Human activities exert global‐scale impacts on our environment with significant implications for freshwater‐driven services and hazards for humans and nature. Our approach to the science of hydrology needs to significantly change so that we can understand and predict these implications. Such an adjustment is a necessary prerequisite for the development of sustainable water resource management strategies and to achieve long‐term water security for people and the environment. Hydrology requires a paradigm shift in which predictions of system behavior that are beyond the range of previously observed variability or that result from significant alterations of physical (structural) system characteristics become the new norm. To achieve this shift, hydrologists must become both synthesists, observing and analyzing the system as a holistic entity, and analysts, understanding the functioning of individual system components, while operating firmly within a well‐designed hypothesis testing framework. Cross‐disciplinary integration must become a primary characteristic of hydrologic research, catalyzing new research and nurturing new educational models. The test of our quantitative understanding across atmosphere, hydrosphere, lithosphere, biosphere, and anthroposphere will necessarily lie in new approaches to benchmark our ability to predict the regional hydrologic and connected implications of environmental change. To address these challenges and to serve as a catalyst to bring about the necessary changes to hydrologic science, we call for a long‐term initiative to address the regional implications of environmental change.

Computations of precipitation recycling using analytical models are generally performed under the assumption of negligible change in moisture storage in the atmospheric column. Because the moisture storage term is nonnegligible at smaller time scales, most recycling studies using analytical models are done at monthly or longer time scales. A dynamic precipitation recycling model, which incorporates the change in moisture storage, is developed. It is derived formally from the conservation of mass equation and is presented in a simple and computationally efficient form. This model allows for recycling analysis at a range of temporal scales, from daily to monthly and longer. In comparison to the traditional models that do not include the storage term, the new model presents almost identical spatial and temporal variability, but predicts recycling ratios that are 12%–33% larger at a monthly level. The dynamic model is used to study the variability of monthly precipitation recycling over the conterminous United States using Reanalysis-II data from 1979 to 2000. On average, the southeastern and southwestern parts of the country exhibit high summer recycling ratios, contrasting with the low values in the northeastern and northwestern United States. The Colorado region also presents high recycling ratios. Dominant modes of spatiotemporal variability in recycling are identified using EOF analysis. The first mode captures strong recycling ratios over the western United States during the summers of 1986, 1992, and 1998. The second mode captures anomalous high recycling ratios during 1988 and 1989 over the central part of the country, and anomalous low ratios during 1980 and 1993.

Issues of scaling characteristics in spatial rainfall have attracted increasing attention over the last decade. Several methods based on simple and multiscaling and multifractal ideas have been proposed and parameter estimation techniques developed for the hypothesized models. Simulations based on these models have realistic resemblance to “generic rainfall fields.” In this research we analyze rainfall data for scaling characteristics without an a priori assumed model. We look at the behavior of rainfall fluctuations obtained at several scales, via orthogonal wavelet transform of the data, to infer the precise nature of scaling exhibited by spatial rainfall. The essential idea behind the analysis is to segregate large‐scale (long wavelength) features from small‐scale features and study each of them independently. The hypothesis is set forward that rainfall might exhibit scaling in small‐scale fluctuations, if at all, and at large scale this behavior will break down to accommodate the effects of external factors affecting the particular rain‐producing mechanism. The validity of this hypothesis is examined. In the first of these papers we develop the methodology for the segregation of large‐ and small‐scale features and apply it to a severe spring time midlatitude squall line storm. The second paper (Kumar and Foufoula‐Georgiou, this issue) develops a framework for testing the presence and studying the nature of self‐similarity in the fluctuations.