Maximum Daily Discharge Research Articles

A hydro-climatic approach for extreme flood estimation in mountainous catchments

Prediction of rainfall-runoff process, peak discharges, and finally flood hydrograph is essential for flood risk management and river engineering projects. In most previous studies in this field, the precipitation rates have been entered into the models without considering seasonal and monthly distribution. In this study, the daily precipitation data of 144 climatology stations in Iran were used to evaluate the seasonal and monthly pattern of flood-causing precipitation. Then, by determining the rainy seasons and seasonal fit of precipitation with a probabilistic model and using regional precipitation, a semi-distributed conceptual model of rainfall-runoff (MORDOR-SD) was trained and validated using the observed discharge data. Flood prediction was performed using climatic data, modeling of hydrological conditions, and extreme flow data with high performance. According to the results, the Nash–Sutcliffe and Kling–Gupta coefficients were 0.69 and 0.82 for the mean daily streamflow, 0.98 and 0.98 for the seasonal streamflow, 0.98 and 0.94 for the maximum discharges, and 0.57 and 0.78 for low flows, respectively. Moreover, the maximum daily discharges in different return periods were estimated using the results of the MORDOR-SD model, considering the probability distribution function of the probabilistic model of central precipitation (MEWP), the probabilistic model of adjacent precipitation, and probability distribution function of the previous precipitation. Finally, the extreme flows were predicted and compared using different methods including the SCHADEX, regional flood analysis, GRADEX, and AGREGEE. The results showed that the methods GRADEX, AGREGEE, and SCHADEX have the highest performance in predicting extreme floods, respectively.

Analysis of Design Flood Discharge and Water Level Case Study of the Batang Singkut River Section Sarolangun Regency

The Batang Singkut River section is a tributary of the Batang Asai River which passes through Pelawan District and Singkut District. This research was carried out by calculating the design flood discharge and modeling the water level using HEC-RAS. The starting point of the research is on the bridge around the Jambi University campus in Sarolangun Regency with the length of the river under review being 300 meters. The discharge data used is the maximum daily discharge for 10 years from 2011 to 2020. The method used to calculate the design flood discharge is the method of frequency analysis and distribution testing using the chi-square method. The analysis was carried out to find the design flood discharge frequency using the Pearson III log distribution type for return periods of 2 and 5 years. The calculated discharge is entered into the HEC-RAS to model the water level. The modeling results showthat at a 2-year return period for cross sections STA 0+00, STA 1+00, STA 1+50, STA 2+00, STA 2+50, and STA 3+00 are able to accommodate water with a discharge of 83.5026 m3/s, but for the STA 0+50 cross section it is unable to accommodate the amount of discharge generated. Whereas for the return period of 5 years the cross section of STA 0+00 to STA 2+50 is unable to accommodate water with a discharge of 118.4132 m3/s, while for STA 3+00 the cross section can hold water.

Supraglacial streamflow and meteorological drivers from southwest Greenland

Abstract. Greenland ice sheet surface runoff is drained through supraglacial stream networks. This evacuation influences surface mass balance as well as ice dynamics. However, in situ observations of meltwater discharge through these stream networks are rare. In this study, we present 46 discrete discharge measurements and continuous water level measurements for 62 d spanning the majority of of the melt season (13 June to 13 August) in 2016 for a 0.6 km2 supraglacial stream catchment in southwest Greenland. The result is an unprecedentedly long record of supraglacial discharge that captures both diurnal variability and changes over the melt season. A comparison of surface energy fluxes to stream discharge reveals shortwave radiation as the primary driver of melting. However, during high-melt episodes, the contribution of shortwave radiation to melt energy is reduced by ∼40 % (from 1.13 to 0.73 proportion). Instead, the relative contribution of longwave radiation, sensible heat fluxes, and latent heat fluxes to overall melt increases by ∼24 %, 6 %, and 10 % (proportion increased from −0.32 to −0.08, 0.28 to 0.34, and −0.04 to 0.06) respectively. Our data also identify that the timing of daily maximum discharge during clear-sky days shifts from 16:00 local time (i.e., 2 h 45 min after solar noon) in late June to 14:00 in late July and then rapidly returns to 16:00 in early August. The change in the timing of daily maximum discharge could be attributed to the expansion and contraction of the stream network, caused by skin temperatures that likely fell below freezing at night. The abrupt shift, in early August, in the timing of daily maximum discharge coincides with a drop in air temperature, a drop in the amount of water temporarily stored in weathering crust, and a decreasing covariance between stream velocity and discharge. Further work is needed to investigate if these results can be transferable to larger catchments and uncover if rapid shifts in the timing of peak discharge are widespread across Greenland supraglacial streams and thus have an impact on meltwater delivery to the subglacial system and ice dynamics.

Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges

Abstract. Non-parametric Bayesian networks (NPBNs) are graphical tools for statistical inference widely used for reliability analysis and risk assessment and present several advantages, such as the embedded uncertainty quantification and limited computational time for the inference process. However, their implementation in hydrological studies is still scarce. Hence, to increase our understanding of their applicability and extend their use in hydrology, we explore the potential of NPBNs to reproduce catchment-scale hydrological dynamics. Long-term data from 240 river catchments with contrasting climates across the United States from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) data set will be used as actual means to test the utility of NPBNs as descriptive models and to evaluate them as predictive models for maximum daily river discharge in any given month. We analyse the performance of three networks, one unsaturated (hereafter UN-1), one saturated (hereafter SN-1), both defined only by hydro-meteorological variables and their bivariate correlations, and one saturated network (hereafter SN-C), consisting of the SN-1 network and including physical catchments' attributes. The results indicate that the UN-1 network is suitable for catchments with a positive dependence between precipitation and discharge, while the SN-1 network can also reproduce discharge in catchments with negative dependence. The latter can reproduce statistical characteristics of discharge (tested via the Kolmogorov–Smirnov statistic) and have a Nash–Sutcliffe efficiency (NSE) ≥0.5 in ∼40 % of the catchments analysed, receiving precipitation mainly in winter and located in energy-limited regions at low to moderate elevation. Further, the SN-C network, based on similarity of the catchments, can reproduce discharge statistics in ∼10 % of the catchments analysed. We show that once a NPBN is defined, it is straightforward to infer discharge and to extend the network itself with additional variables, i.e. going from the SN-1 network to the SN-C network. However, the results also suggest considerable challenges in defining a suitable NPBN, particularly for predictions in ungauged basins. These are mainly due to the discrepancies in the timescale of the different physical processes generating discharge, the presence of a “memory” in the system, and the Gaussian-copula assumption used for modelling multivariate dependence.

АНАЛІЗ ПОВТОРЮВАНОСТІ ДОЩОВИХ ПАВОДКІВ НА РІЧКАХ В БАСЕЙНІ ТИСИ (В МЕЖАХ УКРАЇНИ)

Rain floods on the rivers in the Tisza basin within Ukraine are formed several times during the entire warm period. The purpose of the presented study is a detailed analysis of the frequency of such floods on the rivers of the region under study in terms of the series of annual maximum average daily water discharges and the series of maximum average daily discharges of partial probability. The first series, called the “multi-annual series”, includes only one of the highest discharges each year, in our case, one for the warm period – May-October. The second row includes all values hat exceed some limit value, which corresponds to the smallest value from the series of annual maximum annual water discharges. To carry out the work, we used observational data on the average daily water flow from the following hydrological posts on the rivers: the Uzh River – Uzhgorod, the Latoritsa River – Mukachevo, the Rika River – Mezhgorye, the Teresva River – Ust-Chernaya, the Tisza River – Rakhiv for the observation period 1946 (47 ) to 2019. To obtain the long-term series and partial probability necessary for the study, first, chronological samples of rain flood maxima were formed for both series separately, then they were ranked and the estimated return period was determined for each member of the series. The series of maximums of partial probability according to the data from the studied hydrological stations in their number on average 4-6 times exceed the number of values of the series of annual maxima. The calculated return periods are determined by the inverse formula to the Weibull formula. Graphs of the frequency of rain floods by annual maxima and maxima of partial probability for the stations under study were constructed, which made it possible to notice the following features: 1) the discharges maxima of rain floods with a frequency of less than 5-10 years differ for a number of annual maximum water discharges and a series of partial probability; 2) partial probability series better describe minor floods, especially those with a short return period. The analysis of the frequency of rain floods on the rivers in the Tisza basin within Ukraine made it possible to estimate the possible magnitudes of rain floods that can form during a warm period of time and to estimate their frequency of a certain magnitude. This is of practical interest as knowledge of potential floods can be used to assess the nature of potential floods in the future.

