Daily Streamflow Series Research Articles

Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti

Abstract. Haiti, a Caribbean country, is highly vulnerable to hydroclimatic hazards due to heavy rainfall, which is partly linked to tropical cyclones. Additionally, its steep slopes generate flash floods, particularly in small catchments. Moreover, the hydrology of this region remains poorly understood and understudied. Unfortunately, there is no accessible database for the scientific community to use in this country. To fill this gap, hydroclimatic data were collected to create the first historical database in Haiti. This database, called Simbi (guardian of rivers, freshwater, and rain in Haitian mythology), includes 156 monthly rainfall series over the period 1905–2005, 59 daily rainfall series over the period 1920–1940, 70 daily streamflow series, and 23 monthly temperature series, not necessarily continuous, over the period 1920–1940. It also provides simulated streamflow series over the period 1920–1940 using the GR2M and GR4J rainfall–runoff models for 24 catchments and 49 attributes covering a wide range of topographic, climatic, geological, land use, hydrogeological, and hydrological signature indices. Simbi is the first open-access hydro-meteorological dataset for Haiti and will contribute to a better knowledge of hydrological risk in Haiti. Several sources of uncertainty associated with Simbi are acknowledged, including data quality (historical data), digitisation of paper archives, identification of relevant rain gauges, and rainfall–runoff models. It is important to consider these uncertainties when using Simbi. The database will be regularly updated to include additional historical data that will be digitised in the future. It will thus contribute toward better knowledge of the hydrology of Haitian catchments and will enable the implementation of various hydrological calculations useful for designing structures or flow forecasting. Simbi is an open-access database and is available for download at https://doi.org/10.23708/02POK6 (Bathelemy et al., 2023).

The Value of Large‐Scale Climatic Indices for Monthly Forecasting Severity of Widespread Flooding Using Dilated Convolutional Neural Networks

AbstractSpatially co‐occurring floods pose a threat to the resilience and recovery of the communities. Their timely forecasting plays a crucial role for increasing flood preparedness and limiting associated losses. In this study we investigated the potential of a dilated Convolutional Neural Network (dCNN) model conditioned on large‐scale climatic indices and antecedent precipitation to forecast monthly severity of widespread flooding (i.e., spatially co‐occurring floods) in Germany with 1 month lead time. The severity was estimated from 63 years of daily streamflow series as the sum of concurrent exceedances of at‐site 2‐year return periods within a given month across 172 mesoscale catchments (median area 516 km2). The model was trained individually for the whole country and three diverse hydroclimatic regions to provide insights on heterogeneity of model performance and flood drivers. Our results showed a considerable potential for forecasting widespread flood severity using dCNN especially as the length of training series increases. However, event‐based evaluation of model skill indicates large underestimation for rainfall‐generated floods during dry conditions despite overall lower severity of these events compared to the rain‐on‐snow floods. Feature attribution and wavelet coherence analyses both indicated considerable difference in the major flood drivers in three regions. While the flooding in North‐Eastern region is strongly affected by the Baltic Sea, the North‐Western region is affected more by global patterns associated with the El‐Niño activity. In the Southern region in addition to global patterns we detected the effect of the Mediterranean Sea, while antecedent precipitation plays a less important role in this region.

Daily Streamflow of Argentine Rivers Analysis Using Information Theory Quantifiers.

This paper analyzes the temporal evolution of streamflow for different rivers in Argentina based on information quantifiers such as statistical complexity and permutation entropy. The main objective is to identify key details of the dynamics of the analyzed time series to differentiate the degrees of randomness and chaos. The permutation entropy is used with the probability distribution of ordinal patterns and the Jensen-Shannon divergence to calculate the disequilibrium and the statistical complexity. Daily streamflow series at different river stations were analyzed to classify the different hydrological systems. The complexity-entropy causality plane (CECP) and the representation of the Shannon entropy and Fisher information measure (FIM) show that the daily discharge series could be approximately represented with Gaussian noise, but the variances highlight the difficulty of modeling a series of natural phenomena. An analysis of stations downstream from the Yacyretá dam shows that the operation affects the randomness of the daily discharge series at hydrometric stations near the dam. When the station is further downstream, however, this effect is attenuated. Furthermore, the size of the basin plays a relevant role in modulating the process. Large catchments have smaller values for entropy, and the signal is less noisy due to integration over larger time scales. In contrast, small and mountainous basins present a rapid response that influences the behavior of daily discharge while presenting a higher entropy and lower complexity. The results obtained in the present study characterize the behavior of the daily discharge series in Argentine rivers and provide key information for hydrological modeling.

