Rainfall Time Series Research Articles

Assessment of climate change induced rainfall trend and variability with non-parametric and linear approach for Sirajganj district, Bangladesh

The monthly and annual trends and variance of rainfall have been studied for five stations in an economically important Bangladeshi district named Sirajganj since 1965 to 2021. Natural disasters have prevalent in Sirajganj which is indispensable to assess. But, several researchers have been normally focused on river bank management and flood risk assessment. However, no extensive research has been conducted on Sirajganj based on non-normally distributed time series meteorological data such as rainfall time series so the current study is very important. In this study, the non-parametric Mann-Kendall and Sen's methods have been used to determine the statistical significance of a positive or negative trend in rainfall data. Also, cumulative sum charts and bootstrapping, one-way ANOVA, Tukey's range tests, and linear regression have been used to discover the incidence of abrupt changes, compare the significant difference in monthly and annual rainfall data, multiple comparisons amidst mentioned stations to find changes, and to investigate the changeover on dry and rainy days, respectively. The analysis showed a statistically significant decreasing trends in monthly and annual rainfall series. As well, changes from positive to negative direction have been recognized in the February, May, July, September, and annual rainfall time sequence. Besides, ANOVA and Tukey's range tests revealed a statistically substantial difference in all monthly and annual rainfall volume excluding January, March, and June. Additionally, these two tests demonstrated momentous differences in all monthly and annual frequency of rainfall categories excepting January and April. However, Linear regression analysis revealed that the number of dry days gradually reduced at the end of the dry winter, though the number of rainy days decreased during the rainy season. As in, the number of rainy days replaces the number of dry days during the dry season and vice versa during the rainy season. Even though, with very few exceptions, the volume of rainfall decreases throughout the year. The outcomes of this research might helpful for implementing the planning and evaluating hydrological projects on Sirajganj district.

Nonlinear hydrological time series modeling to forecast river level dynamics in the Rio Negro Uruguay basin.

Floods significantly impact the well-being and development of communities. Hence, understanding their causes and establishing methodologies for risk prevention is a critical challenge for effective warning systems. Complex systems such as hydrological basins are modeled through hydrological models that have been utilized to understand water recharge of aquifers, available volume of dams, and floods in diverse regions. Acquiring real-time hydrometeorological data from basins and rivers is vital for establishing data-driven-based models as tools for the prediction of river-level dynamics and for understanding its nonlinear behavior. This paper introduces a hydrological model based on a multilayer perceptron neural network as a useful tool for time series modeling and forecasting river levels in three stations of the Rio Negro basin in Uruguay. Daily time series of river levels and rainfall serve as the input data for the model. The assessment of the models is based on metrics such as the Nash-Sutcliffe coefficient, the root mean square error, percent bias, and volumetric efficiency. The outputs exhibit varying model performance and accuracy during the prediction period across different sub-basin scales, revealing the neural network's ability to learn river dynamics. Lagged time series analysis demonstrates the potential for chaos in river-level time series over extended time periods, mainly when predicting dam-related scenarios, which shows physical connections between the dynamical system and the data-based model such as the evolution of the system over time.

A modeling study of aerosol effect on summer nocturnal convective precipitation in Beijing

Using the WRF model with chemistry (WRF-Chem), this study investigates the potential contributions of aerosol direct (ADE) and aerosol indirect (AIE) effects on the nocturnal convective precipitation occurred on 9–10 September 2019 in Beijing. It shows that ADE plays a more important role in spatiotemporal changes of heavy precipitation than AIE in urban areas. Further simulation analyses show that ADE suppresses precipitation by inducing greater surface shortwave radiative cooling and stability before 13:00 (UTC, hereafter) on 9 September, which reduces convective available potential energy (CAPE) losses and then increases the later CAPE. The ADE-induced CAPE increase makes updrafts stronger, increasing cloudiness and transporting more cloud water into the upper troposphere. This, in turn, causes greater latent heat release from freezing and then stronger convection, producing higher rain rate during 13:00–21:00. During 12:00–18:00, AIE causes a decrease in CAPE, which weakens updrafts. AIE decreases the snow mixing ratio above 700 hPa except at 15:00–16:00 on 9 September, which can decrease the release of latent heat from freezing, contributing to the decrease in precipitation after 18:00. Due to the greater stability before 10:00 and the longer time required for updrafts to overcome this stability, ADE necessitates 2–3 h to adjust the simulated rainfall time series.