Attribution of climate change and human activities to urban water level alterations and factors importance analysis in Central Taihu Basin

Attribution of climate change and human activities have been extensively discussed over the past few decades, particularly in urbanization area. However, the relationships among different factors may not be well explained by traditional statistical methods. In this study, we took one of the highly urbanized regions, Central Taihu Basin, as an example. Linear regression (LR), random forest (RF) and support vector machine (SVM) were used for regression and the attributions of climate change and human activities on water level alterations at different scales from 1961 to 2018 were quantified by residual analysis. The regression results indicated that SVM performed best. Water level at each scale showed an increasing trend and human activities were the dominant influence. The altered period was further divided into three sub-periods and human activities contributed the most in the sub-period II (2000–2009). Finally, the importance of thirty-eight factors were quantified by RF based on daily data series from 2008 to 2018. The results showed that cumulative antecedent precipitation (CAP) of five days was one of the important climate factors and daily maximum discharge of sluices were the important human activities factors. The methods and results of this study can help to provide support in flood control.

Comparison of different estimation methods for extreme value distribution

The extreme value distribution was developed for modeling extreme-order statistics or extreme events. In this study, we discuss the distribution of the largest extreme. The main objective of this paper is to determine the best estimators of the unknown parameters of the extreme value distribution. Thus, both classical and Bayesian methods are used. The classical estimation methods under consideration are maximum likelihood estimators, moment’s estimators, least squares estimators, and weighted least squares estimators, percentile estimators, the ordinary least squares estimators, best linear unbiased estimators, L-moments estimators, trimmed L-moments estimators, and Bain and Engelhardt estimators. We also propose new estimators for the unknown parameters. Bayesian estimators of the parameters are derived by using Lindley’s approximation and Markov Chain Monte Carlo methods. The asymptotic confidence intervals are considered by using maximum likelihood estimators. The Bayesian credible intervals are also obtained by using Gibbs sampling. The performances of these estimation methods are compared with respect to their biases and mean square errors through a simulation study. The maximum daily flood discharge (annual) data sets of the Meriç River and Feather River are analyzed at the end of the study for a better understanding of the methods presented in this paper.

Projected climate change impacts in the Tahoe Basin: Recent findings from global climate models

Since 2002, the Tahoe Environmental Research Center has been studying the effects of climate change in the Tahoe Basin. We are using the output from four General Circulation Models downscaled by the method of Localized Constructed Analogues and provided by the Scripps Institution of Oceanography (SIO). The daily data were used by SIO to drive the Variable Infiltration Capacity (VIC) model and derive a suite of 24 hydrologic and climate variables at a 1/16° (ca. 6 km) grid scale, for two Representative Concentration Pathways (RCP 4.5 and 8.5). Here we focus on trends in the return levels of extreme climatic events, including annual maximum daily discharge of six basin streams, maximum and total annual kinetic energy (KE) of raindrops falling on snow-free ground, basin-wide climatic water deficit, and wind speed. To analyze time trends in historic and modeled future extreme values, we applied the program extRemes, based on the Generalized Extreme Value (GEV) distribution.Values of KE on snow-free ground were derived by statistically disaggregating daily rainfall to hourly, and using literature values to convert rainfall intensity (mm/hr) for days without snowpack to KE (Joules/m2/hr). We found strong upward trends in extreme values, with most of the effect due to loss of snowpack, but a significant effect of increasing rainfall for one model. To estimate future trends in maximum annual daily discharge of six streams, we adjusted the modeled future annual maximum runoff to the distribution of gage data, using regression of gage data on modeled historic runoff at equal return intervals. The GEV results for the six streams, averaged across the four models, indicate an increase in the 20-year flood of 65–117 percent. Climatic water deficit showed strong upward trends for three of the four models, with a maximum at mid-century for one model. Averaged across the basin and across the four models, average and maximum seasonal winds under RCP 8.5 are projected to decrease slightly in all seasons. The trends in averages and extreme values that we found will have important effects on vegetation, wildfire severity, flood hazards and the clarity of Lake Tahoe.

