Baseflow is generally cooler in temperature and of better quality than storm flow, and it maintains stream flow during dry periods. Decreases in baseflow levels and increases in stream temperatures lead to decreases in the diversity of aquatic species (Brown and Krygier 1970). Human activity such as the irrigation of agriculture and changes in climate can affect the rate at which groundwater discharges to surface waters as baseflow. Understanding the factors that affect baseflow processes is critical to protecting both water quality and supply (Price 2011). The decline of baseflow due to the irrigation of agriculture from unconfined aquifers connected to surface waters has been well documented (e.g., Kraft et al. 2012, Weeks and Stangland 1971, Weeks et al. 1965). However, there are few if any studies on the effects of the withdrawal of groundwater on baseflow from confined aquifers. This is because water stored in the confined aquifer is disconnected from surface waters leading to an unfounded hypothesis that groundwater withdrawal is not related to stream baseflow. In addition, baseflow can increase in basins where the primary land use is agriculture (Gebert et al. 2007), but the mechanism and conditions of such increases are unclear.
Agricultural irrigation was once almost exclusively practiced in the arid western portion of the United States, but in the last few decades, the use of irrigation has accelerated in the humid Great Lakes region of the United States (Kraft et al. 2012). In Wisconsin, the number of high-capacity wells (pumping capacity of at least 265 liters per minute) increased substantially from less than 4,000 in 1983 to over 16,000 in 2014. Historical trends of baseflow in Wisconsin, and the Great Lakes states in general, have not been broadly studied. A national scale baseflow study (Ficklin et al. 2016) found increasing trends in some parts of the Northeast and decreasing in the Southwest between 1980-2010, but there was not sufficient detail at the state level. Baseflow trends in the study were found to be related primarily with precipitation but changes in potential evapotranspiration (PET) were also influencing the trends (Ficklin et al. 2016). With precipitation generally increasing in the second half of the 20th century and projected to increase through the 21st century in Wisconsin (Wisconsin Initiative on Climate Change Impacts 2011), baseflow would be expected to increase as well. However, there are areas in Wisconsin where baseflow has declined. Baseflow increases and declines need to be studied with respect to the source of the groundwater used to irrigate agricultural lands while considering the effects of high-capacity wells.
When groundwater is pumped from a confined aquifer for irrigation, it effectively increases the amount of water that can infiltrate and flow to the unconfined aquifer and increases the groundwater available to recharge the surface waters. Several studies point to land use changes as the only anthropogenic cause of baseflow increases (Juckem et al. 2008, Schilling et al. 2008, Zhang and Schilling 2006). But, when baseflow increases are attributed to land use change alone, without considering the presence of agricultural irrigation or its source aquifer type, the potential large effect of increased infiltration of the irrigation water to the unconfined aquifer is neglected. Baseflow increases can have a positive effect on aquatic ecosystems, but the losses to the fresh water resource of groundwater stored in the confined aquifer may not be replaceable. The current knowledge gap is a hindrance to not only the science but also to policy makers who determine how the permitting of additional high-capacity irrigation wells will affect the baseflow in the basin.
2. Background
2.1 Baseflow trend and climatic factors
Baseflow is the portion of streamflow that is derived from both shallow and deep groundwater storage (Robinson and Ward 1990). Baseflow increases with infiltration and decreases with evapotranspiration (Price 2011). Increases in precipitation can increase storm flow during a single event or increase baseflow over an extended period by increasing the groundwater level. The percent of precipitation that infiltrates the surface to flow vertically to the water table is affected by both soil texture and the soil saturation level. Increased irrigation effectively increases moisture contacting the surface, therefore irrigation water may also increase baseflow. Since agricultural irrigation is only required when soil moisture is low, the moisture contacting the surface infiltrates the surface and does not increase storm flow. The need to irrigate agricultural fields intensifies as fields loose moisture to rising air temperatures and/or decreased amounts of precipitation. Therefore, baseflow decreases from increased temperatures and/or decreased precipitation may be offset by the increases in agricultural irrigation sourced from the confined aquifer.
Preliminary findings for this study suggest declining baseflows in northern Wisconsin, and increasing baseflows in southern portions of the state, between 1984-2014. A study found baseflow trends increasing in basins where the primary land use was agriculture, and no trend for those basins located in forested areas between 1970-1999 in Wisconsin (Gebert et al. 2007). But, declining baseflow trends were found in areas of central Wisconsin, mainly in agricultural areas, between 1977-2009 (Kraft et al. 2012). In portions of Pennsylvania strong downward baseflow trends coincided with decreases in precipitation and increases in temperature between 1971-2001 (Zhu and Day 2005); and in Wisconsin precipitation and temperature were both found to be statistically significant variables in a regression model created to predict variations in baseflow between 1983-2013 (Borchardt et al. 2016). It is crucial to understand the relationship between physical basin properties, such as soil texture and land use, and baseflow; and it is also crucial to understand how both anthropogenic impacts and climate change affect those physical properties (Price 2011). Annual precipitation and temperature have been trending upward over the last 30 years in Wisconsin. Precipitation and temperature both relate to baseflow, but in opposing directions. This study looked at how these climate factors affect baseflow in areas where there is extensive use of high-capacity irrigation wells.
