Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | er a flood ff 3 , and S. Ye 1,2 2012 2011 , Y. Rui 3 , X. Cheng 1,2 ciently and simulate flooding processes with greater accuracy than DEM , J. Wang ffi 1,2,3 ers high precision of elevation parameterization for computational mesh while ff This discussion paper is/has beenSciences under (HESS). review Please for refer the to journal the Hydrology corresponding and final Earth paper System in HESS if available. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Department of Geographic Information Science,Changjiang Nanjing River University, Nanjing, Scientific Jiangsu, Research China Institute, Changjiang Water Resources Commission, simulation method that integrates 2-D hydraulicdata, model enabling results and the high-resolution calculation DEM verse of distance flood weighted water interpolation. levelsinterpolation, in To it get DEM employs grid rid the cells ofgrid run-length through the cells encoding local false method and in- inundation to determinetracing areas mark technique, during the the which real inundated solves DEM inundationDEM the grid areas complicated cells. problem through We constructed of thea a the run-length flood 2-D connectivity hydraulic boundary storage between model areafloodplain for the of flow. Gongshuangcha The Dongting polder, results Lake,problems demonstrate using e that our this integrated methodonly method simulations. can to solve simulate DEM the associated 1 Introduction Floodplain flow simulation isasters. important The for forecasting typical floods focusextent, and of depth assessing simulation and flood duration studies dis- (Garcia,calculation, is 2004; to to build Sanyal a predict et one-dimensional al., accurate (1-D) 2006). and flood two-dimensional In inundation (2-D) the hydraulic field mod- of hydraulic Abstract The rapid progressacquirement of and Light Detection applicationincreasingly And of popular, Ranging high-resolution especially (LiDAR)ing. digital with technology High-resolution elevation regards has DEM model to made a data the (DEM) high include study data amount many ofThese of redundant floodplain calculation, disadvantages flow interpolation and are model- Two-dimensional points, does a (2-D) not needs common hydraulic match modeling, problemflow, the a o for size popular floodplain method of flow ofignoring computational analyzing modeling much mesh. micro-topographic floodplain studies. information of the DEM data itself. We o 1 Nanjing, Jiangsu, China 2 3 Wuhan, Hubei, China Received: 22 January 2015 – Accepted: 1Correspondence February to: 2015 J. – Wang Published: ([email protected]) 13 FebruaryPublished 2015 by Copernicus Publications on behalf of the European Geosciences Union. Integration of 2-D hydraulic modelhigh-resolution and LiDAR-derived DEM for floodplain flow modeling D. Shen Hydrol. Earth Syst. Sci. Discuss.,www.hydrol-earth-syst-sci-discuss.net/12/2011/2015/ 12, 2011–2046, 2015 doi:10.5194/hessd-12-2011-2015 © Author(s) 2015. CC Attribution 3.0 License. 5 15 20 25 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ort in ff erences between models ff erences between the collections of ff ect on the results of 2-D hydraulic mod- ff 2014 2013 ciency demands for model calculations, the mesh ffi cient processing of large amounts of DEM data and opti- ffi erent spatial DEM data resolution on model calculations (Sanders, ff ects of di ff cient and accurate analysis of large-scale automatic floodplain flow models. erent periods and places, and calculate inundation extent and depth. Due to these ering the basis for more precise calculation of flood extent and depth (Horritt et al., As a result, we put forward a new flood simulation method that integrates a 2-D hy- Under mid- or low-resolution DEM, topography appears flatter, allowing the DEM Digital elevation model (DEM) data is the most important data resource for floodplain Although many studies claim that they have achieved satisfying results with the help ff ffi ff (Fewtrell et al., 2011).mizing the E precision of high-resolutione vertical and horizontal information is the key to draulic model with high-resolutionwe DEM constructed data. a Starting comparatively coarse withD computational high-resolution hydraulic mesh DEM model. and data, then The constructed resultsresolution a of DEM 2- the data 2-D and hydraulic theinverse model distance flood were weighted overlaid depth interpolation. with in the During DEMbe high- the grid many process cells false of was flood interpolation, calculatedpolated areas there using despite can in local not being the inundated.the DEM flooded To grid remove areas false-flooded using because areas, run-length partsrun-length we encoding of boundary marked and then all the tracing got of grid technology, the a real cells method flood are extent that inter- through proved to save much e topography data are far greater than those caused by the di nodes. The denser acalculation hydraulic model process, mesh which is, iscomplexity the of limited more model. time by Due should to the high be processing e spent ability on the of computers and the more, the quality of mesh resolutionels. has Horritt little et e al. (2001)same show DEM that increases as from the 1000 resolutionditional to of precision 10 a once m, computational the the mesh resolution calculationto under reaches the result the 100 floodplain m. ceases flow, In to errors applying gain caused 2-D any by hydraulic precision ad- models di elevation change within ahigh-resolution LiDAR-derived mesh DEM element can keep tois much be micro-topography of information ignored, high that while valueIf hundreds in DEM data of calculating only inundation pointsthen serves extent of a for and the lot depth valuation of assignments elevation (Schumann of point et computational information al., mesh is 2008). nodes, largely wasted (Marks et al., 2000). What’s constructed by a hydraulic model is usually sparser than a DEM grid. els is a common methodmodel, (Merwade which et usually al., simplifies 2008).able A hydraulic to 1-D conditions show hydraulic and and model their simulate