Modeling of the Summer 2019 Disastrous Floods on the Iya River (Irkutsk Oblast)

This paper discusses the results from investigating the factors that led to the formation of a catastrophic flood in June 2019 on the Iya river in the city of Tulun, Irkutsk oblast, as well as from calculating and assessing the characteristics of these floods. An analysis of the meteorological environments of flood formation is performed by using the ICON global weather forecast model. The trends in hydrometeorological characteristics for the period 1966–2019 are analyzed. The study revealed an increase in the June precipitation amount by 36–61% (31–46 mm), and negative trends in the maximum water discharges making up 24–37% from May to September. Using data on the water level and historical Q(H) curves, the maximum daily water discharge on June 29, 2019 was estimated at 6800 ± 700 m3/s. A schematization of the Iya river basin is carried out, the parameters are systematized and the distributed Hydrograph hydrological model is verified. For three subbasins within the Iya river basin, the median value of the Nash–Sutcliff criterion was 0.69 for the period 1970–1996. The flood characteristics are simulated and assessed using two sources of meteorological data. According to data from weather stations, the maximum daily discharge was 6570 m3s. This value falls within the range of estimates of observed values but its formation is delayed by 1 day. According to the simulation based on the ICON model data, the maximum 3-hour discharge reached 5260 m3/s, or at least 840 m3/s lower than the observed value; however, the simulated flood hydrograph coincides with observed one for the time intervals. The results of the study confirm a possibility for a short-term flood forecast using the method of deterministic hydrological modeling.

Modeling of runoff formation processes in mountain conditions of the Crimean Peninsula (Russia)

The distributed hydrological model Hydrograph was applied to simulate runoff formation processes in the mountainous conditions of the Crimean Peninsula. The study object is the Derekoyka River watershed (49.7 km2, the altitude difference 14-1398 m). Streamflow simulations were carried out with daily time step for the periods of 1967-1988 and 2016-2017 using the data of two weather stations Ai-Petri and Yalta. Though mean and median Nash-Sutcliffe efficiency was only 0.28 and 0.33, in some years it exceeded 0.60. The analysis has shown that the precipitation input interpolated from weather stations was not always adequate affecting the quality of streamflow simulations. The results of the model verification were accepted as satisfactory. The hazardous flood of 2018 in Yalta was simulated. According to simulations, maximum daily discharge amounted to 14.9 m3/s and exceeded previous maximum value observed in 1962. The results confirm the possibility to apply the model Hydrograph to assess the water balance and hydrological characteristics in the mountainous part of the Crimean Peninsula. We suggest that provided by proper precipitation data the model can be used for short-term forecasts of hazardous floods in the region.

The Impacts of Climate Change on Maximum Daily Discharge in the Payab Jamash Watershed, Iran

Abstract One of the most crucial consequences of climate change involves the alteration of the hydrologic cycle and river flow regime of watersheds. This study was an endeavor to investigate the contributions of climate change to maximum daily discharge (MDD). To this end, the MDD simulation was carried out through implementing the IHACRES precipitation-runoff model in the Payyab Jamash watershed for the 21st century (2016-2100). Subsequently, the observed precipitation and temperature data of the weather stations (1980-2011) as well as 4 multi-model outputs of Global Climate Models (GCMs) under the maximum and minimum Representative Concentration Pathways (RCPs) (2016-2100) were utilized. In order to downscale the output of GCMs, Bias Correction (BC) statistical method was applied. The projections for the 21st century indicated a reduction in Maximum Daily Precipitation (MDP) in comparison with the historic period in the study area. The average projected MDP for the future period was 9 mm/day and 5 mm/ day under 2.6 and 8.5 RCPs (4.6% and 2.6% decrease compared with the historical period), respectively. Moreover, the temperature increased in Jamash Watershed based on 2.6 and 8.5 RCPs by 1∘C and 2∘C(3.7% and 7.4% increase compared with the historical period), respectively. The findings of flow simulation for the future period indicated a decrease in MDD due to the diminished MDP in the study area. The amount of this decrease under RCP8.5 was not remarkable (0.75 m3/s), whereas its value for RCP2.6 was calculated as 40m3/s (respectively, 0.11% and 5.88% decrease compared with the historical period).

Modelling the Present and Future Water Level and Discharge of the Tidal Betna River

Climate change, comprising of changes in precipitation patterns, higher temperatures and sea level rises, increases the likelihood of future flooding in the Betna River basin, Bangladesh. Hydrodynamic modelling was performed to simulate the present and future water level and discharge for different scenarios using bias-corrected, downscaled data from two general circulation models. The modelling results indicated that, compared to the baseline year (2014–2015), the water level is expected to increase by 11–16% by the 2040s and 14–23% by the 2090s, and the monsoon daily maximum discharge is expected to increase by up to 13% by the 2040s and 21% by the 2090s. Sea level rise is mostly responsible for the increase in water level. The duration of water level exceedance of the established danger threshold and extreme discharge events can increase by up to half a month by the 2040s and above one month by the 2090s. The combined influence of the increased water level and discharge has the potential to cause major floods in the Betna River basin. The results of our study increase the knowledge base on climate change influence on water level and discharge at a local scale. This is valuable for water managers in flood-risk mitigation and water management.