2.2 Effects of agricultural irrigation on baseflow
High-capacity wells, used to irrigate agriculture, can significantly impact groundwater storage and the associated interaction of surface to groundwater systems (Sophocleous 2002, Wahl and Tororelli 1997). Several studies have explored the relationship between high-capacity irrigation wells and declines in stream flow in areas where the groundwater flows through highly permeable sand and gravel deposits (Weeks et al. 1965, Kraft et al. 2012, Weeks and Stangland 1971). There is a relationship between declines in both baseflow and groundwater levels and increases in the number of high-capacity wells in the Oklahoma panhandle (Wahl and Tororelli 1997). The decreasing amount of groundwater, which is discharged tostreams, is related to the increased rate of groundwater pumped by high-capacity wells (Barlow and Leake 2012). Ambient groundwater that normally would have discharged as baseflow to surface water can be diverted away from discharge points by the gradients created by high-capacity wells (Sophocleous 2002). It has also been found that high-capacity wells outside the surface water basin, but within the groundwater basin, have a significant effect on the baseflow of the stream within the surface water basin (Borchardt et al. 2016). These studies established that high-capacity wells withdrawing groundwater from an unconfined aquifer that is well connected to the surface waters will lead to declines in the baseflow.
There are few if any studies analyzing the effects on baseflow when the high-capacity wells are withdrawing water from a confined aquifer. Aquifers below the confining layer are not connected to the surface waters. Therefore, withdrawals from them may contribute to increases in baseflow rather than decreases in baseflow. The irrigation of agricultural land increases the soil moisture storage. Increases in soil moisture storage were found to have a moderately strong linkage to increases in baseflow (Shaw et al. 2013, Price 2011). If groundwater withdrawals for irrigation are from the confined aquifer, then they may be contributing to baseflow increases by increasing the amount of water that infiltrates the surface, thereby adding to the soil moisture storage. But, studies which attribute withdrawal rate of high-capacity wells to baseflow change have only analyzed wells withdrawing from above the confining layer (e.g., Borchardt et al. 2016, Kraft et al. 2012, Weeks and Stangland 1971, and Weeks et al. 1965).
2.3 Study area
This study has been conducted for the state of Wisconsin. Wisconsin was chosen in large part because irrigation in the state has grown tremendously over the last few decades, providing an opportunity for trend analysis. Wisconsin also has unique geography with both shallow unconfined aquifers well connected to surface waters in the northern portion of the state and deep confined aquifers in the southern portion of the state. Preliminary findings for this study report increasing baseflow trends in the southern portion of the state over the period 1984-2014, whereas those in northern Wisconsin show a declining trend over the same time
2.4 Climate
The climate in Wisconsin is considered continental but with modifications from the two bordering Great Lakes, Michigan and Superior. Wisconsin’s average annual temperature varies from 4.4oC in the northern regions to 8.9oC in the southern regions. Average monthly temperatures range from a low of -14oC to a high of 28oC. (Netstate 2016). The median date of last spring freeze ranges from early May in southern regions and along the Lake Michigan coast to early June in the Northern portion of the state. Average annual precipitation varies between 76-86 cm across the higher elevations to the west and north. Average precipitation is less, approximately 71 cm, in the lower elevations in the south and along the Lake Michigan shore (Atmospheric and Oceanic Sciences University of Wisconsin-Madison 2003).
2.5 High-Capacity wells
Per the Wisconsin Department of Natural Resources (WDNR), a high-capacity well is defined as a well capable of pumping 100,000 gallons (378,541 liters) per day, or a well that, together with all the other wells on the property, is capable of pumping 100,000 gallons (378,541 liters) per day (Wisconsin DNR 2017). The use of high-capacity wells has increased by almost 400 percent over the period 1984-2014 with only 3,928 wells approved prior to 1984 to over 14,200 approved as of 2014 (Smail 2015). In Wisconsin, groundwater sourced irrigation is used to supplement precipitation, allowing high water demand crops to be grown in soils with low water holding capacity (Kraft et al. 2012).
