Daily Inflow Research Articles

Hybrid DARIMA-NARX model for forecasting long-term daily inflow to Dez reservoir using the North Atlantic Oscillation (NAO) and rainfall data

Proper water resources management cannot be achieved without accessing comprehensive data, suitable resources exploitation programs, and quantified forecasts of water resources. Thus, it is necessary to develop new forecasting models of water resources. Autoregressive integrated moving average (ARIMA) models (classified as time series models) and artificial neural network models have performed well in forecasting linear and non-linear stream flow, respectively. In this paper, a hybrid method was used to evaluate the accuracy of daily flow forecasts through using the capabilities of ARIMA model and nonlinear auto regressive model with exogenous inputs (NARX). Moreover, the efficiency of forecasters such as North Atlantic oscillation (NAO) (as a large scale climate signal) was analyzed for flow forecasts. The forecasting results which compared using proposed error index (IIFFE) to assess mean absolute relative error (MARE), time to peak, and peak flow of forecasted flow. The results showed that forecasting accuracy was enhanced by using the hybrid model. It also displays that using rainfall as a forecaster has the most prominent influence on the increasing forecasting accuracy, while the accuracy is not achieved by using NAO singular or together with rainfall data. Finally, the proposed hybrid model decreased the IIFFE index from 1.25 (achieved by the best ARIMA forecast) to 0.36 and improved the accuracy daily flow forecasting considerably which enhance real time optimal operation of reservoirs.

Can stock market time the FPIs: a study of seasonality

The purpose of the paper is to analyse the presence of the days of the week effect, quarter effect, and month effect on the net investment of foreign portfolio investors (FPI) in India. For this purpose, research has been conducted for the time period from 4th January 2000 till 30th November 2016. FPI's daily net inflows have been analysed for occurrence and existence of month effect along with day of the week and quarter effect. Interaction effect between the quarter, month and nifty returns has been also analysed. Augmented dummy regression with ARIMA and GARCH has been used to analyse presence of seasonal anomalies in FPIs. Results confirmed the presence of calendar effect i.e., December month of the year (December) effect on the FPIs. Also, Friday effect and the Quarter 4 effect were present in the investing pattern of FPIs. Study concluded that FPIs were more biased towards trading more on Fridays and in the month of December in the overall analysis. Also, the presence of quarter effect was seemed to be caused by the presence of month effect along with the nifty returns of those specific months. Hence, there exists the strong interaction effect among the anomalies in the FPI net investment to India.

Can stock market time the FPIs: a study of seasonality

The purpose of the paper is to analyse the presence of the days of the week effect, quarter effect, and month effect on the net investment of foreign portfolio investors (FPI) in India. For this purpose, research has been conducted for the time period from 4th January 2000 till 30th November 2016. FPI's daily net inflows have been analysed for occurrence and existence of month effect along with day of the week and quarter effect. Interaction effect between the quarter, month and nifty returns has been also analysed. Augmented dummy regression with ARIMA and GARCH has been used to analyse presence of seasonal anomalies in FPIs. Results confirmed the presence of calendar effect i.e., December month of the year (December) effect on the FPIs. Also, Friday effect and the Quarter 4 effect were present in the investing pattern of FPIs. Study concluded that FPIs were more biased towards trading more on Fridays and in the month of December in the overall analysis. Also, the presence of quarter effect was seemed to be caused by the presence of month effect along with the nifty returns of those specific months. Hence, there exists the strong interaction effect among the anomalies in the FPI net investment to India.

FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING

ABSTRACT This paper presents the evaluation of a daily inflow forecasting model using a tool that facilitates the analysis of mathematical models for hydroelectric plants. The model is based on a Fuzzy Inference System. An offline version of the Expectation Maximization algorithm is employed to adjust the model parameters. The tool integrates different inflow forecasting models into a single physical structure. It makes uniform and streamlines the management of data, prediction studies, and presentation of results. A case study is carried out using data from three Brazilian hydroelectric plants of the Parana basin, Tiete River, in southern Brazil. Their activities are coordinated by Operator of the National Electric System (ONS) and inspected by the National Agency for Electricity (ANEEL). The model is evaluated considering a multi-step ahead forecasting task. The graphs allow a comparison between observed and forecasted inflows. For statistical analysis, it is used the mean absolute percentage error, the root mean square error, the mean absolute error, and the mass curve coefficient. The results show an adequate performance of the model, leading to a promising alternative for daily inflow forecasting.

