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Machine learning (ML) has gained significant attention for unraveling the complex, nonlinear relationships between soil moisture (SM) and various predictive variables, including remote sensing (RS; reflectance, brightness temperature, backscatter coefficients) and biophysical (topographic, soil, vegetation, and weather) variables. We reviewed the literature to extract and synthesize ML algorithms, reliable input features, and challenges in SM estimation using RS data. We analyzed results from 144 articles published from 2010 to 2024. Random forest (40 out of 67 studies), support vector regressor (13 out of 39 studies), and artificial neural networks (12 out of 27 studies) often outperformed other algorithms to estimate SM using RS datasets. Multi-source RS data often outperformed single-source data in SM estimation. Satellite-derived features, such as vegetation indices and backscattering coefficients, provided critical information on surface SM (SSM) variability to estimate SSM. For root zone SM estimation, soil properties and SSM generally were more reliable predictors than surface information derived solely from RS. Two recent advances—the use of semi-empirical models and L-band SAR to mitigate vegetation effects, and transfer learning to improve model transferability—have shown promise in addressing key challenges in SM estimation.

Agricultural systems exhibit a large degree of within-field yield variability. We require a better understanding of the drivers of this variability in order to optimally manage croplands. We investigated drivers of sub-field spatial variability in yield for three crops (hard red winter wheat, Triticum aestivum L. variety Langin; corn, Zea mays L.; and proso millet, Panicum milaceum L.) usings a multi-year dataset from a dryland research farm in northeastern Colorado, USA. The dataset spanned 18 2.6–4.3 ha management units, over 4 years, and included high-resolution topographic data, densely sampled soil properties, and on-site weather data. We modeled yield for each crop separately using random forest regression and evaluated model performance using spatially blocked cross-validation. The topographic position index (TPI) and increasing percent sand had a strong negative effect on yield, while the nitrogen application rate (N) and total soil carbon had strong positive effects on yield in both the wheat and millet models. Remarkably, TPI had almost as large of an effect size as N, and outperformed other more commonly used topographic predictors of yield such as the topographic wetness index (TWI), elevation, and slope. Despite the size and quality of our dataset, cross-validation results revealed that our models account for approximately one-quarter of the total yield variance, highlighting the need for continued research into drivers of spatial variability within fields.

Weather generators are increasingly used in environmental, water resources, and agricultural applications. Given their potential, it is important that weather generators be evaluated, particularly with respect to their ability to capture extreme events. This study was aimed at evaluating weather generator representation of climate extremes with a focus on LARS-WG applied to three stations in the Western Lake Erie Basin, U.S. Generally, LARS-WG captured the number of days with precipitation greater than 50.8 mm (2 in. and 101.6 mm (4 in), 7-day wet sequences, and wet and dry sequences relatively well. The distribution of 1-day maximum precipitation was also generally captured well based on Q-Q plots, although large deviations were seen at the upper tail at one of the stations. The generator greatly underestimated the number of days per year with maximum temperatures greater than 32.2 °C (90 °F) and overestimated the number of days with temperatures less than 0 °C (32 °F). It also underestimated spring and summer values of one-day maximum temperatures across all stations. Fall and winter values were, however, captured fairly well as were seasonal values of one-day minimum temperatures. Overall, the generator performed relatively well in representing extremes within the basin.

Modeling efforts to simulate hydrologic processes under different climate conditions rely on accurate input data. Among other inaccuracies, errors in climate projections can lead to incorrect decisions. This study aimed to develop a reliable climate (precipitation and temperature) database for the Western Lake Erie Basin for the 21st century. Two statistically downscaled bias-corrected sources of climate projections (GDO: Global Downscaled Climate and Hydrology Projections and MACA: Multivariate Adaptive Constructed Analogs) were tested for their effectiveness in simulating historic climate (1966–2005) using ground-based station data from the National Climatic Data Center. MACA was found to have less bias than GDO and was better at simulating selected climate indices; thus, its climate projections were subsequently tested with different bias correction methods including the power transformation method, variance scaling of temperature, and Stochastic Weather Generators. The power transformation method outperformed the other methods and was used in bias corrections for 2006 to 2099. From the analysis, mean daily precipitation values were expected to remain more or less the same under both RCP (Representative Concentration Pathway) 4.5 and RCP 8.5 scenarios, ranging between 2.4 mm and 3.2 mm, while standard deviations were expected to increase, pointing to a rescaling of the distribution. Maximum one-day precipitation was expected to increase and could vary between 120 and 650 mm across the basin, while the number of wet days could potentially increase under the effects of RCP 4.5 and RCP 8.5. Both mean maximum and mean minimum daily air temperatures were expected to increase by up to 5.0 °C across the basin, while absolute maximum and minimum values could increase by more than 10 °C. The number of days in which precipitation could potentially fall as snow was expected to decrease, as was the annual number of days for optimal corn growth, although an earlier start to the growing season could be expected. Results from this study were very useful in creating a reliable climate database for the entire Western Lake Erie Basin (WLEB), which can be used for hydrologic, water resources, and other applications in the basin. The resulting climate database is published and accessible through the Purdue University Research Repository (Mehan et al., 2019), which is an open-access repository.

