Abstract
This paper applied a HIMS (hydroinformatic modeling system) model to simulate streamflow in the Luanhe River Basin. This model was compared with SIMHYD and XAJ models for eight sub-basins of the Luanhe River. The results showed HIMS model performed better than SIMHYD and XAJ models, in these areas. We then investigated the streamflow response to climate changes in the different sub-basins. Twenty hypothetical climate change scenarios (perturbed temperatures and precipitation) were used to test the sensitivity of HIMS model simulated annual and mean monthly streamflow. Our results demonstrated that: (i) the annual streamflow was positively related to precipitation, and there was a negative relationship between streamflow and temperature for all the eight sub-basins; (ii) in all sub-basins, the relationship of annual streamflow change to precipitation change was highly non-linear, but the relationship of annual streamflow change with temperature change was approximately linear; (iii) the annual streamflow response to precipitation change was more sensitive when increasing than decreasing; (iv) the annual streamflow response to climate change was more sensitive in the Xingzhouhe River sub-basin, followed by the Wuliehe River sub-basin, and the Sahe River sub-basin was least sensitive; (iv) there were few differences in inner-streamflow response to climate change in the Laoniuhe, Yimatuhe, and Yixunhe Rivers. But for other rivers, when the temperature changed, larger streamflow differences happened in winter and summer; when the precipitation decreased or was unchanged, the larger differences happened in winter months, and when the precipitation increased, larger differences happened in winter and summer.
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References
Abdulla F, Eshtawi T, Assaf H (2009) Assessment of the impact of potential climate change on the water balance of a semi-arid watershed. Water Resour Manag 23(10):2051–2068
Arora VK (2001) Streamflow simulations for continental-scale river basins in a global atmospheric general circulation model. Adv Water Resour 24:775–791
Bao H, Zhao L, He Y, Li Z, Wetterhall F, Cloke H, Pappenberger F, Manful D (2011) Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast. Adv Geosci 29:61–67
Bao ZX, Zhang JY, Wang GQ, Fu GB, He RM, Yan XL, Jin JL, Liu YL, Zhang AJ (2012) Attribution for decreasing streamflow of the Haihe River basin, northern China: climate variability or human activities? J Hydrol 460–46:117–129
Bates B, Kundzewicz ZW, Wu S, Palutikof J (2008) Climate change and water. Intergovernmental Panel on Climate Change (IPCC)
Chau KW (2007) A split-step particle swarm optimization algorithm in river stage forecasting. J Hydrol 346:131–135
Chiew FHS (2006) Estimation of rainfall elasticity of streamflow in Australia. Hydrol Sci J 51:613–625
Chiew FHS, Peel MC, Western AW, Singh VP, Frevert D (2002) Application and testing of the simple rainfall-runoff model SIMHYD. Mathematical models of small watershed hydrology and applications, 335–367
Chu W, Gao X, Sorooshian S (2010) Improving the shuffled complex evolution scheme for optimization of complex nonlinear hydrological systems: application to the calibration of the Sacramento soil‐moisture accounting model. Water Resour Res 46(9):W09530. doi:10.1029/2010WR009224
Dibike YB, Coulibaly P (2005) Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. J Hydrol 307:145–163
Duan Q, Sorooshian S, Gupta V (1992) Effective and efficient global optimization for conceptual rainfall‐runoff models. Water Resour Res 28:1015–1031
Duan Q, Sorooshian S, Gupta VK (1994) Optimal use of the SCE-UA global optimization method for calibrating watershed models. J Hydrol 158:265–284
Dumedah G, Berg AA, Wineberg M, Collier R (2010) Selecting model parameter sets from a trade-off surface generated from the non-dominated sorting genetic algorithm-II. Water Resour Manag 24:4469–4489
Franchini M, Lamberti P (1994) A flood routing Muskingum type simulation and forecasting model based on level data alone. Water Resour Res 30(7):2183–2196
Füssel H-M, Klein RJ (2006) Climate change vulnerability assessments: an evolution of conceptual thinking. Clim Chang 75:301–329
