Extreme Rainfall Variability Research Articles

Regionalization of heavy rainfall to improve climatic design values for infrastructure: case study in Southern Ontario, Canada

Southern Ontario, Canada, has been impacted in recent years by many heavy rainfall and flooding events that have exceeded existing historical estimates of infrastructure design rainfall intensity–duration–frequency (IDF) values. These recent events and the limited number of short-duration recording raingauges have prompted the need to research the climatology of heavy rainfall events within the study area, review the existing design IDF methodologies, and evaluate alternative approaches to traditional point-based heavy rainfall IDF curves, such as regional IDF design values. The use of additional data and the regional frequency analysis methodology were explored for the study area, with the objective of validating identified clusters or homogeneous regions of extreme rainfall amounts through Ward's method. As the results illustrate, nine homogeneous regions were identified in Southern Ontario using the annual maximum series (AMS) for daily and 24-h rainfall data from climate and rate-of-rainfall or tipping bucket raingauge (TBRG) stations, respectively. In most cases, the generalized extreme value and logistic distributions were identified as the statistical distributions that provide the best fit for the 24-h and sub-daily rainfall data in the study area. A connection was observed between extreme rainfall variability, temporal scale of heavy rainfall events and location of each homogeneous region. Moreover, the analysis indicated that scaling factors cannot be used reliably to estimate sub-daily and sub-hourly values from 24- and 1-h data in Southern Ontario. Citation Paixao, E., Auld, H., Mirza, M.M.Q., Klaassen, J. & Shephard, M.W. (2011) Regionalization of heavy rainfall to improve climatic design values for infrastructure: case study in Southern Ontario, Canada. Hydrol. Sci. J. 56(7), 1067–1089.

Hydrometeorological thresholds for landslide initiation and forest operation shutdowns on the north coast of British Columbia

Debris flows and debris avalanches are the most widespread and hazardous types of landslides on the British Columbia north coast. Triggered by heavy rain, they pose risks to forestry workers in sparsely developed regions. The scarcity of long-term quality rain gauges and the lack of weather radar information create significant challenge in predicting the timing of landslides, which could be used to warn and, when necessary, evacuate forestry personnel. Traditional methods to relate rainfall antecedents and rainfall intensity to known landslide dates have proven to be unsatisfactory in this study due to extreme spatial variability of rainfall, enhanced by the orographic effect and the scarcity of rain gauges in a very large area. This has led to an integration of meteorological variables in a landslide advisory system that classifies three types of approaching storms by the 850-mbar wind speed and direction, the occurrence of subtropical moisture flow, and the existence of a warm layer characterized by high thickness values of the 500- to 1,000-mbar pressure levels. The storm classification was combined with a 4-week antecedent rainfall and the 24-h rainfall measured near or in the watershed where logging operations are taking place. This system, once implemented, is thought to reduce loss of life, injury, and economic losses associated with forestry works in the study area.

Multifractals, cloud radiances and rain

The extreme variability of rainfall over huge ranges of space–time scales makes direct rain gauge measurements of areal rainfall impossible; assumptions about the rainfall scaling—whether trivial (homogeneous), or multifractal (heterogeneous)—are required even for interpolation. The alternative is to use rain surrogates such as radar reflectivities or those based on visible-infra red radiances. In this paper, we argue that cloud radiances should be studied to obtain basic information about the range and type of scaling in the atmosphere. Since, rain and clouds are strongly non-linearly coupled—and since the scaling of the fields, the scale invariance of the generators/exponents is a symmetry principle—a break in the scaling in one of the fields would cause a break in the other. Using 909 images from three satellites and six sensors (visible and infra red) collectively spanning the range of scales 5000–1 km, we demonstrate that power law scaling is respected to within an error of ±0.3–0.5%; that an upper bound on the deviations from the theoretical universal multifractal scaling is 1–2% per octave in scale. We also show that the outer scale of the cascade is very close to 20,000 km, the largest great circle distance on the earth. Allowing for (one-parameter) subpower law (logarithmic) scaling corrections we show that universal multifractal cascades starting at this scale explain the isotropic moments (order ≤1.6) to within an error of ±0.8%. We argue that the scaling of these isotropic statistics shows that the diversity of cloud morphologies reflects differences in anisotropies which are effectively washed out by the isotropic statistical methods used. We compare and contrast existing multifractal models showing which can be used as realistic cloud and rain models. We go on to use continuous in scale, anisotropic, space–time multifractal rain and cloud simulations (including radiative transfer) to show how diverse cloud, rain and radiance morphologies can be compatible with the observed isotropic scaling statistics. Finally, we argue that these will be necessary for solving measurement problems including the use of rain gauge, radar and visible/infra red surrogate fields.

