Historical Rainfall Time Series Research Articles

Optimal siting of rainwater harvesting systems for reducing combined sewer overflows at city scale

The installation of green infrastructure (GI) is an effective approach to manage urban stormwater and combined sewer overflow (CSO) by restoring pre-development conditions in urban areas. Research on simulation-optimization techniques to aid with GI planning decision-making is expanding. However, due to high computational expense, the simulation-optimization methods are often based on design storm events, and it is unclear how much different rainfall scenarios (i.e., design storm events vs. long-term historical rainfall data) impact the optimal siting of GI. The Parallel Pareto Archived Dynamically Dimensioned Search (ParaPADDS) algorithm in a novel simulation-optimization tool OSTRICH-SWMM was used to leverage distributed computing resources. A case study was conducted to optimally site rainwater harvesting cisterns within 897 potential subcatchments throughout the City of Buffalo, New York. Seven design storm events with different return periods and rainfall durations and a one-month historical rainfall time series were considered. The results showed that the optimal solutions of siting cisterns using event-based scenarios, though less computationally expensive, may not perform well under continuous rainfall scenarios, suggesting design rainfall scenarios should be carefully considered for optimizing GI planning. The impact of rainfall scenarios was particularly significant in the middle region of the Pareto front of multi-objective optimization. Utilizing high-performance parallel computing, OSTRICH-SWMM is a promising tool to optimize GI at large spatial and temporal scales.

The Seasonal Rainfall Forecast in Nanning City in 2019 with the Method of Trend Comparison Ratio (TCR)

In this paper, the monthly rainfall statistical data of Nanning City, Capital of Guangxi Zhuang Autonomous Region, China, from 2006 to 2018, were collected. On the basis of qualitative analysis of the rainfall seasonal changing law, the non-linear seasonal rainfall forecast model on Nanning City with the method of Trend Comparison Ratio (TCR) was established by the statistical analysis software Office Excel 2013. The model was used to predict the rainfall in spring, summer, autumn and winter in Nanning in 2019. The results were: 286.41 mm, 695.79 mm, 292.20 mm and 118.11 mm, respectively. It was also found that the predicted results were consistent with the seasonal distribution characteristics, annual distribution characteristics and the trend of historical rainfall time series fluctuation, through the qualitative analysis of figures. Compared with the actual measured rainfall data of Nanning City in 2019 in the China Statistical Yearbook (2020), the predicted values are basically consistent with the measured values.

The Effectiveness of a Probabilistic Principal Component Analysis Model and Expectation Maximisation Algorithm in Treating Missing Daily Rainfall Data

The reliability and accuracy of a risk assessment of extreme hydro-meteorological events are highly dependent on the quality of the historical rainfall time series data. However, missing data in a time series such as this could result in lower quality data. Therefore, this paper proposes a multiple-imputation algorithm for treating missing data without requiring information from adjoining monitoring stations. The proposed imputation algorithms are based on the M-component probabilistic principal component analysis model and an expectation maximisation algorithm (MPPCA-EM). In order to evaluate the effectiveness of the MPPCA-EM imputation algorithm, six distinct historical daily rainfall time series data were recorded from six monitoring stations. These stations were located at the coastal and inland regions of the East-Coast Economic Region (ECER) Malaysia. The results of analysis show that, when it comes to treating missing historical daily rainfall time series data recorded from coastal monitoring stations, the 2-component probabilistic principal component analysis model and expectation-maximisation algorithm (2PPCA-EM) were found to be superior to the single- and multiple-imputation algorithms proposed in previous studies. On the contrary, the single-imputation algorithms as proposed in previous studies were superior to the MPPCA-EM imputation algorithms when treating missing historical daily rainfall time series data recorded from inland monitoring stations.

Impact of Climate Change on the Development of Rainfall Intensity, Duration and Frequency Curves in <i>Chiro</i> and <i>Hurso</i> Stations of Eastern Ethiopia

