Assessment of Environmental Water Requirement Allocation in Anthropogenic Rivers with a Hydropower Dam Using Hydrologically Based Methods—Case Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area Description
2.2. Data Description
2.3. Methods of Data Analysis
Method | Description | Advantages | Disadvantages | Sources |
---|---|---|---|---|
Tennant and Modified Tennant | -Also known as the Montana Method -Based on testing on 11 rivers -EWR values are recommended based on percentages of MAF | -Simple -Low-cost -Fast -Requires low data -No field works -Adaptability to wet and dry seasons in each region | -Considers only the physical suitability of the habitat -Highly dependent on degree of professional judgement -Not applicable to high gradient rivers (>1% slope) -Not suitable for rivers with varying flow regime -the percentages need to be re-calculated for each region | [26,46,66,67,68] |
Tessman | -Considers flow variations on a monthly basis | -Easy to implement -Applicable for rivers with varying hydrological regimes -No field work | -Low accuracy in low-flow periods -Highly dependent on degree of professional judgement | [69,70] |
Smakhtin | -Uses a combination of HFR as flood events and LFR as minimum water requirements for fish and other aquatic organisms as a percentage of MAF. | -Easy to implement -Low-cost -No field works | -Highly dependent on degree of professional judgement | [70,71] |
Flow duration indices | -Express the percent of time definite flows will be corresponded or exceeded over different time scales -Shows the full range of river flows from low flows to floods as well as the relationship between flow magnitude and frequency. | -Fast -Easy to implement -Inexpensive -Better fits to different geographical regions -Daily, weekly, or monthly discharge data can be used -Provide stable hydrologic conditions that would therefore be beneficial to aquatic habitat | -Low reliability especially during low flows -Highly dependent on degree of professional judgement -Harmful effects on stream biota by application of high flows | [68,71,72] |
Low-flow indices | -Based on more than 50% exceedance obtained from FDC for daily discharge data -Involves a statistical low-flow frequency analysis of the minimum mean daily flow during a given period of 7 days within a 2- or 10-year return period | -Simple -Low cost -Habitat preservation during low-flow seasons -Maintaining water quality under the effect of wastewater | -Low reliability -Not recommended for the variable flow regimes | [62,70,72,73] |
FDC shifting | -Uses monthly flow data -Includes four subsequent steps -An environmental management class should be defined based on ecological conditions and management perspective | -Better fits to different geographical regions -Desktop method -Can be implemented using a free software package | -Highly dependent on degree of professional judgement | [64] |
DRM | -Defines four classes of environmental management -Assumes that EWR increases with increasing base flow contribution and decreases with increasing flow variability. | -Based on monthly flow data which are more readily available or accessible in developing countries. | -Highly dependent on degree of professional judgement -parameter values must be modified for each region | [64,74] |
RVA | -Based on the time series of natural daily flows in a given area, thirty-two hydrological parameters, which reflect different aspects of flow variability including magnitude, duration, frequency and timing are estimated. -Calculates EWR as a range between the 25th and 75th monthly flow percentile using non-parametric analyses or as a range of mean monthly flow using parametric analyses | -Can be implemented as a desktop tool. -Gauged or modelled daily flows can be used | -The number of parameters used is too large | [74,75,76,77] |