3. Research Question
Additional state-wide analysis needs to be completed to determine the spatial distribution of baseflow trends. This research investigated how climate variables and human activity affect baseflow. Specifically, the study aimed to answer the following research questions:
4. Materials and Methods
4.1 Statewide annual baseflow trend analysis
Previous studies have found both increasing and decreasing baseflows in Wisconsin (Gebert et al. 2007, Kraft et al. 2012). Kraft et al. (2012) attributes declining baseflows to the extensive use of irrigated agriculture in the central portion of the state between 1999-2008, but irrigation is found throughout the state. Gebert et al. (2007) reported increasing baseflows in agricultural areas of Wisconsin, and no increases in the forested areas of the state between 1970-1999. This study calculated baseflow for Wisconsin’s streams on an annual basis and determined if the baseflows are trending over time, the direction of the trends, and if there were deviations in any of the baseflow trends over the study period (1984-2014). These finding will determine if baseflow trends over the last decade have changed from previous decades and are the changes related to land use changes and/or increases in agricultural irrigation. The study also mapped the spatial patterns related to decreasing/increasing trends and trend deviations. The study also determined the natural and human-induced characteristics of basins which show significant baseflow trends.
4.1.1 Baseflow determination
Annual mean baseflow was calculated from streamflow data collected for the period 1984-2014 at gauging stations located in Wisconsin by the US Geological Survey (USGS). The RORA method in the USGS computer program, Groundwater Toolbox (USGS 2017) was used to derive baseflow from streamflow. Only gauges that have been continuously recording daily streamflow during the study period were used. The RORA method creates estimates of net recharge using the recession-curve displacement method for baseflow separation (Barlow et al. 2015). The RORA method was chosen over the hydrograph separation methods because it is a measure of the groundwater that drains from storage to the surface water versus a measure of the low flow in the stream (Barlow et al. 2015). Baseflow was plotted against time over the study period and a regression line calculated. The equation for the regression line was used to calculate the percent change in baseflow between 1984 and 2014.
4.1.2 Testing for significance in baseflow trend over time
The significance of the baseflow trends were tested with a nonparametric statistical measure of correlation (Kendall Tau method). The Kendall Tau statistic (Kendall 1938) is used by the USGS in the Groundwater Toolbox program to test the significance of both streamflow and baseflow trend over time (Barlow et al. 2015). Kendall (1938) developed an equation to measure the correlation of ranked pairs of data (equation 1). The Tau equation measured the correlation between the ranked values of time and the calculated baseflow values from Groundwater Toolbox on a scale from -1 to 1, where a value of -1 would indicate a perfect negative relationship, +1 would indicate a perfect positive relationship, and 0 would indicate no ordinal relationship (Kendall 1938). Kendall’s Tau is a measure of the strength of the association between two variables, and it is a ratio between the number of concordant pairs and the total number of possible pairs that can be created between the two variables (Hauke and Kossowski 2011). Kendall’s Tau is more reliable and more interpretable because it is less sensitive to error and discrepancies in the data than Spearman’s rs (Kendall and Gibbons 1990). Both the Kendal Tau statistic and the p-level are available calculations within the Groundwater Toolbox program.
Equation 1
where P = the number of concordant pairs
n = the number of pairs
The total number of possible pairs is calculated as [n(n-1)] and is in the denominator. The number of concordant pairs (pairs where the second data point is numerically greater than the first data point) is in the numerator. The number of concordant pairs that can be created can be obtained by comparing each number with each of its succeeding numbers to calculate the total that are in chronological order (Kendall 1938). Consider two sets of data, the first is put in chronological order from 1 to 10, the second set is random (4,7,2,10,3,6,8,1,5,9), the two sets make up 10 pairs (n=10). To find the number of concordant pairs (P) we find the quantity of numbers to the right of each data point that is greater than the data point and add them together. There are 6 numbers greater than 4 to its right, and 3 numbers greater than 7 to its right and so on. The total number of concordant pairs in this example is 25 (P=25). Substituting 10 and 25 for n and P in equation 1 will gives us +0.11 . Since 0.11 is close to 0, this example represents a low ordinal relationship. Tau values for this study were > 0.34 for baseflows with highly significant trends (p ≤ 0.01).
4.1.3 Divergence detection
A double mass curve analysis was used to detect divergences in the graphed slope of the cumulative annual totals for precipitation versus the cumulative annual total for the baseflow. The double mass curve technique is useful for detecting hydrological changes that may occur due to anthropogenic influence (Choi et al. 2016). The slope of the straight line formed by this proportional relationship represents the relationship between the quantities (Searcy and Hardison 1960), annual precipitation and annual baseflow in this study. A divergence, or a break in the slope, will suggest another variable, potentially the high-capacity well withdrawal rate, is affecting the baseflow rate.
4.1.4 Testing significance of divergence
The data was split at the point of the divergence and the two separate sets of data and their regression slope lines were graphed. The Levene’s test (Levene 1960) available in SPSS Software (IBM) was used to test the homogeneity of the regression slopes. The Levene test was used to test the null hypothesis that the variance of the two sets of data are equal. If the two slopes are different then a covariant (anthropogenic stress) is significantly affecting the independent variable (cumulative baseflow).
