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Maintaining the economic and environmental sustainability of crop production requires optimizing irrigation management using advanced technologies such as soil water sensors. In this study, the performance of a commercially available multi-sensor capacitance probe was evaluated under irrigated field conditions across western Oklahoma. The effects of clay content and salinity on sensor performance were investigated too. In addition, the field capacity (FC) of soil cores collected at study sites was determined in the laboratory. These laboratory FC values were used to assess the performance of two sensor-based approaches for estimating FC: the days to reach laboratory FC after major watering events and the percentile of collected sensor readings that represented laboratory FC. The results showed that among the six calibrations provided by the manufacturer, the default and silty clay loam calibrations produced the largest and smallest soil water content errors, respectively. Errors generally increased with clay and salinity, except for the heavy clay calibration, which showed improved performance with increasing clay content. The default and sand calibrations were more sensitive to increases in clay and salinity compared to other calibrations. In the case of sensor-based FC, on average, one to three days were required to reach laboratory FC, with a large range of one to nine days. The percentiles representing laboratory FC had an average of 56% and a range of 3%-97%. Overall, the sensor-based approaches produced inconsistent and highly variable estimates of FC. [ABSTRACT FROM AUTHOR]

With recent advances in web-based irrigation scheduling tools and mobile applications and the possibility of using more complex modeling approaches, it is important to evaluate the effects of variable input data on the output of these tools and models. Two types of input data that are highly variable across irrigated fields and soil profiles are soil textural data and root water uptake distribution (RWUD). In this study, root zone soil textural data from two sources of commonly used, freely available web soil survey (WSS) and time-consuming, labor-intensive in-situ sampling (ISS) were used in combination with three RWUDs (constant, linear, and sensor-based) to simulate volumetric water content (θv) at four soil layers in six irrigated fields, using the HYDRUS model. The percentage of sand particles based on WSS was about half of the measured amount on average, resulting in a considerable difference in estimated hydraulic properties and soil water thresholds. Sensor data revealed that RWUDs were highly nonuniform, with more than 60% of water extraction occurring from the top 30 cm of the root zone. Among the six combinations of two sources of soil data and three RWUDs, ISS-sensor resulted in the smallest errors in simulated θv, and WSS-constant yielded the largest errors. Simulated θv data were translated to actionable end-user variables of irrigation trigger (IT) and soil water depletion (SWD), which determine the timing and the amount of irrigation applications, respectively. Relying on WSS resulted in irrigation trigger being called about four times more than when measured soil data were used. The average SWD based on WSS was 157 mm, about two times larger than the average SWD based on ISS (68 mm). [ABSTRACT FROM AUTHOR]

Long‐term simulations of agricultural watersheds have often been done assuming constant land use over time, but this is not a realistic assumption for many agricultural regions. This paper presents the soil and water assessment tool (SWAT)‐Landuse Update Tool (LUT), a standalone, user‐friendly desktop‐based tool for updating land use in the SWAT model that allows users to process multi‐year land use data. SWAT‐LUT is compatible with several SWAT model interfaces, provides users with several options to easily prepare and incorporate land use changes (LUCs) over a simulation period, and allows users to incorporate past or emerging land use categories. Incorporation of LUCs is expected to provide realistic model parameterization and scenario simulations. SWAT‐LUT is a public domain interface written in Python programming language. Two applications at the Fort Cobb Reservoir Experimental Watershed located in Oklahoma and pertinent results are provided to demonstrate its use. Incorporating LUCs related to implementation of recommended conservation practices over the years reduced discharge, evapotranspiration, sediment, total nitrogen, and total phosphorus loads by 59%, 9%, 68%, 53%, and 88%, respectively. The user's manual is included in this article as Supporting Information. The SWAT‐LUT executable file and an example SWAT project with three land use rasters and the user's manual are available at the United States Department of Agriculture‐Agricultural Research Service Grazinglands Research Laboratory website under Software. Editor's note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series. Research Impact Statement: SWAT‐LUT, a standalone user‐friendly graphical interface for updating land use in SWAT, simplifies incorporation of multiple land use maps during the simulation period of modeling studies. [ABSTRACT FROM AUTHOR]

Scaling in situ soil water content time series data to a large spatial domain is a key element of watershed environmental monitoring and modeling. The primary method of estimating and monitoring large-scale soil water content distributions is via in situ networks. It is critical to establish the stability of in situ networks when deploying them to study hydrologic systems. Two watersheds in Oklahoma, the Little Washita River Experimental Watershed (LWREW) and the Fort Cobb Reservoir Experimental Watershed (FCREW), are two prime examples of well-equipped research watersheds that provide long-term measurements of atmospheric and soil water content from in situ networks. The soil water content measurement network on the LWREW has been in operation since 2002, with 20 stations available for investigating soil water dynamics at the watershed scale. Temporal stability analysis of the network is complicated by the changing configuration of the network, but it is possible to determine a singular long-term average for the network. The FCREW consists of 15 soil water content stations and began operation in 2007, providing detailed information across a mixed agricultural domain and was determined to be stable and representative of the region. This study reinforces the applicability of temporal stability analysis to very long time scales, which are now becoming available for soil moisture monitoring. Each of these networks is temporally stable with respect to soil water content at each depth on a spatial basis. The LWREW has a persistent pattern through the root zone profile, but the FCREW does not, which requires further investigation. [ABSTRACT FROM AUTHOR]

