Articles | Volume 379
https://doi.org/10.5194/piahs-379-73-2018
© 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-379-73-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatiotemporal variability and assessment of drought in the Wei River basin of China
Siyang Cai
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China
Depeng Zuo
CORRESPONDING AUTHOR
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China
Zongxue Xu
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China
Xianming Han
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China
Xiaoxi Gao
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China
Related authors
Xiaoxi Gao, Depeng Zuo, Zongxue Xu, Siyang Cai, and Han Xianming
Proc. IAHS, 379, 159–167, https://doi.org/10.5194/piahs-379-159-2018, https://doi.org/10.5194/piahs-379-159-2018, 2018
Short summary
Short summary
The blue and green water resources in the upper Yellow River basin (UYRB) were evaluated by the SWAT model in this study. The results show that the average annual total amount of water resources in the UYRB was 140.5 billion m3, in which the blue water resources is 37.8 billion m3, and green water resources is 107.7 billion m3. The intra-annual variability, inter-annual variabilityand spatial distribution of the blue water and green water is relatively similar.
Xianming Han, Depeng Zuo, Zongxue Xu, Siyang Cai, and Xiaoxi Gao
Proc. IAHS, 379, 105–112, https://doi.org/10.5194/piahs-379-105-2018, https://doi.org/10.5194/piahs-379-105-2018, 2018
Short summary
Short summary
To further protect the ecology of the study area, remote sensing image technology is used to analyze the temporal and spatial distribution characteristics of vegetation in the Yarlung Zangbo River Basin by splicing the remote sensing image of a time series (from February 2000 to December 2016). It can be found that vegetation coverage is better in low elevation areas,vegetation change shows a weak sustainability and the vegetation growth is more affected by the temperature than the precipitation.
Hao Li, Baoying Shan, Liu Liu, Lei Wang, Akash Koppa, Feng Zhong, Dongfeng Li, Xuanxuan Wang, Wenfeng Liu, Xiuping Li, and Zongxue Xu
Hydrol. Earth Syst. Sci., 26, 6399–6412, https://doi.org/10.5194/hess-26-6399-2022, https://doi.org/10.5194/hess-26-6399-2022, 2022
Short summary
Short summary
This study examines changes in water yield by determining turning points in the direction of yield changes and highlights that regime shifts in historical water yield occurred in the Upper Brahmaputra River basin, both the climate and cryosphere affect the magnitude of water yield increases, climate determined the declining trends in water yield, and meltwater has the potential to alleviate the water shortage. A repository for all source files is made available.
Depeng Zuo, Guangyuan Kan, Hongquan Sun, Hongbin Zhang, and Ke Liang
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2021-344, https://doi.org/10.5194/nhess-2021-344, 2021
Publication in NHESS not foreseen
Short summary
Short summary
The Generalized Likelihood Uncertainty Estimation (GLUE) method has been thrived for decades, huge number of applications in the field of hydrological model have proved its effectiveness in uncertainty and parameter estimation. In this study, we developed a CPU-GPU hybrid computer cluster-based highly parallel large-scale GLUE method to improve its computational efficiency.
Xiaowan Liu, Zongxue Xu, Hong Yang, Xiuping Li, and Dingzhi Peng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-71, https://doi.org/10.5194/essd-2020-71, 2020
Revised manuscript not accepted
Short summary
Short summary
The retreat of glaciers over the QTP is intensifying. To understand changes in glaciers, the two inventories (RGI 4.0 and GIC-Ⅱ) provide potential, but glacier volumes are not convincing. The study recalculated and compared glacier volumes in RGI 4.0 and GIC-Ⅱ for the QTP. The results indicate the slope-dependent algorithm performs better than area-volume-based equations. The northern QTP has a larger degree of fragmentation. An obvious offset of glacier volumes in different aspects is observed.
Qi Chu, Zongxue Xu, Yiheng Chen, and Dawei Han
Hydrol. Earth Syst. Sci., 22, 3391–3407, https://doi.org/10.5194/hess-22-3391-2018, https://doi.org/10.5194/hess-22-3391-2018, 2018
Short summary
Short summary
The effects of WRF domain configurations and spin-up time on rainfall were evaluated at high temporal and spatial scales for simulating an extreme sub-daily heavy rainfall (SDHR) event. Both objective verification metrics and subjective verification were used to identify the likely best set of the configurations. Results show that re-evaluation of these WRF settings is of great importance in improving the accuracy and reliability of the rainfall simulations in the regional SDHR applications.
