Rainfall In Bangladesh Research Articles

IoT-Based Rainfall Monitoring System to Detect Rainy Flood for Coastal Areas of Bangladesh

Flood disasters in Bangladesh’s coastal regions have been a major source of worry due to their damaging effects on both the natural world and people. For this natural disaster to be tracked and efficiently controlled, rainfall measurement must be precise and trustworthy. The rain gauge, which has acquired global acceptability, is the most commonly used instrument for measuring rainfall. This study presents the design and experimental evaluation of a cloud-based sensor-based rain gauge for the real determination of total rainfall and rate. An ultrasonic sensor is connected to a round water container, a round rainwater acquisition, a Bluetooth module, and a data processing unit for data collecting and processing in the proposed recommendation sensor-based rain gauge. Improved rainfall evaluation and detection methods have been created using cutting-edge sensor data processing techniques. Throughout a local rainfall event, the performance of the electronic sensor-based rain gauge is assessed and contrasted with that of a hand-made rain gauge. The experimental results show that the suggested system offers an accurate reading of rainfall quantity and rate per unit of time and area, real-time rainfall monitoring, and reliable and consistent analysis. The created method is made to maintain rainy floods in Bangladesh’s coastal regions by storing rainfall data. The system is anticipated to help the region establish an efficient rainy flood monitoring system. Overall, the suggested digital sensor-based rain gauge offers a useful and trustworthy instrument for managing and monitoring rainfall in Bangladesh’s coastal regions in real-time. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 52-57

Co-producing representations of summer rainfall in Bangladesh

Climate adaptation governance increasingly investigates the cultural capacities of communities to cope with climate variability and change. This paper reports on research of the symbolic representations of summer rainfall in the cultural repertoires guiding diverse institutionalised fields of activity in Sylhet Division. The research conducted interviews and co-created ‘cognitive maps’ with communities, to critically reflect on their changing seasonal symbols. The study revealed a common stock of summer symbols in Sylhet communities, which individuals reconfigure for strategizing and justifying particular practices. Symbols are stable but not static. As people’s uses of knowledge systems change—moving toward scientific representations—so too does their use of symbols. Moreover, environmental and climatic changes, such as a drying summer, are undermining long-held semiotic templates. Many local and traditional signs no longer hold, leaving communities without cultural templates for timely seasonal action. This work highlights the importance of cultural frameworks for organising communities’ seasonal adaptation, and the imperative for critically revisiting frameworks in rapid flux.

Long-Term Trend and Variability of Rainfall in Bangladesh

The study of long-term rainfall data is significantly important for the country, whose food security and economy depend on the timely availability of water. In this study, spatial and temporal rainfall variability in Bangladesh were analysed for seasonal and annual trends by using monthly rainfall data for 40 years (1981–2020) from 34 stations of Bangladesh Meteorological Department. Linear regression analysis and Mann-Kendall (MK) test were used to identify trends and magnitude of the change and Sen’s slope to estimate the slope of the trend line. The Coefficient of Variation (CV) was used to examine the variability of rainfall. Spatial interpolation technique was done by a Kriging method using Surfer for interpolating the spatial pattern over Bangladesh. With the exception of a few stations, the findings of both parametric and non-parametric tests and trend tests were insignificant for most stations, and these stations revealed a falling trend in annual rainfall. Sen's test showed a decreasing trend for both annual and monsoon rainfall in almost all stations. Over the 40-year periods, among the seasons, the highest percentage of variability was observed in winter rainfall (148.6%) while the lowest percentage variability was observed in annual series (12.1%). The analysis of rainfall data could help irrigation and agricultural management plan for the most efficient use of water resources in Bangladesh.

