Paul Xavier Flanagan

Soil moisture is a fundamental variable controlling hydrologic partitioning, land–atmosphere exchange, and biogeochemical cycling. Although widely characterized through observations, remote sensing, and data-driven approaches, the representation of soil water fluxes and subsurface processes in predictive models remains less systematically synthesized. Here, we present a structured review of widely used predictive models, focusing on their process formulations, spatial discretization, and treatment of soil water fluxes across scales. The synthesis reveals consistent trade-offs among physical realism, scalability, data intensity, and computational efficiency. Conceptual models based on tipping-bucket approaches simplify soil water movement as threshold-driven storage processes and underrepresent transient redistribution and deep soil moisture dynamics. Storage-routing models introduce flux-based redistribution but rely on empirical parameterization that dampen transient flux dynamics. Physically based models resolve hydraulic gradients and provide the highest process fidelity but require extensive data and computational resources. Across scales, the absence or simplification of preferential flow and lateral subsurface flow constitutes the dominant structural limitation, producing systematic smoothing of soil moisture variability. The review provides a comparative basis for model selection and highlights critical directions for improving the representation of subsurface hydrologic dynamics in predictive frameworks.

This study introduces a novel k-nearest neighbors (kNN) method of forecasting precipitation at weather-observing stations. The method identifies numerous monthly temporal patterns to produce precipitation forecasts for a specific month. Compared to climatological forecasts, which average the observed precipitation over the prior thirty years, and other existing contemporary iterations of kNN, the proposed novel kNN method produces more accurate forecasts on a consistent basis. Specifically, the novel kNN method produces improved root mean square errors (RMSE), mean relative errors, and Nash–Sutcliffe coefficients when compared to climatological and other kNN forecasts at five weather stations in Oklahoma. Rather than looking at the daily data for feature vectors, this novel kNN method takes so many days and evenly groups them, using the resulting average as one feature each. All methods tested were lacking in the ability to forecast wet extremes; however, the novel kNN method produced more frequent higher precipitation forecasts compared to climatology and the two other kNN methods tested.

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.

With the importance of agriculture to the southern Great Plains (SGPs), accurate knowledge of growing season (GS) temperatures and precipitation is critical. Previous research into GS precipitation and temperature maxima leads to the development of the asynchronous difference index (ADI) which identified positive and negative ADI GSs (March–September). The goal of this research is to further investigate the ADI within a specific agricultural region of the SGP, the winter wheat region, and to quantify the temporal evolution of temperature and precipitation during positive and negative ADI GSs. For this, Global Historical Climatology Network (GHCN) daily station data were analyzed across the GS (March–September) from 1900 to 2020. Results show that differences appear in the temperature and precipitation fields when comparing positive and negative GSs. Namely, positive (negative) ADI GSs show positive (negative) precipitation and negative (positive) temperature anomalies early in the GS, with these anomalies flipping in the later portion of the GS. Further, the results of this work show that the depicted changes in temperature and precipitation during positive and negative ADI GSs impact winter wheat yields. Overall, these results analyze the implications of positive and negative ADI GSs on the SGP climate, namely, the impact of the seasonal variability of daily maximum temperature and precipitation on agriculture.

The Southern Great Plains (SGP) is defined by hydrometeorological swings between dry and wet extremes. These swings exacerbate the climatological gradients of moisture (east-to-west) and temperature (south-to-north), which can impact the agricultural production of the region. Thus, it is key to understand extremes to sustainably maintain agricultural success in the region. This study investigates the wet extremes, or extreme precipitation events, that have become more prominent in the last two decades. Data from 108 United States Historical Climatology Network (USHCN) stations were analyzed for the 1950 – 2020 period to detect changes in the frequency and magnitude of extreme precipitation events. Results show that changes in the magnitude of extreme precipitation are isolated and scattered across the SGP, with only the winter season showing regional shifts in extreme precipitation magnitude. Changes in the frequency of extreme precipitation events were noted across the entire SGP, although the changes in frequency are more notable in the eastern SGP compared to the western SGP. Analysis shows that the increased number of events detected is driven more, but not exclusively, by the increasing spatial extent of individual extreme precipitation events compared to an increased number of events. Overall, these results depict the changing nature of extreme precipitation within the SGP and differences in extreme precipitation between the eastern and western SGP.

