Daily Rainfall Data Series Research Articles

Variação das características pluviométricas na Planície de Reeds sob os impactos das mudanças climáticas

ABSTRACT Globally, there is enough evidence to affirm that climate change is one of the main factors causing the changing trends in rainfall features (CTRFs). The aim of this study, therefore, is to assess the CTRFs across the rice cultivation regions (RCRs) of the Plain of Reeds in Vietnam. Vietnam is a prime example of the impacts of climate change (ICC) on agricultural activities. Daily rainfall data series from 16 observation stations during the period 1984-2015 were appraised for quality and then the CTRFs were detected by applying the Mann_Kendall test and Sen’s slope estimator. The quality of rainfall data at all observation stations is homogeneous with a significance level of 0.05. Over a year, the trends in rainfall features slightly increased at most obsrvation stations, except for Hong Ngu and Moc Hoa. Additionally, the trends in rainfall features slightly decreased in rainny season rainfall (RSR) at 10 out of 16 observation stations, while insignificant to significant increasesing in dry season rainfall (DSR) were detected at all observation stations.

Autocorrelation Ratio as a Measure of Inertia for the Classification of Extreme Events

One of the problems in modern hydrology concerns the estimation of design events at sites with scarce or null data. This is a challenge for developing countries because they do not have monitoring networks as extensive and reliable as in developed nations. This situation has caused states in the Latin American and Caribbean region, in particular, to rely on hydrological regionalization techniques. These procedures implement clustering algorithms in combination with aggregation rules and metric distances to generate homogeneous groups from which hydrological information can be transferred. In addition, it has been proven that the analysis of spatial variables is sensitive to the magnitudes of extreme events; therefore, a mathematical formulation that adopts this fact into consideration must be included. For this purpose, the autocorrelation distance of the daily rainfall data series is proposed as an estimator of temporal variability. The fit parameters of the mixed Poisson-exponential probability distribution are operated as estimators of spatial variability. These spatio-temporal conditions are combined to obtain a mathematical relation of the autocorrelation as a measure of inertia for the classification of extreme events. This procedure is applied to Hydrologic Region 10 in northwestern Mexico from daily rainfall records. This zone has already been explored in terms of its regional homogeneity, which allows validating the results obtained.

Assessment of soil erosion, sediment yield and basin specific controlling factors using RUSLE-SDR and PLSR approach in Konar river basin, India

The study comprehensively assessed the effects of river basin parameters (basin morphology, drainage network, topography, climate, land use land cover (LULC) composition & pattern, and soil properties) on soil erosion (SE) and specific sediment yield (SSY) in the tropical river basin of Konar, India. The revised universal soil loss equation (RUSLE) along with the sediment delivery ratio (SDR) model has been used to quantify the SE and SSY. As the basin parameters are highly codependent with each other, multivariate partial least square regression (PLSR) with variable importance projection (VIP) statistics has been applied to 32 basin parameters to elucidate the linkage with SE and SSY. Multi-spectral LANDSAT-8 image, topographical sheets (1:50,000), gridded (0.25° × 0.25°) daily rainfall data series, laboratory estimates of soil parameters along with other ancillary data have been employed in this regard. Results revealed that wastelands encouraged moderate to very high (5–40.0 t ha−1 yr−1) annual rate of SE compare to other LULC classes. The PLSR studies revealed that the mean slope gradient (S), basin relief (HD), sediment delivery ratio (SDR), patch density (PD), edge density (ED), hypsometric integral (HI), topographic wetness index (TWI) were very strongly influential (b = 0.1–0.16) in SE. Likewise, SDR, mean annual precipitation (P), rainfall-runoff erosivity (R factor) PD, S, and HD were highly (b=0.12–0.22) important variables (VIP = 1.2–2.3) for SSY. Generated spatial information may help in understanding the SE and SSY dynamics in the basin and the knowledge can be used for soil erosion conservation measures in and around the river basins in India.

Distribution of rainfall probability in the Tapajos River Basin, Amazonia, Brazil

Studies on the probability of rainfall and its spatiotemporal variations are important for the planning of water resources and optimization of the calendar of agricultural activities. This study identifies the occurrence of rain by first-order Markov Chain (MC) and by two states in the Tapajos River Basin (TRB), Amazon, Brazil. Cluster analysis (CA), based on the Ward method, was used to classify homogeneous regions and select samples for checking the probability of rainfall occurrence by season. The historical series of daily rainfall data of 80 stations were used for the period 1990-2014. The CA technique identified 8 homogeneous regions and their probability of occurrence of rainfall, helping to determine which regions and periods have greater need of irrigation. Results of the probability of occurrence of dry and rainy periods in the TRB were used to define the dry (May thru September) and rainy seasons (October thru April). Elements of the matrix transition probabilities showed variability in relation to time and, in addition, the influence of geographical position of seasonal rainfall in determining dry and rainy periods at specific sites in the TRB.

