# An Improved Grid-Xinanjiang Model and Its Application in the Jinshajiang Basin, China

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## Abstract

**:**

## 1. Introduction

## 2. Improved GXAJ Model Formulation

#### 2.1. Two-Source Potential Evapotranspiration (TSPE) Model

^{−2}), respectively; G is the soil heat flux (W m

^{−2}); $\lambda $ is the latent of vaporization (MJ kg

^{−1}); $\rho $ is the air density (kg m

^{−3}); ${C}_{\mathrm{p}}$ is the air specific heat at constant pressure (KJ kg

^{−1}°C

^{−1}); $\gamma $ is the psychrometric constant (kPa °C

^{−1}); $\Delta $ is the slope of the saturation vapor pressure–temperature relationship (kPa °C

^{−1}); ${W}_{fr}$ is the wetted fraction of the canopy; ${r}_{ac}$ and ${r}_{as}$ are the bulk boundary layer resistance of the canopy and the aerodynamic resistance between the soil surface and the canopy air space (sm

^{−1}), respectively; ${r}_{cp}$ and ${r}_{sp}$ are the bulk stomatal resistance of the canopy and the soil surface resistance with the soil moisture at field capacity (sm

^{−1}), respectively; and ${D}_{0}$ is the saturation vapor pressure deficit at the source height (kPa).

#### 2.2. Infiltration and Saturation Excess Runoff Model (ISER)

_{s}(SR) and R

_{g}(groundwater runoff, GR) different. X is the distance from the intersection of curve ${W}^{\prime}~\alpha $ and curve ${F}_{\Delta t}{}^{\prime}~\beta $ to the origin. These two curves can be presented using the following equations:

#### 2.3. Nonlinear Muskingum–Cunge Routing Method

^{−1}) is the kinematic wave celerity; ${q}_{0}$ (m

^{3}s

^{−1}) is the reference discharge; ${S}_{0}$ is the river bed slope, which can be retrieved from DEM data; w (m) and $\Delta {x}^{\prime}$ are the width and length of a river reach; and N stands for Manning’s n and can be estimated with the help of relevant literature [17]. For each cell, ${Q}_{o}^{t-1}({m}^{3}\Delta {t}^{-1})$ represents the discharge at time t − 1. ${Q}_{i}^{t-1}({\mathrm{m}}^{3}\Delta {t}^{-1})$ and ${Q}_{i}^{t}({\mathrm{m}}^{3}\Delta {t}^{-1})$ are the inflows at times t − 1 and t.

#### 2.4. Schematic Diagram of the Integration of the ANNs with Conceptual Models

## 3. A Brief Description of the Study Area

^{3}km

^{2}. The mean annual temperature ranges from −4.5 °C to 16.4 °C. The total annual rainfall varies from 321 to 1662 mm. Approximately 85% of the annual total rainfall occurs during the monsoon months, from May to September, with strong spatial and temporal variation. Based on the river network characteristics, the Yangtze River Basin can be delineated into three sub-basins, as illustrated in Figure 3. Sub-basin 1, located in the Southeastern part of the Tibet Plateau, is upstream of the Jinshajiang River. Table 1 and alpine valleys make the region a transition zone from shrubland and grassland to forest. With a high topographic relief, Sub-basin 2, the largest tributary to the Jinshajiang River, has rich water resources. The main vegetation types of this sub-basin are forest, grassland, and cropland. Sub-basin 3, located downstream, is the more populated area and has ample rainfall. This area is covered by forest, woodland, grassland, and cropland. The annual characteristics of the three sub-basins are listed in Table 1.

## 4. Materials and Methods

#### 4.1. Topographical Land Cover Data

#### 4.2. Modis LAI

#### 4.3. Meteorological Data

#### 4.4. A Prior Parameter Estimation

_{U}(TWC of upper layer), W

_{L}(TWC of lower layer, EX (exponential parameter of C

_{g}(recession constant of groundwater storage), and IMP (fraction of impervious area) have less influence on the model outputs, while K

_{i}(outflow coefficient of free water storage, FES, to interflow), K

_{g}(outflow coefficient of the free water storage (FWS) to groundwater), C (evaporation coefficient of deeper layer), C

_{i}(Recession constant of interflow storage), b, S

_{M}(FWC), and W

_{M}are sensitive parameters [25,26,27,28]. The empirical relations were used to estimate W

