# The WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China

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## Abstract

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_{PM}). Then, we designed two forcing scenarios (WRF-driven rainfall (Wr) + PE

_{PM}, WRF-merged rainfall (Wm) + PE

_{PM}) to drive the Grid-XAJ model for flood forecasting. We found the WRF-driven Grid-XAJ model held significant potential in flood forecasting. The Grid-XAJ model provided only an approximation of flood hygrographs when driven by scenario Wr + PE

_{PM}. The results in scenario Wm + PE

_{PM}showed a high degree-of-fit with observed floods with mean Nash–Sutcliffe efficiency coefficient (NSE) values of 0.94 and 0.68 in two catchments. Additionally, scenario Wm + PE

_{PM}performed better flood hygrographs than scenario Wr + PE

_{PM}. The flood volumes and flow peaks in scenario Wm + PE

_{PM}had an obvious improvement compare to scenario Wr + PE

_{PM}. Finally, we observed that the model exhibited superior performance in forecasting flood hydrographs, flow peaks, and flood volumes in humid catchments compared with semi-humid catchments.

## 1. Introduction

_{oc}) can be estimated utilizing the digital elevation model (DEM), and the leaf area index (LAI) can be estimated based on land use and land cover (LULC) data. On the other hand, other parameters necessitate calibration. Over time, the Grid-XAJ model has undergone continuous development and refinement.

_{pan}). However, usually only one or two pans are available within a catchment, and these pans exclusively capture daily evaporation. So the PE

_{pan}may not resonate with real-world conditions [32,33]. Various methods can improve the spatiotemporal accuracy of PE distribution. With the advancement of remote sensing technology, a growing number of datasets now directly offer high-spatial-resolution PE data with reliable accuracy at no cost. Notable examples include the Moderate Resolution Imaging Spectroradiometer (MODIS) [34] and the Global Land Evaporation Amsterdam Model (GLEAM) [35]. But their temporal resolution is sometimes not satisfactory. Acquiring high-spatiotemporal-resolution meteorological variables such as temperature and wind speed is considerably easy. Hence, machine learning methods with the inputs of meteorological data have been extensively employed for the best PE estimates in recent years [36,37]. In addition, using energy-based methods to estimate PE with high-spatiotemporal-resolution meteorological data is reliable. For example, the Penman–Monteith equation, which is based on energy balance, has been used by Meng [38] to effectively estimate the temporal and spatial distribution of PE in the Grid-XAJ model. We employed the Penman–Monteith equation to evaluate the potential evapotranspiration (PE

_{PM}) using meteorological data generated by the WRF model in this paper.

_{PM}. Finally, to comprehensively assess the WRF-driven Grid-XAJ model’s performance, we conducted a comparative analysis of flood simulations in both the humid Tunxi catchment and the semi-humid Chenhe catchment.

## 2. Methodology

#### 2.1. The WRF Model

#### 2.2. The Grid-XAJ Model

_{PM}). The input data for the Penman–Monteith equation, including radiation, temperature, pressure, humidity, and wind speed, were acquired from the WRF simulation.

#### 2.3. Successive Correction Method

_{ij,k}is the weight factor of grid (i, j) for rain station k; R is the influence radius, and is defined as the minimum value between the number of rows and columns within the WRF domain for each catchment; and d

_{ij,k}is the distance from grid (i, j) to rainfall station k.

_{k}is the correction value of station k, O

_{k}are the gauged data in station k, and G

_{k}is the initial value in station k.

#### 2.4. The Penman–Monteith Equation

_{PM}is the hourly potential evapotranspiration (mm/h); Rn is the net radiation at the crop surface (MJ/(m h)); G is the soil heat flux density (MJ/(m h)); T

_{hr}is the mean hourly air temperature at a 2 m height (°C); u

_{2}is the wind speed at a 2 m height (m/s); ${e}_{{T}_{hr}}^{0}$ is the saturation vapor pressure (kPa); e

_{a}is the actual vapor pressure (kPa); Δ is the slope of the vapor pressure curve (kPa/°C); and γ is the psychrometric constant, whose value is 0.066 kPa/°C. The parameters of the above equation are described below [49]:

^{2}; RL is the incoming long-wave radiation, W/m

^{2}; σ is the Stefan–Boltzmann constant, whose value is 5.67 × 10

^{−8}W/(m

^{2}K

^{4}); and Ts is the land surface temperature, K.

