Low Flow Data Research Articles

Non-stationary low flow frequency analysis under climate change

Analysis of low river flows provides important information for effective management of water resources in a region. Despite the critical importance of understanding low flow dynamics, there is a gap in the literature regarding the use of non-stationary models to analyze low flow data under climate change in Turkey. In this research, low flow series from 80 measuring stations in Turkey are investigated by employing both stationary and non-stationary models based on the Generalized Additive Models for Location, Scale and Shape (GAMLSS). For constructing non-stationary models, 31 explanatory variables consisting of time, precipitation, temperature and atmospheric oscillation indices were used to model the parameters of the chosen distributions. The results show that stationary models are more successful at 7 stations, while non-stationary models are more successful at 73 stations. Comparisons between non-stationary models showed that for most stations, the best performing models were non-stationary models with annual precipitation as covariates. In addition, successful results were obtained when Western Mediterranean Oscillation and North Atlantic Oscillation indices were used as explanatory variables. Additionally, this study investigated 20 and 50-year return levels by fitting the non-stationary frequency distribution models for low flows over historical and projection periods under SSP2-4.5 and SSP5-8.5 climate scenarios. GAMLSS incorporated annual total precipitation, which is the most effective explanatory variable for low flows, as a covariate, and thus changes in low flows were analyzed. The results show that decreases are expected in low flows, except for the stations in the upper Euphrates basin compared to the historical period.

Low Flow Frequency Analysis at Triang River Streamflow Station

Low flow analysis is essential for determining the communities’ water supply. As water supply is a necessity for human daily survival, it is crucial to recognize that the availability of water can have a significant impact on local livelihoods and the sustainability of local populations. Triang River is a tributary of the Pahang River in Peninsular Malaysia. The existence of the Triang Water Intake and Treatment Plant along the Triang River stream serves to determine the low flow magnitudes and frequency curves for the Triang River streamflow station using the Weibull, Gringorten, and Cunnane plotting position formulas. Based on 20 years of historical data, this study analyzed the annual minimum streamflow data for 1-, 4-, 7-, and 30-day durations as well as the average recurrence intervals (ARIs) of 2-, 5-, 10-, 20-, 50-, and 100-year streamflow data. These three plotting position formulas were successfully used to estimate the low flow magnitudes for the various durations and different ARIs. The findings can serve as a guide for developing future water resource projects that take into account the Triang River’s low flow data.

A Turbulent Inflow Generation Method for the LES of High Re Flow by Scaling Low Re Flow Data

The rescaling–recycling method (RRM) is usually used to generate turbulent inflow for the LES of compressible wall-bounded flows, which can lead to relatively high computational cost for high Re flows since the mesh resolution increases exponentially with Re number. A turbulent inflow generation method based on the scaling of low Re flow, referred as TIG-LowRe, is proposed, aiming at reducing the computational cost when applying the RRM. To validate the proposed method, the TIG-LowRe method was applied to generate turbulent inflow for the LES of a non-isothermal round jet flow at Re = 86,000. Two cases were carried out with the inflow generated based on two round pipe flows at Re = 10,000 and 24,000. The results show that the mean and fluctuating temperatures of the two cases agree well with the experimental data. In the case of low Re flow at Re = 10,000, the jet flow decays too fast along the axial direction, the mean and fluctuating axial velocities are over-predicted and the radial fluctuating velocity is under-predicted. By increasing the Re of the low Re flow to 24,000, the decay rate of the jet flow decreases and the accuracies of the mean and fluctuating axial velocities are obviously improved, while the radial fluctuating velocity shifts further away from the experimental data. The main reason for the difference between the two cases is that more fine turbulent structure of the inflow in case-Re10000 is lost than in case-Re24000 during the turbulence generation process.

Low Flow Anesthesia – Mission Impossible?

