Temporal Variability Of Rainfall Research Articles

Spatio‐temporal rainfall variability and trend analysis in the Tekeze‐Atbara river basin, northwestern Ethiopia

AbstractSpatial and temporal rainfall variability analysis at a basin scale is essential to understand water availability for sustainable water resource management. This study investigated the spatial distribution and temporal rainfall trend in the Tekeze river basin using daily rainfall data collected from 43 meteorological stations with records from 1960 to 2019. Coefficient of variation, seasonality index (SI) and trend index were used to evaluate monthly, seasonal and annual rainfall variability in the basin. The non‐parametric Mann‐Kendall's test was used to understand any rainfall trends. Results showed that there is moderate annual rainfall variability in the basin. Rainfall variability is higher in the Tsedey, Bega and Belg than kiremt season. SI results showed that majority of the Tekeze river basin is categorized as seasonal, most of the rain occurring in less than 3 months. Overall, Mann‐Kendall's test result revealed that there is no significant annual rainfall trend in the basin except for four stations. The seasonal rainfall characteristics and short duration of rain in the basin indicate that there is a need to implement water harvesting measures during the wet season for use in the dry periods.

Assessing rainfall global products reliability for water resource management in a tropical volcanic mountainous catchment

Study regionWater resource management relies fundamentally on our ability to monitor climate forcing variability, especially in tropical mountainous environment where the temporal and spatial variability of rainfall strongly controls the dynamics of the water resource. In West Java Island, rainfall temporal and spatial distribution significantly varies in function of regional climatology and volcanoes morphology while accessibility issues and the complexity of climatic phenomena notably are limitations for reliable rainfall ground instrumentation. Study focusHere, we assess the ability of climate reanalyses (CHELSA and TerraClimate) and satellite products (CHIRPS) in capturing rainfall high resolution spatial variability. The accuracy of rainfall amounts, variability and dynamics of each global product is estimated using the three component of Kling-Gupta efficiency score. As direct statistical comparison is influenced by resolution issues, our approach is completed by a process-based one. The spatial and orographic rainfall patterns of the global products are analyzed according to known climatic phenomena. Hydrological InsightsIt appears that, TerraClimate provides the most accurate and stable estimation for temporal monitoring. CHIRPS shows consistent rainfall patterns with atmospheric circulation and volcanoes morphology but overestimates overall rainfall amounts. This study presents a methodology for assessing global climatic products over poorly instrumented areas. The results demonstrate that high resolution global products are somewhat interesting for water resources management. However, several temporal and spatial biases still limit their integration for operational purposes.

The Long-Term ERA5 Data Series for Trend Analysis of Rainfall in Italy

Nowadays, the Mediterranean region is generally recognized as a climate change hot spot given its strong response to global warming, with relevant impacts on rainfall amount and distribution. Within this context, in this work the temporal variability of rainfall at annual, seasonal and monthly scale was analyzed in Italy using rainfall data extracted from the reanalysis dataset ERA5-Land during the period 1950–2020. In particular, rainfall trend magnitude and significance have been estimated by means of non-parametric tests applied to 3215 grid points falling within the Italian territory. The main results of this analysis evidenced only a few relevant trends at the annual scale, mostly involving northern Italy (positive trend) and the Sardinia region (negative trend). At seasonal scale, the results showed a marked negative trend in winter, characterizing almost all the Italian territory, while in the other seasons a positive trend was identified in the majority of grid points, especially in the Alps. Finally, at the monthly scale, September was identified as the month of the year with the highest percentage of grid points with positive trends mainly located in central, southern, and north-eastern Italy.

