1. Introduction
The interactions between water and climate are varied and complex. In the southern Great Plains (SGPs), water is crucial to the success of the agricultural practices in the region (Terrell et al. 2002; Illston et al. 2004; Baumhardt and Salinas-Garcia 2006; Flanagan et al. 2017; Lollato et al. 2017; Ghimire et al. 2018; Maulana et al. 2019; Li et al. 2022), and further the short- and long-term health and socioeconomic success of the population (Baumhardt and Salinas-Garcia 2006; Basara et al. 2013; Lollato et al. 2017; Dhakal et al. 2018; Singh et al. 2023). The interactions between water and climate are becoming even more important as shifts in the regional climate are driving increases in the frequency of climate extremes, such as heatwaves, flash drought, and extreme precipitation events (e.g., Basara et al. 2013; Gherardi and Sala 2019; Hayhoe et al. 2018; Flanagan and Mahmood 2023). In addition, agriculture in the western SGP is highly dependent on water from the Ogallala Aquifer, an underground freshwater source that extends from South Dakota all the way to Texas across the western reaches of the Great Plains (Fig. 1a; Terrell et al. 2002; McGuire et al. 2003; Ghimire et al. 2018; Kukal and Irmak 2018). Given that annual precipitation in the western region of the SGP is typically insufficient to grow the crops planted, irrigation is essential to successfully grow cash crops in the western SGP (McGuire et al. 2003; Basso et al. 2013; Kloesel et al. 2018; Araya et al. 2019). This is further exacerbated by the frequency of heatwaves and drought in the region (Englehart and Douglas 2003; Basara et al. 2013; Kloesel et al. 2018; Tavakol et al. 2020), thus making this issue more complex and urgent.
(a) Station plot of the average ADI across the SGP stations analyzed. The hatched area represents the winter wheat region of the SGP, and the blue outline is the boundary of the Ogallala Aquifer. The station plot displays the average date of daily maximum precipitation (left triangle) and the date of maximum daily maximum temperature (right triangle) for (b) positive ADI GSs and (c) negative ADI GSs across the SGP, with the winter wheat region outlined by the hashed area. For reference, day 120 is 29 Apr and day 245 is 1 Sep.
Citation: Earth Interactions 28, 1; 10.1175/EI-D-23-0019.1
Climate variables not only impact agriculture in terms of water but can directly impact the plants and soil themselves. Temperature, for example, has shown a systemic increase across the globe including the SGP (e.g., Kristensen et al. 2011; Gowda et al. 2018; Kloesel et al. 2018). Warmer temperatures earlier in the growing cycle of plants can negatively impact the overall health of the crop and the resultant yield (Holman et al. 2011; Kristensen et al. 2011; Barlow et al. 2015; Rezaei et al. 2015; Lollato et al. 2017; Dhakal et al. 2018; Maulana et al. 2019). For example, warm temperatures earlier in the year can cause early plant growth and maturity, increasing the time at which a plant requires its maximum water uptake and finally reducing yields (Holman et al. 2011; Kristensen et al. 2011; Lollato et al. 2017). On the other hand, precipitation, while clearly an overall beneficial meteorological phenomenon for agriculture, can have negative impacts on crops as well. Too much precipitation can negatively impact crops by flooding the fields and destroying seeds or seedling plants (Chau et al. 2015; Flanagan et al. 2020; Shirzaei et al. 2021; Yildirim and Demir 2022) or by impacting soil health (Thorup-Kristensen et al. 2009; Frank et al. 2015; Gowda et al. 2018; Shirzaei et al. 2021). Given the links between heavy precipitation and severe weather in the winter wheat growing region of the United States, the impact of these storms can also be seen from the damage caused by other storm-related factors such as agricultural damage due to hail (Changnon and Stout 1967; Vercammen and Pannell 2000). Further, the timing of precipitation can impact crop health as well. Too much time between precipitation events (even if sufficient water is available through previous precipitation) can negatively impact soil health (Fay 2009; Frank et al. 2015; Peltonen-Sainio et al. 2016). If precipitation events occur too far apart, nitrogen availability is decreased (Fay 2009; Thorup-Kristensen et al. 2009) which impacts the overall plant growth and soil health.
