Long-term Rainfall Time Series Research Articles

Assessment of temporal homogeneity of long-term rainfall time-series datasets by applying classical homogeneity tests

Assessment of temporal homogeneity of long-term rainfall time-series datasets by applying classical homogeneity tests

Assessing hydrological performance for optimized integrated grey-green infrastructure in response to climate change based on shared socio-economic pathways

Integrated grey-green infrastructure (IGGI) is considered as a promising facility for mitigating urban flooding during extreme rainfall events. However, most IGGI strategies rely heavily on historical precipitation data, while ignoring the long-term effects of dynamic and non-stationary climate change. This study explored a novel approach to assess the hydrological performance under Shared Socio-economic Pathways (SSPs) based on long-term rainfall time series, facilitating the exploration of the optimal solution to urban runoff alleviation in response to climate change. A case study of Guangzhou, China, was applied for model validation. The results indicated that through non-stationarity analysis and stage division, the future time series of the three SSPs were identified with an abrupt point and divided into two stages. By assessing the hydrological performance of the optimal solutions at various historical and future stages, both fully centralized IGGI and decentralized IGGI are found vulnerable to long-term climate change. Based on a certain extent of trade-off between centralization and decentralization, an appropriate degree of decentralization was observed to significantly enhance the hydrological performance of the whole IGGI system, particularly for reducing peak flow following extreme rainfall events. The authors believe that the findings could provide a novel perspective on hydrological performance assessment for IGGI in response to non-stationary and multi-scenario climate change in urban catchments with high built-up density.

River Stage Modeling with a Deep Neural Network Using Long-Term Rainfall Time Series as Input Data: Application to the Shimanto-River Watershed

The increasing frequency of devastating floods from heavy rainfall—associated with climate change—has made river stage prediction more important. For steep, forest-covered mountainous watersheds, deep-learning models may improve prediction of river stages from rainfall. Here we use the framework of multilayer perceptron (MLP) neural networks to develop such a river stage model. The MLP is constructed for the Shimanto river, which lies in southwestern Japan under a mild, rain-heavy climate. Our input for stage estimation, as well as prediction, is a long-term rainfall time series. With a one-year time series of rainfall, the model estimates the stage with RMSE less than 67 cm for about 10 m of stage peaks, as well as accurately simulating stage-time fluctuations. Furthermore, the forecast model can predict the stage without rainfall forecasts up to three hours ahead. To estimate the base flow stages as well as flood peaks with high precision, we found that the rainfall time series should be at least one year. This indicates that the use of a long rainfall time series enables one to model the contributions of ground water and evaporation. Given that the delay between the arrival time of rainfall at a rain-gauge to the outlet change is well-simulated, the physical concepts of runoff appear to be soundly embedded in the MLP.

Rainfall Threshold for Shallow Landslides Initiation and Analysis of Long-Term Rainfall Trends in a Mediterranean Area

The effects of climate change on landslide activity may have important environmental, socio-economic, and political consequences. In the last decades, several short-term extreme rainfall events affected Mediterranean regions, resulted in damaging geo-hydrological processes and casualties. It is unequivocal that the impact of landslides in several Mediterranean countries is increasing with time, but until now, there has been little or no quantitative data to support these increases. In this paper, both rainfall conditions for the occurrence of shallow landslides and rainfall trends were investigated in the Portofino promontory, which extends in the Ligurian Sea, where heavy rainfall and related ground effects often occur. Adopting a frequentist approach, the empirical intensity-duration threshold was estimated. Our findings highlight that the rainfall intensity required to trigger landslides is lower for the same duration than those expected in other similar environments, suggesting a high susceptibility to rainfall-induced landslides in the Portofino territory. Further, the Mann-Kendall test and Hurst exponent were used for detecting potential trends. Analysis of long-term rainfall time series showed statistically significant increasing trends in short duration precipitation occurrence and rainfall rates, suggesting a possible future scenario with a more frequent exceedance of the threshold triggering value and an increase of landslide risk.

Data assimilation for constructing long-term gridded daily rainfall time series over Southeast Asia

The data scarcity and poor availability of observed daily rainfalls over Southeast Asia has limited the possibility to a wider range of studies in light of impacts from climate change and extreme hydro-meteorological processes such as floods, droughts, and other watershed management practices. To fill such a gap, data assimilation was carried out in this study to construct a long-term gridded daily (0.50° × 0.50°) rainfall time series (1951–2014) over Southeast Asia. In rainfall data assimilation, the available and globally accepted high resolution gridded datasets viz. Southeast Asia observed (SA-OBS) (1981–2014), APHRODITE (1951–2007), TRMM (1998–2018), PRINCETON (1951–2008) along with limited rain gauges-based rainfalls were utilized. In this study, eight gap filling methods were employed and tested at 20 selected rainfall grids to fill the long gaps presented in the SA-OBS gridded dataset. The strength of each method and associated uncertainties were evaluated in the computed rainfalls utilizing multiple functions at missing grids. The accuracy of each method, in case of extreme rainfalls, was tested by quantile–quantile (Q–Q) plots at different quantile intervals. The distance power method based on the Pearson correlation coefficient and the multiple linear regression method performed satisfactorily and produced minimum uncertainties in filling rainfall gaps. To test the accuracy and compatibility of gap-filled SA-OBS gridded dataset with other sources of datasets, the seasonality analysis and rainfall indices comparison were carried out. Results showed that the gap-filled SA-OBS dataset was better comparable to other sources of rainfalls. For the construction of the long-term rainfall time series (1951–2014), quantile mapping was adopted for bias correction and the quality of the final merged dataset was evaluated.