Influence of Spatial Rainfall Gradients on River Longitudinal Profiles and the Topographic Expression of Spatially and Temporally Variable Climates in Mountain Landscapes

AbstractMountain landscapes have dynamic climates that, together with tectonic processes, influence their topographic evolution. Spatial and temporal variations in rainfall are ubiquitous in these settings as orographic precipitation patterns evolve with climate change and topography. Despite important implications such changes have for river incision, their influence is understudied. Here, we investigate how changes in rainfall pattern should affect both the steady state form and transient evolution of river profiles at the catchment scale using the stream power model. We find that spatially varied rainfall patterns can complicate steady state relationships between mean rainfall, channel steepness and fluvial relief, depending on where rainfall is concentrated in catchments, and lead to unexpected transient behavior if they are neglected. Specifically, changes in rainfall pattern cause multi‐stage transient responses that differ from responses to uniform changes in rainfall. Disparate responses by rivers that experience different rainfall conditions, particularly trunk and tributary rivers, are also an important factor in understanding catchment‐wide responses to climate change. Accounting for such disparities in sampling strategies and topographic analyses may, therefore, be vital for detecting and quantifying climate's role in landscape evolution. Lastly, we show how explicitly accounting for rainfall patterns in channel steepness indices, and thus spatial variations in erosional efficiency, may advance understanding of landscape sensitivity to climate. These results have important implications for detecting transient responses to changes in rainfall pattern (and more broadly climate), interpretation of morphometrics in steady state and transient landscapes, and quantifying the sensitivity of landscapes and erosion rates to climate.

Impact of rainfall variability on crop yields and its relationship with sea surface temperature in northern Ethiopian Highlands

Knowing the spatial and temporal variation of rainfall is crucial for the management of agricultural productivity. This study aimed at evaluating rainfall variability impact on crop yields and its relationship with sea surface temperature using GIS and statistical techniques in North and South Wello zones, northern Ethiopian Highlands. In this study, 30 years (1984–2013) time series data of rainfall from National Centers for Environmental Prediction’s Climate Forecast System Reanalysis and National Meteorological Agency, sea surface temperature from National Oceanic and Atmospheric Administration, and crop data from Central Statistical Agency were used. With regard to spatial and temporal distribution of rainfall, the highest amount of mean total Kiremt/summer and annual rainfall was 993.71 mm and 1715.95 mm, respectively. The magnitude of mean total Kiremt and annual rainfall temporal and spatial variations, using percentage of coefficient variation, was ranging from 24.8 to 62% and from 23.13 to 57.86%, respectively. The correlation between total Kiremt and Belg rainfall with mean sea surface temperature was negative (r = −0.457) and positive (r = 0.385), respectively and statically significant at alpha 0.05 level. From the regression analysis, it was observed that the variation of crop production was explained by the variation of rainfall. For instance, the impact of mean Kiremt rainfall accounted for 47.5% (R2 = 0.475) of variations in sorghum. Generally, the analysis of this study revealed that rainfall variability is one of the major determinant factors for variation of major crop yields in the study area, and this information might be used as an input for decision-makers take appropriate adaptive measures in various agricultural and water resource sectors. For instance, this study recommends the expansion of irrigation and water management practice and promoting perennial trees like fruits and drought-resistant crops.

Retraction Note: Neural network–based urban rainfall trend estimation and adolescent anxiety management

The artificial neural network prediction model is a modern prediction method with computational intelligence as the core. Now, the artificial neural network has been applied to various fields. It can play its own role in each field, but the neural network is used in the prediction of rainfall water quality. It is still in its infancy. This article studies the artificial neural network and artificial neural network for water quality prediction. The education system is a very complex mechanism. There are also large differences. The height and space of urban rainfall are prone to variability. This will cause floods and flood disasters. It is very dangerous for the city, and it will also seriously affect the development of the city. Therefore, the artificial neural network is very important for the prediction of rainfall water quality. If we can use the artificial neural network to recognize the temporal and spatial variability of urban rainfall, then flood disasters can be prevented in advance. This paper uses a variety of algorithms and conducts many experiments to discover the small-scale temporal and spatial variation of rainfall in a certain city. The declining mental health status of Chinese adolescents is not optimistic. It is mainly manifested in anxiety, emotional instability, negativity, and psychological disorders. After a long period of observation, it has been found that the psychological problems of adolescents are mainly caused by disharmony in interpersonal relationships. Study pressure is high and emotions cannot be relieved. Therefore, we have conducted special research in these areas, hoping to have a further understanding of the development of anxiety and obtain corresponding solutions to effectively alleviate the psychological problems of adolescents.