Gap infilling of daily streamflow data using a machine learning algorithm (MissForest) for impact assessment of human activities

Machine learning algorithm has been increasingly used to fill missing daily streamflow data from neighboring gauges in data-scarce regions. However, how human activities, especially reservoir construction, may affect the performance of these gap-filling algorithms has not been explicitly assessed. This study applied the MissForest algorithm (MF) to infill daily streamflow gaps at 58 selected stations in the north area of Tianshan Mountains, an arid region in northwest China. The stations without reservoir impacts (i.e., with natural flow regimes) and with reservoir impacts (i.e., with high impacts of human activities) have been separately used to implement the infilling such that the impact of human activities on the performance of MF can be analyzed. Results show that MF using the station without reservoir impacts performed very well in filling daily streamflow gaps. Its performance had been insignificantly influenced by an increasing amount of missing data, different numbers of stations, and different lengths of observed data at each station. Without human activities, MF performance obviously improved with an increasing of 2.2%-3.2% in mean of R2 and a reducing of 21%-40% in mean of SMAPE. However, the performance of MF exhibited a noticeable degradation as a result of human activities that mean of R2 reduced by 1.1%-3.4% and mean of SMAPE increased by −19.5%-30.6%. This could be attributed to the fact that the MF algorithm is unable to fully capture the relevant information regarding human activities. The reconstructed daily streamflow series showed little flow regime change at the stations without reservoir impacts, but an advanced peak flow from July to June and a decrease of peak flow by 13% at the stations with reservoir impacts during the period of 2006–2011 compared with the baseline period of 1964–1989. It is recommended to first fill in the data gaps at stations without human activity, and then fill in the data gaps at stations influenced by human activities. We recommend exercising caution when using the MF method in rivers where human activities are significant or where low flow and intermittent flow events occur frequently.

Physics-Guided Long Short-Term Memory Network for Streamflow and Flood Simulations in the Lancang–Mekong River Basin

A warming climate will intensify the water cycle, resulting in an exacerbation of water resources crises and flooding risks in the Lancang–Mekong River Basin (LMRB). The mitigation of these risks requires accurate streamflow and flood simulations. Process-based and data-driven hydrological models are the two major approaches for streamflow simulations, while a hybrid of these two methods promises advantageous prediction accuracy. In this study, we developed a hybrid physics-data (HPD) methodology for streamflow and flood prediction under the physics-guided neural network modeling framework. The HPD methodology leveraged simulation information from a process-based model (i.e., VIC-CaMa-Flood) along with the meteorological forcing information (precipitation, maximum temperature, minimum temperature, and wind speed) to simulate the daily streamflow series and flood events, using a long short-term memory (LSTM) neural network. This HPD methodology outperformed the pure process-based VIC-CaMa-Flood model or the pure observational data driven LSTM model by a large margin, suggesting the usefulness of introducing physical regularization in data-driven modeling, and the necessity of observation-informed bias correction for process-based models. We further developed a gradient boosting tree method to measure the information contribution from the process-based model simulation and the meteorological forcing data in our HPD methodology. The results show that the process-based model simulation contributes about 30% to the HPD outcome, outweighing the information contribution from each of the meteorological forcing variables (<20%). Our HPD methodology inherited the physical mechanisms of the process-based model, and the high predictability capability of the LSTM model, offering a novel way for making use of incomplete physical understanding, and insufficient data, to enhance streamflow and flood predictions.