The critical role of the routing scheme in simulating peak river discharge in global hydrological models

Global hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge—which is crucial in flood simulations—has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971–2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies.

Relationship between extreme daily rainfall and maximum daily river discharge within Lagos metropolis

Many research works emphasized on rainfall pattern and urban flooding while others examined the relationship between extreme rainfall and its implication for flood frequency with little attempt to establish relationship between extreme rainfall and maximum river discharge. This study therefore, draws interconnection between extreme daily rainfall and maximum daily river discharge within Lagos metropolis. Data on rainfall were collected from Ikeja and Lagos Roof weather stations within the Yewa Basin in Lagos. Also, data on river discharge for Yewa River were obtained from Ogun-Oshun River Basin Development Authority, Abeokuta. Pearson Product Moment Correlation Coefficient (PPMC) was employed to assess the relationship between extreme daily rainfall and maximum daily river discharge at 95% confidence level. Analysis of Variance (ANOVA) employed was used to test for the variability in the means of extreme daily rainfall events across the two weather stations. The result showed that there is a significant relationship between extreme daily rainfall events and maximum daily river discharge for Ikeja station (t value = 2.60, Cv= 2.18, P≤0.05) while no significant relationship was shown in Lagos Roof (t value = 1.60, Cv= 2.18, P≥0.05). It was also revealed that that there are no significant differences in the mean values of extreme daily rainfall across the stations (F 1 30 =2.086, P≥0.05).Therefore, this work has been able to show that the relationship between extreme rainfall and maximum river discharge is not always significant due to several factors such as distance from the sea (continentally) and variation in rainfall across the weather stations. Keywords: Extreme Rainfall, Maximum River Discharge, Ogun-Osun, Flood Frequency

Effect of projected climate change on the hydrological regime of the Yangtze River Basin, China

The hydrological response to the potential future climate change in Yangtze River Basin (YRB), China, was assessed by using an ensemble of 54 climate change simulations. The Coupled Model Intercomparison Project 5 simulations under two new Representative Concentration Pathways (RCP) 4.5 and 8.5 emission scenarios were downscaled and used to drive the Variable Infiltration Capacity hydrological model. This study found that the range of temperature changes is homogeneous for almost the entire region, with an average annual increase of more than 2 °C under RCP4.5 and even more than 4 °C under RCP8.5 in the end of the twenty first century. The warmest period (June–July–August) of the year would experience lower changes than the colder ones (December–January–February). Overall, mean precipitation was projected to increase slightly in YRB, with large dispersion among different global climate models, especially during the dry season months. These phenomena lead to changes in future streamflow for three mainstream hydrological stations (Cuntan, Yichang, and Datong), with slightly increasing annual average streamflows, especially at the end of twenty first century. Compared with the percentage change of mean flow, the high flow shows (90th percentile on the probability of no exceedance) a higher increasing trend and the low flow (10th percentile) shows a decreasing trend or lower increasing trend. The maximum daily discharges with 5, 10, 15, and 30-year return periods show an increasing trend in most sub-basins in the future. Therefore, extreme hydrological events (e.g., floods and droughts) will increase significantly, although the annual mean streamflow shows insignificant change. The findings of this study would provide scientific supports to implement the integrated adaptive water resource management for climate change at regional scales in the YRB.

Comparison and evaluation of uncertainties in extreme flood estimations of the upper Yangtze River by the Delta and profile likelihood function methods

Frequency calculation for extreme flood and methods used for its uncertainty estimation are popular subjects in hydrology research. In this study, uncertainties in extreme flood estimations of the upper Yangtze River were investigated using the Delta and profile likelihood function (PLF) methods, which were used to calculate confidence intervals of key parameters of the generalized extreme value distribution and quantiles of extreme floods. Datasets of annual maximum daily flood discharge (AMDFD) from six hydrological stations located in the main stream and tributaries of the upper Yangtze River were selected in this study. The results showed that AMDFD data from the six stations followed the Weibull distribution, which has a short tail and is bounded above with an upper bound. Of the six stations, the narrowest confidence interval can be detected in the Yichang station, and the widest interval was found in the Cuntan station. Results also show that the record length and the return period are two key factors affecting the confidence interval. The width of confidence intervals decreased with the increase of record length because more information was available, while the width increased with the increase of return period. In addition, the confidence intervals of design floods were similar for both methods in a short return period. However, there was a comparatively large difference between the two methods in a long return period, because the asymmetry of the PLF curve increases with an increase in the return period. This asymmetry of the PLF method is more proficient in reflecting the uncertainty of design flood, suggesting that PLF method is more suitable for uncertainty analysis in extreme flood estimations of the upper Yangtze River Basin.

