Development of a Stepwise‐Clustered Multi‐Catchment Hydrological Model for Quantifying Interactions in Regional Climate‐Runoff Relationships

Abstract

The concurrent variations of multi‐catchment runoffs exist widely in natural hydrological systems. Approaching such variations requires integrated analyses of not only the climate‐runoff relationships within individual catchments but also the distributive interactions among multiple catchments. In this study, a stepwise‐clustered multi‐catchment hydrological model (SCMW) is proposed to tackle the interactive relationships among multi‐catchment runoffs and their concurrent variations within a watershed system. Through multivariate inference based on Wilks likelihood ratio criterions and F tests, the proposed model can deal with both continuous and discrete variables as well as nonlinear relations among multiple variables without the assumptions of functional relationships. The proposed SCMW is applied to the Iskut‐Stikine Watershed, Canada. The effects of multiple uncertain factors are traced through multilevel factorial analysis. Overall, the accuracies of SCMW‐simulated mean and interval flows demonstrate that the developed method can well reproduce the distributive and interactive relationships between climatic variables and multi‐catchment runoffs. At the same time, the contributions of climate variables can be quantified; for example, it is found that near‐surface minimum temperature (at Below Johnson Station) can explain 25.6% concurrent variations of multi‐catchment runoffs, and vapor pressure (at Below Johnson Station) and precipitation (at Telegraph Creek Station) can explain 17.5% and 4.6% of such variations, respectively. The results of the multilevel factorial analysis indicate that the uncertainties in the simulated runoff levels are mainly from the modeling approach (43.3%); also, significant effects exist from interactions among multiple impact factors. The molding of concurrent variations of multi‐catchment runoffs through SCMW is helpful for improving simulation accuracy for watersheds with spatially heterogeneous climate‐runoff relationships.

Ensemble Bayesian Model Averaging Projections of Wind‐Speed Extremes for Wind Energy Applications Over China Under Climate Change

“…In the context of global warming, the state of the atmosphere is projected to change dramatically, leading to fluctuations in regional atmospheric flows (Lu et al., 2022; Michaelis et al., 2022; Wang et al., 2022). Understanding the spatial and temporal variations of regional extreme wind speeds under climate change is critical for regional wind energy utilization and planning.…”

“…Figure 3 shows the spatial distribution of W ref and W 50 for the baseline period and the corresponding margin of error (MOE, defined as half the width of the 90% prediction interval), as well as the lower and upper bounds (LB; UB) of the 90% prediction intervals. As can be seen in In the context of global warming, the state of the atmosphere is projected to change dramatically, leading to fluctuations in regional atmospheric flows (Lu et al, 2022;Michaelis et al, 2022;Wang et al, 2022). Understanding the spatial and temporal variations of regional extreme wind speeds under climate change is critical for regional wind energy utilization and planning.…”

Collided with COVID-19 Pandemic, the 2020 Yangtze Flood Is Exceptionally Severe

“…To avert negative impacts of flood, preparedness measures such as evacuation is increasingly necessary. As of July 28, the flood disaster affected 54.811 million people, and 3.76 million ones were relocated and resettled (Ministry of Emergency Management of the People's Republic of China, 2020; Wang et al, 2022). At the same time, the evacuation of residents affects their capacities to maintain social distancing, lockdown, or other necessary measures to curtail the spread of virus.…”