_{10}) at height h = 10 m, which can be converted to a 2 m wind speed:

_{a}is estimated as follows [50]:

#### 2.5. Evaluation Methods and Metrics

_{i}are the simulated results for each time step i, O

_{i}is the observed value, n is the total number of time series, x is a discrete random variable recording all different values in the grids, p(x

_{k}) is the frequency of the value of x

_{k}appearing in the grids, and N is the length of the x. The RR and NSE quantify the degree-of-fit between simulation and observation. PB measures the errors of the simulated results. In the case of single-value (such as rainfall peak) simulation output, PB represents the relative error between this value and the gauged value. The SE is regarded as a measure for spatial variability, and the higher its values are, the more complex the related spatial information is. The TEP is used to evaluate the time accuracy of the simulated flow peak.

_{pr}) were selected for analyzing the rainfall series [25]. For assessing the cumulative rainfall, the PB of the cumulative rainfall (PB

_{cr}) was selected as an evaluation metric. Due to the distinct spatial distributions of the three types of rainfall, SE was selected to evaluate their spatial characteristics [25]. For PE

_{PM}, we mainly focused on its spatial and temporal variations and possible influencing factors. The PB, NSE, PB of the flow peak (PB

_{pf}), and TEP were used to evaluate the flood simulations.

_{pf}≤ 20%, the flow peak simulation is considered acceptable. Additionally, the TEP within a three-hour window is deemed acceptable for peak occurrence time forecasting. The qualification rate (QR) of flood simulations can be determined as follows:

## 3. Study Area and Data

#### 3.1. Study Catchments

^{2}. It has an elevation ranging from 92 m to 1622 m. The catchment is situated within a subtropical monsoon climate zone and experiences an average annual rainfall of approximately 1600 mm, making it a representative humid catchment. Rainfall is highly unevenly distributed throughout the year, with the months from April to June being rainy, which often leads to flood disasters, while the period from July to September experiences frequent drought. Farmland and woodland collectively occupy over 60% of the Tunxi catchment, with clay loam being the predominant soil type.

^{2}and elevations ranging from 600 m to 3800 m. This catchment represents a typical semi-humid catchment, with an annual runoff depth ranging from 100 to 500 mm and a multi-year average rainfall of 780 mm. The land use in the Chenhe catchment primarily consists of grassland, with a minor proportion of mixed forest and deciduous broadleaf forest. The predominant soil type is loam. The distribution of rivers and stations in the two catchments is shown in Figure 2.

#### 3.2. Gauge Data

#### 3.3. FNL Data

## 4. Results and Discussion

#### 4.1. Parameters Calibration

#### 4.2. Evaluation of Two Rainfall Products

_{pr}values being below 50%. Event 130606 exhibited a relatively large PB

_{cr}value of 60.1%, while the PB

_{cr}values for the other three events remained within 20%. Merging Wr with the gauged rainfall significantly improved rainfall accuracy, as indicated by Wm achieving higher RR values (>0.99). Furthermore, Wm demonstrated notable improvements in the degree-of-fit with Ir, exceeding all NSE values above 0.98. Additionally, the PB

_{pr}of Wm was substantially reduced to below 10%. In terms of cumulative rainfall analysis, there was a significant decrease in PB

_{cr}values across all four events, with values falling below 10%.

_{pr}of Wr for all four events exceeded 40%. Furthermore, the PB

_{cr}values were above 30% for events 030903 and 120830. After merging Wr and gauged rainfall, Wm achieved RR values of at least 0.95, with the NSE values all being above 0.75. The PB

_{pr}of Wm was noticeably reduced compared to that of Wr, and the PB

_{cr}values were also significantly decreased (<37%). In addition, the cumulative rainfall of Wm was consistently higher than that of Ir.

_{pr}, and mean PB

_{cr}were 0.75, 0.42, 30.0%, and 23.0% for Wr; and 0.99, 0.99, 4.8%, and 3.1% for Wm. In the Chenhe catchment, they were 0.43, −0.58, 83.0%, and 30.4% for Wr; and 0.98, 0.89, 34.7%, and 21.9% for Wm.

#### 4.3. Characteristics of PE_{PM}

_{PM}and PE

_{pan}. Then, we mainly focused on some influence factors for PE

_{PM}and its spatiotemporal distribution characteristics.