_{U}, W

_{L}, and C

_{g}, as follows: ${W}_{U}=0.167\times {W}_{M}$, ${W}_{L}=0.5\times {W}_{M}$ (Yao et al. 2012), and ${C}_{g}={Q}_{t+1}/{Q}_{t}$ (outflow of dry season at times t + 1, t), EX, and IMP were set to 1.5, and 0.01, respectively [19]. WM and SM correspond to the soil texture and vegetation type and were estimated from the following equations:

_{i}and K

_{g}control the outflow rate of the interflow and groundwater from FWS. They are estimated according to Koren et al. [30]. The optimum values of b, B, C, C

_{i}, and a for different sub-basins are determined by an optimization algorithm (SCE-UA; Duan et al. [31,32]). The hybrid model was calibrated for the period of 2001–2005 and was validated for the period of 2006–2008. The description and acquisition methods of all model parameters are shown in Table 2.

^{3}/s); ${Q}_{s}$ is the simulated runoff (m

^{3}/s); ${\overline{Q}}_{\mathrm{o}}$ is the mean values of the measured runoff (m

^{3}/s); ${Q}_{s,\mathrm{max}}$ is the simulated peak discharge; ${Q}_{o,\mathrm{max}}$ is the observed peak discharge; ${V}_{s}$ is the simulated volume of hydrograph; and ${V}_{o}$ is the observed volume of hydrograph.

^{3}/s), and $Limi{t}_{u,t}$ and $Limi{t}_{l,t}$ are the upper and lower boundary values of 95% confidence intervals respectively.

## 5. Results and Discussion

#### 5.1. Evaluation of the Simulated Streamflow

#### 5.2. Uncertainty Analysis

_{i}parameters, the values of Subwatersheds 1, 2, and 3 decreased successively. This may be related to the differences in elevation between the basins. The value of b is mainly related to the area of the basin, which increases with area, and vice versa [26,27]. Here, the distribution of B values is relatively close, with little difference in each subwatershed. Parameter a is related to the average basal area of plant stem. The simulation parameters showed that the values of Subwatersheds 3, 2, and 1 increase successively.

#### 5.3. Annual Water Budget Simulated by the Modified Xinanjiang Model

#### 5.4. Seasonal Variation of the Evapotranspiration and the Soil Moisture

_{t}), CT, SE, and the ratio of the mean tension water storage (W) to tension water capacity (W

_{M}) are shown in Figure 7a–c. The maximum monthly precipitation levels of each sub-basin were 105, 155, and 206 mm, respectively. The curve of the runoff hydrograph appeared 1 month after the one of the rainfall in the wet season. The annual evapotranspiration was larger than the runoff at Sub-basin 1 and smaller for Sub-basin 2 and Sub-basin 3. During the period of May to October, the CT exceeded the SE markedly due to the dense vegetation canopy and vice versa. This result shows that the value of W/W

_{M}(taken as the soil moisture content) is high from July to November, coinciding with the summer monsoon, while the value of W/W

_{M}is low from March to May because of the vegetation growth and infrequent rain. Compared to Sub-basin 1, the monthly soil water contents of Sub-basin 2 and Sub-basin 3 were higher. However, even in the rainy season of the humid Sub-basin 3, the soil was not saturated every day. Therefore, it is necessary to take both IER and SER into account in the rainfall runoff process.