Temporal variations in rainfall patterns in Kilembe, Uganda

AbstractThis study investigated rainfall variabilities in Kilembe. Secondary (monthly rainfall) data for the study were obtained from the Uganda National Meteorological Authority in Kampala, Uganda, for the period 1986–2016. To determine the temporal variation of rainfall, a Mann–Kendall method and Sen's slope estimator test were used. The results showed that March, April, May, October, and November represented the smallest coefficient of variation indicating modest variability of rainfall in these months, whereas June and July had the largest coefficient of variation. The month of July recorded an overall lowest average rainfall and a decreasing trend in rainfall. The study further revealed a historical general upward trend in rainfall between 1986 and 2016 in Kilembe. The trend in the monthly and annual rainfall is partly attributed to the Intertropical Convergence Zone, influx of moisture from Congo Forest, and climate change. The area recorded also two peaks of rainfall in the year, one in April and another in September, revealing a bimodal pattern of rainfall. The findings indicate that the government should adopt policy measures in the form of an adaptive strategy in order to save the people in the impacted areas.

Urbanization Exacerbated Rainfall Over European Suburbs Under a Warming Climate

AbstractThe impacts of cities and climate warming on extreme rainfall under strong synoptic conditions are not well understood. Here, we carry out the first model‐based assessment of urban impacts on extreme flood‐producing storms for the European region. We identify contrasting roles of cities and climate warming in determining the space‐time variability of the July 14, 2021 storm over western Europe. While climate warming dominates the temporal rainfall variability over the domain, cities further enhance total rainfall over their suburbs by dynamically modifying the intensity and position of moisture convergence. There is a cyclonic structure of flow anomalies around the city induced by urban surface roughness. The rainfall anomaly induced by the interactive impacts of cities and climate warming is 50% larger than by those urban impacts alone. We highlight a need to develop a regional framework by reconciling the emerging urban‐rural contrasts in vulnerability and preparedness to hydrometeorological extremes.

Spatial-temporal variability and extreme climate indices of precipitation in a coastal watershed of southeastern Brazil.

The analysis of multi-temporal and spatial trends of rainfall in a river basin is an essential approach for water resource planning and management approach. In this study, a combination of trend analysis and spatial-temporal variability of the rainfall for 1970-2017 was applied to examine rainfall distribution patterns in a coastal watershed, Santa Maria da Vitória River Basin (southeastern Brazil). Data from 42 meteorological stations were analyzed using kriging as a geostatistical tool for point data interpolation. Trends in rainfall were computed using the RClimDex package with eleven extreme climate indices. The results have shown spatial and temporal rainfall variability, with drought events becoming more persistent in recent years in the upper sector of the basin, where agricultural land use prevails. Water shortage may impact crops and threatening the water supply and hydropower production. Trend analysis suggests that the annual total wet-day precipitation (PRCPTOT) increases in the coastal section and decreases in the upper basin sector. Consecutive dry days (CDD) and consecutive wet days (CWD) show a strong positive tendency in the lower basin section, where the metropolitan area is located, flooding risks increase in response to positive trends of intensive short-term rainfall events. These results support managers developing and planning sustainability strategies to assure water security and subsidize adaptative responses to extreme hydrological events.

