Deterministic and Fuzzy Evaluation of Human and Climate Contributions in Changing Hydrologic Regime: A Case Study of the Gorganrood Watershed at Tamar River Hydrometric Station

Document Type : Research Article

Authors

1 M.Sc. Student, School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Associate Professor, School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

Human and climate are two major scio-hydrologic drivers that determine hydrological regimes and patterns. In this regards, Land Use and Land Cover (LULC) changes, agricultural development, etc., on global and regional scales, hydro-climatological components have influenced these regimes. The effects of each driver on the variation of hydrological components have been assessed in different studies, but these approaches are not accurate enough at watershed-scales that experience the simultaneous impacts of climate dynamics and LULC changes. Different studies have considered both climate and human altertions in the hydrological cycle, and quantified their contributions in such basin. Results of these researches can help decision makers in water management of the pros and cons of water and land use policies. The Gorganrood watershed is an important basin in the northern part of Iran, especially from the agricultural point of view, which has considerably experienced hydrological and extreme events changes. While the consequence of each climate change and LULC changes have been assessed in the watershed, there is no study, which considers the complicated interactions of these drivers. In this paper, the authors firstly evaluated the contributions of LULC and climate change on the variation of streamflow. Secondly, the modified fuzzy arithmetic method has been used to achieve their fuzzy contributions. To this purpose, the computational period was firstly divided into two different temporal spans known as the reference and affected periods. The reference period is the first temporal span in which climate controls the hydrological responses. Then, the statistical behavior of the time-serries changes due to human activities, and the affected periods. Two hydrological models, Soil and Water Assessment Tool (SWAT) and a black box Artificial Neural Networks (ANNs), were used to simulate the streamflow in the watershed. However, the results of the hydrological models showed their general acceptable performance to simulate the recorded streamflow at Tamar hydrometric station, but the results of the conceptual model (SWAT) showed that the performance of the model in the dry season is not as good as in the wet season. In the next step, the contributions of human and climate activities were assessed via two different methods. The first method is simple differential method, which compares the projection of the calibrated model in the second period with observations in both periods. The second set of contribution rates was calculated using the climate elasticity method via recorded monthly data and implemented derivation rules. In the first method, the contribution rate of human activities is significantly higher than the rate of climate change, and the result of the second method is a reverse. Because of differences in the methods’ concepts, the calculated contributions rates are different. To assess the uncertainty grouped with the estimations, a novel approach was developed using fuzzy mathematics. The uncertain version of the contribution rates showed that in each α-cut (fuzzy uncertainty level), the contribution of human alternation (LULC change) as the most important human interventions is more significant than climate direvers. In other words, during the simulation period, the effect of LULC change on the flow was very noteworthy, while climate change had relatively less effect on the behavioral change of the flow.