Ensemble streamflow forecasting based on variational mode decomposition and long short term memory

Reliable and accurate streamflow forecasting plays a vital role in the optimal management of water resources. To improve the stability and accuracy of streamflow forecasting, a hybrid decomposition-ensemble model named VMD-LSTM-GBRT, which is sensitive to sampling, noise and long historical changes of streamflow, was established. The variational mode decomposition (VMD) algorithm was first applied to extract features, which were then learned by several long short-term memory (LSTM) networks. Simultaneously, an ensemble tree, a gradient boosting tree for regression (GBRT), was trained to model the relationships between the extracted features and the original streamflow. The outputs of these LSTMs were finally reconstructed by the GBRT model to obtain the forecasting streamflow results. A historical daily streamflow series (from 1/1/1997 to 31/12/2014) for Yangxian station, Han River, China, was investigated by the proposed model. VMD-LSTM-GBRT was compared with respect to three aspects: (1) feature extraction algorithm; ensemble empirical mode decomposition (EEMD) was used. (2) Feature learning techniques; deep neural networks (DNNs) and support vector machines for regression (SVRs) were exploited. (3) Ensemble strategy; the summation strategy was used. The results indicate that the VMD-LSTM-GBRT model overwhelms all other peer models in terms of the root mean square error (RMSE = 36.3692), determination coefficient (R2 = 0.9890), mean absolute error (MAE = 9.5246) and peak percentage threshold statistics (PPTS(5) = 0.0391%). The addressed approach based on the memory of long historical changes with deep feature representations had good stability and high prediction precision.

An unbiased estimator of coefficient of variation of streamflow

Given increasing demand for high frequency streamflow series (HFSS) at daily and subdaily time scales there is increasing need for reliable metrics of relative variability for such series. HFSS can exhibit enormous relative variability especially in comparison with low frequency streamflow series formed by aggregation of HFSS. The product moment estimator of the coefficient of variation C, defined as the ratio of sample standard deviation to sample mean, as well as ten other common estimators of C, are shown to provide severely downward biased and highly variable estimates of C for very long records of highly skewed and periodic HFSS particularly for rivers which exhibit zeros. Resorting to the theory of compound distributions, we introduce an estimator of C corresponding to a mixture of monthly zero-inflated lognormal distributions denoted as a delta lognormal monthly mixture ΔLN3MM model. Through monthly stratification, our ΔLN3MM model accounts for the seasonality, skewness, multimodality, and the possible intermittency of HFSS. In comparisons among estimators, our ΔLN3MM based C estimator is shown to yield much more reliable and approximately unbiased estimates of C not only for small samples but also for very large samples (tens of thousands of observations). We document values of C in the range of [0.18, 42,000] with a median of 1.9 and an interquartile range of [1.34, 3.75] for 6807 daily streamflow series across the U.S. from GAGES-II dataset, with the highest values of C occurring in arid and semiarid regions. A multivariate analysis and national contour map reveal that extremely large values of C, never previously documented, tend to occur in arid watersheds with low runoff ratios, which tend to also exhibit a considerable number of zero streamflows.

Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management

Accurate runoff forecasting plays an important role in guaranteeing the sustainable utilization and management of water resources. Artificial intelligence methods can provide new possibilities for runoff prediction when the underlying physical relationship cannot be explicitly obtained. However, few reports evaluate the performances of various artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management by far. To refill this research gap, the potentials of five artificial intelligence methods in daily streamflow series prediction are examined, including artificial neural network (ANN), adaptive neural-based fuzzy inference system (ANFIS), extreme learning machine (ELM), Gaussian process regression (GPR) and support vector machine (SVM). Four quantitative statistical indexes are chosen as the evaluation benchmarks. The results from two huge hydropower reservoirs in China show that five artificial intelligence methods can achieve satisfying forecasting results, while the SVM, GPR and ELM methods can produce better performances than ANN and ANFIS in both training and testing phases with respective to four indexes. Thus, it is of great importance to carefully choose the appropriate forecasting models based on the actual characteristics of the studied reservoir.

Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data

AbstractThe Nash‐Sutcliffe efficiency (NSE) and the Kling‐Gupta efficiency (KGE) are now the most widely used indices in hydrology for evaluation of the goodness of fit between model simulations S and observations O. We introduce two theoretical (probabilistic) definitions of efficiency, E and E′, based on the estimators NSE and KGE, respectively, which enable controlled Monte Carlo experiments at 447 watersheds to evaluate their performance. Although NSE is generally unbiased, it exhibits enormous variability from one sample to another, due to the remarkable skewness and periodicity of daily streamflow data. However, use of NSE with logarithms of daily streamflow leads to estimates of E with almost no variability from one sample to the next, though with high upward bias. We introduce improved estimators of E and E′ based on a bivariate lognormal monthly mixture model that are shown to yield considerable improvements over NSE and slight improvements over KGE in controlled Monte Carlo experiments. Our new estimators of E should avoid most previous criticisms of NSE implied by the literature. Improved estimators of E that account for skewness and periodicity are needed for daily and subdaily streamflow series because NSE is not suited to such applications.

A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series

Data-driven methods are very useful for streamflow forecasting when the underlying physical relationships are not entirely clear. However, obtaining an accurate data-driven model that is sufficiently performant for streamflow forecasting remains often challenging. This study proposes a new data-driven model that combined the variational mode decomposition (VMD) and the prediction models for daily streamflow forecasting. The prediction models include the autoregressive moving average (ARMA), the gradient boosting regression tree (GBRT), the support vector regression (SVR), and the backpropagation neural network (BPNN). The latest decomposition model, the VMD algorithm, was first applied to extract the multiscale features from the entire time series and to decompose them into several subseries, which were predicted after that using forecast models. The ensemble forecast was finally reconstructed by summing. Historical daily streamflow series recorded at the Wushan and Weijiabao hydrologic stations from 1 January 2001 to 31 December 2014 in China were investigated using the proposed VMD-based models. Three quantitative evaluation indexes, including the Nash–Sutcliffe efficiency coefficient (NSE), the root mean square error (RMSE), and the mean absolute error (MAE), were used to evaluate and compare the predicted results of the proposed VMD-based models with two other models such as nondecomposition method (BPNN) and BPNN based on ensemble empirical mode decomposition (EEMD-BPNN). Furthermore, a comparative analysis of the performance of the VMD-BPNN model under different forecast periods (1, 3, 5, and 7 days) was performed. The results evidenced that the proposed VMD-based models could always achieve good performance in the testing stage and had relatively good stability and representativeness. Specifically, the VMD-BPNN model considered both the prediction accuracy and computation efficiency. The results show that the reliability of the forecasting decreased as the foresight period increased. The model performed satisfactorily up to 7-d lead time. The VMD-BPNN model could be applied as a promising, reliable, and robust prediction tool for short-term streamflow forecasting modelling.

Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting

Reliable and accurate streamflow forecasting is vital for water resource management. Many streamflow prediction studies have demonstrated the excellent prediction ability of decomposition ensemble models. Several studies first extracted subsignals from a streamflow series and then split subsignals into training and validation sets to build prediction models, capturing some future information not available in practical streamflow forecasting. Alternatively, dividing a time series into training and validation sets first and later decomposing them into subsignals can expose the subsignals to the boundary effect, making predicting streamflow difficult. Furthermore, building one model for each subsignal is laborious and can cause error accumulation. Therefore, establishing a robust and efficient decomposition ensemble model without future information to predict highly nonstationary and nonlinear streamflow is challenging. Hence, a single-model forecasting (SF) scheme that assesses the validation distribution during the training stage to adapt to the boundary effect was designed. A SF scheme based on variational mode decomposition (VMD) and long short-term memory (LSTM), namely, SF-VMD-LSTM, was proposed to predict daily streamflow 1–7 days ahead. Non-decomposition-based and decomposition-ensemble-based LSTM models established using SF, multi-model ensemble forecasting (MEF) and SF with the most influential subsignals (SFMIS) and the ensemble empirical mode decomposition (EEMD) and discrete wavelet transform (DWT) were compared. Additionally, multi-model ensemble hindcasting (MEH), single-model hindcasting (SH) and SH with the most influential subsignals (SHMIS) were used as benchmarks to evaluate the forecasting schemes’ adaptability to the boundary effect. Two daily streamflow series from Han River and Jing River, China, were investigated. The results indicate that (1) SF is more robust and efficient than MEF and SFMIS; (2) VMD performs better than EEMD and DWT; (3) SF-VMD-LSTM, with NSE values larger than 0.8 for almost all the prediction scenarios, outperformed other comparative models. Therefore, SF-VMD-LSTM is robust and efficient for forecasting highly nonstationary and nonlinear streamflow.