Evaluation of Water Cycle Components with Standardized Indices Under Climate Change in the Pampanga, Solo and Chao Phraya Basins

Drought is a slow-developing disaster of water shortages in water cycle components adversely affecting anthropogenic water use. This study introduces a drought assessment framework of standardized indices in Pampanga (Philippines), Solo (Indonesia), and Chao Phraya (Thailand) basins. We used three existing and developed two new standardized indices to characterize meteorological, agricultural, hydrological and socio-economic droughts. We constructed a 15-arcsec (about 0.45-km) grid block-wise TOP (BTOP) model with multipurpose dam operation at individual river basin using global datasets and calibrated BTOP models with daily river discharge and dam inflow data. The simulated irrigated area is also compared with historical drought damages at each river basin. The calibrated BTOP models were run with bias-corrected MRI-AGCM3.2S precipitation to evaluate droughts under climate change. The calculated standardized indices show similar drought timing of the 1982-1983, 1987-1988, 1991-1992, 1997-1998 and 2002-2003 droughts across three river basins. In addition, the timing of these droughts coincides with historical El Niño-Southern Oscillation (ENSO) cycle events. The projected future climates demonstrate a variability of dam inflows and drought severities between four cases of the worst (RCP8.5) climate change scenario. We conclude that standardized indices are useful tools to characterize droughts at water cycle components.

Yosemite Hydroclimate Network: Distributed stream and atmospheric data for the Tuolumne River watershed and surroundings

AbstractRegions of complex topography and remote wilderness terrain have spatially varying patterns of temperature and streamflow, but due to inherent difficulties of access, are often very poorly sampled. Here we present a data set of distributed stream stage, streamflow, stream temperature, barometric pressure, and air temperature from the Tuolumne River Watershed in Yosemite National Park, Sierra Nevada, California, USA, for water years 2002–2015, as well as a quality‐controlled hourly meteorological forcing time series for use in hydrologic modeling. We also provide snow data and daily inflow to the Hetch Hetchy Reservoir for 1970–2015. This paper describes data collected using low‐visibility and low‐impact installations for wilderness locations and can be used alone or as a critical supplement to ancillary data sets collected by cooperating agencies, referenced herein. This data set provides a unique opportunity to understand spatial patterns and scaling of hydroclimatic processes in complex terrain and can be used to evaluate downscaling techniques or distributed modeling. The paper also provides an example methodology and lessons learned in conducting hydroclimatic monitoring in remote wilderness.

Deep Feature Learning Architectures for Daily Reservoir Inflow Forecasting

Inflow forecasting applies data supports for the operations and managements of reservoirs. To better accommodate the sophisticated characteristics of the daily reservoir inflow, two deep feature learning architectures, i.e., deep restricted Boltzmann machine (DRBM) and stack Autoencoder (SAE), respectively, are introduced in this paper. This study sheds light on the application of deep learning architectures for daily reservoir inflow forecasting, which has been attracting much attention in various areas for its ability to extract and learn useful features from a large number of data. Evaluations are made comparing the basic feed forward neural network (FFNN), the autoregressive integrated moving average (ARIMA), and two categories deep neural networks (DNNs) constructed by the integrations the FFNN with two deep feature learning architectures, named DRBM-based NN and stack SAE-based NN, respectively. Two daily inflow series of the Three Gorges reservoir (1/1/2000–31/12/2014) and the Gezhouba reservoir (1/1/1992–31/12/2014), China, are applied for four modeling exercises, respectively. The results show that, the two DNN models overwhelm the FFNN and the ARIMA models in terms of mean absolute percentage error, normalized root-mean-square error, and threshold statistic criteria.