Extreme precipitation events are challenging to local and regional stakeholders across the United States. The Missouri River Basin (MoRB), covering an area over 1.29 million km², is prone to extreme precipitation events. These events are exacerbated by the complex terrain in the west and the numerous weather and climate features which impact the region on a seasonal/annual basis (low-level jets, mesoscale convective systems, extreme cold air intrusions, etc.). Without an in-depth analysis of extreme precipitation in the MoRB, the evolving nature of extreme precipitation is not known. This warrants an analysis of extreme precipitation, especially relating to sub-annual variations when extreme precipitation is more impactful. To this end, data from 131 United States Historical Climatology Network (USHCN) stations were used to determine the nature of extreme precipitation from 1950 – 2019. Annual 99th percentile and annual station maximum precipitation events occur more frequently in the eastern MoRB than in the western MoRB, in line with the annual precipitation climatology. Results show that 99th percentile events and annual station maximum precipitation events are becoming more frequent across the MoRB. Through analysis of 3-month extreme precipitation trends, areas in the eastern and southern MoRB are shown to have an increasing event frequency and intensity. Frequency shifts in the 99th percentile events, however, have occurred across the entire region. The increasing frequency of extreme events in the western MoRB represent a significant change for the hydroclimate of the region. Overall, the results from this work show that MORB extreme precipitation has increased in frequency and intensity during the 1950 – 2019 period.

Land-use land-cover change (LULCC) has become an important topic of research for the central United States because of the extensive conversion of the natural prairie into agricultural land, especially in the northern Great Plains. As a result, shifts in the natural climate (minimum/maximum temperature, precipitation, etc.) across the north-central United States have been observed, as noted within the Fourth National Climate Assessment (NCA4) report. Thus, it is necessary to understand how further LULCC will affect the near-surface atmosphere, the lower troposphere, and the planetary boundary layer (PBL) atmosphere over this region. The goal of this work was to investigate the utility of a new future land-use land-cover (LULC) dataset within the Weather Research and Forecasting (WRF) modeling system. The present study utilizes a modeled future land-use dataset developed by the Forecasting Scenarios of Land-Use Change (FORE-SCE) model to investigate the influence of future (2050) land use on a simulated PBL development within the WRF Model. Three primary areas of LULCC were identified within the FORE-SCE future LULC dataset across Nebraska and South Dakota. Variations in LULC between the 2005 LULC control simulation and four FORE-SCE simulations affected near-surface temperature (0.5°–1°C) and specific humidity (0.3–0.5 g kg⁻¹). The differences noted in the temperature and moisture fields affected the development of the simulated PBL, leading to variations in PBL height and convective available potential energy. Overall, utilizing the FORE-SCE dataset within WRF produced notable differences relative to the control simulation over areas of LULCC represented in the FORE-SCE dataset.

In the U.S. Great Plains (GP), diagnosing precipitation variability is key in developing an understanding of the present and future availability of water in the region. Building on previous work investigating U.S. GP pluvial years, this study uses ERA twentieth century (ERA-20C) reanalysis data to investigate key circulation anomalies driving GP precipitation anomalies during a subset of GP pluvial years (called in this paper Pattern pluvial years). With previous research showing links between tropical Pacific sea surface temperature (SST) anomalies and GP climate variability, this study diagnoses the key circulation anomalies through an analysis of SSTs and their influence on the atmosphere. Results show that during Pattern southern Great Plains (SGP) pluvial years, central tropical Pacific SST anomalies are coincident with key atmospheric anomalies across the Pacific basin and North America. During northern Great Plains (NGP) Pattern pluvial years, no specific pattern of oceanic anomalies emerges that forces the circulation anomaly feature inherent in specific NGP pluvial years. Utilizing the results for SGP pluvial years, a conceptual model is developed detailing the identified pathway for the occurrence of circulation patterns that are favorable for pluvial years over the SGP. Overall, results from this study show the importance of the identified SGP atmospheric anomaly signal and the potential for predictability of such events.

Precipitation variability has increased in recent decades across the Great Plains (GP) of the United States. Drought and its associated drivers have been studied in the GP region; however, periods of excessive precipitation (pluvials) at seasonal to interannual scales have received less attention. This study narrows this knowledge gap with the overall goal of understanding GP precipitation variability during pluvial periods. Through composites of relevant atmospheric variables from the ECMWF twentieth-century reanalysis (ERA-20C), key differences between southern Great Plains (SGP) and northern Great Plains (NGP) pluvial periods are highlighted. The SGP pluvial pattern shows an area of negative height anomalies over the southwestern United States with wind anomalies consistent with frequent synoptic wave passages along a southward-shifted North Pacific jet. The NGP pattern during pluvial periods, by contrast, depicts anomalously low heights in the northwestern United States and an anomalously extended Pacific jet. Analysis of daily heavy precipitation events reveals the key drivers for these pluvial events, namely, an east–west height gradient and associated stronger poleward moisture fluxes. Therefore, the results show that pluvial years over the GP are likely driven by synoptic-scale processes rather than by anomalous seasonal precipitation driven by longer time-scale features. Overall, the results present a possible pathway to predicting the occurrence of pluvial years over the GP and understanding the causes of GP precipitation variability, potentially mitigating the threats of water scarcity and excesses for the public and agricultural sectors.