution of soil hydraulic properties within this field. While the proposed strategy may increase overall effort, rising scrutiny for agricultural water-use could make this technology cost-effective

Feasibility Assessment on Use of Proximal Geophysical Sensors to Support Precision Management

Soil property maps provide information for field management activities such as irrigation, fertilization, and seeding. Many on-the-go proximal geophysical sensors have been developed in recent decades that can help map agricultural fields without dense soil sampling. To utilize these technologies most profitably in precision management, scientists and precision agriculture dealers must better understand sensors’ performances in given field conditions and the economic value of different proximal soil sensing methods. Chapter two reports the study that was conducted at three sites in North Dakota, United States to strengthen understanding of the usefulness of different proximal geophysical data types in agricultural contexts of varying pedology. This study hypothesizes that electro-magnetic induction (EMI), gamma-ray sensor (GRS), cosmic-ray neutron sensor (CRNS), and elevation data layers are all useful in multiple linear regression (MLR) predictions of soil properties that meet expert criteria at three agricultural sites. In addition to geophysical data collection with vehicle-mounted sensors, 15 soil samples were collected at each site and analyzed for nine soil properties of interest. A set of model training data was compiled by pairing the sampled soil property measurements with the nearest geophysical data. Eleven models passed expert-defined uncertainty criteria at site 1, 16 passed at site 2, and 14 passed at site 3. Electrical conductivity, organic matter, available water holding capacity, silt, and clay were predicted at site 1 with an Rpred2 \u3e .50 and acceptable RMSEP. Bulk density, organic matter, available water capacity, silt, and clay were predicted with Rpred2 \u3e .50 and acceptable RMSEP at site 2. At site 3, no soil properties were predicted with acceptable RMSEP and an Rpred2 \u3e .50. These results confirm feasibility of our method, and the authors recommend the prioritization of EMI data collection if geophysical data collection is limited to a single mapping effort and calibration soil samples are few. Strategies for addressing the remaining needs for better prediction of sensor performance and evaluation of sensing methods’ economic value are discussed in chapter three. Several potential methods for future research from the literature are summarized that can advance understanding of sensors’ best use, sophisticated cost-benefit analysis, and soil sampling optimization. Advisor: Trenton Fran

Soil Salinity Patterns in an Olive Grove Irrigated with Reclaimed Table Olive Processing Wastewater

The agricultural use of saline table olive processing wastewater enables the implementation of closed water cycles in this socioeconomically important industry for rural southern Spain and relieves environmental, economic, and legal burdens. To allow growers to evaluate and guarantee adequate long-term soil and plant conditions when irrigating with such regenerated wastewaters, efficient soil monitoring strategies are needed. Field-scale monitoring with electromagnetic induction sensing, after one (2013) and five years (2017) of irrigation with regenerated wastewater with average electrical conductivity (EC) near 6 dS m−1 in an olive orchard in southern Spain, showed accumulation of highly conductive material in the subsoil in relation to local topography and soil characteristics. Laboratory analysis of the soil water revealed strongly varying patterns of EC during the growing season and across the olive grove, which were attributed to dilution and concentration effects due to rainfall and evaporation, respectively. Visual inspection and leaf analyses revealed no negative effects on the olive trees. Apparent electrical conductivity (ECa), measured in between the tree rows in 2013, showed a linear relationship with surface soil EC1:5 under the drippers and allowed identification of areas with high ECa in the low elevation zones of the farm, due to the presence of shallow perched saline water tables. A second ECa measurement in 2017 showed similar spatial ECa patterns and was used to estimate the distribution of soil EC across the soil profile using inversion software, although no unique field-wide relationships with soil properties could be inferred, possibly as a consequence of spatially variable soil clay and water contents, due to the influence of the topography. Despite the implementation of a more conservative irrigation strategy since 2015, results showed that the salinity has increased since 2013 in about 15% of the study area, with the largest increments in the deepest horizons