#### 5.5. Spatial Patterns of the Annual Precipitation and Evapotranspiration

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

GXAJ | Grid-Xinanjiang model |

TSPE | two-source potential evapotranspiration model |

CT | canopy transpiration |

IE | interception evaporation |

SE | soil evaporation |

PE | potential evapotranspiration |

DEM | digital elevation model |

MODIS | Moderate Resolution Imaging Spectroradiometer |

LAI | leaf area index |

BNU | Beijing Normal University |

XAJ | Xinanjiang |

IER | infiltration excess runoff |

SER | saturation excess runoff |

AE | actual evapotranspiration |

NMC | nonlinear Muskingum–Cunge |

SR | surface runoff |

TWC | tension water capacity |

SIC | soil infiltration capacity |

ISER | Infiltration and saturation excess runoff model |

FWC | free water capacity |

SRTM | shuttle radar topography mission |

MMAP | measured mean annual precipitation |

GR | groundwater runoff |

## References

- Hu, C.; Guo, S.; Xiong, L.; Peng, D. A modified Xinanjiang model and its application in northern China. Nord. Hydrol.
**2005**, 36, 175–192. [Google Scholar] [CrossRef] - Zhao, R.; Liu, X. The Xinanjiang model. In Computer Models of Watershed Hydrology; Singh, V.P., Ed.; Water Resources Publications: Littleton, CO, USA, 1995. [Google Scholar]
- Gan, T.Y.; Dlamini, E.M.; Biftu, G.F. Effects of model complexity and structure, data quality, and objective functions on hydrologic modeling. J. Hydrol.
**1997**, 192, 81–103. [Google Scholar] [CrossRef] - Jayawardena, A.; Zhou, M. A modified spatial soil moisture storage capacity distribution curve for the Xinanjiang model. J. Hydrol.
**2000**, 227, 93–113. [Google Scholar] [CrossRef] - Zhang, D.; Zhang, L.; Guan, Y.; Chen, X.; Chen, X. Sensitivity analysis of Xinanjiang rainfall–runoff model parameters: a case study in Lianghui, Zhejiang province, China. Hydrol. Res.
**2012**, 43, 123–134. [Google Scholar] [CrossRef] - Xu, H.; Xu, C.; Chen, H.; Zhang, Z. Assessing the influence of rain gauge density and distribution on hydrological model performance in a humid region of China. J. Hydrol.
**2013**, 505, 1–12. [Google Scholar] [CrossRef] - Yao, C.; Li, Z.; Bao, H.; Yu, Z. Application of a developed Grid-Xinanjiang model to Chinese watersheds for flood forecasting purpose. J. Hydrol. Eng.
**2009**, 14, 923–934. [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] - Ren, G.; Guo, J. Change in pan evaporation and influential factors over China:1956–2000. J. Nat. Resour.
**2006**, 21, 31–44. (In Chinese) [Google Scholar] - Wang, Y.; Liu, B.; Zhai, J.; Su, B.; Luo, Y.; Zhang, Z. Relationship between potential and actual evaporation in Yangtze River Basin. Adv. Clim. Chang. Res.
**2011**, 6, 393–399. (In Chinese) [Google Scholar] - Penman, H.L. Natural evaporation from open water, hare soil and grass. Proc. R. Soc. Lond. Ser. A
**1948**, 193, 120–145. [Google Scholar] [CrossRef] - Monteith, J.L. Evaporation and the Environment, the State and Movement of Water in Living Organisms; Cambridge University Press: London, UK, 1965; pp. 205–234. [Google Scholar]
- Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop evapotranspiration. In Food and Agriculture Organisation Irrigation and Drainage Paper 56; Food and Agriculture Organization: Rome, Italy, 1988. [Google Scholar]
- Choudhury, B.J.; Monteith, J.L. A four-layer model for the heat budget of homogeneous land surfaces. Q. J. R. Meteorolog. Soc.