Because low flow anesthesia reduces waste of environmentally unfriendly inhaled anesthetics, it is coming in the spotlights – again. Despite a detailed theoretical description, considerable teaching efforts of this simple technique have not succeeded in consistently lowering fresh gas flows (FGF) during manual control1. Worse, even though technology has solved the hurdles of manual delivery and the technology is widely available, we fail to maximally implement it. The delivery of inhaled anesthetics with high FGF prior to securing the airway remains common practice. We fail to consistently adjust MAC to age and poorly titrate opioids to reduce the fraction of the MAC we administer. We fail to incorporate hysteresis which is reflected in the use of excessively high FGF and vaporizer settings during wash-in and in the failure to maintain low FGF prior to emergence (“coasting”). By failing to fully appreciate the quantitative effects of the delivery if inhaled anesthetics we miss the opportunity to reduce waste to the absolute minimum. Belief and myth are strong when the environmental impact of inhaled anesthetics is considered. We need better, detailed life cycle analyses with low flow data before making claims pro/con inhaled/TIVA. We tend to lose sight of perspective, and have to continue to weigh the impact of drug selection on patient care.

Estimation of Low Flow Statistics for Sustainable Water Resources Management in South Australia

The Magnitude and occurrence of extreme low flow events are needed in setting minimum flows to protect the instream users. As the true distribution is not normally known, the identification of the most appropriate distribution function that describes the extreme low flow data of a catchment is essential in estimating reliable low flow quantiles at various average recurrence intervals (ARI). The aim of this study is to conduct a comparative assessment of the performance of three plausible distribution functions for estimating low flow quantiles. The investigation was carried out by using 27-gauge stations within South Australia (SA), the driest state in Australia. The best distribution function out of the three selected distributions; Log Normal (LN), Log Pearson Type 3 (LP3), and Generalized Extreme Value (GEV for each of the three selected annual minima series (7-day, 15-day and 30-day) at each gauged catchments was identified. The estimated low flow quantiles from using these three distribution functions were compared using RMSE values estimated through Monte Carlo simulation studies. For the majority of the selected study catchments, GEV fitted using L moments was found to be the best method for estimating low flow quantiles at ARIs over 10 years (≥14%), while at low ARI, LP3 fitted using the Method of Moments (MOM) was shown to outperform (≥17%) the other methods.

Spatial Hazards of Antibiotic Resistance in Wastewater-Impacted Streams during Low Instream Flow Conditions

Wastewater treatment plants (WWTPs) are a major point source of antibiotics and antimicrobial resistance in the aquatic environment. This study evaluated model predictions for ciprofloxacin (CIP), erythromycin, and sulfamethoxazole (SMX) concentrations, and the potential presence of their respective resistance within U.S. streams affected by WWTP discharges under varying instream flow conditions. We incorporate predicted no-effect concentrations (PNECs) for selection resistance into the previously developed De Facto Reuse Incidence Nations Consumable Supply model to identify potential antibiotic hotspots across the U.S. Our results suggest that under mean annual instream flow, more than one-third of sites (4629 out of 13,245) did not meet the antibiotic resistance (ABR) safety threshold for CIP. Under low instream flow conditions, dilution factors in 76.9% (n = 9885) of sites exceeded the ABR safety threshold for CIP, and ABR safety thresholds for two antibiotics were surpassed in 25.8% (n = 3323) of streams with available low flow data (n = 12,856). Despite considerable therapeutic use and the resulting presence of SMX in the effluent, the threat of SMX resistance is comparatively low due to a higher ABR threshold. We suggest that several streams across the U.S. are vulnerable to ABR development from compounds with relatively low PNECs, such as CIP, during average and low instream flow conditions.

Development of stage-discharge rating curve using ANN

Accurate forecasting of river discharge is essential for the efficient operation of water resources systems. Therefore, researchers are consistently developing and improving various techniques to predict river discharge with relative ease and high accuracy, although traditional methods are available. This paper presents mainly three data-driven modelling techniques, namely the stage rating curve (SRC), generalised reduced gradient (GRG) solver, and an artificial neural network (ANN) to accurately model the stage-discharge relationship for local rivers in Trinidad and Tobago using only low flow data. The model that produced the overall superior results was the ANN.

