Hydrological Series Research Articles

Temporal change of spatial heterogeneity and its effect on regional trend of annual precipitation heterogeneity indices

AbstractA statistical test on climate and hydrological series from different spatial resolution could obtain different regional trend due to spatial heterogeneity and its temporal variability. In this study, annual series of the precipitation heterogeneity indices of concentration index (CI) and the number of wet days (NW) along with annual total amount of precipitation were calculated based on at‐site daily precipitation series during 1962–2011 in the headwater basin of the Huaihe River, China. The regional trends of the indices were first detected based on at‐site series by using the aligned and intrablock methods, and field significance tests that consider spatial heterogeneity over sites. The detected trends were then compared with the trends of the regional index series derived from daily areal average precipitation (DAAP), which averages at‐site differences and thus neglects spatial heterogeneity. It was found that the at‐site‐based regional test shows increasing trends of CI and NW in the basin, which follows the test on individual sites that most of sites were characterized by increasing CI and NW. However, the DAAP‐derived regional series of CI and NW were tested to show a decreasing trend. The disparity of the regional trend test on at‐site‐based regional series and the DAAP‐derived regional series arises from a temporal change of the spatial heterogeneity, which was quantified by the generalized additive models for location, scale, and shape. This study highlights that compared with averaging indices, averaging at‐site daily precipitation could lead to an error in the regional trend inference on annual precipitation heterogeneity indices. More attention should be paid to temporal variability in spatial heterogeneity when data at large scales are used for regional trend detection on hydro‐meteorological events associated with intra‐annual heterogeneity.

Development of a coupled wavelet transform and evolutionary Levenberg‐Marquardt neural networks for hydrological process modeling

AbstractThis research aims to present a general framework by which the most appropriate wavelet parameters including mother wavelet, vanishing moment, and decomposition level can be chosen for a joint wavelet transform and machine learning model. This study is organized in 2 parts: the first part presents an evolutionary Levenberg‐Marquardt neural network (ELMNN) model as the most effective machine learning configuration, and the second part describes how the wavelet transform can be effectively embedded with the developed ELMNN model. In this research, the rainfall and runoff time series data of 2 distinct watersheds at 2 different time scales (daily and monthly) were used to build the proposed hybrid wavelet transform and ELMNN model. The conclusions of this study showed that the Daubechies wavelet more than other wavelet families is capable to extract the informative features of hydrologic series. The vanishing moment and decomposition level of this mother wavelet should be selected based on the watershed behavior and the time resolution of rainfall and runoff time series, respectively. The verification results for both watersheds at daily and monthly time scales indicated root mean square error, peak value criterion, low value criterion, and Kling‐Gupta efficiency as about 0.017, 0.021, 0.023, and 0.91, respectively.

Methodology for Analyzing and Predicting the Runoff and Sediment into a Reservoir

With the rapid economic growth in China, a large number of hydropower projects have been planned and constructed. The sediment deposition of the reservoirs is one of the most important disputes during the construction and operation, because there are many heavy sediment-laden rivers. The analysis and prediction of the runoff and sediment into a reservoir is of great significance for reservoir operation. With knowledge of the incoming runoff and sediment characteristics, the regulator can adjust the reservoir discharge to guarantee the water supply, and flush more sediment at appropriate times. In this study, the long-term characteristics of runoff and sediment, including trend, jump point, and change cycle, are analyzed using various statistical approaches, such as accumulated anomaly analysis, the Fisher ordered clustering method, and Maximum Entropy Spectral Analysis (MESA). Based on the characteristics, a prediction model is established using the Auto-Regressive Moving Average (ARIMA) method. The whole analysis and prediction system is applied to The Three Gorges Project (TGP), one of the biggest hydropower-complex projects in the world. Taking hydrologic series from 1955 to 2010 as the research objectives, the results show that both the runoff and the sediment are decreasing, and the reduction rate of sediment is much higher. Runoff and sediment into the TGP display cyclic variations over time, with a cycle of about a decade, but catastrophe points for runoff and sediment appear in 1991 and 2001, respectively. Prediction models are thus built based on monthly average hydrologic series from 2003 to 2010. ARIMA (1, 1, 1) × (1, 1, 1)12 and ARIMA (0, 1, 1) × (0, 1, 1)12 are selected for the runoff and sediment predictions, respectively, and the parameters of the models are also calibrated. The analysis of autocorrelation coefficients and partial autocorrelation coefficients of the residuals indicates that the models built in this study are feasible for representing and predicting the runoff and sediment inflow into the TGP with a high accuracy.