Developing Non-Stationary Frequency Relationships for Greater Pamba River basin, Kerala India incorporating dominant climatic precursors

Abstract. Global climate changes significantly contribute to increased frequency of hydrologic extremes. This significantly underestimates the hydrologic design parameters, bringing of hydro systems to increased failure risk. In order to address this concern, the current practice of development of hydrologic frequency tools need to be updated accounting for non-stationarity. This study first considered a diverse set of statistical tests to examine the trend, change points, non-stationarity and randomness of streamflow, rainfall and temperature time series of scales ranging from daily to annual. The annual maxima time series indicated non stationarity against the stationary behaviour of daily series of hydro-meteorological datasets of the basin. Subsequently, this study developed the Temperature Duration Frequency (TDF), Rainfall Intensity Duration Frequency (IDF) and Flood Frequency (FF) curves of Greater Pamba river basin in Kerala India, the part of which was most severely affected by the near century return period flood event of 2018. The analysis was performed for a multitude of combinations of variations in distribution parameters with time and climatic drivers as physical covariates in the extreme value formulations. The study proposed a novel wavelet coherence (WC) based driver selection of most dominant combination of climatic precursors in developing FF and IDF relations of three locations of Kalloopara, Malakkara and Thumpamon and TDF curve of Kuttanad region in the basin, considering data of 1985–2015 period. The proposed WC framework considers bi-multi-and partial effects of climatic oscillations (COs) like El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO) in identifying potential drivers. The different WC formulations captured in-phase relationships of streamflows and rainfall with COs at intra-annual, annual and inter annual scales up to 4 years. The methods showed that addition of climatic precursors improved the NS estimates of flood and rainfall quantiles by more accurately capturing the magnitudes of extreme streamflows and rainfalls of 2018, 2021 than the time covariate formulations. However, the role of COs on extreme temperature is not found to be influential in developing TDF relationships, which needs further investigation.

What can we learn from long hydrological time-series? The case of rainfall data at Collegio Romano, Rome, Italy

In this work, we explore the statistical behavior of one of the longest rainfall time-series in Italy and in the world, covering the period 1782–2017. Some standard and innovative statistical tools are applied to test the variability and change of the process across all values (in average, but also in terms of extremes) and scales (from days to years). An oscillation pattern occurs across all the time scales, from years to decades, limited by the sample length. It implies that there are no particular periods of variability, apart from seasonality, and no statistically significant trends, such that the process can be fully characterized in terms of the Hurst coefficient. Despite its exceptional length, the dataset is still insufficient to adequately capture the complex behavior of rainfall over the time scales, especially with regards to extremes, and to separate anthropogenically induced change from natural variability based on the data alone. Our findings suggest that samples of limited length do not allow robust statistical predictions, raising concerns about statistical analyses based on a limited dataset, even a relatively large one.

Analysis of long-term trends of rainfall and extreme rainfall events over Andaman & Nicobar and Lakshadweep Islands of India

The two major archipelagos of India, the Andaman & Nicobar Islands and the Lakshadweep situated in the climate-hazardous areas of the Bay of Bengal and Arabian Sea respectively are largely affected by weather systems developing over the sea and heavy rainfall activities. The recent two daily gridded rainfall data sets published by IMD; Rajeevan et al. (2010) at 1° × 1° spatial resolution and Pai et al. (2014) at 0.25° × 0.25° spatial resolution extending for a period of more than 100 years have been extensively used by researchers to study the rainfall characteristics at various spatiotemporal scales over the Indian mainland. However, these data sets do not include the grids over these two island meteorological subdivisions of India mainly because of the absence of daily rainfall observation for this long period. In this study, an attempt has been made to develop daily gridded rainfall data over these island subdivisions for the recent 70 years (1951 to 2020) in two spatial resolutions, viz., 1° × 1° and 0.25° × 0.25° using all the available islands station data during the period and carry out statistical analyses of various rainfall characteristics over these islands. The 0.25° × 0.25° data set was observed to be more comparable with the official rainfall time series of IMD for both these two Island subdivisions, and hence this data set has been used to carry out the trend analysis of Daily events of rainfall DER (> = 5 mm) for these two island subdivisions for the whole data period of 1951-2020 and the climate regime shift period of 1971-2020. DER was classified into two categories DMR (5-100 mm), daily moderate rainfall events and DHR (100 mm and above) daily heavy rainfall events. Signs and magnitude of the long-term trends in the frequency of DER (with DMR & DHR) showed significant changes during the recent period 1971-2020.