Keywords

Main Subjects

References

آذری، م.، 1392، ارزیابی اثرات تغییر اقلیم و راه‌های سازگاری با آن در مدیریت منابع آب و خاک. رساله دکتری علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی دانشگاه تربیت مدرس، 169 ص.
کلباسی، ا. و زهرایی، ب.، 1394، کمی‌سازی اثر فعالیت‌های انسانی بر رواناب‌های سطحی با تاکید بر آشکارسازی اثرات تغییر اقلیم، پایان نامه برای دریافت درجه کارشناسی ارشد، دانشگاه تهران.
محضری، س.، 1393، شبیه‌سازی هدرروی نیترات توسط رواناب و رسوب در حوه گرگانرود استان گلستان. پایان‌نامه کارشناسی ارشد مهندسی علوم خاک-فیزیک و حفاظت خاک، دانشکده علوم کشاورزی و منابع طبیعی گرگان. 194 ص.
Akoko, G., Le, T. H., Gomi, T. and Kato, T., 2021, A Review of SWAT Model Application in Africa. In Water 13(9), 1313. https://doi.org/10.3390/w13091313.
Asadi, H., Shahedi, K., Jarihani, B. and Sidle, R. C., 2019, Rainfall-Runoff Modelling Using Hydrological Connectivity Index and Artificial Neural Network Approach. In Water 11(2), 212. https://doi.org/10.3390/w11020212.
Bao, Z., Zhang, J., Wang, G., Fu, G., He, R., Yan, X., Jin, J., Liu, Y. and Zhang, A., 2012, Attribution for decreasing streamflow of the Haihe River basin, northern China: Climate variability or human activities? Journal of Hydrology, 460–461, 117–129. https://doi.org/https://doi.org/10.1016/j.jhydrol.2012.06.054.
Clifton, C. F., Day, K. T., Luce, C. H., Grant, G. E., Safeeq, M., Halofsky, J. E. and Staab, B. P., 2018, Effects of climate change on hydrology and water resources in the Blue Mountains, Oregon, USA. Climate Services, 10, 9–19. https://doi.org/https://doi.org/10.1016/j.cliser.2018.03.001.
Dey, P. and Mishra, A., 2017, Separating the impacts of climate change and human activities on streamflow: A review of methodologies and critical assumptions. Journal of Hydrology, 548, 278–290. https://doi.org/https://doi.org/10.1016/j.jhydrol.2017.03.014.
Eisner, S., Flörke, M., Chamorro, A., Daggupati, P., Donnelly, C., Huang, J., Hundecha, Y., Koch, H., Kalugin, A. and Krylenko, I., 2017, An ensemble analysis of climate change impacts on streamflow seasonality across 11 large river basins. Climatic Change, 141(3), 401–417.
Farsi, N. and Mahjouri, N., 2019, Evaluating the contribution of the climate change and human activities to runoff change under uncertainty. Journal of Hydrology, 574, 872–891. https://doi.org/https://doi.org/10.1016/j.jhydrol.2019.04.028.
Gharbia, S. S., Smullen, T., Gill, L., Johnston, P. and Pilla, F., 2018, Spatially distributed potential evapotranspiration modeling and climate projections. Science of The Total Environment, 633, 571–592. https://doi.org/https://doi.org/10.1016/j.scitotenv.2018.03.208.
Gholami, V., 2013, The influence of deforestation on runoff generation and soil erosion (Case study: Kasilian Watershed). Journal of Forest Science, 59(7), 272–278.
Gholami, V., Booij, M. J., Nikzad Tehrani, E. and Hadian, M. A., 2018, Spatial soil erosion estimation using an artificial neural network (ANN) and field plot data. CATENA, 163, 210–218. https://doi.org/https://doi.org/10.1016/j.catena.2017.12.027.
Givati, A., Thirel, G., Rosenfeld, D. and Paz, D., 2019, Climate change impacts on streamflow at the upper Jordan River based on an ensemble of regional climate models. Journal of Hydrology: Regional Studies, 21, 92–109. https://doi.org/https://doi.org/10.1016/j.ejrh.2018.12.004.
Gizaw, M. S., Biftu, G. F., Gan, T. Y., Moges, S. A. and Koivusalo, H., 2017, Potential impact of climate change on streamflow of major Ethiopian rivers. Climatic Change, 143(3), 371–383.
Grillakis, M. G., 2019, Increase in severe and extreme soil moisture droughts for Europe under climate change. Science of The Total Environment, 660, 1245–1255. https://doi.org/https://doi.org/10.1016/j.scitotenv.2019.01.001.
Huang, S., Zheng, X., Ma, L., Wang, H., Huang, Q., Leng, G., Meng, E. and Guo, Y., 2020, Quantitative contribution of climate change and human activities to vegetation cover variations based on GA-SVM model. Journal of Hydrology, 584, 124687. https://doi.org/https://doi.org/10.1016/j.jhydrol.2020.124687.
Jha, M., Pan, Z., Takle, E. S. and Gu, R., 2004, Impacts of climate change on streamflow in the Upper Mississippi River Basin: A regional climate model perspective. Journal of Geophysical Research: Atmospheres, 109(D9). https://doi.org/https://doi.org/10.1029/2003JD003686.
Kim, D.-H., Jang, T. and Hwang, S., 2020, Evaluating impacts of climate change on hydrology and total nitrogen loads using coupled APEX-paddy and SWAT models. Paddy and Water Environment, 18(3), 515–529. https://doi.org/10.1007/s10333-020-00798-4.
Li, C., Wang, L., Wanrui, W., Qi, J., Linshan, Y., Zhang, Y., Lei, W., Cui, X. and Wang, P., 2018, An analytical approach to separate climate and human contributions to basin streamflow variability. Journal of Hydrology, 559, 30–42. https://doi.org/https://doi.org/10.1016/j.jhydrol.2018.02.019.
Li, M., Wu, P. and Ma, Z., 2020, A comprehensive evaluation of soil moisture and soil temperature from third-generation atmospheric and land reanalysis data sets. International Journal of Climatology, 40(13), 5744–5766. https://doi.org/https://doi.org/10.1002/joc.6549.
 
