Simulating Runoff and Actual Evapotranspiration via Time-Variant Parameter Method: The Effects of Hydrological Model Structures
Publication: Journal of Hydrologic Engineering
Volume 27, Issue 12
Abstract
Modeling methodologies capable of coping with changing catchment conditions are becoming more important and are critical to ensure credible model predictions because the hydrologic process of the catchment is inevitably affected by climate change and human activities. A recently developed hydrological modeling framework that treats model parameters to be time-variant by building functions based on identified catchment properties is applied to the Upper Ganjiang River Basin in China experienced soil and water conservation constructions. The efficacy of the time-variant parameter method is investigated, and we further explore the impacts of model structure on hydrological modeling through this method. The approach is tested using two conceptual monthly water balance models, i.e., the model derived from the model, and a two-parameter monthly water balance model (TWBM), which have the same model inputs and outputs, number of parameters and state variables. Results of the case study show that through the comparative analysis with the constant parameter models, both time-variant parameter models provide improved runoff simulations, especially for low and high flows. Moreover, the time-variant parameter method brings relatively larger runoff improvements for the TWBM model. For the actual evapotranspiration simulations, the ab model can yield improvements by treating parameter (i.e., upper soil zone water holding capacity) to be time-variant, whereas the time-variant parameter method shows no efficacy in that for the TWBM model. The results demonstrate that the choice of hydrological model plays an important role in modeling performance of the time-variant parameter framework under a changing environment. Modification of the evapotranspiration calculation module for the TWBM model could be considered as further study to investigate the potential model improvement that compared with only time-variant parameters. This study can provide beneficial reference to comprehensively understand the impacts of changing environment on catchment hydrological modeling and thus improve the regional strategy for future water resource management.
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Data Availability Statement
Some data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Some data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
This study was supported by the Open Research Fund of Key Laboratory of the Pearl River Estuary Regulation and Protection of Ministry of Water Resources (2021KJ07), the National Natural Science Foundation of China (51809071 and 51779073), the National Key Research and Development Program of China (2018YFC0407206), and the Distinguished Young Fund Project of Jiangsu Natural Science Foundation (BK20180021). The authors thank the China Meteorological Data Sharing Service System for providing a part of the data used in this study (http://data.cma.cn/). The authors thank the editor and the anonymous reviewers for their comments that helped improve the quality of the paper.
References
Allen, R. G., L. S. Pereira, D. Raes, and M. Smith. 1998. Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. Rome: Food and Agriculture Organization of the United Nations.
Beven, K., and A. Binley. 1992. “The future of distributed models: Model calibration and uncertainty prediction.” Hydrol. Processes 6 (3): 279–298. https://doi.org/10.1002/hyp.3360060305.
Box, G. E. P., and D. R. Cox. 1964. “An analysis of transformations.” J. R. Stat. Soc. Series B Stat. Methodol. 262: 211–252.
Chlumsky, R., J. Mai, J. R. Craig, and B. A. Tolson. 2021. “Simultaneous calibration of hydrologic model structure and parameters using a blended model.” Water Resour. Res. 57 (5): e2020WR029229. https://doi.org/10.1029/2020WR029229.
Coron, L., V. Andréassian, C. Perrin, J. Lerat, J. Vaze, M. Bourqui, and F. Hendrickx. 2012. “Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments.” Water Resour. Res. 48 (5): W05552. https://doi.org/10.1029/2011WR011721.
Deng, C., P. Liu, S. Guo, Z. Li, and D. Wang. 2016. “Identification of hydrological model parameter variation using ensemble Kalman filter.” Hydrol. Earth Syst. Sci. 20 (12): 4949–4961. https://doi.org/10.5194/hess-20-4949-2016.
Deng, C., P. Liu, S. Guo, H. Wang, and D. Wang. 2015. “Estimation of nonfluctuating reservoir inflow from water level observations using methods based on flow continuity.” J. Hydrol. 529 (Part 3): 1198–1210. https://doi.org/10.1016/j.jhydrol.2015.09.037.
Deng, C., P. Liu, D. Wang, and W. Wang. 2018. “Temporal variation and scaling of parameters for a monthly hydrologic model.” J. Hydrol. 558 (Mar): 290–300. https://doi.org/10.1016/j.jhydrol.2018.01.049.
Deng, C., P. Liu, W. Wang, Q. Shao, and D. Wang. 2019. “Modelling time-variant parameters of a two-parameter monthly water balance model.” J. Hydrol. 573 (Jun): 918–936. https://doi.org/10.1016/j.jhydrol.2019.04.027.
