Articles | Volume 376
https://doi.org/10.5194/piahs-376-51-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/piahs-376-51-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
01 Feb 2018
| 01 Feb 2018
Forecasting domestic water demand in the Haihe river basin under changing environment
Xiao-Jun Wang
CORRESPONDING AUTHOR
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, 210029 Nanjing, China
Research Center for Climate Change, Ministry of Water Resources, 210029 Nanjing, China
State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, 100875 Beijing, China
Jian-Yun Zhang
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, 210029 Nanjing, China
Research Center for Climate Change, Ministry of Water Resources, 210029 Nanjing, China
Shamsuddin Shahid
Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Malaysia
Yu-Xuan Xie
Haihe River Water Conservancy Commission, 300170 Tianjin, China
Xu Zhang
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, 210029 Nanjing, China
Research Center for Climate Change, Ministry of Water Resources, 210029 Nanjing, China
Related authors
Kamal Ahmed, Shamsuddin Shahid, Xiaojun Wang, Nadeem Nawaz, and Najeebullah Khan
Hydrol. Earth Syst. Sci., 23, 3081–3096, https://doi.org/10.5194/hess-23-3081-2019, https://doi.org/10.5194/hess-23-3081-2019, 2019
Short summary
Short summary
The long-term changes (1901–2016) in annual and seasonal aridity in Pakistan and its causes are analyzed in this paper. Gauge-based gridded precipitation and PET data are used to show the spatial and temporal patterns of the changes in aridity over the diverse climate of the country. The present study suggests that the relative influence of precipitation and temperature on aridity determines its trends in the context of climate change.
Kang Xie, Pan Liu, Qian Xia, Xiao Li, Weibo Liu, Xiaojing Zhang, Lei Cheng, Guoqing Wang, and Jianyun Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-217, https://doi.org/10.5194/essd-2022-217, 2022
Revised manuscript not accepted
Short summary
Short summary
There are currently no available common datasets of the Soil moisture storage capacity (SMSC) on a global scale, especially for hydrological models. Here, we produce a dataset of the SMSC parameter for global hydrological models. The global SMSC is constructed based on the deep residual network at 0.5° resolution. SMSC products are validated on global grids and typical catchments from different climatic regions.
A. Dewan, S. Shahid, M. Asaduzzaman, and F. Faruque
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 627–633, https://doi.org/10.5194/isprs-annals-V-3-2022-627-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-627-2022, 2022
Young Hoon Song, Eun-Sung Chung, and Shamsuddin Shahid
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-107, https://doi.org/10.5194/hess-2022-107, 2022
Manuscript not accepted for further review
Short summary
Short summary
This study proposed two new concepts for the previous Double Gamma Quantile Mapping (DGQM), such as the inclusion of flexible dividing point between two individual gamma functions and the use of more probability distributions. As a result, F-DDQM method performed the better bias correction for the GCMs very clearly. This new F-DDQM method can be also applied to the various fields such as the use of satellite climate data, reanalysis climate data and spatial downscaling or interpolation.
Na Yang, Jianyun Zhang, Jiufu Liu, Guodong Liu, Aimin Liao, and Guoqing Wang
Proc. IAHS, 383, 99–110, https://doi.org/10.5194/piahs-383-99-2020, https://doi.org/10.5194/piahs-383-99-2020, 2020
Jie Wang, Jianyun Zhang, Guoqing Wang, Xiaomeng Song, Xiaoying Yang, and Yueyang Wang
Proc. IAHS, 383, 213–222, https://doi.org/10.5194/piahs-383-213-2020, https://doi.org/10.5194/piahs-383-213-2020, 2020
Kamal Ahmed, Dhanapala A. Sachindra, Shamsuddin Shahid, Mehmet C. Demirel, and Eun-Sung Chung
Hydrol. Earth Syst. Sci., 23, 4803–4824, https://doi.org/10.5194/hess-23-4803-2019, https://doi.org/10.5194/hess-23-4803-2019, 2019
Short summary
Short summary
This study evaluated the performance of 36 CMIP5 GCMs in simulating seasonal precipitation and maximum and minimum temperature over Pakistan using spatial metrics (SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency) for the period 1961–2005. NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 were identified as the most suitable GCMs for simulating all three climate variables over Pakistan.