_{PM}and PE

_{pan}series. As shown in the figure, PE

_{pan}remains stable throughout the day, with sudden changes occurring only at the transition to the next day. Contrarily, PE

_{PM}exhibits hourly variations and changes gradually over time. In addition, the range of PE

_{PM}(0.0 mm~0.5 mm for the Tunxi catchment, 0.0 mm~0.4 mm for the Chenhe catchment) is greater than that of PE

_{pan}(0.0 mm~0.3 mm for the Tunxi catchment, 0.0 mm~0.25 mm for the Chenhe catchment).

_{PM}. PE

_{PM}notably decreases during rainy periods in both catchments. Moreover, the influence of different climatic zones is evident in the PE

_{PM}values. The PE

_{PM}values in the Tunxi catchment are considerably higher compared to those in the Chenhe catchment. Specifically, in the Tunxi catchment, the PE

_{PM}values range from 0.0 to 0.5 mm. In the Chenhe catchment, the PE

_{PM}values range from 0.0 to 0.2 mm for events 050928 and 110916, and from 0.0 to 0.4 mm for the other two events.

_{PM}exhibits distinct temporal distribution characteristics. As illustrated in Figure 6, evapotranspiration mainly occurs between 6:00 a.m. and 6:00 p.m., with the peak PE

_{PM}occurring around noon at 12:00 p.m. During the nighttime, the PE

_{PM}values approach zero.

_{PM}exhibits distinct spatial distribution characteristics. Figure 7 depicts the spatial distribution of the mean PE

_{PM}, where the mean PE

_{PM}refers to the average spatial distribution of PE

_{PM}over all time periods. The SE values exceed 2.0 for the Tunxi catchment and surpass 3.0 for the Chenhe catchment. Figure 8 illustrates the relationship between the mean PE

_{PM}and elevation. As shown in Figure 8, there is a relationship between PE

_{PM}and elevation: regions with higher elevations display relatively lower mean PE

_{PM}values, while areas with moderate elevations exhibit the highest mean PE

_{PM}values, and slightly lower values are observed in lower elevation areas. Here are the specifics: (1) In the lower elevation areas of the catchment, the mean PE

_{PM}is moderately high. Within the 0~400 m elevation range in the Tunxi catchment, except for event 170623, there is a slight increase in the mean PE

_{PM}with higher elevations. Similarly, within the 600~800 m elevation range in the Chenhe catchment, except for event 120830, there is also a slight increase in the mean PE

_{PM}with higher elevations. Although the mean PE

_{PM}values of event 170623 in the Tunxi catchment and event 120830 in the Chenhe catchment decreased with increasing elevation, it is worth noting that some lower elevation areas, such as rivers near the catchment outlets, exhibit a relatively low mean PE

_{PM}. (2) In the middle elevation areas of the catchment, the mean PE

_{PM}is the highest. Within the 400~1200 m elevation range of the Tunxi catchment, there is a gradual decrease in the mean PE

_{PM}as the elevation increases. Similarly, within the 800~2400 m elevation range of the Chenhe catchment, although there is a fluctuating trend in the mean PE

_{PM}with increasing elevation for event 110916, for the other three events, there is a gradual decrease in the mean PE

_{PM}with higher elevations. (3) In the high elevation areas of the catchment, the mean PE

_{PM}is the lowest. In the regions with an elevation above 2400 m in the Chenhe catchment and above 1200 m elevation in the Tunxi catchment, the mean PE

_{PM}exhibits a fluctuating pattern. For all events, as the mean PE

_{PM}decreases to a certain extent with increasing elevation, it then shows an upward trend. The possible reason for this phenomenon is that PE is mainly affected by wind speed in the middle and low elevations of the catchment, while PE is mainly affected by temperature in the high elevations of the catchment.

#### 4.4. Evaluation of Discharge

_{pan}with PE

_{PM}in the model. The results are presented in Figure 9. Table 5 shows the values of the metrics used for the simulated floods. It is evident from Figure 9 and Table 5 that directly using Wr and PE

_{PM}to simulate floods yields unsatisfactory results. However, incorporating Wm as input rainfall significantly enhances the forecasting performance.

