# A New Method for Urban Storm Flood Inundation Simulation with Fine CD-TIN Surface

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Data Acquisition and Urban Surface Modeling

#### 2.1. Data Acquisition and Preprocess

Constrain Features | Data Type | Data Organization |
---|---|---|

Water drain grates, Down comers, Outlet of a region, etc. | Constrained point | Shapefile PointZM |

Road cambers, Street curbs, Road isolation strip, Walls, etc. | Constrained polyline | Shapefile PolylineZM |

Buildings (or other man-made structures), Grass lands, Playgrounds, Lakes (or other water bodies), etc. | Constrained polygon | Shapefile PolygonZM |

#### 2.2. Modeling Urban Surface with CD-TIN

**Figure 2.**Urban surface representation with CD-TIN. The blue stars label the water drain grates; the red lines represent the constrained features such as street curbs, walls, grass lands boundaries and buildings; the green lines represent the normal triangle edges; the blue rectangle labels the detailed illustration area.

**Figure 3.**The illustration of property and topology for CD-TIN in terms of point, edge, and triangle.

## 3. Runoff and Confluence

#### 3.1. Computational Urban Water Depression Division

**Figure 4.**Definition of co-fluent edges, trans-fluent edges, and di-fluent edges. (

**a**) Co-fluent edge is (c| Water on the left and right triangle of edge c where both flow to c); (

**b**) Trans-fluent edge is (t| Water on the left and right triangle of edge t where one flows out from t, and the other flows to t); (

**c**) Di-fluent edge is (d| Water on the left and right triangle of edge d where both flow out from d).

**Figure 5.**Illustration of computational water depression; lines with different colors represent different edge types.

#### 3.2. Storm Rainfall Runoff

_{a}is the volume of rainfall excluding water infiltrated into the underground and water conveyed by underground drainage system. Evaporation is not considered in that evaporation can be ignored compared with the accumulated rainfall volume [44]. As shown in Figure 6, the mass balance can be summarized as Equation (1).

_{a}= Q

_{y}+ Q

_{c}= (Q

_{f}- Q

_{u}- Q

_{d}) + (Q

_{i}- Q

_{o})

_{a}is the final

**a**ccumulated water volume (m

^{3}); Q

_{y}is the accumulated

**y**ield water volume (m

^{3}); Q

_{c}is the accumulated confluence water volume (m); Q

_{f}is the rainfall volume during a specified time duration (m

^{3}); Q

_{u}is the volume of water infiltrated into underground (m

^{3}); Q

_{d}is the conveyed water volume by underground drainage sewer system (m

^{3}); Q

_{i}is the water volume flowing in from neighbor depressions (m

^{3}); Q

_{o}is the water volume flowing out to neighbor depressions (m

^{3}).

#### 3.2.1. Rainfall Volume Q_{f}

_{f}= P S

_{p}× 10

^{-3}

_{E}is the return frequency (a); S

_{p}is the horizontal projection area of the depression (m

^{2}).

**Figure 6.**The mass balance illustration in a certain computational water depression, the red dashed line represents the profile crossing one road.

#### 3.3.2. Infiltrated Volume Q_{u}

_{c }, m

^{3}). Equation (1) can then be replaced by Equation (4).

_{a}= (Q

_{f}- Q

_{c}- Q

_{d}) + (Q

_{i}- Q

_{o})

_{c}= (1 − φ) Q

_{f}and φ is the runoff coefficient diversifying with different urban land types; Empirical runoff coefficients can be determined from the Water Supply and Drainage Design Manual [45], as shown in Table 2.

Land type | Runoff coefficient | Value #1 | Value #2 | Value #3 |
---|---|---|---|---|

Building surface, concrete, or asphalt pavement road | 0.85~0.95 | 0.85 | 0.90 | 0.95 |

Large rubble paved road, or gravel road with asphalt surface | 0.55~0.65 | 0.55 | 0.60 | 0.65 |

Gradation macadam road | 0.40~0.50 | 0.40 | 0.45 | 0.50 |

Masonry brick or gravel road | 0.35~0.40 | 0.35 | 0.375 | 0.40 |

Unpaved soil road | 0.25~0.35 | 0.25 | 0.275 | 0.35 |

Garden or green land | 0.10~0.20 | 0.10 | 0.15 | 0.20 |

Water area | 0 | 0 | 0 | 0 |

#### 3.3.3. Drain Volume Q_{d}

_{d}is the designed full capacity of downstream conduit (m

^{3}), calculated as [49]:

_{c}is the Manning’s roughness of the conduit; A

_{d}is the full cross-section area of conduit (m

^{2}); R

_{d}is the hydraulic radius for full conduit flow (m); and S

_{d}is the friction slope of conduit. The friction slope of conduit S

_{d}can be calculated as , where h is the elevation difference of original node and end node; l is the length of conduit.