Context is Everything: Interacting Inputs and Landscape Characteristics Control Stream Nitrogen.

To understand the environmental and anthropogenic drivers of stream nitrogen (N) concentrations across the conterminous US, we combined summer low-flow data from 4997 streams with watershed information across three survey periods (2000-2014) of the US EPA's National Rivers and Streams Assessment. Watershed N inputs explained 51% of the variation in log-transformed stream total N (TN) concentrations. Both N source and input rates influenced stream NO3/TN ratios and N concentrations. Streams dominated by oxidized N forms (NO3/TN ratio > 0.50) were more strongly responsive to the N input rate compared to streams dominated by other N forms. NO3 proportional contribution increased with N inputs, supporting N saturation-enhanced NO3 export to aquatic ecosystems. By combining information about N inputs with climatic and landscape factors, random forest models of stream N concentrations explained 70, 58, and 60% of the spatial variation in stream concentrations of TN, dissolved inorganic N, and total organic N, respectively. The strength and direction of relationships between watershed drivers and stream N concentrations and forms varied with N input intensity. Model results for high N input watersheds not only indicated potential contributions from contaminated groundwater to high stream N concentrations but also the mitigating role of wetlands.

Predictive modelling of the stage–discharge relationship using Gene-Expression Programming

Abstract Modelling the hydrologic processes is an essential tool for the efficient management of water resource systems. Therefore, researchers are consistently developing and improving various predictive/forecasting techniques to accurately represent a river's attributes, even though traditional methods are available. This paper presents the Gene-Expression Programming (GEP) modelling technique to accurately model the stage–discharge relationship for the Arouca River in Trinidad and Tobago using only low flow data. The proposed method uses the stage and associated discharge measurements at one cross-section of the Arouca River. These measurements were used to train the GEP model. The results of the GEP model were also compared to the traditional method of the Stage–Discharge Rating Curve (SRC). Four statistical paraments namely the Pearson's Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Relative Error (MARE) and Nash–Sutcliffe Efficiency (NSE) were used to evaluate the performance of the GEP model and the SRC method. Overall, the GEP model performed exceptionally well with an R2 of 0.990, RMSE of 0.104, MARE of 0.076 and NSE of 0.957.

A review of low-flow hydrology

The purpose of this article is to assess the present state of low-flow hydrology, which is the study of the lowest flow in a river during the dry seasons of the year. The topic begins with an examination of low-flow producing processes under natural circumstances, as well as a description of human variables that influence low flows directly or indirectly. Following that, a review of current low-flow estimate techniques from stream flow time-series is presented, including flow duration curves, frequency analysis of severe low-flow occurrences and continuous low-flow periods, baseflow separation, and stream flow recession characterization. The article goes through a number of low-flow features (indices) and their uses. The connections between low-flow features are shown in a separate section. A regional regression approach, graphical depiction of low-flow features, creation of regional curves for low-flow prediction, and application of time-series simulation methods are among the approaches for low-flow estimate in ungauged river catchments discussed in the article. The article provides an overview of current low-flow-related research efforts from across the world. The issue of fluctuating minimum river flows as a consequence of climatic variability, as well as specific uses of low-flow data in river ecological research and environmental flow management, are also addressed. The review is mainly based on research findings from the previous two decades.

Applicability of Lindley Distribution for Hydroelectric Dam Operations in the Dry Season

The long-term low-flow data are necessary for efficient planning of water resources and for estimating accurate quantiles via runoff data analysis at point. However, the short recording time period, low confidence, inconsistent distribution model, and parameter estimation method, make it difficult to estimate a proper low-flow quantile for each return period. In this study, the Lindley distribution model, which is a mix of the exponential and the gamma distribution models and has been verified as efficient by previous studies, was used to analyze the low-flow frequency using dam inflow data. We studied its applicability via comparison with statistics of observed data and other models already used for low-flow frequency analysis. For this, we carried out a performance analysis through a low-flow frequency analysis of inflow data from the hydroelectric dam and the reappearance capacity assessment of observed data at the Han river watershed. As a result, the hydrological applicability of the Lindley distribution model and its relative qualitative and quantitative excellence compared to the existing model were verified.