Improved estimators of correlation and R2 for skewed hydrologic data

ABSTRACTThe coefficient of determination R2 and Pearson correlation coefficient ρ = R are standard metrics in hydrology for the evaluation of the goodness of fit between model simulations and observations, and as measures of the degree of dependence of one variable upon another. We show that the standard product moment estimator of ρ, termed r, while well-behaved for bivariate normal data, is upward biased and highly variable for bivariate non-normal data. We introduce three alternative estimators of ρ which are nearly unbiased and exhibit much less variability than r for non-normal data. We also document remarkable upward bias and tremendous increases in variability associated with r using both synthetic data and daily streamflow simulations from 905 calibrated rainfall–runoff models. We show that estimators of ρ = R accounting for skewness are needed for daily streamflow series because they exhibit high variability and skewness compared to, for example, monthly/annual series, where r should perform well.

Quantifying the change in streamflow complexity in the Yangtze River

Hydrological processes of the Yangtze River have changed over the past decades due to environmental change and human activity. This paper uses sample entropy to investigate the spatial distribution and dynamic change in streamflow series complexity in the Yangtze River, China. In this study, the complexity of the streamflow series is quantified by entropy analysis. Daily streamflow series for four stations located in the mainstem and two control stations of the two largest freshwater lakes were analysed for the past 60 years. The results showed that the complexity of the streamflow series showed an obvious spatial difference and an increasing trend from upstream to downstream in the Yangtze River. There was a negative relationship between the annual streamflow and the corresponding sample entropy, and their peak-to-valley values showed well-corresponding relationships. The complexity of the runoff series at the Cuntan, Yichang, and Datong stations showed a continuous increasing trend, while that of the Hankou station showed a decreasing trend. The Three Gorges Dam changed the streamflow series complexity in the middle reach of the Yangtze River during the initial impoundment stage, while it had only slight impacts during the fully operational stage. Compared to the mainstem reaches, the streamflow series complexity of the two lakes showed no obvious change. The complexity of the streamflow series in the mainstem of the Yangtze River has been influenced by dam construction. The study could provide a scientific reference for understanding the flow dynamic evolution in the Yangtze River.

Power law distribution characteristics of daily streamflow in the Yellow River Basin under a changing environment

Abstract Natural streamflow series usually exhibit a power law tail with a magnitude–frequency relationship; however, it is not known how the power law distribution of daily streamflow varies under the influence of climate change and human activities. Based on the annual streamflow series of Toudaoguai, Longmen, Sanmenxia, Huayuankou, Gaocun, and Lijin stations located in the Yellow River in China, the Mann–Kendall and Mann–Whitney–Pettitt methods were used to identify change-points. Thereafter, the power law distributions of the daily streamflow series before and after abrupt changes were studied based on the two-parameter power law distribution method. The results reveal that, firstly, under the influence of human activities, abrupt changes have taken place in the streamflow series of the Yellow River, mainly in the storage year of large reservoirs. Secondly, the daily streamflow series at four out of six stations obey a power law distribution, however, with short tails. The power law characteristics of the series before the abrupt change are essentially consistent with the entire streamflow series; however, these characteristics gradually disappear after abrupt changes. Thirdly, human activities are the main factors underlying the variations in the power law distribution of daily streamflow in different periods. This study provides a way for the study of streamflow changes in the Yellow River and may also offer a scientific basis for water resources development and ecological restoration.