Trends of Rainfall and Temperature in Bangladesh: A Comparative Analysis of CMIP5 Results and Meteorological Station Data

The Coupled Model Inter-comparison Project phase 5 (CMIP5) dataset along with rainfall and temperature data, recorded at 34 stations of Bangladesh Meteorological Department (BMD), is analyzed to examine the recent changes in rainfall and temperature over Bangladesh during the period 1981-2019. In the historical period, all three CMIP5 models (MPI-ESM-LR, MPI-ESM-MR and NorESM1-M) underestimated the observed mean rainfall data collected from BMD, whereas for temperature the result is found to be nearly similar between model and observation. The result shows a trend of increasing mean maximum temperature of Bangladesh at a rate of 0.19°C and 0.26°C per decade and increasing mean minimum temperature at a rate of 0.18°C and 0.15°C per decade, for model and observation respectively. The study found a decreasing trend of annual rainfall in Bangladesh observed at a rate of 41.60 mm and 39.77 mm per decade, respectively, for model and observation. Spatial distribution shows an increase in annual rainfall in the northeastern part of Bangladesh. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 9(2), 2020, P 9-18

Rainfall in Bangladesh

Rainfall is the important climatic parameter on which the agriculture and economic condition of Bangladesh depends. For this reason, the scenario of rainfall throughout the country was illustrated briefly in this paper. Secondary data which were annual rainfall data of 34 stations were mainly used for this study, and these data were collected from BBS (Bangladesh Bureau of Statistics) between 2007 and 2019. The data were analyzed and visualized with the aid of Excel. However, the highest rainfall almost 5944 mm was recorded in Sylhet during the year 2017, and the lowest was 792 mm in Rajshahi in 2010. On the contrary, the minimum fluctuation of rainfall was observed in Mongla, whereas, the maximum fluctuation was found in Sylhet.

A Geostatistical Approach to Predict the Average Annual Rainfall of Bangladesh

In this paper we tried to fit a predictive model for the average annual rainfall of Bangladesh through a geostatistical approach. From geostatistical point of view, we studied the spatial dependence pattern of average annual rainfall data (measured in mm) collected from 246 stations of Bangladesh. We have employed kriging or spatial interpolation for rainfall data. The data reveals a linear trend when investigated, so by fitting a linear model we tried to remove the trend and, then we used the trend-free data for further calculations. Four theoretical semivariogram models Exponential, Spherical, Gaussian and Matern were used to explain the spatial variation among the average annual rainfall. These models are chosen according to the pattern of empirical semivariogram. The prediction performance of Ordinary kriging with these four fitted models are then compared through ����-fold cross-validation and it is found that Ordinary Kriging performs better when the spatial dependency in average annual rainfall of Bangladesh is modeled through Gaussian semivariogram model.

Spatio-temporal characterization of rainfall in Bangladesh: an innovative trend and discrete wavelet transformation approaches

The analysis of detailed characteristics of rainfall is of utmost importance for understanding water resources, irrigation, and agriculture. This study investigated detailed characteristics of rainfall of Bangladesh from 1966 to 2019. Rainfall features like the precipitation concentration index (PCI) and seasonality index (SI) were estimated to characterize spatial patterns of rainfall regimes, and innovative trend analysis (ITA) and percent bias (PBIAS) were used to detect the trend, and its reliability was tested by using the Mann–Kendall (MK) or modified Mann–Kendall (mMK) test. The magnitude of changes was computed by using Sen’s slope estimator (Q), and discrete wavelet transform (DWT) was employed to find out the dominant periodicity of the trend of annual series. SI and PCI revealed that rainfall is mainly seasonal and markedly with the long dry season, and the distribution of rainfall is irregular for the major portion of Bangladesh. The result of ITA and PBIAS showed a similar trend for all the stations and time scales. ITA showed that 23 stations experienced significant (α = < 0.05) increasing (39.13%) and decreasing (39.13%) trends in annual rainfall. For the seasonal scale, post-monsoon rainfall is dominated by significant increasing trend (60.87%), whereas winter rainfall is dominated by significant decreasing trend (65.28%); besides, both decreasing and increasing trends were found in pre-monsoon and monsoon in Bangladesh. The analysis of DWT confirmed that 4- to 16-year periodic cycle was the dominant component for annual rainfall. The results of detailed analysis of rainfall will be helpful for policymakers and scientists to focus on regional-scale planning about flood and drought situation of the country that will be ultimately helpful for agricultural development and environmental planning.