Obtaining Homogeneous Regions by Determining the Generalized Fractal Dimensions of Validated Daily Rainfall Data Sets

Extreme rainfall data are widely used in several hydrological models and civil engineering design. Despite high temporal resolution rainfall data are not commonly available, daily rainfall data series are easily found. When these available data series are short in length the Regional Frequency Analysis (RFA) is a good tool to enlarge them by joining stations into homogeneous regions. This is by far, the most complicated step in RFA. This work presents a new method to form homogeneous regions of extreme annual daily rainfall data series. Daily rainfall data series from 53 weather stations in the Maule Region (Chile) have been used. Their fractal dimensions spectra have been obtained by applying the box counting method. Each station has been characterized by the fractal dimensions D1 and D2. A cluster analysis has been carried out based on these at-site characteristics and three regions have been obtained. After performing a RFA of extreme daily annual rainfall data series within each region they have shown as homogeneous. Only one of the available stations has not been possible to be included into any homogeneous regions, being the local frequency analysis the only suitable method to be applied at this location.

Best-Fit Probability Distribution Model for Rainfall Frequency Analysis of Three Cities in South Eastern Nigeria

Rainfall frequency analysis is the estimation of how often rainfall of specified magnitude will occur. Such analyses are helpful in defining policies relating to water resources management. It serves as the source of data for flood hazard mitigation and the design of hydraulic structures aimed at reducing losses due to floods action. In this study rainfall frequency analysis for three (3) cities in South Eastern Nigeria were carried out using annual maximum series of daily rainfall data for the stations. The objective of the study was to select the probability distribution model from among six commonly used probability distribution models namely: Generalized Extreme value distribution (GEV), Extreme value type I distribution (EVI), Generalized Pareto distribution (GPA), Pearson Type III (PIII), log Normal (LN) and Log Pearson Type III (LP111) distributions. These distributions were applied to annual maximum series of daily precipitation data at each station using the parameters of the distributions estimated by the method of moments. The best fit probability distribution model at each location was selected based on the results of seven goodness of fit tests entry: root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute deviation index (MADI) and probability plot correlation coefficient (PPCC), Maximum Absolute Error (MAE), Chi square test and D- Index and a scoring and ranking scheme. Our results indicate that the best fit probability distribution model at all study locations is GEV and this was used to forecast rainfall return values for the stations for return periods of between 5years and 500years. The values obtained are useful for planning, design and management of hydraulic structures for flood mitigation and prevention of flood damage at the location.

Delimiting homogeneous regions using the multifractal properties of validated rainfall data series

Summary In this work, a regional frequency analysis of extreme annual rainfall data in Malaga (Southern Spain) has been performed. Rainfall records have been validated, applying various quality control tests as a pre-requisite before their use, ensuring their reliability and discarding anomalous data. For grouping the stations into potential homogeneous regions, the multifractal properties of daily rainfall data series recorded at 72 locations have been studied. The scaling of the rainfall moments has been analyzed and the empirical moments scaling exponent functions have been obtained. The corresponding multifractal values have been used to group stations into regions, resulting some of them homogeneous.

The use of the exponentK(q)function to delimit homogeneous regions in regional frequency analysis of extreme annual daily rainfall

The regional frequency analysis of extreme annual rainfall data is a useful methodology in hydrology to obtain certain quantile values when no long data series are available. The most crucial step in the analysis is the grouping of sites into homogeneous regions. This work presents a new grouping criterion based on some multifractal properties of rainfall data. For this purpose, a regional frequency analysis of extreme annual rainfall data from the Maule Region (Chile) has been performed. Daily rainfall data series of 53 available stations have been studied, and their empirical moments scaling exponent functions K(q) have been obtained. Two characteristics parameters of the K(q) functions (γmax and K(0)) have been used to group the stations into three homogeneous regions. Only five sites have not been possible to include into any homogenous regions, being the local frequency analysis of extreme daily rainfall the most appropriate method to be used at these locations. Copyright © 2014 John Wiley & Sons, Ltd.

Spatial Variability of the Hurst Exponent for the Daily Scale Rainfall Series in the State of Zacatecas, Mexico

AbstractThe structural pattern of rainfall data exhibits random fluctuations over time and space. Utilizing concepts of fractal theory, it has been possible to identify characteristics of rainfall data beyond simple statistical indicators of their randomness. The objective of this research was to identify the spatial variation of the Hurst exponent, extracted through standard wavelet techniques from time series of daily rainfall data in the state of Zacatecas, Mexico. The Hurst exponent was extracted for 26 locations using the reference techniques for auto-affine traces—in particular, the wavelets method. Results have shown that the Hurst exponents of rainfall time series are negatively influenced by altitude; thus, stations located at higher altitudes were characterized by Hurst exponents indicating more nonpersistent behavior. The trends among geographical variables (west longitude and latitude) and climatic parameters (annual rainfall and number of rainy days) and their relationship with the Hurst exponent were also analyzed.

