Regional frequency analysis of rainfall extremes in Southern Malawi using the index rainfall and L-moments approaches

Rainfall extremes often result in the occurrence of flood events with associated loss of life and infrastructure in Malawi. However, an understanding of the frequency of occurrence of such extreme events either for design or disaster planning purposes is often limited by data availability at the desired temporal and spatial scales. Regionalisation, which involves “trading time for space” by pooling together observations for stations with similar behavior, is an alternative approach for more accurate determination of extreme events even at ungauged areas or sites with short records. In this study, regional frequency analysis of rainfall extremes in Southern Malawi, large parts of which are flood prone, was undertaken. Observed 1-, 3-, 5- and 7-day annual maximum rainfall series for the period 1978–2007 at 23 selected rainfall stations in Southern Malawi were analysed. Cluster analysis using scaled at-site characteristics was used to determine homogeneous rainfall regions. L-moments were applied to derive regional index rainfall quantiles. The procedure also validated the three rainfall regions identified through homogeneity and heterogeneity tests based on Monte Carlo simulations with regional average L-moment ratios fitted to the Kappa distribution. Based on assessments of the accuracy of the derived index rainfall quantiles, it was concluded that the performance of this regional approach was satisfactory when validated for sites not included in the sample data. The study provides an estimate of the regional characteristics of rainfall extremes that can be useful in among others flood mitigation and engineering design.     

Simulating rainfall time-series: how to account for statistical variability at multiple scales?

Daily rainfall is a complex signal exhibiting alternation of dry and wet states, seasonal fluctuations and an irregular behavior at multiple scales that cannot be preserved by stationary stochastic simulation models. In this paper, we try to investigate some of the strategies devoted to preserve these features by comparing two recent algorithms for stochastic rainfall simulation: the first one is the modified Markov model, belonging to the family of Markov-chain based techniques, which introduces non-stationarity in the chain parameters to preserve the long-term behavior of rainfall. The second technique is direct sampling, based on multiple-point statistics, which aims at simulating a complex statistical structure by reproducing the same data patterns found in a training data set. The two techniques are compared by first simulating a synthetic daily rainfall time-series showing a highly irregular alternation of two regimes and then a real rainfall data set. This comparison allows analyzing the efficiency of different elements characterizing the two techniques, such as the application of a variable time dependence, the adaptive kernel smoothing or the use of low-frequency rainfall covariates. The results suggest, under different data availability scenarios, which of these elements are more appropriate to represent the rainfall amount probability distribution at different scales, the annual seasonality, the dry-wet temporal pattern, and the persistence of the rainfall events.     

LE CINQUANTIÈME ANNIVERSAIRE DE L'AISH / THE FIFTHIETH ANNIVERSARY OF THE IAHS

Abstract

One of the scale problems in hydrology is to relate nonlinearity in basin response to size and other factors. On the Sputka basin (103.4 km²), three groups of unit hydrographs were identified, each group having a common shape parameter, N, of the Nash model and each, therefore, representing one dimensionless response. The existence of the three dimensionless responses can be explained in the first place by there being different spatial rainfall patterns for the events from which they were derived. The time parameter, K, within the individual groups depends primarily on the initial flow and on the skewness of the rainfall time pattern. However, when the conditions of rainfall uniformity and of a minimum depth are strictly met, and the initial flow is in a certain range, the basin behaves in a linear fashion.     

Monthly and annual effective infiltration coefficients in Dinaric karst: example of the Gradole karst spring catchment

Abstract

The method of “historic event” is used to generate synthetic hyetographs based on statistical analysis of precipitation data. A synthetic triangular model was developed based on rainfall data of Zioud watershed (central Tunisia) with a standard time step of one hour. A database of 2799 observed rainfall events was used to provide statistical parameters for a simple triangular-shaped hyetograph model. The developed model provides a synthetic hyetograph in dimensionless form for different storm durations (2, 3 and 4 hours). For a given season and location, the variation of the first dimensionless moment with duration was relatively small, with an average range of 13% for all the stations. The resulting dimensionless hyetographs were found to be nearly identical when they were non-dimensionalized using the rainfall depth and duration, showing some seasonal effect and insignificant effects of the rainfall duration. A good agreement between simulated and observed hyetographs was achieved based on not only visual impressions, but also statistical numerical and graphical tests.     

