Bridging river basin scales and processes to assess human‐climate impacts and the terrestrial hydrologic system

Abstract

The increasing expression of human activity, climate variability, and climate change on humid, terrestrial hydrologic systems has made the integrated nature of large river basins more apparent. However, to date, there is no instrument platform sufficient to characterize river basins' hydrologic couplings and feedbacks, with many processes and impacts left almost entirely unobserved (e.g., snowmelt floods). Characterization at the river basin scale will require a more holistic vision and a far greater commitment from the environmental science community. It will require new designs and implementation of integrated instrumentation, a new generation of models, and a management framework that clearly addresses the human‐climate‐terrestrial interactions impacting our watersheds and river basins. Initially, we propose that existing “similarity classifications” (e.g., regional soil, geologic, ecologic, hydrographic digital products) can provide a starting point for organizing historical data and initiating a long‐term adaptive, multiscale observing strategy. This vision paper outlines instrumentation platforms for point, plot, reach, and hillslope scales that could be located within the “characteristic” landscapes of river basins. The network of observing platforms then forms the basis of a “Hydro‐Mesonet” that can potentially support multiscale, multiprocess scientific studies necessary to understand and improve forecasts of our water resources at the river basin scale. This paper concludes with a discussion of how a network of such sites can support research at the level of the individual researcher and scale to the level of community‐wide initiatives.

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

“…To use observations to constrain uncertainty, diagnose model shortcomings and identify change requires overcoming several limitations. Firstly, it is unclear how to prioritize limited resources for observations towards those of the highest value or greatest need in constraining uncertainties or adapting predictive frameworks (Kollat et al, 2011;Mishra and Coulibaly, 2009;Reed et al, 2006;Reed and Kollat, 2012). Secondly, it is unclear which features of model output offer the most diagnostic insight, particularly when attempting to identify the effects of change (Gupta et al, 2008).…”

“…Environmental change should not only drive adaptation of hydrologic models, but also of hydrologic observational strategies (Reed et al, 2006). At present our national, regional, and local observation strategies are largely ad-hoc, non-adaptive, aligned towards understanding status quo behaviors (rather than changing systems) and disconnected from evolving water resources policy and management needs (Davis et al, 1979;Moss, 1979b;Langbein, 1979;United States Geological Survey, 1999;Kaushal et al, 2010;Lindenmayer and Likens, 2009).…”

“…Adaptive observation networks could enable us to transition hydrology towards real-time learning. By coupling model learning and observation strategies, we can envisage a systematic feedback between observation, investigation, prediction and management under continuously changing conditions (Reed et al, 2006).…”

Advances in the identification and evaluation of complex environmental systems models

“…This is particularly challenging and important when seeking to detect and/or predict the impact of long-term systematic changes (non-stationarity). Reed et al (2006) posit that our ability to understand human-climate impacts on environmental systems will require a paradigmatic shift away from static observation network design frameworks. Alternatively, new tools are needed for adaptively characterizing knowledge gaps and critical system gradients in both space and time.…”

“…Problems include those of conceptualization and parameterization of underlying processes, and of our limited ability to observe important subsurface characteristics at the scale of interest (Beven 1989). New theory and new observational capabilities will be needed to achieve better representations of environmental systems (Wagener & Gupta 2005;Kirchner 2006;Reed et al 2006;Gupta et al 2008). New concepts for process-based models have been put forward in recent years, but more testing is required to assess whether previous limitations of physically-based models have yet been overcome (e.g.…”