Non Stationary Analysis of Extreme Events

Traditional approaches to the analysis of extreme hydrological series are based on the stationarity assumption for the underlying processes, namely that the probability distribution of the hydrological variable does not change with time. Over the last decade however, a growing interest has arisen both from a scientific as well as engineering point of view, toward the development of tools able to cope with the apparent non stationary features (either natural or anthropogenic) observed in many hydrological processes. Though most of the works deal with extreme precipitation and floods, less attention has been devoted to modeling droughts under non stationarity paradigm. In the paper, a brief review of the available tools for modeling non stationary series is presented. An extension of such methodologies to drought lenght modeling is developed, taking into account the non stationary nature of the underlying series and/or of the threshold level used for drought definition. An example of application of the developed methods to four precipitation series in Sicily, Italy, exhibiting different degrees of trends is also presented.

Investigation of the complexity of streamflow fluctuations in a large heterogeneous lake catchment in China

The occurrence of flood and drought frequency is highly correlated with the temporal fluctuations of streamflow series; understanding of these fluctuations is essential for the improved modeling and statistical prediction of extreme changes in river basins. In this study, the complexity of daily streamflow fluctuations was investigated by using multifractal detrended fluctuation analysis (MF-DFA) in a large heterogeneous lake basin, the Poyang Lake basin in China, and the potential impacts of human activities were also explored. Major results indicate that the multifractality of streamflow fluctuations shows significant regional characteristics. In the study catchment, all the daily streamflow series present a strong long-range correlation with Hurst exponents bigger than 0.8. The q-order Hurst exponent h(q) of all the hydrostations can be characterized well by only two parameters: a (0.354 ≤ a ≤ 0.384) and b (0.627 ≤ b ≤ 0.677), with no pronounced differences. Singularity spectrum analysis pointed out that small fluctuations play a dominant role in all daily streamflow series. Our research also revealed that both the correlation properties and the broad probability density function (PDF) of hydrological series can be responsible for the multifractality of streamflow series that depends on watershed areas. In addition, we emphasized the relationship between watershed area and the estimated multifractal parameters, such as the Hurst exponent and fitted parameters a and b from the q-order Hurst exponent h(q). However, the relationship between the width of the singularity spectrum (Δα) and watershed area is not clear. Further investigation revealed that increasing forest coverage and reservoir storage can effectively enhance the persistence of daily streamflow, decrease the hydrological complexity of large fluctuations, and increase the small fluctuations.

The Features of the Cyclical Fluctuations, Homogeneity and Stationarity of the Average Annual Flow of the Southern Buh River Basin

Abstract In the last decades, according to researches of scientists of the world becomes more evident that the global and regional climate changes impact on the water regime of rivers and leads to break of the stationarity of observations series. In this study we analysed the long-term (since the beginning of the observations to 2012) average annual flow of the Southern Buh River Basin (37 gauging stations) and its cyclical fluctuations during a long period of time. The methodological approaches based on the using of hydro-genetic methods for estimation the homogeneity and stationarity of hydrological series were used. We researched that series of observations are homogeneous and stationary. However, not all series of observations were representative for determination of the stable average value. The presented results illustrate that the long-term fluctuations of the average annual flow at all gauging stations is synchronous. Therefore, the climatic conditions of the flow formation are homogeneous. However, these fluctuations are not always synchronous phase. We found out that the average annual flow in this basin fluctuates and has decreasing trend in the last decade. It is due to cyclical fluctuations of the river flow. We calculated the statistical parameters for all gauging stations.