Spatial distribution of rainfall trends in Sicily (1921-2000)

The feared global climate change could have important effects on various environmental variables including rainfall in many countries around the world. Changes in precipitation regime directly affect water resources management, agriculture, hydrology and ecosystems. For this reason it is important to investigate the changes in the spatial and temporal rainfall pattern in order to improve water management strategies.In this study a non-parametric statistical method (Mann-Kendall rank correlation method) is employed in order to verify the existence of trend in annual, seasonal and monthly rainfall and the distribution of the rainfall during the year. This test is applied to about 250 rain gauge stations in Sicily (Italy) after a series of procedures finalized to the estimation of missing records and to the verification of data consistency.In order to understand the regional pattern of precipitation in Sicily, the detected trends are spatially interpolated using spatial analysis techniques in a GIS environment.The results show the existence of a generalized negative trend for the entire region.     

Monthly spatial rainfall correlation functions and interpretations for Turkey

Abstract

Monthly spatial rainfall distribution features and their effects on spatial correlation patterns are significant in any regional study. In this paper, first a number of statistical terms and properties are explained with reference to the spatial correlation functions (SCFs). This is followed by the analysis of a theoretical spatial correlation model and its parameter estimation. Monthly empirical SCFs are examined in relation to spatial rainfall characteristics. In order to obtain a definite pattern, the SCF values are averaged in successive equal-distance groups. This average spatial correlation function shows a decreasing pattern with distance. Some interpretations of these spatial correlation functions are given for Turkey with discussion of the results obtained.     

Extraction of information content from stochastic disaggregation and bias corrected downscaled precipitation variables for crop simulation

We applied a simple statistical downscaling procedure for transforming daily global climate model (GCM) rainfall to the scale of an agricultural experimental station in Katumani, Kenya. The transformation made was two-fold. First, we corrected the rainfall frequency bias of the climate model by truncating its daily rainfall cumulative distribution into the station’s distribution based on a prescribed observed wet-day threshold. Then, we corrected the climate model rainfall intensity bias by mapping its truncated rainfall distribution into the station’s truncated distribution. Further improvements were made to the bias corrected GCM rainfall by linking it with a stochastic disaggregation scheme to correct the time structure problem inherent with daily GCM rainfall. Results of the simple and hybridized GCM downscaled precipitation variables (total, probability of occurrence, intensity and dry spell length) were linked with a crop model for a more objective evaluation of their performance using a non-linear measure based on mutual information based on entropy. This study is useful for the identification of both suitable downscaling technique as well as the effective precipitation variables for forecasting crop yields using GCM’s outputs which can be useful for addressing food security problems beforehand in critical basins around the world.     

A two steps disaggregation method for highly seasonal monthly rainfall

 The need for high resolution rainfall data at temporal scales varying from daily to hourly or even minutes is a very important problem in hydrology. For many locations of the world, rainfall data quality is very poor and reliable measurements are only available at a coarse time resolution such as monthly. The purpose of this work is to apply a stochastic disaggregation method of monthly to daily precipitation in two steps: 1. Initialization of the daily rainfall series by using the truncated normal model as a reference distribution. 2.␣Restructuring of the series according to various time series statistics (autocorrelation function, scaling properties, seasonality) by using a Markov chain Monte Carlo based algorithm. The method was applied to a data set from a rainfall network of the central plains of Venezuela, in where rainfall is highly seasonal and data availability at a daily time scale or even higher temporal resolution is very limited. A detailed analysis was carried out to study the seasonal and spatial variability of many properties of the daily rainfall as scaling properties and autocorrelation function in order to incorporate the selected statistics and their annual cycle into an objective function to be minimized in the simulation procedure. Comparisons between the observed and simulated data suggest the adequacy of the technique in providing rainfall sequences with consistent statistical properties at a daily time scale given the monthly totals. The methodology, although highly computationally intensive, needs a moderate number of statistical properties of the daily rainfall. Regionalization of these statistical properties is an important next step for the application of this technique to regions in where daily data is not available.     