Short-term instead of long-term rainfall time series in rainwater harvesting simulation in houses: An assessment using Bayesian Network

Rainwater harvesting simulations usually take long-term rainfall data as input to size system components. However, long-term time series data are not available in many cities around the world. This article aims to investigate the relation between the rainwater harvesting simulation results and the rainfall characteristics of various time series lengths. For 13 cities, 30-year time series were adopted as long-term time series to investigate the influence of time series length on the simulation outcomes. Short-term time series were extracted from those long-term time series and used as input in several computer simulations applied in a generic residential model. Comparison of short-term and long-term time series results allowed to obtain a representative short-term time series that leads to results similar to those obtained using long-term time series. A statistical analysis associated the simulation results of the representative time series with their rainfall characteristics using three indicators: average annual rainfall, seasonality index, and average number of dry days per year. The average number of dry days per year showed to be the most important short-term time series indicator to be considered. Additionally, a Bayesian Network was proposed to serve as a support-decision making tool for future studies. This network uses rainfall indicators and rainwater demand to predict if a given short-term time series leads to results similar to those obtained using a long-term time series. The validation of the Bayesian Network showed the tool is promising and useful to assess the representativeness of short-term time series used in rainwater harvesting simulations in houses.

Assessment of the length of rainfall time series for rainwater harvesting in buildings

In this study, the possibility of using short-term instead of long-term rainfall time series in simulations of rainwater harvesting in buildings was evaluated. The main objective was to determine the required length of a short-term time series in order to produce results similar to those obtained with a long-term time series. The study was conducted using daily rainfall data on thirteen cities located in different countries. The 30-year time series were used as a reference and, from these, short-term time series were extracted, i.e., 30 series of 1-year length, 29 series of 2-year length, and so on. The computer programme Netuno was used to carry out the simulations with short-term and long-term time series and the results were compared. A simulation model was defined and some parameters were fixed, such as the catchment area and number of inhabitants, and others were varied, such as the time series length and the rainwater demand. The results obtained using the short-term time series were compared to those using the long-term time series considering three factors: potential for potable water savings obtained for the same rainwater tank size; the optimal rainwater tank size; and the ideal potential for potable water savings. For each city, the representative time series length was determined based on the frequency of similar results, i.e., when at least 90% of the results were similar to those obtained using a 30-year time series. A validation of the representative time series length was performed using a different period from that used in the initial simulation. Different representative time series lengths were found for each city, the shortest being 6 years and the longest 20 years. A time series of 15 years was sufficient to obtain results similar to the 30-year time series in seven out of the thirteen cities. It was concluded that the use of short-term time series instead of 30-year time series for the simulation of rainwater harvesting systems is valid, depending on the rainfall characteristics of the region.

Non-parametric characterization of long-term rainfall time series

The statistical study of rainfall time series is one of the approaches for efficient hydrological system design. Identifying, and characterizing long-term rainfall time series could aid in improving hydrological systems forecasting. In the present study, eventual statistics was applied for the long-term (1851–2006) rainfall time series under seven meteorological regions of India. Linear trend analysis was carried out using Mann–Kendall test for the observed rainfall series. The observed trend using the above-mentioned approach has been ascertained using the innovative trend analysis method. Innovative trend analysis has been found to be a strong tool to detect the general trend of rainfall time series. Sequential Mann–Kendall test has also been carried out to examine nonlinear trends of the series. The partial sum of cumulative deviation test is also found to be suitable to detect the nonlinear trend. Innovative trend analysis, sequential Mann–Kendall test and partial cumulative deviation test have potential to detect the general as well as nonlinear trend for the rainfall time series. Annual rainfall analysis suggests that the maximum changes in mean rainfall is 11.53% for West Peninsular India, whereas the maximum fall in mean rainfall is 7.8% for the North Mountainous Indian region. The innovative trend analysis method is also capable of finding the number of change point available in the time series. Additionally, we have performed von Neumann ratio test and cumulative deviation test to estimate the departure from homogeneity. Singular spectrum analysis has been applied in this study to evaluate the order of departure from homogeneity in the rainfall time series. Monsoon season (JS) of North Mountainous India and West Peninsular India zones has higher departure from homogeneity and singular spectrum analysis shows the results to be in coherence with the same.

