Assessment of deterministic PMF modelling approaches

Probable maximum flood (PMF) event estimation has challenged the scientific community for many years. Although the concept of the PMF is often applied, there is no consensus on the methods that should be applied to estimate it. In PMF estimation, the spatio-temporal representation of the probable maximum precipitation (PMP) as well as the choice of modelling approach is often not theoretically founded. Moreover, it is not clear how these choices influence PMF estimation itself. In this study, combinations of three different spatio-temporal PMP representations and three different modelling approaches are applied to determine the PMF of a mesoscale basin keeping the antecedent catchment conditions and the total precipitation amount constant. The nine resulting PMF estimations are used to evaluate each combination of methods. The results show that basic methods allow for a rough estimation of the PMF. In cases where a physically plausible and reliable estimation is required, sophisticated PMP and PMF estimation approaches are recommended.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR A. Viglione     

Precipitation patterns and associated hydrological extremes in the Yangtze River basin,China, using TRMM/PR data and EOF analysis

Abstract

A decadal-scale study to retrieve the spatio-temporal precipitation patterns of the Yangtze River basin, China, using the Tropical Rain Mapping Mission, Precipitation Radar (TRMM/PR) data is presented. The empirical orthogonal function (EOF) based on monthly TRMM/PR data extracts several leading precipitation patterns, which are largely connected with physical implications at the basin scale. With the aid of gauge station data, the amplitudes of major principal components (PCs) were used to examine the generic relationships between precipitation variations and hydrological extremes (e.g. floods and droughts) during summer seasons over the past decade. The emergence of such major precipitation patterns clearly reveals the possible linkages with hydrological processes, and the oscillations in relation to the amplitude of major PCs are consistent with these observed hydrological extremes. Although the floods in some sections of the Yangtze River were, to some extent, tied to human activities, such as the removal of wetlands, the variations in major precipitation patterns are recognized as the primary driving force of the flow extremes associated with floods and droughts. The research findings indicate that long-distance hydro-meteorological signals of large-scale precipitation variations over such a large river basin can be successfully identified with the aid of EOF analysis. The retrieved precipitation patterns and their low-frequency jumps of amplitude in relation to PCs are valuable tools to help understand the association between the precipitation variations and the occurrence of hydrological extremes. Such a study can certainly aid in disaster mitigation and decision-making in water resource management.

Editor Z.W. Kundzewicz; Associate editor A. Montanari

Citation Sun, Z., Chang, N.-B., Huang, Q., and Opp, C., 2013. Precipitation patterns and associated hydrological extremes in the Yangtze River basin, China, using TRMM/PR data and EOF analysis. Hydrological Sciences Journal, 57 (7), 1315–1324.     

Dam safety: an evaluation of some procedures for design flood estimation

Abstract

This paper analyses a number of aspects related to the estimation of the design flood for a dam. A new approach to the estimation of the probable maximum precipitation (PMP) is described which takes advantage of the spatial variability of precipitation by using radar-derived distributed rainfall measurements. Procedures which utilize storm transposition and storm maximization are introduced to estimate the probable maximum flood (PMF) and are compared with regionalized statistical methods based upon the Wakeby and generalized extreme value distributions.     

Delineation of precipitation regions in two Canadian study areas: the role of the temporal resolution of the precipitation data

The identification of homogeneous precipitation regions has value in many water resources engineering applications (infrastructure planning, design, operations; climate forecasting, modelling). The objective of this paper is to assess the sensitivity of precipitation regions to the temporal resolution (monthly, seasonal, annual and the annual maximum series) of the data. The presented method uses the fuzzy c-means clustering algorithm to partition climate sites into statistically homogeneous precipitation regions. The regions are validated using an approach based on L-moment statistics. The method is conducted in two climatically different study areas in western and eastern Canada. There does not appear to be a relationship between the spatial distributions of the regions formed using different temporal resolutions of the precipitation data. It is recommended to delineate precipitation regions that are specific to the task at hand, and to select a temporal resolution that is consistent with the final application of the regional precipitation dataset.