is1990). complicated physical a river In processes, traditional networks un- comparison, (Gichamo et adi al., 2-D 2012; hydraulic Samuels, model can simulate the floodplain condition in et al., 1996). Aracy few years of ago, the precise DEMet floodplain data, al., flow 2009). was simulation, However, limited the theing by bottleneck increasing (LiDAR) the problem maturity technology accu- for of hasDEM airborne 2-D made data Light hydraulic the far Detection modeling easier acquirement And in (Apel andthe Rang- recent spatial implement years. resolution By of of processing high-resolution DEM the2005). data point With 1 cloud the m data, high and we resolution, theand can regional vertical micro-topography make topographic error information information 10–15 can of centimeters beo (Haile, the more flattened detailed ditches and2001; balks Li et can al., be 2010; retained, apply Merwade high-resolution et al., LiDAR-derived 2008; DEM French, datathe 2003). to Many e floodplain scholars have flow tried models to and analyze advantages, 2-D models areet commonly al., used 2004). in floodplain flow studies (Archambeau flow models (Garrote etcision al., have 1995). much Its to horizontal resolution do and with vertical the elevation results pre- calculated by 2-D hydraulic modeling (Bates 2007; Raber et al., 2007).has Applying high-resolution become DEM a data common to2000). floodplain trend flow in models flood studies (Archambeau et al., 2004;of Bates high-resolution DEM et data, al., theirIn methods 2-D still hydraulic have models, weaknesses topography (Marks ismesh, et often al., and represented 2000). by DEM unstructured data or structured is used for the valuation assignments of computational mesh 5 5 25 20 15 10 15 20 10 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , is 2 erent periods using our in- ff N), with a total area of 18 780 km 0 20 ◦ , in the middle and lower reaches of Yangtze –30 3 0 2016 2015 m 60. of diversion storage zone around Chenglingji. 30 9 / ◦ 3 m 10 9 × , and is home to 160 000 inhabitants. ects of the Three Gorges Project, which greatly influ- 3 E, 28 ff 10 0 m × 9 10 ◦ 10 × –113 0 40 ◦ in storage area, 121.74 km in levee length, 33.65 m in storage height, has 2 ecting healthy economic development and improvement of living standards in ff By processing the point cloud data, we derived a high-resolution DEM of the Gong- We have employed the airborne laser-measuring instrument HARRIER 86i, from Because of its special location and complex river network system, this area has According to the Report on the Feasibility of the Flood Control Project of Qianliang Gongshuangcha polder, one of the largest flood storage zones in the Dongting Lake 2.2 2-D hydraulic model In 2008, the Changjiang Water Resourcesprehensive Commission Treatment approved Planning a of report Dongting onCommission, the Lake 2008). Com- Area The (Changjiang report Watera Resources surplus highlighted water the volume of serious 21.8–28 threat of floods, which cause shuangcha polder (Fig. 3).ordinate The system spatial with reference Beijing1985 is 1954 national the elevation datum, Gauss–Kruger standard, and projection45.87 of m. the co- which The elevation general the system landscape lowest showninformation is elevation in of is based the levees, DEM 4.55 dikes on m is and the flat, and ridges with highest retained much (Fig. micro 3). topography River. It also stressed that the e German TopoSys Company, for aerial photography ofDecember the Gongshuangcha 2010. polder since Theand digital the camera inertial we navigationof used system 200 Hz. was was The a Applanix80–400 laser KHz POS/AV Trimble and with Rollei scanner scanning a Metric was angle of sampling Riegl AIC 45 frequency LMS-Q680i, Pro with a maximum pulse rate of ences the conditions for incomingtion. water Even and though sediments, the must completion beon the of taken Chin-sha into Three River considera- Gorges, can enhance andat the Xiluodu flood the draining and ability confluence Xiangjiaba around Dams of Chenglingjiurgent (Fig. Dongting 1), need Lake to construct and a Yangtze 10 River, is the report emphasized the a storage volume of 1.85 east and Chi Mountainis to 293 the km west with water in between (Fig. 2). In total, the polder Lake, Gongshuangcha and East Datong Lake of Dongting Lake Areas (Ministry of Wa- been frequently prone towith floods, flooding. lots Due of toprevent labor floods, the and a heavy money total has of burdenthe 266 been on total levees spent length have the on been of masses dike builtFlooding 5812 around to km construction. events in Dongting with In deal this Lake the order area areas,following first to have and individual caused rank events significant 3471 in km destruction. 1996 and TheThe and cost the pressure 1998 of second of was damages rank CNY preventingfactor 15 1509 flooding, km. a and and 8.9 billion, the respectively. Hunan associated province. damage, has been a major area, is located in the north of Yuanjiang county, facing South Dongting Lake to the tegrated method. By analyzingcan and enhance comparing the accuracy the and results, reliability it of floodplain proves flow that modeling. this method 2 Study area and 2-D hydraulic2.1 model Study area and DEM dataset Dongting Lake (111 located in the middle reachesthrough of which Yangtze River the (Changjiang Dongting Lake River, Fig. flowsXiangtan, include 1). and Changde, The Zhuzhou Yiyang, districts Yueyang, in Changsha, Province. Hunan Dongting Province Lake as is wellheads surrounded as are by three varied mountains cities and on in complicated.center. It three Jinzhou It is sides of only a and Hubei flows centripetal into its water the system fountain- Yangtze fanning River out through from Chenglingji the of Yueyang (Fig. 1). verifying the connectivity betweendraulic model DEM for the grid Gongshuangcha cells. polder,Lake, which and Lastly, is calculated we a the flood constructed storage inundation area extent a of and 2-D Dongting depth hy- in di 5 5 15 10 20 25 20 15 25 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , L = 1 di- (3) − ) s q X ( 3 , with ) is: g 1 and d q − ( s direction; b