scholarly journals Numerical modelling - a key tool to support water management decisions in Ontario

2018 ◽  
Author(s):  
S Holysh ◽  
M Marchildon ◽  
P Martin ◽  
D Kassenaar ◽  
C Neville ◽  
...  
Keyword(s):  
Water Management ◽  
Numerical Modelling ◽  
Management Decisions

Stochastic hydrology-how much hydrology does it contain and how much does it matter?

1984 ◽  
Vol 16 (1) ◽  
pp. 19-19
Author(s):  
V. Klemeš
Keyword(s):  
Water Management ◽  
Probability Theory ◽  
Stochastic Models ◽  
Mathematical Statistics ◽  
Stochastic Hydrology ◽  
Management Decisions ◽  
Quality Of Water ◽  
The Way

Most of what is routinely labeled ‘stochastic hydrology’ does not contain any hydrology at all and could be more properly identified as the fitting of stochastic models to samples of data of hydrologic origin. T0 engage in this enterprise, no hydrologic knowledge is necessary, nor do the results contribute to hydrologic knowledge. Moreover, the bulk of the current stochastic hydrology does not appreciably enhance the quality of water management decisions-an aim which provided the original impetus for its development. It seems that the mainstream of stochastic hydrology follows in the steps of ‘dam theory’, the only difference being that while the latter has become a self-contained branch of pure probability theory, the former is on the way to becoming a branch of pure mathematical statistics.

scholarly journals Are General Circulation Models Ready for Operational Streamflow Forecasting for Water Management in the Ganges and Brahmaputra River Basins?

2015 ◽  
Vol 17 (1) ◽  
pp. 195-210 ◽  
Author(s):  
Safat Sikder ◽  
Xiaodong Chen ◽  
Faisal Hossain ◽  
Jason B. Roberts ◽  
Franklin Robertson ◽  
...  
Keyword(s):  
Water Management ◽  
River Basins ◽  
Hydrologic Model ◽  
Streamflow Forecasting ◽  
Management Decisions ◽  
Brahmaputra River ◽  
Ganges Basin ◽  
Monthly Scale ◽  
Brahmaputra Basin ◽  
The Ganges

Abstract This study asks the question of whether GCMs are ready to be operationalized for streamflow forecasting in South Asian river basins, and if so, at what temporal scales and for which water management decisions are they likely to be relevant? The authors focused on the Ganges, Brahmaputra, and Meghna basins for which there is a gridded hydrologic model calibrated for the 2002–10 period. The North American Multimodel Ensemble (NMME) suite of eight GCM hindcasts was applied to generate precipitation forecasts for each month of the 1982–2012 (30 year) period at up to 6 months of lead time, which were then downscaled according to the bias-corrected statistical downscaling (BCSD) procedure to daily time steps. A global retrospective forcing dataset was used for this downscaling procedure. The study clearly revealed that a regionally consistent forcing for BCSD, which is currently unavailable for the region, is one of the primary conditions to realize reasonable skill in streamflow forecasting. In terms of relative RMSE (normalized by reference flow obtained from the global retrospective forcings used in downscaling), streamflow forecast uncertainty (RMSE) was found to be 38%–50% at monthly scale and 22%–35% at seasonal (3 monthly) scale. The Ganges River (regulated) experienced higher uncertainty than the Brahmaputra River (unregulated). In terms of anomaly correlation coefficient (ACC), the streamflow forecasting at seasonal (3 monthly) scale was found to have less uncertainty (>0.3) than at monthly scale (<0.25). The forecast skill in the Brahmaputra basin showed more improvement when the time horizon was aggregated from monthly to seasonal than the Ganges basin. Finally, the skill assessment for the individual seasons revealed that the flow forecasting using NMME data had less uncertainty during monsoon season (July–September) in the Brahmaputra basin and in postmonsoon season (October–December) in the Ganges basin. Overall, the study indicated that GCMs can have value for management decisions only at seasonal or annual water balance applications at best if appropriate historical forcings are used in downscaling. The take-home message of this study is that GCMs are not yet ready for prime-time operationalization for a wide variety of multiscale water management decisions for the Ganges and Brahmaputra River basins.

scholarly journals Interactions between flow and vegetation: Translating knowledge from academic research to daily water management

2018 ◽  
Vol 40 ◽  
pp. 01001
Author(s):  
Ellis Penning
Keyword(s):  
Water Management ◽  
Academic Research ◽  
Management Decisions ◽  
Riverine Ecosystem ◽  
Profound Knowledge ◽  
Rivers And Streams ◽  
Daily Water ◽  
Morphological Processes

Rivers and streams cannot be viewed without the vegetation growing in and alongside it. The riverine ecosystem is strongly organized by the presence of plants in interaction with flow and morphological processes. This creates challenges for water management, as a profound knowledge of these interactions is needed when management decisions must be made. At the same time other aspects of water management, such as societal-economic demands, might compromise the depth at which these processes can be studied and incorporated in the daily management of these systems.

scholarly journals Informing Environmental Water Management Decisions: Using Conditional Probability Networks to Address the Information Needs of Planning and Implementation Cycles

2017 ◽  
Vol 61 (3) ◽  
pp. 347-357 ◽  
Author(s):  
Avril C. Horne ◽  
Joanna M. Szemis ◽  
J. Angus Webb ◽  
Simranjit Kaur ◽  
Michael J. Stewardson ◽  
...  
Keyword(s):  
Water Management ◽  
Conditional Probability ◽  
Information Needs ◽  
Environmental Water ◽  
Management Decisions
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