EDITOR A. Castellarin; ASSOCIATE EDITOR T. Kjeldsen     

Hydrological Sciences Journal Contents 2002

Abstract

Flood frequency estimation is crucial in both engineering practice and hydrological research. Regional analysis of flood peak discharges is used for more accurate estimates of flood quantiles in ungauged or poorly gauged catchments. This is based on the identification of homogeneous zones, where the probability distribution of annual maximum peak flows is invariant, except for a scale factor represented by an index flood. The numerous applications of this method have highlighted obtaining accurate estimates of index flood as a critical step, especially in ungauged or poorly gauged sections, where direct estimation by sample mean of annual flood series (AFS) is not possible, or inaccurate. Therein indirect methods have to be used. Most indirect methods are based upon empirical relationships that link index flood to hydrological, climatological and morphological catchment characteristics, developed by means of multi-regression analysis, or simplified lumped representation of rainfall–runoff processes. The limits of these approaches are increasingly evident as the size and spatial variability of the catchment increases. In these cases, the use of a spatially-distributed, physically-based hydrological model, and time continuous simulation of discharge can improve estimation of the index flood. This work presents an application of the FEST-WB model for the reconstruction of 29 years of hourly streamflows for an Alpine snow-fed catchment in northern Italy, to be used for index flood estimation. To extend the length of the simulated discharge time series, meteorological forcings given by daily precipitation and temperature at ground automatic weather stations are disaggregated hourly, and then fed to FEST-WB. The accuracy of the method in estimating index flood depending upon length of the simulated series is discussed, and suggestions for use of the methodology provided.

Editor D. Koutsoyiannis     

Extreme value statistics for annual minimum and trough-under-threshold precipitation at different spatio-temporal scales

Abstract

The aim of this paper is to quantify meteorological droughts and assign return periods to these droughts. Moreover, the relation between meteorological and hydrological droughts is explored. This has been done for the River Meuse basin in Western Europe at different spatial and temporal scales to enable comparison between different data sources (e.g. stations and climate models). Meteorological drought is assessed in two ways: using annual minimum precipitation amounts as a function of return period, and using troughs under threshold as a function of return period. The Weibull extreme value type 3 distribution has been fitted to both sources of information. Results show that the trough-under-threshold precipitation is larger than the annual minimum precipitation for a specific return period. Annual minimum precipitation values increase with spatial scale, being most pronounced for small temporal scales. The uncertainty in annual minimum point precipitation varies between 68% for the 30-day precipitation with a return period of 100 years, and 8% for the 120-day precipitation with a return period of 10 years. For spatially-averaged values, these numbers are slightly lower. The annual discharge deficit is significantly related to the annual minimum precipitation.

Citation Booij, M. J. & de Wit, M. J. M. (2010) Extreme value statistics for annual minimum and trough-under-threshold precipitation at different spatio-temporal scales. Hydrol. Sci. J. 55(8), 1289–1301.     

LA SÉCHERESSE ACTUELLE EN AFRIQUE TROPICALE QUELQUES DONNÉES HYDROLOGIQUES

ABSTRACT

The drought of the period 1970–1973 in the tropical zone of Africa is analysed by the authors only for 1972 (or 1972–1973), they give attention to the yearly depths of precipitation and the observed discharges of the main rivers. This drought covered a very wide area which stretches from the Sahara Desert to the Equatorial area and crosses all the African Continent from West to East.

Precise data are given for the yearly depths of precipitation for the most representative stations, annual volumes of flow for the main rivers, maximum values of discharge and minimum values after the flood of 1972. Generally, the analysis of precipitation data as with the analysis of discharges leads to the conclusion that the return period of this drought is not far from 50 years. Some areas have been relatively little affected because the drought there has a return period of 10 years or even a return period of 3 years; on the other hand, others have been more severely affected, for instance Senegal and Mauritania with return period of 100 years.

It appears that no influence of man could be responsible for such a drought, at least as far as precipitation and discharge are concerned. It is not the same for the economic consequences.     

Spatio-temporal evaluation of global gridded precipitation datasets across Iran

ABSTRACT

In this work, the accuracy of four gridded precipitation datasets – Climatic Research Unit (CRU), Global Precipitation Climatology Centre (GPCC), PERSIANN-Climate Data Record (PCDR) and University of Delaware (UDEL) – is evaluated across Iran to find an alternative source of precipitation data. Monthly, seasonal and annual precipitation data from 85 synoptic stations for the period 1984–2013 were used as the basis for the evaluations. Our results indicate that all datasets underestimate and overestimate precipitation in stations with annual precipitation greater than 600 and less than 100 mm, respectively. However, all datasets correctly recognize regimes of precipitation, but with a bias in amount of precipitation. Our spatio-temporal assessments show that GPCC is the most suitable dataset to be used over Iran. Both UDEL and CRU can be considered as the second and third most suitable datasets, while PCDR showed the weakest performance among the studied datasets.     

