Accurate simulation of irrigation is key for effective water resources planning and management across various scales. This paper presents SWAT-IRR, a new irrigation algorithm for the Soil and Water Assessment Tool (SWAT) model, designed to enhance the simulation of how different irrigation systems and schedules influence hydrologic fluxes in irrigated agricultural areas. SWAT-IRR enables explicit simulation of three irrigation systems (surface, sprinkler, and drip) parameterized using irrigation application efficiency, conveyance efficiency, surface runoff ratio, and an additional area adjustment factor parameter for drip irrigation. SWAT-IRR offers three simulation options to accommodate varying user needs. Option 0 is the original SWAT model irrigation algorithm. Option 1 addresses the original SWAT model's limitation by improving control of irrigation simulation during growing seasons. Option 2 builds on Option 1 by integrating a new irrigation algorithm to advance the representation of irrigation processes and their associated impacts on hydrologic fluxes. Option 3 further strengthen Option 2 by adopting the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) curve number approach to estimate irrigation surface runoff, as an alternative to the surface runoff ratio. An application of SWAT-IRR at the Fort Cobb Reservoir Experimental Watershed (FCREW) in central Oklahoma illustrates its effectiveness in enhancing irrigation simulation. Comparisons between the original SWAT model and SWAT-IRR, as well as among the various SWAT-IRR options, demonstrates its ability to improve control and representation of irrigation simulation and capture practical aspects of water allocation from various sources and water application using different irrigation systems.

Soil health assessments have become one of the primary measures of successful implementation of the USDA Natural Resources Conservation Service's conservation practice codes and programs. Despite national and global implementation of soil health standards, there is a gap in the adoption and standardization of rapid, high-throughput proximal measures of soil health. This research focuses on a single indicator of soil health, soil organic carbon (SOC), measured via proximal sensing using visible to near infrared (VIS-NIR) sensors in the wavelength region of 400 to 1,000 nm. Our objectives were to determine the minimum pretreatment(s) required to obtain accurate estimates of SOC concentrations previously measured via dry combustion using several radiometers; compare the accuracy of radiometers within the 400 to 2,500 nm range to that of lower cost spectroradiometers in the 400 to 1,000 nm range; and address some of the contradictory results of soil sample treatment effects on radiometric measurements in the current literature with the intent to deploy lower cost, easy-to-use radiometers for field applications. Ranking of the calibration and validation equations verified that prediction efficiencies were sufficiently accurate but lower in the VIS-NIR range relative to the full spectrum. The calibration equations developed using soil spectra did not necessarily improve if pretreatment included sieving and/or air-drying of soil. Thus, direct scanning of soils may be a viable option when large numbers of samples are being collected in a time-limited situation. We recommend baseline evaluation of SOC be conducted with a field radiometer in the range of 400 to 2,500 nm due to the overlap of SOC with carbon constituents that are of plant, animal, or microbial origin and soil pretreatment that initially includes sieving and air drying. After pretreatment, rapid assessment and monitoring of SOC can be accomplished using cheap, easy-to-operate radiometers within the 400 to 1,000 nm range that can undergo calibration transfer to transform spectra prior to use on the secondary instrument located in a different environment.

