Maximum Daily Rainfall Research Articles

Extreme rainfall estimation at ungauged locations: Information that needs to be included in low-lying monsoon climate regions like Bangladesh

Bangladesh, a tropical low-lying region, is stranded between the Bay of Bengal in the south and the Himalayas in the north. The unique characteristics allow receiving huge rainfall, in particular, in the monsoon season. The physical phenomena such as the Bay of Bengal, the Himalayas and the monsoon season dominate the extreme rainfall pattern in Bangladesh. However, how to include that information to good effect in the estimation of extreme rainfall at ungauged locations is yet to be investigated. The objective of this work is to examine the information that needs to be included in the estimation of extreme rainfall in such regions. The approach that permits parameters of an extreme value distribution to vary spatially is used to determine the probability model. The methodology, subsequently, authorizes the frequency analysis to be performed at ungauged conditions. The geostatistical technique in the form of kriging with external drift (KED) which has the ability to take secondary information is used to produce spatial maps of the parameters. This study primarily assessed KED interpolation scheme and its ability to accurately predict the model parameters. The scheme was compared against the more widely used ordinary kriging (OK) model. The cross- validation was used to find the robust scheme for each parameter. Annual maximum daily rainfall data from 34 measuring stations were used for the assessment. The KED with the covariate annual mean monsoon rainfall (AMMR) appears to be the best model for location parameter. However, for the scale and shape parameter the KED models were unable to score past the OK model. The availability of parameter maps allows the estimation of extreme rainfall at locations where rainfall records were absent and offers a much better elucidation than the traditional at-site quantile based interpolation. Overall, the inclusion of monsoon rainfall and spatial information are deemed necessary for the estimation of extreme rainfall at ungauged locations in a low-lying monsoon region.

A comprehensive study on the Bayesian modelling of extreme rainfall: A case study from Pakistan

AbstractIn this paper, the modelling of extreme rainfall is carried out in Pakistan by analysing annual daily maximum rainfall data via frequentist and Bayesian approaches. In frequentist settings, the parameters and return levels of the best fitted probabilistic model (i.e., generalized extreme value) are estimated using maximum likelihood and linear moments method. On the other side, under the Bayesian framework, the parameters and return levels are calculated both for noninformative and informative priors. This task is completed with the help of the Markov Chain Monte Carlo method using the Metropolis‐Hasting algorithm. This study also highlights a procedure to build an informative prior through historical records of the underlying processes from other nearby weather stations. The findings attained from the Bayesian paradigm demonstrate that the posterior inference could be affected by the choice of past knowledge used for the construction of informative priors. Additionally, the best method for the modelling of extreme rainfall over the country is decided with the support of assessment measures. In general, the Bayesian paradigm linked with the informative priors offers an adequate estimations scheme in terms of accuracy as compared to frequentist methods, accounting for ambiguity in parameters and return levels. Hence, these findings are very helpful in adopting accurate flood protection measures and designing infrastructures over the country.

Intensity duration frequency relationship of maximum rainfall in a data scarce urbanized environment: A case study of the Guma Catchment in Sierra Leone

Rainfall intensity for a particular frequency and duration is one of the most important parameter for the hydrologic design of dam, reservoirs, storm sewers, culverts and many other hydraulic structures. This can be obtained from Intensity-Duration-Frequency (IDF) relationship, which is determined by frequency analysis of rainfall data. The goal of this research is to develop rainfall intensity-duration frequency relationship for the data scarce urbanized Guma catchment in Freetown the capital city of Sierra Leone using the Gumbel and Pearson Type III distributions. To achieve this goal, daily rainfall data for the period of 1991 to 2018 for the Guma catchment rainfall station was obtained from the Guma Valley Water Company and were converted to shorter duration (hourly) using the Indian Meteorological Department (IMD) method. Twenty-eight maximum daily rainfall events were converted to hourly rainfall events. Frequency analysis was conducted to develop the rainfall intensity-duration-frequency relationships using the Gumbel and Pearson Type III distributions. The Kolmogorov-Smirnov (K-S) goodness of fit test was utilized to determine which of the distributions have a better fit at 5 and 10% significant levels. The frequency analysis results show that the Gumbel distribution gives higher intensity for all return periods and durations than the Pearson Type III distribution. The result of the K-S goodness of fit test shows that all of the data fit the Gumbel distribution for different return periods (2, 5, 10, 25, 50 and 100 years) at the level of significance of which yield and which yield while the data do not give good fit using the Pearson Type III distribution at both levels of significance for the different return periods. Key words: Rainfall, Intensity-duration-frequency relationships, probability distributions, goodness of fit test.