Winter wheat in the SGP is extensive, with over 9 million hectares planted each year accounting for approximately 30% of the total U.S. wheat production (Lollato et al. 2017). Unlike most crops, which have a growing season contained to the local warm season, winter wheat is planted in the fall and begins growing before falling into dormancy during the winter. Given this, sufficient soil moisture is required during the summer and fall seasons to produce sufficient seedling and tiller growth prior to the first freeze (Bruns and Croy 1983). After the dormant winter period, winter wheat continues to grow in the SGP spring and early summer. During the spring and early summer periods, warm temperatures with sufficient precipitation (around 250 mm; Thapa et al. 2019) are required for winter wheat to reach full maturity (Salazar-Gutierrez et al. 2013; Patrignani et al. 2014; Lollato et al. 2017; Zhao et al. 2022). The resultant wheat crop is harvested after maturity has been reached (once grain filling is complete) approximately in late June to early July (Beuerlein 2001). Due to the gradient of annual precipitation across the SGP, eastern winter wheat production is reliant on sufficient solar radiation to maximize wheat yields, while wheat yields in the western portion of the SGP are more strongly connected to the variability of precipitation (Lollato et al. 2017). Further, eastern SGP winter wheat yields are more connected to early wheat growth precipitation (planting to anthesis; Lollato et al. 2017), and western SGP winter wheat is more dependent on later precipitation (anthesis to maturity; Lollato et al. 2017). Still, the amount of precipitation received from October (planting) to June/July (harvest) is important for a successful SGP winter wheat crop (Patrignani et al. 2014; Lollato et al. 2017). In addition to fluctuations in precipitation, anomalously hot temperatures between anthesis (typically May–June) and maturity (late June or early July) can speed up plant growth and limit grain filling (Hu et al. 2005; Lollato et al. 2017; Ren et al. 2019; Zhao et al. 2022). Overall, understanding the SGP climate and its variability is critical to the continued success of winter wheat production in the future SGP climate.
Flanagan et al. (2017) investigated and quantified the Great Plains climate in terms of temperature and precipitation maxima during the growing season (GS; March–August). Through this study, they identified that there was a significant gap between the dates of daily maximum temperature and precipitation maxima during the GS. The end goal of their study was to identify and quantify the difference in the date of the peak GS daily maximum temperature and the date of the largest GS precipitation total for each GS and to determine if that difference was changing. This was done through their newly developed index, the asynchronous difference index (ADI), which quantified the difference between the dates of daily maximum temperature and precipitation (ADI = DateTemp − DatePrec) during the GS. This has implications on water stress given that the highest precipitation totals typically occur in the mid- to late spring and then precipitation totals decrease during the summer months. Thus, the increased temperatures typically found in the Great Plains summer increase the plant water demand and, without adequate precipitation, quickly deplete water resources and increase the chance of (rapidly) developing drought conditions (flash drought; Otkin et al. 2018). Their results showed that statistically significant trends in ADI were not identified in Texas or Oklahoma, while Kansas showed a statistically significant reduction in ADI from 1895 to 2015, meaning that the dates of daily maximum temperature and precipitation drew closer together. Further, through a statistical analysis of ADI throughout all years for each state, Flanagan et al. (2017) identified two different regimes of ADI, or positive and negative ADI GSs. They showed that positive ADI GSs were associated with an early GS precipitation maximum and a summer daily maximum temperature maximum, while negative ADI years were associated with a late GS daily precipitation maximum and an earlier daily maximum temperature maximum (Figs. 1b,c). However, as their focus was on statewide statistics and trends, their study did not investigate the spatiotemporal evolution of precipitation or temperature during positive or negative ADI years; thus, the hydroclimatic impact of positive and negative ADI regimes was not determined, only theorized.
The overall goal of this study is to further investigate ADI across the winter wheat region of the SGP. The Flanagan et al. (2017) methodology will be utilized to quantify and identify positive and negative ADI GSs, and then, this dataset will be used to investigate the nature of precipitation and temperature during these different ADI regimes. The hope of this work is to identify any specific differences during positive and negative ADI GSs that impact the planting, growth, and harvest of winter wheat in the SGP. Further, given that shifts in the ADI were identified in Kansas (decrease, statistically significant) and Oklahoma (increase, not statistically significant), this study hopes to put those identified ADI changes in the context of their impacts to the GS of the SGP, namely, for the winter wheat crop prevalent in these two states.
2. Data and methods
a. Station data
Global Historical Climatology Network-Daily data (GHCN-Daily; Menne et al. 2012) was used to facilitate the analysis of SGP precipitation and temperature maxima. Only stations that were contained in the states of Texas, Oklahoma, Kansas, and eastern portions of Colorado and New Mexico were included within this analysis. Further, a subset of the SGP stations were used to solely focus on the winter wheat agricultural region of the SGP. To do this, the USDA winter wheat production map (available at https://ipad.fas.usda.gov/rssiws/al/crop_production_maps/US/USA_Winter_Wheat.png) was used to subjectively define an area across the SGP domain that contained the primary areas of winter wheat production. Using the USDA information, the boundary for the winter wheat region was set to be any station east of 103°W and west of 97°W, as well as north of 32°N (Fig. 1). A northern boundary limit was not used as the winter wheat region ends roughly at the Kansas/Nebraska border and only one station, located in eastern Colorado (most northwestern station in the hatched area), was used that is located north of the Kansas/Nebraska border.
