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The optimal sampling plan for groundwater quality monitoring is formulated as a mixed integer programming (MIP) problem. A sampling plan consists of the number and locations of sampling sites as well as the temporal sampling frequency. The MIP network problem is defined by the minimization of the variance of estimation error subject to resource and unbiasedness constraints. The mean and covariance of the spatial/temporal variable (chloride concentration measurements) are derived from the advection-dispersion equation governing mass transport. The solution for the optimal sampling proceeds in two stages: (1) parameter estimation and (2) network optimization. The MIP model was successfully tested with a network design problem in a buried valley aquifer in Butler County, Ohio. The application illustrates the role of objective function, resource constraint, mass transport processes, and hydrogeologic setting in groundwater quality monitoring network design.

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Dendrohydrology provides accurate methods for studying long‐term hydrologic variability at regional scales. A substantial literature and body of knowledge exists, attesting to the value of tree ring based hydrologic reconstructions to discern patterns of long‐term hydrologic variability. Application studies encompass drought analysis, analysis of extremes, periodicity of rare hydrologic phenomena, regional interdependence of surface moisture conditions, and, in general, the probabilistic analysis of key hydroclimatic variables such as runoff, precipitation, and temperature. The probabilistic analysis includes distributional properties, frequency duration analysis, severity of events, and spatial variability of hydrologic indicators. This paper reviews some fundamental aspects of dendrohydrology, with a perspective on its value to hydrologists in pursuit of an understanding of long‐term hydrologic spatial‐temporal behavior and provides also a selective citation of previous work conducted within the dendrohydrologic discipline.