A probabilistic conceptual model to attribute runoff variations to human activity

References

• Ahn, K.H. and Merwade, V., 2014. Quantifying the relative impact of climate and human activities on streamflow. Journal of Hydrology, 515, 257–266. doi:10.1016/j.jhydrol.2014.04.062
   Web of Science ®Google Scholar
• Bao, Z., et al., 2012. Attribution for decreasing streamflow of the Haihe River basin, northern China: climate variability or human activities? Journal of Hydrology, 460–461, 117–129. doi:10.1016/j.jhydrol.2012.06.054
   Web of Science ®Google Scholar
• Budyko, M.I., 1974. Climate and life. New York: Academic Press.
   Google Scholar
• Busuioc, A. and von Storch, H.V., 1996. Changes in the winter precipitation in Romania and its relation to the large-scale circulation. Tellus, 48 (4), 538–552. doi:10.1034/j.1600-0870.1996.t01-3-00004.x
   Google Scholar
• Dey, P. and Mishra, A., 2017. Separating the impacts of climate change and human activitieson streamflow: a review of methodologies and critical assumptions. Journal of Hydrology, 548, 278–290. doi:10.1016/j.jhydrol.2017.03.014
   Web of Science ®Google Scholar
• Fitelson, B.A. and Hájek, 2017. Declarations of independence. Synthese, 194 (10), 3979–3995. doi:10.1007/s11229-014-0559-2
   Web of Science ®Google Scholar
• Friendly, M., Monette, G., and Fox, J., 2013. Elliptic insights: understanding statistical methods through elliptical geometry. Statistical Science, 28, 1–39. doi:10.1214/12-STS402
   Web of Science ®Google Scholar
• Fu, J., 2010. Study on the effects of land-use change on flood in Daqinghe River Bain. Thesis(PhD). Tianjing University. (in Chinese).
   Google Scholar
• Gao, Y., Wang, H., and Long, D., 2009. Changes in hydrological conditions and the eco-environmental problems in Baiyangdian watershed. Resources Science, 31, 1506–1513. (in chinese). Available from: http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZRZY200909010.htm
   Google Scholar
• Honjo, Y., Liu, W.T., and Sakajo, S., 1994. Application of akaike information criterion statistics to geotechnical inverse analysis - the extended bayesian method. Structural Safety, 14, 5–29. doi:10.1016/0167-4730(94)90004-3
   Web of Science ®Google Scholar
• Hu, S., et al., 2012. Assessing the impacts of climate variability and human activities on streamflow in the water’ source area of Baiyangdian Lake. Journal of Geographical Sciences, 22, 895–905. doi:10.1007/s11442-012-0971-9
   Web of Science ®Google Scholar
• Jaramillo, F., et al., 2013. Multimethod assessment of evapotranspiration shifts due to non-irrigated agricultural development in sweden. Journal of Hydrology, 484, 55–62. doi:10.1016/j.jhydrol.2013.01.010
   Web of Science ®Google Scholar
• Jaramillo, F., et al., 2018. Dominant effect of increasing forest biomass on evapotranspiration: interpretations of movement in Budyko space. Hydrology and Earth System Sciences, 22, 567–580. doi:10.5194/hess-22-567-2018
   Web of Science ®Google Scholar
• Jhong, B.C., Tung, C.P., and Tsakiris, G., 2018. Evaluating future joint probability of precipitation extremes with a copula-based assessing approach in climate change. Water Resources Management, 32, 4253–4274. doi:10.1007/s11269-018-2045-y
   Web of Science ®Google Scholar
• 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. Journal of Hydrology, 522, 326–338. doi:10.1016/j.jhydrol.2014.12.060
   Web of Science ®Google Scholar
• Kojadinovic, I., Yan, J., and Holmes, M., 2011. Fast large-sample goodness-of-t tests for copulas. Statistica Sinica, 21, 841–871. Available from: www.jstor.org/stable/24309543
   Web of Science ®Google Scholar
• Kong, D., et al., 2016. Impactassessment of climate change and humanactivities on net runoff in the Yellow River Basin from 1951 to 2012. Ecological Engineering, 91, 566–573. doi:10.1016/j.ecoleng.2016.02.023
   Web of Science ®Google Scholar
• Li, J., Feng, P., and Chen, F., 2014. Effects of land use change on flood characteristics in mountainous area of Daqinghe watershed, China. Natural Hazards, 70, 593–607. doi:10.1007/s11069-013-0830-8
   Web of Science ®Google Scholar
• Liu, J.J., et al., 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, 1–13. doi:10.1016/j.jhydrol.2019.124096
   Web of Science ®Google Scholar
• Liu, M., Gao, Y., and Gan, G., 2011. Long-term trends in annual runoff and the impact of meteorological factors in the Baiyangdian watershed. Resources Science, 33, 1438–1445.