Variabilités interannuelle et intra-saisonnière des pluies aux échelles hydrologiques. La mousson ouest-africaine en climat soudanien / Seasonal cycle and interannual variability of rainfall at hydrological scales. The West African monsoon in a Sudanese climate

Abstract

West African rainfall is characterized by a strong variability, both at decadal and interannual scales. In order to quantify the hydrological impacts of such a variability, analysis of rainfall patterns at fine scales is highly essential. This diagnostic study aims to characterize the Sudanese rainfall regime at hydrological scales, using a raingauge data set collected on the upper Oueme River catchment (Benin) between 1950 and 2002. A long-term drought is observed during the 1970s and 1980s, as in the Sahel. However, the interannual variability remains significant in the Sudanese region. The study of the seasonal cycle, based on the distinction between the oceanic and continental monsoon regimes, shows that the majority of rainfall changes occur in the continental regime. On the one hand, the rainfall peak associated with this regime that has been observed for the last 50 years has occurred increasingly earlier in the season. On the other hand, the annual rainfall deficit is mainly linked to the decrease in the number of large events during the continental part of the season.     

Derivation and verification of empirical catchment response time equations for medium to large catchments in South Africa

Despite uncertainties and errors in measurement, observed peak discharges are the best estimate of the true peak discharge from a catchment. However, in ungauged catchments, the catchment response time is a fundamental input to all methods of estimating peak discharges; hence, errors in estimated catchment response time directly impact on estimated peak discharges. In South Africa, this is particularly the case in ungauged medium to large catchments where practitioners are limited to use empirical methods that were calibrated on small catchments not located in South Africa. The time to peak (T_P), time of concentration (T_C) and lag time (T_L) are internationally the most frequently used catchment response time parameters and are normally estimated using either hydraulic or empirical methods. Almost 95% of all the time parameter estimation methods developed internationally are empirically based. This paper presents the derivation and verification of empirical T_P equations in a pilot scale study using 74 catchments located in four climatologically different regions of South Africa, with catchment areas ranging from 20 km² to 35 000 km². The objective is to develop unique relationships between observed T_P values and key climatological and geomorphological catchment predictor variables in order to estimate catchment T_P values at ungauged catchments. The results show that the derived empirical T_P equation(s) meet the requirement of consistency and ease of application. Independent verification tests confirmed the consistency, while the statistically significant independent predictor variables included in the regressions provide a good estimation of catchment response times and are also easy to determine by practitioners when required for future applications in ungauged catchments. It is recommended that the methodology used in this study should be expanded to other catchments to enable the development of a regional approach to improve estimation of time parameters on a national‐scale. However, such a national‐scale application would not only increase the confidence in using the suggested methodology and equation(s) in South Africa, but also highlights that a similar approach could be adopted internationally. Copyright © 2016 John Wiley & Sons, Ltd.     

Potential impact of climate change on flooding in the Upper Great Miami River Watershed,Ohio, USA: a simulation-based approach

Abstract

This study aims to assess the potential impact of climate change on flood risk for the city of Dayton, which lies at the outlet of the Upper Great Miami River Watershed, Ohio, USA. First the probability mapping method was used to downscale annual precipitation output from 14 global climate models (GCMs). We then built a statistical model based on regression and frequency analysis of random variables to simulate annual mean and peak streamflow from precipitation input. The model performed well in simulating quantile values for annual mean and peak streamflow for the 20th century. The correlation coefficients between simulated and observed quantile values for these variables exceed 0.99. Applying this model with the downscaled precipitation output from 14 GCMs, we project that the future 100-year flood for the study area is most likely to increase by 10–20%, with a mean increase of 13% from all 14 models. 79% of the models project increase in annual peak flow.

Citation Wu, S.-Y. (2010) Potential impact of climate change on flooding in the Upper Great Miami River Watershed, Ohio, USA: a simulation-based approach. Hydrol. Sci. J. 55(8), 1251–1263.     

Frequency analysis of maximum annual suspended sediment concentrations in North America / Analyse fréquentielle des maximums annuels de concentration en sédiments en suspension en Amérique du Nord

Abstract

Suspended sediments are a natural component of aquatic ecosystems, but when present in high concentrations they can become a threat to aquatic life and can carry large amounts of pollutants. Suspended sediment concentration (SSC) is therefore an important abiotic variable used to quantify water quality and habitat availability for some species of fish and invertebrates. This study is an attempt to quantify and predict annual extreme events of SSC using frequency analysis methods. Time series of daily suspended sediment concentrations in 208 rivers in North America were analysed to provide a large-scale frequency analysis study of annual maximum concentrations. Seasonality and the correlation of discharges and annual peak of suspended sediment concentration were also analysed. Peak concentrations usually occur in spring and summer. A significant correlation between extreme SSC and associated discharge was detected only in half of the stations. Probability distributions were fitted to station data recorded at the stations to estimate the return period for a specific concentration, or the concentration for a given return period. Selection criteria such as the Akaike and Bayesian information criterion were used to select the best statistical distribution in each case. For each selected distribution, the most appropriate parameter estimation method was used. The most commonly used distributions were exponential, lognormal, Weibull and Gamma. These four distributions were used for 90% of stations.     

