# A Sink Screening Approach for 1D Surface Network Simplification in Urban Flood Modelling

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## Abstract

**:**

## 1. Introduction

- (i)
- The criteria reflecting the “small/big” conception concerning the sinks’ geometry may ignore the sinks’ primary subcatchment behaviour (“strong/poor”) of retaining runoffs in relation to flood inundations, and therefore may lead to the removal of strong runoff retention sinks, while saving poor ones, and vice versa;
- (ii)
- Due to the accumulated effect of the converging networks, negligible small volume losses from excluded sinks may upscale to substantial amounts, leading to massive overestimated flood volumes concentrated at a specific spot;
- (iii)
- The method tends to be case-dependent and thus challenged by identifying an optimal combination of these two criteria, i.e., a balanced result that achieves the maximal number of removed sinks and the minimal volume losses, simultaneously, across various landscapes;
- (iv)
- Treating the sink screening process homogeneously, the use of uniform criteria may overlook the spatial variability in case of large-scale areas (e.g., basins), where the intensified heterogeneity may affect the final screening results significantly.

## 2. Methodology

#### 2.1. The Volume Ratio Sink Screening Method

#### 2.2. Sink Screening Experiment Design

#### 2.2.1. Sink Reduction Tests

- (i)
- The total number of sink reductions.To quantify the reduction effect of the two approaches in the total number of sinks, an iteration procedure was programmed to obtain the sensitivity analysis results (the detailed workflow is illustrated in Figure A1a, Appendix A). By adopting stepwise incremental threshold values, this procedure was executed using different criteria (i.e., maximum depth, volume and $HR{V}_{ratio}$) within their predefined iteration ranges. For the geometry-based approach, a concatenation of the maximum depth and volume based on the logic operator “AND” is used. In order to clarify their individual reduction effect, each criterion was investigated in an independent iteration. Next, their combined effect was analysed and discussed to address their mutual interference. The detailed parameter settings for the sensitivity analysis are elaborated in Appendix B. To compare the results derived from different criteria, the obtained results were interpreted and analysed by: (i) the curves for the sink reduction reflecting the total number of the reduced sinks in relation to the change of the threshold values, and (ii) boxplots illustrating the distribution of the results. Here, the reduction rate (reduction rate = reduced number of sinks/origin number of sinks × 100%) was taken as the indicator. Six accumulated rainfalls of 30 mm, 50 mm, 70 mm, 90 mm, 110 mm and 130 mm covering rainfall return periods of 10–100 years were used to explore the $HR{V}_{ratio}$’s responses to various rainfall variations.
- (ii)
- Spatially varying reduction of sinks.The use of $HR{V}_{ratio}$ enables adaptive reductions over the variation of rainfalls. To invoke a spatially varying reduction based on rainfall heterogeneity, a rainfall recorded from a radar source (also referred to as “radar rainfall”) was used to compute each sink’s $HR{V}_{ratio}$, and the matching $HR{V}_{ratio}$-derived curves were produced from the same iteration procedures (Figure A1a, Appendix A). For comparison, other $HR{V}_{ratio}$-derived curves, disregarding the rainfall heterogeneity, were generated by assuming statistic values (i.e., maximum, mean and minimum) of associated radar rainfall cells as accumulated rainfalls. In addition, threshold values of 5%, 15% and 25% were selected as representatives of $HR{V}_{ratio}$, and the spatial variations of the removed sinks were summarized cell-wise (1000 m resolution) based on the radar rainfall grids of each of the three cases. Finally, the sink reduction rate was used as the indicator to maintain the comparison consistency for all three case areas.

