# Response of Hydrological Processes to Input Data in High Alpine Catchment: An Assessment of the Yarkant River basin in China

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area

^{4}km

^{2}. It is the largest irrigation region in Xinjiang and a major producer of grain and cotton [25]. Because of the seasonal snow and ice melt, the volume of river runoff in the flood season (from May to September) accounts for 80% of the annual runoff.

## 3. Forcing Data

#### 3.1. Station Data

_{band}(T

_{band}) and R

_{day}(T

_{day}) are the precipitation in mm (average temperature in °C) for the calculated elevation band and at the gauged station, the precipitation (mm H

_{2}O) and average temperature (°C) recorded at a gauging station, respectively; EL

_{band}and EL

_{gage}are the mean elevations (m) of the calculated elevation band and gauging station and days is the average number of rainy days in a year at the station. Based on a previous study [27], the same interpolations of precipitation and temperature were used in SWAT and MIKE SHE. SWAT calculates PET at a sub-basin scale using the Penman-Monteith equation based on station-interpolated data. This semi-distributed PET output from SWAT is chosen as input SBD PET in MIKE SHE.

#### 3.2. TRMM

_{dec}is defined as the total days in which precipitation events were detected by TRMM but not recorded by SBD. SBD

_{dec}is defined as the total number of days in which precipitation events were recorded by SBD but not recorded by TRMM and TRSB

_{dec}is defined as the total number of days in which precipitation events were detected by both TRMM and SBD. In addition, D

_{r}and D

_{w}are the percentages of correct and incorrect precipitation events detected by TRMM for the satellite grids that the stations occupy.

_{w}values are much larger than D

_{r}, indicating that many precipitation events detected by TRMM are redundant. An in-depth analysis estimated the different intensity classes of precipitation using the same approach (Equations (3) and (4)). The results suggest that high values of D

_{w}mainly correspond to low-intensity precipitation events (<0.3 mm) with a high probability of incorrect precipitation events (D

_{w_0.3}) (Table 2).

_{thres}was determined from the raw TRMM data to ensure that the frequency matched the SBD. This value was set as 0.3 because TRMM detected too many redundant rainy days with precipitation amounts smaller than 0.3 mm (Table 2). A scaling factor s

_{m}was then calculated and used to determine that the mean of the corrected precipitation was equal to the observed precipitation, as follows:

_{SBD,m}and P

_{TRMM.raw,m}are the SBD and raw TRMM precipitation respectively in the m

^{th}month, and P

_{TRMM.cor,m}is the corrected TRMM precipitation in the m

^{th}month. $\mu $(.) represents the expectation operator (e.g., $\mu $(P

_{TRMM,.raw,m}) and is the mean value of raw TRMM precipitation in given month m).

_{raw}and r

_{cor}in Table 2. Eventually, this correction approach can be applied to the whole basin based on the ordinary Kriging method, with circular model interpolated s

_{m}using ArcGIS, providing revised TRMM data.

#### 3.3. LST

#### 3.4. PET

## 4. Methodology

#### 4.1. Models

#### 4.2. Calibration

_{obs,i}and Q

_{sim,i}are the measured and simulated discharges respectively, on day i (m

^{3}/s); ${\overline{Q}}_{obs}$ and ${\overline{Q}}_{sim}$ are the average measured and simulated discharges respectively, during the simulated period (m

^{3}/s); and n is the time step.

#### 4.3. Hypothesis Test

_{i,j,k}. The ANOVA model [38] for three factors with fixed effects is as follows:

^{th}case or trial for the scenario based on the i

^{th}level of A, j

^{th}level of B and the k

^{th}level of C; $u\dots $ is the grand mean of all dependent variables u

_{i,j,k}; ${\alpha}_{i}$, ${\beta}_{j}$ and ${\gamma}_{k}$ are the main effects of factors A, B and C, respectively; ${\left(\alpha \beta \right)}_{ij}$, ${\left(\alpha \gamma \right)}_{ik}$ and ${\left(\beta \gamma \right)}_{jk}$ are the main effects of the two-factor interaction$;$ ${\left(\alpha \beta \gamma \right)}_{ijk}$ is the main effect of the three-factor interaction; and ${\epsilon}_{ijkm}$ is the independent random error which follows a normal distribution $\mathrm{N}\left(0,{\delta}^{2}\right)$.