Ordinal pattern dependence between hydrological time series

Ordinal patterns provide a method to measure dependencies between time series. In contrast to classical correlation measures like the Pearson correlation coefficient they are able to measure not only linear correlation but also non-linear correlation even in the presence of non-stationarity. Hence, they are a noteworthy alternative to the classical approaches when considering discharge series. Discharge series naturally show a high variation as well as single extraordinary extreme events and, caused by anthropogenic and climatic impacts, non-stationary behaviour. Here, the method of ordinal patterns is used to compare pairwise discharge series derived from macro- and mesoscale catchments in Germany. Differences of coincident groups were detected for winter and summer annual maxima. Hydrological series, which are mainly driven by annual climatic conditions (yearly discharges and low water discharges) showed other and in some cases surprising interdependencies between macroscale catchments. Anthropogenic impacts as the construction of a reservoir or different flood conditions caused by urbanization could be detected.

Analyse De La Variabilite Hydroclimatique Et Impacts Des Barrages Sur Le Regime Hydrologique D’une Riviere De Zone Semi-Aride : Le Sebou Au Maroc

The analysis and characterization of hydroclimatic variability over a long period of time is fundamental for understanding the impacts of climate change and vulnerability of hydrological regimes. The present work is a study of the spatial and temporal variability of the climatic and hydrological series in the Sebou river basin (Morocco) over the past sixty years using Hanning low-pass filter of second order and statistical tests of stationarity of climate and hydrological time series. These statistic testing methods were applied to analyze the spatial and temporal evolution to the hydroclimatologic data of the AzibEsSoltane, M'jara and MechraBelKsiri hydrometric stations, which are, respectively, located in the Middle Sebou, the Ouerrha Tributary of the Sebou and the Low Sebou. This analysis shows that climate variability has resulted particularly in strong interannual fluctuations in rainfall reflected by a succession of dry and wet periods between 1939 and 1979 and a general decrease in precipitation since 1980. The Water flow time series analysis are marked by ruptures detected mainly between 1972 and 1978 reflecting rainoff declines of about 50% in the Sebou downstream and 70% in the middle Sebou. Several decreases in water discharge (up to 90%) were also detected in the Ouerrhariver since 1996, when the Al Wahda dam started operating.The analysis of the relationships between rainfall and water discharge shows that the impacts of dams constructions are stronger than the climatic effect in the Ouerrha river. The results of this study are important indicators for the assessment of combined climate risks.

Environmental flows and its satisfaction degree forecasting in the Yellow River

Assessment of suitable environmental flows (EFs) and its satisfaction degree is significant for water resources management. We presented a forecasting framework for EFs, IFA and EF satisfaction degree to assess suitable EFs (minimum or threshold) and forecast EF satisfaction degree over the next 20 years. First, we investigated the temporal and spatial distribution characteristics of IFA using statistical analysis and Morlet wavelet analysis of the hydrologic series from 1919 to 2013. Second, forecasting models of IFA were established using hydrologic stochastic simulation based on wavelet analysis. Then, abrupt shifts of baseflow index (BI) were identified based on Mann-Kendall trend detection test. Subsequently, regressions models were developed between a normalized BI and the effect size (ES) of zoobenthos and fish species richness. Accordingly, EFs were calculated. Finally, the IFA and EF satisfaction degree for the forecasting years were calculated. Results show that thresholds of BI, with 0.8 and 0.4 as the lower limit, and 2.3 and 1.1 as the upper limit, were recommended as the limit percentage of the EFs standard during flood and non-flood seasons, respectively. An overall decreasing tendency of IFA was observed in all the seven stations from the 1910s to 2010s, which is especially particularly significant in the lower reaches in the past two or three decades. One major EFs deficit spell occurred throughout the river beginning in the 1990s. Forecasting results indicate that the IFA will satisfy the EFs for the stations located in the upper and middle reaches by 2020 and 2030, except for the stations in the lower reaches. Contradictions of EFs and IFA will be even graver in the future. The results will have important implications for the water management of the riverine ecosystem.