Across agroecosystems, water is a key driver of primary production, and the relationship between precipitation and production (i.e., water-use efficiency; WUE) provides an important indicator for evaluating agroecosystem resilience to changes in water availability. While this relationship has been well-characterized in relatively unmanaged, native ecosystems, cross-site syntheses spanning diverse agroecosystems and climate gradients are lacking. We leveraged the USDA's Long-Term Agroecosystem Research (LTAR) network to assess the relationship between annual precipitation and aboveground net primary production (ANPP) across an extensive set of climate conditions and agroecosystems, representing native rangelands, croplands, and pasturelands and various management intensities. We utilized long-term ANPP data (mean = 17 years) from fifteen sites spanning a large precipitation gradient (265 to 1347 mm yr-1). We observed a positive relationship between annual precipitation and productivity across precipitation gradients; however, this nonlinear pattern differed from native ecosystems and varied by agroecosystem type. Rangeland ANPP was strongly coupled to annual precipitation, increasing nearly 20% for every 100 mm of precipitation. Croplands and pasturelands showed significantly decreased sensitivity, although grouping crops by photosynthetic pathway and crop type revealed some significant patterns. Underlying these patterns in sensitivity were large differences in overall ANPP among agroecosystems; cropland ANPP was up to 6.7-fold greater than rangelands and 2.6-fold greater than pasturelands, despite overlapping precipitation gradients. While agroecosystem type captured much of the variability in the precipitation-production relationship at the continental scale, understanding the more subtle differences in precipitation sensitivities will be fundamental for identifying production vulnerabilities and adapting to changing water resources.

The Southern Plains (SP) is one of 18 Long-Term Agroecosystem Research network sites that combine strategic research projects with common measurements across multiple agroecosystems. Projects at the SP site focus on the use of indicator measurements to aid in assessment of land and nutrient management's impact on soil health, water quality, carbon and water balances, and forage biomass-quality in diversified, adaptive crop-livestock systems designed to overcome shifts in natural resources and climate. The prevailing treatment is tilled winter wheat (Triticum aestivum L.) that is grazed, hayed, harvested for grain, or grazed and harvested for grain. The alternative treatment is year-round annual cover crop forage mixes for cattle (Bos taurus) production planted in fall and spring under conservation tillage management. The area is subject to variable weather and climatic shifts that reduce the potential to diversify forage crops and limit grazing in southern tall grass prairies and small grain systems. Incorporation of fertilized, rain-fed, annual cool and warm season mixtures of cover crops could fill forage gaps. The presence of year-round ground cover reduces sediment and nutrient loading to surface waters while enhancing soil health and water holding capacity. Tools to aid agricultural producers and land and water resource managers have been developed and implemented to determine how climate, topography, and varying conservation management practices alter hydrological structures, greenhouse gas emissions, water usage, and soil resources.

As global climate change poses a challenge to crop production, it is imperative to prioritize effective adaptation of agricultural systems based on a scientific understanding of likely impacts. In this study, we applied an integrated watershed modeling framework to examine the impacts of projected climate on runoff, soil moisture, and soil erosion under different management systems in Central Oklahoma. The proposed model uses measured climate data and three downscaled ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) at the water resources and erosion watershed to understand the impact of climate change and various climate conditions under three management systems: (1) continuous winter wheat (Triticum aestivum) under conventional tillage (WW-CT; baseline system), (2) continuous winter wheat under no-till (WW-NT), and (3) cool and warm season forage cover crop mixes under no-till (CC-NT). The study indicates that the occurrence of agricultural drought is projected to increase while erosion rates will remain unchanged under the WW-CT. In contrast, climate simulations imposed on the WW-NT and CC-NT systems significantly reduce runoff and sediment while preserving soil moisture levels. Especially, implementing the CC-NT system can bolster food security and foster sustainable farming practices in Central Oklahoma in the face of a changing climate.

Sensitivity analysis can be used to identify model parameters driving simulation outputs. However, sensitivity analysis is usually performed on an aggregated output or a model performance metric, calculated over a simulation period. This paper studies the spatial and temporal variations of the SWAT model parameter sensitivities for flow and sediment load modeling in an agricultural watershed. The Willow Creek Sub-watershed in central Oklahoma, characterized by diverse land use and agricultural management, was used as the case study. Parameter uncertainties at the hydrologic response unit (HRU) level were characterized through probability distributions, while parameter sensitivities were calculated using Standardized Regression Coefficients (SRCs) at a daily timescale. Significant temporal variations were observed for different parameters, influenced by changes in climate and management conditions. Additionally, the results indicated certain parameters exhibited high sensitivities only during specific periods, while others maintained high sensitivities throughout most of the simulation period. This study underscores the necessity of integrating both temporal and spatial sensitivity analyses to capture the dynamic nature of parameter sensitivities at a finer level of spatial granularity than the watershed scale.

