Water availability in the Mississippi River alluvial plain: optimized monitoring and modeling for water management

Author(s): Barlow, J.; Haugh, C.

The Mississippi River alluvial plain in northwestern Mississippi (referred to as the Delta), once a floodplain to the Mississippi River covered with hardwoods and marshland, is now a highly productive agricultural region of large economic importance. Water for irrigation in the Delta is supplied primarily by the Mississippi River Valley alluvial aquifer, and although the aquifer has significant storage capacity, there is evidence that the current rate of water use is exceeding the available supply. Groundwater modeling has shown that increasing withdrawals from the aquifer are having a direct impact on the interaction between the groundwater and surface-water systems. Groundwater level declines in the aquifer have resulted in decreased discharge to streams within the Delta to the extent that many stream reaches are presently net-losing streams throughout the year. This decrease in available groundwater discharge is directly impacting many ecosystem services such as maintaining baseflow conditions in streams; regulating temperature regimes for aquatic biota; and buffering contaminant transport at the streambed interface. To better understand and optimize water management and monitoring activities in the Delta, the U.S. Geological Survey and the Mississippi Department of Environmental Quality are collaborating to update and enhance an existing regional groundwater flow model. The model will be used to develop and assess conjunctive water-management optimization scenarios as well as improve and optimize current and future monitoring activities within the Delta. Key revisions include updating the model through 2014 with more recent water use, precipitation and recharge data, and streamflow and water-level observations. In fiscal year 2016, the updated model will be used to develop selected alternative water-supply scenarios to assess relative impacts to the alluvial aquifer and identify data needs for future optimization modeling.

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