Spatiotemporal trends in the frequency of daily rainfall in Bangladesh during 1975–2017

Bangladesh is greatly susceptible to the impacts of climate change. Climate change has enhanced the frequency and intensity of extreme events in Bangladesh, thus resulting in loss of biodiversity and agricultural crop production. However, trend studies in the frequency of climatic variables like rainfall at the micro-scale are less explored in the previous literature. Therefore, this study aims to explore the spatiotemporal trends in the frequency of daily rainfall using different statistical models based on 23 station data from 1975 to 2017 in Bangladesh. The statistical results show that the frequency of rainfall ranges of 10 to ≤ 15 mm, > 15 to ≤20 mm, and > 25 to ≤ 30 mm exhibited an increasing trend while rainfall ranges of > 5 to ≤ 10 mm, > 20 to ≤ 25 mm, > 30 to ≤ 35 mm, and > 35 mm displayed a decreasing trend (p > 0.10). The jump test results indicate that only one jump occurred in the average rainfall ranges of > 15 to ≤ 20 mm (p < 0.10). The changes in temporal trends of different rainfall frequency ranges show an irregular pattern during the study period. The spatial map of trends among various rainfall ranges over sub-climatic regions is heterogeneous nature which varies from region wise and station wise. The probability density function (PDFs) of rainfall frequency ranges will move upward in the future and broaden with an increase in the forthcoming period. To adapt to climate change, the findings highlight the need for the advancement of water resource management and reallocation of agricultural adaptation plans in Bangladesh.

Rainfall in Bangladesh: Is Rainwater Harvesting a Sustainable Approach for Governing Rainwater?

Global environmental change affects the intensity and frequency of rainfall that increases the necessity for sustainable management of rainwater. It is more important for rain intensive country like Bangladesh otherwise it creates further problems like water logging, flood, soil erosion, and water borne diseases. Based on the survey of 80 rainwater harvesters in the Coastal Bangladesh in 2017, this paper evaluates whether rainwater harvesting is sustainable approach to govern rainwater or not by using social and ecological performance measures of Social Ecological System (SES) of Elinor Ostrom. This study found that rainwater harvesting does not affect environment, it is an acceptable approach in family and society, help to develop biodiversity condition, create resilience to climate change impact, improve equitable access to water, efficient use of rainwater, improve women performance regarding water supply to the family, contribute in achieving sustainable development goal, and do not overharvest of rainwater that could create environmental problem. Beyond these measures, it needs rainwater harvesting controlling mechanism like formal operation rule or policy to avoid overharvesting and ensure more sustainability in governing rainwater.Keywords: Accountability; Biodiversity; Efficiency; Resilience; Equity; Global environmental change; Rainwater harvesting; Sustainable approach; Sustainable development goals; Sustainable management of rainwater

Spatiotemporal variability of rainfall linked to ground water level under changing climate in northwestern region, Bangladesh