Intensification, the process of intensifying land management to enhance agricultural goods, results in "intensive" pastures that are planted with productive grasses and fertilized. These intensive pastures provide essential ecosystem services, including forage production for livestock. Understanding the synergies and tradeoffs of pasture intensification on the delivery of services across climatic regions is crucial to shape policies and incentives for better management of natural resources. Here, we investigated how grassland intensification affects key components of provisioning (forage productivity and quality), supporting (plant diversity) and regulating services (CO2 and CH4 fluxes) by comparing these services between intensive versus extensive pastures in subtropical and temperate pastures in the USDA Long-term Agroecosystem Research (LTAR) Network sites in Florida and Oklahoma, USA over multiple years. Our results suggest that grassland intensification led to a decrease in measured supporting and regulating services, but increased forage productivity in temperate pastures and forage digestibility in subtropical pastures. Intensification decreased the net CO2 sink of subtropical pastures while it did not affect the sink capacity of temperate pastures; and it also increased environmental CH4 emissions from subtropical pastures and reduced CH4 uptake in temperate pastures. Intensification enhanced the global warming potential associated with C fluxes of pastures in both ecoregions. Our study demonstrates that comparisons of agroecosystems in contrasting ecoregions can reveal important drivers of ecosystem services and general or region-specific opportunities and solutions to maintaining agricultural production and reducing environmental footprints. Further LTAR network-scale comparisons of multiple ecosystem services across croplands and grazinglands intensively vs extensively managed are warranted to inform the sustainable intensification of agriculture within US and beyond. Our results highlight that achieving both food security and environmental stewardship will involve the conservation of less intensively managed pastures while adopting sustainable strategies in intensively managed pastures.

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.

Current gaps impeding researchers from developing a soil and watershed health nexus include design of long-term field-scale experiments and statistical methodologies that link soil health indicators (SHI) with water quality indicators (WQI). Land cover is often used to predict WQI but may not reflect the effects of previous management such as legacy fertilizer applications, disturbance, and shifts in plant populations) and soil texture. Our research objectives were to use nonparametric Spearman rank-order correlations to identify SHI and WQI that were related across the Fort Cobb Reservoir experimental watershed (FCREW); use the resulting rho (r) and p values (P) to explore potential drivers of SHI-WQI relationships, specifically land use, management, and inherent properties (soil texture, aspect, elevation, slope); and interpret findings to make recommendations regarding assessment of the sustainability of land use and management. The SHI values used in the correlation matrix were weighted by soil texture and land management. The SHI that were significantly correlated with one or more WQI were available water capacity (AWC), Mehlich III soil P, and the sand to clay ratio (S:C). Mehlich III soil P was highly correlated with three WQI: total dissolved solids (TDS) (0.80; P < 0.01), electrical conductivity of water (EC-H2 O) (0.79; P < 0.01), and water nitrates (NO3 -H2 O) (0.76; P < 0.01). The correlations verified that soil texture and management jointly influence water quality (WQ), but the size of the soils dataset prohibited determination of the specific processes. Adoption of conservation tillage and grasslands within the FCREW improved WQ such that water samples met the U.S. Environmental Protection Agency (EPA) drinking water standards. Future research should integrate current WQI sampling sites into an edge-of-field design representing all management by soil series combinations within the FCREW.