_{i}.

#### 3.3. Confluence between Depressions (Q_{i}, Q_{o})

- (1)
- Judge whether the water level of depression A (H
_{a}) arrive at the outlet point O; If not, H_{a}continues to rise, else go to step (2); - (2)
- Refer to the conditions of urban surface (land type, slope) and water level; employ empirical Equation (7) [50] to calculate the overland flow water volume Q
_{v}:_{s}is the cross section of accumulated water on the outlet point O (m^{2}); k is the constant parameter of overland flow (m/s), which can be obtained from Hao’s research [49]; S is the average slope of the ground. To calculate A_{s}, as shown in Figure 8b, we draw the vertical profile P along CD. Then the water surface (blue polygon) insects CO, DO at E and F. Point E, O, F project on P as E’, O’, F’. Thus, A_{s}is calculated as the area of △E’O’F’; - (3)
- Search the water depressions that connect with O; Judge Ha>Hb, then the accumulated water in depression A flows to depression B at the volume of Q
_{v}. Hence, the Q_{o}of depression A is Q_{v}, Q_{i}of depression B is Q_{v}, and vice versa; - (4)
- All urban water depressions repeat Steps (1)–(3) to calculate the water confluence which are quantified as the Q
_{i}and Q_{o}; - (5)
- Calculate the final accumulated water volume in each depression referring to Equation (6).

**Figure 8.**Confluence mode between neighbor water depressions, red point labels the pit point of the depression; point O is the outlet point of the depression A and depression C; blue polygon represents the water surface. (

**a**) The illustration of water confluence from depression A to depression B; (

**b**) the cross section on the outlet point O.

#### 3.4. Inundation Depth Calculation

_{r}= ʃ

_{A}(H

_{w}- H

_{g})ds

_{a}= Q

_{r}

_{r}is the runoff water volume on the urban surface (m

^{3}); A is the inundation area (m

^{2}); ds is the basic integral area (m

^{2}); H

_{w}is the inundated water surface elevation (m); and H

_{g}is the ground elevation (m).

- (1)
- Initialize the maximum (H
_{1}) and minimum (H_{0}) inundation depth; - (2)
- Within each basin which consist of triangular-prism sets, calculate the water volume Q
_{0}, Q_{1}under the inundation depth of H_{0}and H_{1}; - (3)
- If Q
_{0}= Q_{r}then H_{r}= H_{0}; else if Q_{1}= Q_{r}then H_{r}= H_{1}; otherwise calculate the water volume Q_{0.5}under the water surface of H_{0.5}, which equals ; - (4)
- If Q
_{0.5}= Q_{r}, then H_{r}= H_{0.5}; else if Q_{0.5}> Q_{r}, then Q_{1}= Q_{0.5}and H_{1}= H_{0.5}; otherwise if Q_{0.5}< Q_{r}, then Q_{0}= Q_{0.5}and H_{0}= H_{0.5}; - (5)
- Go to Step (2) until obtaining the H
_{r}.

**Figure 9.**Triangular-prism sets of the depression and three inundation phases of one triangular-prism blue represents the accumulated water, h1, h2, h3 representing the water submerged in one, two, and three nodes of the bottom triangle, respectively. (

**a**) Triangular-prism sets of the depression; (

**b**) submerged one node; (

**c**) submerged two nodes; (

**d**) submerged three nodes.

## 4. BNU Main Campus Case Study

#### 4.1. Urban Surface Modeling

#### 4.2. Inundation Simulation

^{®}Core™2 Duo CPU p8600 @ 2.4GHz, 1.93GB RAM). As shown in Figure 14, severely inundated spots are labeled: FuRen Road in front of the Geography Building (labeled as A), Kindergarten (labeled as B), LiYun Building, East gate, North gate, and South gate. To validate the simulation result, we compare the inundation scenario with in-situ captured photos by the authors and BNU logistics office during the storm event. From comparisons in Figure 14, the simulated inundation is generally consistent with the real scenario. Meanwhile, the maximum inundation depth of two severely submerged spots A and B have been captured during the event. The comparison is given as shown in Table 3, which employs the runoff coefficient value #1, #2, #3 in Table 2 for simulation, respectively.