Tropical cyclones and floods in Fiji

Abstract

Daily flow records, rainfall data and tropical cyclone maps during 1970–1998 are used to document the impact of tropical cyclones (TCs) on floods in the Rewa River system, Viti Levu, Fiji. Floods are large, brief, isolated events caused by TCs and non-TC tropical rainstorms. More floods are caused by tropical rainstorms than by TCs, but TC floods are larger. The log Pearson Type III distribution consistently provided the best fit to partial duration flood series and the widely-recommended generalized Pareto distribution performed very poorly, underscoring the need to test a variety of distributions for a particular geographic location. Tropical cyclones occur more often in Fiji during negative values of the Southern Oscillation Index (SOI) and all TCs that occurred during El Niño conditions caused floods. Peak flood discharges caused by TCs are inversely correlated with the SOI, reflecting possible links with tropical cyclone frequency and precipitation intensity.     

STANOVA: a smoothed-ANOVA-based model for spatio-temporal disease mapping

Spatio-temporal disease mapping can be viewed as a multivariate disease mapping problem with a given order of the geographic patterns to be studied. As a consequence, some of the techniques in multivariate literature could also be used to build spatio-temporal models. In this paper we propose using the smoothed ANOVA multivariate model for spatio-temporal problems. Under our approach the time trend for each geographic unit is modeled parametrically, projecting it on a preset orthogonal basis of functions (the contrasts in the smoothed ANOVA nomenclature), while the coefficients of these projections are considered to be spatially dependent random effects. Despite the parametric temporal nature of our proposal, we show with both simulated and real datasets that it may be as flexible as other spatio-temporal smoothing models proposed in the literature and may model spatio-temporal data with several sources of variability.     

Drought assessment in a south Mediterranean transboundary catchment

ABSTRACT

Predicting the impacts of climate change on water resources remains a challenging task and requires a good understanding of the dynamics of the forcing terms in the past. In this study, the variability of precipitation and drought patterns is studied over the Mediterranean catchment of the Medjerda in Tunisia based on an observed rainfall dataset collected at 41 raingauges during the period 1973–2012. The standardized precipitation index and the aridity index were used to characterize drought variability. Multivariate and geostatistical techniques were further employed to identify the spatial variability of annual rainfall. The results show that the Medjerda is marked by a significant spatio-temporal variability of drought, with varying extreme wet and dry events. Four regions with distinct rainfall regimes are identified by utilizing the K-means cluster analysis. A principal component analysis identifies the variables that are responsible for the relationships between precipitation and drought variability.     

A hybrid factorial stepwise-cluster analysis method for streamflow simulation – a case study in northwestern China

ABSTRACT

In this study, a hybrid factorial stepwise-cluster analysis (HFSA) method is developed for modelling hydrological processes. The HFSA method employs a cluster tree to represent the complex nonlinear relationship between inputs (predictors) and outputs (predictands) in hydrological processes. A real case of streamflow simulation for the Kaidu River basin is applied to demonstrate the efficiency of the HFSA method. After training a total of 24?108 calibration samples, the cluster tree for daily streamflow is generated based on a stepwise-cluster analysis (SCA) approach and is then used to reproduce the daily streamflows for calibration (1995–2005) and validation (2008–2010) periods. The Nash-Sutcliffe coefficients for calibration and validation are 0.68 and 0.65, respectively, and the deviations of volume are 1.68% and 4.11%, respectively. Results show that: (i) the HFSA method can formulate a SCA-based hydrological modelling system for streamflow simulation with a satisfactory fitting; (ii) the variability and peak value of streamflow in the Kaidu River basin can be effectively captured by the SCA-based hydrological modelling system; (iii) results from 2⁶ factorial experiments indicate that not only are minimum temperature and precipitation key drivers of system performance, but also the interaction between precipitation and minimum temperature significantly impacts on the streamflow. The findings are useful in indicating that the streamflow of the study basin is a mixture of snowmelt and rainfall water.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR G. Thirel     

Evaluation of satellite-based rainfall products for hydrological modelling in Morocco