#### 2.2.2. Volume Loss Reduction Tests

- (i)
- Quantification of volume losses (the volume loss spreading solver).In order to evaluate the volume loss accumulations over the convergence of stream branches, a volume loss spreading solver was developed to quantify the volume discrepancies in the 1D surface network. As suggested by Figure A1c in Appendix A, the general workflow illustrates two successive computations: (i) flood volume computations (i.e., blue zones, Figure A3c) and (ii) volume loss computations (i.e., red zones, Figure A3c). In order to quantify the flood volumes for each sink (i.e., ${V}_{spilled}$), a link-based fast-inundation spreading algorithm, as reported by [26], was used to enable a filling-and-spilling routine that distributes flood volumes to all sinks rapidly from following the sequence of the Shreve stream order [73]. Furthermore, the removed sinks contain volumes for storing of water, so an exclusion hereof may result in identical volumetric overestimations at downstream via the spillover (Figure A3a). Therefore, we modelled the overestimated volumes as the oil liquids following a spilling-and-remaining routine. Detailed computational equations, processes and the computation example are provided in Appendix C.
- (ii)
- The reduction of volume losses.In order to quantify impacts of the volume losses, which in turn proves the sensitivity of $V{L}_{ratio}$, the redistributed volume losses were computed by using the volume loss spreading solver and an iterative procedure was programmed to conduct sensitivity analysis using different $V{L}_{ratio}$ threshold values (see Figure A1b). Thus, the stepwise reductions in volume losses were investigated from the curves for the volume loss reduction, and were further discussed from the perspectives of (i) the source volume losses, (ii) the spilled volume losses and (iii) the remaining volume losses, where the $RMSE=\sqrt{\frac{1}{n}{\sum}_{i=1}^{n}V{L}_{i}^{2}}$ is taken as the indicator, and n is the total number of streams (polylines) or sink polygons. The detailed parameter settings for the sensitivity analysis are elaborated in Appendix B. To retrieve the consistent source volume losses for each case, the screening results based on $HR{V}_{ratio}^{Radar}$ of 15% were used. The spatially varying reductions in volume losses were investigated from maps, and $V{L}_{ratio}$ values of 50%, 20% and 5% were considered as representative threshold values. Once again, identical grid meshes (Section 2.2.1-(ii)) were used to sum up the $V{L}_{source}$ for each cell, while networks (polylines) and sink polygons were applied to explore the redistributed volume losses in $V{L}_{spilled}$ and $V{L}_{remaining}$.

## 3. Case Studies

## 4. Results

#### 4.1. Total Number of Sink Reductions

^{3}, constituting intuitively fine (detailed) curves. Here, a greater number of screening results (5–8 points, each represents one iteration) were seen before the number of sinks was halved, while minor differences of <20 percentage points were found between screening results. However, a ceiling effect was spotted for the three case areas’ screenings, in which their maximum reduction rates were limited <85%. Due to considerable variation in sinks’ volume values, e.g., the standard deviation of volumes = 33.9 m

^{3}for Greve (Table 1), the use of iteration ranges 0–10 m

^{3}is insufficient and covers only 85% of the total sinks. Whereas extending the iteration range may cover the remaining 15% of the sinks, the subsequent iteration works (iteration times) are tedious particularly when the incremental value of 0.128 m

^{3}is used to include several order of magnitudes higher volumes, e.g., 5,027,476 m

^{3}for Greve.

#### 4.2. Spatially Varying Sink Reductions

#### 4.3. Distribution and Redistribution of Volume Losses

^{3}for Greve, 20,000 m

^{3}and 3000 m

^{3}for the other two cases. In the generated sink polygons, $V{L}_{remaining}$ of 1200–1500 m

^{3}were spotted in the westernmost upstream regions of Greve. With the small accumulated rainfall of 23.6 mm, the generated runoff volumes were insufficient to top the local $V{L}_{source}$ over the spilling level. Thus, these $V{L}_{source}$ were retained locally and converted into equivalent $V{L}_{remaining}$. Likewise, Copenhagen City Center and Amagerbro obtained $V{L}_{remaining}$ of 1000–3000 m

^{3}and 1000–2000 m

^{3}in upstream regions. However, due to the progressively increased rainfalls for the central and downstream regions of Greve, most $V{L}_{source}$ were carried away along with massive spillovers, and, therefore, marginal $V{L}_{remaining}$ were found in those areas. In contrast, for the other two cases, the immense capacity of downstream sinks took in the substantial $V{L}_{spilled}$ from upstream spillovers, and thus higher $V{L}_{remaining}$ (i.e., 6000–10,000 m

^{3}for Copenhagen City Center and 5000–6000 m

^{3}for Amagerbro) were identified for these areas.

#### 4.4. The Reduction of Volume Losses

^{3}was identified for Copenhagen City Center with $V{L}_{ratio}=5$%. As a consequence of the reduced $V{L}_{source}$, the reduced networks and reduced number of sink polygons illustrate considerable reductions in terms of $V{L}_{spilled}$ and $V{L}_{remaining}$. Interestingly, as opposed to the RMSE of $V{L}_{source}$, higher sensitive changes were observed for $V{L}_{spilled}$ and $V{L}_{remaining}$, e.g., RMSE of 5–10 m

^{3}in $V{L}_{source}$ vs. the RMSE of 600–1400 m

^{3}in $V{L}_{spilled}$, Greve. This suggests that the RMSE for $V{L}_{source}$ may be too insensitive to indicate volume losses in 1D surface networks properly. Furthermore, given that the three cases were hit by extreme rainfalls, $V{L}_{spilled}$ performed relatively more responsive reductions in RMSE compared to $V{L}_{remaining}$ in response to the decrease of $V{L}_{ratio}$.