Gill MK, Kaheil YH, Khalil A, McKee M, Bastidas L (2006) Multiobjective particle swarm optimization for parameter estimation in hydrology. Water Resour Res 42:W07417. doi:10.1029/2005WR004528
Guo S, Wang J, Xiong L, Ying A, Li D (2002) A macro-scale and semi-distributed monthly water balance model to predict climate change impacts in China. J Hydrol 268:1–15
Guo J, Zhou JZ, Zou Q, Liu Y, Song LX (2013) A novel multi-objective shuffled complex differential evolution algorithm with application to hydrological model parameter optimization. Water Resour Manag 27(8):2923–2946
IPCC (2001) Third assessment report-climate change 2001. IPCC/WMO/UNEP
Jiang T, Chen YD, Xu C-Y, X. C, Chen X, Singh VP (2007) Comparison of hydrological impacts of climate change simulated by six hydrological models in the Dongjiang Basin, South China. J Hydrol 336:316–333
Jiang Y, Liu C, Huang C, Wu X (2010) Improved particle swarm algorithm for hydrological parameter optimization. Appl Math Comput 217:3207–3215
Jiang Y, Li X, Huang C (2013) Automatic calibration a hydrological model using a master–slave swarms shuffling evolution algorithm based on self-adaptive particle swarm optimization. Expert Syst Appl 40(2):752–757
Jiang SH, Ren LL, Hong Y, Yang XL, Ma MW, Yuan F (2014) Improvement of multi-satellite real-time precipitation products for ensemble streamflow simulation in a middle latitude basin in south China. Water Resour Manag 28(8):2259–2278
Joseph JF, Guillaume JHA (2013) Using a parallelized MCMC algorithm in R to identify appropriate likelihood functions for SWAT. Environ Model Softw 46:292–298
Kaini P, Artita K, Nicklow JW (2012) Optimizing structural best management practices using SWAT and genetic algorithm to improve water quality goals. Water Resour Manag 26:1827–1845
Li JZ, Feng P (2009) Trend ananlysis of runoff generation characteristics of Luanhe River Basin. J Arid Land Resour Environ 23(8):79–85 (in Chinese)
Li H, Zhang Y, Chiew FHS, Xu S (2009) Predicting runoff in ungauged catchments by using Xinanjiang model with MODIS leaf area index. J Hydrol 370:155–162
Liu CM, Wang GT (1980) The estimation of small-watershed peak flows in China. Water Resour Res 16(5):881–886
Liu CM, Zheng HX, Wang ZG (2006) Distributed simulation of catchment water cycle. Yellow River Conservancy Press, Zhengzhou, China
Liu C, Wang Z, Zheng H, Zhang L, Wu X (2008) Development of hydro-informatic modelling system and its application. Sci China Ser Technol Sci 51:456–466
Liu LL, Fischer T, Jiang T, Luo Y (2013) Comparison of uncertainties in projected flood frequency of the Zhujiang River, South China. Quatern Int 304:51–56
Lu E, Takle ES, Manoj J (2010) The relationships between climatic and hydrological changes in the Upper Mississippi River Basin: A SWAT and multi-GCM study. J Hydrometeorol 11:437–451
Mengistu D, Sorteberg A (2012) Sensitivity of SWAT simulated streamflow to climatic changes within the Eastern Nile River basin. Hydrol Earth Syst Sci 16:391–407
Merritt WS, Alila Y, Barton M, Taylor B, Cohen S, Neilsen D (2006) Hydrologic response to scenarios of climate change in sub watersheds of the Okanagan basin, British Columbia. J Hydrol 326:79–108
Miller NL, Bashford KE, Strem E (2003) Potential impacts of climate change on California hydrology. J Am Water Resour Assoc 39:771–784
Muzik I (2002) A first-order analysis of the climate change effect on flood frequencies in a subalpine watershed by means of a hydrological rainfall–runoff model. J Hydrol 267:65–73
Peng D, Xu Z (2010) Simulating the Impact of climate change on streamflow in the Tarim River basin by using a modified semi‐distributed monthly water balance model. Hydrol Process 24:209–216
Samadi S, Carbone GJ, Mahdavi M, Sharifi F, Bihamta MR (2013) Statistical downscaling of river runoff in a semi arid catchment. Water Resour Manag 27(1):117–136
Shi Y, Liu H, Fan M, Huang J (2013) Parameter identification of RVM Runoff forecasting model based on improved particle swarm optimization, advances in swarm intelligence. Springer, pp. 160–167
Shin MJ, Guillaume JH, Croke BF, Jakeman AJ (2013) Addressing ten questions about conceptual rainfall–runoff models with global sensitivity analyses in R. J Hydrol 503:135–152
