The Role of Inflow and Infiltration (I/I) in Urban Water Balances and Streamflow Regimes: A Hydrograph Analysis Along the Sewershed‐Watershed Continuum

Zhang, Kun; Sebo, Spencer; McDonald, Walter; Bhaskar, Aditi S.; Shuster, William D.; Stewart, Ryan D.; Parolari, Anthony J.

doi:10.1029/2022wr032529

Cited by 4 publications

(2 citation statements)

References 78 publications

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“…The lack of stormwater sewer network representation in the model might be a contributing factor to the estimation of the delayed peak flows. A considerable amount of rainwater remains motionless inundating topographic lows and also, sheet overland flow may take more time to reach the watershed outlet than concen trated flows in a sewer network (Zhang et al, 2023). One my note that the surrogate model can capture these characteristics of tRIBS-OFM simulation.…”

Section: Resultsmentioning

confidence: 99%

Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models

Tran,

Ivanov,

et al. 2023

Geophysical Research Letters

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Applications of process‐based models (PBM) for predictions are confounded by multiple uncertainties and computational burdens, resulting in appreciable errors. A novel modeling framework combining a high‐fidelity PBM with surrogate and machine learning (ML) models is developed to tackle these challenges and applied for streamflow prediction. A surrogate model permits high computational efficiency of a PBM solution at a minimum loss of its accuracy. A novel probabilistic ML model partitions the PBM‐surrogate prediction errors into reducible and irreducible types, quantifying their distributions that arise due to both explicitly perceived uncertainties (such as parametric) or those that are entirely hidden to the modeler (not included or unexpected). Using this approach, we demonstrate a substantial improvement of streamflow predictive accuracy for a case study urbanized watershed. Such a framework provides an efficient solution combining the strengths of high‐fidelity and physics‐agnostic models for a wide range of prediction problems in geosciences.

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Section: Resultsmentioning

confidence: 99%

Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models

Tran,

Ivanov,

et al. 2023

Geophysical Research Letters

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“…The DA method, which is developed based on the multiple linear regression (MLR) model, can be used to evaluate the relative importance of a single factor in the MLR model by examining the change in R 2 (coefficient of determination) resulting from adding a predictor to all possible subset regression models [46][47][48]. As a supplement to the regression analysis, when there is multicollinearity between independent variables, the dominance analysis can de-emphasize redundant predictors, thus reducing the impact of collinearity between independent variables and better representing the statistical contribution of independent variables [49,50]. Similar to correlation analysis, for each grid point in the Indian region, the time series of each factor in X and those of LS GW Ep (denoted as Y) are used to build an MLR model:…”

Section: Correlation Analysis and Dominance Analysismentioning

confidence: 99%

Influences of Different Factors on Gravity Wave Activity in the Lower Stratosphere of the Indian Region

Hou,

Luo,

2024

Remote Sensing

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The gravity wave (GW) potential energy (Ep) in the lower stratosphere (LS) of the altitude range between 20 and 30 km over the Indian region (60°E–100°E, 0°–30°N) is retrieved using the dry temperature profiles from the Constellation Observing System for Meteorology Ionosphere and Climate-2 (COSMIC-2) radio occultation (RO) mission from December 2019 to November 2021. Through correlation analysis and dominance analysis (DA) methods, the impacts of multiple influencing factors on the local LS GW activity are quantified and compared. The results demonstrate that in the central and northern part of Indian region, the three factors, including the convective activity (using outgoing long-wave radiation as the proxy) mainly caused by the Indian summer monsoon, the mean zonal wind speed between 15 and 17 km, the height range where the maximum tropical easterly jet (TEJ) wind speed appears, and the mean zonal wind speed between 20 and 30 km, have the greatest impacts on the LS GW activity. In the southern part of the Indian Peninsula and over the Indian Ocean, the mean zonal wind shear between 20 and 30 km plays a dominant role in the LS GW activity, which is due to the fact that the GW energy can be attenuated by large background wind shears. It can be concluded that the LS GW activity in the Indian region is mainly influenced by the Indian summer monsoon, the TEJ, and the wind activity in the LS, while over different local areas, differences exist in which factors are the dominant ones.

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Uncertainty on flow rate and temperature measurement for the detection of illicit flows in sewers

Raimondi,

Sanfilippo,

Becciu

2024

Journal of Hydrology

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The Role of Inflow and Infiltration (I/I) in Urban Water Balances and Streamflow Regimes: A Hydrograph Analysis Along the Sewershed‐Watershed Continuum

Cited by 4 publications

References 78 publications

Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models

Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models

Influences of Different Factors on Gravity Wave Activity in the Lower Stratosphere of the Indian Region

Uncertainty on flow rate and temperature measurement for the detection of illicit flows in sewers

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