Deng, C., and W. Wang. 2019. “Runoff predicting and variation analysis in Upper Ganjiang Basin under projected climate changes.” Sustainability 11 (21): 5885. https://doi.org/10.3390/su11215885.
Duan, Q., V. K. Gupta, and S. Sorooshian. 1993. “Shuffled complex evolution approach for effective and efficient global minimization.” J. Optim. Theory Appl. 76 (3): 501–521. https://doi.org/10.1007/BF00939380.
Evensen, G. 1994. “Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics.” J. Geophys. Res. Oceans 99 (C5): 10143–10162. https://doi.org/10.1029/94JC00572.
Evensen, G. 2003. “The ensemble Kalman filter: Theoretical formulation and practical implementation.” Ocean Dyn. 53 (4): 343–367. https://doi.org/10.1007/s10236-003-0036-9.
Fernandez, W., R. M. Vogel, and A. Sankarasubramanian. 2000. “Regional calibration of a watershed model.” Hydrol. Sci. J. 45 (5): 689–707. https://doi.org/10.1080/02626660009492371.
Fowler, K., et al. 2018. “Simulating runoff under changing climatic conditions: A framework for model improvement.” Water Resour. Res. 54 (12): 9812–9832. https://doi.org/10.1029/2018WR023989.
Gharari, S., M. Hrachowitz, F. Fenicia, and H. H. G. Savenije. 2013. “An approach to identify time consistent model parameters: Sub-period calibration.” Hydrol. Earth Syst. Sci. 17 (1): 149–161. https://doi.org/10.5194/hess-17-149-2013.
Guo, Y., G. Fang, Y.-P. Xu, X. Tian, and J. Xie. 2020. “Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin. East China.” Sci. Total Environ. 710 (Mar): 136275. https://doi.org/10.1016/j.scitotenv.2019.136275.
Gupta, H. V., H. Kling, K. K. Yilmaz, and G. F. Martinez. 2009. “Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modeling.” J. Hydrol. 377 (1): 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003.
Huang, Y., B. Huang, T. Qin, H. Nie, J. Wang, X. Li, and Z. Shen. 2019. “Assessment of hydrological changes and their influence on the aquatic ecology over the last 58 years in Ganjiang Basin, China.” Sustainability 11 (18): 4882. https://doi.org/10.3390/su11184882.
Hwang, J., and N. Devineni. 2022. “An improved Zhang’s dynamic water balance model using Budyko-based snow representation for better streamflow predictions.” Water Resour. Res. 58 (1): e2021WR030203. https://doi.org/10.1029/2021WR030203.
Jeremiah, E., L. Marshall, S. A. Sisson, and A. Sharma. 2013. “Specifying a hierarchical mixture of experts for hydrologic modeling: Gating function variable selection.” Water Resour. Res. 49 (5): 2926–2939. https://doi.org/10.1002/wrcr.20150.
Jiang, C., et al. 2015. “Separating the impacts of climate change and human activities on runoff using the Budyko-type equations with time-varying parameters.” J. Hydrol. 522 (Mar): 326–338. https://doi.org/10.1016/j.jhydrol.2014.12.060.
Jin, Z., et al. 2021. “Quantifying the impact of landscape changes on hydrological variables in the alpine and cold region using hydrological model and remote sensing data.” Hydrol. Processes 35 (10): e14392. https://doi.org/10.1002/hyp.14392.
Kingston, D. G., et al. 2020. “Moving beyond the catchment scale: Value and opportunities in large-scale hydrology to understand our changing world.” Hydrol. Processes 34 (10): 2292–2298. https://doi.org/10.1002/hyp.13729.
Lan, T., K. Lin, C. Y. Xu, X. Tan, and X. Chen. 2020. “Dynamics of hydrological-model parameters: Mechanisms, problems, and solutions.” Hydrol. Earth Syst. Sci. 24 (3): 1347–1366. https://doi.org/10.5194/hess-24-1347-2020.
Leisenring, M., and H. Moradkhani. 2012. “Analyzing the uncertainty of suspended sediment load prediction using sequential data assimilation.” J. Hydrol. 468–469 (Oct): 268–282. https://doi.org/10.1016/j.jhydrol.2012.08.049.
Leuning, R., Y. Q. Zhang, A. Rajaud, H. Cleugh, and K. Tu. 2008. “A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation.” Water Resour. Res. 44 (10): W10419. https://doi.org/10.1029/2007WR006562.