Evidence for inherent nonlinearity in temporal rainfall

We examine the underlying structure of high resolution temporal rainfall by comparing the observed series with surrogate series generated by a invertible nonlinear transformation of a linear process. We document that the scaling properties and long range magnitude correlations of high resolution temporal rainfall series are inconsistent with an inherently linear model, but are consistent with the nonlinear structure of a multiplicative cascade model. This is in contrast to current studies that have reported for spatial rainfall a lack of evidence for a nonlinear underlying structure. The proposed analysis methodologies, which consider two-point correlation statistics and also do not rely on higher order statistical moments, are shown to provide increased discriminatory power as compared to standard moment-based analysis.     

Statistical characterization of temporal structure of storms

The authors present a statistical procedure to estimate the probability distributions of storm characteristics. The approach uses recent advances in stochastic hydrological modeling. The temporal dynamics of rainfall are modeled via a reward alternating renewal process that describes wet and dry phases of storms. In particular, the wet phase is modeled as a rectangular pulse process with dependent random duration and intensity; the global dependence structure is described using multidimensional copulas. The marginal distributions are described by Generalized Pareto laws. The authors derive both the storm volume statistics and the rainfall volume distribution within a fixed temporal window preceding a storm. Based on these results, they calculate the antecedent moisture conditions. The paper includes a thorough discussion of the validity of the assumptions and approximations introduced, and an application to actual rainfall data. The models presented here have important implications for improved design procedures of water resources and hydrologic systems.     

Changes of rainfall and its possible reasons in the Nansi Lake Basin,China

This study investigates the changes of rainfall patterns along with the underlying reasons in the Nansi Lake Basin (NLB), China during 1960–2009. Results show that the annual rainfall increases from the northwest to the southeast of the NLB. From the temporal variation perspective, annual rainfall decreases slightly in the majority of stations. Furthermore, in spite of no pronounced trends are detected in all stations, the annual rainfall series fluctuate intensely, and present step changes around the year of 1974 and 2002. This change pattern of rainfall is verified by the approximately wet–dry–wet phase pattern, which is exhibited in the standardized departures of annual rainfall series, during the three sub-periods divided by the pre-obtained two change years. In particular, the parametric t test demonstrate that the step change in 2002 is significant. The variations in the rainy season (RS, June–September) rainfall contributed mostly to the changes in the annual rainfall, and a high similarity of change patterns between the RS and annual rainfall is also observed. The long term mean RS and annual rainfall decreases largely from the sub-period of 1960–1974 to 1974–2002, and increased largely from the sub-period of 1974–2002 to 2002–2009 in the NLB. Besides, various elements, such as the summer East Asian summer monsoon and summer Pacific decadal oscillation, may together lead to the step changes in summer rainfall over our study area.     

Investigating hydrologic responses to spatio‐temporal characteristics of storms using a Dynamic Moving Storm generator

A synthetic storm generator—Dynamic Moving Storm (DMS)—is developed in this study to represent spatio‐temporal variabilities of rainfall and storm movement in synthetic storms. Using an urban watershed as the testbed, the authors investigate the hydrologic responses to the DMS parameters and their interactions. In order to reveal the complex nature of rainfall–run‐off processes, previously simplified assumptions are relaxed in this study regarding (a) temporal variability of rainfall intensity and (b) time‐invariant flow velocity in channel routing. The results of this study demonstrate the significant contribution of storm moving velocity to the variation of peak discharge based on a global sensitivity analysis. Furthermore, a pairwise sensitivity analysis is conducted to elucidate not only the patterns in individual contributions from parameters to hydrologic responses but also their interactions with storm moving velocity. The intricacies of peak discharges resulting from sensitivity analyses are then dissected into independent hydrologic metrics, that is, run‐off volume and standard deviation of run‐off timings, for deeper insights. It is confirmed that peak discharge is increased when storms travel downstream along the main channel at the speed that corresponds to a temporal superposition of run‐off. Spatial concentration of catchment rainfall is found to be a critical linkage through which characteristics of moving storms affect peak discharges. In addition, altering peak timing of rainfall intensity in conjunction with storm movement results in varied storm core locations in the channel network, which further changes the flow attenuation effects from channel routing. For future directions, the DMS generator will be embedded in a stochastic modelling framework and applied in rainfall/flow frequency analysis.     