## 5. Discussion

#### 5.1. Sink Reductions

#### 5.2. Volume Loss Reductions

#### 5.3. Computational Efficiency and Accuracy in 1D Urban Surface Flood Modelling

## 6. Conclusions

- Considering accumulated rainfalls as the relative reference, $HR{V}_{ratio}$ performs an adaptive reduction in the total number of sinks, which indicates efficient reductions for extreme rainfalls. Based on the comparison of the three distinct cases, the sink screening performance of $HR{V}_{ratio}$ is stable, thus proving the general applicability and robustness of this proposed criterion. Furthermore, the inclusion of a radar rainfall for the computation of $HR{V}_{ratio}$ triggers spatially varying sink reductions. Based on the curve deviation deviations for the three cases, the significance of the rainfall heterogeneity affects the final sink screening result significantly. We therefore recommend the implementation of this method, especially for large-scale studies, in case that the significance of heterogeneity may intensify with the upscaled study area;
- In contrast, the geometry-based sink screening method is less adequate in sink reductions from four aspects: (i) the sink screening process based on the maximum depth is coarse, which reflects an oversensitive response in the total number of sink reductions (i.e., over 60% reduction rates and above 20 percentage points for stepwise changes of reduction rates); (ii) the screening process based on the volume indicates a ceiling effect, which results in incomplete screening results (i.e., covers 85% of sinks only); (iii) the combined reductions triggered by the concatenation of the two criteria are sensitive to distinct topographies, which may hinder its general applicability when dealing with various landscapes; (iv) in the context of urban inundation simulations, sinks’ catchment behaviours (runoff retention performance, strong/poor) are a more suitable criterion than the sinks’ geometries (big/small);
- The volume loss spreading solver reveals a great degree of accumulation and concentration in volume losses over the converging network. The reduction process based on $V{L}_{ratio}$ illustrates efficient reductions in volume losses with respect to the RMSE, as well as the specific sinks. However, the redistributed volume losses depend significantly on the computed flows for the individual case; thus, the corresponding controlling process based on $V{L}_{ratio}$ may vary from one case to another. Here, we recommend that the modeller consider the computed flows of focused sinks, as well as the tolerance level in relation to the specific modelling objective to determine an optimal $V{L}_{ratio}$. In contrast with the geometry-based sink screening method, the VRSS method shows a significant advantage by conducting sink reductions and the volume loss reduction separately from the two independent criteria.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Figure A1.**(

**a**) Iteration procedures for the total number of sink reductions when using different screening criteria: (i) maximum depth, (ii) volume and (iii) $HR{V}_{ratio}$. (

**b**) Iteration procedures of the volume loss reductions when using $V{L}_{ratio}$. (

**c**) The volume loss spreading solver’s workflow, where light grey boxes represent inputs and outputs of procedures; dark grey boxes stand for the major steps and bold fonts represent variables. Equations (A1)–(A6) are provided in Appendix C.

## Appendix B

^{3}and $HR{V}_{ratio}$ [0, 70]) were determined based on boxplots (boxplots are illustrated by Figure A2, Appendix B), which illustrate the distributions of these sink values. In addition, the principle of “as small as permitted” was applied in selections of increment values. Thus, an increment value of 0.05 m for the maximum depth was used corresponding to the DEM’s vertical accuracy, and an increment value of 0.128 m

^{3}for volume was used corresponding to the volume accuracy computed as the vertical accuracy multiplied by the resolution squared (i.e., 0.05 × 1.62 = 0.128 m

^{3}). Considering that the $HR{V}_{ratio}$ is unitless, and thus not limited by the DEM’s accuracy, an increment value of 0.5% was used for $HR{V}_{ratio}$ to merely ensure a discernible resolution of the generated curves.