Ho: =
= … =
Ha: ≠
for at least one pair (i,j).
The Levene test rejects the null hypothesis if the test statistic is greater than the critical value at a given significance level.
4.1.5 Spatial analysis
Stream gauging station locations and their baseflow trend over time (percent declining or increasing) was mapped using Arc GIS desktop 10.4 from Environmental Systems Research Institute (ESRI). A second map was created with the stream gauge locations that exhibited a divergence in the regression slope and it also showed the direction of the divergence. The maps were used to examine the spatial distribution of trend direction and divergence direction.
4.2 Characteristics of basins showing significant trends
4.2.1 Surface basin delineation
Basins that exhibited a divergence were also analyzed for soil and topographic characteristics, landcover, and the aquifer type that high-capacity wells in the basin are withdrawing from. Since gauging stations are often situated in the center of WDNR watersheds, the hydrology tools in Arc GIS were used to delineate only the surface area that contributes water to each gauging station. For the surface basin delineation, the digital elevation model (DEM) was obtained from the USGS web site (USGS n.d.). The DEM was filled to eliminate pits, and both flow direction and accumulation were calculated using the Arc GIS tools. Once the flow accumulation had been calculated, the area contributing to the gauging station could then be delineated using the location of the gauging station as the pour point. The delineated basin raster file was then converted to a poly file to be used to clip landcover and topography data. The surface area contributing to each gauging station can contain more than one WDNR watershed, therefore the area affecting baseflow may encompass a different area than the WDNR watershed the gauge is in.
4.2.2 Soil characteristic determination
This study also analyzed available water storage (AWS) and soil drainage class (SDC). Previous studies have correlated the percentage of sand in the soil (Santhi et al. 2007) and specific yield (Lorenze and Delin 2007) to variations in stream baseflow. Sand is a texture class of soil that is defined as well drained soil. This study used the percent of well drained or very well drained soil the basin contains. Specific yield is a ratio or percent between the volume of water that will drain by gravity from a saturated soil to the total volume of the soil. This study used AWS which is a measure of the amount of water the soil can hold measured in cm.
Available Water Storage (AWS) was downloaded from the Soil Survey Geographic Database (SSURGO) Downloader (ESRI 2018). AWS is a calculation of the difference between soil water content at field capacity and the permanent wilting point. AWS is then adjusted for salinity and fragments at 4 different depths, the top 25cm, 50cm, 100cm, and 150cm of soil. This study uses the calculation for the top 150 cm of soil, the AWS is measured in cm of water (ESRI. n.d.). The AWS mean value was calculated in ArcGIS for each delineated basin.
Soil drainage class (SDC) was also downloaded from the Soil Survey Geographic Database (SSURG) Downloader (ESRI 2018). SDC is a classification of the drainage condition of the soil in the dominant soil component of the map unit (ESRI, n.d.). The drainage classes are divided into 7 conditions; excessively drained, somewhat excessively drained, well drained, moderately drained, somewhat well drained, somewhat poorly drained, poorly drained, and very poorly drained. The percent of well-drained soil for each clipped area was calculated in ArcGIS.
4.2.3. Landcover and topography determination
Topographical characteristics of the basin were analyzed by calculating the average slope over the basin. Landcover type was also analyzed. Previous studies have found correlations between increasing baseflows and the clearcutting of forested regions (Harr et al. 1982, Hicks et al. 1991, Smith 1991). These studies did not take into consideration what landcover replaced the forested land. Baseflow increases have also been attributed to the conversion of perennial grasslands to agriculture row crops (Juckem et al. 2008, Schilling et al. 2008, Zhang and Schilling 2006). The effect of irrigation required to grow the row crops was not taken into consideration in these studies. This study looked at landcover change over the last decade to determine if there is a correlation between landcover change and baseflow variation.
National Land Cover Data (NLCD) was downloaded from the USGS web site (USGS n.d.) for the years 2001 and 2011. The data was then clipped to the area of each delineated basin. Landcover and landcover change over the last decade was analyzed in ArcGIS desktop 10.4 from ESRI for each basin. Percent slope across each basin was calculated using a digital elevation model (DEM) that was obtained from the USGS web site (USGS n.d.). The DEM file was also clipped to each basin. Each DEM basin file was analyzed using the slope tool in the spatial analyst tool set in ArcGIS desk 10.4. The slope tool calculated the maximum rate of elevation change between each raster cell and its neighboring cells (ESRI n.d.). The mean value of all the raster cells in the basin were used as the basin slope value.
