Recent advances in computer technology have led to a proliferation in the development of decision support systems (DSS). Watkins and McKinney [this issue] define a DSS as an integrated, interactive computer system, consisting of analytical tools and information management capabilities, designed to aid decision makers in solving relatively large, unstructured problems. There are many examples of DSS in the field of water resources, yet the most rapid growth has occurred during this past quadrennial see Watkins and McKinney, [this issue]. Most DSS have integrated some combination of classical water resource simulation and/or optimization models, database management systems, geographic information systems (GIS), expert and knowledge-based systems, multiobjective decision support tools and graphical user-friendly interfaces. Watkins and McKinney [this issue] argue that recent developments associated with our ability to access and process very large spatially distributed databases over high-speed and readily accessible networks offers tremendous potential for the development of DSS during the next quadrennial. In addition, improvements in our ability to process very large distributed sources of remotely-sensed and space-based hydrologic and climatic data [ Engman, this issue] combined with advanced data assimilation algorithms [ McLaughlin, this issue] should lead to benefits in both theoretical and applied hydrology. McLaughlin [this issue] suggests that ultimately, data assimilation efforts in the field of subsurface hydrology should be analogous to similar efforts in such fields as geophysics, seismology, and petroleum engineering, to name a few.
Remotely-sensed and space-based observations are particularly important in snow-cover studies due to the difficulty of making field measurements in snow-covered mountainous regions. Bales and Harrington [this issue] review the application of various image-processing methods for use in measuring snow cover properties.
Hornberger and Boyer [this issue] argue that ``creation of, application of, and squabbling over mathematical models of watershed processes have been favorite pastimes of hydrologists for many decades.'' There are many new sources of information which result from recent advances in remote sensing [ Engman, this issue], such as digital terrain data and the use of chemical and isotope data. Nevertheless, Hornberger and Boyer [this issue] argue that many major problems still exist for those wishing to compute the hydrologic response of a watershed. They argue that there is a need to improve our ``empirical'' understanding of watershed processes through experimental studies similar to those reviewed by Kustas [this issue] and Engman [this issue]. Such empirical studies should not be viewed as ``less scientific'' than more mathematical types of research and inquiry.
Most of the following review articles focus on either subsurface or surface water, yet the interaction between these two hydrologic fields cannot be ignored. Due to concerns relating to acid precipitation, eutrophication, land development and water allocation, Winter [this issue] argues that understanding the relationship between subsurface and surface water is a field ripe for future multidisciplinary interactions among hydrologists, geochemists, and biologists because the biogeochemical processes within the upper few decimeters of sediment beneath nearly all surface water bodies can have a profound effect on the chemistry of groundwater entering surface water, as well as on the chemistry of surface water entering groundwater. Winter [this issue] provides an overview of both analytical and numerical procedures for describing the interaction between groundwater and surface water in a variety of landscapes.