Projected changes in monthly baseflow across the U.S. Midwest
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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.Keywords:
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This study investigates the impact of future climate change on heavy metal (i.e., Cd and Zn) transport from soils to surface waters in a contaminated lowland catchment. The WALRUS hydrological model is employed in a semi-distributed manner to simulate current and future hydrological fluxes in the Dommel catchment in the Netherlands. The model is forced with climate change projections and the simulated fluxes are used as input to a metal transport model that simulates heavy metal concentrations and loads in quickflow and baseflow pathways. Metal transport is simulated under baseline climate ("2000-2010") and future climate ("2090-2099") conditions including scenarios for no climate change and climate change. The outcomes show an increase in Cd and Zn loads and the mean flux-weighted Cd and Zn concentrations in the discharged runoff, which is attributed to breakthrough of heavy metals from the soil system. Due to climate change, runoff enhances and leaching is accelerated, resulting in enhanced Cd and Zn loads. Mean flux-weighted concentrations in the discharged runoff increase during early summer and decrease during late summer and early autumn under the most extreme scenario of climate change. The results of this study provide improved understanding on the processes responsible for future changes in heavy metal contamination in lowland catchments.
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Effective control of nonpoint source pollution from contaminants transported by runoff requires information about the source areas of surface runoff. Variable source hydrology is widely recognized by hydrologists, yet few methods exist for identifying the saturated areas that generate most runoff in humid regions. The Soil Moisture Routing model is a daily water balance model that simulates the hydrology for watersheds with shallow sloping soils. The model combines elevation, soil, and land use data within the geographic information system GRASS, and predicts the spatial distribution of soil moisture, evapotranspiration, saturation-excess overland flow (i.e., surface runoff), and interflow throughout a watershed. The model was applied to a 170 hectare watershed in the Catskills region of New York State and observed stream flow hydrographs and soil moisture measurements were compared to model predictions. Stream flow prediction during non-winter periods generally agreed with measured flow resulting in an average r2 of 0·73, a standard error of 0·01 m3/s, and an average Nash-Sutcliffe efficiency R2 of 0·62. Soil moisture predictions showed trends similar to observations with errors on the order of the standard error of measurements. The model results were most accurate for non-winter conditions. The model is currently used for making management decisions for reducing non-point source pollution from manure spread fields in the Catskill watersheds which supply New York City's drinking water. Copyright © 1999 John Wiley & Sons, Ltd.
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Abstract This study uses an empirical downscaling method based on self-organizing maps (SOMs) to produce high-resolution, downscaled precipitation projections over the state of Pennsylvania in the mid-Atlantic region of the United States for the future period 2046–65. To examine the sensitivity of precipitation change to the water vapor increase brought by global warming, the authors test the following two approaches to downscaling: one uses the specific humidity in the downscaling algorithm and the other does not. Application of the downscaling procedure to the general circulation model (GCM) projections reveals changes in the relative occupancy, but not the fundamental nature, of the simulated synoptic circulation states. Both downscaling approaches predict increases in annual and winter precipitation, consistent in sign with the “raw” output from the GCMs but considerably smaller in magnitude. For summer precipitation, larger discrepancies are seen between raw and downscaled GCM projections, with a substantial dependence on the downscaling version used (downscaled precipitation changes employing specific humidity are smaller than those without it). Application of downscaling generally reduces the inter-GCM uncertainties, suggesting that some of the spread among models in the raw projected precipitation may result from differences in precipitation parameterization schemes rather than fundamentally different climate responses. Projected changes in the North Atlantic Oscillation (NAO) are found to be significantly related to changes in winter precipitation in the downscaled results, but not for the raw GCM results, suggesting that the downscaling more effectively captures the influence of climate dynamics on projected changes in winter precipitation.
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