COMPARISON OF TWO AUTOREGRESSIVE MODELS FOR MONTHLY STREAM FLOW GENERATION1

ABSTRACT: Two autoregressive models for monthly stream flow generation are compared based on the reproduction of the historical record in terms of several important statistics such as the mean, standard deviation, skewness coefficient, correlation coefficient, and the reservoir storage components. In the comparison, both theoretical considerations and practical applications are employed to evaluate the performance of each model.     

A STOCHASTIC DYNAMIC PROGRAMMING BASED APPROACH TO THE OPERATION OF A MULTI-RESERVOIR SYSTEM1

ABSTRACT: An heuristic iterative technique based upon stochastic dynamic programming is presented for the analysis of the operation of a three reservoir ‘Y’ shaped hydroelectric system. The technique is initiated using historical inflow data for the downstream reservoir. At each iteration the optimal policies for the downstream hydroelectric generating unit are used to provide relative weightings or targets for operation of upstream reservoirs. New input inflows to the downstream reservoir are then obtained by running the historical streamflow record through the optimal policies for the upstream reservoirs. These flows are then used to develop a new operating policy for the downstream reservoir and hence new targets for the upstream reservoirs. The process is continued until the operating policies for each reservoir provide the same overall system benefit for two successive iterations. Results obtained from the procedure are compared to the results obtained by historical operation of the system. The procedure is shown to develop operating policies which give benefits which are as close to the historical benefits as can be expected given the choice of the number of storage state variables.     

Reservoir Operation with Feedback in a Coupled Land Surface and Hydrologic Model: A Case Study of the Huai River Basin,China

Regarding emerging large‐scale reservoir operation models, reports of reservoir operation feedback for hydrologic modeling are rare, and little attention has been paid to flood control. An operation scheme considering multilevel flood control (MLFC) was first proposed in this study, but more reservoir information was needed. Thus, an alternative scheme was proposed that consisted of a modified version of the reservoir operation scheme in the Soil and Water Assessment Tool Model (MSWAT scheme). These schemes were coupled to a land surface and hydrologic model system with feedback, i.e., a system in which reservoir operation can affect the subsequent simulation, and were investigated in the Huai River Basin. The results show reservoir storage and peak flow were generally overestimated by the original SWAT reservoir scheme (SWAT scheme). Compared with the SWAT scheme, the MSWAT scheme successfully reduced the simulated storage and peak flow at the reservoir stations. For the downstream stations, the streamflow simulations were improved at a significance level of 5%. The performances of the MSWAT and MLFC schemes at the reservoir stations were nearly equivalent. Importantly, reservoir operation feedback to hydrologic modeling was necessary because the reservoir operation effects could not be transferred downstream without it. The streamflow simulation of a reservoir station located on a flat plain was less sensitive to feedback than that of a mountain reservoir station.     

Use of observed hydroclimatic trends to constrain projections of snowmelt season runoff in the Rio Grande headwaters

A method is developed for choosing 21st Century streamflow projections among widely varying results from a large ensemble of climate model-driven simulations. We quantify observed trends in climate–streamflow relationships in the Rio Grande headwaters, which has experienced warming temperature and declining snowpack since the mid-20th Century. Prominent trends in the snowmelt runoff season are used to assess corresponding statistics in downscaled global climate model projections. We define “Observationally Consistent (OC)” simulations as those that reproduce historical changes to linear statistics of diminished snowpack–streamflow coupling in the headwaters and an associated increase in the contribution of spring season (post-peak snowpack) precipitation to streamflow. Only a modest fraction of the ensemble of simulations meets these consistency metrics. The subset of OC simulations projects significant decreases in headwaters flow, whereas the simulations that poorly replicate historical trends exhibit a much wider range of projected changes. These results bolster confidence in model-based projections of declining runoff in the Rio Grande headwaters in the snowmelt runoff season and offer an example of a methodology for evaluating model-based projections in basins with similar hydroclimates that have experienced pronounced climate changes in the recent historical record.     