Once the total list of all SGP stations was identified, stations were removed if their period of record was insufficient. In other words, only stations with a noted period of record, within the station metadata, of over 90 years (between 1900 and 2020) were kept for this study. Thus, any station used had to have been in operation no later than 1930 and have produced data up to, at least, 1990 (i.e., 1900–90, 1910–2000, 1920–2010, or 1930–2020). This period of record requirement was used so that stations with short periods of record were not included to create dataset that was as statistically robust as possible. Note that a requirement for a continuous dataset was not used for this study; thus, data may not be available at each of the used stations for every single year between the station period of record start and end dates. However, given that the results of temporal and spatial aspects of the study agree, this likely did not have any discernable impact on the spatial results shown in this study. With this filtering criterion, there were a total of 143 SGP stations left, with 66 stations analyzed for the winter wheat region of the SGP.
As winter wheat agriculture was the focus of this study, the SGP GS, or from March to September, was used. While winter wheat is typically harvested in June and July in the SGP (USDA-NASS 2010), the precipitation that occurs after harvest is important for the next year’s wheat crop or any cover crop planted after harvest and thus was still analyzed. However, as the study goes through the month of September, historic tropical cyclone events that impacted the Texas coastline were removed from all stations. This was done owing to the rarity of the events, and the fact that tropical cyclone events are not linked to the SGP climatology that is the focus of this paper (i.e., they are very rare for the region and can heavily skew a specific year’s ADI value). A list of tropical cyclone events which impacted the Texas coastline was identified using a National Weather Service Galveston office list of tropical cyclone events (available at https://www.weather.gov/hgx/hurricanes_climatology). Further, an investigation of the top 10 impactful tropical cyclone events to the SGP was completed through various databases, and the dates of those storms were also removed. In total, 149 dates (Table 1) were removed from the dataset owing to an influence from tropical cyclone precipitation.
Dates of all tropical cyclone events that were removed (set to 0.0) from the times series of each USHCN station within the study. Data were gathered from the National Weather Service Galveston office’s list of tropical cyclone events to impact the coastline of Texas (available at https://www.weather.gov/hgx/hurricanes_climatology). Further, the top 10 precipitation or damaging tropical cyclones within the SGP (not on the previous list) were also removed. Note that a lack of records is likely impacting the number of identified cyclones during the earlier periods and that this list is by no means exhaustive, just the best possible list using the data available.
b. Asynchronous difference index
The computed ADI was then used to identify positive and negative ADI GSs from all 66 stations within the SGP winter wheat agricultural area. Daily precipitation totals and daily maximum temperatures across each GS were used to compute relevant statistics and spatial composites (i.e., average time series for positive and negative ADI GSs, histograms, and statistical significance). Further, county-level winter wheat yields from the National Agricultural Statistics Service (NASS) were gathered for the longest available time span within the data for Kansas, Oklahoma, and Texas (1968–2007). Typically, each county was not collocated with a specific station used within this study, as such, the station with the shortest great circle distance [calculated by using “gc_latlon” function within the NCAR Command Language (NCL)] to the center of each county (as depicted within the https://simplemaps.com/data/us-counties county dataset) was used to calculate ADI for each county. After this, ADI was separated into positive and negative ADI years and used to compute relevant statistics of winter wheat yield for each county. To test the difference between the positive and negative ADI distributions, the Kolmogorov–Smirnov two-sample test was used with a significance value of 95% (p value under 0.05). The Kolmogorov–Smirnov two-sample test is a nonparametric statistical significance test which test the chances that two probability distributions are from the same (but unknown) distribution. Unlike Flanagan et al. (2017), who focused on statewide trends of ADI, this study will focus on the distribution of daily maximum temperature and daily precipitation during positive and negative ADI GSs. Given the analyzed trends in temperature and precipitation totals across the region (Kukal and Irmak 2016; Marvel et al. 2023), an analysis of the stationarity of station average (averaged across all stations for each day across each growing season) daily maximum temperatures and precipitation totals was completed (not shown). This analysis depicted no discernible regional growing season trend in either daily maximum temperature or daily precipitation totals across the entire 1900–2020 period.
3. Results
a. Distribution of ADI
The ADI distribution in the SGP winter wheat area follows an approximately normal shape, with a sharp decrease near 0 and 2 peaks near −30 and 55 days with an average value of ∼25 days (Fig. 2a). The histogram peak in the negative ADI values being larger in magnitude than the peak in positive ADI is due to a small number (11) of stations which have a larger frequency of negative ADI events (∼50%–60% negative ADI events over all years). When these stations are removed (not shown), the peak in the distribution is at the same ADI value (∼55 days) with a smaller peak in negative ADI. The ADI distribution is skewed to the right owing to the higher number of events above the 100 ADI mark compared to the abrupt drop-off of events after the −95 ADI bin. This is due to the spread seen in the date of the precipitation maximum distribution (Fig. 2b). While precipitation maxima can occur on every day from March to September, far more precipitation maxima occur later (September, ∼days 240–270) in the GS compared to earlier (March, ∼days 60–90), giving rise to the negative ADI peak. Given that the distribution in the date of temperature maxima (Fig. 2c) has less variability compared to the date of maximum daily precipitation, the distribution in ADI is almost solely due to the date of highest precipitation total in each GS. In total, the SGP winter wheat region shows that 39.5% of the total ADI observations indicate a negative ADI (2690 negative ADI events out of 6807 observations). Compared to the ∼25% of GSs that showed a negative ADI in Flanagan et al. (2017), this is nearly a twofold increase in negative ADI periods in the SGP winter wheat region. Given this, it is worth investigating how late GS precipitation maxima, and the resultant negative ADI value, impact the distribution of precipitation and temperature throughout the SGP GS.
