Uncertainty based modeling of rainfall-runoff: Combined differential evolution adaptive Metropolis (DREAM) and K-means clustering

Zahmatkesh, Zahra; Karamouz, ‪Mohammad; Nazif, Sara

doi:10.1016/j.advwatres.2015.06.012

Cited by 49 publications

(22 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parameters whose variation (over the respective possible range) resulted in more than a 30% change in the annual CSO volume were picked for the uncertainty analysis, which leads to the selection of rainfall (R) and RWH capacity (C). The importance of R and C as significant sources of uncertainty was also confirmed in previous hydrologic studies that focused on rainfall-runoff modeling [57,73] and RWH design [74][75][76][77][78], respectively. Figure 1) Input parameter Sampled from a gamma distribution ( Figure S1).…”

Section: Selected Parameterssupporting

confidence: 69%

Combining Hydrologic Analysis and Life Cycle Assessment Approaches to Evaluate Sustainability of Water Infrastructure: Uncertainty Analysis

Tavakol-Davani

Rahimi

Burian

et al. 2019

Water

View full text Add to dashboard Cite

The goal of this research is identifying major sources of uncertainty of an environmentally-sustainable urban drainage infrastructure design, based on hydrologic analysis and life cycle assessment (LCA). The uncertainty analysis intends to characterize and compare relative roles of unreliability, incompleteness, technological difference, and spatial and temporal variation in life cycle impact assessment (LCIA) data, as well as natural variability in hydrologic data. Uncertainties are analyzed using a robust Monte Carlo simulation approach, performed by High Throughput Computing (HTC) and interpreted by Morse-Scale regression models. The uncertainty analysis platform is applied to a watershed-scale LCA of rainwater harvesting systems (RWH) to control combined sewer overflows (CSOs). To consider the watershed-scale implications, RWH is simulated to serve for both the water supply and CSO control in an urban watershed in Toledo, Ohio, USA. Results suggest that, among the studied parameters, rainfall depth (as a hydrologic parameter) caused more than 86% of the uncertainty, while only 7% of the uncertainty was caused by LCIA parameters. Such an emphasis on the necessity of robust hydrologic data and associated analyses increase the reliability of LCA-based urban water infrastructure design. In addition, results suggest that such a topology-inspired model is capable of rendering an optimal RWH system capacity as a function of annual rainfall depth. Specifically, if the system could capture 1/40th of annual rainfall depth in each event from rooftops, the RWH system would be optimal and, thus, lead to minimized life cycle impacts in terms of global warming potential (GWP) and aquatic eco-toxicity (ETW). This capture depth would be around 2.1 cm for Toledo (given an 85 cm/year rainfall and 200 m2 typical roof area), which could be achieved through an RWH system with 4.25 m3 capacity per household, assuming a uniform plan for the entire studied watershed. Capacities smaller than this suggested optimal value would likely result in loss of RWH potable water treatment savings and CSO control benefits, while capacities larger than the optimal would likely incur an excessive wastewater treatment burden and construction phase impacts of RWH systems.

show abstract

Section: Selected Parameterssupporting

confidence: 69%

Combining Hydrologic Analysis and Life Cycle Assessment Approaches to Evaluate Sustainability of Water Infrastructure: Uncertainty Analysis

Tavakol-Davani

Rahimi

Burian

et al. 2019

Water

View full text Add to dashboard Cite

show abstract

“…; Zahmatkesh et al . ). Moreover, the study of NPS pollution at different spatial scales is challenging due to variability in pollution sources and geochemical changes during the chemical transport (Donohue et al .…”

Section: Introductionmentioning

confidence: 97%

“…and large spatio-temporal variability in rainfall (Andr eassian et al 2001;Antonopoulos et al 2001;Shen et al 2012). The interaction of hydrological processes and variations in the underlying surface conditions contributes to large spatio-temporal variations in NPS pollution in the surface runoff (Ponader et al 2007;Cherry et al 2008;Zahmatkesh et al 2015). Moreover, the study of NPS pollution at different spatial scales is challenging due to variability in pollution sources and geochemical changes during the chemical transport (Donohue et al 2006;Wickham et al 2006;Gemesi et al 2011;Alam & Dutta 2012).…”

Section: Introductionmentioning

confidence: 99%

Characterization of the nonpoint source pollution into river at different spatial scales

Wang

Lin

2018

Water & Environment J

View full text Add to dashboard Cite

The objectives of this study were to investigate the effects of rainfall and underlying surface conditions on nonpoint source (NPS) pollution loads and to identify the uncertainty in NPS pollution loads at different spatial scales in the Fuxi River basin, China. Data on monitored daily flow rates and concentrations of ammonium nitrogen, total nitrogen, total phosphorus and permanganate index at the sub‐basin and basin scales were collected for a period from 2013 to 2015. Dynamic time warping distance and information measures were used to characterize pollution loads and determine the uncertainties. The results indicate that, at both sub‐basin and basin scales, NPS pollution loads increased nonlinearly with rainfall until it reached 38.4 mm, and subsequently, the NPS pollution loads stabilized. The underlying surface conditions affected the NPS pollution loads more profoundly than rainfall. Additionally, the uncertainty in NPS pollution loads increased with the spatial scales.

show abstract

“…Attempts have been made to provide results with high reliability for decision making by accurately predicting the changes in runoff intensity and frequency [4,5]. Zahmatkesh et al [6] proposed a rainfall-runoff model based on K-means clustering to minimize the uncertainties of the parameters constituting the model and applied it to the Bronx River watershed, New York City. Using this model, the runoff based on the predicted rainfall was simulated to predict the future runoff for the watershed.…”

Section: Introductionmentioning

confidence: 99%

Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

Yangho

Park²,

Lee

et al. 2019

Water

View full text Add to dashboard Cite

The runoff from heavy rainfall reaches urban streams quickly, causing them to rise rapidly. It is therefore of great importance to provide sufficient lead time for evacuation planning and decision making. An efficient flood forecasting and warning method is crucial for ensuring adequate lead time. With this objective, this paper proposes an analysis method for a flood forecasting and warning system, and establishes the criteria for issuing urban-stream flash flood warnings based on the amount of rainfall to allow sufficient lead time. The proposed methodology is a nonstructural approach to flood prediction and risk reduction. It considers water level fluctuations during a rainfall event and estimates the upstream (alert point) and downstream (confluence) water levels for water level analysis based on the rainfall intensity and duration. We also investigate the rainfall/runoff and flow rate/water level relationships using the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) and the HEC's River Analysis System (HEC-RAS) models, respectively, and estimate the rainfall threshold for issuing flash flood warnings depending on the backwater state based on actual watershed conditions. We present a methodology for issuing flash flood warnings at a critical point by considering the effects of fluctuations in various backwater conditions in real time, which will provide practical support for decision making by disaster protection workers. The results are compared with real-time water level observations of the Dorim Stream. Finally, we verify the validity of the flash flood warning criteria by comparing the predicted values with the observed values and performing validity analysis.

show abstract

Uncertainty based modeling of rainfall-runoff: Combined differential evolution adaptive Metropolis (DREAM) and K-means clustering

Cited by 49 publications

References 36 publications

Combining Hydrologic Analysis and Life Cycle Assessment Approaches to Evaluate Sustainability of Water Infrastructure: Uncertainty Analysis

Combining Hydrologic Analysis and Life Cycle Assessment Approaches to Evaluate Sustainability of Water Infrastructure: Uncertainty Analysis

Characterization of the nonpoint source pollution into river at different spatial scales

Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

Contact Info

Product

Resources

About