A novel two‐dimensional correlation coefficient for assessing associations in time series data

ABSTRACTThe widely used Pearson's correlation coefficient calculated for assessing the linear relationship between two variables might produce misleading results especially in the comparison of periodic variables. A single correlation coefficient provides a measure of the overall dependence structure and generally might not be sufficient for assessing local differences between the variables (e.g. associations between each individual year might vary in hydrologic series). The reason for this deficiency is the consideration of the averages of the whole series while ignoring the variations of the local averages (e.g. annual averages or long year averages of months) throughout the observations. This study presents a two‐dimensional horizontal (row wise) and vertical (column wise) correlation calculation approach where the compared series are considered as two‐dimensional matrices in which each row represents a sub‐period (e.g. one calendar year of the precipitation data) of the investigated time series data. The method applies a normalization procedure by considering the averages of all rows (namely local averages) for calculating the horizontal correlation and the averages of all columns for calculating the vertical correlation instead of considering the averages of the whole matrices. This enables a separate determination of the degree of relationships between the rows and columns of the compared data matrices by using the horizontal and vertical variance and covariance values that constitute the base of the two‐dimensional correlation. The method is applied on 14 different linearly varying hypothetical matrices, 6 matrices for testing the influence of seasonal and inter‐annual variations and the monthly total precipitation records of 6 stations in southwest Turkey. The results have shown that the developed correlation approach assesses the two‐dimensional behaviour of time series data like precipitation and provides a measure which enables separate assessment of the contributions from the seasonal cycle vs. inter‐annual variability in the association between two time series.

Využití geomorfologických charakteristik pro odhad celkové retence povodí

In the presented study we tested the selected sets of linear and nonlinear regression models, that describe the relationships between the selected parameters of hydrological model Bilan, which were estimated using the meteorological, hydrological series, and between the retention characteristics estimated using the selected geomorphological patterns of the river basin.

Comparison between Deseasonalized Models for Monthly Streamflow Generation in a Hurst–Kolmogorov Process Framework

AbstractThe Hurst–Kolmogorov (HK) behavior in geophysical series is the product of fluctuations occurring simultaneously at several time scales. In streamflow time series, the clustering of similar events characterizes this behavior and brings forth the peculiarly persistent structure of such series. Because the Hurst exponent expresses the intensity of the HK behavior in time series, it is essential to preserve it in modeling hydrological series. This paper addresses an approach for multisite monthly streamflow generation in a HK framework. A deseasonalized symmetric moving average (SMAD) model is proposed, in which the seasonal components are removed from the streamflow series prior to their submission to the model. A case study applied at six gauging stations in the Iguazu River Basin, southern Brazil, is presented. SMAD synthetic series are compared with a multisite contemporaneous autoregressive integrated moving average (CARIMA) model. The results show that both models exhibited similar performance ...

Innovative Analysis of Runoff Temporal Behavior through Bayesian Networks

Hydrological series are largely characterized by a strong random component in their behavior. More noticeable changes in the behavior patterns of rainfall/runoff temporal series are recently being observed. These modifications are not a trivial issue, especially in regards to peculiarities, non-linearities, diffused influences or higher time orders of dependence. This study mainly aimed to analyze the temporal dependence of an annual runoff series dynamically. This approach comprises a coupling between classic techniques (Autoregressive Moving Average Model, ARMA) and novel ones, based on Artificial Intelligent for hydrological research (Bayesian Networks, BNs). An ARMA model was built to provide reliable data to populate BNs. Then, causal reasoning, through Bayes’s theorem, allows the identification of the logic structure of temporal dependences within time series. Furthermore, the resultant conditional probability permits the quantification of the relative percentage of annual runoff change, and provides the right time order of dependence. This research introduces an original methodology able to build a logic structure for a stochastic analysis of temporal behavior. This approach also aimed to provide a powerful and graphic modeling method for improving the understanding of the dynamic runoff series temporal behavior. This was successfully demonstrated in two unregulated river basin stretches, belonging to the Duero river basin which is the largest basin in Spain.