Histograms of (a) GS ADI from all USHCN stations within the SGP winter wheat area with the x axis representing ADI (days), (b) GS date of daily maximum precipitation from all SGP winter wheat stations with the x axis representing the day, and (c) GS date of daily maximum temperature from all SGP winter wheat stations with the x axis representing the day with each bin representing 5 days. For (b) and (c), day 60 represents 1 Mar and day 270 represents 26 Sep (end of September).
Citation: Earth Interactions 28, 1; 10.1175/EI-D-23-0019.1
b. Spatiotemporal evolution of GS precipitation and temperature
Since ∼40% of the observed ADI values were below 0, it is worth investigating the impact to the rest of the GS to have the maxima of precipitation later in the GS. The average daily precipitation totals across all positive ADI GSs show (Fig. 3a) an average to above average (in reference to the black curve in Fig. 3a or the climate normal) amount of daily precipitation from the start of the GS until around day 210 [28/29 July (leap year)], at which point the average daily precipitation total decreases to below the climate normal. This matches closely with what is climatologically expected, more precipitation earlier in the year with a drier period during the summer. The average time series for negative ADI GSs is very similar in the earlier growing season, with daily precipitation values at average to below average through the spring. Around day 120 (beginning of May), the distribution trends toward the below normal distribution and remains there through the remainder of spring and early summer. Around day 190 [8/9 July (leap year)], this trend changes dramatically. Past day 190, the negative ADI precipitation time series shows a large increase in the daily precipitation total, going from an average of 1.8 mm per day to a peak average of over 3.5 mm per day. This increase in the average daily precipitation puts it on par with the average totals seen during later summer wet years.
Time series plot of (a) 5-day running average daily precipitation totals (mm) from all USHCN stations within the SGP winter wheat area and (b) 5-day running average daily maximum temperature (°C) from all USHCN stations within the SGP winter wheat area. In both plots, the green dashed curve represents the average of all above average GSs, the brown dashed curve represents the average of all below average GSs, the red curve is the average across all positive ADI GSs, the blue curve is the average across all negative ADI GSs, and the black curve is the average across all GSs (climate normal). Above and below average refer to GSs in which the GS average of daily maximum temperature or daily precipitation total was above the climatological average for that station’s GS. Time series starts at day 60 (1 Mar) and ends on day 274 (30 Sep).
Citation: Earth Interactions 28, 1; 10.1175/EI-D-23-0019.1
Not as dramatic as precipitation, the daily maximum temperature (Fig. 3b) also shows a change before and after day 190 for positive and negative ADI GSs. This is expected, as the precipitation totals detailed above would cause a shift in the surface energy balance leading to more sensible heating after day 190 as precipitation slowed down during positive ADI GSs. This is reflected in a shift in the average daily maximum temperature for positive ADI GSs before and after day 190. Before day 190, positive ADI daily maximum temperatures were around average. However, around day 190, the first hints of the above average daily temperature regime can be seen, as this is the first point at which the positive ADI average daily maximum temperature curve is consistently above the climatological normal. After day 200 [18/19 July (leap year)], the positive ADI daily maximum temperatures do not go below average for the rest of the GS, detailing an increase in daytime daily maximum temperatures and thus water stress on plants. A negative ADI GS average daily maximum temperature shows nearly the exact opposite trend. The two curves (positive and negative ADI average daily maximum temperatures) approximately follow each other along the climatological normal curve before day 200 and then abruptly the negative ADI daily maximum temperatures shift to below average, and at points mirroring the below normal daily maximum temperature curve. However, statistical significance testing did not show that these daily maximum temperature distributions were different from each other, either before (p value 0.999) or after (p value 0.067) day 190.