THE VALUE OF STREAMFLOW FORECASTING IN RESERVOIR OPERATION1

ABSTRACT: The value of streamflow forecasts in reservoir operation depends on a number of factors and may vary considerably. Assessment of forecast benefits is presented here for three specific systems. Statistical streamflow models of increasing forecasting ability are coupled with a recently developed stochastic control method in extensive simulation experiments. The performance of the system is statisticafly evaluated with regard to energy generation and flood and drought prevention. The results indicate that forecast benefits are system specific and may range from quite substantial to fairly minimal.     

COMPUTATIONAL IMPROVEMENT FOR STOCHASTIC DYNAMIC PROGRAMMING MODELS OF URBAN WATER SUPPLY RESERVOIRS1

ABSTRACT: This paper describes two methods that are introduced to improve the computational effort of stochastic dynamic programming (SDP) as applicable to the operation of multiple urban water supply reservoir systems. The stochastic nature of streamflow is incorporated explicitly by considering it in the form of a multivariate probability distribution. The computationally efficient Gaussian Legendre quadrature method is employed to compute the conditional probabilities of streamflow, which accounts for the serial correlation of streamflow into each storage and the cross correlation between the streamflow into various storages. A realistic assumption of cross correlation of streamflow is introduced to eliminate the need to consider the streamflow combinations which are unlikely to occur in the SDP formulation. A “corridor” approach is devised to eliminate the need to consider the infeasible and/or inferior storage volume combinations in the preceding stage in computing the objective function in the recursive relation. These methods are verified in terms of computational efficiency and accuracy by using a hypothetical example of three interconnected urban water supply reservoirs. Therefore, it can be concluded that these methods allow SDP to be more attractive for deriving optimal operating rules for multiple urban water supply reservoir systems.     

Scenario‐Based and Scenario‐Neutral Assessment of Climate Change Impacts on Operational Performance of a Multipurpose Reservoir

Scenario‐based and scenario‐neutral impacts assessment approaches provide complementary information about how climate change‐driven effects on streamflow may change the operational performance of multipurpose dams. Examining a case study of Cougar Dam in Oregon, United States, we simulated current reservoir operations under scenarios of plausible future hydrology. Streamflow projections from the CGCM3.1 general circulation model for the A1B emission scenario were used to generate stochastic reservoir inflows that were then further perturbed to simulate a potentially drier future. These were then used to drive a simple reservoir model. In the scenario‐based analysis, we found reservoir operations are vulnerable to climate change. Increases in fall and winter inflow could lead to more frequent flood storage, reducing flexibility to store incoming flood flows. Uncertainty in spring inflow volume complicates projection of future filling performance. The reservoir may fill more or less often, depending on whether springs are wetter or drier. In the summer, drawdown may occur earlier to meet conservation objectives. From the scenario‐neutral analysis, we identified thresholds of streamflow magnitude that can predict climate change impacts for a wide range of scenarios. Our results highlight projected operational challenges for Cougar Dam and provide an example of how scenario‐based and scenario‐neutral approaches may be applied concurrently to assess climate change impacts.     

A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models1

Abstract: Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formidable challenge. Recent enhancements in climate forecasting skill and hydrological modeling serve as an impetus for further pursuing models and model combinations capable of delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The framework presented here proposes integration and offline coupling of global climate models (GCMs), multiple regional climate models, and numerous water balance models to improve streamflow forecasting through generation of ensemble forecasts. For demonstration purposes, the framework is imposed on the Jaguaribe basin in northeastern Brazil for a hindcast of 1974‐1996 monthly streamflow. The ECHAM 4.5 and the NCEP/MRF9 GCMs and regional models, including dynamical and statistical models, are integrated with the ABCD and Soil Moisture Accounting Procedure water balance models. Precipitation hindcasts from the GCMs are downscaled via the regional models and fed into the water balance models, producing streamflow hindcasts. Multi‐model ensemble combination techniques include pooling, linear regression weighting, and a kernel density estimator to evaluate streamflow hindcasts; the latter technique exhibits superior skill compared with any single coupled model ensemble hindcast.     