Li, Z., P. Zhou, X. Shi, and Y. Li. 2021. “Forest effects on runoff under climate change in the Upper Dongjiang River Basin: Insights from annual to intra-annual scales.” Environ. Res. Lett. 16 (1): 014032. https://doi.org/10.1088/1748-9326/abd066.
Lü, H. S., et al. 2013. “The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method.” J. Hydrol. 480 (Feb): 102–114. https://doi.org/10.1016/j.jhydrol.2012.12.011.
Martinez, G. F., and H. V. Gupta. 2010. “Toward improved identification of hydrological models: A diagnostic evaluation of the ‘abcd’ monthly water balance model for the conterminous United States.” Water Resour. Res. 46 (8): W08507. https://doi.org/10.1029/2009WR008294.
Meng, S. S., X. H. Xie, and X. Yu. 2016. “Tracing temporal changes of model parameters in rainfall-runoff modeling via a real-time data assimilation.” Water 8 (1): 19. https://doi.org/10.3390/w8010019.
Merz, R., J. Parajka, and G. Blöschl. 2011. “Time stability of catchment model parameters: Implications for climate impact analyses.” Water Resour. Res. 47 (2): W02531. https://doi.org/10.1029/2010WR009505.
Misirli, F., H. V. Gupta, S. Sorooshian, and M. Thiemann. 2003. “Bayesian recursive estimation of parameter and output uncertainty for watershed models.” In Vol. 6 of Advances in calibration of watershed models, AGU Monograph Series on Water Resources. Water Science and Application, edited by Q. Duan, H. V. Gupta, S. Sorooshian, A. N. Rousseau, and R. Turcotte, 113–124. Washington, DC: American Geophysical Union.
Montanari, A., et al. 2013. “‘Panta Rhei—Everything Flows’: Change in hydrology and society—The IAHS Scientific Decade 2013–2022.” Hydrol. Sci. J. 58 (6): 1256–1275. https://doi.org/10.1080/02626667.2013.809088.
Moradkhani, H., and S. Sorooshian. 2008. “General review of rainfall-runoff modeling: Model calibration, data assimilation, and uncertainty analysis.” In Hydrological modeling and the water cycle: Coupling the atmospheric and hydrological models, edited by S. Sorooshian, K. I. Hsu, E. Coppola, B. Tommasseti, M. Verdecchia, and G. Visconti. 1–291. Berlin: Springer.
Moradkhani, H., S. Sorooshian, H. V. Gupta, and P. R. Houser. 2005. “Dual state-parameter estimation of hydrological models using ensemble Kalman filter.” Adv. Water Resour. 28 (2): 135–147. https://doi.org/10.1016/j.advwatres.2004.09.002.
Motavita, D. F., R. Chow, A. Guthke, and W. Nowak. 2019. “The comprehensive differential split-sample test: A stress-test for hydrological model robustness under climate variability.” J. Hydrol. 573 (Jun): 501–515. https://doi.org/10.1016/j.jhydrol.2019.03.054.
Mouelhi, S., C. Michel, C. Perrin, and V. Andréassian. 2006. “Stepwise development of a two-parameter monthly water balance model.” J. Hydrol. 318 (1): 200–214. https://doi.org/10.1016/j.jhydrol.2005.06.014.
Nash, J. E., and J. V. Sutcliffe. 1970. “River flow forecasting through conceptual models Part I: A discussion of principles.” J. Hydrol. 10 (3): 282–290. https://doi.org/10.1016/0022-1694(70)90255-6.
Pathiraja, S., et al. 2018. “Time-varying parameter models for catchments with land use change: The importance of model structure.” Hydrol. Earth Syst. Sci. 22 (5): 2903–2919. https://doi.org/10.5194/hess-22-2903-2018.
Pathiraja, S., L. Marshall, A. Sharma, and H. Moradkhani. 2016a. “Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation.” Adv. Water Resour. 94 (Aug): 103–119. https://doi.org/10.1016/j.advwatres.2016.04.021.
Pathiraja, S., L. Marshall, A. Sharma, and H. Moradkhani. 2016b. “Hydrologic modeling in dynamic catchments: A data assimilation approach.” Water Resour. Res. 52 (5): 3350–3372. https://doi.org/10.1002/2015WR017192.
Samuel, J., P. Coulibaly, G. Dumedah, and H. Moradkhani. 2014. “Assessing model state and forecasts variation in hydrologic data assimilation.” J. Hydrol. 513 (May): 127–141. https://doi.org/10.1016/j.jhydrol.2014.03.048.