ω 3 X , d ; when H was Ω 1 is the net depth ∂ direction − w s q 3 Y , the flux vector of is the normal numerical T ] n ) hv direction; q , ( Y F hu , h [ correspondingly mean the average )] (2) = v f y , Gongshuangcha Polder 3630 m q s 1 − − s and y 3 , and the flux vector of 0 is the gravity and the source term u T s ] erent periods. To ensure precision, we used 2018 2017 ( g ω ff )d gh huv , , with the corresponding water levels for the dikes of rising waters. For the 1954 flood event, the re- . These equations demonstrate that the solution q 1 are the river slope and friction slope on ( u T 2, 1 − w b )] / − s f x q 2 s q Ω 3 ) (1) S is height, ZZ ( 3 + q g ( gh + h ) directions, ), b . L + f x and T q Y d s 2 is the normal numerical flux outside of unit = ( by divergence principle. x n f 2] 0 ) [ − · n / hu Ω S q ) x 2 , ( = and 0 q ) ∂y g ( s hu gh ( X q ∂ [ F ( + Ω Z = + gh F are the river slope and friction slope on ∂ 2 , ) ) − w f y q q hv ( ( q S = , f [ f ∂x ; when H was 31.63–32.60 m, flow volume was 3050 m ∂ ω 1 = d − huv and + t ) s , q y q 3 q ( 0 hv ∂t Ω ∂ could convert 2-D problems into series of local 1-D problems. ary, the outerboundary boundary of of no-water and slow water exchange and unit and rushing tributary boundary flow, of the wetland. innerbe solved boundary, through flowing interactive method over time. flux, where uniform flux of b are surface integration and line integration, and [ In this expression, of water in each timeFormula. unit. The friction slope could be calculated through Manning tion on any unit of ZZ In this expression, S In this expression the conservative vector rection ter Equation: The flood routing model employed 2-D unsteady shallow water equations to describe According to the standard design of the Gongshuangcha Polder diversion, we sim- 3. Boundary Condition. The model sets five kinds of flow boundaries: earth bound- 4. The solution to the equation. The equations, which are explicit finite schemes can 2. The Discretization of Equations. Calculate basic FVM equation through discretiza- 1. Basic Control Equation. The Vector Expression of Conservative 2-D Shallow Wa- The computational mesh of theis 2-D constructed hydraulic model by of a Gongshuangcha non-structural Polder triangular (Fig. 4) mesh in which there are 83 378 triangles, ter Resources of China,Chenglingji 2009), could have flood been restricted waters to fromset safely manageable up events levels to in if local divert 1954, polders 8000–12 were 1966 000 m and 1998, in port shows that the maximum diversion should have been set at 10 000 m Qianliang Lake Polder contributing 4180 m Riemann approximate solver to calculate thementum bulk between and normal the numerical mesh fluxdiscretions elements. of and the The converting mo- model 2-D solvesthe problems coordinate the into rotation equations series of through fluxes. of The FVM 1-D basic problems principles with are as the follows. help of solve the coupled equations,non-structural and discrete simulated mesh flood to routingscape represent of inside the the area the computational and zone polder. theaccurate based We location conservation, of on used water we the conservancy used land- projects.of Then FVM to density to make for ensure decide each bulk, mesh momentum and element the in equilibrium di and Datong Lake Polder 2190 m the water flow, used finite volume method (FVM) and Riemann approximate solvers to lows: when water level3630 m (H) was below32.60–33.65 m, 31.63 m, another flow the diversion flow exit was volume opened. into the sluice was ulated flood flowgraph using acted a as the mode input controlled parameter, with by flood sluice flow behavior. into The the sluice resulting conditioned hydro- as fol- set at 33.06, 33.10 and 33.07 m. 5 5 20 15 10 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (4) (5) } Q erent inundation ⊆ ff usion by four-side ff P , P ∈ fixes the level of floodwater, fixes the level of floodwater, , point Q ∈ water level water level Z Z } is the assemblage of DEM grid cells, and Q Q ∈ 2019 2020 ,cell water level < Z cell connect with point, cell cell ∧ Z cell: { = = water level is the elevation value of DEM grid cell, is the elevation value of DEM grid cell, < Z cell cell is the assemblage of DEM grid cells. Z Z cell Z Q is the assemblage of inundated seeded grid cells among the whole DEM grid. The seeded region growing approach considers DEM grid cell connectivity. The The Bathtub approach, also called “zero-side rule”, does not take connectivity issue The flat-water model assumes that water level is a horizontal plane. In this method, 1-D hydraulic models can divide watercourses into cross sections and get the infor- 2-D hydraulic models, two common modeling computational meshes are regular tes- cell: where and premise of the inundation oflevel DEM and grid also cells next is to that aninundated the inundated DEM elevation DEM is grid grid below cell. cells the This approach as floodwater usually seeds chooses and some then simulates the flood di Flood Extent or eight-side rule. The floodby extent Eq. consists (5): of the coverage of DEM grid, asFlood expressed Extent { of DEM grid cellsare below into floodwater consideration. level Allconsisted are of the regarded DEM DEM grid as coverage, grid flooded as cells areas, expressed by and whose Eq. the (4): elevation inundation values extent region growing approach (Poulter et al., 2008). where point is a real inundatedP seeded grid cell, mation of the water1-D level hydraulic and models flow do oftract not the cross involve parameters sections detailed of topography forsections. inundation information, unique A simply it time good through is points. calculating hard wayDEM Because the to to grid water ex- cell solve level and the offlat-water then problem cross model, decide is this the to model inundationperforming extent has calculate interpolation (Tate to the on et solve DEM al., actual the grid 1999).lated depth cells connectivity Similar cells that of issue to are that during every in-between the are cross thegrid sections. not process cells Interpo- of inundated of the cannot watercourse.ing