4.2.4. Aquifer determination
The correlation between withdraw of groundwater, from the unconfined aquifer, to irrigate agriculture crops and baseflow variation has been well documented (e.g., Weeks et al. 1965, Kraft et al. 2012, Weeks and Stangland 1971). However, the effect groundwater withdrawal from confined aquifers has on surface waters has not been studied. This study determined if there is a relationship between the number of high-capacity wells, withdrawing groundwater from either the confined or the unconfined aquifer, in each study basin and the variation of baseflow in that basin. The study also determined if there was a difference in the relationship direction between the wells pumping from the confined versus the unconfined aquifer.
A geographic information system layer containing high-capacity well data from the WDNR was used to locate the wells within each basin. The number of high-capacity wells in each basin was then calculated in Arc GIS. Well construction reports filed with the WDNR after the completion of a well installation record both the well depth and the depth of the confining layer. By comparing the two depth records a determination was made which wells were drawing from the confined aquifer and which were drawing from the unconfined aquifer. Well construction report data compiled by the Wisconsin Geological Survey (WGS) was received from the University of Wisconsin-Extension via email correspondence from Stephen Mavel, 10 March 2018. The well ID included in the data was used to perform a join operation between the construction report data and the well withdrawal data. The join was performed on each of the 19 basins that contained the well data that had previously been clipped with the basin poly shape file.
5 Results
5.1 Statewide annual baseflow trend results
5.1.1. Baseflow separation
The RORA method of baseflow separation was used to calculate the annual baseflow from stream records available from the USGS. 36 stations across the state of Wisconsin were found to have continuous data over the study period (1984-2014). Trend was calculated using Microsoft Excel. Annual baseflow from the RORA method was plotted in Excel (Appendix A). The percent decline was determined using the equation for the trend line. The calculated baseflow for 1984 and 2014 from the trend line were used in lieu of the calculated values from the RORA method. The trend over the study period ranged from approximately 202 percent to -28 percent, and a mean of approximately 18 percent. The 202 percent baseflow increase (station 05427948) appears to be an outlier. If data for this gauge is removed the results of the study range from approximately 67 percent to -28 percent and a mean of approximately 16 percent (Table 1). Increasing stream baseflows were found mainly in the southern half of the state, while declining stream baseflows were found in the northern half of the state (Figure 1).
5.1.2. Testing for significance
The Kendall Tau method was used to test if the baseflow trends are significant. Half the study basins had a significance level greater than 80 percent. One basin (04074950) had a declining trend that was highly significant (p ≤ 0.01) and a Tau of -0.355. One basin (04060993) had a declining trend that was moderately significant (p ≤ 0.05) and a Tau of -0.306. Three basins (05427948, 05430150, and 05430175) had increasing trends that were highly significant with Tau of 0.463, 0.342 and 0.342 respectively. One basin (05429500) that had an increasing trend that was moderately significant (p ≤ 0.05) with a Tau score of 0.258. Five additional basins (04063700, 05394500, 04024430, 05368000 and 05397500) had declining baseflow trends with a significance score of < -0.200. There were also seven basins with increasing trends (05406500, 05425912, 05431486, 05543830, 05413500, 05426250, and 05414000) that had a Tau significance score of < 0.200. The remaining 18 basins exhibited baseflow trends that were either constant or not significant (Appendix A, Table 3).
5.1.3. Relationship between precipitation and baseflow
It is generally accepted that increases in annual precipitation will lead to increases in annual baseflow and that there is a proportional relationship between the two variables, if all other variables are kept constant. Therefore, if the cumulative value of precipitation is plotted against the cumulative value of baseflow, a slope line will form that represents the relationship between precipitation and baseflow (Choi et al. 2016, Searcy and Hardison 1960). The theory of Double-Mass Curves states that if there is a deviation in the slope line, then there is a change in the relationship between the two variables. Double-mass curve analysis is useful in determining if anthropogenic influences are affecting annual baseflow rate. Deviations in the slope were recorded in 20 of the 35 basins from visual inspection of the graph created in Microsoft Excel. 18 of the 20 basins that exhibited a deviation in the Double Mass Curve slope also exhibited a significant trend over time. There were also an additional 4 basins that did exhibit a deviation in the Double Mass Curve that did not show a significant trend over time. Deviations were evenly split between increases and decreases in slope with 9 basins exhibiting a decrease in slope, and 11 basins exhibiting an increase in slope. 15 basins showed no significant change in the slope line (per visual inspection) representing the relationship between precipitation and baseflow. Declines in the slope were found beginning in approximately 1999 but increases in the slope were not detected until approximately 2007 (Appendix B). All the basins that exhibited an increasing deviation are in the southern half of the state. All except 1 of the basins exhibiting a decreasing deviation are in the northern half of the state (Figure 2).