Senent-Aparicio, J., A. López-Ballesteros, J. Pérez-Sánchez, F. J. Segura-Méndez, and D. Pulido-Velazquez. 2018. “Using multiple monthly water balance models to evaluate gridded precipitation products over Peninsular Spain.” Remote Sens. 10 (6): 922. https://doi.org/10.3390/rs10060922.
Shao, Q. X., A. Traylen, and L. Zhang. 2012. “Nonparametric method for estimating the effects of climatic and catchment characteristics on mean annual evapotranspiration.” Water Resour. Res. 48 (3). https://doi.org/10.1029/2010WR009610.
Thomas, H. A. 1981. Improved methods for national water assessment: Final report, water resources contract. Cambridge, MA: Harvard Water Resources Group.
Vrugt, J. A., C. J. F. ter Braak, C. G. H. Diks, and G. Schoups. 2013. “Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: Theory, concepts, and applications.” Adv. Water Resour. 51 (Jan): 457–478. https://doi.org/10.1016/j.advwatres.2012.04.002.
Wagner, P. D., S. M. Bhallamudi, B. Narasimhan, S. Kumar, N. Fohrer, and P. Fiener. 2019. “Comparing the effects of dynamic versus static representations of land use change in hydrologic impact assessments.” Environ. Model Software 122 (Dec): 103987. https://doi.org/10.1016/j.envsoft.2017.06.023.
Wang, D. 2018. “A new probability density function for spatial distribution of soil water storage capacity leads to the SCS curve number method.” Hydrol. Earth Syst. Sci. 22 (12): 6567–6578. https://doi.org/10.5194/hess-22-6567-2018.
Wang, D., Y. Chen, and X. Cai. 2009. “State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter.” Water Resour. Res. 45 (11): W11416. https://doi.org/10.1029/2008WR007401.
Wang, S., et al. 2017. “Examining dynamic interactions among experimental factors influencing hydrologic data assimilation with the ensemble Kalman filter.” Supplement, J. Hydrol. 554 (SC): 743–757. https://doi.org/10.1016/j.jhydrol.2017.09.052.
Wang, T., H. Yang, D. Yang, Y. Qin, and Y. Wang. 2018a. “Quantifying the streamflow response to frozen ground degradation in the source region of the Yellow River within the Budyko framework.” J. Hydrol. 558 (Mar): 301–313. https://doi.org/10.1016/j.jhydrol.2018.01.050.
Wang, W., et al. 2018b. “Satellite retrieval of actual evapotranspiration in the Tibetan Plateau: Components partitioning, multidecadal trends, and dominated factors identifying.” J. Hydrol. 559 (Apr): 471–485. https://doi.org/10.1016/j.jhydrol.2018.02.065.
Wang, W., Q. Shao, T. Yang, S. Peng, W. Xing, F. Sun, and Y. Luo. 2013. “Quantitative assessment of the impact of climate variability and human activities on runoff changes: A case study in four catchments of the Haihe River basin, China.” Hydrol. Processes 27 (8): 1158–1174. https://doi.org/10.1002/hyp.9299.
Wang, W., S. Zou, Q. Shao, W. Xing, X. Chen, X. Jiao, Y. Luo, B. Yong, and Z. Yu. 2016. “The analytical derivation of multiple elasticities of runoff to climate change and catchment characteristics alteration.” J. Hydrol. 541 (Part B): 1042–1056. https://doi.org/10.1016/j.jhydrol.2016.08.014.
Westra, S., M. Thyer, M. Leonard, D. Kavetski, and M. Lambert. 2014. “A strategy for diagnosing and interpreting hydrological model nonstationarity.” Water Resour. Res. 50 (6): 5090–5113. https://doi.org/10.1002/2013WR014719.
Xie, X., S. Meng, S. Liang, and Y. Yao. 2014. “Improving streamflow predictions at ungauged locations with real-time updating: Application of an EnKF-based state-parameter estimation strategy.” Hydrol. Earth Syst. Sci. 18 (10): 3923–3936. https://doi.org/10.5194/hess-18-3923-2014.
Xie, X., and D. Zhang. 2010. “Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter.” Adv. Water Resour. 33 (6): 678–690. https://doi.org/10.1016/j.advwatres.2010.03.012.