The European Space Agency (ESA) launched the Soil Moisture and Ocean Salinity (SMOS) mission in 2009; currently, multiple global soil moisture (SM) products are based on the measurements of its L-band (1.4 GHz) radiometer. We compared four SMOS products with each other: Level 2, Level 3, IC (INRA-CESBIO), and near real-time products. The comparisons focused on core validation sites (CVS), whose spatial representativeness errors allow the estimation of the SM product performance for bias-insensitive metrics [unbiased root-mean-square error (ubRMSE) and correlation ( $R$ ), and anomaly $R$ ] with negligible uncertainty and for bias-sensitive metrics [mean difference (MD) and root-mean-square difference (RMSD)] with acceptable uncertainty. When the products were compared with CVS independently, the results showed that the ubRMSE, $R$ , and anomaly $R$ of the IC product were better than those of the other products, while the MD was larger. However, the differences between the performances were smaller when the products were assessed using only the data points when each product had a valid retrieval. This indicates that the algorithms have similar performance and that data screening and quality flagging of the retrievals markedly affects the performance. The NASA Soil Moisture Active Passive (SMAP) mission produces a similar SM product as SMOS using an L-band radiometer. The closeness of the ubRMSE, $R$ , and the anomaly $R$ performance of the IC product and the SMAP product (0.039 versus 0.041 $\text{m}<^>{3}/\text{m}<^>{3}$ , 0.80 versus 0.81, and 0.75 versus 0.75) demonstrate that the SMOS and SMAP radiometers can achieve similar SM sensitivity.

A new modeling platform was developed to simulate spatially distributed sediment production and transport in agricultural landscapes. The sediment production at the grid scale was computed using the stand-alone Water Erosion Prediction Project-Hillslope Erosion code, while an advection-dispersion equation represented sediment transport. The model's performance was tested in the Water Resources and Erosion watersheds at the Oklahoma and Central Plains Agricultural Research Center, El Reno, OK, USA. Results showed that this modeling approach could capture the complex behavior of sediment under different management practices in the watersheds. The modeling framework provides mechanistic processes to simulate the fate and transport of sediment across the watershed at spatio-temporal scales that are useful in assessing the environmental impacts of management schemes. Therefore, this study is expected to help advance the current approaches to estimating soil erosion by bridging scale differences to capture the large-scale effects of small-scale soil erosion processes.

The empirical Revised Universal Soil Loss Equation (RUSLE) has been adapted to geographical information system (GIS) frameworks to study the spatial variability of soil erosion across landscapes and has also been used to estimate reservoir sedimentation. The literature presents contradictory results about the efficacy of using RUSLE in a GIS context for quantifying reservoir sedimentation, requiring further evaluation and validation of its estimates relative to measured reservoir sedimentation. Our primary objective was to determine if these contradictory results may be a function of the RUSLE’s inability to account for sediments derived from gullies, stream channels, or stream banks; the temporal variability of some of RUSLE’s empirically based factors such as the land cover/land management (C-) factor; and in some model renditions, the choice of value for the sediment delivery ratio (SDR). The usefulness of adjusting these estimates using a regional representative value of gully/stream bank sediment contributions was also assessed. High-spatial horizontal resolution (2 m) digital elevation models (DEMs) for 12 watersheds were used together with C-factor data for five representative years in a GIS-based RUSLE model that incorporates SDR within a sediment routing routine to study the impacts of choice of C-factor and SDR on reservoir sedimentation estimates. Choice of image date for developing C-factors was found to impact reservoir estimates. We also found that the value of SDR for some of the study watersheds would have to be unrealistically small to produce sedimentation estimates comparable to measured values. Estimates of reservoir sedimentation were comparable to measured data for 5 of the 12 watersheds, when the regionally based adjustment for gully/stream bank contributions was applied. However, differences remained large for the remaining seven watersheds. Statistical analysis revealed that certain combinations of geomorphic, pedologic, or topographic variables could be used to predict the degree of sediment underestimation with a significant and high level of correlation (0.72 < R2 ≤ 0.99; p-value < 0.05). Our findings indicate that the level of agreement between GIS-based RUSLE estimates of reservoir sedimentation and measured values is a function of watershed characteristics; for example, the area-weighted soil erodibility (K-) factor of the soils within the watershed and stream channels, the stream entrenchment ratio and bank full depth, the percentage of the stream corridor having slopes ≥ 21°, and the width of the stream flood way as a percentage of the watershed area. Within the context of GIS, these metrics are easily obtained from digital elevation models and publicly available soils data and may be useful in prioritizing reservoirs’ assessments for function and safety.