Xiaoxi Gao, Depeng Zuo, Zongxue Xu, Siyang Cai, and Han Xianming
Proc. IAHS, 379, 159–167, https://doi.org/10.5194/piahs-379-159-2018, https://doi.org/10.5194/piahs-379-159-2018, 2018
Short summary
Short summary
The blue and green water resources in the upper Yellow River basin (UYRB) were evaluated by the SWAT model in this study. The results show that the average annual total amount of water resources in the UYRB was 140.5 billion m3, in which the blue water resources is 37.8 billion m3, and green water resources is 107.7 billion m3. The intra-annual variability, inter-annual variabilityand spatial distribution of the blue water and green water is relatively similar.
Xianming Han, Depeng Zuo, Zongxue Xu, Siyang Cai, and Xiaoxi Gao
Proc. IAHS, 379, 105–112, https://doi.org/10.5194/piahs-379-105-2018, https://doi.org/10.5194/piahs-379-105-2018, 2018
Short summary
Short summary
To further protect the ecology of the study area, remote sensing image technology is used to analyze the temporal and spatial distribution characteristics of vegetation in the Yarlung Zangbo River Basin by splicing the remote sensing image of a time series (from February 2000 to December 2016). It can be found that vegetation coverage is better in low elevation areas,vegetation change shows a weak sustainability and the vegetation growth is more affected by the temperature than the precipitation.
Zongxue Xu, Dingzhi Peng, Wenchao Sun, Bo Pang, Depeng Zuo, Andreas Schumann, and Yangbo Chen
Proc. IAHS, 379, 463–464, https://doi.org/10.5194/piahs-379-463-2018, https://doi.org/10.5194/piahs-379-463-2018, 2018
Wenchao Sun, Yuanyuan Wang, Guoqiang Wang, Xingqi Cui, Jingshan Yu, Depeng Zuo, and Zongxue Xu
Hydrol. Earth Syst. Sci., 21, 251–265, https://doi.org/10.5194/hess-21-251-2017, https://doi.org/10.5194/hess-21-251-2017, 2017
Short summary
Short summary
The possibility of using a short period of streamflow data (less than one year) to calibrate a physically based distributed hydrological model is evaluated. Contrary to the common understanding of using data of several years, it is shown that only using data covering several months could calibrate the model effectively, which indicates that this approach is valuable for solving the calibration problem of such models in data-sparse basins.
Zongxue Xu and Gang Zhao
Proc. IAHS, 373, 7–12, https://doi.org/10.5194/piahs-373-7-2016, https://doi.org/10.5194/piahs-373-7-2016, 2016
Short summary
Short summary
China is undergoing rapid urbanization during the past decades. For example, the proportion of urban population in Beijing has increased from 57.6 % in 1980 to 86.3 % in 2013. Rapid urbanization has an adverse impact on the urban rainfall-runoff processes, which may result in the increase of urban flooding risk. In this study, the major purpose is to investigate the impact of land use/cover changes on hydrological processes and the flooding risk in Beijing.
Z. X. Xu and Q. Chu
Proc. IAHS, 369, 97–102, https://doi.org/10.5194/piahs-369-97-2015, https://doi.org/10.5194/piahs-369-97-2015, 2015
Short summary
Short summary
Three hourly assimilated precipitation series with 0.1 deg. are used to analyze the features and trends of extreme precipitation in Beijing, China. The results show that: (1) the local climate and topography are two main factors influencing the spatial distributions of precipitation; (2) areas with greater precipitation threshold may have shorter precipitation days; (3) extreme precipitation amount (48% of precipitation) concentrated on urban areas and mountain area within only 5 to 7 days.
Z. X. Xu, X. J. Yang, D. P. Zuo, Q. Chu, and W. F. Liu
Proc. IAHS, 369, 121–127, https://doi.org/10.5194/piahs-369-121-2015, https://doi.org/10.5194/piahs-369-121-2015, 2015
Short summary
Short summary
Spatiotemporal characteristics of extreme precipitation and temperature in Yunnan Province, China, were analyzed by using observed daily data at 28 meteorological stations from 1959-2013 in this study.Both maximum and minimum temperature showed significant increasing tendency while there was not obvious changes for precipitation.It was noted that extreme precipitation and temperature events occurred more frequently in central region where the risk of extreme climatic events was greater.
Cited articles
Abramowitz, M. and Stegun, A. I.: Handbook of mathematical functions with
formulas, graphs, and mathematical tables, Courier Dover Publications, New
York, USA, 1964.