Spatial and Temporal Evolution of Low Water Flows in the Sassandra River Catchment Area in Cote d’Ivoire

In a context of climate change and declining water resources, knowledge of low flow is essential. Present study deals with the spatial and temporal evolution of the streams low flow in Sassandra river. In the study, 12 stations of the Sassandra sub-basins (Sassandra, N’zo, Lobo, and Davo) were selected according to the availability and quality of long-term data. Annual, monthly and daily low flow data from twelve hydrological stations of the Sassandra watershed were used and cover the period from 1970 to 2015. The methodology used is concerned with extracting the low flow data and analyzing and evaluating the trends (Mann-Kendall) and change-point (cumulate sum) of low flow data. Statistical tests are applied to the mean and variance of the low-flow series. The results of the statistical tests show more trends and change points on the mean than on the variance. Significant trends show an increase in low flow waters. The significant change point detected by the cumulative sums test generally occurs between 1990-2007. In addition, tributaries are more affected by significant trends and change point detection than Sassandra river.

Bivariate frequency analysis of low flow using copula functions (case study: Dez River Basin, Iran)

Accurate estimation of low flow as a criterion for different objectives in water resource management, including drought is of crucial importance. Despite the complex nature of water deficits, univariate methods have often been used to analyze the frequency of low flows. In this study, low flows of Dez River basin were examined during period of 1956–2012 using copula functions at the upstream of headbranches’ junction. For this purpose, at first 7-day series of low flow was extracted at the studied stations, then their homogeneity was examined by Mann–Kendall test. The results indicated that 7-day low flow series of Dez basin were homogenous. In the next stage, 12 different distribution functions were fitted onto the low flow data. Finally, for Sepid Dasht Sezar (SDS), Sepid Dasht Zaz (SDZ), and Tang Panj Bakhtiyari (TPB) stations, logistic distribution had the best fit, while for Tang Panj Sezar (TPS) station, GEV distribution enjoyed the best fit. After specifying the best fitted marginal distributions, seven different copula functions including Ali–Mikhail–Haq (AMH), Frank, Clayton, Galambos, Farlie–Gumbel–Morgenstern (FGM), Gumbel–Hougaard (GH), and Plackett were used for bivariate frequency analysis of the 7-day low flow series. The results revealed that the GH copula had the best fitness on paired data of SDS and SDZ stations. For TPS and TPB stations, Frank copula has had the best correspondence with empirical copula values. Next, joint and conditional return periods were calculated for the low flow series at the upstream of branches’ junction. The results of this study indicated that the risk of incidence of severe drought is higher in upstream stations (SDZ and SDS) when compared with downstream stations (TPB and TPS) in Dez basin. Generally, application of multivariate analysis allows researchers to investigate hydrological events with a more comprehensive view by considering the simultaneous effect of the influencing factors on the phenomenon of interest. It also enables them to evaluate different combinations of required scenarios for integrated management of basin and planning to cope with the damages caused by natural phenomena.

Scaling of low flows in Czechia – an initial assessment

Fiala et al. (2010) performed a study focused on the evolution of dry spells at 144 water-gauging stations covering the entire territory of Czechia. They investigated several indicators such as series of 7-day low flows, and deficit volumes and durations (both delimited based on two quantiles, Q330 and Q355, under which the flows are commonly considered as low). Using the Mann-Kendall (MK) test and its reliable modification that accounts for short-term persistence (STP) in data, they evaluated trends in their series. However, as stated in Khaliq et al. (2008), low flow data may also reveal so-called long-term persistence (LTP) that may cause the MK test to detect trends falsely. Therefore, the current study aims to find out if LTP was present before Fiala et al. (2010) applied the MK test to the 7-day low flow series. This was done through the Hurst exponent estimation. Also, testing for a unit root was carried out, similar to Fatichi et al. (2009).