**1988**, 114, 373–398. [Google Scholar] [CrossRef] - Yuan, F.; Ren, L.; Yu, Z.; Xu, J. Potential evapotranspiration computation using a two-source method for the Xin’anjiang hydrological model. J. Hydrol. Eng.
**2008**, 13, 305–316. [Google Scholar] [CrossRef] - Holtan, H.N. A Concept for Infiltration Estimates in Watershed Engineering. Agric. Res.
**1961**, 150, B16–B25. [Google Scholar] - Getirana, A.C.V.; Boone, A.; Peugeot, C. Evaluating LSM-based water budgets over a west African basin assisted with a river routing scheme. J. Hydrometeorol.
**2014**, 15, 2331–2346. [Google Scholar] [CrossRef] - Gangopadhyay, M.; Uryvaev, V.A.; Oman, M.H.; Nordenson, T.J.; Harbeck, G.E. Measurement and Estimation of Evaporation and Evapotranspiration; WMO: Geneva, Switzerland, 1966. [Google Scholar]
- Liu, X.; Ren, L.; Yuan, F.; Singh, V.P.; Fang, X.; Yu, Z.; Zhang, W. Quantifying the effect of land use and land cover changes on green water and blue water in northern part of China. Hydrol. Earth Syst. Sci.
**2009**, 13, 735–747. [Google Scholar] [CrossRef] [Green Version] - MO, X.; LIN, Z.; LIU, S. An improvement of the dual-source model based on Penman-Monteith formula. J. Hydrol. Eng.
**2000**, 5, 6–11. [Google Scholar] - Xu, J.; Ren, L.; Ruan, X.; Liu, X.; Yuan, F. Development of a physically based PDSI and its application for assessing the vegetation response to drought in northern China. J. Geophys. Res. Atmos.
**2012**, 117, D8. [Google Scholar] [CrossRef] - Jinshajiang River Basin in China. Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC). Available online: http://www.resdc.cn/data.aspx?DATAID=141 (accessed on 12 May 2016).
- Changjiang & Southwest Rivers Water Resources Bulletin, Changjiang Water Resources Commission of the Ministry of Water Resources. Available online: http://www.cjw.gov.cn/zwzc/bmgb/ (accessed on 12 May 2016). (In Chinese)
- Fu, B.; Lu, Q. Atlas of Microclimate over Shanxi Basin; Nanjing University Press: Nanjing, China, 1990. (In Chinese) [Google Scholar]
- Song, X.; Kong, F.; Zhan, C.; Han, J.; Zhang, X. Parameter identification and global sensitivity analysis of Xin’anjiang model using meta-modeling approach. Water Sci. Eng.
**2013**, 6, 1–17. [Google Scholar] - Zhao, R.; Wang, P. Parameter analysis for Xinanjiang model. J. China Hydrol.
**1988**, 6, 2–9. Available online: http://www.cnki.com.cn/Article/CJFDTotal-SWZZ198806000.htm (accessed on 12 May 2016). (In Chinese). - Zhao, R.; Wang, P.; Hu, F. Relations between parameter values and corresponding natural conditions of Xinanjiang model. J. Hohai Univ.
**1992**, 20, 52–59. (In Chinese) [Google Scholar] - Shu, C.; Liu, S.; Mo, X.; Liang, Z.; Dai, D. Uncertainty analysis of Xinanjiang model parameter. Geogr. Res.
**2008**, 27, 343–352. (In Chinese) [Google Scholar] - Anderson, R.; Koren, V.; Reed, S. Using SSURGO data to improve Sacramento Model a priori parameter estimates. J. Hydrol.
**2006**, 320, 103–116. [Google Scholar] [CrossRef] - Koren, V.; Smith, M.; Wang, D.; Zhang, Z. Use of Soil Property Data in the Derivation of Conceptual Rainfall–Runoff Model Parameters, 2000. Available online: http://www.nws.noaa.gov/oh/hrl/modelcalibration/3.%20%20A%20priori%20model%20parameters/ams_2000_koren_soils_parameters.pdf (accessed on 12 May 2016).
- Duan, Q.Y.; Gupta, V.K.; Sorooshian, S. Shuffled complex evolution approach for effective and efficient global minimization. J. Optim. Theory Appl.
**1993**, 76, 501–521. [Google Scholar] [CrossRef] - Duan, Q.; Sorooshian, S.; Gupta, V.K. Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol.
**1994**, 158, 265–284. [Google Scholar] [CrossRef] [Green Version] - 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] - Walega, A.; Ksiazek, L. Influence of rainfall data on the uncertainty of flood simulation. Soil Water Res.
**2016**, 11. [Google Scholar] [CrossRef] - Blasone, R.S. Parameter Estimation and Uncertainty Assessment in Hydrological Modelling. Ph.D. Thesis, Technical University of Denmark, Copenhagen, Denmark, August 2007. [Google Scholar]
- Jin, X.; Xu, C.; Zhang, Q.; Singh, V.P. Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model. J. Hydrol.
**2010**, 383, 147–155. [Google Scholar] [CrossRef] - Xiong, L.; Wan, M.; Wei, X. Indices for assessing the prediction bounds of hydrological models and application by generalized likelihood uncertainty estimation. Hydrol. Sci. J.
**2009**, 54, 852–871. [Google Scholar] [CrossRef]