Evaluating Rainwater Harvesting Systems for Water Scarcity Mitigation in Small Greek Islands under Climate Change

Rainfall variability, exacerbated by climate change, poses significant challenges to water resource management, particularly in regions prone to intense droughts and floods. The Greek islands, characterized by poor water potential, face interannual water supply issues dating back to their earliest habitation. Rainwater harvesting (RWH) systems emerge as a promising solution to address water scarcity in these regions. This study simulates RWH systems for two small Greek islands, Fourni and Nisyros, representing similar rainfall regimes. Multiple scenarios are explored, and system reliability is assessed in light of simulated daily rainfall time series incorporating climate change projections. Utilizing eight low/medium (RCP 4.5) and eight high (RCP 8.5) emission scenarios over a future 35-year period, the study evaluates system reliability based on model parameters (collection area: 40 to 140 m2, rainwater tank volume: 5 to 30 m3, number of household members: 2, 3), with 30% coverage of total daily water demand (180 L/d). Negligible evapotranspiration effects are assumed due to closed-type tanks. Results indicate that the RWH system demonstrates high efficiency in general. The investigation for the future period revealed that the system’s performance varies, with instances where daily demand targets are not met, even with a 30 m3 tank. This research underscores the potential of RWH systems as a cost-effective “green” solution, particularly in regions with deficient rainfall regimes. It highlights the importance of localized water management strategies, reducing reliance on mainland water transportation, and assisting desalination unit operations. In conclusion, this study contributes to the assessment of RWH systems, demonstrating their viability as a sustainable water management solution in regions facing water scarcity, contingent on local rainfall conditions and system design parameters.

Modelling urban stormwater and irrigation management with coupled blue-green infrastructure in the context of climate change

ABSTRACT Urban planners must consider stormwater infrastructure to prevent floods, enhance resilience and promote sustainability, ultimately benefiting cities by minimizing damage and fostering sustainable growth. This is leading cities to consider the implementation of urban blue-green infrastructure (BGI) as an integrated approach to stormwater management. An urban irrigation model, blue-green infrastructure irrigation (B-GRIIN), has been developed that incorporates BGI and the possibility of reusing stormwater for irrigation to facilitate the design of zero-runoff urban blocks. Simulations based on rainfall time series, including an extremely dry year, have shown that it is possible to achieve a zero-water balance and provide sufficient water for irrigation by implementing coupled BGI. However, water availability in extremely dry years may limit the full irrigation of all green areas. The results have also shown that the evapotranspiration scaling factor kc has a large influence on the predicted irrigation volume and thus on the overall water balance. The B-GRIIN model makes it possible to couple the rainwater management functions of different BGIs, determine their water requirements and provide sufficient irrigation water. As a result, it can serve as a basis for holistic planning and operation of BGI in order to achieve a zero urban water balance.

Evaluating the impacts of anthropogenic, climate, and land use changes on streamflow

Abstract Several factors, including natural and human-induced, can affect river discharge. This study aims to examine the influence of land use changes and climate change on the monthly average streamflow time series in the Talar River basin, in northern Iran. To investigate the impact of human factors, namely land use change and point source operations, on monthly average streamflow, the DBEST method was used to detect any breakpoint in the streamflow time series caused by gradual changes in land use and climate. The SWAT model was used to simulate the basin at Kiakola and Shirghah stations, between 2001 and 2020. The land use maps were created for the years 2001 and 2019. Calibration and validation at the Kiakola station showed that the Nash-Sutcliffe model (NSE) had an efficiency of 0.8 and 0.76, respectively, while at the Shirghah station, the same values were 0.84 and 0.75. Findings revealed that human activities, specifically the combined impact of land use change and point source operations, had a 60% influence on the monthly average streamflow of the Talar River. They further showed that the combination of land use and harvesting played the most significant role in the basin's outflow on a monthly scale.