Contribution of piezometric measurement to knowledge and management of low water levels: examples on the chalk aquifer in the Champagne Ardennes region

Abstract. This article is based on a BRGM study on piezometric indicators, threshold values of discharge and groundwater levels for the assessment of potentially-exploitable water resources of chalky watersheds. A method for estimating low water levels based on groundwater levels is presented from three examples representing chalk aquifers with different cycles: annual, combined and interannual. The first is located in Picardy and the two others in the Champagne-Ardennes region. Piezometers with annual cycles, used in these examples, are supposed to be representative of the aquifer hydro-dynamics. Except for multi-annual systems, the analysis between discharge measurements at a hydrometric station and groundwater levels measured at a piezometer representative of the main aquifer, leads to relatively precise and satisfactory relationships within a chalky context. These relationships may be useful for monitoring, validation, extension or reconstruction of the low water flow data. On the one hand, they allow definition of the piezometric levels corresponding to the different alert thresholds of river discharges. On the other hand, they clarify the proportions of low surface water flow from runoff or drainage of the aquifer. Finally, these correlations give an assessment of the minimum flow for the coming weeks. However, these correlations cannot be used to optimize the value of the exploitable water resource because it seems to be difficult to integrate the value of the effective rainfall that could occur during the draining period. Moreover, in the case of multi-annual systems, the solution is to attempt a comprehensive system modelling and, if it is satisfactory, using the simulated values to get rid of parasites or running the model for forecasting purposes.

Spatial and Temporal Streamflow Trends in Northern Taiwan

Streamflow is an important factor in the study of water resource management, floods, and droughts. Dramatic climate change has created extreme rainfall distributions, making the study of streamflow trends and variability even more crucial. In this study, the long-term streamflow data and trends recorded at gauging stations in Northern Taiwan are analyzed using the Mann-Kendall test. The data used for trend analysis are the average annual streamflow, the average seasonal streamflow, and the high and low flows. The slope trend is calculated using the Theil-Sen estimator. Finally, change point analysis is conducted using the Mann-Whitney-Pettit test and the cumulative deviation test to gain further information about the change points and to understand the changes in streamflow before and after the change points. The average annual streamflow of the 12 gauging stations in the study area is analyzed using the Mann-Kendall test. The results show that of the 12 gauging stations, only the Ximen Bridge Station in the Lanyang River basin show a significant downward streamflow trend. Results of the monthly and seasonal average streamflow analysis show that in the spring, 72.2% of the gauging stations showed upward streamflow trends, most of which were located in the Tamsui River and the Touqian River basins. The high and low flow data analysis shows that the Ximen Bridge Station was the only gauging station to feature a significant downward streamflow trend for both high and low flows. This distribution pattern provides valuable information for regional hydrological studies and water management.

Biohydrology of low flows in the humid Ethiopian highlands: The Gilgel Abay catchment

In Ethiopia the population is rapidly expanding. As a consequence the landscape is rapidly changing. Eucalyptus plantations are increasing and irrigation projects are implemented. The hydrological effects of the changing landscape on river (low) flows have not been well documented and therefore the amount of water available in the future might be over optimistic. The objective of this paper is to establish how low flows have been impacted by new developments in irrigation and by landscape change. For this paper, we choose the Gilgel Abay in the headwaters of the upper Blue Nile basin, since it has both good quality discharge data and it is located in the Tana Beles growth corridor. Numerical and statistical means were used to analyze the 25 years of available low flow data. We found a statistically significant decreasing trend (P < 0.00001) of low flow in the Gilgel Abay. From 1980’s to 1990’s the low flow decreased by 25% and from 1990’s to 2000’s the low flow was reduced by 46%. The deterministic analysis with the Parameter Efficient Distributed (PED) model supported the statistical findings and indicated that in the middle of the nineteen nineties, after irrigation projects and eucalyptus plantations increased greatly, the low flows decreased more rapidly.