**Figure 14.**BNU main campus “7.21 storm” inundation scenario and comparison with in-situ captured photos. The most severe submerged spots are labelled with A and B on the FuRen Road in front of Geography Building and Kindergarten.

_{n}), whose contribution area is about 5374 m

^{2}, is a composite of grass land and road surface. While the water depression where B is located (called depression B

_{n}), whose contribution area is about 409 m

^{2}, is a composite of road surface and building surface. We conduct the simulation to obtain the accumulated water volume with different runoff coefficients (#1, #2, #3) and rainfall duration (1 h, 2 h, 3 h). As shown in Figure 15: (1) the same underlying land type can yield more accumulated water varying with larger coefficients, vice versa; (2) A

_{n}has the similar accumulative pattern that the accumulative volume rises with the increasing rainfall duration on the same underlying surface with the same coefficient; whereas, the accumulative pattern of B

_{n}is irregular due to uncertainty of in-flow of water from adjacent depressions. As Table 3 shows, the simulated inundation depths are compared with the in-situ captured maximum inundation depth. From that we know several facts: (1) The variety of runoff coefficient has certain influence on the simulation that depends on urban surface; (2) Due to A

_{n}composite of grass land and road surface, the yield water of unit area on the grass land is less than that on the road surface. Thus, the simulated accumulative water volume is relatively small so that the water depth is relatively small; (3) B

_{n}is a composite of road surface and building surface with a similar runoff coefficient, and so the simulation result should be stable. However, the simulated depth with coefficient value #2 dramatically changes in that the accumulative water is affected by the water flow from adjacent water depressions.

**Figure 15.**Excess runoff volume with different runoff coefficient at rainfall duration of 1 h, 2 h, and 3 h, respectively. (

**a**) The accumulated water in A

_{n}; (

**b**) the accumulated water in B

_{n}.

Used Runoff Coefficient | Record Depth A | Simulation Result | Relative Error | Record Depth B | Simulation Result | Relative Error |
---|---|---|---|---|---|---|