**Figure A2.**(

**a**) Boxplots of maximum depth, volume and $HV{R}_{ratio}$, where iteration ranges were determined for three case areas. (

**b**) Boxplots of $V{L}_{ratio}$ and $V{L}_{Aggr}$ ($V{L}_{source}$) when $HV{R}_{ratio}$ of 15% was used, where iteration ranges of $V{L}_{ratio}$ were determined for three case areas. Note: A = maximum depth; B = volume; HR = $HV{R}_{ratio}^{Radar}$; H3 = $HV{R}_{ratio}^{30mm}$; H5 = $HV{R}_{ratio}^{50mm}$; H7 = $HV{R}_{ratio}^{70mm}$; H9 = $HV{R}_{ratio}^{90mm}$; H11 = $HV{R}_{ratio}^{110mm}$; H13 = $HV{R}_{ratio}^{130mm}$.

## Appendix C

**Figure A3.**(

**a**) The generation of source volume losses due to removed sinks (A) and the redistribution of volume losses (B). (

**b**) Network geometry features, where Points A–I illustrate sinks, and blue points represent the sinks that contain $V{L}_{remaining}$. Edges S1–S8 stand for stream links and red edges represent the stream links that contain $V{L}_{spilled}$. (

**c**) Attribute table containing the computational information corresponding to the geometry features, where blue zones represent link-based fast-inundation spreading computations, and red zones represent volume losses spreading computations.

## Appendix D

- Stream order I: S1, S2, S4, S5.

- For S1, ${V}_{runoff}=20$ m
^{3}, ${V}_{received}=0$ m^{3}and ${C}_{sink}=5$ m^{3}.

- (i)
- For flood volume computations (blue zones):${V}_{spilled}={V}_{runoff}+{V}_{received}-{C}_{sink}=20+0-5=15$ m
^{3}; - (ii)
- For volume loss computations (red zones):$V{L}_{source}=1$ m
^{3}and $V{L}_{received}=0$ m^{3};Here, $V{L}_{received}+V{L}_{source}\le {V}_{spilled}$;$V{L}_{spilled}=V{L}_{received}+V{L}_{source}=1$ m^{3};$V{L}_{remaining}=0$ m^{3}.

- For S2, ${V}_{runoff}=30$ m
^{3}, and ${V}_{received}=0$ m^{3}, ${C}_{sink}=30$ m^{3}.

- (i)
- For flood volume computations (blue zones):${V}_{received}+{V}_{runoff}=30$ m
^{3}$\le {C}_{sink}=30$ m^{3};${V}_{spilled}=0$ m^{3}; - (ii)
- For volume loss computations (red zones):$V{L}_{source}=5$ m
^{3}and $V{L}_{received}=0$ m^{3};Here, ${V}_{spilled}=0$ m^{3};$V{L}_{spilled}=0$ m^{3};$V{L}_{remaining}=V{L}_{source}+V{L}_{received}=5$ m^{3}.

- For S4, ${V}_{runoff}=100$ m
^{3}, ${V}_{received}=0$ m^{3}and ${C}_{sink}=90$ m^{3}.

- (i)
- For flood volume computations (blue zones):${V}_{spilled}={V}_{runoff}+{V}_{received}-{C}_{sink}=100+0-90=10$ m
^{3}; - (ii)
- For volume loss computations (red zones):$V{L}_{source}=30$ m
^{3}and $V{L}_{received}=0$ m^{3};Here, $V{L}_{received}+V{L}_{source}>{V}_{spilled}$;$V{L}_{spilled}={V}_{spilled}=10$ m^{3};$V{L}_{remaining}=V{L}_{source}+V{L}_{received}-{V}_{spilled}=20$ m^{3}.

- For S5, ${V}_{runoff}=120$ m
^{3}, ${V}_{received}=0$ m^{3}and ${C}_{sink}=100$ m^{3}.

- (i)
- For flood volume computations (blue zones):${V}_{spilled}={V}_{runoff}+{V}_{received}-{C}_{sink}=120+0-100=20$ m
^{3}; - (ii)
- For volume loss computations (red zones):$V{L}_{source}=27$ m
^{3}and $V{L}_{received}=0$ m^{3};Here, $V{L}_{received}+V{L}_{source}>{V}_{spilled}$;$V{L}_{spilled}={V}_{spilled}=20$ m^{3};$V{L}_{remaining}=V{L}_{source}+V{L}_{received}-{V}_{spilled}=7$ m^{3}.

- Stream order II: S3, S7.

- For S3, ${V}_{runoff}=50$ m
^{3}, and ${V}_{received}={V}_{spilled}^{S1}+{V}_{spilled}^{S2}=15+0=15$ m^{3}, ${C}_{sink}=40$ m^{3}.

