# Integrated Hydrologic and Hydrodynamic Models to Improve Flood Simulation Capability in the Data-Scarce Three Gorges Reservoir Region

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}and a storage capacity of 393 billion m

^{3}at a normal water level (175 m, Wusong Elevation System). The length of the mainstream in the study area is approximately 760 km, and the reservoir surface width of the mainstream is generally 700–1700 m. The topography of the study area (Figure 1, 90 × 90 m, Digital Elevation Model) shows great variation, with the highest elevation (3090 m) in the northeast and the lowest elevation (40 m) in the northwest.

#### 2.2. Dataset

#### 2.3. Watershed Division

^{2}, 15,086 km

^{2}, 8611 km

^{2}, 31,215 km

^{2}. The area of the whole study area is 69,467 km

^{2}. Each sub-region contains several rainfall sites, evaporation sites, and several sub-catchments. Sub-catchments are extracted based on terrain using the ArcGIS tool.

#### 2.4. The Xinanjiang Model

- Evapotranspiration module: the three-layer evapotranspiration mode—that is, the total evaporation is composed of three parts, namely, surface evaporation, shallow evaporation, and deep evaporation.
- Runoff generation module: runoff generation under saturated conditions—that is, the precipitation does not produce flow before the field water capacity is satisfied, and all precipitation is absorbed by the soil. After the precipitation meets the field water holding capacity, all precipitation (excluding the evaporation in the same period) produces flow.
- Runoff separation module: the division of the three water sources—that is, according to the free water capacity distribution curve, the total production flow is divided into the surface runoff, soil middle flow, and underground runoff.
- Runoff routing module: this module is divided into the river network confluence and the river channel confluence. The river network confluence is runoff that flows directly into the sub-catchment outlet; the river confluence is the runoff at the sub-catchment outlet that uses the Muskingum method to evolve the basin exit.

#### 2.5. The One-Dimensional (1-D) Hydrodynamic Model

_{f}is the friction slope, and t is the time.

_{f}is calculated using the Manning formula [51], as follows:

#### 2.6. Coupled Models

^{2}, the catchment concentration time of the river basin is short, the river basin confluence is mainly affected by the river network confluence, and the river channel confluence can be ignored. Furthermore, the Muskingum method is derived on the assumption of a single function relationship between the outflow and the tank storage. This assumption is consistent with the actual water flow in the upper reaches of the basin with sufficient accuracy. In the lower reaches of the basin, especially in the plain estuary area, the flow is affected by the combined action of the incoming upstream flow and the downstream tidal level. Due to the support of the downstream water level, the flow is not a free outflow. The above assumption no longer exists. In the Three Gorges reservoir area, the free water flow is affected by the water level of the Three Gorges, and the assumption of the Muskingum method is no longer valid.

#### 2.6.1. XAJ-H1DM Model

#### 2.6.2. H1DM-XAJ Model

#### 2.7. Model Calibration and Validation

#### 2.7.1. The Xinanjiang Model

_{e}is the total number of calibration events, and E

_{N,j}is the error indices for the event j.

_{o}value (close to 0) corresponds to a better match between the simulated streamflow and the observed data. Thus, the parameters were calibrated by minimizing the objective function. Simulations of the sub-regions used a parameter set transposed from their sub-catchments that were expanded according to the area ratio of the sub-region to sub-catchment. The formulas to calculate the RRE, RPE, and peak time error (PTE) values are as follows:

_{s}is the simulation value of flow, R

_{o}is the observation value of flow, n is the total number of years, m is the total number of flood periods in one year, and T(X) represents the day X occurred.

#### 2.7.2. The One-Dimensional Hydrodynamic Model

#### 2.8. Parameter Replacement

## 3. Results

#### 3.1. Results of Xinanjiang Model

#### 3.2. Results of the H1DM Model

#### 3.3. Results for Parameter Replacement

#### 3.4. Results of XAJ-H1DM Model

^{3}/s). The NSE values for WX were 0.33 and 0.394 from 2008 to 2009. The terrain in the WX sub-catchment is complex, ravines and gullies crisscross the area, the flood routing time is short; therefore, the simulation results of the Xinanjiang module without the routing module will produce large errors. This may indicate that the major source of uncertainty in the application of the Xinanjiang module in these ungauged catchments lies in the model process of runoff routing [27].

#### 3.5. Results of H1DM-XAJ Model

## 4. Discussion

^{2}[68] is used as a similarity index on the basis of the analysis of Figure 11. Furthermore, Figure 11 illustrates the R

^{2}between the flow simulations of the four models and the measured values at different hydrometric stations, while the R

^{2}of water level simulations are presented in Figure 12.