**Figure 3.**The Jinshajiang River basin, China [22].

**Figure 4.**Time series of observed and estimated daily streamflow hydrographs from the modified GRAJ model and the TSPE+GRAJ model in the calibration period; (

**a**) Sub-basin 1; (

**b**) Sub-basin 2; (

**c**) Sub-basin 3.

**Figure 5.**The distribution curves of parameters of the three sub-basins simulated by the modified GXAJ model.

**Figure 7.**The series of monthly precipitation, runoff, evapotranspiration (E

_{t}), canopy transpiration (CT), potential soil evaporation (SE), and the ratio of mean tension water storage (W) to tension water capacity (W

_{M}); (

**a**) Sub-basin 1; (

**b**) Sub-basin 2; (

**c**) Sub-basin 3.

**Figure 8.**The spatial distribution of the mean annual precipitation (

**a**), evapotranspiration (

**b**), and fraction of evapotranspiration to precipitation (

**c**), land cover (

**d**), elevation (

**e**), and mean annual temperatures (

**f**).

**Table 1.**The annual characteristics of three sub-basins of the Jinshajiang River basin [23].

Watershed | Area (km^{2}) | Evaporation (mm) | PE (mm) | Temperature (°C) | Runoff (mm) | Runoff Coeff | Climate Zone |
---|---|---|---|---|---|---|---|

Sub-basin 1 | 214,184 | 461.5 | 1351.54 | 0.44 | 201.90 | 0.44 | Semi-arid, semi-humid |

Sub-basin 2 | 136,000 | 753.16 | 1264.57 | 7.43 | 446.16 | 0.59 | Semi-humid |

Sub-basin 3 | 108,616 | 909.76 | 1267.21 | 13.52 | 479.53 | 0.53 | Humid |

Module | Parameter | Description | Acquisition Method |
---|---|---|---|

Two-source potential evapotranspiration | r_{min} | minimum stamatal resistance(sm^{−1}) | based on land data assimilation system (LDAS) |

a_{c} | albedo | based on land data assimilation system (LDAS) | |

h | height of vegetation(m) | based on land data assimilation system (LDAS) | |

W_{max} | maximum leaf width | based on land data assimilation system (LDAS) | |

d_{0} | monthly zero-plane displacement | based on land data assimilation system (LDAS) | |

z_{0} | monthly roughness length | based on land data assimilation system (LDAS) | |

LAI | Leaf area index | from the BNU MODIS LAI dataset | |

Actual evaporation | W_{um} | tension water capacity (TWC) of upper layer (mm) | Based on WM |

W_{lm} | TWC of lower layer (mm) | Based on WM | |

C | evapotranspiration coefficient of deeper layer | determined by optimization algorithm | |

Runoff generation | W_{m} | TWC (mm) | using θ_{f}_{c}, θ_{w}_{p}, and aeration zone thickness |

θ_{s} | saturated moisture content | from literature | |

θ_{fc} | field capacity | from literature | |

θ_{wp} | wilting point | from literature | |

S_{m} | free water capacity (FWC; mm) | using θ_{s}, θ_{fc}, and humus layer thickness | |

K_{i} | outflow coefficient of FWS to interflow | on the basis of soil properties | |

K_{g} | outflow coefficient of FWS to groundwater | on the basis of soil properties | |

C_{i} | recession coefficient of interflow water | determined by optimization algorithm | |

C_{g} | recession coefficient of groundwater water | outflow of dry season at time t+1, t | |

Nonlinear Muskingum–Cunge routing method | n | Manning roughness coefficient | from literature |

q_{0} | water discharge of reference(m^{3}s^{−1}) | using outflow and inflow of each grid | |

S_{0} | river bed slope | derived from the shuttle radar topography mission (SRTM) digital elevation model (DEM) | |

w | width of a river reach (m) | derived from the drainage area | |

Dx′ | length of a river reach(m) | using total river length within each grid | |

Infiltration and saturation excess runoff | b | exponent of TWC capacity distribution curve | determined by optimization algorithm |

B | exponent of soil infiltration capacity distribution curve | determined by optimization algorithm | |

a | average basal area of plant stems | determined by optimization algorithm |

**Table 3.**Performances of the three sub-basins in the modified GXAJ model, ISER + GRAJ model, and TSPE + GRAJ model (daily).

Parameter | Category | Calibration | Validation | ||||
---|---|---|---|---|---|---|---|

Sub-Basin 1 | Sub-Basin 2 | Sub-Basin 3 | Sub-Basin 1 | Sub-Basin 2 | Sub-Basin 3 | ||