Deformation characteristics and reactivated failure modes of the massive Baimu ancient landslide deposit on the northern margin of the East Himalayan syntaxis

AbstractIn the Eastern Himalayan syntaxis, massive Quaternary loose deposits represent a typical product of abrupt topographic changes. Influenced by the Qinghai‐Tibet Plateau uplift and rapid river incision, the partial or complete failure of these deposits is a recurrent phenomenon in alpine‐gorge regions, posing substantial threats to major infrastructure development. Through topographic interpretation and field investigation, this study clarifies the geological features and genetic mechanisms of the Baimu ancient landslide deposit (BALD), located on the northern margin of the East Himalayan syntaxis. The surface deformation time series spanning October 2017 to December 2019 was estimated using employing multitemporal synthetic aperture radar interferometry. Then, the spatiotemporal deformation characteristics of the BALD was analysed by superposing terrain factors, and hazard division was implemented. The results indicate that high‐hazard zones are mainly distributed on high and steep free faces of the BALD. Deformation evolution and rainfall time series showed obvious correlation. Lastly, a generalized failure model is established for the BALD, which will serve as a valuable reference for disaster prevention and is applicable to the study of similar deposits in the Qinghai‐Tibet Plateau.

RAINFALL FORECASTING WITH AN INTERMITTENT APPROACH USING HYBRID EXPONENTIAL SMOOTHING NEURAL NETWORK

Rainfall forecasting is crucial in agriculture, water resource management, urban planning, and disaster preparation. Traditional approaches fail to capture complicated and intermittent rainfall patterns. The “Hybrid Exponential Smoothing Neural Network” is introduced in this study to handle intermittent rainfall forecasting issues. Exponential Smoothing, an established approach for discovering underlying patterns and seasonal fluctuations in time series data, is combined with Neural Networks, which are good at capturing complex linkages and nonlinearities. Using these two methods, this model hopes to deliver a complete rainfall forecasting solution that accounts for short-term changes and long-term patterns. This research uses residuals from the exponential smoothing model and is modeled using a Neural Network. The residual input is transformed using rolling mean. The results show that the hybrid model is able to capture patterns well, but there are still patterns that experience time lag. Experimental results obtained reveal that the hybrid methodology performs better than the model exponential smoothing, implying that the proposed model hybrid synergy approach can be used as an alternative solution to the rainfall time series forecasting. The results show that the Hybrid method can form patterns better than individual exponential smoothing models or neural networks. The RMSSE values for all areas are 1.0185, 1.55092, 1.0872.

Pyraingen: A python package for constrained continuous rainfall generation

Continuous rainfall is often required for flood estimation and water resources assessment. However, stochastically generated continuous rainfall records are typically inconsistent with intensity-frequency-durations (IFDs) used in design, and do not simulate the rainfall behaviour we can expect in a future warmer climate. Here, we present the python package pyraingen to generate ensembles of continuous subdaily rainfalls at a single location that preserve IFD relationships. Rainfall generation can also be conditioned on a climate covariate, in this case temperature, to produce rainfall timeseries reflective of a future climate. We present an implementation at five climatologically unique sites across Australia and demonstrate its ability to produce rainfall timeseries that reflect both present day and future changes in rainfall persistence, temporal patterns, means, and extremes. This package will enable studies using continuous simulation to assess the impacts of climate change using the current guidance on changes to both IFDs and mean annual rainfalls.