_{0}, meaning that the influence of the factor on the result is not significant, is acceptable. Thus, ${\alpha}_{1}={\alpha}_{2}=0$. In this case, the probability p = P(F

_{[(a-1),abc(n-1)]}>F

_{A}) can be calculated. When the calculated p-value is larger than the significance level (α), set as 0.05 in this study, suggesting that the assumption is correct, the null hypothesis H

_{0}will be accepted. Otherwise, the alternative hypothesis H

_{a}should be accepted. Other hypotheses could be tested in the same way.

## 5. Results and Discussion

#### 5.1. Simulated Discharges

#### 5.2. Sensitivities of Water Components

_{a}was divided into different categories: snow sublimation (SNOWS), canopy interception (CI), river and pond water evaporation (WE), soil evaporation (SOILE) and plant transpiration (PT). These five ET

_{a}sources, overland flow (OLF), base flow (BF) and snow storage (SS) were employed for the hypothesis test. The probability p value results are provided in Table 6.

#### 5.3. Responses of Hydrological Processes

#### 5.3.1. Snow Storage

_{5}), 10 mm (SS

_{10}) and 15 mm (SS

_{15}) in the LST model decreased by 37%, 46% and 47% relative to STA model, respectively. However, the area with snow depth less than 5 mm (SS

_{0}) was simulated to be 77% larger by the LST model. These changes could be caused by the distinct distributions of the SBD and LST data sets (Figure 8). For SBD, a piecemeal spatial distribution similar to that of precipitation was observed. Variation in the spatial distribution generally was not substantial. Compared with SBD in the zone with elevations lower than 3600 m, the LST data were lower by 3.2 °C. In the 5000–5700 m elevation band, the LST data were higher than SBD by 1.1 °C. In the 3600 m–5000 m region, there was little distinction between the data sets.

_{0}in the LST model. In the higher 5000 m region where the dominant snowpack was located, the calculated higher air temperature in the LST model caused a 41.2% reduction in the annual snowpack. Figure 8 also shows a continuous increase in snow storage in the region higher than 5700 m, largely because there is little snowmelt in this region because of the very low temperature. Snowdrifts and snow slides become the primary movement methods. Unfortunately, these movements have not yet been included in MIKE SHE. Thus, this snow storage generally increased as snowfall accumulates.

#### 5.3.2. Plant Transpiration

_{a}sources are calculated based on different parameters, all of these ET

_{a}sources are sensitive to PET and can yield clearer trends regarding the uncertainty of the input PET.