Changes in large rainstorm magnitude–frequency over the last century in Sabah, Malaysian Borneo and their geomorphological implications

Increases in the frequency of large to extreme rainfall events are widely predicted with global warming, but evidence from the humid tropics is hampered by a paucity of long-term data. This paper assesses changes in daily rainfall magnitude–frequency and their geomorphological consequences in the equatorial environment of northern Borneo using (1) rare daily rainfall data series for 1906–2012 assembled from archival and current sources and (2) geomorphological process data from the Danum Valley area of eastern Sabah. Rainstorm changes are explored using (1) analysis of frequencies of daily falls above threshold values, (2) extreme-value analysis comparing differences in locally weighted scatterplot smoothing (LOESS) best-fit curves for successive 20-year periods and (3) a novel approach constructing graphs of long-term change in daily falls of 1- to 10-year return period derived from overlapping 20-year LOESS curves. Substantial increases, some statistically significant, since 1980 (intensifying since 1999) are detected at most stations in (1) the frequency of daily falls ≥50, 100 and 200 mm and (2) the magnitudes of daily falls with return periods of 0.2–5.0 years. The scale and temporal patterns of historical change vary with (1) coastal aspect and (2) different parts of the return period spectrum. The period 1921–1940 exhibited higher rainstorm magnitude–frequency than 1952–1979 and demonstrates the dangers of simple deductions derived from post-1960 data comparisons. Geomorphological responses indicated by long-term monitoring at Danum (eastern Sabah), where there has been a marked upswing in heavy rainfalls since 1999, include a threefold increase in slopewash rates, more frequent high flows and higher sediment loads. The very high soil pipe sediment yields, rates of pipe roof collapse and stream channel extension recorded from 2002 to 2012 in pipe systems at Danum may be a consequence of this upswing. Increased landsliding is also a likely consequence, exacerbated in logged, agricultural and urban terrain.

Estimation of extreme flash flood evolution in Barcelona County from 1351 to 2005

Abstract. Every year, flash floods cause economic losses and major problems for undertaking daily activity in the Catalonia region (NE Spain). Sometimes catastrophic damage and casualties occur. When a long term analysis of floods is undertaken, a question arises regarding the changing role of the vulnerability and the hazard in risk evolution. This paper sets out to give some information to deal with this question, on the basis of analysis of all the floods that have occurred in Barcelona county (Catalonia) since the 14th century, as well as the flooded area, urban evolution, impacts and the weather conditions for any of most severe events. With this objective, the identification and classification of historical floods, and characterisation of flash-floods among these, have been undertaken. Besides this, the main meteorological factors associated with recent flash floods in this city and neighbouring regions are well-known. On the other hand, the identification of rainfall trends that could explain the historical evolution of flood hazard occurrence in this city has been analysed. Finally, identification of the influence of urban development on the vulnerability to floods has been carried out. Barcelona city has been selected thanks to its long continuous data series (daily rainfall data series, since 1854; one of the longest rainfall rate series of Europe, since 1921) and for the accurate historical archive information that is available (since the Roman Empire for the urban evolution). The evolution of flood occurrence shows the existence of oscillations in the earlier and later modern-age periods that can be attributed to climatic variability, evolution of the perception threshold and changes in vulnerability. A great increase of vulnerability can be assumed for the period 1850–1900. The analysis of the time evolution for the Barcelona rainfall series (1854–2000) shows that no trend exists, although, due to changes in urban planning, flash-floods impact has altered over this time. The number of catastrophic flash floods has diminished, although the extraordinary ones have increased.

The role of rainfall in the landslide hazard: the case of the Avigliano urban area (Southern Apennines, Italy)

Mass movements varying in type and size, some of which are periodically reactivated, affect the urban area of Avigliano. The disturbed and remoulded masses consist of sandy–silty or silty–clayey plastic material interbedded with stone fragments and conglomerate blocks. Five landslides that were markedly liable to rainfall-associated instability phenomena were selected. The relationships between landslides and rainfall were investigated using a hydrological and statistical model based on long-term series of daily rainfall data. The model was used to determine the return period of cumulative daily rainfall over 1–180 days. The resulting hydrological and statistical findings are discussed with the aim of identifying the rainfall duration most critical to landslides. The concept of a precipitation threshold was generalized by defining some probability classes of cumulative rainfall. These classes indicate the thresholds beyond which reactivation is likely to occur. The probability classes are defined according to the return period of the cumulative rainfall concomitant with landslide reactivation.