Bangladesh is one of the most climatic hazard-prone countries in the world. Rainfall in Bangladesh is highly variable which depends on each season and location. This study aims to investigate spatiotemporal variability of rainfall linked to groundwater level under changing climate in the northwestern region, Bangladesh during the period of 1976-2016 using rainfall and groundwater level data. A number of statistical approaches such as Mann-Kendall (MK) test, linear regression model, Sen’s slope estimator and Spearman’s rho (SK) test, Coefficient of Variation (CV) were employed to reveal trends and variability of rainfall and groundwater level. Additionally, wavelet transform was applied to assess temporal variability of rainfall time series in the northwestern region, Bangladesh. The results show that the annual rainfall variability was the highest in the Ishwardi station (25.39%) and the lowest was found in Rajshahi (20.70%). The MK test results reveal that the trends of annual and seasonal rainfall decreased gradually, where the highest significant decreasing trend of annual rainfall was found in Rajshahi (-2.976 mm/year) and the lowest decreasing trend was found in Syedpur (-1.278 mm/year). Furthermore, monsoon and winter seasons represent the decreasing trends of rainfall for all the stations. The results of wavelet analysis demonstrate that the monthly rainfall variance of average rainfall in the northwestern region was low from the period of 2006 to 2014, indicating the dry years. The annual and seasonal rainfall trends in the northwestern region were decreased, particularly in the recent decade. The relationship between rainfall and groundwater level indicates that rainfall was decreased and the ground water level was also declining at the same time in the study region. Interestingly, it is observed that a significant high decreasing trend of annual rainfall is found in Rangpur but the annual groundwater level depth is less increased in Rangpur at the same time.

Evaluation of spatio‐temporal rainfall variability and performance of a stochastic rainfall model in Bangladesh

Rainfall in Bangladesh exhibits persistent wet and dry anomalies associated with occurrence of floods and droughts. Assessing inter‐annual variability of rainfall is vital to account these hydrological extremes in the design and operations of water systems. However, the inter‐annual variability obtained from short record rainfall data might be misleading as it does not contain whole climate variability which signifies the utmost importance of stochastic rainfall models. Since the inter‐annual variability and stochastic models have not been explored adequately for rainfall in Bangladesh, this study evaluated (a) the spatio‐temporal variability of rainfall focusing on inter‐annual variability, and (b) applicability of a stochastic daily rainfall model, referred as the Decadal and Hierarchical Markov Chain (DHMC) model. Daily rainfall data of 1973–2012 for 18 stations across Bangladesh were used to investigate the probability distributions and autocorrelations of rainfall, and the model performances. Results show a higher magnitude of inter‐annual variabilities of rainfall depth (standard deviation 80–250 mm) and wet spells (standard deviation 4–6 days) in wetter months (June to September) across rainfall stations in the east region of the country. In contrast, higher rates of inter‐annual variabilities (i.e., coefficients of variations) were observed in drier months across the west region. Spatially, the dry spells were observed consistent across the country. Monthly rainfall showed decreasing trend over the region from west to the middle part of the country, whereas monthly number of wet days showed increasing trend over the eastern part. The DHMC was found to preserve the observed variabilities of rainfall at daily to multiyear resolutions at all stations, except a tendency to underestimate the autocorrelation of monthly rainfall depth. Despite this limitation, DHMC can be considered as a suitable stochastic rainfall simulator for a tropical monsoon climate like Bangladesh.

Evaluation of a large ensemble regional climate modelling system for extreme weather events analysis over Bangladesh

Potential increases in the risk of extreme weather events under climate change can have significant socio‐economic impacts at regional levels. These impacts are likely to be particularly high in South Asia where Bangladesh is one of the most vulnerable countries. Regional climate models (RCMs) are valuable tools for studying weather and climate at finer spatial scales than are typically available in global climate models. Quantitative assessment of the likely changes in the risk of extreme events occurring requires very large ensemble simulations due to their rarity. The weather@home setup within the climateprediction.net distributed computing project is capable of providing the necessary very large ensembles at regionally higher resolution, but has only been evaluated over the South Asia region for its representation of seasonal climatological and monthly means. Here, we evaluate how realistically the HadAM3P‐HadRM3P model setup of weather@home can reproduce the observed patterns of temperature and rainfall in Bangladesh with focus on the modelled extreme events. Using very large ensembles of regional simulations, we find that there are substantial spatial and temporal variations in rainfall and temperature biases compared with observations. These are highest in the pre‐monsoon, which are largely caused by timing issues in the model. Modelled mean monsoon and post‐monsoon temperatures are in good agreement with observations, whereas there is a dry bias in the modelled mean monsoon rainfall. The rainfall bias varies both spatially and with the data set used for comparison. Despite of these biases, the model‐simulated temperature and rainfall extremes in summer monsoon over Bangladesh are approximately representative of the observed ones. At the wettest parts of northeast Bangladesh, rainfall extremes are underestimated compared to GPCC and APHRODITE but are within the range of CPC observations. Therefore, the weather@home RCM, HadRM3P may provide a sufficiently reliable tool for studying the extreme weather events in Bangladesh.