Xing, W., et al. 2014. “Changes of reference evapotranspiration in the Haihe River Basin: Present observations and future projection from climatic variables through multi-model ensemble. Global Planet.” Change 115 (Apr): 1–15. https://doi.org/10.1016/j.gloplacha.2014.01.004.
Xing, W., et al. 2018. “Estimating monthly evapotranspiration by assimilating remotely sensed water storage data into the extended Budyko framework across different climatic regions.” J. Hydrol. 567 (Dec): 684–695. https://doi.org/10.1016/j.jhydrol.2018.10.014.
Xiong, L., and S. Guo. 2012. “Appraisal of Budyko formula in calculating long-term water balance in humid watersheds of southern China.” Hydrol. Processes 26 (9): 1370–1378. https://doi.org/10.1002/hyp.8273.
Xiong, L., and S. L. Guo. 1999. “A two-parameter monthly water balance model and its application.” J. Hydrol. 216 (1–2): 111–123. https://doi.org/10.1016/S0022-1694(98)00297-2.
Xiong, L., K.-X. Yu, and L. Gottschalk. 2014. “Estimation of the distribution of annual runoff from climatic variables using copulas.” Water Resour. Res. 50 (9): 7134–7152. https://doi.org/10.1002/2013WR015159.
Xiong, M., et al. 2019. “Identifying time-varying hydrological model parameters to improve simulation efficiency by the ensemble Kalman filter: A joint assimilation of streamflow and actual evapotranspiration.” J. Hydrol. 568 (Jan): 758–768. https://doi.org/10.1016/j.jhydrol.2018.11.038.
Xu, C.-Y. 2021. “Issues influencing accuracy of hydrological modeling in a changing environment.” Water Sci. Eng. 14 (2): 167–170. https://doi.org/10.1016/j.wse.2021.06.005.
Ye, W., B. C. Bates, N. R. Viney, M. Sivapalan, and A. J. Jakeman. 1997. “Performance of conceptual rainfall-runoff models in low-yielding ephemeral catchments.” Water Resour. Res. 33 (1): 153–166. https://doi.org/10.1029/96WR02840.
Zhang, S., H. Yang, D. Yang, and A. W. Jayawardena. 2016a. “Quantifying the effect of vegetation change on the regional water balance within the Budyko framework.” Geophys. Res. Lett. 43 (3): 1140–1148. https://doi.org/10.1002/2015GL066952.
Zhang, X., and P. Liu. 2021. “A time-varying parameter estimation approach using split-sample calibration based on dynamic programming.” Hydrol. Earth Syst. Sci. 25 (2): 711–733. https://doi.org/10.5194/hess-25-711-2021.
Zhang, Y., et al. 2016b. “Multi-decadal trends in global terrestrial evapotranspiration and its components.” Sci. Rep. 6 (Jan): 19124. https://doi.org/10.1038/srep19124.
Zhang, Y., R. Leuning, L. B. Hutley, J. Beringer, I. McHugh, and J. P. Walker. 2010. “Using long-term water balances to parameterize surface conductances and calculate evaporation at 0.05 spatial resolution.” Water Resour. Res. 46 (5): W05512. https://doi.org/10.1029/2009WR008716.
Zhang, Y., N. Wang, C. Tang, S. Zhang, Y. Song, K. Liao, and X. Nie. 2021. “A new indicator to better represent the impact of landscape pattern change on basin soil erosion and sediment yield in the Upper Reach of Ganjiang, China.” Land 10 (9): 990. https://doi.org/10.3390/land10090990.
Zhou, L., P. Liu, Z. Gui, X. Zhang, W. Liu, L. Cheng, and J. Xia. 2022. “Diagnosing structural deficiencies of a hydrological model by time-varying parameters.” J. Hydrol. 605 (Feb): 127305. https://doi.org/10.1016/j.jhydrol.2021.127305.
Zhu, J. 2016. “Study on runoff responses to land use/cover change in Ganjiang watershed.” [In Chinese.] Master’s thesis, School of Geography and Environment, Jiangxi Normal Univ.
Zhu, Z., J. Bi, Y. Pan, S. Ganguly, A. Anav, L. Xu, A. Samanta, S. Piao, R. R. Nemani, and R. B. Myneni. 2013. “Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011.” Remote Sens. 5 (2): 927–948. https://doi.org/10.3390/rs5020927.
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Received: Aug 12, 2021
Accepted: Jul 14, 2022
Published online: Sep 27, 2022
Published in print: Dec 1, 2022
Discussion open until: Feb 27, 2023
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