be Previous methods work connected such suggests as with that geostatistical the this interpolationet issue real routines al., can (March 1996), inundated be et neighbourhood DEM al., solved analysis(Werner, 1990; us- 2001). (Jonge Sorensen et al., 1996), and cost distance mapping sellation and triangular irregular net-TIN. Triangular irregular net-TIN is more popular flooding of cities oreled coastal relatively easily areas (Demirkesen duecommon et to methods al., storms are 2007; or usedapproach Wang rise (Moorhead to et and of decide al., Brinson, water the 2002; 1995; level John, inundation Titus can 2001). extent and be Two from Richman, mod- DEM: 2001) the and bathtub the seeded models, there are three main computationmodel methods: and the the flat-water 2-D model, hydraulic 1-D model. hydraulic each of whose side lengthlevees is with between triangulars 100–150 m. (each The sideDEM model length mesh data, is densifies between we the 60–80 main m). getthrough With the nearest the elevation 1 interpolation m-resolution value andcomputes of make the the water the mesh level value of nodeit as each simulates and the triangular 50 triangles periods’ initial mesh’s central inundation centre condition. processes point points The (8 every h model 10 and min. 20 Finally, min in total). 3 Methodology 3.1 Overview The inundation process is veryparticular hard time, there to is simulate a becausecalculated winding it from curved varies water a surface. over certain If time.water we For time level overlay is each with the greater water DEM than surface topographydation data, elevation. simulation As then is a the result, to the inundation calculate key water area point surface is of height. flood where According inun- to the di 5 5 25 15 20 25 20 10 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (7) (6) ) is i x erent is the ( ff z C and col- erent so- I ff erent time. As the hydraulic com- ff 2022 2021 is the distance between each pair of unknown r − ij d erent. One reasonable way is to calculate water ff , i x ) is the water level value of No.i known point, i C is set as 2 for spatial data interpolation. In a high-resolution ( r z is expressed as: stands for the central point which is located at row 2 J − ix x d 2 · − ix r ) stands for the unknown points that need to be interpolated, i − d ij j C 1 d x r ( = · 13 P i − z ij ) i 1 d = x of DEM grid, 13 1 P i ( n = P and column i z J = 1 I n ) = P i x ( = triangle. Firstly, get theC13 coordinate of P1P2P3. and Then its search allwith water the P1P2P3, level triangles and that value calculate share the of the(C1–C12) coordinate nodes the and of P1, their central the P2 water central and point P3 points levelrow values. of The these triangles Equation of water level of grid cell at z umn In this equation, ) j It is of high importance to choose computational mesh nodes as the known interpo- 1. The case is that the computational water level value locates at the center of the x ( solutions of the equation.we For can the decide cell the locateda location at DEM of row I the grid and cell cellused column by to (the J the choose black of spatial the square) nodes the coordinate of DEM is of water grid, the inside level interpolation: central P1P2P3, point. the If following methods can be C13 shows) or on the node of the triangle (as P1–P12 shows) according to di lated points for theto water get level interpolation all oftens the or DEM nodes even grid hundreds in cells ofshows a thousands because computational a hydraulic it mesh non-structural is model nodes modeling are improper level involved value involved. computation in Figure of 5 mesh the water model (TIN). level could The interpolation be located computational when on water the central point of every triangle (as C1– the elevation of No.i known point through interpolation based on thewith equation the above, elevation and value compare of thethe DEM DEM water grid level grid value cell, cell. it Ifgrid means the this cell grid water is cell level the is value water inundated. is level The value higher inundation minus than depth the of that grid the of cell DEM elevation value. DEM, we could get a water level value for each central point of every DEM grid cell In this equation, and known points. Usually, With high-resolution DEMwhole data, DEM it grid is cells inof not the each precise mesh cell element to directly in give because the the the actual DEM floodwater elevation grid level value for is the di z lutions of hydraulic computational equations, thismesh model node can or get the the central water pointputation level of of mesh every mesh element is at di inundation an depth approximate of expression each ofvalue. mesh Floodplain unit digital extent can terrain, and be inundation flooddemand derived depth for water after can result calculating be level precision calculated every (Marks and directly water et if al., level 2000). there is3.2 low Local inverse distance weighted interpolation level of every DEM gridmodeling. cell Inverse distance through weighted spatial interpolation interpolationthe is technology a spatial like comparatively interpolation 1-D simple data, way hydraulic given to which the get location can and interpolate value of theweighted known interpolation points. value The is of common as equation unknown follows: of points inverse distance with has an advantage on showingsity the of topographic some areas reliefs of because thea it triangular better can mesh realization improve to of adjust the to topographic den- the relief changes (Casas of et terrain al., and provide 2006). According to di 5 5 15 20 10 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | - J ffi (8) 8 Bytes × and column I and column I , which is set as r raised to the power ix d ) is the water level value i P 51 000 Columns ( z × , x er from the flood. This is a typ- ff raised to the power ix is represented by d x 2024 2023 ciency of computation. Problems like recursion might be ffi is the vertex of the triangle, and the distance between each is represented by P x ) is the water level elevation of 2 stands for the central point which is located at