_{PM}and Wm + PE

_{PM}forcing scenarios were designed for the Grid-XAJ model. In the Tunxi catchment, for scenario Wr + PE

_{PM}, only one flood event exhibits an NSE value exceeding 0.90. The PB values are relatively small for events 130606 and 150607. However, for the other two events, particularly event 120423, the PB values are significantly larger. All simulated flow peaks fail to meet the qualification criteria, with all PB

_{pf}values exceeding 20%, despite having a small TEP. For scenario Wm + PE

_{PM}, all NSE values surpass 0.90. The flood volume simulation is robust, with all PB values below 5%. For flow peaks, the PB

_{pf}values for all four events are below 20%, with events 130606 and 170623 achieving PB

_{pf}values below 10%. The TEP for the four floods remain within a 3 h margin, demonstrating significant reductions compared to scenario Wr + PE

_{PM}.

_{PM}, the NSE values for all events remain below 0.55, indicating a suboptimal degree-of-fit between the simulation and observation. Except for event 110916, all PB values exceed 20%. Additionally, notable deficiencies are observed in peak flows. Although the PB

_{pf}values of events 030903 and 050928 meet the criteria, significant temporal discrepancies exist for the TEP (−9 h, −42 h). For events 110916 and 120830, neither the flow peaks nor the peak occurrence times align with the gauged discharges. For scenario Wr + PE

_{PM}, all four events achieve NSE values surpassing 0.5. Although event 030903 does not meet the criteria for PB

_{pf}and TEP, these deviations are relatively small. The other three events yield satisfactory results in terms of PB

_{pf}and TEP.

_{PM}significantly improves the degree-of-fit, flood volume, and flow peaks over scenario Wr + PE

_{PM}. In the Tunxi catchment, compared to scenario Wr + PE

_{PM}, the mean NSE value of scenario Wm + PE

_{PM}increased by 0.3, the mean PB value decreased from 21.5% to 3.5%, the QR of PB

_{pf}increased from 0 to 100%, and the TEP decreased one hour. In the Chenhe catchment, compared to scenario Wr + PE

_{PM}, the NSE value of scenario Wm + PE

_{PM}increased from −1.65~0.54 to 0.54~0.87. The PB values exhibit minor fluctuations, with reductions of 8.26% and 0.89% for events 030903 and 120830, and a slight increase of 1.6% and 2.77% for events 050928 and 110916. The QR of PB

_{pf}increased from 50% to 100% and the TEP decreased by seventeen hours.

_{PM}, the mean NSE value in the Tunxi catchment is 66.4% higher than that in the Chenhe catchment, and the mean PB is 7.3% lower than that in the Chenhe catchment. Additionally, the QR of the TEP is 100% in the Tunxi catchment but 0% in the Chenhe catchment. For scenario Wr + PE

_{PM}, all NSE values exceed 0.90 in the Tunxi catchment, whereas in the Chenhe catchment, all NSE values fall below 0.90. In the Tunxi catchment, the QRs for PB, PB

_{pf}, and TEP are all 100%, while in the Chenhe catchment, the QRs for the PB, PB

_{pf}, and TEP are 50%, 75%, and 75%, respectively. These outcomes indicate that the results of the model in the semi-humid catchment can only serve as a reference.

#### 4.5. Discussion

_{PM}, which exhibited a reasonable spatiotemporal distribution (Figure 7 and Figure 8), was also consistent with other research that used it with NWM output for PE calculation [59].

_{pr}of Wr, which positively influences the simulation of flow peaks. An accurate estimate for the cumulative rainfall will improve the precision of simulated flood volume. The error in the cumulative rainfall of Wm is smaller than that of Wr, which indicates that Wm is more suitable to simulate flood volume than Wr. In addition, the error of each metric for Wr and Wm in the Tunxi catchment is lower than that in the Chenhe catchment, suggesting that the Grid-XAJ model may yield more accurate flood simulations in the Tunxi catchment. Figure 6 shows that PE

_{PM}exhibits a correlation with rainfall. This association can be attributed to the concurrent decrease in air temperature and increase in moisture content during rainy periods. Furthermore, PE

_{PM}in the Tunxi catchment was higher compared to that in the Chenhe catchment (Figure 6). This may be attributed to the elevated soil moisture content and a greater availability of water vapor within the humid catchment. Research has shown a strong correlation between PE

_{PM}and elevation [60], and that was proved in our study (Figure 7).

## 5. Conclusions

_{PM}) were utilized in this paper. Then, two forcing scenarios (Wr + PE

_{PM}and Wm + PE

_{PM}) were designed to drive the Grid-XAJ model in both the Tunxi and the Chenhe catchments, aiming to enhance the accuracy of flood simulations. The main conclusions are as follows.