- in the simulation of rainfall-runoff, only the effects of rainfall and evaporation on the runoff generation and routing in the basin were considered, and no consideration was given to meteorological factors (such as humidity, wind direction), land use, and vegetation cover;
- compared with the distributed hydrological model, the Xinanjiang model is faster in the calculation and requires less data, but the physical meaning is not clear enough and there are more empirical parameters;
- the H1DM used in this paper has fast calculation speed and high accuracy but does not consider the different roughness of the riverbed and the flood plain.

## 5. Conclusions

- Results indicate that the regionalization approaches can be successfully used in the application of the integrated hydrologic and hydrodynamic model.
- Our results show that both coupled models are capable of providing satisfactory and comparative simulations of runoff volume, peak discharge, peak time, and flood hydrograph for the data-scarce catchments once they are well-calibrated. Integrated models also have the ability to simulate floods using the regionalization approaches. The coupled models produced markedly improved estimates of peak discharge and runoff volume as compared to the single H1DM model, indicating that the intermediate flow is a major uncertainty source for the application of the H1DM model in large scale watersheds. Coupling Xinanjiang with H1DM has the potential to substantially improve the flood simulation capability of the H1DM model in poorly gauged catchments.
- The coupled models have shown improvements in peak discharge, runoff volume, peak time, and hydrograph as compared to the XAJ model. Moreover, the ability of the coupled models to simulate peak water level and hydrograph, which any hydrological model lack, is significantly better than that of the single H1DM model. This study demonstrates the importance of incorporating intermediate inflows, which can be obtained from rainfall-flow model predictions in data-deficient areas, in hydraulic models.
- The coupled way of the H1DM-XAJ model provides a realizable direction to improve the flood simulation capability of the integrated hydrologic and hydrodynamic models in ungauged intermediate catchments.