row x − ix ( x d 2 z · − ix ) i d P 1 ( = 12 P z i 1 = 12 P i = ) of DEM grid, x ( , which is set as 2 for spatial data interpolation. J In this equation, of No.i known point, 2 for spatial data interpolation. get the coordinate and its waterThen level value search of the all nodes the P1, P2and triangles and calculate P3 that the of coordinate share P1P2P3. of the allwater nodes the level nodes P1, values. of P2 The these Equation and trianglesis of (P4–P12) P3 expressed and water as: with their level P1P2P3, of grid cell at row z pair of No.i known point and grid node central point of thepoint triangle, and grid and node the distance between each pair of No.i known r Due to a large amount of DEM data, which is hard to read for one time, it is better To solve the problem, calculated the actual flood extent based on the connectivity 2. The case is that the computational water level value is located at the node. Firstly, 8.36 GB), stored in the memory of a computer is required when dealing with the DEM Every time individual stripsponding water raster file. level To process is largetaken interpolated, volumes up of the by DEM result the data, previous the is strip memory stored is that has on released been before a next corre- data strip is read. to divide the data intospatially strips with to each read. being As read Fig.concurrently at stored 7 one on shows, time. DEM a The raster data results file is of with divided a water into null level 5 interpolations value strips grid are equal to the source DEM data. too deep when dealingoverflowed to with the a extent largeidealistic that way amount computation to of failures employ data such cannectivity neighborhood problems and occur. when analysis As the facing methods a a stack large toof scale, result, of solve DEM high it DEM resolution, data. a and grid is an computer con- enormous not is amount an ≈ data of our study area.cursive On algorithm the with other low hand, e the seeded region growing method is a re- not be applied tothe high-resolution computation DEM capability data required. because Using of the the seeded prohibitive region DEM growing method, size a and di principle. However, some judgment methodswater to and solve 1-D the hydraulic connectivity models problem are of based flat- on the entire DEM. These methods can- ical false inundation area.of Another some issue areas is amongflooded ringed mountains because mountains, is of although lower the protection than the of flood elevation the water mountains. level, these areas are not cult amount of data to process, 8.36 GB (220 000 Rows dams and trenches and the surfacesor of ponds low-resolution that DEM cannot be (Fig. representedfour-sides. 6). on Although some Suppose mid- the that pond there becomesit is inundated is a during not pond the actually surrounded process flooded by of because levees interpolation, the in levees do not su 3.3 Inundated grid cells storing andBecause labelling much micro-topographyderived information DEM data, is many man-made retained surface in features become high-resolution a part LiDAR- of the DEM, like The method mentioned above canwater level interpolate elevation the of inside all the ofto known actual the points flood DEM that extent. are grid Aswithout calculated cell the in interpolating, elevation, local which DEM areas are reduces gridbe equal the cells employed for that amount other are of kinds not calculation. of inundated The computational grid, can method like be can a decided also quadrilateral grid. 5 5 25 20 15 10 10 20 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (9) (10) } } 2026 2025 (RowIndex, RLNum, RLS) = RLList:RLList { = (RLIndex, StartCol, EndCol) = RL:RL { = Take Fig. 9-(1) for example, the inundated central point of ABC can be found on the If the boundary trace from the left of the run-length of Inundated cell 1 meets the right There are two states for every grid cell during DEM grid interpolation: un-inundated As Fig. 8 shows, three inundated cells in a raster field are marked in purple. To first run-length on thethis eleventh run-length, raster the outer throughthe boundary its right of spatial flood of coordinate. extent this From can run-length, be the one traced left of (Fig. of the 9-(1)) inner and boundaries from of flood extent can be traced is only expressed by fourvalue triangles, of and the the central run-lengths pointwe are of can simplified. the calculate The mesh the water elementcan level flood is be extent calculated searched by by on model tracing the computation, central the points so boundaries of of the the whole computational run-lengths, model which elements. 3.5 False inundation area exclusion Based on the methodrun-length mentioned boundary above, tracing we and canshows the get remove run-length the false boundary inundation tracing map andin areas of flood red from extent, flood rectangles. in DEM extent which data run-lengths and only is includes depth. marked 25 Figure raster rows, 9-(1) the model computation mesh already carried the informationconnectivity of problem connectivity could between bethe inundation worked seeded grid region out growing cells through method, andborders this boundary method the to tracing. only need Compared prove search with speed. the along the connectivity run-length between cells, allowing for far faster computation of the run-length ofrun-length Inundated cell of 3, 1 then andthen the the they cells run-length are can not of be connected. 