Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding

Abstract

Abstract. This paper provides an overview of this IAHS symposium and PIAHS proceeding on "hydrologic nonstationarity and extrapolating models to predict the future". The paper provides a brief review of research on this topic, presents approaches used to account for nonstationarity when extrapolating models to predict the future, and summarises the papers in this session and proceeding. Hydrologic nonstationarity and implicationsOverviewThe commentary by Milly et al. (2008) has initiated significant discussions and continuing progression of research on hydrologic nonstationarity. The term "hydrologic nonstationarity" has been used to describe many things, ranging from different climate-runoff relationships evident in different periods within a long hydroclimate time series to changes in hydroclimate characteristics and dominant hydrological processes in an increasingly warmer and higher CO 2 world. Hydrologists have always represented stationarity and nonstationarity (which is difficult to distinguish statistically in natural systems) as best they could and their implications on water resources and related systems, but modelling this adequately will become increasingly challenging in a world driven by anthropogenic changes. The constancy of laws and patterns has always been and will always be "stationary". It is our understanding or lack of these and the constancy of variables or characteristics at different times that may appear "nonstationary". For example, a hydroclimate time series can be considered "stationary" over thousands or millions of years, in that we can represent statistically or stochastically the characteristics and variability over time and space scales or even develop a precise understanding of the processes from the very long record. But of course, the characteristics of the different periods will always be different (exhibiting variability over different time and space scales), that is, nonstationary over time. The practical issue then is not whether hydroclimate systems are stationary or nonstationary, but whether the nonstationarity is substantial enough to require a change in existing system characterisation, conceptualisation or modelling for a particular hydrologic design, operation and planning.Hydrologists have excelled in developing models for numerous applications, through analysing and interpreting climate and hydrologic data to understand hydrologic processes, conceptualising the processes in hydrological models, and calibrating and testing models against observations. These models are particularly good in predicting the streamflow response to changes in the climate inputs and catchment characteristics. These models, when developed adequately using relatively long historical records that encapsulate the range of hydroclimate conditions, should be able to predict hydrologic responses to changes in the climate inputs over the near and medium term.However, extrapolating hydrological models to predict further into the future that is influenced by anthropogenic change is challenging as ...

A comprehensive sensitivity and uncertainty analysis for discharge and nitrate-nitrogen loads involving multiple discrete model inputs under future changing conditions

“…Yet often different model setups and different sets of parameters in a model can perform equally well to reproduce historical observations of the variables of interest. Equifinality is a well-known issue in hydrologic modeling that has been extensively addressed in the literature (e.g., Schulz et al, 1999;Beven, 1996Beven, , 2006Beven and Freer, 2001), where multiple model structures (e.g., Clark et al, 2008) and model parametrizations (e.g., Schulz et al, 1999) represent observations equally well and thus cannot be rejected (Beven, 2006). An adequate representation of historical data does not necessarily assure that different model setups agree when extrapolating to future conditions (Chiew and Vaze, 2015;Milly et al, 2008).…”

Assessment of the effects of climate change on water balance components in the upper Erer subbasin, Ethiopia

“…The CMhyd models that defined as Climatic Model Data for the Hydrologic Modeling Tool have well performance in other watersheds [ 43 , 44 ] in extracting and bias-correction of CORDEX RCMs for hydrological modelling. Several hydrological models have been in use to model hydrological processes using spatially distributed information and time series data [ [45] , [46] , [47] , [48] ] under data scarce. The Soil and Water Assessment Tool (SWAT) model is implemented to quantify hydrological response and climate change studies under data scarce regions [ 17 , 18 , 38 , [49] , [50] , [51] , [52] ] and also used in this study.…”

Improving Efficiency of Hydrological Prediction Based on Meteorological Classification: A Case Study of GR4J Model

“…Therefore, under the influence of climate change, it is more realistic to think that the rainfall-runoff model parameters have the characteristics of changing with time, i.e., time varying [5][6][7]. The change of basin climate and underlying surface conditions is very likely to cause the change of rainfall-runoff model parameters [8][9][10][11][12].…”