Trend analysis and estimation of monthly and annual precipitation, reference evapotranspiration ET_o, and rainfall deficit are essential for water-resources management and cropping-system design. Rainfall, ET_o, and water-deficit patterns and trends at Macon in eastern Mississippi for a 120-yr period (1894–2014) were analyzed for annual, seasonal, and monthly periods. The analysis showed historical average annual rainfall, ET_o, and dryness index (DI) in the location to be 1307 mm, 1210 mm, and 0.97, respectively. Monthly rainfall and ET_o ranged from 72 to 118 mm and from 94 to 146 mm, respectively, between May and October, resulting in a monthly rain deficit from 22 to 62 mm. Annual rainfall showed an increasing trend of 1.17 mm yr⁻¹ while annual ET_o exhibited a decreasing trend of −0.51 mm yr⁻¹, resulting in an annual DI reduction of 0.001 per year. Seasonal trends were found for rainfall in autumn (1.06 mm yr⁻¹), ET_o in summer (−0.29 mm yr⁻¹) and autumn (−0.18 mm yr⁻¹), and DI in autumn (−0.006). An autoregressive, integrated, and moving-average (ARIMA) approach was used to model monthly and annual rainfall, ET_o, and DI and to predict those values in the future. Low values of the root-mean-square error (RMSE) and mean absolute error (with both statistics being normalized to the mean of the observed values), low values of average percent bias, and low values of the ratio of the RMSE to the standard deviation of observed data, along with values of 1.0 for Nash–Sutcliffe modeling efficiency and the index of agreement, all suggest that the performance of the models is acceptable. The ARIMA models forecast 1319 mm of mean annual rainfall, 1203 mm of mean annual ET_o, and 0.82 of mean annual DI from 2015 to 2024. The results obtained from this research can guide development of water-management practices and cropping systems in the area that rely on this weather station. The approaches used and the models fitted in this study can serve as a demonstration of how a time series trend can be analyzed and a model fitted at other locations.

Forest ecosystems in an ecotone and their dynamics to climate change are growing ecological and environmental concerns. Phenology is one of the most critical biological indicators of climate change impacts on forest dynamics. In this study, we estimated and visualized the spatiotemporal patterns of forest phenology from 2001 to 2017 in the Qinling Mountains (QMs) based on the enhanced vegetation index (EVI) from MODerate-resolution Imaging Spectroradiometer (MODIS). We further analyzed this data to reveal the impacts of climate change and topography on the start of the growing season (SOS), end of the growing season (EOS), and the length of growing season (LOS). Our results showed that forest phenology metrics were very sensitive to changes in elevation, with a 2.4 days delayed SOS, 1.4 days advanced EOS, and 3.8 days shortened LOS for every 100 m increase in altitude. During the study period, on average, SOS advanced by 0.13 days year⁻¹, EOS was delayed by 0.22 days year⁻¹, and LOS increased by 0.35 day year⁻¹. The phenological advanced and delayed speed across different elevation is not consistent. The speed of elevation-induced advanced SOS increased slightly with elevation, and the speed of elevation-induced delayed EOS shift reached a maximum value of 1500 m from 2001 to 2017. The sensitivity of SOS and EOS to preseason temperature displays that an increase of 1 °C in the regionally averaged preseason temperature would advance the average SOS by 1.23 days and delay the average EOS by 0.72 days, respectively. This study improved our understanding of the recent variability of forest phenology in mountain ecotones and explored the correlation between forest phenology and climate variables in the context of the ongoing climate warming.

Temperatures over the past three decades have exhibited an asymmetric warming pattern between night and day throughout the Tibetan Plateau. However, the implications of such diurnally heterogeneous warming on vegetation growth is still poorly understood. In this paper, we evaluate how vegetation growth has responded to daytime and night-time warming at the regional, biome, and pixel scales based on normalized difference vegetation index (NDVI) and meteorological data from 1982 to 2015. We found a persistent increase in the growing seasonal minimum temperature (T_min) and maximum temperature (T_max) over the Tibetan Plateau between 1982–2015, whereas the rate of increase of T_min was 1.7 times that of T_max. After removing the correlations between T_min, precipitation, and solar radiation, we found that the partial correlation between T_max and NDVI was positive in wetter and colder areas and negative in semi-arid and arid regions. In contrast, the partial correlation between T_min and NDVI was positive in high-cold steppe and meadow steppe and negative in montane steppe or wet forest. We also found diverse responses of vegetation type to daytime and night-time warming across the Tibetan Plateau. Our results provide a demonstration for studying regional responses of vegetation to climate extremes under global climate change.