- (i)
- For flood volume computations (blue zones):${V}_{spilled}={V}_{runoff}+{V}_{received}-{C}_{sink}=50+15-40=25$ m
^{3}; - (ii)
- For volume loss computations (red zones):$V{L}_{source}=15$ m
^{3}and $V{L}_{received}=V{L}_{spilled}^{S1}+V{L}_{spilled}^{S2}=1+0=1$ m^{3};Here, $V{L}_{received}+V{L}_{source}\le {V}_{spilled}$;$V{L}_{spilled}=V{L}_{received}+V{L}_{source}=16$ m^{3};$V{L}_{remaining}=0$ m^{3}.

- For S7, ${V}_{runoff}=400$ m
^{3}, and ${V}_{received}={V}_{spilled}^{S5}=20$ m^{3}, ${C}_{sink}=2000$ m^{3}.

- (i)
- For flood volume computations (blue zones):${V}_{received}+{V}_{runoff}=420$ m
^{3}$\le {C}_{sink}=2000$ m^{3}; - (ii)
- For volume loss computations (red zones):$V{L}_{source}=500$ m
^{3}and $V{L}_{received}=V{L}_{spilled}^{S5}=20$ m^{3};Here, ${V}_{spilled}=0$ m^{3};$V{L}_{spilled}=0$ m^{3};$V{L}_{remaining}=V{L}_{source}+V{L}_{received}=500+20=520$ m^{3}.

- Stream order III: S6.

- For S6, ${V}_{runoff}=400$ m
^{3}, and ${V}_{received}={V}_{spilled}^{S3}+{V}_{spilled}^{S4}=25+10=35$ m^{3}, ${C}_{sink}=200$ m^{3}.

- (i)
- For flood volume computations (blue zones):${V}_{spilled}={V}_{runoff}+{V}_{received}-{C}_{sink}=400+35-200=235$ m
^{3}; - (ii)
- For volume loss computations (red zones):$V{L}_{source}=75$ m
^{3}and $V{L}_{received}=V{L}_{spilled}^{S3}+V{L}_{spilled}^{S4}=16+10=26$ m^{3};Here, $V{L}_{received}+V{L}_{source}\le {V}_{spilled}$;$V{L}_{spilled}=V{L}_{received}+V{L}_{source}=26+75=101$ m^{3};$V{L}_{remaining}=0$ m^{3}.

- Stream order IV: S8.

- For S8, ${V}_{runoff}=500$ m
^{3}, and ${V}_{received}={V}_{spilled}^{S6}+{V}_{spilled}^{S7}=235+0=235$ m^{3}, ${C}_{sink}=150$ m^{3}.

- (i)
- For flood volume computations (blue zones):${V}_{spilled}={V}_{runoff}+{V}_{received}-{C}_{sink}=500+23-150=585$ m
^{3}; - (ii)
- For volume loss computations (red zones):$V{L}_{source}=20$ m
^{3}and $V{L}_{received}=V{L}_{spilled}^{S6}+V{L}_{spilled}^{S7}=101+0=101$ m^{3};Here, $V{L}_{received}+V{L}_{source}\le {V}_{spilled}$;$V{L}_{spilled}=V{L}_{received}+V{L}_{source}=101+20=121$ m^{3};$V{L}_{remaining}=0$ m^{3}.

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**Figure 1.**(

**a**) The workflow behind the VRSS method, where light grey boxes represent input data; Step I represents the removal of artefact sinks; Step II represents a computationally significant sink selection; Step III represents the control of volume losses. (

**b**) The sink screening process: before and after, where pour points denote a transition point indicating runoff converted from sheet flow (orange) to channel flow (blue lines). (

**c**) Rainfall hyetograph, where the poor runoff retention sinks tend to be filled up rapidly before the time of ${t}_{i}$, and a smaller $HR{V}_{ratio}$ indicates a smaller proportion of ${S}_{1}$ with earlier ${t}_{i}$ in relation to the temporal variation of a rainfall event. Notes: increasing blue colouring for sinks symbolizes larger volumes.

**Figure 2.**Locations and radar rainfall datasets for the three case areas: Greve, Copenhagen City Center and Amagerbro. Each radar rainfall cell represents the accumulated rainfall for an area of 1000 m × 1000 m. Base map shown in this paper is from source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community.