This study is a part of the Conservation Effects Assessment Project (CEAP) aimed to quantify the environmental and economic benefits of conservation practices implemented in the cultivated cropland throughout the United States. The Soil and Water Assessment Tool (SWAT) model under the Hydrologic United Modeling of the United States (HUMUS) framework was used in the study. An automated flow calibration procedure was developed and used to calibrate runoff for each 8-digit watershed (within 20% of calibration target) and the partitioning of runoff into surface and sub-surface flow components (within 10% of calibration target). Streamflow was validated at selected gauging stations along major rivers within the river basin with a target R2 of >0.6 and Nash and Sutcliffe Efficiency of >0.5. The study area covered the entire Mississippi and Atchafalaya River Basin (MARB). Based on the results obtained, our analysis pointed out multiple challenges to calibration such as: (1) availability of good quality data, (2) accounting for multiple reservoirs within a sub-watershed, (3) inadequate accounting of elevation and slopes in mountainous regions, (4) poor representation of carrying capacity of channels, (5) inadequate capturing of the irrigation return flows, (6) inadequate representation of vegetative cover, and (7) poor representation of water abstractions (both surface and groundwater). Additional outstanding challenges to large-scale hydrologic model calibration were the coarse spatial scale of soils, land cover, and topography.

Amidst the growing population, urbanization, globalization, and economic growth, along with the impacts of climate change, decision-makers, stakeholders, and researchers need tools for better assessment and communication of the highly interconnected food–energy–water (FEW) nexus. This study aimed to identify critical periods for water resources management for robust decision-making for water resources management at the nexus. Using a 4610 ha agricultural watershed as a pilot site, historical data (2006–2012), scientific literature values, and SWAT model simulations were utilized to map out critical periods throughout the growing season of corn and soybeans. The results indicate that soil water deficits are primarily seen in June and July, with average deficits and surpluses ranging from −134.7 to +145.3 mm during the study period. Corresponding water quality impacts include average monthly surface nitrate-N, subsurface nitrate-N, and soluble phosphorus losses of up to 0.026, 0.26, and 0.0013 kg/ha, respectively, over the growing season. Estimated fuel requirements for the agricultural practices ranged from 24.7 to 170.3 L/ha, while estimated carbon emissions ranged from 0.3 to 2.7 kg CO2/L. A composite look at all the FEW nexus elements showed that critical periods for water management in the study watershed occurred in the early and late season—primarily related to water quality—and mid-season, related to water quantity. This suggests the need to adapt agricultural and other management practices across the growing season in line with the respective water management needs. The FEW nexus assessment methodologies developed in this study provide a framework in which spatial, temporal, and literature data can be implemented for improved water resources management in other areas.

Climate forcing data accuracy drives performance of hydrologic models and analyses, yet each investigator needs to select from among the numerous gridded climate dataset options and justify their selection for use in a particular hydrologic model or analysis. This study aims to provide a comprehensive compilation and overview of gridded datasets (precipitation, air temperature, humidity, wind speed, solar radiation) and considerations for historical climate product selection criteria for hydrologic modeling and analyses based on a review and synthesis of previous studies conducting dataset intercomparisons. All datasets summarized here span at least the conterminous US (CONUS), and many are continental or global in extent. Gridded datasets built on ground-based observations (G; n= 20), satellite imagery (S; n= 20), and/or reanalysis products (R; n= 23) are compiled and described, with focus on the characteristics that hydrologic investigators may find useful in discerning acceptable datasets (variables, coverage, resolution, accessibility, and latency). We provide best-available-science recommendations for dataset selection based on a thorough review, interpretation, and synthesis of 29 recent studies (past 10 years) that compared the performance of various gridded climate datasets for hydrologic analyses. No single best source of gridded climate data exists, but we identified several common themes that may help guide dataset selection in future studies: Gridded daily temperature datasets improved when derived over regions with greater station density. Similarly, gridded daily precipitation data were more accurate when derived over regions with higher-density station data, when used in spatially less-complex terrain, and when corrected using ground-based data. In mountainous regions and humid regions, R precipitation datasets generally performed better than G when underlying data had a low station density, but there was no difference for higher station densities. G datasets were generally more accurate in representing precipitation and temperature data than S or R datasets, although this did not always translate into better streamflow modeling. We conclude that hydrologic analyses would benefit from guided dataset selection by investigators, including justification for selecting a specific dataset, and improved gridded datasets that retain dependencies among climate variables and better represent small-scale spatial variability in climate variables in complex topography. Based on this study, the authors' overall recommendations to hydrologic modelers are to select the gridded dataset (from Tables 1, 2, and 3) (a) with spatial and temporal resolutions that match modeling scales, (b) that are primarily (G) or secondarily (SG and RG) derived from ground-based observations, (c) with sufficient spatial and temporal coverage for the analysis, (d) with adequate latency for analysis objectives, and (e) that includes all climate variables of interest (so as to better represent interdependencies).

