Resilience changes in watershed systems: A new perspective to quantify long-term hydrological shifts under perturbations

Abstract

Keywords:Critical slowing down Resilience indicator Convex model Hydrological regime shift Watershed systems s u m m a r y Natural hydrological regimes are essential to the stability of river basins. While numerous efforts have been put forth to characterize flow regime alterations driven by climate change and human activities, few approaches have been proposed to explore changes in watershed resilience. The present study attempted to introduce a systematic approach that can be used to identify the resilience change of river basins based on annual river discharge through the application of a convex model and the principle of critical slowing down. Specifically, a resilience indicator (p i ) that reflects streamflow autocorrelation at a given time was proposed to represent the temporal variation of the system resilience, and annual water discharge at representative hydrological stations located in upstream, midstream, and downstream regions of a river basin was used to reflect alterations in long-term hydrological processes and the stability of river basins. The application of this method to the Yellow River basin and Yangtze River basin indicated that the system resilience was lower in downstream regions compared to upstream regions. The Yellow River basin has suffered a decrease in resilience in its lower reaches since 1971, which extended to the middle reaches in 1987 and upper reaches in 1990. Similarly, recent observation of the resilience change in the Yangtze River basin indicated that resilience in its lower reaches has likely decreased since 2002, and this low resilience extended to the middle reaches in 2005. Overall, our study presents a new method to predict potential decreases of resilience in complex large scale watershed systems where mechanistic insight is insufficient to build reliable basin-scale hydrological, climate, ecosystem integrated models.

An Early Warning System for Flood Detection Using Critical Slowing Down

“…Note that hydrologists have a long history of research using indicators such as autocorrelation, variance, and power spectrum to study hydrological data [ 22 , 23 ] even though not for critical slowing down or early warning system purposes. We also found one paper [ 24 ] that uses the theory of critical slowing down in the hydrological field to quantify the long-term hydrological shifts based on river discharge, however, the authors just used one indicator which was autocorrelation for their analysis. Further, throughout this study, the term of an early warning signal is meant by any signal that is determined before the day of the flood event.…”

Quantifying groundwater sensitivity and resilience over peninsular India

“…In the present study, we use a systematic approach to understand the stability and resilience of the complex groundwater system towards natural and anthropogenic disturbances based upon a convex model using the principle of critical slowing down theory. This theory suggests that the system with high autocorrelation within the state variable would improve slowly, following disturbances, pertaining to low resilience attributes and vice versa (Lenton, 2011;Qi et al, 2016;Scheffer et al, 2009). Here, we use groundwater sensitivity (GWS) as the state variable to understand the temporal changes in resilience of groundwater system over the period (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007).…”

“…Sensitivity of groundwater recharge due to precipitation is a critical issue, and evaluation of its ability to recover from the disturbed state is of paramount importance. A new approach of quantifying the resilience potential of a dynamic system incorporating critical slowing down theory has been suggested (Qi, Feng, Sun, & Yang, 2016; Scheffer et al, 2009). In the present study, it is used as a tool to assess the ability of the system to recover from the perturbations (due to climatic and anthropogenic factors) by quantifying the autocorrelations of state variables.…”

A Systematic Review of Quantitative Resilience Measures for Water Infrastructure Systems

“…The higher positive value of the WSR shows a more resilient reservoir system, since it indicates sufficient reservoir storage to meet demand. Qi et al [43] described resilience of a river basin and its time variation based on the concept of "critical slowing down" as a generic leading indicator of low resilience of system, which has been used for description of stability and resilience in ecology fields (For more information, see [68]). A system state of critical slowing down is generally determined by the increasing autocorrelation in time-series of a system state variable.…”

“…It was also identified that the selected measures, except Hashimoto et al [29], Mehran et al [37], Qi et al [43], Todini [31], and Jayaram and Srinivasan [34], have not addressed long-term variation of system resilience. Many of the selected measures evaluate system resilience as "a snapshot in time [60]", so they could not reflect the variable nature of resilience.…”