Conditional modelling approach to multivariate extreme value distributions: application to extreme rainfall events in South Africa

Multivariate extreme value models are used to investigate the combined behaviour of several weather variables. To investigate joint dependence of extreme rainfall events, a multivariate conditional modelling approach was considered to analyse the behaviour of joint extremes of rainfall events at selected weather stations in South Africa. The results showed that the multivariate conditional modelling fitted to daily maximum rainfall events provided apparent benefits in terms of improved precision in the estimation of the marginal parameters of generalised Pareto distribution. The conditional modelling approach provided all forms of dependence using Laplace marginal transformations, for which all weather stations are not extreme equally. Bootstrap sampling was also employed to account for models uncertainty in computing the prediction standard errors, and compared with the prediction obtained from the conditional model fitted to extreme data. The results obtained from predictions reflected both the marginal and the dependence features, and the extremal dependence structure described consistently for extreme daily maximum rainfall events between weather stations. The current study contributes towards understanding the salient features on the extremal dependence of rainfall extremes which are associated with e.g., flash floods and landslides. This knowledge has practical applications in disaster risk preparedness by responsible authorities.

Debilitating floods in the Sahel are becoming frequent

Despite the long-lasting and widespread drought in the Sahel, flood events did punctuate in the past. The concern about floods remains dwarf on the international research and policy agenda compared to droughts. In this paper, we elucidate that floods in the Sahel are now becoming more frequent, widespread, and more devastating. We analyzed gridded daily rainfall data over the period 1981–2020, used photographs and satellite images to depict flood areas and threats, compiled and studied flood-related statistics over the past two decades, and supported the results with peer-reviewed literature. Our analysis revealed that the timing of the maximum daily rainfall occurs from the last week of July to mid-August in the Eastern Sahel, but from the last week of July to the end of August in the Western Sahel. In 2019 and 2020, flash and riverine floods took their toll in Sudan and elsewhere in the region in terms of the number of affected people, direct deaths, destroyed and damaged houses and croplands, contaminated water resources, and disease outbreaks and deaths. Changes in rainfall intensity, human interventions in the physical environment, and poor urban planning play a major role in driving catastrophic floods. Emphasis should be put on understanding flood causes and impacts on vulnerable societies, controlling water-borne diseases, and recognizing the importance of compiling relevant and reliable flood information. Extreme rainfall in this dry region could be an asset for attenuating the regional water scarcity status if well harvested and managed. We hope this paper will induce the hydroclimate scholars to carry out more flood studies for the Sahel. It is only then encumbered meaningful opportunities for flood risk management can start to unveil.

Population‐specific resilience of Halophila ovalis seagrass habitat to unseasonal rainfall, an extreme climate event in estuaries

Abstract Extreme climate events are predicted to alter estuarine salinity gradients exposing habitat‐forming species to more frequent salinity variations. The intensity and duration of these variations, rather than the mean salinity values ecosystems are exposed to, may be more important in influencing resilience but requires further investigation. Precipitation, including the frequency, intensity and timing of occurrence, is shifting due to climate change. A global analysis on the timing of rainfall in estuarine catchments was conducted. In 80% of the case studies, the maximum daily rainfall occurred in the dry season at least once over the 40‐year period and could be classified as an extreme event. We selected an estuary in southwestern Australia and investigated the effects of an extreme rainfall event in 2017 resulting in an excess discharge of freshwater on seagrass Halophila ovalis. Adapting an approach applied for marine heatwaves using salinity data, we quantified metrics and characterised the event along the estuarine gradient. We assessed seagrass resilience by calculating resistance times based on the comparisons of biomass and leaf density data prior to, and during the event, and recovery times through assessment against historical condition. Where salinity is historically more variable, reductions in biomass were lower (higher resistance via plasticity in salinity tolerance) and meadows recovered within 9–11 months. Where salinity is historically more stable, loss of biomass was greatest (low resistance) post‐event and recovery may exceed 22 months, and potentially due to the rapid decline in salinity (−3 PSU/day). As estuaries become more hydrologically variable, these metrics provide a baseline for retrospective and future comparisons. Our results suggest seagrass resilience to hyposalinity is population specific. This understanding enables more accurate predictions about ecological responses to climate change and identifies which populations may ‘future proof’ ecosystem resilience. Synthesis. Following an extreme rainfall event, we found seagrass populations that are exposed to variable salinities recovered while those from a stable salinity environment were unable to recover within the study time frame. These findings expand upon existing evidence, derived primarily from other ecosystems, that show new sources of resilience may be uncovered by accounting for between‐population variation.

