Flood Control Versus Water Conservation in Reservoirs: A New Policy to Allocate Available Storage

The aim of this study is to contribute to solving conflicts that arise in the operation of multipurpose reservoirs when determining maximum conservation levels (MCLs). The specification of MCLs in reservoirs that are operated for water supply and flood control may imply a reduction in the volume of water supplied with a pre-defined reliability in the system. The procedure presented in this study consists of the joint optimization of the reservoir yield with a specific reliability subject to constraints imposed by hydrological dam safety and downstream river safety. We analyzed two different scenarios by considering constant or variable initial reservoir level prior to extreme flood events. In order to achieve the global optimum configuration of MCLs for each season, we propose the joint optimization of three variables: minimize the maximum reservoir level (return period of 1000 years), minimize the maximum released outflow (return period of 500 years) and maximize the reservoir yield with 90% reliability. We applied the methodology to Riaño Dam, jointly operated for irrigation and flood control. Improvements in the maximum reservoir yield (with 90% reliability) increased up to 10.1% with respect to the currently supplied annual demand (545 hm3) for the same level of dam and downstream hydrological safety. The improvement could increase up to 26.8% when compared to deterministic procedures. Moreover, dam stakeholders can select from a set of Pareto-optimal configurations depending on if their main emphasis is to maintain/increase the hydrological safety, or rather to maintain/increase the reservoir yield.

Influence of sediment distribution on the relationships among reservoir yield, spill, and evaporation losses

ABSTRACT This study shows how the sedimentation process in reservoirs affects the yield-spill-evaporation losses in reservoirs of Ceará State, Brazilian Northeast. Reservoirs are assumed to have, initially, inverted conical shape. Three forms of sedimentation were investigated: type 1, with deposition occurring parallel to the wetted perimeter; type 2, deposition distributed proportionally to the water depth; and, type 3, deposition concentrated in the reservoir bottom. These sedimentation patterns were found in many reservoirs in Ceará, with capacity ranging from about 0.5 to 100 hm3. Nevertheless, type 2 pattern was the most frequent. In this paper, five large reservoirs, over 100 hm3, were studied using Monte Carlo approach, and considering the silting over the time horizon. It was found that sediment distribution can significantly affect the yield-spill-evaporation trade-off on large reservoirs. Type 1 results have the lowest impact on reservoir yield, followed by type 2 and type 3. For Cedro reservoir, the yield would go to zero in 2115, assuming a type 3 deposition pattern. These results reinforce the need for monitoring sedimentation in large reservoirs in the Brazilian semiarid region. In addition, this study provides a relatively simple methodology to predict the impact of siltation on reservoir yield-spill-evaporation relationships, for the three most found patterns of sedimentation.

Reservoir Yield Analysis Using Multiple Variable Regression Screening Model and Its Comparison: A Study of 28 Water Supply Dams in Malaysia

This paper presents the study of water supply dams in Malaysia using the Storage Yield Reliability (SYR) model. The model is a linearized regressed equation with five independent variables comprising of hydrological and physical properties of the reservoir system, namely dam inflows and its statistical moment properties, reservoir storage capacity, and designated return periods or probability of non-exceedance of low flow. A total of twenty eight water supply reservoir schemes were selected for comparison in this study. Seventeen and eleven reservoirs respectively operated under direct supply (DS) and regulating reservoir (RR) modes. The estimated SYR yields were compared to the known water treatment plant (WTP) capacities of these reservoir schemes. Out of five variables, catchment area (indirectly proportionate to dam inflows) and storage capacity are positively correlated to the estimated SYR yields. The SYR model adopted in this study could provide quick yield assessment for all the twenty eight DS and RR reservoir schemes in Malaysia. In summary, the multivariate regression model SYR approach can be used as the first screening process of DS and RR operation mode reservoir yield estimation in Malaysia.

Reservoir yield intercomparison of large dams in Jaguaribe Basin-CE in climate change scenarios

ABSTRACT Climate changes can have different impacts on water resources. Strategies to adapt to climate changes depend on impact studies. In this context, this study aimed to estimate the impact that changes in precipitation, projected by Global Circulation Models (GCMs) in the fifth report by the Intergovernmental Panel on Climate Change (IPCC-AR5) may cause on reservoir yield (Q90) of large reservoirs (Castanhão and Banabuiú), located in the Jaguaribe River Basin, Ceará. The rainfall data are from 20 GCMs using two greenhouse gas scenarios (RCP4.5 and RCP8.5). The precipitation projections were used as input data for the rainfall-runoff model (SMAP) and, after the reservoirs’ inflow generation, the reservoir yields were simulated in the AcquaNet model, for the time periods of 2040-2069 and 2070-2099. The results were analyzed and presented a great divergence, in sign (increase or decrease) and in the magnitude of change of Q90. However, most Q90 projections indicated reduction in both reservoirs, for the two periods, especially at the end of the 21th century.

Trade-off between reservoir yield and evaporation losses as a function of lake morphology in semi-arid Brazil

This study investigates the relationships between yield and evaporation as a function of lake morphology in semi-arid Brazil. First, a new methodology was proposed to classify the morphology of 40 reservoirs in the Ceará State, with storage capacities ranging from approximately 5 to 4500 hm3. Then, Monte Carlo simulations were conducted to study the effect of reservoir morphology (including real and simplified conical forms) on the water storage process at different reliability levels. The reservoirs were categorized as convex (60.0%), slightly convex (27.5%) or linear (12.5%). When the conical approximation was used instead of the real lake form, a trade-off occurred between reservoir yield and evaporation losses, with different trends for the convex, slightly convex and linear reservoirs. Using the conical approximation, the water yield prediction errors reached approximately 5% of the mean annual inflow, which is negligible for large reservoirs. However, for smaller reservoirs, this error became important. Therefore, this paper presents a new procedure for correcting the yield-evaporation relationships that were obtained by assuming a conical approximation rather than the real reservoir morphology. The combination of this correction with the Regulation Triangle Diagram is useful for rapidly and objectively predicting reservoir yield and evaporation losses in semi-arid environments.

Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data

Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between global climate models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) data sets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to present a proof-of-concept approximation of within-GCM uncertainty for monthly precipitation and temperature projections and to assess the impact of within-GCM uncertainty on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. We adopt stochastic replicates of available GCM runs to approximate within-GCM uncertainty because large ensembles, hundreds of runs, for a given GCM and scenario are unavailable, other than the Climateprediction.net data set for the Hadley Centre GCM. To date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2015) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), mean annual temperature (MAT), mean annual runoff (MAR), the standard deviation of annual precipitation (SDP), standard deviation of runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 worldwide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainties from the 17 catchments and 5 GCMs for 2015–2044 (A1B) were MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould–Dincer Gamma (G-DG) procedure was applied to each annual runoff time series for hypothetical reservoir capacities of 1 × MAR and 3 × MAR and the average uncertainties in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were 25.1% (1 × MAR) and 11.9% (3 × MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1 × MAR or 3 × MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable – these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.