Observed trends in temperature and rainfall in Bangladesh using pre-whitening approach

Abstract Understanding the trend of temperature and rainfall is of prime importance for climate change related research. This research aims to identify the trends of temperature and rainfall for the last 50 years (1966–2015) for Bangladesh. To detect the trends, the non-parametric Mann–Kendall test in combination with a trend-free pre-whitening approach for correcting the time series to eliminate the influence of serial correlation has been used whereas the Sen's slope method is used for identification of the trend value. Maximum, minimum, and mean monthly temperatures have been analyzed to identify the monthly, seasonal and annual trend. Four seasons are distinct in Bangladesh, namely, pre-monsoon, monsoon, post-monsoon and winter. In case of rainfall, mean monthly rainfall, 1-day maximum rainfall and consecutive 3-day maximum rainfall are analyzed in a similar fashion. For maximum, minimum and mean monthly temperature, the majority of the stations show an increasing trend for all three indices. Monsoon season observes highest increasing trend of 0.015 °C/yr for mean monthly temperature whereas the winter season shows the negative trend of −0.002 °C/yr. On an annual basis, the trend is increasing with a value of 0.008 °C/yr or 0.4 °C in 50 years having significance level, p .10. The trend of mean rainfall for annual basis shows an increasing trend of 5.675 mm/yr. The trend in one day and consecutive three-day maximum rainfall depicts that the trend is increasing in few places whereas in other places the trend is decreasing with no significant change. The research concludes that the climate of Bangladesh is getting cooler and drier for winter, on the other hand, warmer and wetter for the rest of the year.

The Impact of Extreme Precipitation on the Vulnerability of a Country

Recently, the impact of climate change on national instability is getting more and more attention. However, there is no explanation to how climate affects country’s fragility. Based on some data and researches, we get eight indices provided by The World Bank and two climate indices, temperature difference and extreme precipitation. Researches show that temperature difference affects the fragility directly and extreme precipitation causes indirect influence. We choose South Sudan as the study target. In order to find out how climate change increases this state’s fragility, multiple linear regression analysis is applied to get the relationship between extreme precipitation and three indices. After that, we choose Bangladesh, which is not in the top ten most fragile states to analyse. By calculating the weights of the ten indices above, the two indices, economy and refugees &IDPs (Internally-Displaced Persons), are more significant. Therefore, we conclude that climate change increases fragility by affecting rainfall in Bangladesh. The tipping point is defined as the year when rainfall exceeds 2100 mm and we adopt the GM (1,1) model based on gray prediction theory to get the point. Prediction shows that the next two tipping points of Bangladesh are 2022 and 2027.

Model output statistics downscaling using support vector machine for the projection of spatial and temporal changes in rainfall of Bangladesh