Identification of long-term high-flow regime changes in selected stations along the Danube River

Abstract The aim of the paper is to study spatial and temporal changes in the magnitude, duration and frequency of high flows in the Danube basin. A hydrological series of the mean daily discharges from 20 gauging stations (operated minimally since 1930) were used for the analysis of changes in the daily discharges. The high flow events were classified into three classes: high flow pulses, small floods, and large floods. For each year and for each class, the means of the peak discharges, the number and duration of events, and the rate of changes of the rising and falling limbs of the waves were determined. The long-term trends of the annual time series obtained were analyzed and statistically evaluated. The long-term high flow changes were found to be different in three individual high flow classes. The duration of the category of high flow pulses is decreasing at 19 stations on the Danube and is statistically significant at the Linz, Vienna, Bratislava and Orsova stations. The frequency of the high flow pulses is increasing in all 20 stations. Also, the rising and falling rates of the high flow pulse category are increasing at the majority of the stations. The long-term trends of the selected characteristics of the small floods are very similar to the trends of the high flow pulses, i.e., the duration of small floods is decreasing, and their mean number per year is increasing. In the category of large floods the changes were not proved.

Impact of Hydrological Uncertainty on Water Management Decisions

This study focuses on the analysis of uncertainties in hydrologic series and their impact on water resources management decisions. The methodology is based on Monte Carlo simulation, where relevant parameters on the rainfall-runoff processes of a hydrologic model were sampled from suitable probability distributions in order to analyse the probability distribution of output variables in a water resources system model. The procedure was applied to three water resources systems in the Duero basin (Spain). Results showed that the performances of the water resources systems are very sensitive to uncertainties in the model input. The deviation from the reliabilities or cumulated supply deficits obtained in the water resources systems compared to the reference inflows was much larger when the perturbations were unfavourable than when they were favourable. This suggests that the uncertainty analysis may have important implications when using these performance measures of the water resources system in decision making, particularly in dry years. The uncertainty on hydrologic variables may change dramatically our assessment of the performance of a water resources system and thus hydrologic inputs should be evaluated with extreme care.

Scenario Optimisation of Pumping Schedules in a Complex Water Supply System Considering a Cost–Risk Balancing Approach

The optimisation of water pumping plant activation schedules is a significant issue when managing emergency and costly water transfer under a drought risk. This problem needs specific optimisation tools to deal with complex multi-reservoir supply systems and to consider different alternative scenarios. The effectiveness of emergency transfers alleviating droughts requires early warning and activation; on the other hand, the high operating costs of pump stations require system managers to take a robust approach that defines activation rules. The proposed optimisation procedure combines scenario optimisation analysis with a cost-risk balancing approach. The model searches for the identification of optimal decision rules by balancing the risk of water shortages and the operating costs of pumping stations. Scenario optimisation provides ‘barycentric’ values that define the activation threshold by comparing hydrological synthetic series results. A multi-objective approach is also required in order to balance energy cost minimisation requirements and a reduction of damage needs that can be caused by water shortages. Consequently, a scenario optimisation has been developed considering the multi-objective and cost-risk balancing problem. A model application has been developed optimising water management and energy costs in a real water system with shortage risks in the South Sardinia (Italy) region.

Characterizing the Seasonal Changing Patterns of Hydrological Variables in the East River, Southern China

AbstractTrends and variability of hydrological variables can reflect the redistribution of seasonal patterns that can be caused by both climatic and anthropic factors. In this paper, China’s East River, the most regulated tributary in the Pearl River Delta (PRD), is selected to be a typical study case. Annual and seasonal-scale analyses of the river regime are performed, in conjunction with a correlation analysis between runoff and suspended sediment concentration (SSC). Noteworthy results include the strong increasing trends in dry season (October–March) runoff, as well as a significant decreasing trend in annual and wet season (April–September) SSC. Moreover, the continuous wavelet transform, runoff, and SSC display a degenerative annual fluctuation signal gain and a nearly total signal loss after 1990, respectively. All these results can be ascribed to an intra-annual redistribution of hydrological series, which bridges the gap between the wet and dry season and leads to a weakened seasonal difference....