Singh P, Arora M, Goel NK (2006) Effect of climate change on runoff of a glacierized Himalayan basin. Hydrol Process 20:1979–1992
Van Griensven A, Francos A, Bauwens W (2002) Sensitivity analysis and auto-calibration of an integral dynamic model for river water quality. Water Sci Technol 45:325–332
Varis O, Kajander T, Lemmelä R (2004) Climate and water: from climate models to water resources management and vice versa. Clim Chang 66:321–344
Vicuna S, Dracup J (2007) The evolution of climate change impact studies on hydrology and water resources in California. Clim Chang 82:327–350
Vrugt JA, Gupta HV, Bastidas LA, Bouten W, Sorooshian S (2003a) Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resour Res 39:1214. doi:10.1029/2002WR001746
Vrugt JA, Gupta HV, Bouten W, Sorooshian S (2003b) A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour Res 39:1201. doi:10.1029/2002WR001642
Wang QJ (1991) The genetic algorithm and its application to calibrating conceptual rainfall‐runoff models. Water Resour Res 27:2467–2471
Wang QJ (1997) Using genetic algorithms to optimise model parameters. Environ Model Softw 12:27–34
White ED, Easton ZM, Fuka DR, Collick AS, Adgo E, McCartney M, Awulachew SB, Selassie YG, Steenhuis TS (2011) Development and application of a physically based landscape water balance in the SWAT model. Hydrol Process 25:915–925
Wu Y, Liu S, Gallant AL (2012) Predicting impacts of increased CO2 and climate change on the water cycle and water quality in the semiarid James River Basin of the Midwestern USA. Sci Total Environ 430:150–160
Xie ZH, Su FG, Liang X, Zeng QC (2003) Applications of a surface runoff model with Horton and Dunne runoff for VIC. Adv Atomospheric Sci 20(2):165–172
Xu C-Y (2000) Modelling the effects of climate change on water resources in central Sweden. Water Resour Manag 14:177–189
Xu C-Y, Widén E, Halldin S (2005) Modelling hydrological consequences of climate change—progress and challenges. Adv Atmos Sci 22:789–797
Xu ZX, Zhao FF, Li JY (2009) Response of streamflow to climate change in the headwater catchment of the Yellow River basin. Quat Int 208:62–75
Yuan F, Xie ZH, Xia J (2005) Simulating hydrologic changes with climate change scenarios in the Haihe River Basin. Pedosphere 15:595–600
Zhan CS, Zeng SD, Jiang SS, Wang XH, Ye W (2014) An integrated approach for partitioning the effect of climate change and human activities on surface runoff. Water Resour Manag 28(11):3843–3858
Zhang Y, Chiew FHS (2009) Relative merits of different methods for runoff predictions in ungauged catchments. Water Resour Res 45, doi: 10.1029/2008WR007504
Zhang X, Srinivasan R, Hao E (2007) Predicting hydrologic response to climate change in the Luohe River basin using the SWAT model. Trans ASABE 50:901–910
Zhang X, Srinivasan R, Zhao K, Liew MV (2009) Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model. Hydrol Process 23:430–441
Zhao R (1992) The Xinanjiang model applied in China. J Hydrol 135:371–381
Zhao RJ, Liu XR, Singh VP (1995) The Xinanjiang model. Comput Models Watershed Hydrol, 215–232
Zuo DP, Xu ZX, Wu W, Zhao J, Zhao FF (2014) Identification of streamflow response to climate change and human activities in the Wei River Basin, China. Water Resour Manag 28(3):833–8551
Acknowledgments
We are grateful to Prof. Hongrui Wang for his helpful suggestions and to Dr. Yaomin Qin for his help in mapping. Funding was supported by the National Natural Science Foundation of China (Nos. 50809004, 51279006), the Key Project of the National Natural Science Foundation of China (No. 41330529) and the National Major Science and Technology Projects for Water Pollution Control and Management (Nos.2012ZX07203-002, 2012ZX07203-003). The HIMS model was programmed and provided for their studies by Prof. Changming Liu, Zhonggen Wang and Hongxing zheng (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, China).
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Jiang, Y., Liu, C. & Li, X. Hydrological Impacts of Climate Change Simulated by HIMS Models in the Luanhe River Basin, North China. Water Resour Manage 29, 1365–1384 (2015). https://doi.org/10.1007/s11269-014-0881-y
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DOI: https://doi.org/10.1007/s11269-014-0881-y