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Todini, E. Rainfall-Runoff Models for Real-Time Forecasting. Encycl. Hydrol. Sci.
**2006**. [Google Scholar] [CrossRef] - WMO. Manual on Flood Forecasting and Warning; World Meteorological Organization: Geneva, Switzerland, 2011. [Google Scholar]
- Alho, P.; Aaltonen, J. Comparing a 1D hydraulic model with a 2D hydraulic model for the simulation of extreme glacial outburst floods. Hydrol. Process. Int. J.
**2008**, 22, 1537–1547. [Google Scholar] [CrossRef] - Anderson, E.J.; Schwab, D.J.; Lang, G.A. Real-time hydraulic and hydrodynamic model of the St. Clair River, Lake St. Clair, Detroit River system. J. Hydraul. Eng.
**2010**, 136, 507–518. [Google Scholar] [CrossRef] [Green Version] - Brunner, G.W. HEC-RAS River Analysis System. Hydraulic Reference Manual. Version 1.0; Hydrologic Engineering Center: Davis, CA, USA, 1995. [Google Scholar]
- Correia, F.N.; Rego, F.C.; Saraiva, M.D.G.; Ramos, I. Coupling GIS with hydrologic and hydraulic flood modelling. Water Resour. Manag.
**1998**, 12, 229–249. [Google Scholar] [CrossRef] - Liu, Q.; Qin, Y.; Zhang, Y.; Li, Z. A coupled 1D–2D hydrodynamic model for flood simulation in flood detention basin. Natl. Hazards
**2015**, 75, 1303–1325. [Google Scholar] [CrossRef] - Mu, J.-B.; Zhang, X.-F. Real-time flood forecasting method with 1-D unsteady flow model. J. Hydrodyn.
**2007**, 19, 150–154. [Google Scholar] [CrossRef] - Nandalal, K. Use of a hydrodynamic model to forecast floods of Kalu River in Sri Lanka. J. Flood Risk Manag.
**2009**, 2, 151–158. [Google Scholar] [CrossRef] - Panda, R.K.; Pramanik, N.; Bala, B. Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model. Comput. Geosci.
**2010**, 36, 735–745. [Google Scholar] [CrossRef] - Seyoum, S.D.; Vojinovic, Z.; Price, R.K.; Weesakul, S. Coupled 1D and noninertia 2D flood inundation model for simulation of urban flooding. J. Hydraul. Eng.
**2011**, 138, 23–34. [Google Scholar] [CrossRef] - Shamsudin, S.; Hashim, N. Rainfall runoff simulation using MIKE11 NAM. Malays. J. Civ. Eng.
**2002**, 15, 26–38. [Google Scholar] - Ye, J.; McCorquodale, J. Simulation of curved open channel flows by 3D hydrodynamic model. J. Hydraul. Eng.
**1998**, 124, 687–698. [Google Scholar] [CrossRef] - Jiang, J. Coupled Modeling and Application of Hydrological, Hydrodynamic and Water Quality Models for Typical Coastal Basins: A Case Study of the Yongjiang River Basin. Ph.D. Thesis, Zhejiang University, Hangzhou, China, 2018. [Google Scholar]
- Xu, Z.; Godrej, A.N.; Grizzard, T.J. The hydrological calibration and validation of a complexly-linked watershed—Reservoir model for the Occoquan watershed, Virginia. J. Hydrol.
**2007**, 345, 167–183. [Google Scholar] [CrossRef] - Inoue, M.; Park, D.; Justic, D.; Wiseman Jr, W.J. A high-resolution integrated hydrology—Hydrodynamic model of the Barataria Basin system. Environ. Model. Softw.
**2008**, 23, 1122–1132. [Google Scholar] [CrossRef] - Morita, M.; Yen, B.C. Modeling of conjunctive two-dimensional surface-three-dimensional subsurface flows. J. Hydraul. Eng.
**2002**, 128, 184–200. [Google Scholar] [CrossRef] - Lian, Y.; Chan, I.-C.; Singh, J.; Demissie, M.; Knapp, V.; Xie, H. Coupling of hydrologic and hydraulic models for the Illinois River Basin. J. Hydrol.
**2007**, 344, 210–222. [Google Scholar] [CrossRef] - Liu, H.-L.; Chen, X.; Bao, A.-M.; Wang, L. Investigation of groundwater response to overland flow and topography using a coupled MIKE SHE/MIKE 11 modeling system for an arid watershed. J. Hydrol.
**2007**, 347, 448–459. [Google Scholar] [CrossRef] - De Paiva, R.C.D.; Buarque, D.C.; Collischonn, W.; Bonnet, M.P.; Frappart, F.; Calmant, S.; Bulhoes Mendes, C.A. Large-scale hydrologic and hydrodynamic modeling of the Amazon River basin. Water Resour. Res.
**2013**, 49, 1226–1243. [Google Scholar] [CrossRef] [Green Version] - Paiva, R.; Collischonn, W.; Bonnet, M.-P.; De Goncalves, L.; Calmant, S.; Getirana, A.; Santos da Silva, J. Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon. Hydrol. Earth Syst. Sci.
**2013**, 17, 2929–2946. [Google Scholar] [CrossRef] [Green Version] - Mateo, C.M.; Hanasaki, N.; Komori, D.; Tanaka, K.; Kiguchi, M.; Champathong, A.; Sukhapunnaphan, T.; Yamazaki, D.; Oki, T. Assessing the impacts of reservoir operation to floodplain inundation by combining hydrological, reservoir management, and hydrodynamic models. Water Resour. Res.
**2014**, 50, 7245–7266. [Google Scholar] [CrossRef] - Bellos, V.; Tsakiris, G. A hybrid method for flood simulation in small catchments combining hydrodynamic and hydrological techniques. J. Hydrol.