## 6. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Beven, K.; Binley, A. The future of distributed models: Model calibration and uncertainty prediction. Hydrol. Process.
**1992**, 6, 279–298. [Google Scholar] [CrossRef] - Jasper, A.V.; Hoshin, V.G.; Willem, B.; Soroosh, S. A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model paremeters. Water Resour. Res.
**2003**, 39, 1–16. [Google Scholar] - Misgana, K.M.; John, W.N. Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model. J. Hydrol.
**2005**, 306, 127–145. [Google Scholar] - Zhang, X.S.; Raghavan, S.; David, B. Calibration and uncertainty analysis of the SWAT model using Genetic Algorithm and Bayesian Model Averaging. J. Hydrol.
**2009**, 374, 307–317. [Google Scholar] [CrossRef] - Jin, X.L.; Xu, C.Y.; 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] - Ajami, N.K.; Duan, Q.; Gao, X.; Soroosh, S. Multimodel combination techniques for analysis of hydrological simulations application to distributed model intercomparison project results. J. Hydrometeorol.
**2006**, 4, 755–768. [Google Scholar] [CrossRef] - Ajami, N.K.; Duan, Q.; Sorooshian, S. An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction. Water Resour. Res.
**2007**, 43, 1–10. [Google Scholar] - Kavetski, D.; Kuczera, G.; Franks, S.W. Bayesian analysis of input uncertainty in hydrological modeling: 2. Application. Water Resour. Res.
**2006**, 42, 1–9. [Google Scholar] [CrossRef] - Xu, C.Y.; Tunemar, L.; Chen, Y.D.; Singh, V.P. Evaluation of seasonal and spatial variations of lumped water balance model sensitivity to precipitation data errors. J. Hydrol.
**2006**, 324, 80–93. [Google Scholar] [CrossRef] - Thompson, J.R.; Green, A.J.; Kingston, D.G. Potential evapotranspiration-related uncertainty in climate change impacts on river flow: An assessment for the Mekong River basin. J. Hydrol.
**2014**, 510, 259–279. [Google Scholar] [CrossRef] - Dobler, C.; Bürger, G.; Stötter, J. Assessment of climate change impacts on flood hazard potential in the Alpine Lech watershed. J. Hydrol.
**2012**, 460, 29–39. [Google Scholar] [CrossRef] - Chien, H.; Yeh, P.J.; Knouft, J.H. Modeling the potential impacts of climate change on streamflow in agricultural watersheds of the Midwestern United States. J. Hydrol.
**2013**, 491, 73–88. [Google Scholar] [CrossRef] - Xu, Y.P.; Zhang, X.; Ran, Q.; Tian, Y. Impact of climate change on hydrology of upper reaches of Qiantang River Basin, East China. J. Hydrol.
**2013**, 483, 51–60. [Google Scholar] [CrossRef] - Zhang, X.; Xu, Y.P.; Fu, G. Uncertainties in SWAT extreme flow simulation under climate change. J. Hydrol.
**2014**, 515, 205–222. [Google Scholar] [CrossRef] - Blazkova, S.; Beven, K. Flood frequency estimation by continuous simulation for a catchment treated as ungauged (with uncertainty). Water Resour. Res.
**2002**, 38, 1411–1414. [Google Scholar] [CrossRef] - Stisen, S.; Jensen, K.H.; Sandholt, I.; Grimes, D.I. A remote sensing driven distributed hydrological model of the Senegal River basin. J. Hydrol.
**2008**, 354, 131–148. [Google Scholar] [CrossRef] - Liu, T.; Willems, P.; Feng, X.W.; Li, Q.; Huang, Y.; Bao, A.M.; Chen, X.; Veroustraete, F.; Dong, Q.H. On the usefulness of remote sensing input data for spatially distributed hydrological modelling: Case of the Tarim River basin in China. Hydrol. Process.
**2012**, 26, 335–344. [Google Scholar] [CrossRef] - Sun, W.; Ishidaira, H.; Bastola, S. Calibration of hydrological models in ungauged basins based on satellite radar altimetry observations of river water level. Hydrol. Process.
**2012**, 26, 3524–3537. [Google Scholar] [CrossRef] - Deus, D.; Gloaguen, R.; Krause, P. Water Balance Modeling in a Semi-Arid Environment with Limited in Situ Data Using Remote Sensing in Lake Manyara, East African Rift, Tanzania. Remote Sens.
**2013**, 5, 1651–1680. [Google Scholar] [CrossRef] - Van, D.A. Model-data fusion: Using observations to understand and reduce uncertainty in hydrological models. In Proceedings of the 19th International Congress on Modelling and Simulation (MODSIM 2011), Perth, Australia, 12–16 December 2011.
- McMichael, C.E.; Hope, A.S.; Loaiciga, H.A. Distributed hydrological modelling in California semi-arid shrublands: MIKE SHE model calibration and uncertainty estimation. J. Hydrol.
