Initial conditions of urban permeable surfaces in rainfall-runoff models using Horton's infiltration

Davidsen, Steffen; Löwe, Roland; Ravn, Nanna Høegh; Jensen, Lina N.; Arnbjerg‐Nielsen, Karsten

doi:10.2166/wst.2017.580

Cited by 21 publications

(13 citation statements)

References 11 publications

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“…This runoff generation process is referred to as the saturation-excess runoff. Q/P (%) of a watershed will become larger when effective soil water-storage is reducing [19][20][21][22]. Even a medium-sized storm event over a saturated watershed with a high runoff coefficient may cause severe flooding; therefore, it is critical to assess the performance of different runoff models under different runoff coefficient conditions in order to select the best performing parsimonious urban rainfall-runoff model for urban runoff estimation.…”

Section: Methodology and Study Sitementioning

confidence: 99%

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

et al. 2022

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The curve number (CN) rainfall–runoff model is widely adopted. However, it had been reported to repeatedly fail in consistently predicting runoff results worldwide. Unlike the existing antecedent moisture condition concept, this study preserved its parsimonious model structure for calibration according to different ground saturation conditions under guidance from inferential statistics. The existing CN model was not statistically significant without calibration. The calibrated model did not rely on the return period data and included rainfall depths less than 25.4 mm to formulate statistically significant urban runoff predictive models, and it derived CN directly. Contrarily, the linear regression runoff model and the asymptotic fitting method failed to model hydrological conditions when runoff coefficient was greater than 50%. Although the land-use and land cover remained the same throughout this study, the calculated CN value of this urban watershed increased from 93.35 to 96.50 as the watershed became more saturated. On average, a 3.4% increase in CN value would affect runoff by 44% (178,000 m3). This proves that the CN value cannot be selected according to the land-use and land cover of the watershed only. Urban flash flood modelling should be formulated with rainfall–runoff data pairs with a runoff coefficient > 50%.

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Section: Methodology and Study Sitementioning

confidence: 99%

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

et al. 2022

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“…Contrarily, some reported that the usage of CN in representing a watershed is often contradictory in describing related land cover areas [13]. Some researchers still reported difficulty to calibrate the existing model [14,15] while other studies started to incorporate soil moisture and saturation-excess concepts in their modelling approach [16][17][18][19]. US researchers [2,20] were first to conduct large scale studies on the SCS CN model by analyzing more than half a million rainfall events across 24 states in the USA and reported an optimum λ = 0.05 to achieve better runoff modelling results than Equation (2) in USA.…”

Section: Introductionmentioning

confidence: 99%

Statistical and Type II Error Assessment of a Runoff Predictive Model in Peninsula Malaysia

2021

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Flood related disasters continue to threaten mankind despite preventative efforts in technological advancement. Since 1954, the Soil Conservation Services (SCS) Curve Number (CN0.2) rainfall-runoff model has been widely used but reportedly produced inconsistent results in field studies worldwide. As such, this article presents methodology to reassess the validity of the model and perform model calibration with inferential statistics. A closed form equation was solved to narrow previous research gap with a derived 3D runoff difference model for type II error assessment. Under this study, the SCS runoff model is statistically insignificant (alpha = 0.01) without calibration. Curve Number CN0.2 = 72.58 for Peninsula Malaysia with a 99% confidence interval range of 67 to 76. Within these CN0.2 areas, SCS model underpredicts runoff amounts when the rainfall depth of a storm is < 70 mm. Its overprediction tendency worsens in cases involving larger storm events. For areas of 1 km2, it underpredicted runoff amount the most (2.4 million liters) at CN0.2 = 67 and the rainfall depth of 55 mm while it nearly overpredicted runoff amount by 25 million liters when the storm depth reached 430 mm in Peninsula Malaysia. The SCS model must be validated with rainfall-runoff datasets prior to its adoption for runoff prediction in any part of the world. SCS practitioners are encouraged to adopt the general formulae from this article to derive assessment models and equations for their studies.

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“…This may indicate that either the initial loss in the model needs to be differentiated according to soil moisture, which indicates a parameter assessment error, or that the imperviousness is lower for events with a low soil moisture content, which indicates a process knowledge error. Without giving a precise answer, we refer to the literature dealing with this specific knowledge gap in urban drainage modelling (Thorndahl et al 2008;Davidsen et al 2018;Nielsen et al 2019).…”

Section: Signatures and Surrounding States Illustrating Process Knowl...mentioning

confidence: 99%

Using multi-event hydrologic and hydraulic signatures from water level sensors to diagnose locations of uncertainty in integrated urban drainage models used in living digital twins

Pedersen

Borup

et al. 2022

Water Science and Technology

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Digital twins of urban drainage systems require simulation models that can adequately replicate the physical system. All models have their limitations, and it is important to investigate when and where simulation results are acceptable and to communicate the level of performance transparently to end-users. This paper first defines a classification of four possible ‘locations of uncertainty’ in integrated urban drainage models. It then develops a structured framework for identifying and diagnosing various types of errors. This framework compares model outputs with in-sewer water level observations based on hydrologic and hydraulic signatures. The approach is applied on a real case study in Odense, Denmark, with examples from three different system sites: a typical manhole, a small flushing chamber, and an internal overflow structure. This allows diagnosing different model errors ranging from issues in the underlying asset database and missing hydrologic processes to limitations in the model software implementation. Structured use of signatures is promising for continuous, iterative improvements of integrated urban drainage models. It also provides a transparent way to communicate the level of model adequacy to end-users.

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Initial conditions of urban permeable surfaces in rainfall-runoff models using Horton's infiltration

Cited by 21 publications

References 11 publications

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

Statistical and Type II Error Assessment of a Runoff Predictive Model in Peninsula Malaysia

Using multi-event hydrologic and hydraulic signatures from water level sensors to diagnose locations of uncertainty in integrated urban drainage models used in living digital twins

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