FII, Stock Exchange Return, and the Leverage Effect: Evidence from India

The present study examines the impact of foreign institutional investment (FII) on SENSEX returns and exchange rate and vice versa. The study considers the monthly data of five years (from January 1, 2010 to December 31, 2014) of BSE SENSEX, exchange rate (INR/USD), and net FIIs. The results of vector autoregression analysis, impulse response, variance decomposition, and Granger causality indicates that net FIIs are neither affected by exchange rates nor by SENSEX movements, but exchange rates and SENSEX returns are affected by FIIs. Higher foreign inflow needs more liberalized policies along with better economic and political environment to gain the confidence of foreign investors. Foreign institutional investors are looking for enhanced investment in emerging markets, and India is among them. The share of FII in the Indian market is increasing largely as it was INR 16,364.60 crores in January 2004, compared to INR 82,420.23 crores in December 2014, an increase of 400%. There is an emergent need to gain the confidence of foreign institutional investors to invest aggressively in India by implementing liberalized policies that boost the buoyancy of investors as far as India is concerned. Along with its relationship with SENSEX and exchange rate, we tried to capture the impact of news on the volatility of foreign institutional investors through threshold autoregressive conditional heteroskedasticity and threshold autoregressive conditional heteroskedasticity models with pictorial representation of the news impact curve on daily inflows of foreign institutional investment.

Development of an optimal reservoir pumping operation for adaptation to climate change

Optimization of operation rule curve of Layang reservoir located in southern part of peninsular Malaysia under climate change uncertainties has been presented in this study. Downscaled climate data were incorporated in a lumped rainfall-runoff model to project daily reservoir inflow, which was then used to determine the reservoir operation rule curves to minimize the operation cost. The results reveal that climatic change will cause a rise of temperature in the range of 1.5-2.9°C, but decrease in rainfall in most of the months in the end of this century which will casue a change in the seasonal pattern of inflow to reservoir. Comparison of historical and model generated operation costs indicates that annual savings of 23% and 33% is possible with optimized reservoir operation rule curves. Assessment of the performance of the optimized operation curve under climate change scenarios shows that it will be able to cope with the changing pattern of reservoir inflow with proper adjustment. It is expected that the method presented in this paper to derive pumping operation curves to optimize the total volume of water to be pumped from various sources to reservoir can be useful for reservoir operators in the context of climate change.

Analysis of hydraulic load of a wastewater treatment plant in Jasło

Abstract The paper presents an analysis of hydraulic load in a wastewater treatment plant (WTP) in Jasło. The study was based on the records of daily sewage volume entering the treatment plant within a multi-year period of 2010–2014. The analysis took into account the average daily amount of incoming sewage, the maximum daily peaking factor for the incoming sewage, changes in the sewage volume depending on specific month and day of a week, and class intervals with the greatest frequency of occurrence. The analysis revealed that the average daily volume of the sewage entering the WTP in Jasło in the investigated multi-year period was 13 045 m3·d−1. The amount of incoming sewage was variable, as evidenced by the maximum peaking factors of daily sewage inflow that ranged from 1.07 to 2.78, depending on a specific month. The sewage admission was the largest in March, May and June and on Saturdays. Sewage volume interval most often occurring at the WTP in Jasło was the one between 8 000 and 10 000 m3·d−1. The study results indicated that the facility was hydraulically underloaded.

Simulation Of Mine Water Inflow: Case Study Of The Štavalj Coal Mine (Southwestern Serbia)

Abstract The inflow of mine water to mining operations is often caused by random events such as precipitation. Consequently, the mine water inflow regime can be defined as a function of random events applying the theory of random processes. Regression models of the multiple linear correlation type have been used to simulate the inflow of mine water into mining operations, produce short-range predictions and facilitate rapid response inside the mine. The significance of such models lies in the ability to simulate and predict the consequences (mine water inflow), caused by events of a random nature (meteorological parameters: precipitation and air temperature). The presented prognostic models have been calibrated for mine water inflow to the Štavalj Coal Mine in southwestern Serbia. Mathematical dependencies were defined based on daily mine water inflow rates recorded during the period from 2003 to 2011, which can be used to generate short-range (1-7 day) predictions of mean daily mine water inflow rates to the Štavalj Coal Mine. A strong correlation (coefficient of correlation r = 0.93, Sig. = 0.00) was derived for the one-day forecast. The coefficients of correlation for predictions of mean daily mine water inflow rates related to time periods of two, three...seven days gradually declined to 0.63 (7-day mean daily inflow rate).

Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models

Summary Inflow forecasting applies data supports for the operations and managements of reservoirs. A multiscale deep feature learning (MDFL) method with hybrid models is proposed in this paper to deal with the daily reservoir inflow forecasting. Ensemble empirical mode decomposition and Fourier spectrum are first employed to extract multiscale (trend, period and random) features, which are then represented by three deep belief networks (DBNs), respectively. The weights of each DBN are subsequently applied to initialize a neural network (D-NN). The outputs of the three-scale D-NNs are finally reconstructed using a sum-up strategy toward the forecasting results. A historical daily inflow series (from 1/1/2000 to 31/12/2012) of the Three Gorges reservoir, China, is investigated by the proposed MDFL with hybrid models. For comparison, four peer models are adopted for the same task. The results show that, the present model overwhelms all the peer models in terms of mean absolute percentage error (MAPE = 11.2896%), normalized root-mean-square error (NRMSE = 0.2292), determination coefficient criteria (R2 = 0.8905), and peak percent threshold statistics (PPTS(5) = 10.0229%). The addressed method integrates the deep framework with multiscale and hybrid observations, and therefore being good at exploring sophisticated natures in the reservoir inflow forecasting.

Performance evaluation and hydrological trend detection of a reservoir under climate change condition

Water is the key of development and stability of any region. Under climate change condition, water systems are less reliable and more vulnerable. In this study, performance of Saline reservoir in Odisha, India has been evaluated under the climate change condition. The study mainly focused on the trend analysis for identified the temporal changes in precipitation and inflow time series using Mann–Kendall test, quantification of inflows to reservoir using ARNO model and performance evaluation of reservoir using WEAP model under climate change condition. In the results, it has been observed that there is no significant trend for annual, seasonal and for any monthly rainfall. Variation in precipitation shows that rainy days are decreasing whereas number of intense rainy days (>100 mm) are increasing. A significant rising trend with test statistic value of +3.18 is observed for daily inflow series at 95 % confidence level. Evaluation of inflow to reservoir, ARNO model shows that the change in simulated flow is directly proportional to the change in rainfall. In order to 25 % Change, decrease or increase in rainfall amount resulted in equal amount of decrease or increase in the inflow. Performance evaluation of reservoir using WEAP model shows that if inflows reduced by 20 % there would be a decrease in supply reliability and it would not be possible to increase supplies. Reducing live storage by 10 % influence supply delivered in May and June, but again has little impact on the rest of the year.

Estimation of historic flows and sediment loads to San Francisco Bay, 1849–2011

Summary River flow and sediment transport in estuaries influence morphological development over decadal and century time scales, but hydrological and sedimentological records are typically too short to adequately characterize long-term trends. In this study, we recover archival records and apply a rating curve approach to develop the first instrumental estimates of daily delta inflow and sediment loads to San Francisco Bay (1849–1929). The total sediment load is constrained using sedimentation/erosion estimated from bathymetric survey data to produce continuous daily sediment transport estimates from 1849 to 1955, the time period prior to sediment load measurements. We estimate that ∼55% (45–75%) of the ∼1500 ± 400 million tons (Mt) of sediment delivered to the estuary between 1849 and 2011 was the result of anthropogenic alteration in the watershed that increased sediment supply. Also, the seasonal timing of sediment flux events has shifted because significant spring-melt floods have decreased, causing estimated springtime transport (April 1st to June 30th) to decrease from ∼25% to ∼15% of the annual total. By contrast, wintertime sediment loads (December 1st to March 31st) have increased from ∼70% to ∼80%. A ∼35% reduction of annual flow since the 19th century along with decreased sediment supply has resulted in a ∼50% reduction in annual sediment delivery. The methods developed in this study can be applied to other systems for which unanalyzed historic data exist.

Hydrology Properties At Sembrong Dam Reservoir in Johor

Water is fresh potable water is not always available at the right time or the right place for human or ecosystem use [1]. According to Straskraba and Tundisi (1999), water impoundments constructed by damming rivers are called dam reservoirs. Under the Malaysian Western Johore Agricultural Development Project, the main function of the Sembrong dam is flood mitigation. The secondary function of the dam is to provide clean water supply to 240,000 consumers in Kluang district area [2]. Water from the Sembrong dam reservoir is treated at the West Sembrong Water Treatment Plant before the distribution. Daily reservoir inflow data were extracted by applying the water balance model to the Sembrong dam reservoir. Developing hydrologic hazard curves for risk assessment uses the length of record and type of data to determine the extrapolation limits for flood frequency analysis [5]. Extrapolation beyond the data is often necessary to provide information needed for dam safety risk assessments [6]. The sources of information used for flood hazard analyses include stream flow and precipitation records and pale flood data.

