Abstract Baseflow is an essential water resource because it is the groundwater discharged to streams and represents long‐term storage. Understanding its future changes is a major concern for water supply and ecosystem health. This study examines the impacts of climate and agriculture on monthly baseflow in the U.S. Midwest through the end of the 21st century. We use a statistical approach to evaluate three scenarios. The first scenario is based on downscaled and bias corrected global climate model (GCM) outputs and the representative concentration pathway (RCP) 8.5, and agriculture is held constant (and equal to the mean from 2013 to 2019). In the next two scenarios, climate is held constant (2010–2019) to isolate the impact of agriculture on baseflow. In terms of agricultural changes, we consider scenarios representative of either increases or decreases with respect to the production of corn and soybeans. Changes in the climate system point to increases in baseflow that are likely a result of increased precipitation and antecedent wetness. Seasonally, warmer temperature in the winter and spring (i.e., February to July) is expected to cause increasing trends in baseflow. Changes in land use showed that agriculture would either mitigate the impact of climate change or possibly amplify it. Expanding corn and soybean areas would increase baseflow in the Corn Belt region. On the other hand, converting land back to perennial vegetation would decrease baseflow throughout the entire year. Despite its simplicity, this study can provide basic information to understand where to expect adverse effects on baseflow and thus improve land management practices in those areas.
Baseflow is the portion of streamflow that comes from groundwater and subsurface sources. Although baseflow is essential for sustaining streams during low flow and drought periods, we have little information about how and why it has changed over large regions of the continental United States. The objective of this study was to evaluate how changes in the climate system have affected observed monthly baseflow records at 3,283 USGS gauges over the last 30 years (1989–2019). We developed a statistical modeling framework to determine the relationship between monthly baseflow and monthly climate predictors (i.e., precipitation, temperature, and antecedent wetness). Overall, we found that baseflow trends and the factors influencing them vary by region and month. In the US Northeast, increases were detected earlier in the year (February and March) and in the summer (May and June), and were likely due to increasing precipitation, warmer temperature, and subsequent changes in snowmelt. Increasing baseflow in the US Pacific Northwest and Midwest were associated with increases in precipitation and antecedent wetness throughout the year. Decreasing trends were located in the US Southeast and Southwest. Baseflow trends in the US Southeast were only detected in March, possibly as a result of decreased precipitation during the spring. On the other hand, decreases in baseflow in the Central Southwestern United States occurred throughout the year. These trends were associated with a lack of precipitation and increases in temperature. Finally, we examined the relationship between monthly baseflow trends and changes in total water storage using monthly Gravity Recovery and Climate Experiment mascon products from the Jet Propulsion Laboratory. In this study, trends in total water storage were strongly associated with baseflow trends across the United States. The spatial and temporal variability in baseflow response to climate reported here can aid water managers in adapting to future climate change.
Abstract Various techniques exist to estimate stream nitrate loads when measured concentration data are sparse. The inherent uncertainty associated with load estimation, however, makes tracking progress toward water quality goals more difficult. We used high‐frequency, in situ nitrate sensors strategically deployed across the agricultural state of Iowa to evaluate 2016 stream concentrations at 60 sites and loads at 35 sites. The generated data, collected at an average of 225 days per site, show daily average nitrate‐N yields ranging from 12 to 198 g/ha, with annual yields as high as 53 kg/ha from the intensely drained Des Moines Lobe. Thirteen of the sites that capture water from 82.5% of Iowa's area show statewide nitrate‐N loading in 2016 totaled 477 million kg, or 41% of the load delivered to the Mississippi–Atchafalaya River Basin (MARB). Considering the substantial private and public investment being made to reduce nitrate loading in many states within the MARB, networks of continuous, in situ measurement devices as described here can inform efforts to track year‐to‐year changes in nitrate load related to weather and conservation implementation. Nitrate and other data from the sensor network described in this study are made publicly available in real time through the Iowa Water Quality Information System.
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