5.1.4. Testing for significance in slope deviation
The Leven test was performed on the 20 basins that exhibited a deviation in the regression slope from visual inspection of the Microsoft Excel graph. All except one (basin 05432500, p = 0.072) had p values ≤ 0.05, therefore the deviations were significant. A visual inspection of the Excel graph from basin 05432500 verifies that the deviation was only marginally deviated and likely not statistically significant. Since the two regression lines in the other 19 basins have slopes that are statistically different, then it is highly probable that an anthropogenic variable is affecting stream baseflow in the 19 basins (Table 3).
5.2 Basin Characteristics
5.2.1. Land Cover and topography relationship to baseflow
The 4 principle landcovers found in the 20 delineated basins are listed in table 1 below. The landcovers were calculated as percent of total landcover in each basin in 2011. Agriculture was the most prevalent landcover ranging from ≈ 85% to ≈ 1% with a mean value of ≈ 42%. The next highest landcover is forested ranging from ≈ 66% to ≈ 4% and a mean value of ≈ 28%. The 2 following landcover are Wetlands and Developed land having mean values of ≈ 12% and 15% respectively. Developed lands include both high and low intensity residential, and commercial/industrial/transportation land use. Increasing baseflow trends are more likely to be found in basins with a lower percent of forested landcover, and a higher percentage of agricultural land cover with a r2 0.6499 and 0.5851 respectively. Percent land cover of wetland and developed land had a less significant effect on baseflow trend with a r2 of 0.394 and 0.0114 respectively (Figure 3a-d).
Mean slope and basin area of each basin is recorded in table 2 below. Mean slope for the 20 basins ranges between ≈ 13 and ≈ 2 with a mean of ≈ 5. Mean slope was calculated as percent rise, which is the rise divided by the run and the result multiplied by 100. This translates to a mean slope of 5, which would be equivalent to a 5 cm rise per 1 meter of run or a relatively gentle slope across all 20 basins. The area of the basins varies between 1,350 square kilometers to 48 square kilometers. The mean area of the basins is ≈ 573 square kilometers. Neither the mean slope nor the basin area has a significant relationship with the baseflow trend with an r2 of 0.0215 and 0.0076 respectively (Fig. 4a-b)
5.2.2. Aquifer type and number of wells
Well construction data was incomplete, but 100 percent of the wells that did have data and were in the same basin were drawing water from the same aquifer type. Therefore, it is assumed that all the high-capacity wells in the same basin were drawing from the same aquifer type. It is a generally accepted practice to drill new wells to approximately the same depth as other wells in the area if those wells have been producing a reliable supply of water. 5 basins were found to have wells withdrawing from an unconfined aquifer, and 15 basins were found to have wells withdrawing from a confined aquifer. The number of wells is recorded as a negative number if the aquifer type is unconfined, and the number of wells is recorded as a positive number if the aquifer type is confined. The number of high-capacity wells in operation during 2015 in each surface basin is recorded in table 2. The number of wells varies between 0 to 317, with the average number of wells calculated at 63.5 per basin. The number of wells in a basin is related to the baseflow trend percent with an r2 of 0.4714 (Figure 5). Table 2 shows that in basins with no high-capacity wells, baseflow had a declining trend over the study period (1984-2014). As the number of wells withdrawing from the confined aquifer increased, the baseflow tread increased from a declining trend of approximately 15% to an increasing trend of almost 67%. This increase illustrates a mitigating effect to the decreasing trend related to environmental variables alone. As the number of wells withdrawing from an unconfined aquifer increases, the already declining baseflow trend intensifies from 18% to over 28%, illustrating the contribution high-capacity wells have in basin baseflow decline in areas where aquifers are connected to surface water.
6. Discussion
This study examined the annual baseflow of 36 streams across the state of Wisconsin that had a minimum of 30 years of continuous stream flow data using the USGS program Groundwater Tool Box. The baseflow trend over the study period (1984-2014) was found to be spatially separated between declining baseflows in northern Wisconsin and increasing baseflows in the southern portion of the state. Cumulative baseflows were graphed against cumulative precipitation to detect any deviations in the relationship slope. Twenty basins exhibited a deviation in the slope suggesting an anthropogenic variable was affecting baseflows.
ESRI’s ArcMap 10.4 was used to delineate the surface area contributing to the baseflow at each of the twenty gauging stations that exhibited a deviation. The aquifer type (confined or unconfined) that high-capacity wells in each basin are drawing water from was determined from well construction data obtained from WGS. The number of wells was recorded as a negative number if the aquifer type was unconfined, and the number of wells was recorded as a positive number if the aquifer type was confined. As the number of wells withdrawing from the confined aquifer increased, the baseflow tread increased from a declining trend of approximately 15% to an increasing trend of almost 67%. This increase illustrates a mitigating effect to the decreasing trend related to climate variables alone. As the number of wells withdrawing from an unconfined aquifer increases, the already declining baseflow trend intensifies from 18% to over 28%, illustrating the contribution high-capacity wells have in basin decline in areas where aquifers are connected to surface water as previously documented by others (e.g., Kraft et al. 2012, Weeks and Stangland 1971, Weeks et al. 1965).