INDEXED SEQUENTIAL HYDROLOGIC MODELING FOR HYDROPOWER CAPACITY ESTIMATION1

ABSTRACT: The indexed sequential hydrologic modeling (ISM) methodology is utilized by the Western Area Power Administration as the basis for risk-based estimation of project-dependable hydropower capacity for several federally owned/operated projects. ISM is a technique based on synthetic generation of a series of overlapping short-term inflow sequences obtained directly from the historical record. The validity of ISM is assessed through application to the complex multireservoir hydropower system of the Colorado River basin for providing risk estimates associated with determination of reliable hydrogeneration capacity. Performance of ISM is compared with results from stochastically generated streamflow input data to the Colorado River Simulation System (CRSS). Statistical analysis and comparison of results are based on monthly power capacity, energy generation, and downstream water deliveries. Results indicate that outputs generated from ISM synthetically generated sequences display an acceptable correspondence with those obtained from stochastically generated hydrologic data for the Colorado River Basin.     

OPTIMIZATION OF MULTIPURPOSE RESERVOIR SYSTEM OPERATION1

ABSTRACT: The goal programming approach for multipurpose reservoir operation has been proposed and applied to the Bhadra reservoir system, having irrigation and hydropower production as dual purposes, in India. The objective of the model is to satisfy sequentially a series of operating criteria. Two goal programming models, one with the objective function as minimizing the deviations from storage targets and the other with the objective function as minimizing the deviations from release targets, have been formulated and applied to the reservoir system under study. The results proved that the model with release targets is preferred over the model with storage targets for determining operational policies for multipurpose reservoir system.     

BENEFITS OF TRANSFERRING STREAMFLOW PRIORITY FROM AGRICULTURAL TO NON-AGRICULTURAL USE1

ABSTRACT: In Virginia, as in many states, priority to streamflow is held by riparian landowners who are predominantly agricultural users. The streamfiow may also have a high potential value to non-agricultural users who do not have riparian rights. The potential benefits of transferring streamfiow priority rights from agricultural to non-agricultural use were evaluated using simulation for an eastern Virginia watershed. Lowering irrigators' priority to streamflow reduced crop yields and irrigated returns in some years because of inadequate water supplies. However, the transfer of priorities increased the likelihood that the urban reservoir would be able to withdraw water from the stream without interruption. As a result, priority trades reduced the size of reservoir needed to meet a given water requirement by municipal users. The resulting savings in reservoir construction and maintenance costs more than offset the losses to irrigators. Net savings could be achieved even if the reservoir were required to release water periodically to maintain a minimum level of instream flow. The conclusion is that the state should encourage trading of access to streamflow in order to increase the use efficiency of streamfiows. Alternative means by which the state can facilitate water exchanges are discussed.     

GENERATING STREAMFLOW SEQUENCES WITH TREND AND CYCLICAL MOVEMENTS1

ABSTRACT: A first-order autoregressive model had been modified with the trend and cyclical movements to generate the streamflow sequences. Three main portions were involved in this modified model, i.e., six-year cycle with trend changing; six-year cycle without trend changing; and both annual and six-year cycles with trend changing. The synthetic sequences of monthly streamflow sequences were compared with the historical records obtained from the Kissimmee River basin by using the Chi-Square test for goodness-of-fit. The results indicated that the newly proposed model has a better solution than the original model because the trend and cyclical movements involved in generating sequences are much closer to the historical records.     

A STOCHASTIC DYNAMIC PROGRAMMING MODEL FOR THE OPTIMUM OPERATION OF A MULTI-PURPOSE RESERVOIR1

ABSTRACT: The main objective of this paper is to present a stockastic dynamic programming model useful in determining the optimal operating policy of a single multipurpose surface reservoir. It is the unreliability of forecasting the amount of future streamflow which makes the problem of a reservoir operation a stochastic process. In this paper the stochastic nature of the streamflow is taken into account by considering the correlation between the streamflows of each pair of consecutive time intervals. This interdependence is used to calculate the probability of transition from a given state and stage to its succeeding ones. A dynamic programming model with a physical equation and a stochastic recursive equation is developed to find the optimum operational policy. For illustrative purposes, the model is applied to a real surface water reservoir system.     

Comparison of Machine Learning Models Performance on Simulating Reservoir Outflow: A Case Study of Two Reservoirs in Illinois,U.S.A.