_{PM}demonstrates significant spatiotemporal heterogeneity, exhibiting hourly variations compared to PE

_{pan}. Additionally, PE

_{PM}can be influenced by rainfall, and its values are higher in the humid catchment than in the semi-humid catchment. The spatial distribution of PE

_{PM}exhibits a gradual decrease from moderately elevated regions towards areas characterized by both higher and lower elevations.

_{PM}, shows a broad capacity to simulate floods. In contrast, under scenario Wm + PE

_{PM}, the model demonstrates a superior flood forecasting performance. Additionally, the WRF-driven Grid-XAJ model demonstrates superior flood forecasting performance in the humid catchment compared to the semi-humid catchment.

## 6. Study Limitations and Prospects

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**Study areas: (

**a**) the location of the two catchments within China, (

**b**) the channels and rainfall/discharge gauges of the Tunxi catchment, and (

**c**) the channels and rainfall/discharge gauges of the Chenhe catchment.

**Figure 3.**Rainfall series: (

**a1**–

**a4**) show the hourly precipitation and cumulative rainfall for events 120423, 130606, 150607, and 170623 in the Tunxi catchment, and (

**b1**–

**b4**) show the hourly precipitation and cumulative rainfall for events 030903, 050928, 110916, and 120830 in the Chenhe catchment.

**Figure 4.**The spatial distribution of cumulative rainfall in the Tunxi catchment. The left panel (

**a1**,

**b1**,

**c1**,

**d1**) shows the spatial distribution of Ir, the middle panel (

**a2**,

**b2**,

**c2**,

**d2**) shows the spatial distribution of Wr, and the right panel (

**a3**,

**b3**,

**c3**,

**d3**) shows the spatial distribution of Wm.

**Figure 5.**The spatial distribution of cumulative rainfall in the Chenhe catchment. The left panel (

**a1**,

**b1**,

**c1**,

**d1**) shows the spatial distribution of Ir, the middle panel (

**a2**,

**b2**,

**c2**,

**d2**) shows the spatial distribution of Wr, and the right panel (

**a3**,

**b3**,

**c3**,

**d3**) shows the spatial distribution of Wm.

**Figure 6.**The PE

_{PM}series: (

**a1**–

**a4**) show the hourly PE

_{PM}of events 120423, 130606, 150607, and 170623 in the Tunxi catchment, and (

**b1**–

**b4**) show the hourly PE

_{PM}of events 030903, 050928, 110916, and 120830 in the Chenhe catchment.

**Figure 7.**The spatial distribution of PE

_{PM}: (

**a1**–

**a4**) show the spatial distribution of the mean PE

_{PM}for events 120423, 130606, 150607, and 170623 in the Tunxi catchment, and (

**b1**–

**b4**) show the spatial distribution of the mean PE

_{PM}for events 030903, 050928, 110916, and 120830 in the Chenhe catchment.

**Figure 8.**The relationship between the mean PE

_{PM}and DEM in the Tunxi catchment (

**a**) and in the Chenhe catchment (

**b**).

**Figure 9.**The hydrograph of flood events: (

**a1**–

**a4**) show the flood hydrograph of events 120423, 130606, 150607, and 170623 in the Tunxi catchment, and (

**b1**–

**b4**) show the flood hydrograph of events 030903, 050928, 110916, and 120830 in the Chenhe catchment.

Category | Parameterization Selected | Reference |
---|---|---|

Microphysics option | Thompson | Thompson [41] |

Cumulus option | Kain–Fritsch | Kain and Fritsch [42] |

Planetary boundary layer | YSU | Hong and Pan [43] |

Radiation physics option | RRTMG | Shin [44] |

Catchment | Year | Events | Simulation Period | Peak Discharge (m^{3}/s) |
---|---|---|---|---|

Tunxi | 2012 | 120423 | 23 April, 14:00–27 Aapril, 06:00 | 3170 |

2013 | 130606 | 6 June, 14:00–11 June, 20:00 | 3610 | |

2015 | 150607 | 7 June, 08:00–10 June, 17:00 | 3010 | |

2017 | 170623 | 23 June, 08:00–28 June, 00:00 | 4210 | |

Chenhe | 2003 | 030903 | 3 September, 02:00–8 September, 20:00 | 740 |

2005 | 050928 | 28 September, 08:00–3 October, 20:00 | 1740 | |

2011 | 110916 | 16 September, 14:00–21 September, 08:00 | 1200 | |

2012 | 120830 | 30 August, 20:00–3 September, 18:00 | 1710 |

Parameter | Description | Optimal Estimate of the Tunxi Catchment | Optimal Estimate of the Chenhe Catchment |
---|---|---|---|