A thermal hydraulic disaggregation of soil moisture (THySM) algorithm was implemented to downscale NASA's soil moisture active passive (SMAP) enhanced soil moisture (SM) product to 1 km over the continental United States (CONUS). This algorithm was developed by combining thermal inertia theory with a soil hydraulic-based approach that considers fine-scale SM spatial distribution driven by both heat fluxes and hydraulic conductivity in soils. Relative soil wetness values were estimated using land surface temperature and normalized difference vegetation index for the thermal inertia model and using soil properties for the hydraulic model. The relative soil wetness values at 1 km from both models were then combined by using weighting functions whereby the spatial distribution of SM was governed more by thermal fluxes during times of strong heat transport and infiltration during moisture abundant soil conditions. THySM values were evaluated using in situ SM measurements from SMAP Core Validation Sites (CVS), the US Department of Agriculture Soil Climate Analysis Network, and the National Oceanic and Atmospheric Administration Climate Reference Network over CONUS. THySM shows higher accuracy than the SMAP / Sentinel-1 (SPL2SMAP_S) 1 km SM product when compared to in situ measurements. The accuracy of THySM is 0.048 m3/m3 based on unbiased root mean square error (ubRMSE), outperforming SPL2SMAP_S by 0.01–0.02 m3/m3. The ubRMSE of THySM 1 km SM over the SMAP grassland/rangeland-dominated CVS sites is better than 0.04 m3/m3, which meets the SMAP mission SM accuracy requirement applied at 9 and 36 km.

The vision of the Long Term Agroecosystem Research (LTAR) network is to enable multi‐decadal, trans‐disciplinary, and cross‐location science to ensure the long‐term sustainability of U.S. agriculture. LTAR's primary goals are to: (1) Intensify agricultural productivity, (2) Improve ecosystem services related to agricultural production, and (3) Improve rural prosperity. The LTAR network includes 18 locations (sites). It includes 10 existing hydrologic observatories from the Agricultural Research Service‐Experimental Watershed Network (ARS‐EWN) that were established before the creation of LTAR. Background and an overview of the network are presented.

Short-range predictions of crop yield provide valuable insights for agricultural resource management and likely economic impacts associated with low yield. Such predictions are difficult to achieve in regions that lack extensive observational records. Herein, we demonstrate how a number of basic or readily available input data can be used to train an Artificial Neural Network (ANN) model to provide months-ahead predictions of cotton yield for a case study in Menemen Plain, Turkey. We use limited reported yield (13 years) along cumulative precipitation, cumulative heat units, two meteorologically-based drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)), and three remotely-sensed vegetation indices (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI)) as ANN inputs. Results indicate that, when EVI is combined with the preceding 12-month SPEI, it has better sensitivity to cotton yield than other indicators. The ANN model predicted cotton yield four months before harvest with R2 > 0.80, showing potential as a yield prediction tool. We discuss the effects of different combinations of input data (explanatory variables), dataset size, and selection of training data to inform future applications of ANN for early prediction of cotton yield in data-scarce regions.