Estimate of Design Hydrographs for the Angostura Dam, Sonora, Using Statistical and Spectral Methods

Large dams require not only the definition of peak flow but also description of the shape of the hydrograph because both aspects are considered in the design and operation of the spills. There are several methods to do this, but these are subjective and questionable, and rely on the time-base definition of the design hydrograph, and on the historical samples used for estimation. In this paper a new procedure is proposed to obtain the design hydrographs for this class of spills by means of daily mean inflow historical data, selecting the time base of the design hydrograph using spectral analysis of the historical floods and eliminating the subjectivity on its selection. Additionally, a procedure to give the shape of the flood is presented, that is different from the traditional procedure of alternating blocks or utilising Gamma or Hermitian Hydrographs because it is added a statistical analysis of the block to be alternated. Results were obtained using the different methods for a return period of 10,000 years, using the historical data of the Angostura dam at Sonora in Mexico. The design hydrograph obtained was compared with the Method of Institute of Engineering UNAM (IIUNAM) and a bivariate probabilistic method. Using the spectral method the time base of multi peak floods were obtained, similar to those which were recorded by the historical events.

Ensemble seasonal forecast of extreme water inflow into a large reservoir

Abstract. An approach to seasonal ensemble forecast of unregulated water inflow into a large reservoir was developed. The approach is founded on a physically-based semi-distributed hydrological model ECOMAG driven by Monte-Carlo generated ensembles of weather scenarios for a specified lead-time of the forecast (3 months ahead in this study). Case study was carried out for the Cheboksary reservoir (catchment area is 374 000 km2) located on the middle Volga River. Initial watershed conditions on the forecast date (1 March for spring freshet and 1 June for summer low-water period) were simulated by the hydrological model forced by daily meteorological observations several months prior to the forecast date. A spatially distributed stochastic weather generator was used to produce time-series of daily weather scenarios for the forecast lead-time. Ensemble of daily water inflow into the reservoir was obtained by driving the ECOMAG model with the generated weather time-series. The proposed ensemble forecast technique was verified on the basis of the hindcast simulations for 29 spring and summer seasons beginning from 1982 (the year of the reservoir filling to capacity) to 2010. The verification criteria were used in order to evaluate an ability of the proposed technique to forecast freshet/low-water events of the pre-assigned severity categories.

Impacts of Large-Scale Stormwater Green Infrastructure Implementation and Climate Variability on Receiving Water Response in the Salt Lake City Area

This study evaluated impacts of Green Infrastructure (GI) as a stormwater management practice on return flows and the further Implications of climate variability. The goal was to create a model to explore the impacts that bioretention and Rainwater Harvesting (RWH) representing GI had using goldsim and Stormwater Management Modeling (SWMM) software. The software was used to represent impacts that climate variability individually and combined, may have on downstream stakeholders and receiving water systems in Salt Lake city, Utah, USA. Primary stakeholders included downstream water rights users, Farmington Bay waterfowl management area and the migratory birds that rely on Farmington Bay and the advocates that represent them. The steps to reach this goal were broken down incrementally to: (1) Characterize daily inflows to Farmington Bay, (2) Provide daily inflows from natural and urban runoff to the Jordan river, (3) Create a daily water balance model of Farmington Bay, (4) Demonstrate the model with and without stormwater GI and climate variability scenarios and (5) Determine trends of inflow to the Jordan River, duck clubs and Farmington Bay under various scenarios. The simulation results demonstrated that bioretention and RWH individually and combined had minimal impact on downstream water users, Jordan River flows and ultimately Farmington Bay water levels. Bioretention reduced the flow in the Jordan River minimally, with reductions primarily during peak flow. RWH actually kept more water in the natural system on average because less water was needed from the water treatment facilities when outdoor irrigation was supplemented with rainwater. The user reliability did not differ for any of the bioretention and RWH scenarios. The climate variability scenario had the greatest impact to Jordan River flows, Farmington Bay water levels and user reliability. When analyzed without GI implementation, the climate variability induced reduction in tributary flows and precipitation led to an average decrease of 11% in the Jordan River streamflow when compared to average baseline scenario over a 25 year simulation. The user reliability decreased by 5% and most importantly there was found to be an average of 36% decrease in the water levels in Farmington Bay. The resultant of the decrease in Farmington Bay water level is a loss of up to 61 square kilometers (15,000 acres) of open bay that would impact bird habitat, brine shrimp grounds, recreationalists, bird watchers, hunters and more. For this case study the implications of climate variability on the water system are much greater than implementing GI.