Comprehensive Extreme Rainfall Analysis: A study on Ahmedabad region, India

ABSTRACT Recently, the Central Public Health and Environmental Engineering Organization (CPHEEO), India, emphasized extreme rainfall analysis based on the probabilistic approach. The extreme rainfall analysis was performed using hourly rainfall data for Ahmedabad city, India. The analysis was carried out to obtain (a) clock hour correction factor (CHCF) for annual maximum rainfall series (AMS) and partial duration maximum rainfall series (PDS); (b) empirical factors for converting PDS to AMS for different return period extreme rainfall; and (c) conversion factors for various return period 24-h maximum rainfall. The AMS and PDS were fitted with generalized extreme value and generalized Pareto distribution, respectively, for fixed window (FW) and sliding window (SW). The CHCF for daily maximum rainfall for AMS and PDS were 1.134 and 1.139, respectively, against 1.15 suggested by IS 5542: 2003. The empirical factor for converting PDS to AMS for a 2-year return period of 24-h extreme rainfall (FW) was found to be 0.86. The conversion factor for 24-hextreme rainfall PDS (FW) from a 50-year return period to a 5-year return period was found to be 0.54. The empirical and conversion factors derived in the present study for Ahmedabad city are site-specific.

Impact of Climate Change on Flood Hazard at Airports on Pacific Islands: A Case Study of Faleolo International Airport, Samoa

Climate change is believed to have increased the intensity and frequency of heavy rainfall, and also to have caused sea level rises over this century and beyond. There is widespread concern that small-island nations are particularly vulnerable to increasing risk of inland flood due to such climate change. Understanding the impact of climate change on flood hazard is of great importance for these countries for the development of better protection and adaptation strategies. This study conducted a case study focusing on the impact of climate change on flood hazard at Faleolo International Airport (FIA), Samoa. FIA is a typical small islands airport, located on the lowland along the coast fronted by a fringing reef. Annual maximum daily rainfalls for different return periods were first estimated for the present and future climate around FIA. The estimated rainfalls were input as the forcing of a two-dimensional flood inundation model to investigate the flooding behavior and effectiveness of probable drainage systems. Results showed that a part of the runway can be inundated under heavy rainfall. Construction of drainage pipes significantly contributes to reducing the flood hazard level. Sensitivity analysis showed that the astronomical tide level has relatively little influence on the performance of the drainage system, while the combination of sea level rise and the sea level anomaly induced by stormy waves on the fringing reef could have non-negligible impacts on the drainage system. Location of the drainage pipe is also important to effectively mitigate flooding. The time-concentration of torrential rainfall may also significantly impact the overall performance of the drainage system.

Selection of the best probability models for daily annual maximum rainfalls in Egypt

Twelve commonly used probability distributions are evaluated to identify the most suitable model that could provide accurate extreme rainfall estimates in Egypt. Three popular parameter estimation methods are applied: the method of moments, L-moments and maximum likelihood. The performance of the models is evaluated based on several numerical and graphical goodness-of-fit criteria. The proposed procedure is applied to annual maximum daily rainfall data from a network of 31 stations located in Egypt. The results indicate that no single distribution performed the best at all stations. Log-Normal, Log-Pearson Type III and Exponential are the top three distributions for the frequency analysis of daily annual extreme rainfalls in Egypt, i.e. they are selected as the “optimum” models for 23%, 19% and 19% of the total stations, respectively. In contrast, the distributions: Normal, Gumbel, Logistic and Generalized Logistic are not suitable for describing the extreme rainfalls in the country. The performances of both L-moments and maximum likelihood methods are almost equal and much better than that of the method of moments. Additionally, Depth-Duration-Frequency curves were established for 18 stations by using the “optimum” model, which can support the design of hydraulic structures. The findings from this study would be helpful for rainfall frequency analysis in similar arid countries.