Impacts of rainfall spatial and temporal variabilities on runoff quality and quantity at the watershed scale

Due to climate change and human disturbances, rainfall spatial and temporal variabilities at the watershed have become increasingly prominent, significantly impacting the patterns of runoff generation and pollutant transportation. Originally, rainfall variabilities in space and time were frequently neglected or idealized in the traditional applications of hydrology and water quality models. This research attempted to investigate the responses of runoff quality and quantity simulation to rainfall spatial and temporal variabilities. We used Rainfall Hazard Modeling System (RainyDay model) combined with TRMM (Tropical Rainfall Measuring Mission) rainfall products to generate rainfall events for 500 return periods. Such events were resampled into eight rainfall scenarios with multiple levels of spatial and temporal variabilities. They were used to drive the Soil and Water Assessment Tool (SWAT) to simulate runoff quality (TP as the index) and quantity under multiple watershed scales and return periods at the Dongjiang River watershed. Analysis of Variance was employed to analyze the relative contributions of rainfall spatial and temporal variabilities to the total variabilities of runoff quality and quantity. The results highlighted that rainfall spatial and temporal variabilities would have notable impacts on runoff quality and quantity under multiple watershed scales and event magnitudes. For headwater at small watershed scales, rainfall spatial variability would have a relatively significant impact on runoff quality and quantity. For relatively larger watershed scales, rainfall temporal variability showed an increasingly important impact than spatial variability for smaller return periods, whereas the opposite results can be obtained for relatively larger return periods. The results can be caused by the following reasons:1) rainfall was concentrated in the middle and lower reaches of the watershed; 2) it was based on the daily scale. Our research suggested that attention should be paid to spatial and temporal variabilities of rainfall inputs in hydrological and water quality models.

Regional spatial and temporal variability of rainfall, temperature over Bangladesh and Northern Bay of Bengal

The spatial and temporal variation of rainfall, Tmax and Tmin (mean maximum and minimum temperature) and ΔT (Tmax -Tmin) of different Regions of Bangladesh and Sea Surface Temperature (SST) of Northern Bay of Bengal at seasonal and annual timescales for the period of 1981–2019 have been investigated using a Linear regression test (LRT) and a student t-test in this study. Correlation analysis has been investigated between SST of the Bay of Bengal, and temperature or rainfall of the entire Coast. The regional differences of climatic trends were tested in each region against other regions via a student t-test. Results suggest that there is regional climatic variation between the Northwest and South Zones. There are significant regional differences in climatic trends found in this study. All seasons mean Tmax and Tmin are increasing except in winter when mean Tmax for the Northwest Region (- 0.005 °C/year), and Middile Zone (-0.003#x00A0;°C/year) and mean Tmin for some Southern Regions (e.g., Hilly Region) are decreasing in some seasons. Hilly Region is showing the highest increasing rates of mean Tmax across all seasons (annually at the rate of 0.04 °C/ year (p < 0.01) except in the monsoon season when mean Tmax for the Northwest region is highest (0.042 °C/year (p < 0.01)). Overall, the time series analysis suggests that in Bangladesh, annual mean Tmax and Tmin are increasing at the rate of 0.026 °C/year (p < 0.001) and 0.013 °C/year (p < 0.001), which is concurrent with the global temperature increase. The study also found that SST in Northern Bay of Bengal is increasing in all seasons except winter. The study revealed that the average temperature of most of the Coastal stations and the entire Coast have significant moderate correlations with SST, except for winter seasons for a few stations. At the same time, there is no to weak correlation between SST and rainfall across most seasons. A negative trend in rainfall across most seasons is visible, with an especially large decline during the summer monsoon. However, annual rainfall is decreasing (albeit not significantly) across the whole of Bangladesh since 1981, at a rate of - 6.7 mm/year.

Crop resilience to climate change: A study of spatio-temporal variability of sugarcane yield in a subtropical region, China