A model output statistics (MOS) downscaling approach based on support vector machine (SVM) is proposed in this study for the projection of spatial and temporal changes in rainfall of Bangladesh. A combination of past performance assessment and envelope-based methods is used for the selection of GCM ensemble from Coupled Model Intercomparison Project phase 5 (CMIP5). Gauge-based gridded monthly rainfall data of Global Precipitation Climatological Center (GPCC) is used as a reference for downscaling and projection of GCM rainfall at regular grid intervals. The obtained results reveal the ability of SVM-based MOS models to replicate the temporal variation and distribution of GPCC rainfall efficiently. The ensemble mean of selected GCM projections downscaled using MOS models show changes in annual precipitation in the range of −4.2% to 24.6% in Bangladesh under four Representative Concentration Pathways (RCP) scenarios. Annual rainfalls are projected to increase more in the western part (5.1% to 24.6%) where average annual rainfall is relatively low, and less in the eastern part (−4.2 to 12.4%) where average annual rainfall is relatively high, which indicates more homogeneity in the spatial distribution of rainfall in Bangladesh in future. A higher increase in rainfall is projected during monsoon compared to other seasons, which indicates more concentration of rainfall in Bangladesh during monsoon.

Nutritional effects of flooding due to unseasonably early monsoon rainfall in Bangladesh: a cross-sectional study in an ongoing cluster-randomised trial

BackgroundSubsistence farmers worldwide experience food insecurity and undernutrition and are particularly affected by natural disasters. In 2017, unseasonably heavy rains in Bangladesh in mid-April filled the northeastern floodplain early and destroyed the annual rice crop before it was ready to be harvested. We aimed to quantify the effect of this flood on food security and the dietary diversity of women and children in the area, describe families' coping strategies, and assess whether a dietary diversification programme could attenuate negative effects of flooding. MethodsThis study is nested in the Food and Agricultural Approaches to Reducing Malnutrition (FAARM) cluster-randomised trial (NCT02505711) in rural Sylhet Division, Bangladesh, which is investigating the effect of a homestead food production programme on undernutrition. FAARM includes nearly 2700 young women in 96 villages, of which 48 villages were randomly assigned to the intervention and 48 are controls. We visited all trial participants every 2 months to collect data on diet, morbidity, and pregnancies. We also collected data on overall perceived effect of the flood on the household, coping strategies, and household food security roughly 6 months after the flooding event. We did multilevel regression analysis, adjusting for intervention allocation, to quantify the effect of the flood on food security and nutrition outcomes. This study is ongoing, and we will continue to collect data on diets and health. FindingsWe interviewed 2405 women about the flooding, of whom 1335 (56%) reported that their families were affected by the flood to a great extent, whereas 505 (21%) reported that they were not affected at all. Of the 1577 families who usually grow rice and grew rice in 2017, 528 (33%) harvested at least as much as usual, whereas 445 (28%) expected a shortfall of 5 months or more of rice consumption compared with a usual year. Income losses were also reported by many households. At 6 months after the flood, only 722 (30%) of 2423 families were food secure and 356 (40%) of 901 women interviewed about their diet consumed an adequately diverse diet. Families with a large rice shortfall (5 months or more) had more than double the odds of food insecurity than did those with no shortfall (odds ratio 2·5, 95% CI 1·8–3·6), adjusting for baseline food insecurity and usual rice harvest. Dietary diversity among women in these families was 0·4 (95% CI 0·1–0·7) food groups lower than in families with no shortfall, adjusting for dietary diversity 1 year previously. The most commonly reported coping strategy among the families who perceived their livelihoods to be affected to a great extent was borrowing money (777 [58%] of 1335 families), mostly from outside lenders. InterpretationNatural disasters exacerbate food insecurity and worsen dietary diversity among subsistence farmers. In a changing climate, natural disasters are becoming more frequent in Bangladesh and elsewhere. FundingGerman Federal Ministry for Education and Research, UK Department for International Development, Dutch Ministry of Foreign Affairs, and the Alexander von Humboldt Foundation.