ABSTRACT

Several satellite-based precipitation estimates are becoming available at a global scale, providing new possibilities for water resources modelling, particularly in data-sparse regions and developing countries. This work provides a first validation of five different satellite-based precipitation products (TRMM-3B42 v6 and v7, RFE 2.0, PERSIANN-CDR, CMORPH1.0 version 0.x) in the 1785 km² Makhazine catchment (Morocco). Precipitation products are first compared against ground observations. Ten raingauges and four different interpolation methods (inverse distance, nearest neighbour, ordinary kriging and residual kriging with altitude) were used to compute a set of interpolated precipitation reference fields. Second, a parsimonious conceptual hydrological model is considered, with a simulation approach based on the random generation of model parameters drawn from existing parameter set libraries, to compare the different precipitation inputs. The results indicate that (1) all four interpolation methods, except the nearest neighbour approach, give similar and valid precipitation estimates at the catchment scale; (2) among the different satellite-based precipitation estimates verified, the TRMM-3B42 v7 product is the closest to observed precipitation, and (3) despite poor performance at the daily time step when used in the hydrological model, TRMM-3B42 v7 estimates are found adequate to reproduce monthly dynamics of discharge in the catchment. The results provide valuable perspectives for water resources modelling of data-scarce catchments with satellite-based rainfall data in this region.

Editor M.C. Acreman; Associate editor N. Verhoest     

Alpine Waters edited by Ulrich Bundi

Abstract

A snowmelt runoff model is derived for relatively small rivers. The model involves the main components of the catchment water budget, physiographical and some other factors: water equivalent of snow cover, precipitation, antecedent moisture content, daily snowmelt, non-uniformity of snow cover, retention capacity of the basin, and percentage of forest area. The model structure includes calculations of the daily values of snowmelt excess and the transformation of these values into discharges at the outlet of the basin based on meteorological observations and appropriate distribution functions. Both calculations are made separately for open and forest areas. The parameters of the model were derived by optimization methods. The linear model based on the superposition principle is used to transform the discharges of a small river into total inflow into a large reservoir. The combined model was used to forecast for five days in advance daily mean inflows into the Gorky and Kuibyshev reservoirs (on the River Volga), using the observed and forecast discharges of the small rivers as input.     

Direct relationships of discharges to the mean and standard deviation of the intervals between their exceedences

Abstract

This article paves a way for assessing flood risk by the use of two-parameter distributions, for the intervals between threshold exceedences rather than by the traditional exponential distribution. In a case study, the apparent properties of intervals between exceedences of runoff events differ from those anticipated for exponentially distributed series. A procedure is proposed to relate two statistical parameters of the intervals to threshold discharges. It considers partial duration series (PDS) with thresholds equal to all high enough observed discharges. To avoid unnecessary assumptions on the behaviour of those parameters and effects of dependence between parameters for different PDS, a non-parametric trend-free pre-whitened scheme is applied. It leads to power-law relationships between a discharge and the mean and standard deviation of the intervals between its exceedences. Predicted mean inter-exceedence intervals, for the highest observed discharges at the stations, are closer to the observational periods than those predicted by GEV distributions fitted to AMS, and by GP distributions to fitted PDS. In the present case, the latter predictions are longer than the observational periods whereas some of the predicted mean inter-exceedences are shorter than the corresponding observational periods and some others are longer.

Citation Ben-Zvi, A. & Azmon, B. (2010) Direct relationships of discharges to the mean and standard deviation of the intervals between their exceedences. Hydrol. Sci J. 55(4), 565–577.     

Robust flood statistics: comparison of peak over threshold approaches based on monthly maxima and TL-moments

ABSTRACT

Flood quantile estimation based on partial duration series (peak over threshold, POT) represents a noteworthy alternative to the classical annual maximum approach since it enlarges the available information spectrum. Here the POT approach is discussed with reference to its benefits in increasing the robustness of flood quantile estimations. The classical POT approach is based on a Poisson distribution for the annual number of exceedences, although this can be questionable in some cases. Therefore, the Poisson distribution is compared with two other distributions (binomial and Gumbel-Schelling). The results show that only rarely is there a difference from the Poisson distribution. In the second part we investigate the robustness of flood quantiles derived from different approaches in the sense of their temporal stability against the occurrence of extreme events. Besides the classical approach using annual maxima series (AMS) with the generalized extreme value distribution and different parameter estimation methods, two different applications of POT are tested. Both are based on monthly maxima above a threshold, but one also uses trimmed L-moments (TL-moments). It is shown how quantile estimations based on this “robust” POT approach (rPOT) become more robust than AMS-based methods, even in the case of occasional extraordinary extreme events.

Editor M.C. Acreman Associate editor A. Viglione