Skip to content
Research Article Open access CC BY 4.0

A Review of Models for Evaluation of Climate Change Impact on Water Resources

A. W. Salami, A. A. Mohammed, D. O. Olukanni

Current Journal of Applied Science and Technology · pp. 226–237 · Published 1 Apr 2015

10.9734/BJAST/2015/16886

Abstract

The use of models to simulate or predict impact of climate change on water resources management is very vital due to continual increase in global warming which invariably affects most important natural resources in the environment. This paper provides an overview of the existing models used for evaluating climate change impact on water resources management. It also compares their relative advantages and drawbacks. It was found that no model can perform satisfactorily the assessment of climate change impact; hence it may be necessary to use one model to compliment the weakness of another. Global Circulation Model (GCM) is not easily accessible in developing countries due to sophistications and processes involved in running it. Moreso, the nature of available data and cost of acquiring it is high. The main advantage of Water Balance (WATBAL) model is that it can model climate change impact in water resources but its major drawback is that it requires many inputs of hydro-meteorological parameters. Regression and Artificial Neural Network (ANN) models are readily available and not too expensive. They can model climate change impact on water resources and hydropower operation. However, the drawback is that enormous data are required for ANN model calibration and operation. It is imperative therefore to anticipate and efficiently prepare for future water resources management and suggest necessary measures to mitigate the effect of climate change.

ANN BILAN data GCMs regression and WATBAL.

References (19)

  1. 1 River flow forecasting using artificial neural networks [DOI]
  2. 2 Assessment of impact of climate change on water resources: a long term analysis of the Great Lakes of North America [DOI]
  3. 3 Using MATLAB to Develop Artificial Neural Network Models for Predicting Global Solar Radiation in Al Ain City – UAE [DOI]
  4. 4 Impacts and Uncertainty of Climate Change on Water Resource Management of the Peribonka River System (Canada) [DOI]
  5. 5 Rainfall-runoff prediction based on artificial neural network (a case study: Jarahi watershed).
  6. 6 Hydrologic effects of climate change in the Yukon River Basin [DOI]
  7. 7 Climate change impacts and responses in the Philippines coastal sector [DOI]
  8. 8 Assessing Hydrological Impacts of Climate Change: Modeling Techniques and Challenges [DOI]
  9. 9 Heuristic Modelling of the Water Resources Management in the Guadalquivir River Basin, Southern Spain [DOI]
  10. 10 Climate change impacts and responses in the Philippines: water resources [DOI]
  11. 11 Simulation of Climate Change Impacts on Streamflow in the Bosten Lake Basin Using an Artificial Neural Network Model [DOI]
  12. 12 Artificial Neural Network Model for Rainfall–Runoff Relationship [DOI]
  13. 13 Using Neural Networks to Model the Impacts of Climate Change on Water Supplies [DOI]
  14. 14 Assessment of Impact of Hydropower Dams Reservoir Outflow on the Downstream River Flood Regime – Nigeria’s Experience [DOI]
  15. 15 Estimation of climate change impact on water resources by using Bilan water balance model [DOI]
  16. 16 Estimation of uncertainty in catchment-scale modeling of climate change impact (case of the Merkys river, Lithuania).
  17. 17 Fitting Probability Distribution Functions To Reservoir Inflow At Hydropower Dams In Nigeria
  18. 18 Identification of an appropriate low flow forecast model for the Meuse River [DOI]
  19. 19 Automatic calibration of the Watbal model [DOI]

Cited by 3

Comparing ANN and ARIMA model in predicting the discharge of River Opeki from 2010 to 2020

Olutoyin A. Fashae, Adeyemi O. Olusola, Ijeoma Ndubuisi · River Research and Applications · 2018

Showing 1 of 3 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

65

Page views

0

PDF downloads

37

Outbound clicks

3

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.