Environmental sensitivity of flash flood hazard based on surface water model and GIS techniques in Wadi El Azariq, Sinai, Egypt

Wadi El Azariq basin constitutes the administrative division of the province in the northeastern part of the Sinai Peninsula, Egypt. This wadi represents one of the most promising areas for different activities due to its hydrological and location importance. This study aims to implement a new methodology for flash flood assessment and modeling in this strategic region through the integration between physiographic features, surface water models (WMS and HEC-HMS), as well as Geographic Information System (GIS). Thirty-eight morphometric elements were analyzed qualitatively and quantitatively using topographic maps and recent satellite images. These parameters were inter-linked to yield nine important factors for assessment of the flash flood hazardous, which were determined and grouped in both high and low degrees. Also, hydrographs for Wadi El Azariq basin were constructed with different recurrence stages of extremely (maximum) daily rainfall using the last phase carried out in the modeling procedure. The obtained result proved that the total surface runoff ranges from 4.1×106 m3 to 13.8×106 m3 at recurrence stages of 5 years and 100 years respectively based on the maximum daily rainfall events. This study draws specifics about the flash flood-prone areas, planning rainwater harvesting and flood management approaches of Wadi El Azariq basin. Besides, areas of inundation and groundwater potentiality were also determined as the surface runoff potentiality.

The Kiryu Experimental Watershed: 50‐years of rainfall‐runoff data for a forest catchment in central Japan

AbstractThe Kiryu Experimental Catchment (KEW) is a small (5.99 ha) forest catchment located in Shiga Prefecture, central Japan (34°58′ N, 136°00′ E; www.bluemoon.kais.kyoto-u.ac.jp/kiryu/contents.html). Around this area, forest devastation occurred from ca. 1250 to ca. 150 years ago because of overuse of forest and timbers. Then, hillside forestation was carried out for more than 100 years to prevent soil erosion and support the timber industry, and consequently, most of this area is now covered with plantation forests mainly by Chamaecyparis obtusa Sieb. et Zucc. (Japanese cypress) planted around 1960's. This plantation forest is not actively managed. The KEW is one of the leading experimental forests with long‐term monitoring data in Japan. Research in the KEW began in 1967 to elucidate the hydrological and biogeochemical processes in the forested catchment in relation to climate, geology, soil, and vegetation growth. Since then, the long‐term hydrological data of precipitation, runoff and sediment transport are continuously monitoring. In this study, we provide the data and preliminarily discuss the rainfall–runoff patterns and sediment transport through 50 years in the KEW. The annual precipitation and the maximum daily rainfall have been greater than the average over the last decade. In response to the rainfall patterns, the ratio of annual direct runoff to precipitation was also larger in the last decade. The sediment transport in this decade was consequently larger than the preceding decades. Our data presented here suggest that a close relationship exists between the climate condition, rainfall–runoff response, sediment dynamics, as well as a slowly progressing change of forest condition.

Evaluation of Reanalysis Precipitation Data and Potential Bias Correction Methods for Use in Data-Scarce Areas

Data availability and accessibility often present challenges to resolving regional water management issues. One primary input essential to models and other tools used to inform policy decisions is daily precipitation. Since observed datasets are not always present or accessible, data from the Climate Forecast System Reanalysis (CFSR) have become a potential alternative. A comparison of CFSR precipitation data to available observed data from stations in the East African countries Kenya, Uganda, and Tanzania showed notable differences between the two datasets, particularly with respect to precipitation totals and number of days receiving rainfall. A sliding window bias correction approach evaluated using 3 methods with 8 different window length and timestep variations showed that empirical quantile mapping with a 30-day sliding window length and 1-day timestep achieved the best performance. A comparison of bias corrected CFSR precipitation data against observed data showed marked improvement in the similarity of the number of wet days and maximum daily rainfall between the two datasets. For precipitation totals, bias correction reduced underprediction errors by 32% and overprediction errors by 81%. Results indicate that bias-corrected CFSR precipitation data provides an improved basis for water resources applications in the study region. Methodologies and approaches are extendable to other data-scarce regions or areas where complete and consistent data are not easily accessible.