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Description of Flows | Recommended EWR (% of MAF) | Recommended EWR (m3/s) | ||
---|---|---|---|---|
October–March | April–September | October–March | April–September | |
Flushing or maximum | 200 | 200 | 52.62 | 52.62 |
Optimum range | 60–100 | 60–100 | 15.79–26.31 | 15.79–26.31 |
Outstanding | 40 | 60 | 10.52 | 15.79 |
Excellent | 30 | 50 | 7.89 | 13.16 |
Good | 20 | 40 | 5.26 | 10.52 |
Fair or degrading | 10 | 30 | 2.63 | 7.89 |
Poor or minimum | 10 | 10 | 2.63 | 2.63 |
Severe degradation | <10 | <10 | <2.63 | <2.63 |
Month | MMF | MAF | Tenant | Tessman | FDC Shifting | DRM | Flow Duration Indices | RVA | |||
---|---|---|---|---|---|---|---|---|---|---|---|
Traditional | Modified | Q50 | Q90 | Q95 | |||||||
October | 10.13 | 26.31 | 2.63 | 2.63 | 10.13 | 5.00 | 5.34 | 8.95 | 6.31 | 6.07 | 8.10 |
November | 12.96 | 26.31 | 2.63 | 2.63 | 10.53 | 6.30 | 5.75 | 11.01 | 6.68 | 6.28 | 7.55 |
December | 24.32 | 26.31 | 2.63 | 2.63 | 10.53 | 11.00 | 6.42 | 16.15 | 10.78 | 10.38 | 10.19 |
January | 33.63 | 26.31 | 2.63 | 2.63 | 13.45 | 13.90 | 7.60 | 20.06 | 12.48 | 11.95 | 14.15 |
February | 42.07 | 26.31 | 2.63 | 7.89 | 16.83 | 15.70 | 8.38 | 33.82 | 15.51 | 13.83 | 15.36 |
March | 56.76 | 26.31 | 2.63 | 7.89 | 22.71 | 19.50 | 9.97 | 45.48 | 20.5 | 18.20 | 22.50 |
April | 57.02 | 26.31 | 7.89 | 7.89 | 22.81 | 19.50 | 11.88 | 46.95 | 24.66 | 22.61 | 31.64 |
May | 30.43 | 26.31 | 7.89 | 7.89 | 12.17 | 12.20 | 9.50 | 24.71 | 12.02 | 10.85 | 31.94 |
June | 15.81 | 26.31 | 7.89 | 2.63 | 10.53 | 8.00 | 8.14 | 12.92 | 6.50 | 5.91 | 15.05 |
July | 12.03 | 26.31 | 7.89 | 2.63 | 10.53 | 6.30 | 4.30 | 10.28 | 6.40 | 6.04 | 10.71 |
August | 10.60 | 26.31 | 7.89 | 2.63 | 10.53 | 5.40 | 4.11 | 9.22 | 6.17 | 5.89 | 9.24 |
September | 9.98 | 26.31 | 7.89 | 2.63 | 9.98 | 5.00 | 3.81 | 8.64 | 5.67 | 5.40 | 8.66 |
MAF | 10% MAF | 20% MAF | 30% MAF | LFR | HFR | EWR |
---|---|---|---|---|---|---|
26.31 | 2.63 | 5.26 | 7.89 | 15.13 | 0 | 15.13 |
Indices | Q70 | Q75 | Q80 | Q85 | Q90 | Q95 |
---|---|---|---|---|---|---|
Flow (m3/s) | 18.41 | 17.51 | 16.66 | 15.87 | 15.13 | 14.42 |
Environmental Management Class | Class A | Class B | Class C | Class D | Class E | Class F |
---|---|---|---|---|---|---|
EWR (% of MAF) | 69.7 | 49.9 | 36.4 | 27.2 | 20.4 | 15.0 |
EWR (m3/s) | 18.3 | 13.1 | 9.6 | 7.2 | 5.4 | 3.9 |
Probability Distribution | Error (%) |
---|---|
Normal | 0.59 |
Log-normal | 0.80 |
Pearson type III | 0.51 |
Log-Pearson type III | 1.21 |
Gumbel | 0.65 |
7Q2 | 7Q10 | ||
---|---|---|---|
Flow (m3/s) | Percent of MAF (%) | Flow (m3/s) | Percent of MAF (%) |
7.2 | 27.3 | 10.7 | 40.6 |
Environmental Management Class | A | A/B | B | B/C | C | C/D | D |
---|---|---|---|---|---|---|---|
EWR (% of MAF) | 64.40 | 51.90 | 40.80 | 33.20 | 25.50 | 20.80 | 16.20 |
EWR (m3/s) | 16.94 | 13.65 | 10.73 | 8.73 | 6.71 | 5.47 | 4.26 |
Month | Before Dam Construction | After Dam Construction | RVA Boundaries | |||||
---|---|---|---|---|---|---|---|---|
Ave. | Min. | Max. | Ave. | Min. | Max. | Lower | Upper | |
October | 9.38 | 5.25 | 22.40 | 4.49 | 0.77 | 24.05 | 8.10 | 11.12 |
November | 9.30 | 4.90 | 22.40 | 3.02 | 1.64 | 23.90 | 7.55 | 10.72 |
December | 11.44 | 6.20 | 29.60 | 4.87 | 3.29 | 36.60 | 10.19 | 13.58 |
January | 16.00 | 6.90 | 71.00 | 5.42 | 2.66 | 20.50 | 14.15 | 21.19 |
February | 24.00 | 7.52 | 83.00 | 7.79 | 3.47 | 40.70 | 15.36 | 29.28 |