K | Ratio of potential evapotranspiration to pan evaporation | 0.98 | 0.7 |

C | Evapotranspiration coefficient of deeper layer | 0.18 | 0.08 |

Ci | Recession constant of interflow storage | 0.3 | 0.55 |

Cg | Recession constant of groundwater storage | 0.98 | 0.87 |

Cs | Recession constant in the lag and route technique | 0.93 | 0.89 |

Lag | Lag time | 1.0 | 2.0 |

Catchment | Events | Wr | Wm | ||||||
---|---|---|---|---|---|---|---|---|---|

RR | NSE | PB_{pr}% | PB_{cr}% | RR | NSE | PB_{pr}% | PB_{cr}% | ||

Tunxi | 120423 | 0.66 | 0.32 | −40.1 | −60.1 | 0.99 | 0.98 | −2.6 | −0.4 |

130606 | 0.90 | 0.80 | −10.3 | −14.3 | 0.99 | 0.99 | 3.5 | −2.0 | |

150607 | 0.73 | 0.18 | 33.9 | −4.6 | 0.99 | 0.98 | −4.8 | 3.3 | |

170623 | 0.69 | 0.36 | −35.8 | 13.1 | 0.99 | 0.99 | −8.1 | 6.7 | |

Chenhe | 030903 | 0.39 | −1.29 | 142.6 | 51.2 | 0.95 | 0.75 | 88.1 | 36.8 |

050928 | 0.18 | −1.62 | 83.7 | 15.9 | 0.98 | 0.91 | 10.3 | 15.8 | |

110916 | 0.45 | 0.18 | −58.0 | −16.0 | 0.99 | 0.96 | 17.3 | 13.8 | |

120830 | 0.70 | 0.42 | −47.8 | −38.3 | 0.99 | 0.92 | 23.0 | 21.1 |

Catchment | Event | Wr + PE_{PM} | Wm + PE_{PM} | ||||||
---|---|---|---|---|---|---|---|---|---|

NSE | PB% | PB_{pf}% | TEP (Hour) | NSE% | PB% | PB_{pf}% | TEP (Hour) | ||

Tunxi | 120423 | 0.12 | −67.26 | −77.55 | −3 | 0.94 | 3.85 | −7.60 | 0 |

130606 | 0.93 | −5.42 | −25.38 | −2 | 0.91 | 4.18 | −16.24 | 1 | |

150607 | 0.82 | −1.51 | 36.41 | −2 | 0.94 | 1.71 | 14.51 | −2 | |

170623 | 0.68 | 11.96 | 31.36 | 0 | 0.96 | 4.14 | −5.59 | 0 | |

Chenhe | 030903 | 0.46 | 31.97 | −1.20 | −9 | 0.66 | 23.71 | 21.15 | 4 |

050928 | −1.65 | 24.48 | −1.49 | −42 | 0.54 | 26.08 | −9.06 | −3 | |

110916 | 0.54 | −16.43 | −29.75 | 20 | 0.87 | 19.20 | 16.27 | 0 | |

120830 | 0.54 | −42.58 | −48.40 | −5 | 0.66 | 41.69 | 7.10 | −2 |

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## Share and Cite

**MDPI and ACS Style**

Gong, J.; Hu, Y.; Yao, C.; Ma, Y.; Sun, M.; Gong, J.; Shi, Z.; Li, J.
The WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China. *Water* **2024**, *16*, 103.
https://doi.org/10.3390/w16010103

**AMA Style**

Gong J, Hu Y, Yao C, Ma Y, Sun M, Gong J, Shi Z, Li J.
The WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China. *Water*. 2024; 16(1):103.
https://doi.org/10.3390/w16010103

**Chicago/Turabian Style**

Gong, Junchao, Youbing Hu, Cheng Yao, Yanan Ma, Mingkun Sun, Junfu Gong, Zhuo Shi, and Jingbing Li.
2024. "The WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China" *Water* 16, no. 1: 103.
https://doi.org/10.3390/w16010103