   Google Scholar
• Ma, H., et al., 2010. Impact of climate variability and human activity on streamflow decrease in the Miyun Reservoir catchment. Journal of Hydrology, 389 (3–4), 317–324. doi:10.1016/j.jhydrol.2010.06.010
   Web of Science ®Google Scholar
• Marsaglia, G., Tsang, W.W., and Wang, J.B., 2003. Evaluating Kolmogorov’s Distribution. Journal of Statistical Software, 8, 1–4. doi:10.18637/jss.v008.i18
   Google Scholar
• Milly, P.C.D., et al., 2008. Stationarity is dead: whither water management? Science, 319, 573–574. doi:10.1126/science.1151915
   PubMed Web of Science ®Google Scholar
• Ministry of Water Resources of the P.R.China, 2012. Hydrological data of Haihe River Basin. Annual hydrological report, Beijing, China.
   Google Scholar
• Mitchell, T. and Jones, P., 2005. An improved method of constructing a database of monthly climate observations and associated high-resolution grids RID C-8718-2009. International Journal of Climatology, 25(6), 693–712. doi:10.1002/joc.1181
   Web of Science ®Google Scholar
• Mwangi, H.M., et al., 2016. Relative contribution of land use change and climate variability on discharge of upper Mara River, Kenya. Journal of Hydrology Regional Studies, 5, 244–260. doi:10.1016/j.ejrh.2015.12.059
   Google Scholar
• Nelsen, R.B., 2006. An introduction to copulas (Springer Series in Statistics). New York: Springer.
   Google Scholar
• Pandey, P.K., et al., 2018. Modelling of interdependence between rainfall and temperature using copula. Modeling Earth Systems and Environment, 4 (2), 867–879. doi:10.1007/s40808-018-0454-9
   Web of Science ®Google Scholar
• Paul, S.S., et al., 2018. Application of object oriented image classification and Markov Chain Modeling for Land Use and Land Covet change analysis. Journal of Environmental Informatics, 31 (1), 30–40. doi:10.3808/jei.201700368
   Web of Science ®Google Scholar
• Paul, S.S., et al., 2019. Assessing land use–land cover change and soil erosion potential using a combined approach through remote sensing, RUSLE and random forest algorithm. Geocarto International, 1–13. doi:10.1080/10106049.2019.1614099
   Web of Science ®Google Scholar
• Pettitt, A.N., 1979. A non-parametric approach to the change-point problem. Journal of the Royal Statistical Society, 28, 126–135. doi:10.2307/2346729
   Google Scholar
• Polajzer, B., Brezovnik, R., and Ritonja, J., 2016. Evaluation of load frequency control performance based on standard deviational ellipses. IEEE Transactions on Power Systems, 32, 2296–2304. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7560610
   Web of Science ®Google Scholar
• Ruxton, G.D. and Neuhäuser, M., 2010. When should we use one-tailed hypothesis testing? Methods in Ecology & Evolution, 1, 114–117. doi:10.1111/j.2041-210X.2010.00014.x
   Web of Science ®Google Scholar
• Ryberg, K.R., Hodgkins, G.A., and Dudley, R.W., 2020. Change points in annual peak streamflows—Method comparisons and historical change points in the United States. Journal of Hydrology, 583, 124307. doi:10.1016/j.jhydrol.2019.124307
   Web of Science ®Google Scholar
• Suttles, K.M., et al., 2018. Assessment of hydrologic vulnerability to urbanization and climate change in a rapidly changing watershed in the Southeast US. Science of the Total Environment, 645, 806–816. doi:10.1016/j.scitotenv.2018.06.287
   PubMed Web of Science ®Google Scholar
• Tu, X., et al., 2015. Intra-annual distribution of streamflow and individual impacts of climate change and human activities in the Dongijang River Basin, China. Water Resources Management, 29, 2677–2695. doi:10.1007/s11269-015-0963-5
   Web of Science ®Google Scholar
• Velpuri, N.M. and Senay, G.B., 2013. Analysis of long-term trends (1950–2009) in precipitation, runoff and runoff coefficient in major urban watersheds in the United States. Environmental Research Letters, 8, 24020–24026. doi:10.1088/1748-9326/8/2/024020
   Web of Science ®Google Scholar
• Wahl, T., et al., 2015. Increasing risk of compound flooding from storm surge and rainfall for major US cities. Nature Climate Change, 5, 1093–1097. doi:10.1038/NCLIMATE2736
   Web of Science ®Google Scholar
• Wang, D. and Hejazi, M., 2011. Quantifying the relative contribution of the climate and direct human impacts on mean annual streamflow in the contiguous United States. Water Resources Research, 47, 411. doi:10.1029/2010WR010283
   Web of Science ®Google Scholar
• 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, W00A11. doi:10.1029/2007WR006768
   Web of Science ®Google Scholar
• Wang, W., et al., 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. Hydrological Processes, 27, 1158–1174. doi:10.1002/hyp.9299