**Figure 3.**(

**a**) Curves for sink reduction rate. The upper two x-axes represent the maximum depth and the volume; the lower x-axis stands for $HR{V}_{ratio}$. (

**b**) Boxplots for the distribution of sink reduction rate. A and B belong to the geometry-based screening, with maximum depth and volume threshold values shown as grey boxes. H3–H13 belong to the $HR{V}_{ratio}$ computed based on six accumulated rainfalls of 30–130 mm with threshold values shown as orange boxes. The upper and lower ends of the bars represent maximum and minimum value of sink reduction rate; the upper and lower ends of the boxes represent third quartile (0.25) and first quartile (0.75); the red lines represent the median value. Note: A = maximum depth; B = volume; HR = $HV{R}_{ratio}^{Radar}$; H3 = $HV{R}_{ratio}^{30mm}$; H5 = $HV{R}_{ratio}^{50mm}$; H7 = $HV{R}_{ratio}^{70mm}$; H9 = $HV{R}_{ratio}^{90mm}$; H11 = $HV{R}_{ratio}^{110mm}$; H13 = $HV{R}_{ratio}^{130mm}$.

**Figure 4.**Spatially varying reductions of $HR{V}_{ratio}$ computed based on the radar rainfalls of the three case areas. The curves present the deviation of the screening processes due to the impact of rainfall heterogeneity. The maps demonstrate how the reductions vary spatially for the three $HR{V}_{ratio}$ of 5%, 15% and 25%.

**Figure 5.**(

**a**) Distribution and redistribution of volume losses for three case areas. A: Distribution of source volume losses. B: Re-distribution of volume losses illustrated as networks ($V{L}_{spilled}$) and sinks ($V{L}_{remaining}$). (

**b**) Variation in volume losses of three selected sinks when using different $V{L}_{ratio}$ threshold values, demonstrated for the three case areas. Above: Location of selected sinks. Below: Curves of volume losses. The associated table represents flow conditions for the three selected sinks (P10837: Greve; P3313: Copenhagen City Center; P345: Amagerbro) and their redistributed volume losses when no $V{L}_{ratio}$ is used.

**Figure 6.**RMSE curves of volume losses and maps, where the general reduction in volume losses was demonstrated, and detailed spatial variations were presented from the perspective of spilled volume losses, remaining volume losses and source volume losses.

**Table 1.**Topographic overviews, sink statistics and radar rainfall statistics for the three case areas on 2 July 2011.

Greve | Copenhagen City Center | Amagerbro | |||
---|---|---|---|---|---|

Topographic overviews | Elevation (m) | Min. | −1.29 | −0.89 | −5.16 |

Max. | 80.62 | 98.55 | 87.8 | ||

Mean | 22.37 | 12.87 | 4.26 | ||

St. dev. | 15.08 | 7.91 | 4.99 | ||

Slope (%) | Mean | 9 | 30 | 17 | |

St. dev. | 19 | 49 | 36 | ||

Sink statistics | Total number | 30,556 | 13,899 | 7356 | |

Max. depth (m) | Min. | 0.05 | 0.05 | 0.05 | |

Max. | 20.6 | 22.27 | 26.3 | ||

Mean | 0.18 | 0.28 | 0.22 | ||

St. dev. | 0.48 | 1.26 | 0.95 | ||

Volume (m ^{3}) | Min. | 0.13 | 0.13 | 0.13 | |

Max. | 5,027,476 | 602,870 | 564,441 | ||

Mean | 485 | 210 | 278 | ||

St. dev. | 33,930 | 6200 | 9976 | ||

Sum. | 14,819,660 | 2,918,790 | 2,044,968 | ||

Radar rainfall statistics | Rain amounts (mm) | Min. | 23.6 | 77.4 | 88.09 |

Max. | 109.19 | 147.5 | 133 | ||

Mean | 58.97 | 104.68 | 111.46 | ||

St. dev. | 26.82 | 12.2 | 14.69 |

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## Share and Cite

**MDPI and ACS Style**

Zhao, G.; Mark, O.; Balstrøm, T.; Jensen, M.B.
A Sink Screening Approach for 1D Surface Network Simplification in Urban Flood Modelling. *Water* **2022**, *14*, 963.
https://doi.org/10.3390/w14060963

**AMA Style**

Zhao G, Mark O, Balstrøm T, Jensen MB.
A Sink Screening Approach for 1D Surface Network Simplification in Urban Flood Modelling. *Water*. 2022; 14(6):963.
https://doi.org/10.3390/w14060963

**Chicago/Turabian Style**

Zhao, Guohan, Ole Mark, Thomas Balstrøm, and Marina B. Jensen.
2022. "A Sink Screening Approach for 1D Surface Network Simplification in Urban Flood Modelling" *Water* 14, no. 6: 963.
https://doi.org/10.3390/w14060963