Impact of Climate Change on Reservoir Reliability: A Case of Bhumibol Dam in Ping River Basin, Thailand

Bhumibol Dam is the largest dam in the central region of Thailand and it serves as an important water resource. The dam’s operation relies on reservoir operating rules that were developed on the basis of the relationships among rainfall-inflow, water balance, and downstream water demand. However, due to climate change, changing rainfall variability is expected to render the reliability of the rule curves insecure. Therefore, this study investigated the impact of climate change on the reliability of the current reservoir operation rules of Bhumibol Dam. The future scenarios from 2000 to 2099 are based on EC-EARTH under RCP4.5 and RCP8.5 scenarios downscaled by RegCM4. MIKE11 HD was developed for the inflow simulation. The model generates the inflow well (R2=0.70). Generally, the trend of increasing inflow amounts is expected to continue in the dry seasons from 2000-2099, while large fluctuations of inflow are expected to be found in the wet seasons, reflecting high uncertainties. In the case of standard deviations, a larger deviation is predicted under the RCP8.5 scenario. For the reservoir’s operation in a climate change study, standard operating procedures were applied using historical release records to estimate daily reservoir release needed to serve downstream water demand in the future. It can be concluded that there is high risk of current reservoir operating rules towards the operation reliability under RCP4.5 (80% reliability), but the risk is lower under RCP8.5 (87% reliability) due to increased inflow amounts. The unmanageability occurs in the wet season, cautioning the need to redesign the rules.

Daily RAPID streamflow and reservoir release flow in the Mississippi River Basin

This dataset includes RAPID streamflow simulation correspond to the selected gages in the Mississippi River Basin. RAPID was run from 2005 to 2014 with and without reservoir releases. 175 USACE dams and reservoirs were considered in this study. The daily reservoir releases are included in this dataset.

Water Reservoir Multiobjective Optimal Operation Using Grey Wolf Optimizer

This study develops the Multiobjective Grey Wolf Optimization (MOGWO) algorithm to obtain the optimum rules on the operation of the Golestan Dam in Golestan Province, Iran, under the climate change conditions. The tow objective functions defined in the optimization process include minimizing the vulnerability and maximizing the reliability indices of the model under baseline and climate change conditions periods. Results showed that the river flow would decline by 0.17 percent of the baseline period under climate change conditions in addition to increasing the temperature by 20%, as well as decreasing the rainfall by 21.1%. Moreover, the extent of vulnerability index variations in baseline and climate change conditions was 16–45% and 10–43%, respectively. The range of reliability index variations in baseline and climate change conditions was 47–90% and 27–93%. On the other hand, the vulnerability index has also been measured at 29% and 27% for baseline and climate change conditions, respectively, with 75 percent of reliability. Comparison of the reservoir release rate and water demands for all of the Pareto points indicates a rise in release rates for climate change conditions relative to the baseline one; as the result, the higher adjustment in the reservoir release rates to its demand volumes will be highlighted as the higher dam efficiency in climate change conditions.

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Medium- to long-range forecasts often guide reservoir release decisions to support water management objectives, including mitigating flood and drought risks. While there is a burgeoning field of science targeted at improving forecast products and associated decision support models, data describing how and when forecasts are applied in practice remain undeveloped. This lack of knowledge may prevent hydrological modelers from developing accurate reservoir release schemes for large-scale, distributed hydrology models that are increasingly used to assess the vulnerabilities of large regions to hydrological stress. We address this issue by estimating seasonally varying, regulated inflow forecast horizons used in the operations of more than 300 dams throughout the conterminous United States (CONUS). For each dam, we take actual forward observed inflows (perfect foresight) as a proxy for forecasted flows available to the operator and then identify for each week of the year the forward horizon that best explains the release decisions taken. Resulting “horizon curves” specify for each dam the inferred inflow forecast horizon as a function of the week of the water year. These curves are analyzed for strength of evidence for contribution of medium- to long-range forecasts in decision making. We use random forest classification to estimate that approximately 80 % of large dams and reservoirs in the US (1553±50 out of 1927 dams with at least 10 Mm3 storage capacity) adopt medium- to long-range inflow forecasts to inform release decisions during at least part of the water year. Long-range forecast horizons (more than 6 weeks ahead) are detected in the operations of reservoirs located in high-elevation regions of the western US, where snowpack information likely guides the release. A simulation exercise conducted on four key western US reservoirs indicates that forecast-informed models of reservoir operations may outperform models that neglect the horizon curve – including during flood and drought conditions.

Policy Design for Optimizing the Hydropower Generation Potential of Maithon Multi-purpose Reservoir System

It is proposed to develop an optimal operating policy for the Maithon multi-purpose reservoir system, situated at Maithon, which is approximately 48 km from the district of Dhanbad (Jharkhand), India. The present objective is to maximize hydropower generation subjected to reservoir mass balance, release, storage, reservoir-drawdown level, overflow, maximum flood zone space, maximum and minimum storage constraints under three different dependable inflow conditions namely 50%, 70% and 90%. The storage curves also been derived after analyzing the various policies and was observed to be persistent with that of demand requirements. The derived policy is capable of producing maximum annual hydropower of 133394.43 MWh, 103015.14 MWh and 61782.77 MWh for 50%, 70% and 90% dependable inflow conditions respectively against the existing generated values of hydropower as 102958.3 MWh which has been averaged over last 10 years. Further the firm hydropower power values obtained under 50%, 70% and 90% dependable inflow conditions are 5.773 MW, 3.421 MW and 2.67 MW respectively. In this study the potential of hydropower energy production of the reservoir system is explored extensively, and a trade-off between reservoir release especially for irrigation purpose and maximum energy production has been established for the use of various stakeholders as well as managers of reservoir operations.

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Medium to long-range forecasts often guide reservoir release decisions to support water management objectives, including mitigating flood and drought risks. While there is a burgeoning field of science targeted at improving forecast products and associated decision support models, data describing how and when forecasts are applied in practice remains undeveloped. This lack of knowledge may prevent hydrological modelers from developing accurate reservoir release schemes for large-scale, distributed hydrology models that are increasingly used to assess the vulnerabilities of large regions to hydrological stress. We address this issue by estimating seasonally-varying, regulated inflow forecast horizons used in the operations of more than 300 dams throughout the Conterminous United States. For each dam, we take actual forward observed inflows (perfect foresight) as a proxy for forecasted flows available to the operator, and then identify for each week of the year the forward horizon that best explains the release decisions taken. Resulting horizon curves specify for each dam the inferred horizon as a function of the week of the water year. These curves are analyzed for strength of evidence for contribution of medium to long-range forecasts in decision making. We use random forest classification to estimate that approximately 80 % of large dams and reservoirs in the US (1553 ± 50 out of 1927 dams with at least 10 Mm3 storage capacity) adopt medium to long-range inflow forecasts to inform release decisions during at least part of the water year. Long-range forecast horizons (more than six weeks ahead) are detected in the operations of reservoirs located in high elevation regions of the Western US, where snowpack information likely guides the release. A simulation exercise conducted on a selection of key reservoirs demonstrates that forecast-informed models of reservoir operations outperform models that neglect the horizon curve – including during flood and drought conditions.