Liu, J., Zhou, Z., Yan, Z., Gong, J., Jia, Y., Xu, C. Y. and Wang, H., 2019, A new approach to separating the impacts of climate change and multiple human activities on water cycle processes based on a distributed hydrological model. Journal of Hydrology, 578, 124096.
Liu, J., Zhang, Q., Singh, V. P. and Shi, P., 2017, Contribution of multiple climatic variables and human activities to streamflow changes across China. Journal of Hydrology, 545, 145-162.
Loiselle, D., Du, X., Alessi, D. S., Bladon, K. D. and Faramarzi, M., 2020, Projecting impacts of wildfire and climate change on streamflow, sediment, and organic carbon yields in a forested watershed. Journal of Hydrology, 590, 125403. https://doi.org/https://doi.org/10.1016/j.jhydrol.2020.125403.
Mahmoodi, N., Kiesel, J., Wagner, P. D. and Fohrer, N., 2020, Integrating water use systems and soil and water conservation measures into a hydrological model of an Iranian Wadi system. Journal of Arid Land, 12(4), 545–560. https://doi.org/10.1007/s40333-020-0125-3.
McInerney, D., Thyer, M., Kavetski, D., Githui, F., Thayalakumaran, T., Liu, M. and Kuczera, G., 2018, The Importance of Spatiotemporal Variability in Irrigation Inputs for Hydrological Modeling of Irrigated Catchments. Water Resources Research, 54(9), 6792–6821. https://doi.org/https://doi.org/10.1029/2017WR022049.
Mehdizadeh, S., Behmanesh, J. and Khalili, K., 2018, New Approaches for Estimation of Monthly Rainfall Based on GEP-ARCH and ANN-ARCH Hybrid Models. Water Resources Management, 32(2), 527–545. https://doi.org/10.1007/s11269-017-1825-0.
Moradi, Z. and Mikaeili T., A. R., 2020, Relationship between Land Use Change and Water Yield in Gorgan-rood Watershed. Journal of Watershed Management Research, 11(21), 269–280.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D. and Veith, T. L., 2007, Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900.
Nash, J. E. and Sutcliffe, J. V., 1970, River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), 282–290. https://doi.org/https://doi.org/10.1016/0022-1694(70)90255-6.
Nasseri, M., Ansari, A. and Zahraie, B., 2014, Uncertainty assessment of hydrological models with fuzzy extension principle: Evaluation of a new arithmetic operator. Water Resources Research, 50(2), 1095–1111.
Nilawar, A. P. and Waikar, M. L., 2019, Impacts of climate change on streamflow and sediment concentration under RCP 4.5 and 8.5: A case study in Purna river basin, India. Science of The Total Environment, 650, 2685–2696. https://doi.org/https://doi.org/10.1016/j.scitotenv.2018.09.334.
Nourani, V., 2017, An Emotional ANN (EANN) approach to modeling rainfall-runoff process. Journal of Hydrology, 544, 267–277. https://doi.org/https://doi.org/10.1016/j.jhydrol.2016.11.033.
Nourani, V., Komasi, M. and Mano, A., 2009, A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling. Water Resources Management, 23(14), 2877. https://doi.org/10.1007/s11269-009-9414-5.
Patterson, L. A., Lutz, B. and Doyle, M. W., 2013, Climate and direct human contributions to changes in mean annual streamflow in the South Atlantic, USA. Water Resources Research, 49(11), 7278–7291. https://doi.org/https://doi.org/10.1002/2013WR014618.
Pettitt, A. N., 1979, A non‐parametric approach to the change‐point problem. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28, 126–135.
Qi, J., Zhang, X., Yang, Q., Srinivasan, R., Arnold, J. G., Li, J., Waldholf, S. T. and Cole, J., 2020, SWAT ungauged: Water quality modeling in the Upper Mississippi River Basin. Journal of Hydrology, 584, 124601. https://doi.org/https://doi.org/10.1016/j.jhydrol.2020.124601.
Rajib, A., Merwade, V. and Yu, Z., 2018, Rationale and Efficacy of Assimilating Remotely Sensed Potential Evapotranspiration for Reduced Uncertainty of Hydrologic Models. Water Resources Research, 54(7), 4615–4637. https://doi.org/https://doi.org/10.1029/2017WR021147.
Ran, L., Yuan, Y., Cooter, E., Benson, V., Yang, D., Pleim, J., Wang, R. and Williams, J., 2019, An Integrated Agriculture, Atmosphere, and Hydrology Modeling System for Ecosystem Assessments. Journal of Advances in Modeling Earth Systems, 11(12), 4645–4668. https://doi.org/https://doi.org/10.1029/2019MS001708.