Value #1 | 42 | 35 | 16.7% | 36 | 34 | 5.6% |

Value #2 | 42 | 37 | 9.6% | 36 | 33 | 8.3% |

Value #3 | 42 | 38 | 9.5% | 36 | 33 | 8.3% |

## 5. Conclusions and Discussion

- (a)
- The model focuses on the detailed surveyed urban surfaces with fine CD-TIN representation instead of raster. It couples the fine constrained urban features, such as street curbs, road cambers, ans water drain grates, which greatly influence urban water flow;
- (b)
- The model can handle the storm flood with a lack of detailed drainage data, especially the discharge of the subsurface drainage system, or urban environments with a built-in sewer system;
- (c)
- The model employs the GIS-based dichotomy numerical simulation method, which avoids the time-consuming problem of 1D or 2D equation numerical calculation. It can provide a practical rapid inundation solution for urban storm flood simulation.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Fewtrell, T.J.; Duncan, A.; Sampson, C.C.; Neal, J.C.; Bates, P.D. Benchmarking urban flood models of varying complexity and scale using high resolution terrestrial LiDAR data. Phys. Chem. Earth Parts A B C
**2011**, 36, 281–291. [Google Scholar] [CrossRef] - Smith, M.B. Comment on ‘Analysis and modeling of flooding in urban drainage systems’. J. Hydrol.
**2006**, 317, 355–363. [Google Scholar] [CrossRef] - Schmitt, T.G.; Thomas, M.; Ettrich, N. Analysis and modeling of flooding in urban drainage systems. J. Hydrol.
**2004**, 299, 300–311. [Google Scholar] [CrossRef] - Maksimović, Č.; Prodanović, D.; Boonya-Aroonnet, R.S.; Leitão, J.P.; Djordjević, S.; Allitt, D.R. Overland flow and pathway analysis for modelling of urban pluvial flooding. J. hydraul. Res.
**2009**, 47, 512–523. [Google Scholar] [CrossRef] - Djordjević, S.; Prodanović, D.; Maksimović, C.; Ivetić, M.; Savić, D. SIPSON—Simulation of interaction between pipe flow and surface overland flow in networks. Water Sci. Technol.
**2005**, 52, 275–289. [Google Scholar] - Djordjević, S.; Prodanović, D.; Maksimović, Č. An approach to simulation of dual drainage. Water Sci. Technol.
**1999**, 39, 95–103. [Google Scholar] [CrossRef] - Carr, R.S.; Smith, G.P. Linking of 2D and Pipe Hydraulic Models at Fine Spatial Scales. In Proceedings of the 4th International Conference on Water Sensitive Urban Design, Melbourne, Australia, 3–7 April 2006; pp. 888–895.
- Chen, A.; Hsu, M.; Chen, T.; Chang, T.J. An integrated inundation model for highly developed urban areas. Water Sci. Technol.
**2005**, 51, 221–230. [Google Scholar] - Chen, A.S.; Djordjevic, S.; Leandro, J.; Savic, D. The Urban Inundation Model with Bidirectional Flow Interaction between 2D Overland Surface and 1D Sewer Networks. In Proceedings of the 6th International Conference on Sustainable Techniques and Strategies in Urban Water Management (NOVATECH), Lyon, France, 25–28 June 2007; pp. 465–472.
- Dey, A.K.; Kamioka, S. An integrated modeling approach to predict flooding on urban basin. Water Sci. Technol.
**2007**, 55, 19–29. [Google Scholar] - Seyoum, S.; Vojinovic, Z.; Price, R.; Weesakul, S. Coupled 1D and noninertia 2D flood inundation model for simulation of urban flooding. J. Hydraul. Eng.
**2012**, 138, 23–34. [Google Scholar] [CrossRef] - Li, W.; Chen, Q.; Mao, J. Development of 1D and 2D coupled model to simulate urban inundation: An application to Beijing Olympic Village. Chin. Sci. Bull.
**2009**, 54, 1613–1621. [Google Scholar] [CrossRef] - Bates, P.D.; De Roo, A.P.J. A simple raster-based model for flood inundation simulation. J. Hydrol.
**2000**, 236, 54–77. [Google Scholar] [CrossRef] - Hunter, N.M.; Horritt, M.S.; Bates, P.D.; Wilson, M.D.; Werner, M.G.F. An adaptive time step solution for raster-based storage cell modelling of floodplain inundation. Adv. Water Resour.
**2005**, 28, 975–991. [Google Scholar] [CrossRef] - Leandro, J.; Chen, A.; Djordjević, S.; Savić, D.D.A. Comparison of 1D/1D and 1D/2D coupled (Sewer/Surface) hydraulic models for urban flood simulation. J. Hydraul. Eng.
**2009**, 135, 495–504. [Google Scholar] [CrossRef] - Leandro, J.; Djordjević, S.; Chen, A.S.; Savić, D.A.; Stanić, M. Calibration of a 1D/1D urban flood model using 1D/2D model results in the absence of field data. Water Sci. Technol.