Today environmental issue becomes the biggest concern of humankind because of scientific evidence about the increasing concentration of greenhouse gases in the atmosphere and the changing climate of the Earth. This study was conducted in eastern Ethiopia specifically at <i>Chiro</i> and <i>Hurso</i> stations. The study assessed quantitatively the rainfall Intensity Duration Frequency (IDF) relationships under changing climate condition and compare with the existing rainfall- Intensity Duration- Frequency (IDF) relationships. Rainfall intensity duration and frequency curves were developed using historical rainfall time series data under the assumption that climate is stationary. This assumption is not valid under changing climatic conditions that may shifts in the magnitude and frequency of extreme rainfall. Such shifts in extreme rainfall at the local level demand new regulations for any intervention management as well as changes in design practices. In order to estimate the level of climate change impact on the rainfall Intensity Duration Frequency (IDF) relationships, these changes of the climate variables were applied to Hyetos Temporal Rainfall Disaggregation model to simulate future IDF relationships. From the results can see graphical presentation of IDF curves for return periods of 2, 5, 10, 25, 50 and 100 years for durations of 1, 2, 3, 6, 12 and 24 hours. The comparison results indicate that, difference between rainfall intensities (percentage) of climate change scenario and historic rainfall for 2020s ranges between 1.58% and 10.92% for 2050s, ranges between 0.07% and 20.22% and for 2080s, ranges between 0.71% and 55.93% in <i>Chiro</i> station, respectively. Similarly, in the case of <i>Hurso</i> station, the difference between climate change scenario and historic rainfall for 2020s ranges between1.10% and 27.83% for 2050s ranges between 110.5% and 40.21% and for 2080s that ranges between 19.44% and 67.75%, respectively. Therefore, the outputs of the study indicates that the rainfall magnitude will be different in the future and thereby the decrease and increase in rainfall intensity and magnitude may have major implications on ways in which current and future intervention is designed, operated, and maintained. Therefore, design standards and guidelines currently employed in the study area should be revised with the confirmation of the impacts of climate change.

Combined use of satellite estimates and rain gauge observations to generate high‐quality historical rainfall time series over Ethiopia

ABSTRACTClimate data are used in a number of applications including climate risk management and adaptation to climate change. However, the availability of climate data, particularly throughout rural Africa, is very limited. Available weather stations are unevenly distributed and mainly located along main roads in cities and towns. This imposes severe limitations to the availability of climate information and services for the rural community where, arguably, these services are needed most. Weather station data also suffer from gaps in the time series. Satellite proxies, particularly satellite rainfall estimate, have been used as alternatives because of their availability even over remote parts of the world. However, satellite rainfall estimates also suffer from a number of critical shortcomings that include heterogeneous time series, short time period of observation, and poor accuracy particularly at higher temporal and spatial resolutions. An attempt is made here to alleviate these problems by combining station measurements with the complete spatial coverage of satellite rainfall estimates. Rain gauge observations are merged with a locally calibrated version of the TAMSAT satellite rainfall estimates to produce over 30‐years (1983‐todate) of rainfall estimates over Ethiopia at a spatial resolution of 10 km and a ten‐daily time scale. This involves quality control of rain gauge data, generating locally calibrated version of the TAMSAT rainfall estimates, and combining these with rain gauge observations from national station network. The infrared‐only satellite rainfall estimates produced using a relatively simple TAMSAT algorithm performed as good as or even better than other satellite rainfall products that use passive microwave inputs and more sophisticated algorithms. There is no substantial difference between the gridded‐gauge and combined gauge‐satellite products over the test area in Ethiopia having a dense station network; however, the combined product exhibits better quality over parts of the country where stations are sparsely distributed.

Building hazard maps of extreme daily rainy events from PDF ensemble, via REA method, on Senegal River Basin

Abstract. The Sudano-Sahelian zone of West Africa, one of the poorest of the Earth, is characterized by high rainfall variability and rapid population growth. In this region, heavy storm events frequently cause extensive damage. Nonetheless, the projections for change in extreme rainfall values have shown a great divergence between Regional Climate Models (RCM), increasing the forecast uncertainty. Novel methodologies should be applied, taking into account both the variability provided by different RCMs, as well as the non-stationary nature of time series for the building of hazard maps of extreme rainfall events. The present work focuses on the probability density functions (PDFs)-based evaluation and a simple quantitative measure of how well each RCM considered can capture the observed annual maximum daily rainfall (AMDR) series on the Senegal River basin. Since meaningful trends have been detected in historical rainfall time series for the region, non-stationary probabilistic models were used to fit the PDF parameters to the AMDR time series. In the development of PDF ensemble by bootstrapping techniques, Reliability Ensemble Averaging (REA) maps were applied to score the RCMs. The REA factors were computed using a metric to evaluate the agreement between observed -or best estimated- PDFs, and that simulated with each RCM. The assessment of plausible regional trends associated to the return period, from the hazard maps of AMDR, showed a general rise, owing to an increase in the mean and the variability of extreme precipitation. These spatial-temporal distributions could be considered by Organization for the Development of the Senegal River (Organisation pour la mise en valeur du fleuve Sénégal, OMVS), in such a way as to reach a better balance between mitigation and adaptation.