Interannual variability of European extreme winter rainfall and links with mean large‐scale circulation

AbstractDecember–February (DJF) extreme rainfall was analysed at 347 European stations for the period 1958–2000. Two indices of extreme rainfall were examined: the maximum number of consecutive dry days (CDD); and the number of days above the 1961–90 90th percentile of wet‐day amounts (R90N). A principal component analysis of CDD found six components that accounted for 52.4% of the total variance. Six components of DJF R90N were also retained that accounted for 39.1% of the total variance. The second component of R90N has a very significant trend and the factor loadings closely resemble the observed linear trend in this index, suggesting that the analysis has isolated the mode of variability causing the trend as a separate component. The principal components of the indices were correlated with surface and upper‐air observations over the North Atlantic. The best correlations were generally found to be with sea‐level pressure (SLP) observations. A separate canonical correlation analysis of each of the two indices with SLP revealed several coupled modes of variability. The North Atlantic oscillation (NAO) was isolated as the first canonical pattern for R90N. For CDD the first two canonical coefficients of CDD were significantly correlated with the NAO index. Generally, the canonical coefficients with the highest correlations with the NAO had the most significant trends, suggesting that the observed trend in the NAO has strongly contributed to the observed trends in the indices. Two other important canonical patterns were isolated: a pattern of anomalous mean SLP (MSLP) centred over the North Sea, which seems to be related to local sea‐surface temperature over this region; and a dipole‐like pattern of MSLP with poles over the eastern Mediterranean and the central North Atlantic. Repeating the canonical correlation analysis with two other indices of extreme rainfall, the 90th percentile of wet day amounts and the maximum 10 day rainfall total, gives very similar coupled patterns. Copyright © 2004 Royal Meteorological Society.

Risk management strategies using seasonal climate forecasting in irrigated cotton production: a tale of stochastic dominance

Decision-making in agriculture is carried out in an uncertain environment with farmers often seeking information to reduce risk. As a result of the extreme variability of rainfall and stream-flows in north-eastern Australia, water supplies for irrigated agriculture are a limiting factor and a source of risk. The present study examined the use of seasonal climate forecasting (SCF) when calculating planting areas for irrigated cotton in the northern Murray Darling Basin. Results show that minimising risk by adjusting plant areas in response to SCF can lead to significant gains in gross margin returns. However, how farmers respond to SCF is dependent on several other factors including irrigators' attitude towards risk.

Risk management strategies using seasonal climate forecasting in irrigated cotton production: a tale of stochastic dominance *

Decision‐making in agriculture is carried out in an uncertain environment with farmers often seeking information to reduce risk. As a result of the extreme variability of rainfall and stream‐flows in north‐eastern Australia, water supplies for irrigated agriculture are a limiting factor and a source of risk. The present study examined the use of seasonal climate forecasting (SCF) when calculating planting areas for irrigated cotton in the northern Murray Darling Basin. Results show that minimising risk by adjusting plant areas in response to SCF can lead to significant gains in gross margin returns. However, how farmers respond to SCF is dependent on several other factors including irrigators’ attitude towards risk.

Estimation of Rainfall Interstation Correlation

Abstract This study discusses questions of estimating correlation coefficient of point rainfall as observed at two measuring stations. The focus is on issues such as sensitivity to sample size, extreme rain events, and distribution of rainfall. The authors perform extensive analysis based on a two-point data-driven rainfall model that simulates the intermittence and extreme variability of rainfall using a bivariate mixed-lognormal distribution. The study examines the commonly used product–moment estimator along with an alternative transformation-based estimator. The results show a high level of bias and variance of the traditional correlation estimator, which are caused mostly by significant skewness levels that characterize rainfall observations. Application using data from a high-density cluster indicated the advantages of using the alternative estimator. The overall aim of the study is to draw the attention of researchers working with rainfall to some commonly disregarded issues when they seek accurate...

Microphysical and Large-Scale Dependencies of Temporal Rainfall Variability over a Tropical Ocean

Abstract The focus of this paper is the elucidation of the physical origins of the observed extreme rainfall variability over tropical oceans. A simple statistical–dynamical model, suitable for use in repetitive Monte Carlo experiments, is formulated as a diagnostic tool for this purpose. The model is based on three partial differential equations that describe airmass, water substance, and vertical momentum conservation in a column of air extending from the ocean surface to the top of the storm clouds. Tropospheric conditions are specified for the model state variables (such as updraft–downdraft velocity, precipitation water and cloud content, or saturation vapor deficit) in accordance with past observations in oceanic convection, to allow for vertical integration of the model equations and the formulation of a computationally efficient diagnostic tool. Large-scale forcing is represented by stochastic processes with temporal structure and parameters estimated from observed large-scale data. This model for...