Stochastic multi-site generation of daily rainfall occurrence in south Florida

This paper presents a stochastic model to generate daily rainfall occurrences at multiple gauging stations in south Florida. The model developed in this study is a space–time model that takes into account the spatial as well as temporal dependences of daily rainfall occurrence based on a chain-dependent process. In the model, a Markovian method was used to represent the temporal dependence of daily rainfall occurrence and a direct acyclic graph (DAG) method was introduced to encode the spatial dependence of daily rainfall occurrences among gauging stations. The DAG method provides an optimal sequence of generation by maximizing the spatial dependence index of daily rainfall occurrences over the region. The proposed space–time model shows more promising performance in generating rainfall occurrences in time and space than the conventional Markov type model. The space–time model well represents the temporal as well as the spatial dependence of daily rainfall occurrences, which can reduce the complexity in the generation of daily rainfall amounts.     

A storm rainfall pattern above the Central African Plateau / Vue d'ensemble de la répartition de pluie sur le Plateau Central Africain

Abstract

The spatial and temporal distribution of tropical rainfall above a small tropical basin on the Central African Plateau was studied by using a dense network of rainfall recorders. The results indicate a high variability of the rainfall pattern within a small area of less than 5 km². Formulae for the calculation of the rainfall rate and depth-area-duration relationship are presented for the purpose of network construction and engineering design in tropical Africa.     

Statistical identification of orographic effects in the regional analysis of extreme rainfall

Regional models of extreme rainfall must address the spatial variability induced by orographic obstacles. However, the proper detection of orographic effects often depends on the availability of a well‐designed rain gauge network. The aim of this study is to investigate a new method for identifying and characterizing the effects of orography on the spatial structure of extreme rainfall at the regional scale, including where rainfall data are lacking or fail to describe rainfall features thoroughly. We analyse the annual maxima of daily rainfall data in the Campania region, an orographically complex region in Southern Italy, and introduce a statistical procedure to identify spatial outliers in a low order statistic (namely the mean). The locations of these outliers are then compared with a pattern of orographic objects that has been a priori identified through the application of an automatic geomorphological procedure. The results show a direct and clear link between a particular set of orographic objects and a local increase in the spatial variability of extreme rainfall. This analysis allowed us to objectively identify areas where orography produces enhanced variability in extreme rainfall. It has direct implications for rain gauge network design criteria and has led to promising developments in the regional analysis of extreme rainfall. Copyright © 2015 John Wiley & Sons, Ltd.     

Teleconnections between the rainfall over Northeast Brazil and the winter circulation of Northern Hemisphere

Linear correlation coefficients are calculated between the geopotential heights for the winter months (December, January, and February) at 700 mb in the Northern Hemisphere and the March rainfall over Northeast Brazil. Isolines of correlation coefficients showed interesting patterns and regions of significantly high correlation. The occurrence of PNA pattern is interpreted as a connection between the Northern Hemisphere winter circulation and NE Brazil rainfall through El Niño—Southern oscillation phenomena. The negative center over North-West United States in the PNA pattern also has a direct relationship to the NE Brazil rainfall. Further studies are needed to substantiate and understand the teleconnections noted here.     