A comparative copula‐based bivariate frequency analysis of observed and simulated storm events: A case study on Bartlett‐Lewis modeled rainfall

Because of a lack of historical rainfall time series of considerable length, one often has to rely on simulated rainfall time series, e.g., in the design of hydraulic structures. One way to simulate such time series is by means of stochastic point process rainfall models, such as the Bartlett‐Lewis type of model. For the evaluation of model performance, with a focus on the reproduction of extreme rainfall events, often a univariate extreme value analysis is performed. Recently developed concepts in statistical hydrology now offer other means of evaluating the overall performance of such models. In this study, a copula‐based frequency analysis of storms is proposed as a tool to evaluate differences between the return periods of several types of observed and modeled storms. First, this study performs an analysis of several storm variables, which indicates a problem with the modeling of the temporal structure of rainfall by the models. Thereafter, the bivariate frequency analysis of storms, defined by their duration and volume, illustrates the underestimation and overestimation of the return period of the storms simulated by the models, which is partially explained by a large difference in the marginal distribution functions of the storm duration and storm volume, the difference in the degree of association between the latter, and a different mean storm interarrival time. The proposed methodology allows for the identification of some problems with the rainfall simulations from which recommendations for possible improvements to rainfall models can be made.

On the effect of spatial variances in historical rainfall time series to CSO performance evaluation

Historical, high-resolution rain series are the backbone of modern combined sewer overflow (CSO) structure design. These rain series are the input to the computational estimation of the performance of the measures with respect to CSO pollution abatement. However, those historical precipitation measurements are available at only a few locations. Frequently rain series have to be used from gauging stations at a significant distance. In order to judge and to compensate for this influence an estimate between rain characteristics and combined sewer outflow (CSO) performance indicators would be useful. In this paper such correlations have been sought for a collection of 37 rain series covering large areas of Europe. It was found that the mean annual rain volume can explain most of the variances for the performance indicators Number of overflows and CSO volume. For explaining the spatial differences in the efficiency of the CSO structure another rain characteristic, i.e. the maximum event with a return period of one year, is to be used.

Stochastic Point Rainfall Modeling for Correlated Rain Cell Intensity and Duration

A new stochastic point rainfall model which considers the correlation structure between rain cell intensity and duration is developed. In order to consider the positive and negative correlations simultaneously, the Gumbel’s Type-II bivariate distribution is applied. For cluster characteristics of rainfall events, the Neyman–Scott cluster point process is applied. The application is performed using the rainfall station of Jeonju situated on the southwest part of the Korean peninsula. Results from generating long time rainfall events show that the proposed model reproduces well the statistical characteristics of the historical rainfall time series and the model-generated data are robust with different parameter sets when the correlation parameter is appropriately taken.

Selection of regional historical rainfall time series as input to urban drainage simulations at ungauged locations

It has become established practice during the past 20 years to use high-resolution historical rainfall time series as input to hydrological model packages for detailed simulation of urban drainage systems. However, sufficiently long rain series are rarely available from the exact catchment in question and simulations are hence often based on available rain series from other locations. Extreme rainfall properties of importance to the performance of urban storm drainage systems vary significantly even in regions with only minor physiographic differences. Part of this variation can be explained by regional variations of the mean annual rainfall and the remaining statistical residue can be interpreted as statistical uncertainty. In Denmark, more than 75 high-resolution rain gauges are installed across a total area of 43,000 m. About 40 gauges had sufficiently long records to be included in a comprehensive national investigation where newly developed statistical regionalisation procedures were used to model the regional variation of extreme rainfalls. On this basis, a spreadsheet model was made available for estimation of extreme design rainfalls and the associated uncertainty at any location in the country. Statistics were furthermore computed to classify historical rainfall time series according to the developed regional model, and this makes it possible to assess the uncertainty related with using different historical rain series for simulations at ungauged locations. This research indicates that use of historical point rainfall data at ungauged locations introduces a significant uncertainty that is largely overlooked in today's practice. The engineering recommendation is to select historical rain series based on an evaluation of the local physiographic characteristics (e.g., the mean annual rainfall) and a (pre-defined) desired safety level of the simulations.

On the dependent structure between rainfall intensity and duration

In this study, new stochastic point rainfall models which can consider the correlation structure between rainfall intensity and duration are developed. In order to consider the negative and positive correlation simultaneously, the Gumbel’s type-II bivaria te distribution is applied, and for the cluster structure of rainfall events, the Neyman-Scott cluster point process is selected. In the theoretical point of view, it is shown that the models considering the dependent structure between rainfall intensity and duration have slightly heavier tail autocorrelation functions than the corresponding independent models. Results from generating long time rainfall events show that the dependent models better reproduce historical rainfall time series than the corresponding independent models in the sense of autocorrelation structures, zero rainfall probabilities and extreme rainfall events.