Multiple streamflow time series modeling using VAR–MGARCH approach

Multivariate time series modeling approaches are known as valuable methods for simulating and forecasting the temporal evolution of hydroclimatic variables. These approaches are also useful for modeling the temporal dependence and cross-dependence between variables and sites. Although multiple linear time series approaches, such as vector autoregressive (VAR) and multiple generalized autoregressive conditional heteroscedasticity (MGARCH) approaches are ordinarily applied in finance and econometrics, these methods have not been broadly applied in hydrology science. The present research employs the VAR and VAR–MGARCH methods to model the mean and conditional variance (heteroscedasticity) of daily streamflow data in the Zarrineh Rood dam watershed, in northwestern Iran. The bivariate diagonal vectorization heteroscedasticity (DVECH) model, as one of the key MGARCH models, demonstrates how the conditional variance, covariance, and correlation structures change in time between the residual time series from VAR model. In this regards, in the present study, five experiments which present different combinations of twofold streamflows (including both upstream and downstream stations) are conducted. The VAR approach is fitted to the twofold daily time series in each of the experiments with different orders. The Portmanteau test, as a formal test for demonstrating time-varying variance (or so-called ARCH effect), indicates the existence of conditional heteroscedastic behavior in the twofold residual time series obtained from the VAR models fitted to the twofold streamflows. Thus, the VAR–DVECH approach is suggested to capture the inherent heteroscedasticity in daily streamflow series. The bivariate DVECH approach indicates short-term and long-term persistency in the conditional variance–covariance structure of the twofold residuals of streamflows. Results show also that the use of the nonlinear bivariate DVECH model improves streamflow modeling efficiency by capturing the heteroscedasticity in the twofold residuals obtained from the VAR model for all experiments. The assessment criteria indicate also that the VAR–DVECH approach leads to a better performance than the VAR model.

Statistical classification of streamflow based on flow variability in west flowing rivers of Kerala, India

Measured streamflow and flood series of 43 gauging stations from 25 west flowing rivers in Kerala, India, were analysed for their descriptive characteristics to study their spatial and temporal variation. The spatial and temporal variations in streamflow are influenced by many factors including climatic and basin characteristics. Streamflow data from each station (length varies from 14 to 43 years) is analysed for their internal characteristics such as trend, stationarity, homogeneity, noise and periodicity to incorporate it in hydrological models, so that their predictions would be more accurate. The internal characteristics were studied along with the statistical analysis. For analysing each internal characteristic, more than one method of analysis has been used to have reliable result. The trend characteristic was analysed using Mann-Kendall (MK) test, Sen’s slope test, Spearman’s rank correlation coefficient and Pearson correlation coefficient methods. Stationarity characteristics have been tested using augmented Dickey-Fuller test (ADF), Phillips-Perron test and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test. For identifying homogeneous nature, Pettitt test, standard normal homogeneity (SNH) test, Buishand test and von Neumann tests were used. Noise in the streamflow was verified using Box-Peirce, Ljung-Box and McLeod-Li tests. From the study, it is found that the daily series is non-homogeneous, stationary data with white noise, whereas flood series shows mixed characteristics. Based on the variations in the time series, the daily streamflow and flood series are classified into different categories such as high, average, moderate, minor and no variation stations. In most of the river basins, daily flow shows average variation. Flood series shows average variation in 44% of stations and moderate variation in 28% of stations.

Rainfall-Runoff Modelling Considerations to Predict Streamflow Characteristics in Ungauged Catchments and under Climate Change

This paper investigates the prediction of different streamflow characteristics in ungauged catchments and under climate change, with three rainfall-runoff models calibrated against three different objective criteria, using a large data set from 780 catchments across Australia. The results indicate that medium and high flows are relatively easier to predict, suggesting that using a single unique set of parameter values from model calibration against an objective criterion like the Nash–Sutcliffe efficiency is generally adequate and desirable to provide a consistent simulation and interpretation of daily streamflow series and the different medium and high flow characteristics. However, the low flow characteristics are considerably more difficult to predict and will require careful modelling consideration to specifically target the low flow characteristic of interest. The modelling results also show that different rainfall-runoff models and different calibration approaches can give significantly different predictions of climate change impact on streamflow characteristics, particularly for characteristics beyond the long-term averages. Predicting the hydrological impact from climate change, therefore, requires careful modelling consideration and calibration against appropriate objective criteria that specifically target the streamflow characteristic that is being assessed.