2the connected. Based real cannot Likewise, on inundation if meet mutual area, the exclusion, each all asthe the other long areas areas by as connected connected boundary we to to know 1 2 tracing, that are are real 1 false inundation is inundation areas, areas. and As all a result, the run-lengths have Inundated cell 1 (the firstrun-length run-length on on the the raster rasterboundary row) and row) trace Inundated from must cell the be 3right left (the found. of of fourth If 3 the as these run-length long of run-lengths as 1 are it (as is connected is on the shown the from left the of 1. graph) to the The following are the equations of run-lengthRun data Length and Dataset run-length list on the raster: and might-be-inundated. Thiscompressed is encoding typical to binary therows, raster sequential we data. can might-be-inundated If mark DEMlength we all grid encoding perform the cells is might-be-inundated run-length in a cellsHe raster typical and et compressed al., store 2011), method them whichlength for in only encodes raster memory. needs the data to Run- cells mark (Chang withthe the et same cells storage where al., value of it in 2006; data starts compression.of across remarkably. Every the Figure where run- it might-be-inundated 7 ends, DEM shows whichwhere grid the reduces there cells. run-length are Area three compressed A islands. encoding There in is blue a is false inundation the area real inside inundation the middle extent island. prove the connectivity between Inundated cell 1 and Inundated cell 3 the run-length of RLS As Eq. (9)raster shows, row. The run-length listRowIndex, data means RLNum the is and run-lengths RLS. mainly ofraster In row, current comprised and Eq. raster each (10), of rows, run-length RLS carries on the consists its which RLIndex, RL of there StartCol3.4 all are Lists and the EndCol. on run-lengths Connectivity on detection every principle one After finishing DEM grid water levelencoding interpolation of and inundated storage of cells, run-length therun-length compressed connectivity boundary issue tracing of technology DEM (Quek,inundated grid 2000). cells cells To of prove can DEM the be randomly, connectivity onlying solved of the by run-lengths two judgment is of connectivity needed.vertically of Both and the horizontally. correspond- the if right thebe two and traced run-lengths to left are form borders connected, a then of closed their loop. a borders run-length can are traced 5 5 25 20 15 10 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent from each other. ff 2028 2027 erent periods. In the 2-D hydraulic model, the flood extent ff erence of flood depths between ridges of paddy fields. In Fig. 11-(3) ff er little, the distributions of flood depth are very di ff Figure 11 shows the No.50 period of process of inundation and its regional enlarged After boundary tracing through all the central points of the inundated computational We compare the flood extents calculatedmentioned from above the for 2-D 50 hydraulic model di and the method there are three linear areasthose which are are levee crests not on inundated. which From houses Fig. and 11-(4) roads4.2 we are can being tell constructed. that Inundation area and volume statistics is calculated by adding uptional every mesh, inundated while triangle’s in area the method fromevery of the real this hydraulic inundated paper, computa- cell the flood area extent basedthat is on calculated the of by 1 flood summing m-resolution DEM extentthe data. calculated It method from can be of the expected this 2-Dtopography information hydraulic paper model into (Fig. is full 12). larger consideration, The than and 2-D that many hydraulic details from model cannot cannot be shown take on the micro- The maximum inundation depth calculatedthe by 2-D our hydraulic model. method is 70 cm higher thanview. that of Figure 11-(1) isairborne LiDAR the system, whose high-resolution spatialthe resolution aerial distribution is of remote 0.3 m. farmlands, sensing roads, From this channels,the image levees image houses and we taken are houses can clearly, see by constructed among2-D which along an hydraulic rivers model and and levees.The our Figure mesh methods 11-(2) resolution of regional and a enlarged 11-(3) 2-Dfeatures hydraulic are view of model roads of the is and coarser the compared houses,area inundation with so area. the is the geographic lower result can whilecannot only show the prove the ponds that flood the condition onimportant of flood the geographic every depth features house. left of can However, with that of bethat the clearly the help not expressed. of image only From our the cannot Fig. method, floodbut 11-(3) be condition it also expressed. of is the channels, It obvious di ponds also and levees are clearly expressed, water level through 1extents m di high-resolution DEM. As a result, although the whole flood model mesh is above 100 m, whereas this method mentioned above interpolates the 4.1 Flood inundation results According to the principledepth mentioned of above, Gongshuangcha we Polder ofNo.50 get periods) Dongting the shows Lake the 50 area. comparison periods’ Figure betweenand 10 flood the the result (No.10, extent from No.30 result and the and 2-D of hydraulic the model method mentioned above. The resolution of the 2-D hydraulic and 12–17 in Fig.through 9-(2). the They run-lengths are located toa run-length at search is the the traced islands islandsthe while of untraced the of the side other the flood and not, flood extent.only performing all Traverse boundary the extent. two tracing islands Once kinds (Fig. can one ofthe 9-(3)). be side At found other run-length. by this of is One tracing time, that islengths from there neither are that shows side false each of inundation sideautomatically the removed areas. of run-length by Therefore, boundary the is the tracing. run-length traced.interpolated Meanwhile, false automatically flood The is inundation extent from extent traced, and the extent of depth traced and can can run-length untraced be (Fig. be run- 9-(4)). 4 Results and discussion mesh elements, some of the run-lengths are only traced by one side, like rows 5–8 (Fig. 9-(2)). The outerboundary boundary tracing of of the the entire run-length(the that flood 9th can row). extent be To can avoid searched be repetitionlengths, from of also it the run-length traced is central tracing, through point important andwhether of to to CDE a mark set real run-length two inundated hasonce run- labels previously one along been of two traced. the sidespoint Run-lengths sides of are of is the marked traced. CDE run-length Boundary as isABC to tracing traced has located indicate of been is the traced. not run-length performed where when the central the run-length of the central point of 5 5 20 25 10 15 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . 3 m 6 10 × ect the simulation and analysis of flood- ff 2030 2029 erence is 9.689 ff erence of results is from 3 to 8 %, and in the 50th time- ff erent reasons can be analyzed in greater detail. These factors demon- ff er a method, which integrates a 2-D hydraulic model with high-resolution LiDAR- ff To employ LiDAR-derived DEM to simulate flood routing directly is not realistic be- However, a coin has two sides. With more man-made architectures using high- Among the 50 periods, the flood area calculated by a 2-D hydraulic model surpasses As for the inundation volume, the result calculated by the 2-D hydraulic model is With high-resolution topography data, flood simulation can be analyzed from the ba- tion of high-resolution DEMwhich have has direct been relation to ignoredto inundation extent when o and we depth. As deal a with result, this flood paper parameters, hopes digital topography and datacaused of by residential di houses and public infrastructure, the floods cause of the complexityresolution of mesh. calculation Thus, ofbasis we a of need hydraulic high-resolution model digital to topography. with However, construct lots a of a prohibitively micro-topography relative informa- high- coarse model mesh on the derived DEM to simulateand floodplain depth with flow. much This more method precision can during floodplain calculate flow the modeling. flood With extent this kind of over roads. The redundant information couldplain a flow model. To remove redundantand might information, complicate much later the situation. treatments are needed 5 Conclusions With the help ofdigital photogrammetry terrain and of remote a sensingtopography large technology, materials we scale and can of lack surveyfor reaches of the progressively with greater data high precision accuracy precision. forThe are analysis Problems rapid being and development like assessment of gradually a of LiDAR solved, lossand technology flood allowing has disaster update of especially risks. of promoted digital theapplication terrain acquirement to data flood and disaster shown studies. its great potential for relevant study and strate the great applicationaccurate potential assessment of of our potential method loss for from predictive flooding flood events. simulation and period, the inundation volume di resolution digital topography, newraphy problems information might might be occur involved. Forcould because example, not the some be airborne distinguished false LiDAR from point topog- the cloud data data of channels, bridges over reaches or viaducts method are larger thantopography the could 2-D be hydraulic highertopography model, if directly. which The we means di employ that model the computational whole mesh digital to express the sis of topography to geographictopography elements information because is some accounted oflevees, the for ponds most by and important digital man-made micro- topographyextent architectures. data, and Once especially depth we map that into take of consideration, the we factors can from get the the results flood with more precision. that of our methodperiod, by the 5–17 flood %.The area of exceedingof the flood 2-D this area hydraulic paper’s model method. is is getting 6 square larger. kilometers In larger the than that smaller 50th than that calculated bythe our method maximum (Fig. inundation 13). depth According to and the the previous graphs regional inundation depth calculated by our The precision of digitalSpatial resolution topography and is Vertical precision aand are key depth. both factor important Employing for for high-resolution mappinga flood topography of 2-D data flood simulation hydraulic model. extent can and make analysis. up for the errors of the model computational mesh, likeWe some get secondary a levees, smaller ponds areamesh and result whose steep because elevation slopes. values we are can higher get than rid the of interpolated the water parts levels. in the computational 4.3 Discussion 5 5 15 20 10 25 10 20 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2032 2031 ects of spatial resolution on a raster based model of flood ff ects of DEM sources on hydrologic application, Comput. Environ. ff imagery and SRTM DEM: a case study of Izmir, Turkey, Environ. Monit. + Urban Syst., 34, 251–261, 2010. Hydraul. Res., 28, 629–643, 1990. models, Hydrol. Process., 14, 2109–2122, 2000. hydrodynamic modeling and flood1311, 2008. inundation mapping, Environ. Model. Softw., 23,coastal 1300– plain of North Carolina, Ecol. Appl., 5, 261–271,and 1995. East Datong Lake of2009. Dongting Lake Areas, Ministry of Water Resources, Beijing, China, Geogr. Inform. Sci., 22, 167–182, 2008. forecasting, Trans. GIS, 8, 245–254, 2004. elevation models, J. Hydrol., 167, 279–306, 1995. from the ASTER global DEM for flood modeling, Environ.elling, Modell. M.S. Softw., thesis, 31, International 37–46, Institute 2012. Enschede, for Geo-Information the Science Netherlands, and 2005. Earth Observation length, Pattern Recogn. Lett., 32, 1591–1597, 2011. flow, J. Hydrol., 253, 239–249, 2001. in: HydroGIS96: Application ofResources Geographical Management Information and Planning: Systems Proceedings1996, in of IAHS Hydrology the Publ., and 235, Vienna 299–306, Conference, Water 1996. 