Quantifying flood risks during monsoon and post-monsoon seasons: An integrated framework for resource-constrained coastal regions

Global coastal regions are transforming into “flooding hot-spots” due to the multi-faceted confluence of several flood-drivers. Unfortunately, these regions are often poorly gauged, hindering comprehensive flood hazard quantification. Besides summer-monsoon floods, post-monsoon rainfalls induced by cyclonic-depressions have escalated the flood risks in the coastal regions. For the first time, the present study captures the seasonal flood hazards by disentangling monsoon (June, July, August, and September) and post-monsoon (October, November, and December) periods of rainfall. An integrated MIKE + 1D-2D coupled model was used to generate flood hazard risks for Jagatsinghpur district (Odisha, India), a resource-constrained multi-hazard catchment in the Lower Mahanadi River Basin. Due to limited data for this coastal region, we investigated the performance of CHIRPS v2.0, a global Satellite Precipitation Product (SPP) with high spatiotemporal characteristics, for capturing extreme rainfall events and deriving flood hazards at the inundation scale. We observed higher magnitude of flood hazards are prominent in the coastal villages, emerging from a complex interaction between rainfall and storm-tide events. Three widely used Archimedean copulas were implemented to capture the rainfall and storm-tide events' joint (bivariate) behavior and their implications on population exposure, which is valuable for mapping flood hazard risks. We observed that univariate analysis (Ua) significantly underestimates the flood hazard over bivariate analysis (Ba) during both periods. Interestingly, the results are more pronounced in the population exposure estimates as ∼47 % (monsoon) and ∼54 % (post-monsoon) of the population are exposed based on univariate analysis against ∼65 % (monsoon) and ∼60 % (post-monsoon) under bivariate analysis. Our study highlights the necessity of disentangling rainfall time-series for foreseeing under-shadowed flood risks, vital for designing flood combating structures, evacuation plans, land use planning, and flood governance. The results further enhance our understanding of monsoon and post-monsoon flood hazards and their associated risks under different return periods.

Optimization of waterproofing and drainage measures for open-pit mines based on seasonal rainfall time series prediction

Influenced by seasonal rainfall, the total groundwater inflow at the bottom of open-pit mines exhibits seasonal fluctuations. However, effective methods are lacking for forecasting groundwater inflow because groundwater inflow mechanisms remain unclear, thus hindering targeted seasonal adjustments of drainage measures. Consequently, the drainage capacity may be underutilized and emergency response capabilities may be insufficient. Addressing the aforementioned issues, this study investigated the intrinsic relationship between rainfall and groundwater inflow using seasonal time series analysis and proposes a groundwater inflow prediction model, aiming to explore the inherent correlation between precipitation and runoff. Moreover, seasonal adjustment strategies for drainage measures were explored. The proposed model can enhance the economic viability of drainage projects while ensuring safety. It addresses the key challenge of adapting measures to seasonal variations and provides a conceptual framework for optimizing drainage measures in open-pit mining.

A Deep Neural Network Framework for Landslide Susceptibility Mapping by Considering Time-Series Rainfall

A Deep Neural Network Framework for Landslide Susceptibility Mapping by Considering Time-Series Rainfall

Моделювання впливу кліматичних змін на продуктивність агросектора

The article proposes a conceptual approach to modeling the impact of climate change on the productivity of the agrarian sector, substantiates the factors of climate change, and forms the information base of the study. Specifically, rainfall data was taken from the Climate Hazards Group InfraRed Precipitation with Station database (CHIRPS), which is a quasi-global dataset of precipitation amounts over 30+ years. CHIRPS incorporates 0.05° resolution satellite imagery together with data from local stations to create gridded rainfall time series to analyze trends and monitor seasonal drought. In order to obtain rainfall data for each of the selected countries, the Large-Scale International Borders (LSIB) database was used. This data is taken from two other datasets: the LSIB Lines Vector File and the World Vector Shoreline (WVS) from the National Geospatial-Intelligence Agency (NGA). Additional precipitation information was obtained using the Google Earth Engine after performing procedures with a sample of geolocation data for each country, obtaining actual precipitation data for the observation period, exporting the data and processing them to bring the results to the annual figure. The formed information base on the factors of climate change has allowed to develop models of productivity of grain production in the countries that are the largest wheat producers in the world. The results of the development of models led to the conclusion that climate change affects crop yields in countries that are leading producers of cereals. The obtained results can be used to predict changes in yield and production depending on climatic parameters such as temperature and precipitation, as well as to determine the optimal and extreme values of climatic factors.