Statistical evaluation of rainfall time series in concurrence with agriculture and water resources of Ken River basin, Central India (1901–2010)

Trend analysis of long-term rainfall records can be used to facilitate better agriculture water management decision and climate risk studies. The main objective of this study was to identify the existing trends in the long-term rainfall time series over the period 1901–2010 utilizing 12 hydrological stations located at the Ken River basin (KRB) in Madhya Pradesh, India. To investigate the different trends, the rainfall time series data were divided into annual and seasonal (i.e., pre-monsoon, monsoon, post-monsoon, and winter season) sub-sets, and a statistical analysis of data using the non-parametric Mann–Kendall (MK) test and the Sen’s slope approach was applied to identify the nature of the existing trends in rainfall series for the Ken River basin. The obtained results were further interpolated with the aid of the Quantum Geographic Information System (GIS) approach employing the inverse distance weighted approach. The results showed that the monsoon and the winter season exhibited a negative trend in rainfall changes over the period of study, and this was true for all stations, although the changes during the pre- and the post-monsoon seasons were less significant. The outcomes of this research study also suggest significant decreases in the seasonal and annual trends of rainfall amounts in the study period. These findings showing a clear signature of climate change impacts on KRB region potentially have implications in terms of climate risk management strategies to be developed during major growing and harvesting seasons and also to aid in the appropriate water resource management strategies that must be implemented in decision-making process.

Influence of the length of rainfall time series on rainwater harvesting systems: A case study in Berlin

This study evaluated the influence of using different rainfall time series lengths on the rainwater harvesting systems sizing. The main objective was to determine a short-term rainfall time series length can lead to results similar to those obtained with long-term rainfall time series. The study was conducted using daily rainfall data from Berlin, Germany. A 30-year time series was used as a reference and then shorter series were detached, i.e., 30 sets of 1-year, 29 sets of 2-year, and so on. A single-family house was used as a simulation model fixing the catchment area and a number of residents, and varying the time series length and the rainwater demand. Two simulation variables were evaluated: “optimal rainwater tank capacity” and “ideal potential for potable water savings”. The results obtained from the simulations for each short-term time series were compared to the results for the 30-year time series. The frequency of the optimal rainwater tank capacity results that were similar to those obtained for the 30-year time series and the relative and absolute differences between the ideal potential for potable water savings were evaluated. It was found that a series of 10 years of daily rainfall data is enough to generate results significantly similar to those of a 30-year time series. To validate the results obtained, the model was simulated again, using a series of 10-years of daily rainfall data not included in the 30-year time series used in the experiment. The validation has confirmed the experiment results.

Short-term versus long-term rainfall time series in the assessment of potable water savings by using rainwater in houses

The main objective of this article is to assess the possibility of using short-term instead of long-term rainfall time series to evaluate the potential for potable water savings by using rainwater in houses. The analysis was performed considering rainfall data from 1960 to 1995 for the city of Santa Bárbara do Oeste, located in the state of São Paulo, southeastern Brazil. The influence of the rainfall time series, roof area, potable water demand and percentage rainwater demand on the potential for potable water savings was evaluated. The potential for potable water savings was estimated using computer simulations considering a set of long-term rainfall time series and different sets of short-term rainfall time series. The ideal rainwater tank capacity was also assessed for some cases. It was observed that the higher the percentage rainwater demand and the shorter the rainfall time series, the larger the difference between the potential for potable water savings and the greater the variation in the ideal rainwater tank size. The sets of short-term rainfall time series considered adequate for different scenarios ranged from 1 to 13 years depending on the roof area, percentage rainwater demand and potable water demand. The main finding of the research is that sets of short-term rainfall time series can be used to assess the potential for potable water savings by using rainwater, as the results obtained are similar to those obtained from the long-term rainfall time series.

Automatic Curve Extraction for Digitizing Rainfall Strip Charts

Abstract A method has been developed that largely automates the labor-intensive extraction work for large amounts of rainfall strip charts and paper rolls. The method consists of the following five basic steps: 1) scanning the charts and rolls to high-resolution digital images, 2) manually and visually registering relevant meta information from charts and rolls and preprocessing rolls to locate day transitions, 3) applying automatic curve extraction software in a batch process to determine the coordinates of cumulative rainfall lines on the images, 4) postprocessing the curves that were not correctly determined in step 3, and 5) aggregating the cumulative rainfall in pixel coordinates to the desired time resolution. The core of the method is in step 3. Here a color detection procedure is introduced that automatically separates the background of the charts and rolls from the grid and subsequently the rainfall curve. The rainfall curve is detected by minimization of a cost function. In total, 321 station years of locations in the Netherlands have successfully been digitized and transformed to long-term rainfall time series with 5-min resolution. In about 30% of the cases, semiautomatic postprocessing of the results was needed using a purpose-built graphical interface application. This percentage, however, strongly depends on the quality of the recorded curves and the charts and rolls. Although developed for rainfall, the method can be applied to other elements as well.