   Web of Science ®Google Scholar
• Wang, W., et al., 2018. Two-dimension monthly river flow simulation using hierarchical network-copula conditional models. Water Resources Management, 32, 3801–3820. doi:10.1007/s11269-018-1968-7
   Web of Science ®Google Scholar
• Wang, X., et al., 2014. Trend and extreme occurrence of precipitation in a mid-latitude Eurasian steppe watershed at various time scales. Hydrological Processes, 28, 5547–5560. doi:10.1002/hyp.10054
   Web of Science ®Google Scholar
• Wang, X., et al., 2015. Variation analysis of streamflow and ecological flow for the twin rivers of the Miyun Reservoir Basin in northern China from 1963 to 2011. Science of the Total Environment, 536, 739–749. doi:10.1016/j.scitotenv.2015.07.088
   PubMed Web of Science ®Google Scholar
• Wang, X., et al., 2017. Identifying priority management intervals of discharge and TN/TP concentration with copula analysis for Miyun Reservoir inflows, North China. Science of the Total Environment, 609, 1258. doi:10.1016/j.scitotenv.2017.07.135
   PubMed Web of Science ®Google Scholar
• Wang, X.X., 2014. Advances in separating effects of climate variability and human activity on stream discharge: an overview. Advances in Water Resources, 71, 209–218. doi:10.1016/j.advwatres.2014.06.007
   Web of Science ®Google Scholar
• Wang, Y., et al., 2013b. Contributions of climate and human activities to changes in runoff of the Yellow and Yangtze rivers from 1950 to 2008. Science China-Earth Sciences, 56, 1398–1412. doi:10.1007/s11430-012-4505-1
   Web of Science ®Google Scholar
• Wei, C. and Zhang, M., 2010. Quantifying streamflow change caused by forest disturbance at a large spatial scale: a single watershed study. Water Resources Research, 46, W12525. doi:10.1029/2010WR009250
   Web of Science ®Google Scholar
• Wu, J., et al., 2017. Detecting the quantitative hydrological response to changes in climate and human activities. Science of the Total Environment, 586, 328–337. doi:10.1016/j.scitotenv.2017.02.010
   PubMed Web of Science ®Google Scholar
• Xu, X., et al., 2014. Attribution analysis based on the Budyko hypothesis for detecting the dominant cause of runoff decline in Haihe basin. Journal of Hydrology, 510, 530–540. doi:10.1016/j.jhydrol.2013.12.052
   Web of Science ®Google Scholar
• Ye, X.C., et al., 2013. Distinguishing the relative impacts of climate change and humanactivities on variation of streamflow in the Poyang Lake catchment,China. Journal of Hydrology, 494, 83–95. doi:10.1016/j.jhydrol.2013.04.036
   Web of Science ®Google Scholar
• Yuan, Y., et al., 2016. Quantitative assessment of the contribution of climate variability and human activity to streamflow alteration in Dongting Lake, China. Hydrological Processes, 30, 1929–1939. doi:10.1002/hyp.10768
   Web of Science ®Google Scholar
• Yue, S., et al., 2002. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological Processes, 16, 1807–1829. doi:10.1002/hyp.1095
   Web of Science ®Google Scholar
• Zeng, S., et al., 2015. Effects of climate change and human activities on surface runoff in the Luan River Basin. Advances in Meteorology, 2015, 1–12. doi:10.1155/2015/740239
   Web of Science ®Google Scholar
• Zhang, L., Dawes, W.R., and Walker, G.R., 2001. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resources Research, 37, 701–708. doi:10.1029/2000WR900325
   Web of Science ®Google Scholar
• Zhang, Q., et al., 2016. Evaluation of impacts of climate change and human activities on streamflow in the Poyang Lake basin, China. Hydrological Processes, 30, 2562–2576. doi:10.1002/hyp.10814
   Web of Science ®Google Scholar
• Zhang, Z.X., et al., 2011. Evaluating the non-stationary relationship between precipitation and streamflow in Nine Major Basins of China during the past 50 years. Journal of Hydrology, 409, 81–93. doi:10.1016/j.jhydrol.2011.07.041
   Web of Science ®Google Scholar
• Zhao, G.J., et al., 2014. Quantifying the impact of climate variability and human activities on streamflow in the middle reaches of the Yellow River basin. China. Journal of Hydrology, 519, 387–398. doi:10.1016/j.advwatres.2014.06.007
   Web of Science ®Google Scholar
• Zhao, Y.F., et al., 2015. Quantifying the anthropogenic and climatic contributions to changes in water discharge and sediment load into the sea: a case study of the Yangtze River, China. Science of the Total Environment, 536, 803–812. doi:10.1016/j.scitotenv.2015.07.119
   PubMed Web of Science ®Google Scholar
• Zipper, S.C., et al., 2018. Continuous separation of land use and climate effects on the past and future water balance. Journal of Hydrology, 565, 106–122. doi:10.1016/j.jhydrol.2018.08.022
   Web of Science ®Google Scholar