Abstract
Changes in amount and frequency of runoff are caused by interaction of the climatic and anthropogenic factors in human-nature system. Those part of the runoff variations caused by anthropogenic loadings are more manageable compared with climate effects occurring over a long period. In this paper, we propose a framework to decompose the effect of anthropogenic and climatic time series on streamflow. To fulfill this task, anthropogenic time series attribution (ATSA) method is introduced. ATSA employs the output of climate elasticity (CE) method to extract human-affected time series and naturalizes the annual streamflow. In other words, ATSA makes it possible to exploit CE output in time series analysis. Furthermore, temporal downscaling process performed by a hybrid discrete wavelet transform and artificial neural network (DWT-ANN) enhances the temporal resolution of ATSA. We applied ATSA framework on Urmia Lake (northwest of Iran) inflow over the 1972–2010 period. CE application indicated that the contribution of climatic and anthropogenic factors in reduction of the mean inflow to the lake is about 60 and 40%, respectively. ATSA also revealed how human exploitation affected the time series of the lake inflow. Roughly, 14,000 MCM of the lake inflow reduction was attributed to the anthropogenic impacts between 1995 and 2010 such that the removal of anthropogenic loading could have led to rising water level up to 1275.5 meters above the sea level (i.e., 1.4 m above the ecological level) in 2010. Furthermore, the effect of de-trended inflow time series was investigated via DWT-ANN. Trend-free pre-whitening Mann-Kendall (TFPW-MK) test indicated significant increase in temperature, potential evapotranspiration, population, reservoir capacity, irrigated area, and the number of wells, over 39 years of the study period.
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References
Ahn K-H, Merwade V (2014) Quantifying the relative impact of climate and human activities on streamflow. J Hydrol 515:257–266. https://doi.org/10.1016/j.jhydrol.2014.04.062
Angélil O, Stone D, Wehner M, Paciorek CJ, Krishnan H, Collins W (2017) An independent assessment of anthropogenic attribution statements for recent extreme temperature and rainfall events. J Clim 30:5–16. https://doi.org/10.1175/JCLI-D-16-0077.1
Arora VK (2002) The use of the aridity index to assess climate change effect on annual runoff. J Hydrol 265:164–177. https://doi.org/10.1016/S0022-1694(02)00101-4
Brutsaert W, Parlange M (1998) Hydrologic cycle explains the evaporation paradox. Nature 396:30. https://doi.org/10.1038/23845
Budyko MI, Miller DH & Miller DH (1974) Climate and life (Vol. 508). New York: Academic press
Chang J, Wang Y, Istanbulluoglu E, Bai T, Huang Q, Yang D, Huang S (2015) Impact of climate change and human activities on runoff in the Weihe River Basin, China. Quat Int 380–381:169–179. https://doi.org/10.1016/J.QUAINT.2014.03.048
Delju AH, Ceylan A, Piguet E, Rebetez M (2012) Observed climate variability and change in Urmia Lake Basin, Iran. Theor Appl Climatol 111:285–296. https://doi.org/10.1007/s00704-012-0651-9
Fabre J, Ruelland D, Dezetter A, Grouillet B (2016) Sustainability of water uses in managed hydrosystems: human- and climate-induced changes for the mid-21st century. Hydrol Earth Syst Sci 20:3129–3147. https://doi.org/10.5194/hess-20-3129-2016
Gao G, Fu B, Wang S, Liang W, Jiang X (2016) Determining the hydrological responses to climate variability and land use/cover change in the Loess Plateau with the Budyko framework. Sci Total Environ 557–558:331–342. https://doi.org/10.1016/J.SCITOTENV.2016.03.019
Ghobadi Hamzekhani F, Saghafian B, Araghinejad S (2015) Environmental management in Urmia Lake: thresholds approach. Int J Water Resour Dev 32:1–12. https://doi.org/10.1080/07900627.2015.1024829
Hargreaves GH, Samani ZA (1982) Estimating potential evapotranspiration. J Irrig Drain Div 108:225–230
Hassanzadeh E, Zarghami M, Hassanzadeh Y (2011) Determining the main factors in declining the Urmia Lake level by using system dynamics modeling. Water Resour Manag 26:129–145. https://doi.org/10.1007/s11269-011-9909-8
Henriques C, Garnett K, Weatherhead EK, Lickorish FA, Forrow D, Delgado J (2015) The future water environment — using scenarios to explore the significant water management challenges in England and Wales to 2050. Sci Total Environ 512–513:381–396. https://doi.org/10.1016/J.SCITOTENV.2014.12.047
Hosseini-Moghari S-M, Araghinejad S, Tourian MJ et al (2018) Quantifying the impacts of human water use and climate variations on recent drying of Lake Urmia basin: the value of different sets of spaceborne and in-situ data for calibrating a hydrological model. Hydrol Earth Syst Sci Discuss:1–29. https://doi.org/10.5194/hess-2018-318
Jalili S, Hamidi SA, Ghanbari RN (2015) Climate variability and anthropogenic effects on Lake Urmia water level fluctuations, northwestern Iran. Hydrol Sci J:150527103244004. https://doi.org/10.1080/02626667.2015.1036757