**2006**, 317, 307–324. [Google Scholar] - Knoche, M.; Fischer, C.; Pohl, E.; Krause, P.; Merz, R. Combined uncertainty of hydrological model complexity and satellite-based forcing data evaluated in two data-scarce semi-arid catchments in Ethiopia. J. Hydrol.
**2014**, 519, 2049–2066. [Google Scholar] - Prigent, C. Precipitation retrieval from space: An overview. Comptes Rendus Geosci.
**2010**, 342, 380–389. [Google Scholar] - Shi, Y.F. Brief of Glacier Inventory of China; Shanghai Popular Science Press: Shanghai, China, 2005. (In Chinese) [Google Scholar]
- Tang, H.; Yang, D.G.; Zhang, Y.F. Food security and agricultural structural adjustment in Yarkant River Basin, northwest China. J. Food Agricul. Environ.
**2013**, 11, 324–328. [Google Scholar] - Gao, X.; Zhang, S.Q.; Ye, B.S.; Qiao, C.J. Glacier Runoff Change in the Upper Stream of Yarkant River and Its Impact on River Runoff during 1961–2006. J. Glaciol. Geocryol.
**2010**, 32, 445–453. (In Chinese) [Google Scholar] - Liu, J.; Liu, T.; Bao, A.M.; Philippe, D.M.; Feng, X.W.; Scott, N.M.; Chen, X. Assessment of Different Modelling Studies on the Spitial Hydrological Processes in an Arid Alpine Catchment. Water Resour. Manag.
**2016**, 30, 1757–1770. [Google Scholar] [CrossRef] - Huffman, G.J.; Adler, R.F.; Bolvin, D.T.; Gu, G.; Nelkin, E.J.; Bowman, K.P.; Hong, Y.; Stocker, E.F.; Wolff, D.B. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydeometeorol.
**2007**, 8, 38–55. [Google Scholar] [CrossRef] - Xuan, J.; Luo, Y. Quality Assessment of the TRMM Precipitation Data in Mid Tianshan Mountains. Arid Land Geogr.
**2013**, 36, 253–262. (In Chinese) [Google Scholar] - Schmidli, J.; Frei, C.; Vidale, P.L. Downscaling from GCM precipitation: A benchmark for dynamical and statistical downscaling methods. Int. J. Climatol.
**2006**, 26, 679–689. [Google Scholar] [CrossRef] - Hulley, G.C.; Hook, S.J. Intercomparison of versions 4, 4.1 and 5 of the MODIS Land Surface Temperature and Emissivity products and validation with laboratory measurements of sand samples from the Namib desert, Namibia. Remote Sens. Environ.
**2009**, 113, 1313–1318. [Google Scholar] [CrossRef] - Vogt, J.V.; Viau, A.A.; Paquet, F. Mapping regional air temperature fields using satellite-derived surface skin temperatures. Int. J. Climato.
**1997**, 17, 1559–1579. [Google Scholar] [CrossRef] - Cristóbal, J.; Ninyerola, M.; Pons, X. Modeling air temperature through a combination of remote sensing and GIS data. J. Geophy. Res.
**2008**, 113. [Google Scholar] [CrossRef] - Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56; FAO: Rome, Italy, 1998; Volume 300, p. D05109. [Google Scholar]
- Abbott, M.; Bathurst, J.; Cunge, J.; O’connell, P.; Rasmussen, J. An introduction to the European Hydrological System-Systeme Hydrologique Europeen, “SHE”, 2: Structure of a physically-based, distributed modelling system. J. Hydrol.
**1986**, 87, 61–77. [Google Scholar] [CrossRef] - Duan, Q.; 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] - Nash, J.; Sutcliffe, J.V. River flow forecasting through conceptual models part I-A discussion of principles. J. Hydrol.
**1970**, 10, 282–290. [Google Scholar] [CrossRef] - Michael, H.K.; Christopher, J.N.; John, N.; William, L. Applied Linear Statistical Models; McGraw-Hill Education: New York, NY, USA, 2005. [Google Scholar]
- Fabio, F.; Christian, R.; Tobias, J.; Gabriel, A.; Stefan, W. Alpine Grassland Phenology as Seen in AVHRR, VEGETATION, and MODIS NDVI Time Series-a Comparison with in Situ Measurement. Sensors
**2008**, 8, 2833–2853. [Google Scholar] - Kristensen, K.J.; Jensen, S.E. A model for estimating actual evapotranspiration from potential evapotranspiration. Nordic Hydrol.
**1975**, 6, 170–188. [Google Scholar]