Rouhani, H. and Jafarzadeh, M. S., 2018, Assessing the climate change impact on hydrological response in the Gorganrood river basin, Iran. Journal of Water and Climate Change, 9(3), 421–433.
Schaake, J. C., 1990, From climate to flow. Climate Change and US Water Resources., 177–206.
Sunde, M. G., He, H. S., Hubbart, J. A. and Urban, M. A., 2017, Integrating downscaled CMIP5 data with a physically based hydrologic model to estimate potential climate change impacts on streamflow processes in a mixed-use watershed. Hydrological Processes, 31(9), 1790–1803. https://doi.org/https://doi.org/10.1002/hyp.11150.
Tan, X. and Gan, T. Y., 2015, Contribution of human and climate change impacts to changes in streamflow of Canada. Scientific Reports, 5(1), 17767. https://doi.org/10.1038/srep17767.
Tian, P., Lu, H., Feng, W., Guan, Y. and Xue, Y., 2020, Large decrease in streamflow and sediment load of Qinghai–Tibetan Plateau driven by future climate change: A case study in Lhasa River Basin. CATENA, 187, 104340. https://doi.org/https://doi.org/10.1016/j.catena.2019.104340.
Wang, G., Xia, J. and Chen, J., 2009, Quantification of effects of climate variations and human activities on runoff by a monthly water balance model: A case study of the Chaobai River basin in northern China. Water Resources Research, 45(7). https://doi.org/https://doi.org/10.1029/2007WR006768.
Xiang, Y., Gou, L., He, L., Xia, S. and Wang, W., 2018, A SVR–ANN combined model based on ensemble EMD for rainfall prediction. Applied Soft Computing, 73, 874–883. https://doi.org/https://doi.org/10.1016/j.asoc.2018.09.018.
Xin, Z., Li, Y., Zhang, L., Ding, W., Ye, L., Wu, J. and Zhang, C., 2019, Quantifying the relative contribution of climate and human impacts on seasonal streamflow. Journal of Hydrology, 574, 936–945. https://doi.org/https://doi.org/10.1016/j.jhydrol.2019.04.095.
Yan, B., Fang, N. F., Zhang, P. C. and Shi, Z. H., 2013, Impacts of land use change on watershed streamflow and sediment yield: An assessment using hydrologic modelling and partial least squares regression. Journal of Hydrology, 484, 26–37. https://doi.org/https://doi.org/10.1016/j.jhydrol.2013.01.008.
Yin, J., Yuan, Z., Yan, D., Yang, Z. and Wang, Y., 2018, Addressing Climate Change Impacts on Streamflow in the Jinsha River Basin Based on CMIP5 Climate Models. In Water 10(7). https://doi.org/10.3390/w10070910.
Zadeh, L. A., 1975, The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), 199–249.
Zeng, S., Zhan, C., Sun, F., Du, H. and Wang, F., 2015, Effects of climate change and human activities on surface runoff in the Luan River Basin. Advances in Meteorology, 2015.
Zhang, C., Wang, X., Li, J. and Hua, T., 2020a, Identifying the effect of climate change on desertification in northern China via trend analysis of potential evapotranspiration and precipitation. Ecological Indicators, 112, 106141. https://doi.org/https://doi.org/10.1016/j.ecolind.2020.106141.
Zhang, H., Wang, B., Liu, D. L., Zhang, M., Leslie, L. M. and Yu, Q., 2020b, Using an improved SWAT model to simulate hydrological responses to land use change: A case study of a catchment in tropical Australia. Journal of Hydrology, 585, 124822. https://doi.org/https://doi.org/10.1016/j.jhydrol.2020.124822.
Zhang, S. and Lu, X. X., 2009, Hydrological responses to precipitation variation and diverse human activities in a mountainous tributary of the lower Xijiang, China. Catena, 77(2), 130–142.
Zheng, K., Wei, J.-Z., Pei, J.-Y., Cheng, H., Zhang, X.-L., Huang, F.-Q., Li, F.-M. and Ye, J.-S., 2019, Impacts of climate change and human activities on grassland vegetation variation in the Chinese Loess Plateau. Science of The Total Environment, 660, 236–244. https://doi.org/https://doi.org/10.1016/j.scitotenv.2019.01.022.
 
Journal of the Earth and Space Physics
Volume 48, Issue 2
online publication date: 31 August 2022
August 2022
Pages 403-420

Files

Share

How to cite

Statistics