Increasing sugarcane production to meet sugar industry demands is a major challenge facing agricultural sector in a more vulnerable environment to climate change in subtropical China. Numerous studies have shown the impact of climate change on sugarcane production, but most of these studies were conducted in a plot or catchment scale without assessing the impacts at regional scale. In this paper, climatic elements of temperature, rainfall, and radiation data, simulated by 28 global climate models, were evaluated using the Taylor chart. The two greenhouse gas emission scenarios, RCP4.5 and RCP8.5, were used to generate climate changes in the region. Combined with the spatial analysis method, the spatial and temporal variation of temperature, rainfall and radiation in Guangxi region from 2010 to 2100 were studied. A validated APSIM-sugarcane model was selected to simulate sugarcane yield at 22 research stations in Guangxi region under different RCP scenarios in the future, and to analyze the three research stages (2030s: 2021-2040; 2060s: 2051-2070 and 2090s: 2081-2100). The spatio-temporal changes of aboveground biomass weight (DM), sugarcane stalk and sucrose (S) yields of the years 2021-2100 were compared with those in the base years (1961-2010), and the effects of climate change on sugarcane yield were quantified. On average, the DM yields were projected to increase by 3.6 and 4.7 and 5.8 t ha−1 for RCP4.5 and 2.9, 5.4 and 8.5t ha−1 for RCP8.5 in 2030s, 2060s and 2090s as compared to 1961–2010, respectively. Similarly, the projected increase in S yields were 5.2, 7.0, and 8.3 t ha−1for RCP4.5 and 4.3, 7.2 and 10.8 t ha−1 for RCP8.5 in the respective future years in comparison with baseline years. The results showed that sugarcane yield had no significant relationship with changes in solar radiation, but the yield had significant correlations with rainfall and temperature changes. Combine changes in radiation, rainfall, temperature and elevated CO2 might be accounted for 69 and 76% changes in sugarcane DM and S yields, respectively, across the entire location. An increase in temperature caused a little increase in sugarcane yield, while an increase in rainfall increased sugarcane yields significantly, but decreased when it exceeded a certain limit. On the other hand, the S yield increased with increase in temperature at constant radiation, but decreased when the radiation exceeded a certain limit. Temperature changes only caused S yield increase when radiation and temperature increased simultaneously. The effects of temperature and rainfall on S yield were similar to those on biomass yield. The effects of radiation and rainfall on S yield showed that both rainfall and radiation increased S yields, but the amplitude was limited. Therefore, to mitigate climate change in the future, supplemental irrigation can be used to increase soil moisture content to improve sugarcane yield.

Exploring Temporal Rainfall Variability and Trends Over a Tropical Region Using Tropical Rainfall Measurement Mission (TRMM) and Observatory Data

Kerala is the gateway of the Indian southwest monsoon. The Tropical Rainfall Measurement Mission (TRMM) rainfall data is an efficient approach to rainfall measurement. This study explores the temporal variability in rainfall and trends over Kerala from 1998-2019 using TRMM data and observatory data procured from India Meteorological Department (IMD). Direct comparison with observatory data at various time scales proved the reliability of the TRMM data (monthly, seasonal and annual). The temporal rainfall converted by averaging the data on an annual, monthly and seasonal time scale, and the results have confirmed that the rainfall estimated based on satellite data is dependable. The station wise comparison of rainfall in monsoon season provides satisfactory results. However, estimation of rainfall in mountainous areas is challenging task using the TRMM. In the basins of humid tropical regions, TRMM data can be a valuable source of rainfall data for water resource management and monitoring with some vigilance. In Kerala, the study found an insignificant increase in the southwest monsoon and winter season rainfall during last two decades. The rainfall over Kerala showed uncertainty in the distribution of monthly, seasonal and yearly time scales. This study provides a preview of recent weather patterns that would enable us to make better decisions and improve public policy against climate change.

Impact of Soil Moisture Initialization in the Simulation of Indian Summer Monsoon Using RegCM4