A review of environmental flow assessment: methodologies and application in the Qianhe River

Environmental flow is of great significance for maintenance of ecological services in the riverine ecosystem. The paper reviews present methodologies for environmental flow assessment on the basis of three classifications, including the Hydrological Index Methodologies, the Hydraulic Rating Methodologies and the Habitat Simulation Methodologies. Both advantages and disadvantages of each classification are fully analysed, as well as applicable conditions. Moreover, representative methods of different classifications are applied to prescribe environmental flow in the Qianhe River of north China with consideration of hydrological series, hydraulic characteristics and habitat suitability of targeted species. The results of environment flow by the Montana Method, Wetted Perimeter Method and Physical Habitat Simulation System are 1.23 m3/s, 2.07 m3/s and 0.52 m3/s for the Qianyang section in middle reach of the Qianhe River. In view of seasonal variation of the Qianhe River, the paper recommends 1.23 m3/s as the minimum runoff in the dry season and 2.07 m3/s in the wet season. For further improvement of environmental flow assessment, studies of quantitative correspondence relationship between each component of instream flow and its ecological functions of riverine ecosystem, and the development of Holistic Methodologies by combination of various methodologies and multi-disciplinary information have great potential.

Dynamic changes of sediment load and water discharge in the Weihe River, China

The abrupt reduction of the water discharge and sediment load in the Yellow River has attracted much attention during the past several decades. This paper investigates the temporal and spatial changes of annual sediment load and water discharge in the Weihe River basin, the largest tributary of the Yellow River, which contributes 40 % of the sediment and 26 % of the water to the Yellow River. The results suggest that sediment load and water discharge in the Weihe River basin from both mainstream and tributaries show significant decreasing trends (significance level of 0.05). The trends show the following three distinct stages: the fluctuating stage (1956–1969), the slowly decreasing stage (1970–1979) and the accelerated decreasing stage (1980–2010). Additionally, the water discharge decreases more quickly than sediment. Spatially, the greatest reduction in water discharge and sediment load occurs upstream of the river. Additionally, the decrease in water discharge and sediment load in the main stream is greater than in the tributary. The focal years of water discharge and sediment changes are the same for each station except for the Zhuangtou Station, which were approximately 1970 and during the 1990s. Human activities contribute much more to changes in the hydrological series, and the percentage of human activity impacts on the water discharge are much larger than the sediment load for most of the periods. The relationship between the average monthly water discharge and the sediment load shows a clockwise loop curve. The change points for the annual water discharge and the sediment load relationship occurred mainly in 1983 for most stations. The reduction in the water discharge and sediment load is caused mainly by human activities, especially soil and water conservation projects (such as afforestation, terraces, reservoirs and dams). In summary, the results in this study provide further evidence of the need for river basin management.

Period analysis of hydrologic series through moving-window correlation analysis method

Summary Period analysis is of great significance for understanding various hydrologic processes and predicting the future hydrological regime of a watershed or region. Hence, many period analysis methods including fast Fourier transform (FFT), maximum entropy spectral analysis (MESA) and wavelet analysis (WA) have been developed to study this issue. However, due to the complex components of hydrologic series and the limitations of these conventional methods, the problem is still difficult to be solved. In this paper, the moving-window correlation analysis method (MWCA) has been proposed for analyzing the periodic component of hydrologic series, which includes construction of periodic processes, significant test of periods and time frequency analysis. Three commonly used methods (FFT, MESA and WA) and MWCA are employed to investigate the periods of synthetic series and observed hydrologic series, respectively. The results show that FFT, MESA and WA are not always as good as expected when detecting periods of a time series. By contrast, MWCA has better application effects, which could identify the true periods of time series, extract the reliable periodic components, find the active time ranges of periodic components and resist the disturbance of noises. In conclusion, MWCA is suitable to identify the true periods of hydrologic time series.