Alam, M. M., Siwar, C., Toriman, M. E., and Molla, R. I.: Climate change
induced adaptation by paddy farmers in Malaysia, Mitig. Adapt. Strat. Gl.,
17, 173–186, 2012.
Chen, H. and Sun, J.: Changes in Drought Characteristics over China Using the
Standardized Precipitation Evapotranspiration Index, J. Climate, 28,
5430–5447, https://doi.org/10.1175/JCLI-D-14-00707.1, 2015.
CMDC (China Meteorological Data Service Center): Dataset of daily climate
data from China Ground Climate Data Monthly Dataset, available at:
http://data.cma.cn/, last access: 1 May 2018.
Cong, D., Zhao, S., Chen, C., and Duan, Z.: Characterization of droughts
during 2001-2014 based on remote sensing: A case study of Northeast China,
Ecol. Inform., 39, 56–67, https://doi.org/10.1016/j.ecoinf.2017.03.005, 2017.
Dai, A.: Drought under global warming: a review, Wires Clim. Change, 2,
45–65, https://doi.org/10.1002/wcc.81, 2011.
Food and Agriculture Organization of the United Nations: FAOSTAT, The
Statistics Division of FAO, Rome, Italy, 2014.
Karavitis, C. A., Alexandris, S., Tsesmelis, D. E., and Athanasopoulos, G.:
Application of the Standardized Precipitation Index (SPI) in Greece, WATER,
3, 87–805, https://doi.org/10.3390/w3030787, 2011.
Kogan, F. N.: Droughts of the late 1980s in the United States as derived from
NOAA polar-orbiting satellite data, B. Am. Meteorol. Soc., 76, 655–668,
1995.
Lei, T., Wu, J., Li, X., Geng, G., Shao, C., Zhou, K., Wang, Q., and Liu, L.:
A new framework for evaluating the impacts of drought on net primary
productivity of grassland, Sci. Total Environ., 536, 161–172,
https://doi.org/10.1016/j.scitotenv.2015.06.138, 2015.
Lei, Y., Zhang, H., Chen, F., and Zhang, L.: How rural land use management
facilitates drought risk adaptation in a changing climate – A case study in
arid northern China, Sci. Total Environ., 550, 192–199,
https://doi.org/10.1016/j.scitotenv.2016.01.098, 2016.
Liu, Z., Zhou, P., Zhang, F., Liu, X., and Chen, G.: Spatiotemporal
characteristics of dryness/wetness conditions across Qinghai Province,
Northwest China, Agr. Forest Meteorol., 182–183, 101–108,
https://doi.org/10.1016/j.agrformet.2013.05.013, 2013.
Long, D., Shen, Y., Sun, A., Hong, Y., Longuevergne, L., Yang, Y., Li, B.,
and Chen, L.: Drought and flood monitoring for a large karst plateau in
Southwest China using extended GRACE data, Remote Sens. Environ., 155,
145–160, https://doi.org/10.1016/j.rse.2014.08.006, 2014.
Martinez-Fernandez, J., Gonzalez-Zamora, A., Sanchez, N., Gumuzzio, A., and
Herrero-Jimenez, C. M.: Satellite soil moisture for agricultural drought
monitoring: Assessment of the SMOS derived Soil Water Deficit Index, Remote
Sens. Environ., 177, 277–286, https://doi.org/10.1016/j.rse.2016.02.064, 2016.
McKee, T. B., Doedken, N. J., and Kleist, J.: The relationship of drought
frequency and duration to time scales, Proceeding of the 8th Conference on
Applied Climatology, Anaheim, Calif., USA, 17–22 January 1993, Am. Meteorol.
Soc., 17, 179–182, 1993.
Mishra, A. K. and Singh, V. P.: A review of drought concepts, J. Hydrol.,
391, 202–216, 2010.
Moreira, E. E., Martins, D. S., and Pereira, L. S.: Assessing drought cycles
in SPI time series using a Fourier analysis, Nat. Hazards Earth Syst. Sci.,
15, 571–585, https://doi.org/10.5194/nhess-15-571-2015, 2015.
Oloruntade, A. J., Mohammad, T. A., Ghazali, A. H., and Wayayok, A.: Analysis
of meteorological and hydrological droughts in the Niger-South Basin,
Nigeria, Glob. Planet. Change, 155, 225–233,
https://doi.org/10.1016/j.gloplacha.2017.05.002, 2017.
Palmer, W. C.: Meteorological drought, US Department of Commerce, Weather
Bureau, Washington, DC, USA, 1965.
