A Framework to Assess the Reliability of a Multipurpose Reservoir under Uncertainty in Land Use
2. Materials and Methods
2.1. The Framework
2.1.1. SWAT Modelling and Uncertainties
2.1.2. Optimisation Tool
- Step 1: Determine the following key factors for the optimisation tool: (i) Objective function (Equation (1)), (ii) Decision variables (water releases), and (iii) Constraints (Equations (2)–(7)). Step 2: Set up a deterministic optimisation model using monthly deterministic input data.
- Step 3: Replace monthly deterministic input data including monthly inflows, sediment values, and water demands by monthly probability distributions to include uncertainties.
- Step 4: Generate a number of random possible combinations (n = 180 was chosen for this study because it balances computational cost vs. accuracy in obtaining a reliable solution) of water inflow, sediment inflow and water demand for the optimisation model using the Latin Hypercube sampling method in @RISK. Run the genetic optimisation algorithm in @RISK for each possible combination and evaluate the possible range of reservoir reliabilities.
- Penalty function when reservoir storage is greater than active storage (P1)
- Penalty function when water release is lower than minimum allowable release (P2)
2.1.3. Model Performance Indicators
2.2. Nuicoc Watershed Case Study
2.2.1. Watershed and Reservoir
2.2.2. Data Sources and Pre-Processing for the Case Study
2.2.3. Case Study Calibration and Validation
2.2.4. Land-Use Change Scenarios
- The baseline map (BL) using the land-use map in 2014.
- Scenario 1 (S1) shows a slight decrease in forest area, by 5%. The paddy and rural area decline considerably due to increase in the urban area, while the urban area will increase up to 8%.
- Scenario 2 (S2) will witness a significant reduction in the forest area, by 8% while the urban and agricultural area will rise to over 10% and 4% respectively.
- Scenario 3 (S3) is an extreme scenario with the highest urban and agricultural area and the lowest forest area.
2.2.5. Accounting for Uncertainties in Inflows and Water Demands
- Uncertainty in future potential land-use scenariosThe main drivers for land-use changes are urbanisation and conversion from forest to agricultural area. The study considers three possible scenarios (S1, S2, and S3) in the watershed.
- Uncertainty of water inflows to the reservoir due to uncertainty in parametersMonthly inflows generated by SUFI2 in SWAT-CUP within 95PPU vary based on their frequency distributions. To simplify the quantification of inflow uncertainties, we assumed that monthly inflows to the reservoir are independent and uniformly distributed within an area bounded by the lower values and upper values of 95PPU. The combination of the random monthly inflows over the simulation period creates a unique inflow time series that was fed to the optimisation tool.
- Uncertainty of demandsAs this study only considers the impact of land-use changes on reservoir reliability, the climate data and water demands were kept constant. The uncertainty in monthly water demands during the operational timeframe is considered. Based on the summary of the historical data (Figure 5), the monthly demands from urban use, agriculture and downstream flow requirements during the operational period are assumed to follow uniform (max, min) and triangular distributions (max, median, min), respectively. The combination of the random monthly water demand generates a water demand time series for the optimisation tool.
- Uncertainty in sediment inflowsParameter uncertainty will in turn result in uncertainty in streamflow, which is described by the 95% prediction uncertainty (95PPU) in SWAT-CUP. This also leads to uncertainty in sediment inflows, which is also expressed as 95PPU. To quantify the uncertainty of water inflow in simulations, a uniform distribution was applied for each month to generate monthly water inflows randomly. However, this approach cannot be used for sediment inflows as it depends on water inflows. It is therefore assumed that the relation between water inflows and sediment inflows is linear. The study also assumes that, at the beginning of simulation, sediment will be distributed equally on the bottom of the reservoir within the active storage since the reservoir dead storage has been full after 40 years of operation, from 1982; and that, the inclusion of sediment will not affect the reservoir surface.
3.1. SWAT Model Calibration and Validation for Water Inflows and Evapotranspiration
3.2. SWAT Model Output
3.3. The Impact of Land-Use Changes on Performance Indicators of the Reservoir
4.1. The Modelling Framework
4.2. Impact of the Change in Urban Areas and Conversion from Forest to Agricultural Areas on Performance Indicators
4.3. Impact of Spatial Distribution of Land-Uses
Data Availability Statement
Conflicts of Interest
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|Digital Elevation Model (DEM) (30 m × 30 m)||The Shuttle Radar Topography Mission database |
|Land-use||2004, 2014||Thainguyen Department of Resources and Environment, 2018||2/2018|
|Soil map and properties||FAO |
|Rainfall||2002–2013||Vietbac Centre for Hydrology and Meteorology, 2018||2/2018|
|Calculated inflow||2004–2013||Thainguyen Irrigation Management Company, 2018||2/2018|
|Other climate data||1979–2013||Climate Forecast System Reanalysis |
|Growth phases of crops||Handbook of plantings |
|Modelling Period||Land-Use Map||Evaluation Statistics for Model Uncertainty||The Best Simulation|
|Water Inflows in Wet Seasons (m3/s)||Water Inflows in Dry Seasons|
|n-Value||Range of Reliability|
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Nguyen, A.; Cochrane, T.A.; Pahlow, M. A Framework to Assess the Reliability of a Multipurpose Reservoir under Uncertainty in Land Use. Water 2021, 13, 287. https://doi.org/10.3390/w13030287
Nguyen A, Cochrane TA, Pahlow M. A Framework to Assess the Reliability of a Multipurpose Reservoir under Uncertainty in Land Use. Water. 2021; 13(3):287. https://doi.org/10.3390/w13030287Chicago/Turabian Style
Nguyen, Anh, Thomas A. Cochrane, and Markus Pahlow. 2021. "A Framework to Assess the Reliability of a Multipurpose Reservoir under Uncertainty in Land Use" Water 13, no. 3: 287. https://doi.org/10.3390/w13030287