**2011**, 64, 1016–1024. [Google Scholar] [CrossRef] - Chen, A.S.; Evans, B.; Djordjević, S.; Savić, D.A. Multi-layered coarse grid modelling in 2D urban flood simulations. J. Hydrol.
**2012**, 470–471, 1–11. [Google Scholar] [CrossRef] [Green Version] - Hunter, N.; Bates, P.; Neelz, S.; Pender, G.; Villanueva, I.; Wright, N.; Liang, D.; Falconer, R.; Lin, B.; Waller, S. Benchmarking 2D hydraulic models for urban flooding. Proc. ICE Water Manag.
**2008**, 161, 13–30. [Google Scholar] [CrossRef] [Green Version] - Neal, J.; Fewtrell, T.; Trigg, M. Parallelisation of storage cell flood models using OpenMP. Environ. Model. Softw.
**2009**, 24, 872–877. [Google Scholar] [CrossRef] - Kalyanapu, A.J.; Shankar, S.; Pardyjak, E.R.; Judi, D.R.; Burian, S.J. Assessment of GPU computational enhancement to a 2D flood model. Environ. Model. Softw.
**2011**, 26, 1009–1016. [Google Scholar] [CrossRef] - Néelz, S.; Pender, G. Benchmarking of 2D Hydraulic Modeling Packages; Environment Agency: Bristol, UK, 2010. [Google Scholar]
- Chen, J.; Hill, A.; Urbano, L.D. A GIS-based model for urban flood inundation. J. Hydrol.
**2009**, 373, 184–192. [Google Scholar] [CrossRef] - Zerger, A.; Smith, D.I.; Hunter, G.J.; Jones, S.D. Riding the storm: A comparison of uncertainty modelling techniques for storm surge risk management. Appl. Geogr.
**2002**, 22, 307–330. [Google Scholar] [CrossRef] - Zerger, A. Examining GIS decision utility for natural hazard risk modelling. Environ. Model. Softw.
**2002**, 17, 287–294. [Google Scholar] [CrossRef] - Bates, P.D.; Horritt, M.S.; Fewtrell, T.J. A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. J. Hydrol.
**2000**, 387, 33–35. [Google Scholar] - Liu, Y.; Snoeyink, J. Flooding Triangulated Terrain. In Developments in Spatial Data Handling; Springer Berlin: Heidelberg, Germany, 2005; pp. 137–148. [Google Scholar]
- Zhou, Q.; Liu, X. Error assessment of grid-based flow routing algorithms used in hydrological models. Int. J. Geogr. Inf. Sci.
**2002**, 16, 819–842. [Google Scholar] [CrossRef] - Liu, X.J.; Wang, Y.J.; Ren, Z. Algorithm for extracting drainage network based on triangulated irregular network. J. Hydraul. Eng.
**2008**, 39, 27–35. (In Chinese) [Google Scholar] - Leitão, J.P.; Boonya-aroonnet, S.; Prodanovic, D.; Maksimovic, C. The influence of digital elevation model resolution on overland flow networks for modelling urban pluvial flooding. Water Sci. Technol.
**2009**, 60, 3137–3149. [Google Scholar] [CrossRef] - Chou, T.-Y.; Lin, W.-T.; Lin, C.-Y.; Chou, W.-C.; Huang, P.-H. Application of the PROMETHEE technique to determine depression outlet location and flow direction in DEM. J. Hydrol.
**2004**, 287, 49–61. [Google Scholar] [CrossRef] - Martz, L.W.; Garbrecht, J. The treatment of flat areas and depressions in automated drainage analysis of raster digital elevation models. Hydrol. Processes
**1998**, 12, 843–855. [Google Scholar] [CrossRef] - Marks, D.; Dozier, J.; Frew, J. Automated basin delineation from digital elevation data. Geo Process.
**1984**, 2, 299–311. [Google Scholar] - Vogt, J.V.; Colombo, R.; Bertolo, F. Deriving drainage networks and catchment boundaries: A new methodology combining digital elevation data and environmental characteristics. Geomorphology
**2003**, 53, 281–298. [Google Scholar] [CrossRef] - Lin, W.T.; Chou, W.C.; Lin, C.Y.; Huang, P.H.; Tsai, J. WinBasin: Using improved algorithms and the GIS technique for automated watershed modelling analysis from digital elevation models. Int. J. Geogr. Inf. Sci.
**2008**, 22, 47–69. [Google Scholar] [CrossRef] - Theobald, D.M.; Goodchild, M.F. Artifacts of TIN-based surface flow modeling. Proc. GIS LIS 90
**1990**, 2, 955–967. [Google Scholar] - Bänninger, D. Technical note: Water flow routing on irregular meshes. Hydrol. Earth Syst. Sci. Discuss.
**2006**, 3, 3675–3689. [Google Scholar] [CrossRef] - Tucker, G.E.; Lancaster, S.T.; Gasparini, N.M.; Bras, R.L.; Rybarczyk, S.M. An object-oriented framework for distributed hydrologic and geomorphic modeling using triangulated irregular networks. Comput. Geosci.