A comparative study of Machine Learning and Deep Learning methods for flood forecasting in the Far-North region, Cameroon

Flood crises are the consequence of climate change and global warming, which lead to an increase in the frequency and intensity of heavy rainfall. Floods are, and remain, natural disasters that result in huge loss of lives and material damage. Flood risks threaten all countries of the globe in general. The Far-North region of Cameroon has suffered of flood crises on several occasions, resulting in significant loss of human lives, infrastructural and socio-economic damage, with the destruction of homes, crops and grazing areas, and the halting of economic activities. The models used for flood forecasting in this region are generally physical-based, and produce unsatisfactory results. The use of artificial intelligence based methods for flood forecasting in order to limit its consequences is a way to be explored in the Far-North region of Cameroon. The aims of the present research work is to design and compare the performance of Machine Learning and Deep Learning based models such as one dimensional Convolutional Neural Network, Long and Short Term Memory and Multi Layer Perceptron for short-term and long-term flood forecasting in the Far-North region of Cameroon. The models designed take as input the temperature and rainfall time series recorded in this region. Performance criteria used for evaluating models are Nash–Sutcliffe Efficiency, Percent Bias, Coefficient of Determination and Root Mean Squared Error. As the results of the design and performance comparison of the models, the best model for short-term flood forecasting is the LSTM model , and the best model for long-term flood forecasting is still the LSTM model. The best models obtained from the comparisons have satisfactory performance and good generalization capabilities, as reflected by the performance criteria. The results of our research work can be used for implementation of floods warning systems and for definition of an effective and efficient flood risk management policies in order to make the Far-North region of Cameroon more resilient to flood crises.

Generating The Rainfall Time Series Using Arima Model in Kurdistan Region, Iraq

Generating The Rainfall Time Series Using Arima Model in Kurdistan Region, Iraq

Modeling and Simulating Rainfall and Temperature Using Rotated Bivariate Copulas

Climate change is a significant environmental challenge that affects water resources, agriculture, health, and other aspects of human life. Bivariate modeling is a statistical method used to analyze the relationship between variables such as rainfall and temperature. The Pearson correlation coefficient, Kendall’s tau, or Spearman’s rank correlation are some measures used for bivariate modeling. However, copula functions can describe the dependence structure between two or more variables and can be effectively used to describe the relationship between rainfall and temperature. Despite the literature on bivariate modeling of rainfalls and temperature being extensive, finding flexible and sophisticated bivariate models is sometimes difficult. In this paper, we use rotated copula functions that can arrange any type of dependence that is empirically detected, especially negative dependence. The methodology is applied to an Italian municipality’s bivariate daily time series of rainfall and temperature. The estimated rotated copula is significant and, therefore, can be used for simulating the effects of extreme events.

Changes in geochemical and isotopic contents in groundwater before seismic events in Ischia Island (Italy)

We analysed the hydrogeochemical and isotopic contents in groundwater for the period 2002–2020, in the Ischia Island, a volcanic island in Southern Italy, and compared them with seismic events that occurred in the same period. The study is based on a large hydrochemical database, which includes chemical (major and minor compounds, metals and trace elements) and isotopic analyses (δ18O and δ2H). For each of the 34 seismic events occurred in the studied period, we considered coordinates, date, time, depth and magnitude. To exclude the influence of meteorological variability on the hydrochemistry, we examined rainfall time series measured in four stations located in the island. Results show hydrogeochemical anomalies for some chemical elements observed months before the seismic events. Arsenic, electrical conductivity, chromium and vanadium have been identified as potentially affected by hydrogeochemical anomalies related to the earthquakes. The variations in stable isotopes (δ2H and δ18O) in groundwater also seem associated with the earthquakes.This study aims to contribute to the individuation of components in groundwater prone to register sudden changes related to seismic events and it highlights the need of a continuous and long-term hydrogeochemical monitoring in seismic areas. Indeed, the conclusions of this study must be further confirmed by a future continuous monitoring of major compounds, trace elements and isotopes in groundwater to evaluate the effective temporal coincidence/lag with the seismic events.