Assessment of the spatial-temporal distribution of groundwater recharge in data-scarce large-scale African river basin

被引:16
|
作者
Gelebo, Ayano Hirbo [1 ,2 ]
Kasiviswanathan, K. S. [1 ]
Khare, Deepak [1 ]
机构
[1] Indian Inst Technol Roorkee, Dept Water Resources Dev & Management, Roorkee 247667, Uttar Pradesh, India
[2] Arba Minch Water Technol Inst, Fac Water Resources & Irrigat Engn, Arba Minch, SNNPR, Ethiopia
关键词
Groundwater recharge; Model parameter sensitivity; WetSpass-; M; Omo river basin; WATER-BALANCE; WETSPASS MODEL; GEBA BASIN; RESOURCES; VARIABILITY; TOOLBOX; NETWORK; TIGRAY;
D O I
10.1007/s10661-022-09778-z
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The systematic assessment of spatial and temporal distribution of groundwater recharge (GWR) is crucial for the sustainable management of the water resources systems, especially in large-scale river basins. This helps in identifying critical zones in which GWR largely varies and thus leads to negative consequences. However, such analyses might not be possible when the models require detailed hydro-climate and hydrogeological data in data-scarce regions. Hence, this calls for alternate suitable modeling approaches that are applicable with the limited data and, however, includes the detailed assessment of the spatial-temporal distribution of different water balance components especially the GWR component. This paper aimed at investigating the spatial and temporal distribution of the GWR at monthly, seasonal and annual scales using the WetSpass-M physically distributed hydrological model, which is not requiring the detailed catchment information. In addition, the study conducted the sensitivity analysis of model parameters to assess the significant variation of GWR. The large-scale river basins such as the Omo river basin, Ethiopia, were chosen to demonstrate the potential of the WetSpass-M model under limited data conditions. From the modeling results, it was found that the maximum average monthly GWR of 13.4 mm occurs in July. The estimated average seasonal GWR is 32.5 mm/yr and 47.6 mm/yr in the summer and winter seasons, respectively. Further, it was found that GWR is highly sensitive to the parameter such as average rainfall intensity factor.
引用
收藏
页数:17
相关论文
共 50 条
  • [31] Identifying Dynamic Changes with Noisy Labels in Spatial-temporal Data: A Study on Large-scale Water Monitoring Application
    Jia, Xiaowei
    Chen, Xi
    Karpatne, Anuj
    Kumar, Vipin
    2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2016, : 1328 - 1333
  • [32] Transfer learning framework for streamflow prediction in large-scale transboundary catchments:Sensitivity analysis and applicability in data-scarce basins
    MA Kai
    SHEN Chaopeng
    XU Ziyue
    HE Daming
    Journal of Geographical Sciences, 2024, 34 (05) : 963 - 988
  • [33] Modeling the multiple time scale response of hydrological drought to climate change in the data-scarce inland river basin of Northwest China
    Zhu, Nina
    Xu, Jianhua
    Wang, Chong
    Chen, Zhongsheng
    Luo, Yang
    ARABIAN JOURNAL OF GEOSCIENCES, 2019, 12 (07)
  • [34] Spatial-temporal evolution of the distribution pattern of river systems in the plain river network region of the Taihu Basin, China
    Deng, Xiaojun
    Xu, Youpeng
    Han, Longfei
    Yang, Mingnan
    Yang, Liu
    Song, Song
    Li, Guang
    Wang, Yuefeng
    QUATERNARY INTERNATIONAL, 2016, 392 : 178 - 186
  • [35] The large-scale crowd analysis based on sparse spatial-temporal local binary pattern
    Hua Yang
    Yihua Cao
    Hang Su
    Yawen Fan
    Shibao Zheng
    Multimedia Tools and Applications, 2014, 73 : 41 - 60
  • [36] Metrics Assessment and Streamflow Modeling under Changing Climate in a Data-Scarce Heterogeneous Region: A Case Study of the Kabul River Basin
    Akhtar, Fazlullah
    Borgemeister, Christian
    Tischbein, Bernhard
    Awan, Usman Khalid
    WATER, 2022, 14 (11)
  • [37] The large-scale crowd analysis based on sparse spatial-temporal local binary pattern
    Yang, Hua
    Cao, Yihua
    Su, Hang
    Fan, Yawen
    Zheng, Shibao
    MULTIMEDIA TOOLS AND APPLICATIONS, 2014, 73 (01) : 41 - 60
  • [38] Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins
    Ma, Kai
    Shen, Chaopeng
    Xu, Ziyue
    He, Daming
    JOURNAL OF GEOGRAPHICAL SCIENCES, 2024, 34 (05) : 963 - 984
  • [39] Spatial-Temporal Distribution Analysis of Industrial Heat Sources in the US with Geocoded, Tree-Based, Large-Scale Clustering
    Ma, Yan
    Ma, Caihong
    Liu, Peng
    Yang, Jin
    Wang, Yuzhu
    Zhu, Yueqin
    Du, Xiaoping
    REMOTE SENSING, 2020, 12 (18)
  • [40] Trajectory analysis of land use and land cover maps to improve spatial-temporal patterns, and impact assessment on groundwater recharge
    Zomlot, Z.
    Verbeiren, B.
    Huysmans, M.
    Batelaan, O.
    JOURNAL OF HYDROLOGY, 2017, 554 : 558 - 569