This study combines geospatial data and a classification scheme that uses landform elements to derive landform complexes that codify the collection of soils data at variable scale within a single field site. Our experiment was initiated in 2018 on three, 1.6 ha self-contained watersheds representing a southern tall grass prairie (STGP), a system of continuous winter wheat (Triticum aestivum) tilled via offset disking and chisel plow (WWCT), and a minimally disturbed winter wheat system that was periodically planted to a warm season forage, typically sorghum-sudangrass (Sorghum bicolor L.) (WWMT) from 1978 to 2018. A class I soil survey was conducted in 2018 by grid sampling the landscape of all watershed systems at the site. The survey indicated four distinct catena were present across all watersheds, which enabled us to utilize a split block design. This statistical approach allowed for testing of interactions among management practices, landscape position, and soil depth to obtain means and standard errors for different edaphic properties. Using a hydraulic probe, 144 random soil cores were collected to a 30 cm depth at each of the four 4.6 m by 3.8 m replicated blocks per landscape position (tread, riser, and toe) within the three watersheds. Cores were further divided into three depths (0 to 5, 5 to 15, and 15 to 30 cm). Baseline analyses included Mehlich-3, soil sulfate (SO4) and DTPA-sorbitol extractions, soil texture, bulk density, pH, total soil organic carbon (TSOC) and total soil nitrogen (TSN), particulate organic matter (POM), and non-hydrolysable C (RCAH), the resistant fraction of soil organic C. The majority of edaphic properties associated with soil classification varied with landscape position and depth. These included clay content, base saturation (calcium [Ca], magnesium [Mg], and potassium [K]), pH, and sulfur in the form of sulfate (SO4-S) and phosphorus (P). Carbon and N fractions varied with land use, conservation practices, and/or depth. The establishment of replicate sampling stations that account for and limit the spatial variability of edaphic properties within defined landform complexes enables researchers to more accurately quantify the effects of conservation practices and land management.

Soil moisture is of great importance to disciplines such as agriculture, hydrology and meteorology. Over the past three decades, passive microwave remote sensing has been demonstrated as a promising tool for global soil moisture estimation and several missions have been launched over the past years. This study focuses on the parametrization of the tau-omega model at L-, C- and X-band for the Yanco site in New South Wales, Australia, and compares the resulting forward-simulated brightness temperatures with two missions: NASA's Soil Moisture Active Passive (SMAP) mission and JAXA's Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W mission. Preliminary comparison of SMAP and AMSR2 brightness temperatures and forward-simulated brightness temperatures at the Yanco site showed a generally good agreement and higher correlation for the vertical polarization. This is consistent with other studies analyzing the SMAP soil moisture products. Simultaneous calibration of the vegetation parameter b and roughness parameter h was also performed for the L-, C- and X-band data sets, respectively, at both horizontal and vertical polarizations.

Carbon dioxide (CO2) fluxes and evapotranspiration (ET) during the non-growing season can contribute significantly to the annual carbon and water budgets of agroecosystems. Comparative studies of vegetation phenology and the dynamics of CO2 fluxes and ET during the dormant season of native tallgrass prairies from different landscape positions under the same climatic regime are scarce. Thus, this study compared the dynamics of satellite-derived vegetation phenology (as captured by the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI)) and eddy covariance (EC)-measured CO2 fluxes and ET in six differently managed native tallgrass prairie pastures during dormant seasons (November through March). During December–February, vegetation phenology (EVI and NDVI) and the dynamics of eddy fluxes were comparable across all pastures in most years. Large discrepancies in fluxes were observed during March (the time of the initiation of growth of dominant warm-season grasses) across years and pastures due to the influence of weather conditions and management practices. The results illustrated the interactive effects between prescribed spring burns and rainfall on vegetation phenology (i.e., positive and negative impacts of prescribed spring burns under non-drought and drought conditions, respectively). The EVI better tracked the phenology of tallgrass prairie during the dormant season than did NDVI. Similar EVI and NDVI values for the periods when flux magnitudes were different among pastures and years, most likely due to the satellite sensors’ inability to fully observe the presence of some cool-season C3 species under residues, necessitated a multi-level validation approach of using ground-truth observations of species composition, EC measurements, PhenoCam (digital) images, and finer-resolution satellite data to further validate the vegetation phenology derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) during dormant seasons. This study provides novel insights into the dynamics of vegetation phenology, CO2 fluxes, and ET of tallgrass prairie during the dormant season in the U.S. Southern Great Plains.

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 (theta(v)) 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 theta(v), and WSS-constant yielded the largest errors. Simulated theta(v) 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).