Spatially, the temperature and precipitation results are much the same as the temporal analysis. Prior to day 190, more precipitation (primarily in the 50–100-mm average per GS difference range across the region) occurs across the SGP winter wheat region in positive ADI GSs compared to negative ADI GSs (Fig. 4a). After day 190, the opposite result is seen, with larger precipitation totals (50–150-mm average per GS difference range across the region) analyzed during negative ADI GSs (Fig. 4b). Further, the widespread statistical significance in the spatial precipitation results drives the point that the primary difference between positive and negative ADI GSs in the SGP winter wheat region is the regionwide shift in precipitation across the two separate GS types. As for daily maximum temperature (Figs. 4c,d), the results are much less distinct. Neither the before (Fig. 4c) or after (Fig. 4d) day 190 difference plot shows large-scale statistical significance within the SGP winter wheat region. Still, the stations showing statistical significance do match the time series analysis, with positive (negative) ADI GSs showing lower (higher) daily maximum temperatures before day 190 and higher (lower) daily maximum temperatures after day 190. Interestingly, the spatial patterns of statistically significant stations shift when comparing the before and after day 190 analyses. Before day 190, statistical significance is solely near and west of 100°W longitude, while after day 190 statistical significance is primarily located east of 100°W longitude and in the farthest western stations in the SGP winter wheat area (Colorado and western Texas).
SGP winter wheat region USHCN station plots of (a) day 61–190 total precipitation differences, (b) day 190–274 total precipitation differences, (c) day 61–190 average daily maximum temperature differences, and (d) day 190–274 average daily maximum temperature differences. Differences were computed by subtracting the average composites for positive ADI periods vs negative ADI periods (positive ADI − negative ADI). Units for (a) and (b) are in millimeters; units for (c) and (d) are in degrees Celsius. Statistical significance is denoted by color-filled circles (p value of 0.05), with black circles and a color ring denoting stations without statistically significant differences.
Citation: Earth Interactions 28, 1; 10.1175/EI-D-23-0019.1
A look into the distribution of precipitation totals and average daily maximum temperatures was completed in the form of box plots (Fig. 5). Before day 190 (Spr in Fig. 5), positive ADI GSs depict higher precipitation totals on average, with less spread, while negative ADI GSs show large variability in total precipitation amounts, with a lower mean value compared to the positive ADI distribution. After day 190 (Smr in Fig. 5), the positive ADI GSs show reduced precipitation totals compared to negative ADI GSs (after day 190), which depict nearly the same precipitation amounts as occurred prior to day 190. While the distributions depict a change in overall total precipitation amounts after day 190, positive ADI GSs still show reduced variability when compared to the spread of the distribution of negative ADI precipitation totals. While the tails of the distribution, the distance between the highest and lowest quartiles and the maximum and minimum, respectively, do overlap, the bulk of the distribution, between the upper and lower quartiles, shows no overlap either before or after day 190 when comparing positive and negative ADI GS Spr/Smr total precipitation. The daily maximum temperature distribution (Fig. 5b) depicts the same differences seen in the time series analysis in Fig. 3b. Before day 190 (early July), the two temperature distributions are nearly identical, with a slight increase in the center of the distribution in negative ADI GSs compared to positive ADI GSs. After day 190, the two distributions are further apart, with only a small amount of overlap in the interquartile range. Thus, after day 190, negative ADI GSs are, on average, slightly cooler than positive ADI GSs.
Box plots of (a) total precipitation (mm) accumulated over spring {Spr; before day 190 [9/8 Jul (leap year)]} and summer (Smr; after day 190) and (b) average daily maximum temperature averaged over Spr (before day 190) and Smr (after day 190) for all USHCN stations within the SGP winter wheat area. Pos and Neg represent positive ADI and negative ADI, respectively.
Citation: Earth Interactions 28, 1; 10.1175/EI-D-23-0019.1
Owing to the abrupt shifts in daily maximum temperature and daily precipitation that occur when analyzing their time series, it was determined that a month-to-month analysis of daily maximum temperature and total precipitation was warranted. While the analysis was completed from March through September, the results for March, April, and July showed little to no statistical significance and thus are not discussed here or shown. The monthly total precipitation difference plot for May (Fig. 6a) and June (Fig. 6b) both show that positive ADI GSs produce more precipitation during May/June compared to negative ADI GSs in a large swath of the SGP winter wheat region. Although it should be noted that the results in June are not as widespread as in May, with far fewer statistically significant monthly total precipitation differences. During August (Fig. 6c) and September (Fig. 6d), the results flip, with widespread statistically significant negative differences showing that more precipitation occurs in August and September during negative ADI GSs compared to positive ADI GSs. As with the previous analyses, the results for daily maximum temperature are not as widespread or statistically significant as for precipitation. While results do show that the primary statistically significant signal is for cooler (warmer) daily maximum temperatures in May (Fig. 7a) and June (Fig. 7b) and warmer (cooler) daily maximum temperatures in August (Fig. 7c) and September (Fig. 7d) during positive (negative) ADI GSs, these results are not widespread compared to the month-to-month results for precipitation.
SGP winter wheat region USHCN station plots of total precipitation differences (positive ADI − negative ADI) for the months of (a) May, (b) June, (c) August, and (d) September. Units are in millimeters. Statistical significance is denoted by color-filled circles (p value of 0.05), with black circles and a color ring denoting stations without statistically significant differences.