Derivation of low flow distribution functions using copulas

Derivation of low flow distribution using recession functions has been introduced in previous studies, but without taking into consideration the statistical dependence structure between the characteristics of low flow event, i.e. the duration of dry spell t and the recession parameter k. Low flow data of three basins in China with different climates demonstrate that statistical dependence actually exists between t and k. A copula-based derived distribution is proposed in this paper to take full account of this internal dependence within the low flow event. The proposed derived distribution can be flexibly constructed using a wide variety of copula functions and marginal distributions. Four types of copula functions (i.e. Student, Clayton, Gumbel, and Frank), each with twelve combinations of marginal distributions, are all employed to derive low flow distributions to find out which component, copula function or marginal distribution, has the most impact on the performance of derived low flow distributions in fitting the observed data. It turns out that the capability of this copula-based derived distribution is strongly influenced by the choice of marginal distribution, while different copula functions have more than negligible impacts on the tails’ goodness-of-fit. Student copula is preferred to model the chosen (t, k) samples with both lower and upper tail dependence. But the copula-based derived distribution is not recommended to describe low flow samples with long lower tails. The performance of the copula-based derived distributions is compared with that of the derived truncated Weibull distribution whose parameters are also process-oriented but without considering the statistical dependence structure of (t, k) in low flow events. The results highlight that the copula-based derived distribution is more flexible and can more reasonably describe both the upper and lower tails of low flow series than the derived truncated Weibull distribution. Two traditional fitted distributions, fitted truncated Weibull distribution and fitted Pearson Type III distribution, are also applied to describe the low flow series to evaluate the capability of the copula-based derived distribution. The fitted Pearson Type III distribution always provides highest accuracy, while copula-based derived distributions perform comparably given the appropriate marginal distributions and copula function. In general, the copula-based derived distribution can be a potential attractive alternative in low flow frequency analysis, for it can be used in studying the impacts of climate change and human activities on the frequency of low flows.

A method for low-flow estimation at ungauged sites: a case study in Wallonia (Belgium)

Abstract. Well-integrated water management can notably require estimating low flows at any point of a river. Depending on the management practice, it can be needed for various return periods. This is seldom addressed in the literature. This paper shows the development of a full analysis chain including quality analysis of gauging stations, low-flow frequency analysis, and building of a global model to assess low-flow indices on the basis of catchment physical parameters. The most common distributions that fit low-flow data in Wallonia were two-parameter lognormal and gamma. The recession coefficient and percolation were the most explanatory variables, regardless of the return period. The determination coefficients of the models ranged from 0.51 to 0.67 for calibration and from 0.61 to 0.80 for validation. The regression coefficients were found to be linked to the return period. This was used to design a complete equation that gives the low-flow index based on physical parameters and the desired return period (in a 5 to 50 yr range). The interest of regionalisation and the development of regional models are also discussed. Four homogeneous regions are identified, but to date the global model remains more robust due to the limited number of 20-yr-long gauging stations. This should be reconsidered in the future when enough data will be available.

Joint mapping of statistical streamflow descriptors

Summary An approach is presented for a joint mapping and regionalization of statistical descriptors of streamflow like the mean value and the coefficient of variation of daily data, the flow duration curve, moments and distributions of annual minima and maxima. Probability theory, and specifically extreme value theory, offers possible parametric relations to be tested, linking knowledge of the basic mean value and coefficient of variation with the other descriptors. This initial study is limited to theoretical results derived from the assumption that the original daily data follow the lognormal or the gamma distribution. With these standard distributions the statistical regularity found is surprisingly good when confronting the theory against the empirical data of daily streamflow from 35 catchments in the Moselle drainage basin in France. The lognormal distribution appears to be the best candidate for the flow duration curve, as well as maximum and minimum streamflow. The tail behaviour of streamflow maxima is well described, while for minima the results are more difficult to interpret. One problem might be the relative higher uncertainty in annual low flow data, and especially the sensitivity to outliers towards high values.