The groundwater level in the Big Sunflower River Watershed (BSRW) in the U.S. has declined significantly in the past 30 years. Therefore, it is imperative to assess surface water resources (SWR) availability in BSRW to mitigate groundwater use for irrigation. This research applied the coupled Soil and Water Assessment Tool–Modular Groundwater Flow model (SWAT–MODFLOW) to assess SWR in BSRW. This study aimed at: (1) Assessing the reliability of SWAT–MODFLOW in BSRW, (2) analyzing temporal and spatial variations of SWR, and (3) assessing the potential availability of SWR in BSRW. Calibration and validation results showed that SWAT–MODFLOW can well simulate streamflow and groundwater levels in BSRW. Our results showed that BSRW had lower average monthly total stream resources (MSR = 8.8 × 10⁷ m³) in growing seasons than in non-growing seasons (MSR = 11.0 × 10⁷ m³), and monthly pond resources (MPR from 30,418 to 30,494 m³) varied less than stream resources. The proportion of sub-basins in BSRW with stream water resources greater than 700 mm was 21% in dry years (229 to 994 mm), while this increased to 35% in normal years (296 to 1141 mm) and 57% in wet years (554 to 991 mm). The Water Stress Index (WSI) ranged from 0.4 to 2.1, revealing that most of the sub-basins in BSRW have net SWR available for irrigation. Our results suggested that surface water resources might be supplementary irrigation sources to mitigate the water resources scarcity in this region.

Water scarcity is the most critical constraint for sustainable cotton production in Xinjiang Province, northwest China. Drip irrigation under mulch is a major water-saving irrigation method that has been widely practiced for cotton production in Xinjiang. The performance of such an irrigation system should be evaluated for proper design and management. Therefore, a field experiment and a simulation study were conducted to (1) determine a modeling approach that can be applied to manage drip irrigation under mulch for cotton production in this region; and (2) examine the effects of irrigation amount and mulch on soil water distribution and root zone water balance components. In the experiment, four irrigation treatments were used: T1, 166.5 m³; T2, 140.4 m³; T3, 115.4 m³; and T4: 102.3 m³. The HYDRUS-2D model was calibrated, validated, and applied with the data obtained in this experiment. Soil water balance in the 0–70 cm soil profile was simulated. Results indicate that the observed soil water content and the simulated results obtained with HYDRUS-2D are in good agreement. The radius of the wetting pattern, root water uptake, and evaporation decreased as the amount of irrigation was reduced from T1 to T4, while a lot of stored soil water in the root zone was utilized and a huge amount of water was recharged from the layer below 70 cm to compensate for the decrease in irrigation amount. Mulch significantly reduced evaporation by 11.7 mm and increased root water uptake by 11.2 mm. Our simulation study suggests that this model can be applied to provide assistance in designing drip irrigation systems and developing irrigation strategies.

The application of remote sensing, which is non-destructive and cost-efficient, has been widely used in crop monitoring and management. This study used a built-in multispectral imager on a small unmanned aerial vehicle (UAV) to capture multispectral images in five different spectral bands (blue, green, red, red edge, and near-infrared), instead of satellite-captured data, to monitor soybean growth in a field. The field experiment was conducted in a soybean field at the Mississippi State University Experiment Station near Pontotoc, MS, USA. The experiment consisted of five cover crops (Cereal Rye, Vetch, Wheat, Mustard plus Cereal Rye, and native vegetation) planted in the winter and three fertilizer treatments (Fertilizer, Poultry Liter, and None) applied before planting the soybean. During the soybean growing season in 2022, eight UAV imaging flyovers were conducted, spread across the growth season. UAV image-derived vegetation indices (VIs) coupled with machine learning (ML) models were computed for characterizing soybean growth at different stages across the season. The aim of this study focuses on monitoring soybean growth to predict yield, using 14 VIs including CC (Canopy Cover), NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), EVI2 (Enhanced Vegetation Index 2), and others. Different machine learning algorithms including Linear Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) are used for this purpose. The stage of the initial pod development was shown as having the best predictability for earliest soybean yield prediction. CC, NDVI, and NAVI (Normalized area vegetation index) were shown as the best VIs for yield prediction. The RMSE was found to be about 134.5 to 511.11 kg ha⁻¹ in the different yield models, whereas it was 605.26 to 685.96 kg ha⁻¹ in the cross-validated models. Due to the limited number of training and testing samples in the K-fold cross-validation, the models’ results changed to some extent. Nevertheless, the results of this study will be useful for the application of UAV remote sensing to provide information for soybean production and management. This study demonstrates that VIs coupled with ML models can be used in multistage soybean yield prediction at a farm scale, even with a limited number of training samples.