Low-flow frequency analysis at ungauged sites based on regionally estimated streamflows

Estimation of low-flow quantiles or indices at ungauged sites is traditionally done through regional low-flow frequency analysis. However, traditional methods imply a prior aggregation of the regional information due to the usual focus on a given quantile. This leads to loss of information for estimating additional quantiles or performing additional applications. In the present study, the recently proposed regional streamflow based frequency analysis (RSBFA) approach is evaluated for the estimation of low-flow quantiles. The approach, originally applied to floods, is based on regionally estimating the daily streamflow series at the ungauged site to later obtain hydrological quantiles through a local frequency analysis. In the present study, the RSBFA approach is applied to low flows through a case study in the province of Quebec, Canada, considered in prior traditional regional low-flow studies. Although the RSBFA approach does not systematically lead to the best results, its relevance resides in the fact that the whole regionally estimated daily streamflow series is provided at the ungauged site. This allows an easy estimation of low-flow quantiles associated to particular durations, such as the 7-day (30-day) average low flows corresponding to a return period of 2 or 10 years (5 years). Furthermore, a large range of additional low-flow quantiles, such as q95% (streamflow expected to be exceeded 95% of the time divided by the catchment area), may be obtained from the regionally estimated streamflow series with a very good performance. Therefore, any specific or absolute, annual or seasonal low-flow quantile may be easily obtained by a local low-flow frequency analysis without repeating the regional procedure. The approach does not require complex models, and it may also allow combining regional and local information in a straight forward manner.

Nonstationarity in maximum annual daily streamflow series from Southern Brazil

ABSTRACT Extreme streamflow values estimates are important for flood risk assessment and also for the design and operation of hydraulic structures. The behavior of this hydrological variable is under climate and land use changes effects and river’s course modifications caused by construction and operation of large reservoirs. The assumption of stationarity commonly adopted in flood frequency and magnitude analysis studies is questionable under such circumstances. In this work we identified nonstationary annual maximum streamflow series corresponding to fluviometric gauges located in Southern Brazil. A nonstationary frequency model was applied to those series and the results were compared with those of a stationary model. We also evaluated the presence of abrupt changes in the series. The results indicate that 75 of 157 series of Southern Brazil may be considered nonstationary, most of which are in the Iguazu, the Paranapanema and the Uruguay basins. For a planning horizon equal to 10 years, the return period of the present 100 years flood changes to 48-75 years when considering the nonstationary model, respectively. Abrupt changes were identified mainly as occurring in the 70’s.

Flood Frequency Analysis at Ungauged Sites Based on Regionally Estimated Streamflows

Abstract Estimation of flood events at ungauged sites is often performed through regional flood frequency analysis (RFFA). RFFA uses the available information at gauged sites to estimate the desired design events at the ungauged site. These regional methods are based on a prior aggregation of the hydrological information at the gauged sites, which implies loss of information. In the present study, a different approach or path for conducting RFFA is presented. First, the daily streamflow series at the ungauged site is regionally estimated from daily information at the gauged sites through a regional flow duration curve approach. Then, a local flood frequency analysis is performed on the extracted maximum peak flow series. The proposed approach, referred to as regional streamflow-based frequency analysis (RSBFA), is applied to a case study in the province of Quebec, Canada. Results indicate that the performance of the RSBFA approach is comparable to traditional methods. However, the proposed method has the advantages of being simple, flexible, and of providing the whole daily streamflow series at the ungauged site, which allows the direct estimation of a large number of other flow characteristics, such as low-flow features. The RSBFA approach also avoids performing a complete at-site flood frequency analysis at each gauged site. The fact that all the regional information is included in the regionally estimated daily streamflow series implies a number of benefits: annual or seasonal, absolute or specific, stationary or nonstationary, and univariate or multivariate flood quantiles corresponding to any return period may then be obtained through the estimated series without reconducting a regional analysis.