**2016**, 540, 331–339. [Google Scholar] [CrossRef] - Zhang, L.; Lu, J.; Chen, X.; Liang, D.; Fu, X.; Sauvage, S.; Sanchez Perez, J.-M. Stream flow simulation and verification in ungauged zones by coupling hydrological and hydrodynamic models: A case study of the Poyang Lake ungauged zone. Hydrol. Earth Syst. Sci.
**2017**, 21, 5847–5861. [Google Scholar] [CrossRef] [Green Version] - Wu, B.; Wang, G.; Wang, Z.; Liu, C.; Ma, J. Integrated hydrologic and hydrodynamic modeling to assess water exchange in a data-scarce reservoir. J. Hydrol.
**2017**, 555, 15–30. [Google Scholar] [CrossRef] - Montanari, A.; Toth, E. Calibration of hydrological models in the spectral domain: An opportunity for scarcely gauged basins? Water Resour. Res.
**2007**, 43. [Google Scholar] [CrossRef] [Green Version] - Yao, C.; Zhang, K.; Yu, Z.; Li, Z.; Li, Q. Improving the flood prediction capability of the Xinanjiang model in ungauged nested catchments by coupling it with the geomorphologic instantaneous unit hydrograph. J. Hydrol.
**2014**, 517, 1035–1048. [Google Scholar] [CrossRef] - Vergara, H.; Kirstetter, P.-E.; Gourley, J.J.; Flamig, Z.L.; Hong, Y.; Arthur, A.; Kolar, R. Estimating a-priori kinematic wave model parameters based on regionalization for flash flood forecasting in the Conterminous United States. J. Hydrol.
**2016**, 541, 421–433. [Google Scholar] [CrossRef] - Yamanaka, T.; Ma, W. Runoff prediction in a poorly gauged basin using isotope-calibrated models. J. Hydrol.
**2017**, 544, 567–574. [Google Scholar] [CrossRef] [Green Version] - Wagener, T.; Wheater, H.S. Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty. J. Hydrol.
**2006**, 320, 132–154. [Google Scholar] [CrossRef] - Yang, T.; Zhang, Q.; Chen, Y.D.; Tao, X.; Xu, C.Y.; Chen, X. A spatial assessment of hydrologic alteration caused by dam construction in the middle and lower Yellow River, China. Hydrol. Process. Int. J.
**2008**, 22, 3829–3843. [Google Scholar] [CrossRef] - Yang, T.; Shao, Q.; Hao, Z.-C.; Chen, X.; Zhang, Z.; Xu, C.-Y.; Sun, L. Regional frequency analysis and spatio-temporal pattern characterization of rainfall extremes in the Pearl River Basin, China. J. Hydrol.
**2010**, 380, 386–405. [Google Scholar] [CrossRef] - Yang, T.; Zhang, Q.; Wang, W.; Yu, Z.; Chen, Y.D.; Lu, G.; Hao, Z.; Baron, A.; Zhao, C.; Chen, X. Review of advances in hydrologic science in China in the last decades: Impact study of climate change and human activities. J. Hydrol. Eng.
**2012**, 18, 1380–1384. [Google Scholar] [CrossRef] - Yao, C.; Li, Z.; Yu, Z.; Zhang, K. A priori parameter estimates for a distributed, grid-based Xinanjiang model using geographically based information. J. Hydrol.
**2012**, 468, 47–62. [Google Scholar] [CrossRef] - Singh, R.; Archfield, S.; Wagener, T. Identifying dominant controls on hydrologic parameter transfer from gauged to ungauged catchments–A comparative hydrology approach. J. Hydrol.
**2014**, 517, 985–996. [Google Scholar] [CrossRef] - Oudin, L.; Andréassian, V.; Perrin, C.; Michel, C.; Le Moine, N. Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments. Water Resour. Res.
**2008**, 44. [Google Scholar] [CrossRef] - Zhang, Y.; Chiew, F.H. Relative merits of different methods for runoff predictions in ungauged catchments. Water Resour. Res.
**2009**, 45. [Google Scholar] [CrossRef] - Beck, H.E.; van Dijk, A.I.; De Roo, A.; Miralles, D.G.; McVicar, T.R.; Schellekens, J.; Bruijnzeel, L.A. Global-scale regionalization of hydrologic model parameters. Water Resour. Res.
**2016**, 52, 3599–3622. [Google Scholar] [CrossRef] [Green Version] - Zhao, R.; Zhang, Y.; Fang, L.; Liu, X.; Zhang, Q. The Xinanjiang Model Hydrological Forecasting Proceedings Oxford Symposium; IASH: Edinburgh, UK, 1980. [Google Scholar]
- Ren-Jun, Z. The Xinanjiang model applied in China. J. Hydrol.
**1992**, 135, 371–381. [Google Scholar] [CrossRef] - Pramanik, N.; Panda, R.K.; Sen, D. One dimensional hydrodynamic modeling of river flow using DEM extracted river cross-sections. Water Resour. Manag.
**2010**, 24, 835–852. [Google Scholar] [CrossRef] - Thakur, B.; Parajuli, R.; Kalra, A.; Ahmad, S.; Gupta, R. Coupling HEC-RAS and HEC-HMS in Precipitation Runoff Modelling and Evaluating Flood Plain Inundation Map. In Proceedings of the World Environmental and Water Resources Congress 2017, Sacramento, CA, USA, 21–25 May 2017; pp. 240–251. [Google Scholar]
- Chengwei, L.U.; Zhou, J.; Dechao, H.U.; Zhang, Y. Real-time Simulation of Hydrodynamic Process in Dendritic River Network in Three Georges Reservoir Area. J. Yangtze River Sci. Res. Inst.