Hydraulic analysis and ecological and physical study of hydraulic Alas-Singkil river Southeast Aceh, Indonesia

The problem of flooding that occurs around the Lawe Alas-Singkil, Aceh Province is the conservation function of the area from upstream to downstream needs serious attention. The area around the watershed is degraded due to several factors. This factor is closely related to illegal logging, exploitation of nature with mineral mining to the conversion of area functions. This study aims to identify the existing conditions of the riverbanks, how the relationship between community social activities and the banks of the Alas-Singkil River, and to study the physical conditions of the river from upstream to downstream. The study method in this research includes a literature study, hydrological conditions of the Alas-Singkil Watershed, field surveys, identification of existing conditions, and study of the ecology and physicality of the river. Survey the condition of flora and fauna in the study area and the economic and social activities of the community. The results of the study on the hydrological conditions of the Alas-Singkil watershed with an area of 10,090.13 km2 and a river length of 316.26 km, in the upstream form of mountains and natural forests where deforestation has occurred due to forest conversion to cocoa plantations, planting of corn on the hills and so on, The maximum annual daily rainfall is high (151-300 mm), so the potential for flood disaster is even greater. In the rainy season, large amounts of water cause mountains or hills in Southeast Aceh to melt and become a flash flood disaster. Many of the rivers in the middle and downstream have turned into plantations and rice fields and some are still swamps. The condition of the river in the Alas-Singkil watershed generally occurs silting or sedimentation in the river body, the existing condition of the Alas-Singkil riverbank, where there are still many settlements on the riverbanks. The downstream Krueng Singkil is experiencing more frequent flooding, either due to the overflow of Krueng Singkil in the upstream part and the blocked river flow and rising tides. The frequency of inundation and flood disasters in Singkil City reaches 3 to 4 times each year with an inundation height of 0.5-3.0 meters. It is necessary to repair the riverbanks by using vegetation components found on the banks of the Alas-Singkil River and overcoming floods in the upstream and downstream rivers to protect the ecosystem and people living on the riverbanks. The solution that can be done is to restore the conservation function of the area around the Lawe Alas watershed. and built channel diversion flood from the Lae Soraya river in the village of Gelombang directly to the sea.

Climate trends and rainfall patterns in the Ciliwung watershed, West Java of Indonesia

Jakarta city is one of the capitals that prone to frequent flooding. The objective of this study was to reveal climate trends and rainfall patterns upstream that were deemed to contribute to the flooding. Based on the daily climate data over 35 years from 1985 to 2019 and by applying Mann-Kendall Test and Gumbel distribution model, the watershed has become hotter and drier as indicated by a significant increase of air temperature (between 0.48 °C – 1.26 °C) and decrease of the relative humidity (over 2.9%). While the annual rainfall ranged around 3158±432 mm and did not indicate any changes. There were 5 extreme rainfalls in 1992 amounted to 4104 mm, 1995 (3758 mm), 2000 (3593 mm), 2012 (3779 mm), and 2013 (4283 mm). The maximum daily rainfall tended to increase at the slope of 0.94 mm/year, but the trend was not considered significant. The most probable event (63%) of the maximum daily rainfall was 98 mm with its return period of 1.6 years. The largest maximum daily rainfall was 245 mm in 2007 with its return period of 187 years causing the heaviest flooding with the highest casualty. There were clear indications that the rainfalls upstream considerably correlated with flooding.

Sensitivity of Statistical Models for Extremes Rainfall Adjustment Regarding Data Size: Case of Ivory Coast

The objective of this study is to analyze the sensitivity of the statistical models regarding the size of samples. The study carried out in Ivory Coast is based on annual maximum daily rainfall data collected from 26 stations. The methodological approach is based on the statistical modeling of maximum daily rainfall. Adjustments were made on several sample sizes and several return periods (2, 5, 10, 20, 50 and 100 years). The main results have shown that the 30 years series (1931-1960; 1961-1990; 1991-2020) are better adjusted by the Gumbel (26.92% - 53.85%) and Inverse Gamma (26.92% - 46.15%). Concerning the 60-years series (1931-1990; 1961-2020), they are better adjusted by the Inverse Gamma (30.77%), Gamma (15.38% - 46.15%) and Gumbel (15.38% - 42.31%). The full chronicle 1931-2020 (90 years) presents a notable supremacy of 50% of Gumbel model over the Gamma (34.62%) and Gamma Inverse (15.38%) model. It is noted that the Gumbel is the most dominant model overall and more particularly in wet periods. The data for periods with normal and dry trends were better fitted by Gamma and Inverse Gamma.