Additionally, the delineated surface basins were used to analyze the relationship between the basin characteristics (land cover, size, soil, topography) and basin baseflow and/or baseflow trend. None of the basin characteristics had a significant relationship with baseflow on a state-wide basis. On the other hand, several of the characteristics have a moderate relationship with baseflow trend. Increases in agricultural land cover, increases in the percent of well drained soils, and increases in the amount of available water storage were related to increased trend rates in stream baseflow. Decreased baseflow trend rates were observed, as the percent of forested land cover increased, within the delineated basin area.
The variables that were found to be related to baseflow trend rates will be used in a follow up study covering stream baseflow in the state of Wisconsin. The follow up study’s purpose will be to develop a model that uses free and easily downloadable data to predict baseflow change. The model will be used to predict the effect of either adding an additional high-capacity well or the abandonment of an existing high-capacity well within a basin, on the baseflow of the stream in that basin. Current modeling techniques are time consuming and require large quantities of data. It is hoped that an easy to use model can be created for preliminary investigation of the effects of high-capacity well permitting and/or abandonment.
7. Conclusion
This study highlights that environmental stresses are related to baseflow declines across the state of Wisconsin, and that the decreases are being mitigated or completely reversed by the addition of groundwater to the surface from below the confining layer. These artificial additions to baseflow may be beneficial to aquatic species that rely on the cooler groundwater recharges to their streams. But, these withdrawals from our groundwater resources to produce agricultural products in unsuitable environments may not be the best use of this nonrenewable resource. This knowledge will both add to science’s understanding of hydrological processes and provide scientific basis for policy makers to determine how the permitting of additional high-capacity irrigation wells will affect the baseflow of streams in the basin.
Conflict of Interest: The author declares that they have no conflict of interest
Atmospheric and Oceanic Sciences University of Wisconsin-Madison. (2003). Climate of Wisconsin retrieved 29 October 2018 from http://www.aos.wisc.edu/~sco/clim-history/state/stateclimate.html
Barlow, P. M., Cunningham, W. L., Zhai, T., & Gray, M. (2015). US Geological Surveygroundwater toolbox, a graphical and mapping interface for analysis of hydrologic data (version 1.0): user guide for estimation of base flow, runoff, and groundwater recharge from streamflow data: U.S. Geological Techniques and Methods3-B10, 27p., http://dx.doi.org/10.3133/tm3B10
Barlow, P. M., & Leake, S. A. (2012). Streamflow depletion by wells–Understanding and managing the effects of groundwater pumping on streamflow (No. 1376, pp. i-84). US Geological Survey.
Borchardt, S., Choi, W., & Han, W. S. (2016). High-capacity wells and baseflow decline in the Wolf River Basin, northeastern Wisconsin (USA). Environmental Earth Sciences, 75(16), 1176.
Brown, G. W., & Krygier, J. T. (1970). Effects of clear‐cutting on stream temperature. Water resources research, 6(4), 1133-1139.
Choi, W., Nauth, K., Choi, J., & Becker, S. (2016). Urbanization and rainfall–runoff relationships in the Milwaukee River Basin. The Professional Geographer, 68(1), 14-25.
ESRI. (2018) SSURGO Downloader. Retrieved 29 October 2018 from http://www.arcgis.com/home/item.html?id=cdc49bd63ea54dd2977f3f2853e07fff
ESRI. (n.d.) ArcGIS Resources – Available Water Storage Retrieved 29 October 2018 from http://resources.arcgis.com/en/communities/soils/02ms00000009000000.htm
ESRI. (n.d.) ArcGIS Resources – Soil Hydrology of the United States Retrieved 29 October 2018 from http://resources.arcgis.com/en/communities/soils/02ms00000008000000.htm
ESRI. (n.d.) ArcMap – How Slope works Retrieved 29 October 2018 from http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/how-slope-works.htm
Ficklin, D. L., Robeson, S. M., & Knouft, J. H. (2016). Impacts of recent climate change on trends in baseflow and stormflow in United States watersheds. Geophysical Research Letters, 43(10), 5079-5088.
Gebert, W. A., Radloff, M. J., Considine, E. J., & Kennedy, J. L. (2007). Use of Streamflow Data to Estimate Base Flow/Ground‐Water Recharge for Wisconsin1. JAWRA Journal of the American Water Resources Association, 43(1), 220-236.
Harr, R. D., Levno, A., & Mersereau, R. (1982). Streamflow changes after logging 130‐year‐old Douglas fir in two small watersheds. Water Resources Research, 18(3), 637-644.