Pengcheng Xu, Dong Wang, Vijay P. Singh, Yuankun Wang, Jichun Wu, Huayu Lu, Lachun Wang, Jiufu Liu, and Jianyun Zhang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-358, https://doi.org/10.5194/hess-2019-358, 2019
Revised manuscript not accepted
Short summary
Short summary
In this study, a multivariate nonstationary risk analysis of annual extreme rainfall events, extracted from daily precipitation data observed at six meteorological stations in Haihe River basin, China, was done in three phases: (1) Several statistical tests, were applied to both the marginal distributions and the dependence structures to decipher different forms of nonstationarity; (2) Time-dependent copulas were adopted to model the distribution structure.
Kamal Ahmed, Shamsuddin Shahid, Xiaojun Wang, Nadeem Nawaz, and Najeebullah Khan
Hydrol. Earth Syst. Sci., 23, 3081–3096, https://doi.org/10.5194/hess-23-3081-2019, https://doi.org/10.5194/hess-23-3081-2019, 2019
Short summary
Short summary
The long-term changes (1901–2016) in annual and seasonal aridity in Pakistan and its causes are analyzed in this paper. Gauge-based gridded precipitation and PET data are used to show the spatial and temporal patterns of the changes in aridity over the diverse climate of the country. The present study suggests that the relative influence of precipitation and temperature on aridity determines its trends in the context of climate change.
Wenqing Shi, Qiuwen Chen, Jianyun Zhang, Cheng Chen, Yuchen Chen, Yuyu Ji, Juhua Yu, and Bryce R. Van Dam
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-380, https://doi.org/10.5194/bg-2018-380, 2018
Preprint withdrawn
Short summary
Short summary
As the second most important greenhouse gas, the production of methane undermines the green credentials of hydropower. We investigated methane emissions from a reservoir island and discovered self-mitigation of methane emissions in a ring-like zone around the island edge. Understanding the self-mitigation of methane emissions in dammed rivers will help to screen effective strategies for lessening the global warming effects of hydropower systems.
Cited articles
Babel, M. S., Maporn, N., and Shinde, V. R.: Incorporating Future Climatic and
Socioeconomic Variables in Water Demand Forecasting: A Case Study in
Bangkok, Water Resour. Manage., 28, 2049–2062, 2014.
Blokker, E. J. M., Vreeburg, J. H. G., and van Dijk, J. C.: Simulating residential water
demand with a stochastic end-use model, J. Water Res. Pl.-ASCE, 136, 19–26,
2010.
Browne, A. L., Medd, W., and Anderson, B: Developing Novel Approaches to Tracking
Domestic Water Demand Under Uncertainty – A Reflection on the “Up Scaling”
of Social Science Approaches in the United Kingdom, Water Resour.
Manage., 27, 1013, https://doi.org/10.1007/s11269-012-0117-y, 2013.
Dursun, S.: Effect of Global Climate Change on Water Balance of
Beyşehir Lake (Konya – Turkey), BALWOIS 2010, Ohrid, Republic of
Macedonia, 25–29 May 2010.
Froelich, W. and Magiera, E.: Forecasting Domestic Water Consumption Using
Bayesian Model, Intelligent Decision Technologies 2016, 337–346, 2016.
HRCC: Haihe River water resources bulletin, Haihe River
Conservancy Commission, Tianjin, 2001–2012.
Jakimavičius, D. and Kriaučiūnienė, J.: The climate change
impact on the water balance of the Curonian Lagoon, Water Resour., 40,
120–132, 2013.
Karamouz, M., Yazdi, M. S. S., Ahmadi, B., and Zahraie, B.: A system dynamics approach
to economic assessment of water supply and demand strategies. Paper presented
at the World Environmental and Water Resources Congress 2011: Bearing
Knowledge for Sustainability, Proceedings of the 2011 World Environmental
and Water Resources Congress, 1194–1203, 2011.
Liu, B. and Speed, R.: Water resources management in the People's Republic of
China, Int. J. Water Resour. Dev., 25, 193–208, 2009.