**2018**, 35, 153–156. [Google Scholar] - Merz, R.; Blöschl, G. Regionalisation of catchment model parameters. J. Hydrol.
**2004**, 287, 95–123. [Google Scholar] [CrossRef] [Green Version] - Doummar, J.; Sauter, M.; Geyer, T. Simulation of flow processes in a large scale karst system with an integrated catchment model (Mike She)—Identification of relevant parameters influencing spring discharge. J. Hydrol.
**2012**, 426, 112–123. [Google Scholar] [CrossRef] - Zhang, G.; Xie, T.; Zhang, L.; Hua, X.; Liu, F. Application of Multi-Step Parameter Estimation Method Based on Optimization Algorithm in Sacramento Model. Water
**2017**, 9, 495. [Google Scholar] [CrossRef] [Green Version] - Wang, B.; Tian, F.; Hu, H. Analysis of the effect of regional lateral inflow on the flood peak of the Three Gorges Reservoir. Sci. China Technol. Sci.
**2011**, 54, 914–923. [Google Scholar] [CrossRef] - Sun, N.; Zhou, J.; Zhang, H.; Lezhuang, G.E. Application of Xin’anjiang Model and Tank Model in Zhexi Basin. J. China Hydrol.
**2018**, 3, 6. [Google Scholar] - Alaghmand, S.; bin Abdullah, R.; Abustan, I.; Eslamian, S. Comparison between capabilities of HEC-RAS and MIKE11 hydraulic models in river flood risk modelling (a case study of Sungai Kayu Ara River basin, Malaysia). Int. J. Hydrol. Sci. Technol.
**2012**, 2, 270–291. [Google Scholar] [CrossRef] - Xia, J.; Zhang, X.; Deng, S.; Li, J. Modelling of hyperconcentrated floods in the lower Yellow River using a coupled approach. Adv. Water Sci.
**2015**, 26, 686–697. [Google Scholar] - Wang, C.; Li, G. Practical River Network Flow Calculation; Nanjing Department of Water Resources and Hydrology, Hohai University: Nanjing, China, 2003. (In Chinese) [Google Scholar]
- Hu, D.; Zhang, H.; Zhong, D. Properties of the Eulerian–Lagrangian method using linear interpolators in a three-dimensional shallow water model using z-level coordinates. Int. J. Comput. Fluid Dyn.
**2009**, 23, 271–284. [Google Scholar] [CrossRef] - Hu, D.-C.; Zhong, D.-Y.; Wang, G.-Q.; Zhu, Y.-H. A semi-implicit three-dimensional numerical model for non-hydrostatic pressure free-surface flows on an unstructured, sigma grid. Int. J. Sedim. Res.
**2013**, 28, 77–89. [Google Scholar] [CrossRef] - Hu, D.; Zhong, D.; Zhang, H.; Wang, G. Prediction–correction method for parallelizing implicit 2D hydrodynamic models. I: Scheme. J. Hydraul. Eng.
**2015**, 141, 04015014. [Google Scholar] [CrossRef] - Hoitink, A.; Jay, D.A. Tidal river dynamics: Implications for deltas. Rev. Geophys.
**2016**, 54, 240–272. [Google Scholar] [CrossRef] - Sanders, B.F. Hydrodynamic modeling of urban flood flows and disaster risk reduction. Oxf. Res. Encycl. Natl. Hazard Sci.
**2017**. [Google Scholar] [CrossRef] - Duan, Q.; Sorooshian, S.; Gupta, V. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res.
**1992**, 28, 1015–1031. [Google Scholar] [CrossRef] - Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol.
**1970**, 10, 282–290. [Google Scholar] [CrossRef] - MWR. Standard for Hydrological Information and Hydrological Forecasting (GB/T 22482–2008); Chinese Ministry of Water Resources: Beijing, China, 2008; p. 16. (In Chinese)
- Bao, W.-M.; Zhang, X.-Q.; Qu, S.-M. Dynamic correction of roughness in the hydrodynamic model. J. Hydrodyn.
**2009**, 21, 255–263. [Google Scholar] [CrossRef] - Maimon, O.; Rokach, L. Data Mining and Knowledge Discovery Handbook; Springer: Berlin/Heidelberg, Germany, 2005. [Google Scholar]
- Yadav, M.; Wagener, T.; Gupta, H. Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins. Adv. Water Resour.
**2007**, 30, 1756–1774. [Google Scholar] [CrossRef] - Oudin, L.; Kay, A.; Andréassian, V.; Perrin, C. Are seemingly physically similar catchments truly hydrologically similar? Water Resour. Res.
**2010**, 46. [Google Scholar] [CrossRef] - Beven, K.J.; Kirkby, M.J. A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol. Sci. J.
**1979**, 24, 43–69. [Google Scholar] [CrossRef] [Green Version] - O’Callaghan, J.F.; Mark, D.M. The extraction of drainage networks from digital elevation data. Comput. Vis. Graph. Image Process.
**1984**, 28, 323–344. [Google Scholar] [CrossRef] - Tarboton, D.G. A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resour. Res.
**1997**, 33, 309–319. [Google Scholar] [CrossRef] [Green Version] - Pan, F.; Peters-Lidard, C.D.; Sale, M.J.; King, A.W. A comparison of geographical information systems–based algorithms for computing the TOPMODEL topographic index. Water Resour. Res.
**2004**, 40, 6. [Google Scholar] [CrossRef] [Green Version] - Nagelkerke, N.J. A note on a general definition of the coefficient of determination. Biometrika
**1991**, 78, 691–692. [Google Scholar] [CrossRef]