Our study aims to understand how the hydrological cycle is affected by climate change in river basins. This study focused on the Karnali River Basin (KRB) to examine the impact of extreme weather events like floods and heat waves on water security and sustainable environmental management. Our research incorporates precipitation and temperature projections from ten Global Circulation Models (GCMs) under the Coupled Model Intercomparison Project Phase 6 (CMIP6). We applied thirteen statistical bias correction methods for precipitation and nine for temperatures to make future precipitation and temperature trend projections. The research study also utilized the Soil and Water Assessment Tool (SWAT) model at multi-sites to estimate future streamflow under the Shared Socioeconomic Pathway (SSP) scenarios of SSP245 and SSP585. Additionally, the Web-based Hydrograph Analysis Tool (WHAT) was used to distinguish between baseflow and streamflow. Our findings, based on the Multi-Model Ensemble (MME), indicate that precipitation will increase by 7.79–16.25% under SSP245 (9.43–27.47% under SSP585) and maximum temperatures will rise at rates of 0.018, 0.048, and 0.064 °C/yr under SSP245 (0.022, 0.066, and 0.119 °C/yr under SSP585). We also anticipate that minimum temperatures will increase at rates of 0.049, 0.08, and 0.97 °C/yr under SSP245 (0.057, 0.115, and 0.187 °C/yr under SSP585) for near, mid, and far future periods, respectively. Our research predicts an increase in river discharge in the KRB by 27.12% to 54.88% under SSP245 and 45.4% to 93.3% under SSP585 in different future periods. Our finding also showed that the expected minimum monthly baseflow in future periods will occur earlier than in the historical period. Our study emphasizes the need for sustainable and adaptive management strategies to address the effects of climate change on water security in the KRB. By providing detailed insights into future hydrological conditions, this research serves as a critical resource for policymakers and stakeholders, facilitating informed decision-making for the sustainable management of water resources in the face of climate change.

Manual transplanting, a pre-dominant practice in almost all the paddy growing areas in India, is laborious, burdensome, and has many expenses on raising, settling, and transplanting nursery. The transplanting process’s limitations motivated the replacement of conventional paddy transplanting methods. The study was divided into two phases. The first phase included laboratory testing of three levels of metering mechanisms, namely cell type (M1) with 10 cells grooved around a circular plate having a 13 cm diameter, inclined plate (M2) containing 24 U shaped cells provided on an 18 cm diameter plate, and fluted roller (M3) with 10 flutes on a 5 cm diameter shaft. The testing matrix included a missing index, multiple index, and seed damage with forward speeds (2.5, 3.0, and 3.5 km/h), and pre-germination levels of 24 h soaked (P1), 24 h pre-germinated (P2), and 36 h pre-germinated paddy seeds (P3)). The second phase included selecting the best combination obtained from the laboratory study and developing a new efficient planter for the puddled field. The inclined plate metering mechanism operating at 2.5 km/h for 24 h pre-germinated seeds was reported most efficient from the first phase. Therefore, a self-propelled 8-row planter equipped with an inclined plate metering mechanism having a row-to-row spacing of 22.5 cm was developed, fabricated, and evaluated in the puddled field. The designed planter was assessed on two different soils: sandy loom (ST1) and clay loom (ST2) and at two different hopper fill levels as ½ filled hopper (F1) and ¾ filled hopper (F2). The number of plants per square meter and hill-to-hill spacing was measured. The on-field evaluation revealed that the number of plants per square meter was non-significantly affected by the type of soil but was significantly affected by hopper fill.