Citation: Earth Interactions 28, 1; 10.1175/EI-D-23-0019.1
SGP winter wheat region USHCN station plots of average daily maximum temperature differences (positive ADI − negative ADI) for the months of (a) May, (b) June, (c) August, and (d) September. Units are in degrees Celsius. Statistical significance is denoted by color-filled circles (p value of 0.05), with black circles and a color ring denoting stations without statistically significant differences.
Citation: Earth Interactions 28, 1; 10.1175/EI-D-23-0019.1
c. Winter wheat yields
With the focus of this work on impacts of ADI to the winter wheat GS, an analysis of winter wheat yields in relation to ADI is necessary. State-level winter wheat yields and statewide composite ADI time series show very little correlation (correlations ranged from 0.09 to 0.12 for both positive and negative ADIs with statewide winter wheat differences near 0.0). However, when winter wheat yields are separated into county-level statistics, different results are seen. A statistically significant winter wheat yield difference (Fig. 8) occurs between positive and negative ADI GSs across most of central and western Oklahoma, along with central Kansas. While sporadic negative differences are seen (meaning more winter wheat during negative ADI years), there does not appear to be an overall pattern to these negative differences across the winter wheat region. Additionally, the entire Texas Panhandle within the winter wheat boundary considered was removed owing to a lack of statistical significance in the results, even with winter wheat yields (not shown) in that region being consistently higher than in the dryland areas of Oklahoma, Kansas, and the eastern portions of Texas. Since the regionwide average winter wheat yield is approximately 23 BU per acre, small differences of 1–2 BU per acre represent approximately 5%–10% of the average annual total yield, and thus, the small (2–6 BU per acre) differences seen when comparing positive and negative ADI GSs are impactful.
County-level winter wheat yield (bushel per acre; data downloaded from the NASS archive) differences for positive and negative ADI years (positive ADI − negative ADI) from 1968 to 2007. Counties with color show statistically significant differences with a p value less than 0.05, while white counties do not show statistical significance. Black dots depict the locations of the stations used to calculate ADI across the region. The station used to calculate ADI for each county was chosen based on the shortest great circle distance between the latitude/longitude of the center of the county and the latitude/longitude of each station used. The latitude/longitude values for each county were gathered from https://simplemaps.com/data/us-counties.
Citation: Earth Interactions 28, 1; 10.1175/EI-D-23-0019.1
4. Discussion
Flanagan et al. (2017) noted that positive and negative ADI GSs were distinctly different owing to the identified average dates of maxima during positive and negative ADI GSs [Table 1 in Flanagan et al. (2017)]. They further stated that lower ADI GSs (less time between the maxima of precipitation and temperature) would be beneficial as this would theoretically mean more soil moisture during peak water demand owing to the reduced time between the two maxima. Increases in ADI are more complex, as shifting the dates of maximum precipitation and temperature further apart could both be beneficial and harmful to agriculture. For example, an earlier date of maximum precipitation could provide additional moisture for early plant growth prior to temperatures beginning to seasonally increase; at the same time, an earlier date of the precipitation maximum could mean a prolonged period of water stress as drier periods set in before the middle of summer when daytime temperatures maximize. However, without knowledge of the temporal evolution of precipitation and maximum temperature during positive and negative ADI GSs, firm conclusions on the impact of increasing or decreasing ADI are difficult. Results from this work show that not only are positive and negative ADI GSs distinctly different in the timing of precipitation and temperature maxima, the daily evolution of temperature and primarily precipitation are notably different as well.
Positive and negative ADI GSs were analyzed, prior to day 190 (early July), with similar time series of daytime daily maximum temperature. While some small discrepancies were identified in the two daily maximum temperature time series, more above average temperature days for negative ADI and below average temperature days for positive ADI, no statistically significant difference could be seen between the two different ADI distinctions with regard to daily maximum temperatures. However, after day 190, negative ADI GSs showed cooler daytime daily maximum temperatures compared to positive ADI GSs. Spatially, results were like the time series analysis. Statistically significant daily maximum temperature differences before day 190 showed that positive (negative) ADI GSs were cooler (warmer) in May and June, with the opposite being true in August and September. Thus, not only does negative ADI GSs show a later precipitation maximum, this later precipitation maxima also consistently shifts the later GS daytime daily maximum temperatures downward such that the GS maxima of daytime daily maximum temperatures have less of a chance of happening after day 190. This is a result that was seen in the Flanagan et al. (2017) analysis, as the average date of GS temperature maxima of negative ADI was earlier in the year compared to positive ADI GSs.