**2001**, 27, 959–973. [Google Scholar] [CrossRef] - Ivanov, V.Y.; Vivoni, E.R.; Bras, R.L.; Entekhabi, D. Catchment hydrologic response with a fully distributed triangulated irregular network model. Water Resour. Res.
**2004**, 40. [Google Scholar] [CrossRef] - Sampson, C.C.; Fewtrell, T.J.; Duncan, A.; Shaad, K.; Horritt, M.S.; Bates, P.D. Use of terrestrial laser scanning data to drive decimetric resolution urban inundation models. Adv. Water Resour.
**2012**, 41, 1–17. [Google Scholar] [CrossRef] - Mason, D.C.; Horritt, M.S.; Hunter, N.M.; Bates, P.D. Use of fused airborne scanning laser altimetry and digital map data for urban flood modelling. Hydrol. Processes
**2007**, 21, 1436–1447. [Google Scholar] [CrossRef] - Fewtrell, T.J.; Bates, P.D.; Horritt, M.; Hunter, N.M. Evaluating the effect of scale in flood inundation modelling in urban environments. Hydrol. Processes
**2008**, 22, 5107–5118. [Google Scholar] [CrossRef] - Wu, L.; Shi, W. Principle and Algorithem of GIS; Science Press: Beijing, China, 2003; pp. 156–160. [Google Scholar]
- Frank, A.U.; Palmer, B.; Robinson, V. Formal Methods for the Accurate Definition of Some Fundamental Terms in Physical Geography. In Proceedings of 2nd International Symposium on Spatial Data Handling, Seattle, WA, USA, 5–10 July 1986; pp. 583–599.
- Apirumanekul, C. Modeling of Urban Flooding in Dhaka City; No. WM-00-13; Asian Institute of Technology: Bangkok, Thailand, 2001. [Google Scholar]
- Beijing General Municipal Engineering Design and Research Institute. Water Supply and Drainage Design Manual: Volume 5th Urban Drainage; China Building Industry Press: Beijing, China, 2004. [Google Scholar]
- Holtan, H.N. Time condensation in hydrograph analysis. Trans. Am. Geophys. Union
**1945**, 26, 407–413. [Google Scholar] [CrossRef] - Sharp, A.L.; Holtan, H.N. Extension of graphic methods of analysis of sprinkled-plot hydrographs to the analysis of hydrographs of control plots and small homogeneous watersheds. Trans. Am. Geophys. Union
**1942**, 23, 578–593. [Google Scholar] [CrossRef] - Mays, L.W. Water Resources Engineering; Wiley: New York, NY, USA, 2001. [Google Scholar]
- Hsu, M.H.; Chen, S.H.; Chang, T.J. Inundation simulation for urban drainage basin with storm sewer system. J. Hydrol.
**2000**, 234, 21–37. [Google Scholar] [CrossRef] [Green Version] - Hao, Z.; Li, L.; Wang, J.; Luo, J. Distributed Hydrological Model Theory and Method; Science Press: Beijing, China, 2010; pp. 256–260. [Google Scholar]
- Li, Z.F.; Wu, L.X.; Zhang, Z.X.; Xu, Z.H. Triangular-Prism-Based Algorithm on Urban Flood Inundation Simulation by Employing Dichotomy Numerical Solution. In Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS), Melbourne, Austrilia, 21–26 July 2013; 2013; pp. 790–793. [Google Scholar]
- Wu, L.X.; Wang, Y.B.; Shi, W.Z. Integral ear elimination and virtual point-based updating algorithms for constrained Delaunay TIN. Sci. China Ser. E Technol. Sci.
**2008**, 51, 135–144. [Google Scholar] [CrossRef] - Beijing “7.21” Storm Event. Available online: http://www.weather.com.cn/zt/kpzt/696656.shtml (accessed on 20 August 2013). (In Chinese)
- Beijing encountered the strongest storm with 61 year return period and the average precipitation as high as 215 mm. Available online: http://news.sohu.com/20120722/n348744539.shtml (accessed on 20 August 2013). (In Chinese)
- Ministry of Development of the People’s Republic of China. Outdoor Drainage Design Standard (GB 50014–2006); China Planning Press: Beijing, China, 2011.

© 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

## Share and Cite

**MDPI and ACS Style**

Li, Z.; Wu, L.; Zhu, W.; Hou, M.; Yang, Y.; Zheng, J.
A New Method for Urban Storm Flood Inundation Simulation with Fine CD-TIN Surface. *Water* **2014**, *6*, 1151-1171.
https://doi.org/10.3390/w6051151

**AMA Style**

Li Z, Wu L, Zhu W, Hou M, Yang Y, Zheng J.
A New Method for Urban Storm Flood Inundation Simulation with Fine CD-TIN Surface. *Water*. 2014; 6(5):1151-1171.
https://doi.org/10.3390/w6051151

**Chicago/Turabian Style**

Li, Zhifeng, Lixin Wu, Wei Zhu, Miaole Hou, Yizhou Yang, and Jianchun Zheng.
2014. "A New Method for Urban Storm Flood Inundation Simulation with Fine CD-TIN Surface" *Water* 6, no. 5: 1151-1171.
https://doi.org/10.3390/w6051151