**Figure 3.**Regression relationship between daily air temperature and LST at the Tashkurgan and Pishan stations in 2000–2009.

**Figure 4.**Boxplots of monthly mean discharges at the outlet station derived from eight models from 2003 to 2009.

**Figure 5.**Spatial distributions of simulated annual mean snow storage based on the STA, TRMM, LST and GPET models in the Yarkant River basin from 2003 to 2009.

**Figure 7.**Spatial distributions of the annual mean precipitation based on TRMM and SBD in Yarkant River basin from 2003–2009.

**Figure 8.**Spatial distributions of the annual mean temperature between station and TRMM data in the Yarkant River basin from 2003–2009.

**Figure 9.**The spatial distributions of simulated annual mean transpiration based on the STA, TRMM, LST and GPET models in the Yarkant River basin from 2003–2009.

**Figure 10.**The temporal distributions of mean monthly PET based on SBD and RSD in the entire Yarkant River basin from 2003–2009.

Altitude Group (m) | PCG (mm/km/year) | TCG (°/km) |
---|---|---|

<3000 | 0.0 | −6.5 |

3000–5000 | −70.0 | −6.8 |

5000–7000 | 100.0 | −7.0 |

>7000 | 70.0 | −6.8 |

Station | D_{r} | D_{w} | D_{w_0.3} | r_{raw} | r_{cor} |
---|---|---|---|---|---|

Tashkurgan | 0.54 | 0.75 | 0.95 | 0.11 | 0.45 |

Shache | 0.21 | 0.87 | 0.96 | 0.67 | 0.77 |

Pishan | 0.15 | 0.88 | 0.99 | 0.36 | 0.67 |

Data Source | Model Abbreviation | ||
---|---|---|---|

Rainfall | Temperature | Evapotranspiration | |

station | station | station | STA |

TRMM | station | station | TRMM |

station | LST | station | LST |

station | station | GPET | GPET |

TRMM | LST | station | TRLS |

TRMM | station | GPET | TRGP |

station | LST | GPET | LSGP |

TRMM | LST | GPET | RSD |

Models | STA | TRMM | LST | GPET | TRLS | TRGP | LSGP | RSD |
---|---|---|---|---|---|---|---|---|

DDF (mm/day/°C) | 2.01 | 2.03 | 1.25 | 1.98 | 1.25 | 2.00 | 1.23 | 1.25 |

TMT (°C) | −0.98 | −1.00 | −0.56 | −1.01 | −0.57 | −1.02 | −0.56 | −0.55 |

LAI_NLT * | 3.81 | 3.82 | 3.78 | 2.65 | 3.82 | 2.64 | 2.63 | 2.66 |

RD_NLT (mm) * | 4500 | 4500 | 4500 | 4000 | 4500 | 4000 | 4000 | 4000 |

Model | STA | TRMM | LST | GPET | TRLS | TRGP | LSGP | RSD |
---|---|---|---|---|---|---|---|---|

NSC | 0.65 | 0.50 | 0.52 | 0.61 | 0.55 | 0.42 | 0.52 | 0.46 |

R | 0.84 | 0.73 | 0.75 | 0.81 | 0.74 | 0.68 | 0.74 | 0.70 |

RMSE | 172.10 | 207.15 | 204.49 | 183.52 | 197.35 | 222.81 | 204.11 | 214.66 |

Groups | A | B | C | A*B | A*C | B*C | A*B*C |
---|---|---|---|---|---|---|---|

OLF | 0.000 | 0.221 | 0.691 | 0.040 | 0.714 | 0.399 | 0.336 |

BF | 0.003 | 0.436 | 0.008 | 0.087 | 0.619 | 0.450 | 0.553 |

SS | 0.003 | 0.000 | 0.945 | 0.001 | 0.828 | 0.415 | 0.430 |

SNOWS | 0.271 | 0.000 | 0.224 | 0.789 | 0.546 | 0.120 | 0.282 |

CI | 0.000 | 0.000 | 0.000 | 0.007 | 0.089 | 0.028 | 0.431 |

WE | 0.000 | 0.000 | 0.000 | 0.937 | 0.085 | 0.072 | 0.573 |

SOILE | 0.138 | 0.238 | 0.000 | 0.747 | 0.241 | 0.739 | 0.932 |

PT | 0.541 | 0.535 | 0.000 | 0.751 | 0.637 | 0.809 | 0.905 |

© 2016 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/).

## Share and Cite

**MDPI and ACS Style**

Liu, J.; Liu, T.; Bao, A.; De Maeyer, P.; Kurban, A.; Chen, X.
Response of Hydrological Processes to Input Data in High Alpine Catchment: An Assessment of the Yarkant River basin in China. *Water* **2016**, *8*, 181.
https://doi.org/10.3390/w8050181

**AMA Style**

Liu J, Liu T, Bao A, De Maeyer P, Kurban A, Chen X.
Response of Hydrological Processes to Input Data in High Alpine Catchment: An Assessment of the Yarkant River basin in China. *Water*. 2016; 8(5):181.
https://doi.org/10.3390/w8050181

**Chicago/Turabian Style**

Liu, Jiao, Tie Liu, Anming Bao, Philippe De Maeyer, Alishir Kurban, and Xi Chen.
2016. "Response of Hydrological Processes to Input Data in High Alpine Catchment: An Assessment of the Yarkant River basin in China" *Water* 8, no. 5: 181.
https://doi.org/10.3390/w8050181