We present Multiscale Extrapolative Learning Algorithm (MELA) as a novel artificial-intelligence (AI)-based data extrapolator. MELA is capable of extending temporally limited local hydroclimatic measurements at fine spatial resolution to longer periods, using remotely-sensed hydroclimatic data readily available for longer periods but at coarse spatial resolution. We demonstrate the implementation of MELA to extrapolate the monthly local soil moisture measurements at multiple depths from 2015–2021 to 1958–2021 in a semi-arid region. Such data extrapolators are imperative to generate longer historical data needed to adequately train and test AI models while enhancing the chance of capturing the effects of extreme climates on spatially variable soil moisture. The MELA-extrapolated local soil moisture subsequently allowed the construction of monthly time-series of field-scale soil moisture distributions with a normalized accuracy of 72% and prediction of countywide annual winter wheat yields – using MELA-extrapolated soil moisture data and eXplainable AI (XAI) – with a normalized accuracy of 81%. Furthermore, the XAI model ranked the predictors based on their importance in estimating winter wheat yields, in which the soil moisture near the surface and in the root zone and precipitation totals were found to be more influential than temperature on crop yields in the semi-arid region. The XAI model also unveiled the inflection points of the predictors beyond which crop yields would increase or decrease. Moreover, the AI-based analyses in conjunction with climate projections from global climate models suggest potential reductions in rainfed crop yields in the study area by 2050 and 2100 in the absence of climate-resilient mitigation and adaptation plans. • Introducing MELA and demonstrating its data generation capabilities. • Extrapolating temporally limited local soil moisture measurements utilizing MELA. • Predicting crop yields using extrapolated soil moisture data and Explainable AI. • Analyzing crop yields based on climatic features and climate scenarios. • Providing insights for policy decision and developing mitigation & adaptation plans. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines the potential for improving Soil and Water Assessment Tool (SWAT) hydrologic predictions of root-zone soil moisture, evapotranspiration, and stream flow within the 341km2 Cobb Creek Watershed in southwestern Oklahoma through the assimilation of surface soil moisture observations using an Ensemble Kalman filter (EnKF). In a series of synthetic twin experiments assimilating surface soil moisture is shown to effectively update SWAT upper-layer soil moisture predictions and provide moderate improvement to lower layer soil moisture and evapotranspiration estimates. However, insufficient SWAT-predicted vertical coupling results in limited updating of deep soil moisture, regardless of the SWAT parameterization chosen for root-water extraction. Likewise, a real data assimilation experiment using ground-based soil moisture observations has only limited success in updating upper-layer soil moisture and is generally unsuccessful in enhancing SWAT stream flow predictions. Comparisons against ground-based observations suggest that SWAT significantly under-predicts the magnitude of vertical soil water coupling at the site, and this lack of coupling impedes the ability of the EnKF to effectively update deep soil moisture, groundwater flow and surface runoff. The failed attempt to improve stream flow prediction is also attributed to the inability of the EnKF to correct for existing biases in SWAT-predicted stream flow components. [Copyright &y& Elsevier]

• Single-domain models can misrepresent the hydrologic system. • Multi-domain models can reduce equifinality. • Unsupervised calibration techniques may result in distorted parameters. • Any modeling repertory should consider equifinality and model-constraint inadequacy. The multi-metric assessment of model performance in a dominantly single-domain modeling approach (i.e., surface) may not be sufficient to gauge the validity of the model to represent the hydrologic system. Consequently, rating metrics can mathematically validate model results as satisfactory even when some of the simulated hydrologic processes are incorrectly represented during the calibration process. In this paper, we argue that to properly represent the hydrologic system, calibration tasks focused on modifying model parameters should account for equifinality, model inadequacy, and constraint inadequacy. To demonstrate our argument, we conducted a traditional calibration using the Soil and Water Assessment Tool (SWAT) coupled to the Modular Finite-difference Flow Model (MODFLOW) to simulate the hydrologic processes in the Fort Cobb Reservoir Experimental Watershed (FCREW) in central western Oklahoma. The results indicated that model calibration based on metrics from a single domain, in this case using SWAT, did not necessarily guarantee an appropriate representation of water circulation. Moreover, unsupervised calibration techniques based on the objective-function optimization (e.g., inverse calibration using SWAT-CUP SUFI2 algorithm) may not be sufficient if model constraints in multiple domains are not properly set, resulting in distorted parameters. This practice can result in the misrepresentation of water circulation and can have adverse impacts on environmental model results used to support policies and decisions. [ABSTRACT FROM AUTHOR]