Optimal Site Selection of Watershed Hydrological Monitoring Stations Using Remote Sensing and Grey Integer Programming

Abstract

Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are all intimately related with each other to form water balance dynamics on the surface of the Earth. To monitor change in hydrological systems with minimum effort, however, hydrological monitoring networks at the watershed scale should be deployed at critical locations to advance the monitoring and sensing capability. One of the science questions is how to develop an optimum arrangement/ distribution strategy of those monitoring platforms with respect to hydrological components subject to technical and resources constraints. While the complexities arise from the integration of highly heterogeneous data streams in the hydrological cycle under uncertainty, there is an acute need to develop a site screening and sequencing procedure permitting a cost-effective search for final site selection. This paper purports to develop such an approach to address the optimal site selection strategy by integrating satellite remote sensing images with a grey integer programming (GIP) model. The approach uses spatial information on the range of likely values temporally encountered for a number of biophysical descriptors in support of the optimization analysis under uncertainty. Practical implementation was assessed by a case study in a semi-arid watershed-the Choke Canyon Reservoir watershed, south Texas. GIS-based GIP modeling technique successfully supports the screening and sequencing mechanism based on the composite satellite images, which smoothly prioritizes the relative importance and provides the rank order scores across all candidate sites. With the aid of such a synergistic approach, seven locations out of 563 candidate sites were eventually selected and confirmed by a field investigation.

A GIS‐based siting technique for automatic weather stations in Trabzon, Turkey

“…The number of existing AWSs in Turkey was considered insufficient for the prediction of natural disasters; thus, the aim was to establish new AWSs with the TEFER project. Based on published site selection criteria researched in the literature (WMO, ; ; EPA, ; Shepherd, ; Yalcın, ; Baltas and Mimikou, ; Chang and Makkasorn, ; Kidd and Chapman, ) and on the distances from suitable or non‐suitable areas (EPA, ; Oke, ; NWCG, ; Overton, ), six primary criteria were determined, as listed below.…”

A Surrogate‐Based Optimization Methodology for the Optimal Design of an Air Quality Monitoring Network

“…Since not all violations have the same severity, a weighting factor has been used to characterize the violations to each range for the pollutants CO, SO 2 , and NO x . The segmented non‐linear weighting function has been chosen in this research: where is the violation score for the i th candidate location, w k is the weighing factor corresponding to threshold x k , x k is the k th threshold ( X = 0, if ( x i – x k ) ≤ 0 and X = 1, otherwise), N t is the total number of thresholds, and T is the total number of simulated observations.…”

“…Since not all violations have the same severity, a weighting factor has been used to characterize the violations to each range for the pollutants CO, SO 2 , and NO x . The segmented non-linear weighting function has been chosen in this research: [30][31][32][33][34]…”

Methodology for Identifying Optimal Locations of Water Quality Sensors in River Systems

“…Distributed monitoring systems are predicated on the development of new sensors capable of monitoring the contaminants of interest. A critical component of implementing such a network is the identification of the optimal locations to deploy environmental sensors or establish sampling sites [4,6,22].…”