March | 33.10 | 12.75 | 113.70 | 18.95 | 4.08 | 57.10 | 22.50 | 48.56 |
April | 53.20 | 130 | 134.00 | 13.6 | 3.07 | 35.00 | 31.64 | 66.40 |
May | 42.90 | 8.38 | 108.00 | 13.95 | 3.39 | 43.40 | 31.94 | 53.72 |
June | 19.70 | 2.25 | 77.60 | 5.28 | 2.92 | 27.30 | 15.05 | 25.22 |
July | 11.92 | 3.40 | 43.50 | 5.22 | 1.00 | 14.35 | 10.71 | 16.02 |
August | 10.66 | 3.10 | 33.10 | 9.24 | 0.61 | 37.50 | 9.236 | 12.64 |
September | 9.88 | 3.40 | 22.70 | 6.38 | 0.11 | 10.70 | 8.66 | 11.39 |
Month | Tenant | Tessman | FDC Shifting | DRM | Flow Duration Indices | RVA | DEWR | Average | |||
---|---|---|---|---|---|---|---|---|---|---|---|
Trad. | Modi. | Q50 | Q90 | Q95 | |||||||
October | 61.21 | 61.21 | −49.41 | 26.03 | 21.24 | −32.01 | 6.93 | 10.47 | −19.47 | 33.33 | 9.58 |
November | 71.29 | 71.29 | −14.96 | 31.43 | 37.23 | −20.20 | 27.07 | 31.44 | 17.58 | 33.33 | 28.02 |
December | 72.40 | 72.40 | −10.49 | −15.52 | 32.63 | −69.46 | −13.12 | −8.92 | −6.93 | 66.67 | 5.89 |
January | 78.91 | 78.91 | −7.86 | −11.57 | 39.05 | −60.87 | −0.08 | 4.17 | −13.47 | 55.56 | 11.91 |
February | 87.97 | 63.92 | 23.05 | 28.26 | 61.68 | −54.64 | 29.08 | 36.76 | 29.77 | 22.22 | 33.98 |
March | 89.37 | 68.10 | 8.17 | 21.20 | 59.68 | −83.91 | 17.10 | 26.41 | 9.02 | 22.22 | 23.90 |
April | 63.04 | 63.04 | −6.84 | 8.63 | 44.36 | −119.91 | −15.50 | −5.90 | −48.20 | 55.56 | −1.92 |
May | 39.35 | 39.35 | 6.46 | 6.13 | 26.98 | −89.93 | 7.61 | 16.60 | −145.50 | 22.22 | −10.33 |
June | −19.18 | 60.27 | −59.06 | −20.92 | −22.96 | −95.17 | 1.81 | 10.73 | −127.34 | 66.67 | −30.20 |
July | 6.29 | 68.76 | −25.06 | 24.99 | 48.93 | −22.09 | 23.99 | 28.27 | −27.20 | 33.33 | 14.10 |
August | −10.20 | 63.27 | −47.07 | 24.58 | 42.60 | −28.77 | 13.83 | 17.74 | −28.99 | 44.44 | 5.22 |
September | −40.64 | 53.12 | −77.90 | 11.05 | 32.09 | −54.01 | −1.07 | 3.74 | −54.37 | 55.56 | −14.22 |
Average | 41.65 | 63.64 | −21.75 | 11.19 | 35.29 | −60.91 | 8.14 | 14.29 | −34.59 |
Method | Description | EWR | EWR |
---|---|---|---|
(% of MAF) | (m3/s) | ||
Tennant | October–March | 10 | 2.63 |
April–September | 30 | 7.89 | |
Tennant Modified | February–May | 30 | 7.89 |
June–January | 10 | 2.63 | |
Tessman | 51 | 13.39 | |
Smakhtin | 58 | 15.13 | |
Flow duration indices | Q70 | 70 | 18.41 |
Q75 | 67 | 17.51 | |
Q80 | 63 | 16.66 | |
Q85 | 60 | 15.87 | |
Q90 | 58 | 15.13 | |
Q95 | 55 | 14.42 | |
Low-flow indices | 7Q10 | 40.6 | 10.65 |
7Q2 | 27.3 | 7.17 | |
FDC shifting | Class C | 36.4 | 9.60 |
DRM | Class C | 25.5 | 7.10 |
RVA | Low RVA limit | 59 | 15.42 |
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Hamidifar, H.; Akbari, F.; Rowiński, P.M. Assessment of Environmental Water Requirement Allocation in Anthropogenic Rivers with a Hydropower Dam Using Hydrologically Based Methods—Case Study. Water 2022, 14, 893. https://doi.org/10.3390/w14060893
Hamidifar H, Akbari F, Rowiński PM. Assessment of Environmental Water Requirement Allocation in Anthropogenic Rivers with a Hydropower Dam Using Hydrologically Based Methods—Case Study. Water. 2022; 14(6):893. https://doi.org/10.3390/w14060893
Chicago/Turabian StyleHamidifar, Hossein, Farzaneh Akbari, and Paweł M. Rowiński. 2022. "Assessment of Environmental Water Requirement Allocation in Anthropogenic Rivers with a Hydropower Dam Using Hydrologically Based Methods—Case Study" Water 14, no. 6: 893. https://doi.org/10.3390/w14060893
APA StyleHamidifar, H., Akbari, F., & Rowiński, P. M. (2022). Assessment of Environmental Water Requirement Allocation in Anthropogenic Rivers with a Hydropower Dam Using Hydrologically Based Methods—Case Study. Water, 14(6), 893. https://doi.org/10.3390/w14060893