Completing the loop: from data to decisions and back to data

Received 4 July 2007 Accepted 4 July 2007 The role of science in decision-making has been compared to the role of meat in a hamburger. By itself, it is messy, but when contained by the bun of policy it becomes more palatable. Extending this metaphor, data can be thought of as the cow that is processed into meat by the scientific process. However, the topic of where the meat comes from rarely comes up in polite conversation. Unfortunately, there is a ‘tragedy of the commons’ occurring— everyone wants a piece of the cow but no one wants to feed it. Furthermore, no one wants to buy a bull that will allow the cow to reproduce. The cow is languishing for the very reason that grain given to the cow is at the expense of the starving graduate students needed to produce prodigious volumes of output. In the meantime, information husbandry is left in the hands of bureaucrats who are accountable only for their ability to balance a budget and who are not held accountable for the legacy they leave. There is incessant demand for quick answers to complex questions. This appetite for ‘fast food’ has resulted in resources being diverted from information husbandry to computer modeling (Hartemink et al., 2001). Scientists have to resort to data scavenging—obtaining whatever ‘road-kill’ they can find to grind up into meat to serve policy objectives. Contemporary environmental science is calorie-rich but low in essential nutrients. When more money is invested in science it results in a ‘super-size’ meal that has extra bulk provided by modeling, but with little added nutritional value. This is because the scientific community is focused on their contemporary needs—to fund graduate students and to publish papers, forgetting the ethics of their profession, which would have them leave a rich legacy upon which their proteges can build their careers. We are consuming the information legacies of previous generations, but leaving the soil barren for future generations. Well-maintained data appreciate in value like a vintage car. In contrast, model output is like an ice cream cone on a hot summer day. It is intended for immediate consumption with no residual value. Many modelers view the world through the lens of their model algorithms, and sometimes this view of reality is as if seen through a kaleidoscope. In the absence of independent observations of reality how is any modeler to know whether their view of reality is realistic? As our models increase in sophistication we should be investing in more comprehensive monitoring to shed light on how well the models reflect reality (Silberstein, 2006). Industrial-scale data production is replacing gauge data as our primary source of information about reality. We can provide cheaper ‘fast food’ if we are supplied by these data factories, but the sheer volume of remotely sensed data is overwhelming our ability to understand those data. The algorithms that convert any remotely sensed electromagnetic signal into a measure of some hydrologic variable are useful but they are also imperfect. We now understand many of the problems complicating the simple conversion of a radar signal into an absolute quantity of precipitation but only after decades of concurrent gauging since the notion of radar-derived precipitation measurement was first conceived (Salek et al., 2004; Saltikoff et al., 2004). If we do not monitor for the infection of technological artifact in remotely sensed estimates of