Reservoir outflow is an important variable for understanding hydrological processes and water resource management. Natural streamflow variation, in addition to the streamflow regulation provided by dams and reservoirs, can make streamflow difficult to understand and predict. This makes them a challenge to accurately simulate hydrologic processes at a daily scale. In this study, three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were examined and compared to model reservoir outflow. Past, current, and future hydrologic and meteorological data were used as model inputs, and the outflow of next day was used as prediction. Simulation results demonstrated that all three models can reasonably simulate reservoir outflow. For Carlyle Lake, the coefficient of determination and Nash–Sutcliffe efficiency were each close to one for the three models. The coefficient of determination, relative mean bias, and root mean square error indicated that the SVM performed better than the RF and ANN, but the SVM output displayed a larger relative mean bias than that from RF and ANN. For Lake Shelbyville, the ANN model performed better than RF and SVM when considering the coefficient of determination, Nash–Sutcliffe efficiency, relative mean bias, and root mean square error. The study results demonstrate that the three ML algorithms (RF, SVM, and ANN) are all promising tools for simulating reservoir outflow. Both the accuracy and efficacy of the three ML algorithms are considered to support practitioners in planning reservoir management.     

ESTIMATION OF WATER TEMPERATURE EXCEEDANCE PROBABILITIES USING THERMO-HYDRODYNAMIC MODELING1

ABSTRACT: A continuous simulation approach is proposed for estimating water temperature exceedance probabilities using thermo-hydrodynamic modeling. The approach uses (1) a deterministic unsteady flow and heat transport model, (2) continuous hydrological and meteorological data for a long historical period, and (3) synthetic records of tributary water temperatures and other model inputs. Representative historical records of streamflow, air temperatures, and other hydrometeorological variables are obtained from nearby gages. Stochastic modeling methods are used to construct synthetic records for other model inputs, including inflow water temperatures. An application of this deterministic-stochastic approach is presented for a complex waterway in northeastern Illinois with heat discharges from several power plants and wastewater treatment plants. Statistical results from the continuous simulations are compared to results obtained from traditional event simulations. The application illustrates the information that engineers and biologists can obtain for (1) evaluating compliance with water temperature standards, and (2) assessing the effect of water temperatures on aquatic habitat.     

APPLICATION OF THE HEC-4 MONTHLY STREAM FLOW SIMULATION MODEL1

ABSTRACT: The HEC-4 monthly stream flow simulation model, developed by the Hydrologic Engineering Center, Davis, California, is used to extend the available historical stream flow records in the Central Ohio area. The principal objective of this paper is to examine the effectiveness of the HEC-4 model in generating synthetic monthly flows. Important statistical parameters are evaluated in order to relate the statistical properties of the historical and generated flows. In doing so, it is observed that the mean, standard deviation, and skewness of the generated flows are consistently larger than the corresponding estimates based on historical flows. However, results show that these statistics, as well as the lag-1 serial correlation, are generally well maintained by the generated sequences. The degree to which any statistical dissimilarities would be critical, from an engineering design point of view, is demonstrated by utilizing their low flow characteristics. Estimates of reservoir safe-yields, based on a nonsequential mass-curve analysis of the historical and generated low flows, indicate a nominal difference in this particular study.     

CLIMATE CHANGE IMPACTS ON WATER RESOURCES OF THE TSENGWEN CREEK WATERSHED IN TAIWAN1

ABSTRACT: This study presents a methodology to evaluate the vulnerability of water resources in the Tsengwen creek watershed, Taiwan. Tsengwen reservoir, located in the Tsengwen creek watershed, is a multipurpose reservoir with a primary function to supply water for the ChiaNan Irrigation District. A simulation procedure was developed to evaluate the impacts of climate change on the water resources system. The simulation procedure includes a streamflow model, a weather generation model, a sequent peak algorithm, and a risk assessment process. Three climate change scenarios were constructed based on the predictions of three General Circulation Models (CCCM, GFDL, and GISS). The impacts of climate change on streamflows were simulated, and, for each climate change scenario, the agricultural water demand was adjusted based on the change of potential evapotranspiration. Simulation results indicated that the climate change may increase the annual and seasonal streamflows in the Tsengwen creek watershed. The increase in streamflows during wet periods may result in serious flooding. In addition, despite the increase in streamflows, the risk of water deficit may still increase from between 4 and 7 percent to between 7 and 13 percent due to higher agricultural water demand. The simulation results suggest that the reservoir capacity may need to be expanded. In response to the climate change, four strategies are suggested: (1) strengthen flood mitigation measures, (2) enhance drought protection strategies, (3) develop new water resources technology, and (4) educate the public.     

