Jonas Olsson

Fractal Analysis of High-Resolution Rainfall Time Series

Two-year series of 1-min rainfall intensities observed by rain gages at six different points are analyzed to obtain information about the fractal behavior of the rainfall distribution in time. First, the rainfall time series are investigated using a monodimensional fractal approach (simple scaling) by calculating the box and correlation dimensions, respectively. The results indicate scaling but with different dimensions for different time aggregation periods. The time periods where changes in dimension occur can be related to average rainfall event durations and average dry period lengths. Also, the dimension is shown to be a decreasing function of the rainfall intensity level. This suggests a multidimensional fractal behavior (multiscaling), and to test this hypothesis, the probability distribution/multiple scaling method was applied to the time series. The results confirm that the investigated rainfall time series display a multidimensional fractal behavior, at least within a significant part of the studied timescales, which indicates that the rainfall process can be described by a multiplicative cascade process.

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.

Multifractal analysis of daily spatial rainfall distributions

The multifractal behavior of daily spatial rainfall distributions observed by a dense gage network in southern Sweden was analyzed by studying the variation of average statistical moments with scale. The data were analyzed both separated into groups depending on the rainfall generating mechanism (warm fronts, cold fronts, and convection, respectively) and pooled into one group representing the total rainfall process in the area. The results indicated that the daily spatial rainfall distributions were well characterized by a multifractal behavior both separated into mechanism groups and pooled into one group. The multifractal properties, however, displayed distinct differences which were related to physical differences of the rainfall generating mechanisms. The multifractal properties of the total rainfall process agreed well with the properties of the cold front group. Investigations of interpolated grids showed that these well preserve the multifractal behavior of the original data, but modify the multifractal properties.

Evidence of chaos in the rainfall-runoff process

Abstract The transformation of rainfall into runoff is one of the most important processes in hydrology. In the past few decades, a wide variety of automated or computer-based approaches have been applied to model this process. However, many such approaches have an important limitation in that they treat the rainfall-runoff process as a realization of only a few parameters of linear relationships rather than the process as a whole. What is required, therefore, is an approach that can capture not only the overall appearance but also the intricate details of the nonlinear behaviour of the process. The purpose of this study is to investigate the possibility of understanding the dynamics of the rainfall-runoff process from a new perspective, as a chaotic process. The possible existence of chaotic behaviour in the rainfall-runoff process is studied by investigating the rainfall and runoff time series: (a) separately; and (b) jointly (using the runoff coefficient). Monthly rainfall and runoff observed over a period of 131 years (January 1807-December 1937) at the Göta River basin in the south of Sweden are analysed. The correlation dimension method is employed to identify the presence of chaos. The correlation dimensions obtained for the rainfall and runoff time series are 6.4 and 5.5, respectively. The finite dimensions obtained for the rainfall and runoff time series indicate the possible existence of chaos in these processes, implying that the joint rainfall-runoff process might also exhibit chaotic behaviour. The correlation dimension of about 7.8 obtained for the runoff coefficient also indicates the possible presence of chaos and supports the above results.

Impacts of climate change on rainfall extremes and urban drainage systems: a review

A review is made of current methods for assessing future changes in urban rainfall extremes and their effects on urban drainage systems, due to anthropogenic-induced climate change. The review concludes that in spite of significant advances there are still many limitations in our understanding of how to describe precipitation patterns in a changing climate in order to design and operate urban drainage infrastructure. Climate change may well be the driver that ensures that changes in urban drainage paradigms are identified and suitable solutions implemented. Design and optimization of urban drainage infrastructure considering climate change impacts and co-optimizing these with other objectives will become ever more important to keep our cities habitable into the future.

Impacts of Climate Change on Rainfall Extremes and Urban Drainage Systems

Impacts of Climate Change on Rainfall Extremes and Urban Drainage Systems provides a state-of-the-art overview of existing methodologies and relevant results related to the assessment of the climate change impacts on urban rainfall extremes as well as on urban hydrology and hydraulics. This overview focuses mainly on several difficulties and limitations regarding the current methods and discusses various issues and challenges facing the research community in dealing with the climate change impact assessment and adaptation for urban drainage infrastructure design and management. ISBN: 9781780401256 (Print) ISBN: 9781780401263 (eBook)

Statistical atmospheric downscaling of short-term extreme rainfall by neural networks

Abstract Statistical atmospheric rainfall downscaling, that is, statistical estimation of local or regional rainfall on the basis of large-scale atmospheric circulation, has been advocated to make the output from global and regional climate models more accurate for a particular location or basin. Neural networks (NNs) have been used for such downscaling, but their application has proved problematic, mainly due to the numerous zero-values present in short-term rainfall time series. In the present study, using serially coupled NNs was tested as a way to improve performance. Mean 12-hour rainfall in the Chikugo River basin, Kyushu Island, Southern Japan, was downscaled from observations of precipitable water and zonal and meridional wind speed at 850 hPa, averaged over areas within which the temporal variation was found to be significantly correlated with basin rainfall. Basin rainfall was ranked into four categories: no-rain (0) and low (1), high (2) and extreme (3) intensity. A series of NN experiments showed that the best overall performance in terms of hit rates was achieved by a two-stage approach in which a first NN distinguished between no-rain (0) and rain (1–3), and a second NN distinguished between low, high, and extreme rainfalls. Using either a single NN to distinguish between all four categories or three NNs to successively detect extreme values proved inferior. The results demonstrate the need for an elaborate configuration when using NNs for short-term downscaling, and the importance of including physical considerations in the NN application.

Estimating Catchment Nutrient Flow with the HBV-NP Model: Sensitivity To Input Data

The dynamic catchment model HBV-N has been further developed by adding routines for phosphorus transport and is now called the HBV-NP model. The model was shown to satisfactorily simulate nutrient dynamics in the Rönneå catchment (1,900 km2). Its sensitivity to input data was tested, and results demonstrated the increased sensitivity to the selection of input data on a subcatchment scale when compared with the catchment scale. Selection of soil and land use databases was found to be critical in some subcatchments but did not have a significant impact on a catchment scale. Although acceptable on a catchment scale, using templates and generalization, with regards to emissions from point sources and rural households, significantly decreased model performance in certain subcatchments when compared with using more detailed local information. A division into 64 subcatchments resulted in similar model performance at the catchment outlet when compared with a lumped approach. Adjusting the imported matrixes of the regional leaching of nitrogen, from agricultural land, against mean subcatchment water percolation did not have a significant impact on the model performance.

Validity and applicability of a scale-independent, multifractal relationship for rainfall

Abstract The resolution of available rainfall data used as input in hydrological models is often insufficient to correctly describe the full variability of the modeled processes. This paper describes a method to use scale-independent, multifractal temporal rainfall properties to extract statistical information about the rainfall process at a scale smaller than the observation scale of the data. The method is tested on a 2-year series of 64-min rainfall intensity observations. The empirical probability distribution functions (pdfs) for scales down to 8 min are estimated by the method and compared to real pdfs obtained from the data. The overall agreement is good, but differences exist and these are thoroughly discussed. The results show that this approach has a potential to reduce the problem of inadequate rainfall data for hydrological calculations.

An analysis of the rainfall time structure by box counting—some practical implications

The scale-invariant behavior of the rainfall time structure was investigated by applying the box counting method to rainfall time series. Two years of minute observations, 90 years of daily observations and 170 years of monthly observations were analyzed and the results were interpreted and related to physical properties of the rainfall process. This paper discusses the question of whether an hypothesis of temporal scale invariance is valid for rainfall and the possibilities of using it in practical hydrology.