A Spatially Distributed Groundwater Metric for Describing Hydrologic Changes in a Regional Population of Wetlands North of Tampa Bay, Florida, from 1990 to 2015

A groundwater condition metric is presented and used to evaluate hydrologic changes in a regional population of wetlands in and around municipal well fields with large groundwater withdrawals. The approach compares a 26-year, monthly time series of groundwater potentiometric surfaces to light detection and ranging (LiDAR) land-surface elevations at 10,516 wetlands in a 1505-square-kilometer area. Elevation differences between the potentiometric surface and wetland land surface provide a flow direction (upward or downward) and a proxy for vertical hydraulic head difference in Darcy’s groundwater flow equation. The resulting metric quantifies the groundwater condition at a wetland through time as the potential for groundwater to discharge upward into a wetland or for water in a wetland to leak downward to recharge the underlying aquifer. The potential for wetland leakage across the regional wetland population decreased by 33% in the 13 years after cutbacks in groundwater withdrawals (2003-2015) compared to years before cutbacks (1990-2002). Inside well field properties, wetland leakage potential decreased by 24%. In the wet season month of September, wetlands with the potential to receive groundwater discharge increased to 21.6% of the regional population after cutbacks compared to 13.3% before cutbacks. When mapped across regional drainage basins, discharging wetlands formed spatial connections suggesting they play a critical role in generating streamflow.

A weighted multi-output neural network model for the prediction of rigid pavement deterioration

A novel weighted multi-output neural network (NN) model is proposed for predicting the deterioration of rigid pavements based on Iowa pavement management system data. This first-of-a-kind model simultaneously predicts four pavement condition metrics concerning rigid pavements, including IRI, faulting, longitudinal crack and transverse crack. It provides an opportunity to efficiently evaluate pavement conditions and to make treatment decisions based on multi-condition metrics, such as the pavement condition index (PCI) for budget allocation models. Compared to traditional single-output NN models, this multi-output model is capable of incorporating correlations among different condition metrics. During model training, each condition metric is assigned a weight to reflect its relative importance. When the weights equal to those in the formula for the multi-condition metric, the prediction performance for PCI is optimal (13% lower MSE than optimal, single-output models). The multi-output model improves on the prediction performance for three of the four individual condition metrics compared to optimal single-output models. Results show that the consideration of correlations could improve the prediction performance for single and multi-condition metrics. Finally, variable weighting is critical for achieving the optimal balance of prediction performance among the various metrics as dictated by the needs of the decisionmaker.