Precipitation, on the other hand, depicts much more concise results. The time series plot (Fig. 3a) shows a split in the daily average precipitation that occurs in early July between positive and negative ADI GSs. This split is highlighted by the above (below) normal precipitation before day 190 and below (above) normal precipitation after day 190 for positive (negative) ADI GSs, a result that was statistically significant in both the time series and spatial analyses. In fact, the spatial analyses (both month-to-month and before/after day 190) showed a regionwide pattern of statistical significance in total precipitation differences when comparing positive and negative ADI GSs in the winter wheat region. While Flanagan et al. (2017) showed that the date of daily maximum temperature was the driver of ADI trends, these results show that the temporal evolution of GS precipitation is the primary driver of differences in year-to-year ADI.
The differing temporal evolutions of precipitation between positive and negative ADI GSs would alter the seasonality of soil moisture and distinctly impact winter wheat in different ways. Positive ADI GSs depict the climatological pattern of precipitation that the SGP is known for, in other words, a wet spring and a drier summer (e.g., Illston et al. 2004; Flanagan et al. 2017; Mauget et al. 2020). This is the primary reason that winter wheat is one of the most abundant crops grown in this region, as winter wheat is typically planted in the fall and then remains relatively dormant until the early spring where it takes advantage of the spring precipitation and warmth to rapidly grow and mature (Thorup-Kristensen et al. 2009; Holman et al. 2011). Then, winter wheat is harvested in June and July before being stressed by the hot temperatures notable in the SGP summer. The positive ADI temperature and precipitation regime, occurring in approximately 60% of GSs, represent a climate that can support this crop and explain the climatological reasons why many other cash crops cannot be grown in this region without abundant irrigation, which is exemplified by the larger winter wheat yields shown in Fig. 8 during positive ADI GSs. This is further proven due to the positive differences seen in Fig. 8 being in regions (central Kansas and Oklahoma) with predominantly dryland production of winter wheat (little to no irrigation) compared to the lack of results in the Texas Panhandle which is likely due to the large reliance on irrigation water in that region. In fact, irrigation could explain the scattering of negative winter wheat yield differences seen in some counties. If irrigation were to be applied in the early growing season (May and June) each year for a specific winter wheat area, then more consistent yield totals would be expected GS to GS, and thus, the warmer temperatures earlier in the GS during negative ADI years could serve to enhance winter wheat yields if enough irrigation water was applied. Given that most crops are planted in the spring and harvested in the late summer and early fall, an SGP-positive ADI environment (wet spring, hot and dry summer) would make growing other cash crops more difficult. This is because the portion of the year that most cash crops are reaching maturity happens to be when the positive ADI GS depicts less precipitation and warmer temperatures. Thus, the maturing crops would be subject to harsh atmospheric conditions and a relative minimum of soil moisture.
On the other hand, negative ADI GSs are not ideal for maximizing winter wheat harvests given that negative ADI GSs are linked to an earlier shift of the warmest GS temperatures. On average, negative ADI GS temperatures are warmest around day 190 (Flanagan et al. 2017), which is around the time of winter wheat harvest. Thus, during a negative ADI year, the chances of above average temperatures just before and during harvest are increased. While this is not as problematic as warmer temperatures during the seedling and heading stages of winter wheat growth, the warmer temperatures in June and July would impact the maturity phenostage and thus negatively impact yields, which is shown by decreased county-level winter wheat county yields during negative ADI years in Fig. 8. Further, given that negative ADI springs are more variable (compared to positive ADI springs) in terms of precipitation, less water is likely available during the early summer for the mature crops’ growth, and thus, the increased temperatures earlier in the year would increase water stress on the crop. Last, while not directly impacting that season’s winter wheat crop, the above average precipitation [after day 190 (early July)] noted during negative ADI GSs could negatively impact postharvest soil in June and July. Heavy rainfall events on soils with less plant matter cause an increase in surface runoff, thus decreasing the replenishment of soil moisture necessary for the next crop, along with leaching valuable nutrients from the soil that are needed for the next crop to be successful. Overall, negative ADI years depict an environment that is not conducive for a healthy and maximized winter wheat crop without additional resources or proper management of the agricultural land. Not only does it impact the current GS, but the heavier rainfall during the summer could impact the tilling of the field and the timing of planting the next crop as the fields could be inundated with additional water. While only representing 40% of the analyzed GSs, negative ADI GSs represent the harsh years that winter wheat stakeholders need to be prepared for.
Lastly, it would be remiss to not mention linkages of ADI to large-scale features of the climate system and the central U.S. hydroclimate. However, the results computed from station-based ADI within this study have shown little to no regional correlations to any climate system drivers (ENSO, PDO, PNA, Gulf of Mexico sea surface temperatures, southwestern U.S. summer Monsoon, etc.) that were tested within this work. While a station-by-station analysis (not shown) did show individual stations with larger (>0.3) correlation values to different climate indices, these larger correlations were not widespread and could not reliably be used to explain the variability of ADI within the SGP winter wheat region. While further analysis and investigation on ADI need to be completed, these results show that the SGP winter wheat region ADI variability is predominantly localized, and thus, the ADI within the SGP winter wheat region is primarily of diagnostic utility. However, the results of this work show that the primary differences between positive and negative ADI GSs occur during the summer period; a likely source of variability impacting daily precipitation and maximum temperature variability (especially in the western extent of the winter wheat region in the SGP) is the North American Monsoon. As the monsoon is the dominant form of precipitation variability in the southwestern U.S. summer period (Adams and Comrie 1997; Vera et al. 2006), it is worth investigating further in future work.