Rayne, S. and Forest, K.: Evidence for increasingly variable Palmer Drought
Severity Index in the United States since 1895, Sci. Total Environ., 544,
792–796, https://doi.org/10.1016/j.scitotenv.2015.11.167, 2016.
Rhee, J., Im, J., and Carbone, G. J.: Monitoring agricultural drought for
arid and humid regions using multi-sensor remote sensing data, Remote Sens.
Environ., 114, 2875–2887, 2010.
Solomon, S.: The Physical Science Basis. Working Group I Contribution to the
Fourth Assessment Report of the IPCC[J], edited by: Chang, I. C., 17 November
2007, Comp. Geom., 18, 95–123, 2007.
Steinemann, A.: Drought indicators and triggers: A stochastic approach to
evaluation, J. Am. Water Resour. As., 39, 1217–1233,
https://doi.org/10.1111/j.1752-1688.2003.tb03704.x, 2003.
Thom, H. C. S.: Some methods of climatological analysis, World Meteorological
Organization (WMO), Technical Note no. 82, Geneva, Switzerland, 20–22, 1966.
Unganai, L. S. and Kogan, F. N.: Drought monitoring and corn yield estimation
in Southern Africa from AVHRR data, Remote Sens. Environ., 63, 219–232,
https://doi.org/10.1016/S0034-4257(97)00132-6, 1998.
Vicente-Serrano, S. M., Begueria, S., and Lopez-Moreno J. I.: A Multi-scalar
Drought Index Sensitive to Global Warming: The Standardized Precipitation
Evapotranspiration Index, J. Climate, 23, 1696–1718,
https://doi.org/10.1175/2009JCLI2909.1, 2010.
Wilhite, D. A. and Glantz, M. H.: Understanding: the Drought Phenomenon: The
Role of Definitions, Water Int., 10, 111–120, https://doi.org/10.1080/02508068508686328,
1985.
Yang, B., Ma, S., Li, J., Liao, Y., Zhao, B., and Claudia, K.: Agriculture
drought monitoring in Dongting lake basin by MODIS data, 1st International
Conference on Agro-Geoinformatics, 2–4 August 2012, Shanghai, China, 2012.
Yu, M., Li, Q., Hayes, M. J., Svoboda, M. D., and Heim, R. R.: Are droughts
becoming more frequent or severe in China based on the Standardized
Precipitation Evapotranspiration Index: 1951–2010?, Int. J. Climatol., 34,
545–558, https://doi.org/10.1002/joc.3701, 2014.
Zarch, M. A. A., Sivakumar, B., and Sharma, A.: Droughts in a warming
climate: A global assessment of Standardized precipitation index (SPI) and
Reconnaissance drought index (RDI), J. Hydrol., 526, 183–195,
https://doi.org/10.1016/j.jhydrol.2014.09.071, 2015.
Zhai, L. and Feng, Q.: Spatial and temporal pattern of precipitation and
drought in Gansu Province, Northwest China, Nat. Hazards, 49, 1–24,
https://doi.org/10.1007/s11069-008-9274-y, 2009.
Zhang, A. and Jia, G.: Monitoring meteorological drought in semiarid regions
using multi-sensor microwave remote sensing data, Remote Sens. Environ., 134,
12–23, 2013.
Zhang, L., Jiao, W., Zhang, H., Huang, C., and Tong, Q.: Studying drought
phenomena in the Continental United States in 2011 and 2012 using various
drought indices, Remote Sens. Environ., 190, 96–106,
https://doi.org/10.1016/j.rse.2016.12.010, 2017.
Zuo, D., Cai, S., Xu, Z., Yang, X., and Li, F.: Spatiotemporal patterns of
drought at various time scales in Shandong Province of Eastern China, Theor.
Appl. Climatol., 2016, 1–14, https://doi.org/10.1007/s00704-016-1969-5, 2016.
Zou, L., Xia, J., and She, D.: Drought Characteristic Analysis Based on an
Improved PDSI in the Wei River Basin of China, WATER, 9, 1783,
https://doi.org/10.3390/w9030178, 2017.
Short summary
Drought is a natural and recurring feature of climate; occurring in virtually all climatic regimes. Wei River is of great importance in social and economic in China. The temporal and spatial variations of drought in the Wei River basin were investigated by calculating the drought indexes. Through analysis of the historical precipitation and temperature data, it was found that precipitation had a greater contribution to creating agricultural drought conditions than temperature.
Drought is a natural and recurring feature of climate; occurring in virtually all climatic...