Modified GRAJ Model | |||||||

NSE (%) | mean annual flow | 95.39 | 88.97 | 94.80 | 94.81 | 88.35 | 95.63 |

rainy season | 91.26 | 83.54 | 92.19 | 91.58 | 85.03 | 93.86 | |

dry season | 81.68 | 61.06 | 82.82 | 76.94 | 58.49 | 87.70 | |

RE | mean annual flow | −2.60 | −6.51 | 1.41 | 4.26 | −6.88 | 1.20 |

rainy season | −3.65 | −5.97 | 1.11 | 3.46 | −4.53 | 2.97 | |

dry season | 4.95 | −11.10 | 2.43 | 8.27 | −12.77 | −3.97 | |

PEP | rainy season | −3.80 | −9.80 | −4.40 | −4.80 | −8.60 | −3.40 |

PEV | rainy season | −6.50 | −11.50 | 2.60 | 6.13 | −8.65 | 5.35 |

ISER + GRAJ Model | |||||||

NSE | mean annual flow | 94.22 | 86.68 | 93.34 | 93.59 | 85.87 | 94.66 |

rainy season | 89.29 | 81.87 | 90.05 | 90.02 | 83.10 | 93.00 | |

dry season | 80.34 | 62.18 | 80.95 | 74.34 | 56.67 | 85.37 | |

RE | mean annual flow | −3.70 | −6.88 | −2.10 | 4.65 | −6.30 | -2.50 |

rainy season | −4.50 | −6.60 | −1.82 | 4.12 | −5.70 | −2.90 | |

dry season | 6.20 | −12.00 | −4.20 | 8.67 | −11.90 | 3.50 | |

PEP | rainy season | −4.60 | −10.10 | −5.10 | −5.30 | 9.50 | −4.10 |

PEV | rainy season | −7.10 | −13.20 | −4.30 | 7.32 | −12.46 | −5.12 |

TSPE + GRAJ Model | |||||||

NSE | mean annual flow | 92.33 | 81.90 | 91.16 | 92.59 | 76.98 | 90.66 |

rainy season | 87.17 | 80.44 | 89.15 | 87.66 | 80.30 | 92.32 | |

dry season | 75.60 | 60.66 | 76.90 | 69.38 | 56.99 | 82.46 | |

RE | mean annual flow | −8.03 | −7.83 | −2.01 | −5.12 | −8.94 | −3.31 |

rainy season | −7.90 | −7.37 | −2.53 | −4.17 | −7.24 | −2.90 | |

dry season | −8.23 | −12.00 | 5.62 | −9.46 | −15.30 | −7.42 | |

PEP | rainy season | −9.70 | −12.40 | −7.70 | −9.20 | −11.70 | −6.20 |

PEV | rainy season | −14.50 | −16.30 | −7.05 | −8.47 | −14.87 | −5.31 |

Uncertainty Parameter | Catchment | ||
---|---|---|---|

Sub-Basin 1 | Sub-Basin 2 | Sub-Basin 3 | |

average relative length (ARIL) | 0.265 | 0.513 | 0.267 |

average asymmetry degree (AAD) | 0.300 | 0.324 | 0.298 |

average deviation amplitude (ADA) | 285.878 | 406.498 | 108.610 |

Watershed | Year | Precipitation | Storage Change | Modeled Annual Actual Evaporation | Measured Discharge |
---|---|---|---|---|---|

Sub-basin 1 | mean annual (mm) | 461.50 | −1.08 | 258.63 | 201.90 |

σ/Mean | 0.23 | - | 0.09 | 0.38 | |

correlation with Precipitation | 1.00 | 0.85 | 0.41 | 0.81 | |

Sub-basin 2 | mean annual (mm) | 753.16 | −1.40 | 338.12 | 446.16 |

σ/Mean | 0.20 | - | 0.07 | 0.40 | |

correlation with Precipitation | 1.00 | 0.66 | 0.64 | 0.95 | |

Sub-basin 3 | mean annual (mm) | 909.76 | 1.62 | 422.21 | 479.53 |

σ/Mean | 0.27 | - | 0.07 | 0.46 | |

correlation with Precipitation | 1.00 | 0.62 | 0.59 | 0.94 |

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**MDPI and ACS Style**

Meng, C.; Zhou, J.; Zhong, D.; Wang, C.; Guo, J.
An Improved Grid-Xinanjiang Model and Its Application in the Jinshajiang Basin, China. *Water* **2018**, *10*, 1265.
https://doi.org/10.3390/w10091265

**AMA Style**

Meng C, Zhou J, Zhong D, Wang C, Guo J.
An Improved Grid-Xinanjiang Model and Its Application in the Jinshajiang Basin, China. *Water*. 2018; 10(9):1265.
https://doi.org/10.3390/w10091265

**Chicago/Turabian Style**

Meng, Changqing, Jianzhong Zhou, Deyu Zhong, Chao Wang, and Jun Guo.
2018. "An Improved Grid-Xinanjiang Model and Its Application in the Jinshajiang Basin, China" *Water* 10, no. 9: 1265.
https://doi.org/10.3390/w10091265