16–19 April we need to be?, Nat. Hazards, 49, 79–98, 2009. risk analysis: working with 2D flowthe model, 2nd laser International DEM and Conference a onNapoli, GIS Fluvial Italy. environment: A.A. Hydraulics: Proceedings Balkema River of Publ, Flow Leiden, 2004, the 23–25 Netherlands, June 2, 2004, 1395–1401,on 2004. flood inundation predictions26, using 115–134, 1996. a time/space distributed sensitivity analysis,J. Catena, Hydrol., 236, 54–77, 2000. terrain models as aProc. Land., key 31, element 444–456, 2006. in the accuracy ofapproach, Inf. hydraulic Science, flood 176, modelling, 3393–3408, 2006. Earth Surf. 373, 184–192, 2009. sources Commission, Wuhan, China, 2008. Landsat-7 ETM Assess., 131, 293–300, 2008. coastal flood studies along the8, shoreline 11–20, of 2001. Raritan Bay, New Jersey, Environ. Eng. Geosci., flood models of varyingPhys. Chem. complexity Earth, and 36, scale 281–291, using 2011. high resolutionSurf. terrestrial Proc. LiDAR Land., data, 28, 321–335, 2003. March, C., Lessard, G., and Gharbi, B. El.:Marks, Kriging K. technique and for river Bates, flood P.: representation, Integration J. ofMerwade, high-resolution V., Cook, topographic A., data and with Coonrod, floodplain J.: GIS flow techniques for creatingMoorhead, river K. terrain K. models and for Brinson, M.MWR: M.: Report Response on the of Feasibility of wetlands the to Flood Control rising Project of sea Qianliang Lake, level Gongshuangcha in thePoulter, B. lower and Halpin, P. N.: Raster modelling of coastal flooding from sea-level rise, Int. J. Garcia, S. G.: GRASS GIS-embedded decisionGarrote, support L. framework and for Bras, flood R. simulation L.: and Gichamo, A T. distributed Z., model Popescu, for I., real-time Jonoski, flood A.,Haile, forecasting A. and T.: using Integrating Solomatine, Hydrodynamic digital D.: Models and River High cross-section Resolution DEM(LiDAR) extraction for flood mod- He, Z., Sun, W., Lu, W., and Lu, H.: DigitalHorritt, image M. splicing S. detection and based on Bates, approximate P. run D.: E Jonge, T. de, Kok, M., and Hogeweg, M.: Modelling floods and damage assessment using GIS, Li, J. and Wong, D. W. S.: E References Apel, H., Aronica, G. T., Kreibich, H., and Thieken,Archambeau, A. P.,Dewals, B., H.: Erpicum, Flood S., risk Detrembleur, S., analyses-how and detailed Pirotton, M.: do New trends in flood Bates, P. D. and Anderson, M. G.: A preliminary investigation into the impact of initial conditions Bates, P. D. and De Roo., A. P. J.: A simpleCasas, raster-based A., model Benito, for G., flood Thorndycraft, inundation V. simulation, R., and Rico, M.: The topographic data sourceChang, of C. digital C., Lin, C. Y., and Wang, Y. Z.: New imageChen, steganographic J., methods Hill, using A. run-length A., and Urbano, L. D.: A GIS-based modelCWRC: for urban Comprehensive flood inundation, Treatment J. Hydrol., Planning of Dongting LakeDemirkesen, A. Area, C., Evrendilek, Changjiang F., Berberoglu, Water S., and Re- Kilic, S.: Coastal flood risk analysis using Dobosiewicz, J.: Application of digital elevation models and geographic information systems to Fewtrell, T. J., Duncan, A., Sampson, C. C., Neal, J. C., and Bates, P. D.:French, Benchmarking J. urban R.: Airborne LiDAR in support of geomophological and hydraulic modelling, Earth 5 5 20 25 30 10 15 20 25 10 15 30 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent administrative scales: ff mann, L., and Pfister, L.: Comparison ff cient method for mapping flood extent in ffi 2034 2033 The location of Dongting Lake in Yangtze River Basin. of remotely sensed waterPhotogramm., 63, stages 283–296, from 2008. LiDAR, topographic contours andical SRTM, and ISPRS hydraulic modelling: J. DLISical and MIKE Information II-GIS, Systems HydroGISProceedings 96: in of Application Hydrology the of Vienna Geograph- and Conference, 16–19 Water April Resources 1996, Management IAHS Publ.,Online and 235, Report 149–156, Planning: 99-1, 1996. Center for1999. Research in Water Resources, University of Texas at Austin, along the US Atlantic and Gulf coasts, Clim. Res.,a 18, costal 205–228, floodplain 2001. using2002. Landsat TM and DEM data, Int. J. RemotePhys. Sens., Chem. 23, Earth 3681–3696, B, 26, 517–522, 2001. Softw., 17, 287–294, 2002. a case study in Gangetic West Bengal, India, Singapore J. Trop. Geogr., 27, 207–220, 2006. regions, Pattern Recogn., 33, 1637–1649, 2000. of lidar nominal post-spacingEngin. Remote on Sens., DEM 73, accuracy 793–804, 2007. and flood zoneon delineation, River Photogramm. Flood Hydraulics, edited by: White, W. R., Wiley,30, Chichester, 1831–1843, 339–350, 2007. 1990. Figure 1. Sorensen, H. R., Kjelds, J. T., Deckers, F., and Waardenburg, F.: Application of GIS in hydrolog- Tate, E. and Maidment, D.: Floodplain mapping using HEC-RASTitus, and J. ArcView G. GIS, and CRWR Richman, C.: Maps ofWang, lands Y., Colby, vulnerable J. to D., sea and level rise: Mulcahy, K. modeled elevations A.: AnWerner, e M. G. F.: Impact of grid size in GIS basedZerger, flood A.: extent mapping Examining using GIS a 1D decision flow model, utility for natural hazard risk modeling, Environ. Model. Schumann, G., Matgen, P.,Cutler, M. E. J., Black, A., Ho Quek, F. K. H.: An algorithm forRaber, the G. T., rapid Jensen, computation J. R., of Hodgson, boundaries M. E., of Tullis, run-length J. A., encoded Davis, B. A.,Samuels, and P. G.: Berglund, Cross J.: section Impact location in one-dimensional models, in: InternationalSanders, Conference B. F.: Evaluation of on-line DEMs for floodSanyal, inundation J. modeling, Adv. and Water Lu, Resour., X. X.: GIS-based flood hazard mapping at di 5 15 20 25 30 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | × 51 000 columns, and file size is × 2036 2035 1 m-resolution DEM data for the Gongshuangcha Polder. Coverage shown is 50 The location of Gongshuangcha Polder in Dongting Lake Area. 4.18 GB. Figure 3. 20 km, Space resolution of 1 m, DEM grid is 22 000 rows Figure 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2038 2037 The 2-D hydraulic model mesh of Gongshuangcha Polder and its regional enlarged The scheme of spatial interpolation. Figure 5. Figure 4. view. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2040 2039 Run-length compressed encoding of DEM. The micro-topography information of DEM. Figure 7. Figure 6. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2042 2041 The scheme of run-length boundary tracing and the derived flood extent. Connectivity detection between DEM grid cells. Figure 9. Figure 8. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent ff 2044 2043 The scheme of the inundation process on the 50th time period and its regional The scheme of the inundation process of Gongshuangcha Polder in three di Figure 11. enlarged view. time periods. Figure 10. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent time periods. ff erent time periods. ff 2046 2045 The comparison of inundation volumes in di The comparison of inundation areas in di Figure 13. Figure 12.