The effects of river normalization on flood risk

A river is a naturally occurring flow system that flows from upstream to downstream. Rivers have many benefits. Similarly, rivers have a negative impact if rainfall is high. It can lead to catastrophic flooding. Flooding often occurs along the central roads of Padang City when it rains, especially in the Maransi and Aie Pacah areas. The purpose of this study is to estimate the height of water level by using software HEC – RAS and estimate areas that have the potential for flooding in conditions before and after river normalization by using the software Arc-GIS. Data collection is carried out to achieve this goal, namely the maximum daily rainfall data, Digital Elevation Model (DEM) data obtained from the processing of aerial photo data, and cross-sectional data of the river. Then, a numerical simulation is performed to estimate the height of the water level, and then a numerical simulation is performed to determine the extent of the flood area. The results of this study are in the form of flood areas in conditions before and after river normalization with a period of 25, 50, and 100 years that can be used to see the effectiveness of river normalization work in the river.

Actualization of Climatic Parameters of the Empirical Methods of ORSTOM and CIEH: Case of Ivory Coast

Climate change is a reality that affects several climate variables including precipitation. This change and the variability of the climate are considered to be the greatest challenges facing humanity in the decades to come, on all geographic scales and in all economic sectors. In this context of severe climate change, it will be judicious to review the sizing parameters of hydraulic structures. The determination of hydrological standards is necessary within the framework of development projects for the design of hydraulic structures and the storm water management. The objective of this study is to update the parameters of the ORSTOM and CIEH methods. These are annual rainfall normals, extreme rainfall norms, extreme rainfall quantiles as well as the extreme rainfall gradex. The study carried out was based on annual maximum daily rainfall data and annual rainfall data collected over the period 1931-2020 from twenty-six (26) rainfall stations. The methodological approach is based on the one hand on the evaluation of the normals (normals of annual rainfall, norms of extreme rainfall) and on the other hand on the determination of the quantiles of the daily maximum annuals. These quantiles were used to assess the extreme rainfall gradex. The annual rainfall norms evaluated over the period 1931-2020 vary between 1,180 and 1,457.4 mm. As for the normals of the maximum daily rains, they oscillate between 73.61 mm (Agnibilékro) and 136.59 mm (Tabou) with an average of 94.57 mm. The 10-year return period quantiles evaluated over the period 1931-2020 vary between 103 mm (Dimbokro) and 222 mm (Tiassalé) with an average of 143.6 mm. As for the centennial quantiles, they oscillate between 132 mm (Dimbokro) and 326 mm (Tiassalé) with an average of 211.5 mm. An analysis of the extreme rainfall gradex revealed values fluctuating between 0.28 (Dimbokro) and 0.71 (Guiglo) with an average of 0.47 on Ivorian territory. The gradex coefficients determined are all greater than the regional value of 0.38 defined by ORSTOM except for the Dimbokro station (0.28). Indeed, the calculated gradex coefficient biases are all positive (ranging from +2.63 to + 86.84%) except that of Dimbokro (-26.32%). The use of the regional value of 0.38 reflects an under designing of the values of the gradex coefficient for the whole country except the Dimbokro station.

Analysis of Annual Maximum Rainfall Series using Method of L-Moments in Some Selected Sites in South-South Geographical Zone, Nigeria

Determination of the extent of peak rainfall for different return periods is an essential ingredient for the accurate design of hydraulic structures such as drains, dams and culverts as well as detection of flood risk areas. The focus of this study is to analyze annual maximum daily rainfall series in some selected sites within the coastal region of Nigeria using three parameter probability distribution models, namely, Generalized Logistics (GLO), Generalized Extreme Value (GEV) and Generalized Pareto (GPA) with the view of identifying the best fit probability distribution model per station that can be employed to estimate the rainfall magnitude for selected return periods. Specific time series analysis test, namely, detection of outlier and homogeneity test were performed to certify that the data utilized are adequate and suitable. Descriptive statistics such as sample mean, variance, standard deviation, kurtosis, skewness, and coefficient of variation were computed using basic statistical equations. The probability weighted moment parameters (b0, b1, b2 and b3), L-moment values (λ1, λ2, λ3 and λ4) and ratios (τ2, τ 3 and τ4) including the distribution parameters, namely, shape (k), scale (α) and location (ξ) parameters were computed based on L-moments procedures. To select the best-fit probability distribution model per station, carefully chosen goodness-of-fit statistics, namely, root mean square error, relative root mean square error, maximum absolute deviation index, maximum absolute error and probability plot correlation coefficient were employed since they can adequately assess the fitted distribution at a site. Results obtained indicate that the GLO is the best fit distribution for analyzing annual maximum daily rainfall series from Warri and Calabar while GPA for Port Harcourt and Uyo.