Assessment of design rainfall uncertainty for hydrologic engineering applications in Hong Kong

Hydrologic engineering designs and analyses often require the specification of design storm which involves rainfall amount, duration and hyetograph. In practice, the determination of design rainfall in hydrologic engineering applications involves the frequency analysis of extreme rainfalls of different durations and the establishment of rainfall hyetograph for the design event under consideration. Sampling errors exist in the estimation of rainfall depth (or intensity) quantiles from frequency analysis, which will be transmitted in the process of determining the design rainfall hyetograph. This paper presents a practical methodological framework based on the bootstrap resampling scheme to assess the uncertainty features associated with the magnitude of estimated rainfall depth/intensity quantiles and the corresponding design hyetographs. The procedure is implemented to quantify uncertainty of design rainfall hyetograph following the Stormwater Drainage Manual of Hong Kong involving the use of rainfall intensity–duration–frequency (IDF) model. Of particular interesting is that the bootstrap resampling scheme implemented herein is modified to handle unequal record period of annual maximum rainfall data series of different durations and to account for their intrinsic correlations. According to the adopted rainfall IDF model, the design rainfall hyetograph is a function of the IDF model coefficients. Due to the correlation among rainfall quantiles of different durations, the IDF coefficients are found to be strongly related in a nonlinear fashion which should not be ignored in the establishment of the design hyetographs.     

Effects of rainfall patterns on runoff and rainfall-induced erosion

Rainfall-induced erosion involves the detachment of soil particles by raindrop impact and their transport by the combined action of the shallow surface runoff and raindrop impact.Although temporal variation in rainfall intensity(pattern)during natural rainstorms is a common phenomenon,the available information is inadequate to understand its effects on runoff and rainfall-induced erosion processes.To address this issue,four simulated rainfall patterns(constant,increasing,decreasing,and increasing-decreasing)with the same total kinetic energy were designed.Two soil types(sandy and sandy loam)were subjected to simulated rainfall using 15 cm×30 cm long detachment trays under infiltration conditions.For each simulation,runoff and sediment concentration were sampled at regular intervals.No obvious difference was observed in runoff across the two soil types,but there were significant differences in soil losses among the different rainfall patterns and stages.For varying-intensity rainfall patterns,the dominant sediment transport mechanism was not only influenced by raindrop detachment but also was affected by raindrop-induced shallow flow transport.Moreover,the efficiency of equations that predict the interrill erosion rate increased when the integrated raindrop impact and surface runoff rate were applied.Although the processes of interrill erosion are complex,the findings in this study may provide useful insight for developing models that predict the effects of rainfall pattern on runoff and erosion.     

The role of the stratosphere in Iberian Peninsula rainfall: A preliminary approach in February

This paper attempts to establish a connection between stratospheric anomalies in the North Pole and rainfall on the Iberian Peninsula through the occurrence of major midwinter warmings (MMWs) and cold events (CEs), taking February as a preliminary approach. We define the MMWs as the warmings which break down the polar vortex, whereas the CEs are the episodes in which the polar vortex remains cold and undisturbed. Both anomalies lead to a wind anomaly around the north polar stratosphere, which is connected with a shortly lagged tropospheric anomaly through a stratosphere–troposphere coupling in winter. A T-mode principal component analysis (PCA) was used as an objective pattern classification method for identifying the main daily surface-level pressure (SLP) patterns for February for the 1961–1990 reference period. Subsequently, those February months with an MMW or a CE influence in the troposphere are identified in the whole study period (1958–2000) by means of the Arctic Oscillation Index (AOI). Thus, performing the same analysis for the selected February months, new principal patterns for detecting changes in surface circulation structure and morphology are obtained. The results show a significant decrease in the westerlies and a southward shift of the storm tracks in Western Europe some weeks after an MMW occurrence, leading to an increase in precipitation in western Iberia and a slight decrease on the eastern Mediterranean fringe. The results are quite the opposite under a CE influence: the westerlies are strengthened and shifted northwards due to the displacement of the Atlantic anticyclone towards Central Europe; dry conditions are established throughout Iberia, except for the Mediterranean fringe, where precipitation shows a considerable increase due to the greater frequency of the northeasterly winds. Finally, an 11-year sunspot cycle–quasi-biennial oscillation (QBO) modulation might be demonstrated in Iberian rainfall in February through the occurrence of these stratospheric anomalies.