A Critical Review of the Australian Experience in Cloud Seeding

Abstract From 1947 to 1994 a number of cloud-seeding experiments were done in Australia based on the static cloud-seeding hypothesis. A critical analysis of these successive cloud-seeding experiments, coupled with microphysical observations of the clouds, showed that the static cloud-seeding hypothesis is not effective in enhancing winter rainfall in the plains area of Australia. However, there is evidence to suggest that cloud seeding is effective for limited meteorological conditions in stratiform clouds undergoing orographic uplift. In particular, two successive experiments in Tasmania show strong statistical evidence for rainfall enhancement when cloud-top temperatures are between -10° and -12°C in a southwesterly stream. The evidence for similar effects on the Australian mainland is more controversial. In the summer rainfall regions of northern Australia, the extreme rainfall variability makes it impossible to design a statistical experiment that can to be evaluated in a reasonable time using current...

Structural adjustment and drought in Sub-Saharan Africa

Sub-Saharan Africa (SSA) has been affected by very frequent and severe droughts in the form of extreme rainfall variability and almost a secularly declining precipitation in the sizeable arid and semi-arid zones of the sub-continent.2 It is estimated that as much as 60 per cent of SSA is vulnerable to drought and about 30 per cent is considered to be highly vulnerable (Clay, 1995). Because of the heavy dependence on extensive resource utilisation in the context of a technologically poor agriculture and substantially disarticulated economies, the human and socioeconomic impacts of these droughts have been quite substantial. A revealing account of the impacts of recent droughts in SSA is documented by Rasheed (1993): the 1968–1973 drought in the Sahel caused more than 100,000 deaths, the loss of twelve million cattle, large-scale migration of herders and farmers and massive degradation of rangelands … The 1984–1985 severe drought in this zone and other regions claimed over 100,000 lives, caused famine among forty million people, displaced another ten million people and had a negative impact on the economies of many countries. The drought has not spared even the normally rain-bountiful Eastern and Southern African sub-region. The failure of rain in late 1991 and early 1992 caused the food harvest to drop by about half the levels of previous years, necessitated four million tons of food aid, and imports of over $200 million worth of emergency non-food aid, and caused the death of large numbers of livestock and wildlife, power cuts and the closing down of factories.’3

Multifractal Properties of Daily Rainfall in Two Different Climates

The multifractal properties of daily rainfall were investigated in two contrasting climates: an east Asian monsoon climate (China) with an extreme rainfall variability and a temperate climate (Sweden) with a moderate rainfall variability. First, daily time series were studied. The results showed that daily rainfall in both climates can be viewed as the result of a multiplicative cascade process for the range 1–32 days. The temporal data exhibited scaling for moments of orders up to 2.5 in the monsoon area and up to 4.0 in the temperate area and showed clear multifractal properties in both climates. Second, daily spatial rainfall distributions were pooled into different rainfall‐generating mechanism groups, and each group was analyzed separately. The spatial data for all rainfall mechanisms in the two climates exhibited scaling for moments of orders up to 4.0. The scaling regime was 15–180 km (225–32,400 km2) in the monsoon climate and 7.5–90 km (55–8100 km2) in the temperate climate. A multifractal framework seemed well suited for description of convective‐type rainfall in both climates, but its suitability for frontal rainfall in the two regions was less clear. Although the frontal rainfall exhibited scaling, the almost linear τ(q) functions suggested monofractality.

On the Possible Use of Fractal Theory in Rainfall Applications

The rainfall data used in hydrological calculations are seldom sufficient to correctly represent the extreme variability of rainfall in time and space. Regarding for example traditional sewer design, the errors may be negligible, but the successively more complex hydrological models used today are much more sensitive to inadequate input data. The problem can be solved either by extensive data collection or by developing methods for extracting more information from limited data. An approach based on fractal theory has been shown to provide a suitable framework for this purpose. In this paper we apply two fractal-related analyzing methods to a two-year series of one-minute rainfall observations. The results indicate that the series exhibit statistical properties that are independent of scale, i.e., scale invariant. A possibility emerging from these findings is to make a shift from the scale defined by the available data to the scale needed for the hydrological problem in question, a prospect of tremendous practical importance.