5. Conclusions
The goal of this study was to investigate ADI across the SGP winter wheat region and quantify the GS temporal evolution of daily maximum temperature and daily precipitation in relation to the positive and negative ADI signals analyzed in Flanagan et al. (2017). To this end, GHCN data were used with the methodology designed in Flanagan et al. (2017) to identify positive and negative ADI GSs within the SGP winter wheat region and to subset the daily data for analysis. Primarily, this meant identifying the dates of maximum daytime temperature and daily precipitation totals throughout the GS (March–September) and then analyzing the distribution and time series of daytime daily maximum temperature and daily precipitation totals for the identified positive and negative ADI GSs.
The primary findings of this work are that positive and negative ADI GSs show distinctly different evolutions of daily maximum temperature and daily precipitation totals throughout the SGP winter wheat region. Positive ADI GSs display average to above average precipitation with average daily maximum temperatures before day 190 (early July) and below average precipitation and increased daily maximum temperatures after day 190. Negative ADI GSs are the opposite, with increased daily maximum temperatures and average to below average precipitation before day 190 shifting to decreased daily maximum temperatures and above average precipitation after day 190. These differing evolutions of temperature and precipitation depict two distinctly different sets of environmental conditions for the growth of winter wheat. Positive ADI GSs are beneficial, with reduced temperatures and more precipitation early in the growing season and the low precipitation totals and higher daily maximum temperatures occurring primarily after the dates of harvest for winter wheat (after day 190) and thus are not detrimental to the production of winter wheat. However, negative ADI GSs with reduced precipitation and higher temperatures during the early GS lead to an increased risk of crop water stress earlier in the year, made worse by an earlier date of the GS daily maximum temperature prior to the onset of above average precipitation after day 190. County-level yields of winter wheat across the region for positive and negative ADI years agree with these results, with positive ADI years showing statistically significant higher county-level yields across the dryland agricultural areas in eastern Kansas and western Oklahoma.
These results show why the SGP climate is conducive for the growth of the winter wheat crop. Positive ADI GSs, which represent approximately 60% of all GSs and thus the primary GS climate of the region, are ideal for the growth of winter wheat. However, the idea that the SGP climate is represented by a maximum in precipitation during the late spring (May/June), a temperature peak in the middle of summer (July/August), and then a secondary peak in precipitation during the early fall (September/October; Illston et al. 2004; Flanagan et al. 2017) may not be as fundamental as once thought. These results depict a possible shift in this thinking, that a “typical” year for the SGP may be noted with a single wet period in the spring and a temperature maximum in the summer, but without the noted increased chance of higher (compared to summer) precipitation totals in the late GS (September). These results break this paradigm, depicting the climatological secondary peak in the precipitation during the latter half of the year during negative ADI years rather than being a climatological feature of all years in the SGP winter wheat region. In other words, the increase of precipitation in the spring climatologically occurs during all years (maximum during positive ADI years, while muted in the negative ADI years), while the later precipitation “peak” (September/October) appears to occur during negative ADI years. However, given that this analysis does not include the later portions of the year, October and November, this theory needs to be further tested before firm conclusions can be made.
While the ADI, in reference to the SGP winter wheat region, appears to be a diagnostic variable more so than a prognostic feature, future work could investigate specific subsets of positive and negative ADI years at local scales to investigate possible connects to specific types of ADI GSs, extreme ADI GSs, neutral ADI GS, etc., to large-scale climate features. While the initial analysis completed for this study did not show any concrete regionwide links to climate variability patterns, the local nature of the ADI suggests that a more in-depth, local-scale investigation into ADI and climate variability could provide more information into the nature of ADI and the SGP. Finally, with the local-scale variability of ADI identified through the course of this investigation, a study analyzing ADI, and its links to the local ecosystem and agricultural, at a small subset of locations, or a singular location, is warranted to provide a more in-depth understanding of how ADI increases our understanding of local-scale hydroclimate in the SGP.
Acknowledgments.
The author would like to thank the contributions of Dr. Jeffrey Basara and Dr. Michael Richman for their past help in developing the ADI. I would also like to thank the three anonymous reviewers for their help in reviewing this work. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer.
Data availability statement.
Data analyzed in this study were a reanalysis of existing data, which are openly available at locations cited in the reference section. The NCAR Command Language (NCL) was used for all data analysis; it is available at https://doi.org/10.5065/D6WD3XH5.
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