Jiang S, Wang M, Ren L, Xu CY, Yuan F, Liu Y, Lu Y, Shen H (2019) A framework for quantifying the impacts of climate change and human activities on hydrological drought in a semiarid basin of Northern China. Hydrol Process 33:1075–1088. https://doi.org/10.1002/hyp.13386
Kendall MG (1975) Rank Correlation Methods. 4th Edition, Charles Griffin, London
Khalaj M, Kholghi M, Saghafian B, Bazrafshan J (2019) Impact of climate variation and human activities on groundwater quality in northwest of Iran. J Water Supply Res Technol 68:121–135. https://doi.org/10.2166/aqua.2019.064
Leys C, Ley C, Klein O, Bernard P, Licata L (2013) Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J Exp Soc Psychol 49:764–766. https://doi.org/10.1016/j.jesp.2013.03.013
Li B, Liang Z, Zhang J, Wang G, Zhao W, Zhang H, Wang J, Hu Y (2018a) Attribution analysis of runoff decline in a semiarid region of the Loess Plateau, China. Theor Appl Climatol 131:845–855. https://doi.org/10.1007/s00704-016-2016-2
Li C, Wang L, Wanrui W, Qi J, Linshan Y, Zhang Y, Lei W, Cui X, Wang P (2018b) An analytical approach to separate climate and human contributions to basin streamflow variability. J Hydrol 559:30–42. https://doi.org/10.1016/j.jhydrol.2018.02.019
Ma H, Yang D, Tan SK, Gao B, Hu Q (2010) Impact of climate variability and human activity on streamflow decrease in the Miyun Reservoir catchment. J Hydrol 389:317–324. https://doi.org/10.1016/j.jhydrol.2010.06.010
Mann HB (1945) Nonparametric tests against trend. Econometrica: J Economet Soc 245–259
Mezentsev V (1955) More on the computation of actual evaporation (Yechio raz o rastchetie srednevo summarnovo ispareniia). Meteorol Gidrol 5:24–26
Mwangi HM, Julich S, Patil SD, McDonald MA, Feger KH (2016) Relative contribution of land use change and climate variability on discharge of upper Mara River, Kenya. J Hydrol Reg Stud 5:244–260. https://doi.org/10.1016/J.EJRH.2015.12.059
Nabaei S, Saghafian B (2019) Cellular time series: a data structure for spatio-temporal analysis and management of geoscience information. J Hydroinformatics 21:999–1013. https://doi.org/10.2166/hydro.2019.012
Pettitt AN (1979) A non-parametric approach to the change-point problem. Appl Stat 28:126–135
Pike JG (1964) The estimation of annual run-off from meteorological data in a tropical climate. J Hydrol 2:116–123. https://doi.org/10.1016/0022-1694(64)90022-8
Pires A, Morato J, Peixoto H, Botero V, Zuluaga L, Figueroa A (2017) Sustainability assessment of indicators for integrated water resources management. Sci Total Environ 578:139–147. https://doi.org/10.1016/J.SCITOTENV.2016.10.217
Safavi S, Shamsai A, Saghafian B (2018) Reduced-order salinity modeling of the Urmia Lake using MIKE3 and proper orthogonal decomposition models. Water Resour 45(5):728–737. https://doi.org/10.1134/S0097807818050196
Schaake JS (1990) From climate to flow. In: Waggoner, P.E., Ed., Climate Change and US Water Resources, John Wiley, New York, pp 177–206
Shahid M, Cong Z, Zhang D (2018) Understanding the impacts of climate change and human activities on streamflow: a case study of the Soan River basin, Pakistan. Theor Appl Climatol 134:205–219. https://doi.org/10.1007/s00704-017-2269-4
Sharafati A, Nabaei S, Shahid S (2020) Spatial assessment of meteorological drought features over different climate regions in Iran. Int J Climatol:joc.6307. https://doi.org/10.1002/joc.6307
Shrestha S, Shrestha M, Babel MS (2017) Assessment of climate change impact on water diversion strategies of Melamchi Water Supply Project in Nepal. Theor Appl Climatol 128:311–323. https://doi.org/10.1007/s00704-015-1713-6
Tan X, Gan TY (2016) Contribution of human and climate change impacts to changes in streamflow of Canada. Sci Rep 5:17767. https://doi.org/10.1038/srep17767
Turc L (1954) Le bilan d’eau des sols: relations entre les précipitations, l’évaporation et l’écoulement. Institut national de la recherche agronomique
Wang W, Shao Q, Yang T, Peng S, Xing W, Sun F, Luo Y (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 Process 27:1158–1174. https://doi.org/10.1002/hyp.9299
Yue S, Pilon P, Phinney B, Cavadias G (2002) The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol Process 16:1807–1829. https://doi.org/10.1002/hyp.1095
Zeng S, Zhan C, Sun F, du H, Wang F (2015) Effects of Climate Change and Human Activities on Surface Runoff in the Luan River Basin. Adv Meteorol 2015:1–12. https://doi.org/10.1155/2015/740239
Zhang Q, Liu J, Singh VP, Gu X, Chen X (2016) Evaluation of impacts of climate change and human activities on streamflow in the Poyang Lake basin, China. Hydrol Process 30:2562–2576. https://doi.org/10.1002/hyp.10814
Zhang L, Nan Z, Wang W, Ren D, Zhao Y, Wu X (2019) Separating climate change and human contributions to variations in streamflow and its components using eight time-trend methods. Hydrol Process 33:383–394. https://doi.org/10.1002/hyp.13331
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Nabaei, S., Saghafian, B. Quantifying streamflow drivers by anthropogenic time series attribution method in human-nature system. Theor Appl Climatol 144, 1335–1348 (2021). https://doi.org/10.1007/s00704-021-03598-w
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DOI: https://doi.org/10.1007/s00704-021-03598-w