Soil moisture is one of the key components of land surface processes and a potential source of atmospheric predictability that has received little attention in regional scale studies. In this study, an attempt was made to investigate the impact of soil moisture on Indian summer monsoon simulation using a regional model. We conducted seasonal simulations using a regional climate model (RegCM4) for two different years, viz., 2002 (deficit) and 2011 (normal). The model was forced to initialize with the high-resolution satellite-derived soil moisture data obtained from the Climate Change Initiative (CCI) of the European Space Agency (ESA) by replacing the default static soil moisture. Simulated results were validated against high-resolution surface temperature and rainfall analysis datasets from the India Meteorology Department (IMD). Careful examination revealed significant advancement in the RegCM4 simulation when initialized with soil moisture data from ESA-CCI despite having regional biases. In general, the model exhibited slightly higher soil moisture than observation, RegCM4 with ESA setup showed lower soil moisture than the default one. Model ability was relatively better in capturing surface temperature distribution when initialized with high-resolution soil moisture data. Rainfall biases over India and homogeneous regions were significantly improved with the use of ESA-CCI soil moisture data. Several statistical measures such as temporal correlation, standard deviation, equitable threat score (ETS), etc. were also employed for the assessment. ETS values were found to be better in 2011 and higher in the simulation with the ESA setup. However, RegCM4 was still unable to enhance its ability in simulating temporal variation of rainfall adequately. Although initializing with the soil moisture data from the satellite performed relatively better in a normal monsoon year (2011) but had limitations in simulating different epochs of monsoon in an extreme year (2002). Thus, the study concluded that the simulation of the Indian summer monsoon was improved by using RegCM4 initialized with high-resolution satellite soil moisture data although having limitations in predicting temporal variability. The study suggests that soil moisture initialization has a critical impact on the accurate prediction of atmospheric circulation processes and convective rainfall activity.

Spatial and temporal trends and variability of rainfall using long-term satellite product over the Upper Blue Nile Basin in Ethiopia

The present study aimed to analyze the long-term trends and variability of rainfall in the Upper Blue Nile (UBN) basin in Ethiopia using Climate Hazards Group Infrared Precipitation with Stations (CHIRPS-v2) rainfall product from 1981 to 2018. A pixel-based rainfall variability and trend analysis was made at the annual, seasonal, and monthly temporal scales. The coefficient of variation (CV) was applied to compute the rainfall variability. The trends for each pixel were analyzed using Mann–Kendall (MK) trend test, while the Theil-Sen slope was used to estimate the magnitude of the trend. Results indicated that monthly and seasonal rainfall variabilities are high with the CV up to 140% and 70%, respectively. Annual rainfall variability was found to be low with CV ranging from 6 to 18%. The annual rainfall anomaly analysis, on the other hand, indicated that there are variations in the amount of rainfall received at different years and parts of the basin. It captured the spatial distributions of below- and above-average rainfall periods and its associated drought and flood events, respectively, observed in the basin. In general, the MK monotonic trend analysis for the annual, summer, spring, and autumn rainfall over the entire basin showed an average increasing trend at a rate of 2.48 mm year−1,1.16 mm year−1, 0.92 mm year−1, and 0.67 mm year−1, respectively. In contrast, the rainfall average trend during the winter season (− 0.15 mm year−1) indicated a decreasing trend. The consistency of per-pixel trends with previous studies, based on rain gauge observations, demonstrates the robustness of the trends. Furthermore, the completely raster-based analysis made in this study has provided essential information regarding the spatial variability and trends of rainfall in the basin, which was not satisfactorily captured by the conventional systems due to the larger inter-station distance between rain gauges. Thus, this could be a valuable addition to the existing knowledge of rainfall characteristics in the UBN basin.

Urban intensification of convective rainfall over the Singapore – Johor Bahru region

AbstractSimulations of five November months (2010–2014) using the urban version of the numerical weather prediction system of the Meteorological Service Singapore (uSINGV) are used to analyse the urban effect on convective precipitation over Singapore and Johor Bahru (Malaysia). The model is able to closely predict locations where rainfall peaks occur, but rainfall totals are overestimated compared to radar data. The temporal variability of rainfall in the region shows that urban areas increase the frequency and severity of rainfall events and that such impact increases with the rainfall intensity. Results show that low‐level moisture advection is enhanced in this coastal conurbation as a result of the strengthening of wind convergence. The latter is likely caused by increasing sea‐breeze strength due to lower surface pressure over the urban area, and higher urban surface roughness, respectively. As a consequence, more precipitable water is available in the region, enhancing convection and increasing the probability of heavy rainfall over the centre and north of Singapore island and Johor Bahru. Stronger convection further increases moisture advection from the vicinity. By studying the temporal variability and the spatial distribution of rainfall events, the present study provides new insights on the urban impact on heavy rainfall in tropical areas. The conclusions are only valid for the November inter‐monsoon period, when local forcing, rather than large‐scale influences, dominates rainfall generation.

