CLIMACS: A method for stochastic generation of continuous climate projected point rainfall for urban drainage design

Thorndahl, Søren Liedtke; Andersen, Christoffer Bang

doi:10.1016/j.jhydrol.2021.126776

Cited by 8 publications

(4 citation statements)

References 37 publications

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“…These design events have been used to drive hydrodynamic models, from “rain on grid” models often used over large areas (Bates et al., 2023) to models also representing linkages with sewer networks which are often applied over small urban domains (Chen et al., 2010). Some studies also stochastically generate design rainfall events for a particular return period using a weather generator approach, allowing the spatiotemporal features of the IDF data to be modeled (Burton et al., 2008; Kilsby et al., 2007; Thorndahl & Andersen, 2021). Model outputs typically provide estimates of inundation depth and extent based on the rainfall return period as opposed to the return period of a given flood frequency.…”

Section: Introductionmentioning

confidence: 99%

Future Change in Urban Flooding Using New Convection‐Permitting Climate Projections

Archer,

Hatchard,

Devitt

et al. 2024

Water Resources Research

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Rainfall intensity in the United Kingdom is projected to increase under climate change with significant implications for rainfall‐driven (combined pluvial and fluvial) flooding. In the UK, the current recommended best practice for estimating changes in pluvial flood hazard under climate change involves applying a simple percentage uplift to spatially uniform catchment rainfall, despite the known importance of the spatial and temporal characteristics of rainfall in the generation of pluvial floods. The UKCP Local Convective Permitting Model (CPM) has for the first time provided the capacity to assess changes in flood hazard using hourly, 2.2 km CPM precipitation data that varies in space and time. Here, we use an event set of ∼13,500 precipitation events across the three UKCP Local epochs (1981–2000, 2021–2040, and 2061–2080) to simulate rainfall‐driven flooding using the LISFLOOD‐FP hydrodynamic model at 20 m resolution over a 750 km2 area of Bristol and Bath, UK. We find that both the event set and uplift approaches indicate an increase in flood hazard under near‐term (2021–2040) and future (2061–2080) climate change. However, the event set produces markedly higher estimates of flood hazard when compared to the uplift approach, ranging from 19% to 49% higher depending on the return period. This suggests including the full spatiotemporal rainfall variability and its future change in rainfall‐driven flood modeling is critical for future flood risk assessment.

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Section: Introductionmentioning

confidence: 99%

Future Change in Urban Flooding Using New Convection‐Permitting Climate Projections

Archer,

Hatchard,

Devitt

et al. 2024

Water Resources Research

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“…CLIMACS generates ensembles of stochastically sampled rain events not aiming to match the true climate and precipitation pattern but to statistically represent rainfall for designing hydrological systems. The paper investigates ensemble variability and the required ensemble size in a given design process by Thorndahl and Andersen [17]. Historical research efforts have predominantly emphasized the fitting and evaluation of statistical distributions to analyze rainfall patterns.…”

Section: Introductionmentioning

confidence: 99%

Probability distributions in Kerala’s rainfall: implications for hydro energy planning

Baranitharan,

Chandran,

Subramaniyan Mathan

et al. 2024

IJECE

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Heavy rainfall has consistently acted as the primary catalyst for floods, resulting in numerous casualties and significant economic losses globally. Rainfall forecasting is accomplished by analysing existing rainfall data, which is then used to analyse the hydraulic system’s features. Gaining an understanding of rainfall requirements is a crucial challenge for every location, particularly in the case of India, given its diverse geographical area, population, and other influencing factors that impact various demands. This study evaluated the rainfall data for a span of 1990-2021 in six districts of Kerala State, India. To match the rainfall data from all districts, we utilized both Kaumarasamy-distribution and Dagum-distributions. Various Probabilistic tests, were employed to comparing these distributions. The results revealed that, in Kasargod, the Kumarasamy distribution demonstrates superior goodness-of-fit with the lowest Kolmogorov-Smirnov statistic (0.0597) and Anderson-darling statistic (2.271). However, in Wayanad, Malappuram, Palakkad, Idukki, and Trivandrum, the Dagum distribution consistently exhibits the most accurate fit, evident from its lowest Kolmogorov-Smirnov statistics (0.07447, 0.05435, 0.0556, 0.03636, 0.04291) and favourable Chi-Squared statistics (19.471, 8.4907, 19.239, 5.7318, 7.5297). These results emphasize the regional variation in precipitation data and the suitability of specific distribution models for accurate representation across differentlocations.

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“…In the overall planning of the city, the relevant departments of the municipality should grasp the key and difficult points of water supply and drainage design [5]. And taking it as the fundamental starting point, we strive to develop a scientific and reasonable urban water supply and drainage design scheme to further promote the economic construction and future development of the city [6][7].…”

Section: Introductionmentioning

confidence: 99%

The Planning & Designing of Urban Building Water Supply and Drainage Engineering in AI Era

Jiang

2023

Applied Mathematics and Nonlinear Sciences

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To explore how to improve the quality of urban building water supply and drainage engineering and further promote the economic construction of urban buildings, this paper combines artificial intelligence with water supply and drainage engineering of urban buildings. A detailed analysis of water supply and drainage engineering is unfolded, and the combination of BP neural network algorithm and particle swarm algorithm is used to improve the convergence accuracy of the algorithm and network generalization ability to optimize the BP neural network algorithm and construct the artificial intelligence algorithm model. Through experiments in the artificial intelligence algorithm model, the actual output quality parameter is 82, and the difference with the desired output quality parameter of 81 is 1, while in the ordinary design, the actual output quality parameter is 70, and the difference with the desired output quality parameter is 11, get in the artificial intelligence model the desired output and the expected output close. It shows that combining artificial intelligence with urban building water supply and drainage engineering can improve the quality of the project, which is more accurate and superior to the ordinary design and reduce the project cost.

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CLIMACS: A method for stochastic generation of continuous climate projected point rainfall for urban drainage design

Cited by 8 publications

References 37 publications

Future Change in Urban Flooding Using New Convection‐Permitting Climate Projections

Future Change in Urban Flooding Using New Convection‐Permitting Climate Projections

Probability distributions in Kerala’s rainfall: implications for hydro energy planning

The Planning & Designing of Urban Building Water Supply and Drainage Engineering in AI Era

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