Spatio-temporal rainfall trend and homogeneity analysis in flood prone area: case study of Odaw river basin-Ghana

Accurately forecasting rainfall trends is vital for the socio-economic development of a nation. Observed daily rainfall data from the Ghana Meteorological Agency (GMet) spanning 1980 – 2015 was deployed to study the spatio-temporal rainfall trend in the Odaw river basin (ORB) in Accra. Using Mann–Kendall (MK), Sen’s slope and set of homogeneity tests, the monthly, annual and decadal rainfall variability based on the indices – rainfall total, daily maximum and rainy/wet day count were examined. Principal Component Analysis (PCA) and wavelet (WT) analysis were applied on the monthly time series in order to verify the spatio-temporal variability. It was observed that a mean value ranging from 760 to 1200 mm, 77.1–94.4 mm and 56–90 day count were recorded for Annual Rainfall Total (ART), Annual Daily Maximum (ADM) and Annual Rainy Days (ARD) respectively in the ORB. Mann–Kendal test recorded an overall non-significant positive trend in the Odaw basin for ART and ARD with an average positive slope of 3.7 mm/year and 0.23 wet days/year and a significant positive trend with slope value of 0.55 mm/year for ADM in the basin. On the monthly scale, a significant upward trend was observed for the dry seasonal months; December, January and February for the Monthly Rainfall Total (MRT) and Monthly Daily Maximum (MDM) rainfall series during the period at all stations. Homogeneity for both annual and monthly rainfall time series were observed over the stations in the basin by the Pettitt, Alexandersson’s SNHT and Buishand’s tests. PCA revealed that the spatial variability of rainfall in ORB is very diverse with 55.9% of the variability located in the middle to highland part while 44.0% of the variation is occurring mostly in the low-lying area at the southeastern part of the ORB. The wavelet analysis also revealed a strong annual periodicity at all the selected stations in the basin. The outcome of this study provides valuable information to formulate adaptation measures through appropriate strategies for managing flood in the study area.

Analyzing the spatiotemporal dynamics of flood risk and its driving factors in a coastal watershed of southeastern China

Rapid urbanization and climate change can cause more extensive flood risk, in the absence of urgent and efficient adaptation measures. As the occurrence of floods varies with time and space, comprehensive and dynamical assessment of the spatiotemporal variability of flood risk and understanding of its drivers is vital for flood risk management. In this study, we developed a spatial multi-criteria analysis (SMCA) framework for quantifying the spatiotemporal dynamics of flood risk through a case study in a coastal watershed of southeastern China from 1990 to 2015. A comprehensive framework for flood risk assessment was constructed from the hazard, exposure, sensitivity and adaptive capacity components with 23 indicators. The results showed that the highest risk happened in the stage of 2006–2010, while the lowest risk stage was 2011–2015, with higher flood risk in the downstream areas of Jiulong River watershed (JRW). The contribution of each indicator reflects the difference in temporal, spatial and quantity aspects. The top 5 driving factors for JRW included: peak discharge, maximum daily rainfall, age structure, wetland, and reservoir. The risk perception showed a continuous growing impact on flood risk. However, some indicators only showed obvious contributions in the specified area: for example, the built-up expansion in Zhangzhou city; the increase of dike length and the improvement of dike standard in Xinluo district; and the increase of government financial investment in Zhangping and Liancheng district. This study demonstrates the well-performance of our proposed novel approach for flood risk assessment. Our results and conclusions are also of significance for policymakers to understand and point out the deficiencies in the current actions of flood adaption, and consequently develop more targeted and spatially-specific strategies for flood adaptation, in the context of climate change and rapid urbanization.