Discussion of “Comparison of Wavelet-Based ANN and Regression Models for Reservoir Inflow Forecasting” by Krishna Budu

The discusser wishes to thank the author for investigating the accuracy of the wavelet neural network (WNN) technique in predicting daily reservoir inflows to the Malaprabha Reservoir in Belgaum, India, using daily values of rainfall, inflow, and streamflow at an upstream gauging station. The WNN results were compared with two standard neural network models along with a multiple linear regression (MLR) model and moving average (MA). It was found that the WNN model performed better than the artificial neural network (ANN) and MLR models in inflow forecasting. The discusser would like to point out some important points that the author and other researchers might consider: • According to the author, the testing data-set extremes (Xmin 1⁄4 0 and Xmax 1⁄4 1,016 m=s) fell within the training data-set limits (Xmin 1⁄4 0 and Xmax 1⁄4 1,036.4 m=s) and therefore the trained neural network models did not face difficulties in extrapolating. It is clear from Table 1, however, that Xmin 1⁄4 0.14 m=s, which was higher than the corresponding value of the testing data set and implies that these models faced difficulties in extrapolating low flow values. This can also be seen from Fig. 10, in which the WNN-BP and WNN-RB models (here BP = back propagation and RB = radial basis) slightly overestimated the low flow values. • In the study, the iteration numbers for the optimal ANN-BP, ANN-RB, WNN-BP, and WNN-RB were not provided. As seen from Table 3, the fitting accuracy of the ANN-BP model in the training period was generally similar to that of the ANN-RB model. However, WNN-BP approximated flow values in the training period much better than WNN-RB did. What might be the reason? • According to the author, it is seen from the scatter plot in Fig. 7 that the WMLR model had more values close to its predicted line and its R value was slightly higher. The discusser agrees with the author on this. However, the fit line coefficients should also have been considered in comparing the two methods. It is clear from the scatter plots (assuming that the fit line equation was y 1⁄4 axþ b) that the WNN-BP a and b coefficients were respectively closer to 1 and 0 (the ideal line is y 1⁄4 x) than were the WMLR a and b coefficients. This was also valid for Fig. 9. • The “Conclusion” section states, “Overall analysis indicates that the performance of ANN and regression models using undecomposed data is relatively lower compared to that of wavelet-decomposed data used NN and regression models; this may be plausible due to the variation in nonlinear dynamics of the rainfall runoff process that mapped effectively by waveletbased models.” In the study, however, the WMLR model was found to be better than the WNN model coupled with the RB and BP methods. The discusser wonders how the linear WMLR model could perform better than the nonlinear WNN models in such a nonlinear rainfall-runoff process, as also stated by the author. This needs clarification.

The Role of Reservoirs in Shaping The Dominant Cyclicity And Energy Of Mountain River Flows

The main objective of the paper is to determine the role of mountain reservoirs in shaping the dominant cyclicity and energy of river flows. The paper compares a large complex composed of two reservoirs (Czorsztyn–Sromowce Wyzne) with a smaller reservoir (Besko). These reservoirs are located in the Polish Carpathians and are characterised by very different parameters and functions. Moreover, they operate on rivers (the Dunajec and the Wislok) with different hydrologic regimes. Using Fourier spectral analysis of daily inflows, outflows and water levels in reservoirs for the period 1998–2012, it has been possible to identify the dominant frequencies of the considered time series with a percentage of spectral energy for these frequencies. Moreover, for signals created by river flow rates, changes in signal energy are associated with adequate changes in river energy. Therefore, by calculating the energy of the signal in the time domain, changes in signal energy can be observed before and after the water passes through the reservoir. Data on cyclicity of water levels in these reservoirs forms the background to the analysis of reservoir-induced changes in cyclicity of river flows. The conducted analysis revealed that reservoirs strengthen the regularity of the annual periodicity of stream flow in the studied rivers. Besides, these reservoirs significantly reduce the energy of water flowing out of them compared to the energy of inflow. Copyright © 2015 John Wiley & Sons, Ltd.