**Figure 1.**Map of the study area and locations of the river gauging stations. ZT, CT, QXC, WX, SX, BB and WUL stands for the Zhutuo, Cuntan, Qingxichang, Wanxian, Sanxia, Beibei and Wulong hydrometric station respectively.

**Figure 3.**Flow chart of the Xinanjiang model with the one-dimension hydrodynamic model, XAJ-H1DM (

**left**) and H1DM-XAJ (

**right**).

**Figure 5.**Relative positioning of the sub-catchments and sub-regions. WC, SZ, WQ, CTA, WXI and XS stand for the Wucha, Shizhu, Wenquan, Changtan, Wuxi, and Xingshan hydrometric station respectively.

**Figure 6.**Topographic index probability density distribution curve for six sub-basins in different groups. The topographic index probability density distribution curves of the 10 regions (

**a**), the first group (

**b**), the second group (

**c**), the third group (

**d**).

**Figure 7.**Streamflow hydrographs for several sites. (

**a**) CT-2009-Flow; (

**b**) CT-2009-Water level; (

**c**) QXC-2007-Flow; (

**d**) QXC-2007-Water level; (

**e**) QXC-2009-Flow; (

**f**) QXC-2009-Water level.

**Figure 11.**Flow scatter diagram and correlation coefficient (R

^{2}) of the four models in CT (

**a**), QXC (

**b**), WX (

**c**), SX (

**d**) for all periods.

**Figure 12.**Water level scatter diagram and R

^{2}of three models in CT (

**a**), QXC (

**b**), WX (

**c**), SX (

**d**) for all periods.

Subdomains | Range of Roughness |
---|---|

ZT-CT | 0.025–0.028 |

CT-QXC | 0.028–0.033 |

QXC-WX | 0.033–0.035 |

WX-SX | 0.035–0.056 |

Xinanjiang Model | H1DM-XAJ Model | XAJ-H1DM Model | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Module | Para | ZT-CT | CT-QXC | QXC-WX | WX-SX | ZT-CT | CT-QXC | QXC-WX | WX-SX | WC | SZ | WQ | CTA | WXI | XS |

Evapotranspiration | K | 0.500 | 0.501 | 1.100 | 0.953 | 0.500 | 0.500 | 0.500 | 0.500 | 0.500 | 0.500 | 1.002 | 0.701 | 0.587 | 0.501 |

UM | 65 | 75 | 74 | 74 | 65 | 73 | 70 | 71 | 13 | 13 | 26 | 21 | 29 | 16 | |

LM | 76 | 77 | 77 | 61 | 85 | 64 | 65 | 62 | 66 | 66 | 90 | 74 | 79 | 71 | |

DM | 31 | 26 | 37 | 18 | 60 | 15 | 16 | 15 | 15 | 15 | 49 | 15 | 60 | 40 | |

C | 0.16 | 0.11 | 0.15 | 0.10 | 0.14 | 0.15 | 0.12 | 0.13 | 0.13 | 0.13 | 0.11 | 0.09 | 0.12 | 0.16 | |

Runoff generation | IM | 0.00 | 0.03 | 0.01 | 0.01 | 0.03 | 0.03 | 0.02 | 0.01 | 0.007 | 0.007 | 0.030 | 0.000 | 0.029 | 0.023 |

B | 0.1 | 0.2 | 0.2 | 0.3 | 0.4 | 0.2 | 0.2 | 0.3 | 0.1 | 0.1 | 0.3 | 0.4 | 0.5 | 0.3 | |

Runoff separation | SM | 44.00 | 16.50 | 40.81 | 10.04 | 22.02 | 16.72 | 10.00 | 10.01 | 4.55 | 4.55 | 1.00 | 6.93 | 1.00 | 8.63 |