Rapid Global Exposure Assessment for Extreme River Flood Risk Under Climate Change

Globally, large-scale floods are one of the most serious disasters, considering increased frequency and intensity of heavy rainfall. This is not only a domestic problem but also an international water issue related to transboundary rivers in terms of global river flood risk assessment. The purpose of this study is to propose a rapid flood hazard model as a methodological possibility to be used on a global scale, which uses flood inundation depth and works reasonably despite low data availability. The method is designed to effectively simplify complexities involving hydrological and topographical variables in a flood risk-prone area when applied in an integrated global flood risk assessment framework. The model was used to evaluate flood hazard and exposure through pixel-based comparison in the case of extreme flood events caused by an annual maximum daily river discharge of 1/50 probability of occurrence under the condition of climate change between two periods, Present (daily data from 1980 to 2004) and Future (daily data from 2075 to 2099). As preliminary results, the maximum potential extent of inundation area and the maximum number of affected people show an upward trend in Present and Future.

Relation between design floods based on daily maxima and daily means: use of the Peak Over Threshold approach in the Upper Nysa Kłodzka Basin (SW Poland)

ABSTRACTThe estimation of flood quantiles is crucial in the assessment of the magnitude and frequency of floods. We carried out a comparative analysis of design discharges estimated from both daily maximum flows and daily mean flows for four mountainous catchments located in the Upper Nysa Kłodzka river basin (SW Poland). After separation of baseflow, split of the riverflow time series in independent events, and selection of the Peak Over Threshold sample, the parameters of the Generalized Pareto Distribution were estimated using the Hill statistic, after bias correction, and considering asymptotic properties. The comparison was performed for various return periods, where the long return periods were of main concern. The jack-knife approach was used to assess the uncertainty of the predicted flood quantiles, and comparison was made with an alternative approach based on annual maxima. We found a meaningful level of differences between daily maximum and mean design discharges and between the rate of change of flood magnitude for which the level (i) stabilized with increasing return period, (ii) decreased downstream, and (iii) was large for catchments susceptible to flooding and with high elevation change. Results are useful in practice when daily maximum discharge is not routinely recorded.

The Change of Tidal Characteristics under the Influence of Human Activities in the Yangtze River Estuary

Tan, Y.; Yang, F.; Xie, D, H, 2016. The Change of Tidal Characteristics under the Influence of Human Activities in the Yangtze Estuary. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 163–167. Coconut Creek (Florida), ISSN 0749-0208.The aim of this study is to understand the change of tidal wave characteristics in the Yangtze River Estuary by human activities. The harmonic analysis results based on measurement shows that the semidiurnal tides are dominant in Yangtze River Estuary. The shallow water constituents are significant. Tidal constituent Sa, which is one of main meteorological tides, is gradually increasing with the increase of upper stream river discharge. The amplitude of Sa is highly correlated to the maximum anomaly discharge value (maximum daily discharge differing from the long term mean). Due to human activities, the changes of the river discharge, the water depth and the shape of the costal line in the Yangtze River cause the change of tidal wave propagation. A 2-D numerical model is established to simulate the evolution of main tidal constituents in the Yangtze Estuary under the influence of human activities.

Flood Discharge Estimation using Wavelet Transform (Case Study: Tamer Watershed)

In this study, we present a method to carry out flood frequency analysis when the assumption of stationary is not valid. A wavelet transform model is used to flood discharge estimation. A full series is applied to flood discharge estimation using two different wavelet functions. The energy function of wavelet was used to estimate flood discharge. The data were decomposed into some details and one approximation through different wavelet functions and decomposition levels. The approximation series was employed to estimate flood discharge. This was performed using daily maximum discharge data from on the Tamer hydrodynamic station in the north of Iran. In this way, the data from 1970 to 2009 were evaluated by wavelet analysis. Results illustrate that the decomposition levels in wavelet transform have a significant role in the flood discharge estimation. For instance, in 100years return period, the flood discharges are 13.06 and 110.92 by Haar (db1) mother wavelet in decomposition level of 1 and 8, respectively. It is shows a more than 8 time growth in flood discharge. The higher decomposition levels are closer to traditional statistical methods such as annual maximum and partial duration series.