Adaptation of Climate Model Projections of Streamflow to Account for Upstream Anthropogenic Impairments

A statistical procedure is developed to adjust natural streamflows simulated by dynamical models in downstream reaches, to account for anthropogenic impairments to flow that are not considered in the model. The resulting normalized downstream flows are appropriate for use in assessments of future anthropogenically impaired flows in downstream reaches. The normalization is applied to assess the potential effects of climate change on future water availability on the Rio Grande at a gage just above the major storage reservoir on the river. Model‐simulated streamflow values were normalized using a statistical parameterization based on two constants that relate observed and simulated flows over a 50‐year historical baseline period (1964–2013). The first normalization constant is a ratio of the means, and the second constant is the ratio of interannual standard deviations between annual gaged and simulated flows. This procedure forces the gaged and simulated flows to have the same mean and variance over the baseline period. The normalization constants can be kept fixed for future flows, which effectively assumes that upstream water management does not change in the future, or projected management changes can be parameterized by adjusting the constants. At the gage considered in this study, the effect of the normalization is to reduce simulated historical flow values by an average of 72% over an ensemble of simulations, indicative of the large fraction of natural flow diverted from the river upstream from the gage. A weak tendency for declining flow emerges upon averaging over a large ensemble, with tremendous variability among the simulations. By the end of the 21st Century the higher‐emission scenarios show more pronounced declines in streamflow.     

Hydrologic Model Framework for Water Resources Planning in the Santa Cruz River,Southern Arizona1

Abstract: The authors develop a model framework that includes a set of hydrologic modules as a water resources management and planning tool for the upper Santa Cruz River near the Mexican border, Southern Arizona. The modules consist of: (1) stochastic generation of hourly precipitation scenarios that maintain the characteristics and variability of a 45‐year hourly precipitation record from a nearby rain gauge; (2) conceptual transformation of generated precipitation into daily streamflow using varied infiltration rates and estimates of the basin antecedent moisture conditions; and (3) surface‐water to ground‐water interaction for four downstream microbasins that accounts for alluvial ground‐water recharge, and ET and pumping losses. To maintain the large inter‐annual variability of streamflow as prevails in Southern Arizona, the model framework is constructed to produce three types of seasonal winter and summer categories of streamflow (i.e., wet, medium, or dry). Long‐term (i.e., 100 years) realizations (ensembles) are generated by the above described model framework that reflects two different regimes of inter annual variability. The first regime is that of the historic streamflow gauge record. The second regime is that of the tree ring reconstructed precipitation, which was derived for the study location. Generated flow ensembles for these two regimes are used to evaluate the risk that the regional four ground‐water microbasins decline below a preset storage threshold under different operational water utilization scenarios.     

The Sensitivity of California Water Resources to Climate Change Scenarios1

Abstract: Using the latest available General Circulation Model (GCM) results we present an assessment of climate change impacts on California hydrology and water resources. The approach considers the output of two GCMs, the PCM and the HadCM3, run under two different greenhouse gas (GHG) emission scenarios: the high emission A1fi and the low emission B1. The GCM output was statistically downscaled and used in the Variable Infiltration Capacity (VIC) macroscale distributed hydrologic model to derive inflows to major reservoirs in the California Central Valley. Historical inflows used as inputs to the water resources model CalSim II were modified to represent the climate change perturbed conditions for water supply deliveries, reliability, reservoir storage and changes to variables of environmental concern. Our results show greater negative impacts to California hydrology and water resources than previous assessments of climate change impacts in the region. These impacts, which translate into smaller streamflows, lower reservoir storage and decreased water supply deliveries and reliability, will be especially pronounced later in the 21st Century and south of the San Francisco bay Delta. The importance of considering how climate change impacts vary for different temporal, spatial, and institutional conditions in addition to the average impacts is also demonstrated.