EX | 0.50 | 0.50 | 1.16 | 2.00 | 0.50 | 0.50 | 0.50 | 0.50 | 0.66 | 0.66 | 0.50 | 0.50 | 0.50 | 0.50 | |

KI | 0.376 | 0.449 | 0.350 | 0.390 | 0.425 | 0.380 | 0.429 | 0.426 | 0.450 | 0.450 | 0.406 | 0.450 | 0.350 | 0.450 | |

KG | 0.305 | 0.250 | 0.350 | 0.250 | 0.250 | 0.250 | 0.250 | 0.250 | 0.252 | 0.252 | 0.307 | 0.350 | 0.269 | 0.250 | |

Runoff routing | CS | 0.500 | 0.412 | 0.164 | 0.500 | 0.408 | 0.500 | 0.495 | 0.469 | 0.410 | 0.410 | 0.269 | 0.273 | 0.545 | 0.467 |

CI | 0.567 | 0.900 | 0.754 | 0.604 | 0.503 | 0.565 | 0.500 | 0.559 | 0.691 | 0.691 | 0.790 | 0.900 | 0.726 | 0.898 | |

CG | 0.992 | 0.990 | 0.998 | 0.998 | 0.994 | 0.990 | 0.990 | 0.990 | 0.998 | 0.998 | 0.998 | 0.996 | 0.998 | 0.998 | |

KE | 13.289 | 2.877 | 11.324 | 0.214 | - | - | - | - | - | - | - | - | - | - | |

XE | 0.282 | 0.000 | 0.122 | 0.003 | - | - | - | - | - | - | - | - | - | - |

**Table 3.**Accuracy statistics of the four model flow simulations with calibrated parameters for both calibration and validations events.

2007 | 2008 | 2009 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

XAJ | H1DM | XAJ-H1DM | H1DM-XAJ | XAJ | H1DM | XAJ-H1DM | H1DM-XAJ | XAJ | H1DM | XAJ-H1DM | H1DM-XAJ | ||

CT | RRE (%) | 1.70% | −2.39% | −0.20% | −0.51% | 1.76% | −2.54% | −0.83% | −0.67% | 2.34% | −3.94% | −2.19% | −1.51% |

RPE (%) | −4.75% | 0.78% | 1.17% | 1.05% | −3.08% | −3.53% | −3.40% | −3.15% | 8.77% | −10.95% | −1.92% | −1.32% | |

PTE (day) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

NSE | 0.9891 | 0.9960 | 0.9975 | 0.9976 | 0.9909 | 0.9949 | 0.9976 | 0.9976 | 0.9899 | 0.9925 | 0.9975 | 0.9978 | |

QXC | RRE (%) | 3.06% | −5.84% | −3.92% | −1.14% | 3.42% | −6.40% | −4.80% | −1.38% | 2.82% | −5.56% | −3.68% | −0.57% |

RPE (%) | 2.12% | −9.24% | −8.09% | −3.52% | 3.12% | −5.18% | −4.39% | −0.88% | 1.46% | −15.25% | −5.98% | −1.60% | |

PTE (day) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

NSE | 0.9910 | 0.9810 | 0.9883 | 0.9955 | 0.9900 | 0.9774 | 0.9827 | 0.9896 | 0.9915 | 0.9776 | 0.9867 | 0.9913 | |

WX | RRE (%) | 1.01% | −4.68% | −1.84% | 2.16% | 1.03% | −3.37% | −0.87% | 3.92% | 1.05% | −3.18% | −0.47% | 3.88% |

RPE (%) | −2.90% | −10.77% | −8.42% | −0.72% | 0.31% | −2.40% | 0.56% | 6.94% | −3.77% | −13.69% | −3.87% | 2.09% | |

PTE (day) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

NSE | 0.9930 | 0.9834 | 0.9921 | 0.9951 | 0.9936 | 0.9885 | 0.9936 | 0.9886 | 0.9937 | 0.9864 | 0.9935 | 0.9893 | |

SX | RRE (%) | 4.80% | −10.19% | −2.28% | 3.70% | 4.27% | −8.95% | −2.09% | 5.63% | 3.58% | −7.28% | −0.85% | 3.20% |

RPE (%) | 6.42% | −20.56% | −14.38% | −2.89% | 12.24% | −7.75% | 5.27% | 6.07% | 0.20% | −1.24% | 1.76% | 8.92% | |

PTE (day) | 0 | 0 | 0 | 0 | −1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

NSE | 0.9776 | 0.9212 | 0.9766 | 0.9915 | 0.9808 | 0.9426 | 0.9871 | 0.9788 | 0.9895 | 0.9524 | 0.9803 | 0.9750 |

**Table 4.**Accuracy statistics of the four model water level simulations with calibrated parameters for both calibration and validations events.