MWR: China Water Resources Bulletin, Ministry of Water Resources
(MWR), Beijing, 2000–2012.
Price, J. I., Chermak, J. M., and Felardo, J.: Low-flow appliances and household
water demand: an evaluation of demand-side management policy in Albuquerque,
New Mexico, J. Environ. Manage., 133, 37–44, 2014.
Protopapas, A. L., Katchamart, S., and Platonova, A.: Weather effects on daily
water use in New York City, J. Hydrol. Eng., 5, 332–338, 2000.
Sebri, M.: A meta-analysis of residential water demand studies, Environ.
Dev. Sustain., 16, 499–520, 2014.
Shahid, S., Wang, X.-J., Harun, S. B., Shamsudin, S. B., Ismail, T., and Minhans, A.:
Climate variability and changes in the major cities of Bangladesh:
observations, possible impacts and adaptation, Reg. Environ. Change,
16, 459–471, 2016.
Slavíková, L., Malý, V., Rost, M., Petružela, L., and Vojáček,
O.: Impacts of Climate Variables on Residential Water Consumption in the
Czech Republic, Water Resour. Manage., 27, 365–379, 2013.
Walker, D., Creaco, E., Vamvakeridou-Lyroudia, L., Farmani, R., Kapelan, Z., and Savic,
D.: Forecasting Domestic Water Consumption from Smart Meter Readings Using
Statistical Methods and Artificial Neural Networks, Procedia Engineering,
119, 1419–1428, 2016.
Wang, X.-J., Zhang, J.-Y., Wang, J.-H., He, R.-M., ElMahdi, A., Liu, J.-H., Wang, X.-G., King, D.,
and Shahid, S.: Climate change and water resources management in Tuwei river
basin of Northwest China, Mitigation and adaptation strategies for global
change, 19, 107–120, 2014.
Wang, X.-J., Zhang, J.-Y., Yang, Z., Shahid, S., He, R.-M., Xia, X.-H., and Liu, J.-H.:
Historic water consumptions and future management strategies for Haihe River
basin of Northern China, Mitigation and adaptation strategies for global
change, 20, 371–387, 2015.
Wang, X.-J., Zhang, J.-Y., Shahid, S., Guan, E., Wu, Y., Gao, J., and He, R.-M.: Adaptation
to climate change impacts on water demand. Mitigation and Adaptation
Strategies for Global Change, 21, 81–99, 2016.
Wang, X.-J., Zhang, J.-Y., Shahid, S., Xie, W., Du, C.-Y., Shang, X.-C., and Zhang,
X.: Modeling domestic water demand in Huaihe River Basin of China under climate
change and population dynamics, Environment, Development and Sustainability,
online first, 1–14, https://doi.org/10.1007/s10668-017-9919-7, 2017a.
Wang, X.-J., Zhang, J.-Y., Shahid, S., Oyang, R.-L., Guan, T.-S., Xue, J.-G., and Zhang, X.:
Impacts of climate variability and changes on domestic water use in the
Yellow River Basin of China, Mitigation and Adaptation Strategies for Global
Change, 22, 595–608, 2017b.
Zachariadis, T.: Residential water scarcity in Cyprus: impact of climate
change and policy options, Water, 2, 788–814, 2010.
Zhang, H.: Strategic study for water management in China, Southeast
University Press, Nanjing, 2005.
Zhou, M.: Residential Water Demand in China: Applications of Double-Log
Model and EDM System, The Southern Agricultural Economics Association's 2016
Annual Meeting, San Antonio, Texas, 6–9 February 2016.
Short summary
A statistical model has been developed for forecasting domestic water demand in Haihe river basin of China due to population growth, technological advances and climate change. Historical records of domestic water use, climate, population and urbanization are used for the development of model. An ensemble of seven general circulation models (GCMs) namely, BCC-CSM1-1, BNU-ESM, CNRM-CM5, GISS-E2-R, MIROC-ESM, PI-ESM-LR, MRI-CGCM3 were used for the projection of climate and the changes in water demand.
A statistical model has been developed for forecasting domestic water demand in Haihe river...