Rainfall Characteristics in Ahmednagar District of Maharashtra State

Rainfall is one of the vital form of precipitation which affects not only agricultural activity but also entire ecology in any region. Hence rainfall distribution and its trends in district is important to understand water availability and to take decisions for the agricultural activities in area. This research paper is an effort to assess the spatial and temporal rainfall variability of Ahmednagar district of Maharashtra State. Ahmednagar is popularly known as the largest district of Maharashtra with fourteen Talukas. The average annual rainfall of this district is 621 mm with an average of 46 rainy days. In this study the spatial and temporal rainfall distribution of this district is taken in to account. Short-term annual rainfall data are considered from 1998 to 2014. The daily rainfalls of monsoon months of all the fourteen Taluka are analyzed for the year 2015.It was found that spatial and temporal variability is high in the District.

Quantifying rainfall in Greenland: a combined observational and modelling approach

AbstractThis paper estimates rainfall totals at 17 Greenland meteorological stations, subjecting data from in-situ precipitation gauge measurements to seven different precipitation phase schemes to separate rain- and snowfall amounts. To correct the resulting snow/rain fractions for undercatch, we subsequently use a Dynamic Correction Model (DCM) for Automatic Weather Stations (AWS, Pluvio gauges) and a regression analysis correction method for staffed stations (Hellmann gauges). With observations ranging from 5% to 57% for cumulative totals, rainfall accounts for a considerable fraction of total annual precipitation over Greenland’s coastal regions, with the highest rain fraction in the south (Narsarsuaq). Monthly precipitation and rainfall totals are used to evaluate the regional climate model RACMO2.3. The model realistically captures monthly rainfall and total precipitation (R=0.3-0.9), with generally higher correlations for rainfall for which the undercatch correction factors (1.02-1.40) are smaller than those for snowfall (1.27-2.80), and hence the observations more robust. With a horizontal resolution of 5.5 km and simulation period from 1958-present, RACMO2.3 therefore is a useful tool to study spatial and temporal variability of rainfall in Greenland, although further statistical downscaling may be required to resolve the steep rainfall gradients.

Spatial and temporal rainfall variability and its controlling factors under an arid climate condition: case of Gabes Catchment, Southern Tunisia

Spatial and temporal rainfall variability and its controlling factors under an arid climate condition: case of Gabes Catchment, Southern Tunisia

Trends and Frequencies of Extreme Rainfall Events in the Urban Catchments of Dar es Salaam, Tanzania

Understanding the characteristics of extreme rainfall events is necessary for proper planning and management of urban flooding impacts. In this paper, daily rainfall data from four key weather stations for the period 1967-2017 were used to investigate temporal variability in annual, seasonal, and extreme rainfall in the urban catchments of Dar es Salaam, Tanzania. The MannKendall trend analysis and Sen’s slope estimator were used to quantify the magnitudes and significance of long-term trends in rainfall. The frequencies of extreme rainfall events were modelled using the Generalized Pareto model. Results of trend analysis provided evidence of a decrease in total annual rainfall, with the highest decrement being 6.59mm per year. The statistical significance of the decrease in total annual rainfall was inconclusive. Observations of increase in both annual and seasonal maximum rainfall were also made; with the highest increments being 1.01mm and 0.79mm per event, for annual and seasonal maximum rainfall, respectively. The statistical significance of the increase in annual maximum rainfall was certain at 3 out of 4 stations. Frequencies of extreme rainfall events investigated using the R6 threshold provided reasonable results based on actual experience in the study area. Results indicated that most of the pluvial and fluvial flooding are from rainfall events with a 2 to 10-year return period. This is indicative of issues with the drainage systems in the area; either in their designed capacity or the reduction of their water carrying capacity due to anthropogenic factors.