2007 | 2008 | 2009 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

XAJ | H1DM | XAJ-H1DM | H1DM-XAJ | XAJ | H1DM | XAJ-H1DM | H1DM-XAJ | XAJ | H1DM | XAJ-H1DM | H1DM-XAJ | ||

CT | AWME (m) | - | −0.340 | −0.204 | −0.237 | - | −0.215 | −0.112 | −0.099 | - | −0.114 | −0.028 | 0.003 |

RMWE (%) | - | −0.76% | −0.74% | −0.74% | - | −0.64% | −0.60% | −0.60% | - | −2.19% | −1.37% | −1.38% | |

NSE | - | 0.9839 | 0.9885 | 0.9867 | - | 0.9922 | 0.9942 | 0.9943 | - | 0.9890 | 0.9938 | 0.9940 | |

QXC | AWME (m) | - | −0.046 | 0.046 | 0.132 | - | 0.017 | 0.083 | 0.180 | - | −0.044 | 0.015 | 0.083 |

RMWE (%) | - | −1.09% | −0.88% | −0.36% | - | −0.13% | 0.03% | 0.05% | - | −0.04% | −0.03% | −0.02% | |

NSE | - | 0.9896 | 0.9934 | 0.9920 | - | 0.9993 | 0.9990 | 0.9977 | - | 0.9991 | 0.9995 | 0.9990 | |

WX | AWME (m) | - | −0.046 | 0.026 | 0.064 | - | 0.007 | 0.057 | 0.096 | - | −0.009 | 0.030 | 0.061 |

RMWE (%) | - | −0.02% | −0.01% | 0.02% | - | −0.01% | 0.06% | 0.07% | - | −0.01% | 0.00% | 0.01% | |

NSE | - | 0.9966 | 0.9981 | 0.9974 | - | 0.9998 | 0.9996 | 0.9993 | - | 0.9998 | 0.9987 | 0.9995 | |

SX | AWME (m) | - | 0.000 | 0.000 | 0.000 | - | 0.000 | 0.000 | 0.000 | - | 0.000 | 0.000 | 0.000 |

RMWE (%) | - | 0.00% | 0.00% | 0.00% | - | 0.00% | 0.00% | 0.00% | - | 0.00% | 0.00% | 0.00% | |

NSE | - | 1.0000 | 1.0000 | 1.0000 | - | 1.0000 | 1.0000 | 1.0000 | - | 1.0000 | 1.0000 | 1.0000 |

Catchment | NSE | |||
---|---|---|---|---|

2007 | 2008 | 2009 | Average | |

WC | 0.514 | 0.799 | 0.826 | 0.713 |

SZ | 0.291 | 0.223 | 0.235 | 0.249 |

WQ | 0.848 | 0.662 | 0.832 | 0.781 |

CTA | 0.741 | 0.741 | 0.590 | 0.690 |

WXI | 0.830 | 0.330 | 0.394 | 0.518 |

XS | 0.640 | 0.642 | 0.602 | 0.628 |

Catchment | 2007 | 2008 | 2009 |
---|---|---|---|

ZT-CT | 2.22%/1.5% | 1.83%/2.09% | 1.88%/2.55% |

CT-QXC | 2.08%/3.94% | 1.97%/5.26% | 2.12%/5.38% |

QXC-WX | 3.15%/5.94% | 2.85%/7.45% | 2.92%/7.38% |

WX-SX | 8.06%/12.02% | 7.07%/14.47% | 6.64%/13.85% |

© 2020 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 (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Zhang, Y.; Zhou, J.; Lu, C.
Integrated Hydrologic and Hydrodynamic Models to Improve Flood Simulation Capability in the Data-Scarce Three Gorges Reservoir Region. *Water* **2020**, *12*, 1462.
https://doi.org/10.3390/w12051462

**AMA Style**

Zhang Y, Zhou J, Lu C.
Integrated Hydrologic and Hydrodynamic Models to Improve Flood Simulation Capability in the Data-Scarce Three Gorges Reservoir Region. *Water*. 2020; 12(5):1462.
https://doi.org/10.3390/w12051462

**Chicago/Turabian Style**

Zhang, Yulong, Jianzhong Zhou, and Chengwei Lu.
2020. "Integrated Hydrologic and Hydrodynamic Models to Improve Flood Simulation Capability in the Data-Scarce Three Gorges Reservoir Region" *Water* 12, no. 5: 1462.
https://doi.org/10.3390/w12051462