Hauke, J., & Kossowski, T. (2011). Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaestiones geographicae, 30(2), 87-93.
Hicks, B. J., Beschta, R. L., & Harr, R. D. (1991). LONG‐TERM CHANGES IN STREAMFLOW FOLLOWING LOGGING IN WESTERN OREGON AND ASSOCIATED FISHERIES IMPLICATIONS 1. JAWRA Journal of the American Water Resources Association, 27(2), 217-226.
Juckem, P. F., Hunt, R. J., Anderson, M. P., & Robertson, D. M. (2008). Effects of climate and land management change on streamflow in the driftless area of Wisconsin. Journal of Hydrology, 355(1), 123-130
Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1/2), 81-93.
Kendall, M. G. and J. D. Gibbons. 1990. Rank Correlation Methods. 5th ed. London: Griffin.
Kraft, G. J., Clancy, K., Mechenich, D. J., & Haucke, J. (2012). Irrigation effects in the northern lake states: Wisconsin central sands revisited. Ground water, 50(2), 308-318.
Levene, H. (1960, January). A robust approximate confidence-interval for components of variance. In Annals of Mathematical Statistics (Vol. 31, No. 2, pp. 534-535). IMS BUSINESS OFFICE-SUITE 7, 3401 INVESTMENT BLVD, HAYWARD, CA 94545: INST MATHEMATICAL STATISTICS.
Lorenz, D.L., & Delin, G.N. (2007). A regression model to estimate regional ground water recharge. Groundwater, 45(2), 196-208.
Mavel, Stephen (2018) Email correspondence, 3/10/2018
Netstate. (2016). Wisconsin. Retrieved 29 October 2018 from http://www.netstate.com/states/geography/wi_geography.htm
Price, K. (2011). Effects of watershed topography, soils, land use, and climate on baseflow hydrology in humid regions: A review. Progress in physical geography, 35(4), 465-492.
Santhi, C., Allen, P.M., Muttiah, R.S., Arnold, J.G., & Tuppad, P. (2008). Regional estimation of base flow for the conterminous United States by hydrologic landscape regions. Journal of Hydrology, 351(1), 139-153.
Schilling, K. E., Jha, M. K., Zhang, Y. K., Gassman, P. W., & Wolter, C. F. (2008). Impact of land use and land cover change on the water balance of a large agricultural watershed: Historical effects and future directions. Water Resources Research, 44(7).
Searcy, J. K., & Hardison, C. H. (1960). Double-mass curves. Geological Survey Water-Supply Paper 1541-B, United States Government Printing Office, Washington, D.C
Shaw, S. B., McHardy, T. M., & Riha, S. J. (2013). Evaluating the influence of watershed moisture storage on variations in base flow recession rates during prolonged rain‐free periods in medium‐sized catchments in New York and Illinois, USA. Water Resources Research, 49(9), 6022-6028.
Sophocleous, M. (2002). Interactions between groundwater and surface water: the state of the science. Hydrogeology journal, 10(1), 52-67.
Smail, Robert A. (2015) Email correspondence, 1/6/2015
Smith, R. E. (1991). Effect of clearfelling pines on water field in a small eastern transvaal catchment, South Africa. Water S. A., 17(3), 217-224.
USGS. (2017). Groundwater Toolbox: A graphical and mapping interface for analysis of hydrologic data Retrieved 29 October 2018 from https://water.usgs.gov/ogw/gwtoolbox/
USGS. (n.p.). The National Map Download. Retrieved 29 October 2018 from https://viewer.nationalmap.gov/basic/
Wahl, K. L., & Tortorelli, R. L. (1997). Changes in flow in the Beaver-North Canadian river basin upstream from Canton Lake, western Oklahoma. US Department of the Interior, US Geological Survey
Weeks, E. P., Ericson, D. W., & Holt, C. L. R. (1965). Hydrology of the Little Plover River basin, Portage County, Wisconsin, and the effects of water resource development. US Government Printing Office.
Weeks, E. P., & Stangland, H. G. (1971). Effects of irrigation on streamflow in the Central Sand Plain of Wisconsin. US Department of the Interior, Geological Survey, Water Resources Division.
Wisconsin, WICCI (2011). Wisconsin’s Changing Climate: Impacts and Adaptation 2011, Wisconsin Initiative on Climate Change Impacts. Nelson Institute for Environmental Studies, University of Wisconsin-Madison and the Wisconsin Department of Natural Resources, Madison, last accessed on, 01-27.
Zhang, Y. K., & Schilling, K. E. (2006). Increasing streamflow and baseflow in Mississippi River since the 1940s: Effect of land use change. Journal of Hydrology, 324(1), 412-422.
Zhu, Y., & Day, R. L. (2005). Analysis of Streamflow Trends and the Effects of Climate in Pennsylvania, 1971 TO 20011.
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