A study of Bangladesh's sub-surface water storages using satellite products and data assimilation scheme

Climate change can significantly influence terrestrial water changes around the world particularly in places that have been proven to be more vulnerable such as Bangladesh. In the past few decades, climate impacts, together with those of excessive human water use have changed the country's water availability structure. In this study, we use multi-mission remotely sensed measurements along with a hydrological model to separately analyze groundwater and soil moisture variations for the period 2003–2013, and their interactions with rainfall in Bangladesh. To improve the model's estimates of water storages, terrestrial water storage (TWS) data obtained from the Gravity Recovery And Climate Experiment (GRACE) satellite mission are assimilated into the World-Wide Water Resources Assessment (W3RA) model using the ensemble-based sequential technique of the Square Root Analysis (SQRA) filter. We investigate the capability of the data assimilation approach to use a non-regional hydrological model for a regional case study. Based on these estimates, we investigate relationships between the model derived sub-surface water storage changes and remotely sensed precipitations, as well as altimetry-derived river level variations in Bangladesh by applying the empirical mode decomposition (EMD) method. A larger correlation is found between river level heights and rainfalls (78% on average) in comparison to groundwater storage variations and rainfalls (57% on average). The results indicate a significant decline in groundwater storage (∼32% reduction) for Bangladesh between 2003 and 2013, which is equivalent to an average rate of 8.73 ± 2.45mm/year.

Multiple Linear Regressions for Predicting Rainfall for Bangladesh

Agricultural economy is largely based upon crop productivity and rainfall. For analyzing the crop productivity, rainfall prediction is require and necessary to all farmers. Rainfall Prediction is the application of science and technology to predict the state of the atmosphere. It is important to exactly determine the rainfall for effective use of water resources, crop productivity and pre planning of water structures. Data mining might be used to make precise predictions for rainfalls. Most widely used techniques for rainfall is clustering, artificial neural networks, linear regression etc. In this article multiple linear regressions used for predicting rainfall in Bangladesh.

Pattern Recognition of Rainfall Using Wavelet Transform in Bangladesh

The aim of the study is to explore the regional variation of changing patterns of rainfall in Bangladesh using wavelet transform. The study is completed using rainfall variation of the five regions of Bangladesh as Dhaka, Cox’s Bazar, Rajshahi, Bogra and Sylhet. The duration of the study period was 69 years for Dhaka, 64 years for Cox’s Bazar, 40 years for Rajshahi, 54 years for Bogra and 55 years for Sylhet. The results of the wavelet analysis reveals that, in Rajshahi the amount of rainfall are decreasing in a significant rate among the other study regions. It also explores the annual periodicity of rainfall for all the study regions along with a special 6-month periodicity in the Cox’s Bazar. In addition, this analysis also explores a dominating 3 - 4 year cycle of rainfall in all the study regions. Besides the climate change in Cox’s Bazar and Sylhet are pretty much alarming.

Assessment of TRMM data for precipitation measurement in Bangladesh

ABSTRACTApplication of the Tropical Rainfall Measuring Mission (TRMM) can be an efficient method for precipitation measurement in Bangladesh. The TRMM was a joint US−Japan space programme used to measure tropical rainfall with spatial and temporal distribution. This study analyses the reliability of TRMM data by comparing them with the rain gauge data of Bangladesh Meteorological Department at 35 different locations. The comparison is made for the data from 1998 to 2010. Extensive analyses were performed on the basis of different temporal scales (daily, monthly, seasonal and yearly). Both datasets were compared for average, peak and total values. The correlation co‐efficient and bias of TRMM rainfall data with respect to rain gauge values were also determined. However, the rainfall products are also compared at the two most extreme elevation stations to observe the influence of station topography (if any). Results show that TRMM rainfall data closely match with rain gauge data for average values on an annual basis, and the correlation co‐efficient is found to be greater than 0.90. However, significant deviation is found for daily peak rainfall values. Again, a seasonal comparison shows that the TRMM data outputs match with station data except during the dry season. The rainfall data at the highest and lowest elevations show that the TRMM data follow a similar trend to the station data but they underestimate the value to some extent, especially during the wet season. Finally, it is inferred that the TRMM data are reliable in estimating the average rainfall in Bangladesh for hydrological analyses of watersheds.