Predictors and their domain for statistical downscaling of climate in Bangladesh

Reliable projection of future rainfall in Bangladesh is very important for the assessment of possible impacts of climate change and implementation of necessary adaptation and mitigation measures. Statistical downscaling methods are widely used for downscaling coarse resolution general circulation model (GCM) output at local scale. Selection of predictors and their spatial domain is very important to facilitate downscaling future climate projected by GCMs. The present paper reports the finding of the study conducted to identify the GCM predictors and demarcate their climatic domain for statistical downscaling in Bangladesh at local or regional scale. Twenty-six large scale atmospheric variables which are widely simulated GCM predictors from 45 grid points around the country were analysed using various statistical methods for this purpose. The study reveals that large-scale atmospheric variables at the grid points located in the central-west part of Bangladesh have the highest influence on rainfall. It is expected that the finding of the study will help different meteorological and agricultural organizations of Bangladesh to project rainfall and temperature at local scale in order to provide various agricultural or hydrological services

Historical trends and future projection of climate at Dhaka city of Bangladesh

Dhaka, the capital city of Bangladesh is considered as one of the most vulnerable cities of the world to climate change. A study has been carried out to assess the historical changes as well as future changes in the climate of Dhaka city in order to propose necessary mitigation and adaptation measures. Statistical downscaling model (SDSM) was used for the projection of future changes in daily rainfall and temperature and non-parametric trend analysis was used to assess the changes in rainfall, temperature and related extremes. The impacts of projected changes in climate on urban infrastructure and livelihood in Dhaka city was finally assessed to propose necessary adaptation measures. The study revealed that night time temperature in Dhaka city has increased significantly at a rate of 0.22ºC/decade in last fifty year, which is support to increase continually in the future. Different temperature related extreme events are also found to increase significantly in Dhaka. On the other hand, no significant change in rainfall or rainfall related extremes are observed. Therefore, it can be remarked that imminent impacts of climate change will be due to the increase in temperature and temperature related extremes. The public health and the water and energy supply are likely to be imminent affected sector in the city due to climate change

Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia

A genetic programming (GP)-based logistic regression method is proposed in the present study for the downscaling of extreme rainfall indices on the east coast of Peninsular Malaysia, which is considered one of the zones in Malaysia most vulnerable to climate change. A National Centre for Environmental Prediction reanalysis dataset at 42 grid points surrounding the study area was used to select the predictors. GP models were developed for the downscaling of three extreme rainfall indices: days with larger than or equal to the 90th percentile of rainfall during the north-east monsoon; consecutive wet days; and consecutive dry days in a year. Daily rainfall data for the time periods 1961-1990 and 1991-2000 were used for the calibration and validation of models, respectively. The results are compared with those obtained using the multilayer perceptron neural network (ANN) and linear regression-based statistical downscaling model (SDSM). It was found that models derived using GP can predict both annual and seasonal extreme rainfall indices more accurately compared to ANN and SDSM

Spatiotemporal changes in aridity and the shift of drylands in Iran

The spatial and temporal changes in annual and seasonal aridity, the shift of land from one arid class to another and the effect of this shift on different landuses in Iran during 1951–2016 have been assessed in this study. The monthly rainfall data of global precipitation climatology center (GPCC), and the monthly mean temperature and potential evapotranspiration (PET) data of climate research unit (CRU) having a spatial resolution of 0.5° were used for this purpose. The novelty of the study is the assessment of the significance in the shift of arid land between 1951–1980 and 1987–2016. Besides, the association of rainfall and temperature with aridity in different arid zones were assessed to understand the driving factors of the shift of arid lands. The results revealed an increase in annual and seasonal aridity in Iran, which caused expansion of arid land. The most remarkable changes include conversion of 4.84% semi-arid land to arid land due to an increase in annual aridity, shift of 4.84% arid land to hyper-arid during summer and 6.45% semi-arid land to arid during winter. However, only the expansion of semi-arid land to dry-subhumid land was found statistically significant. Analysis of results revealed different contributions of rainfall and temperature in the expansion of different classes of arid lands. The decrease in rainfall was the cause of the increasing aridity in the arid and semi-arid region, while the increasing temperature was found to play a major role in increasing aridity in the humid region. The overlapping of landuse map on aridity shift map revealed that the rangelands and farmlands in the north and the northwest were more affected by the expansion of aridity which might have severe consequences on agricultural production and food security of the country

Physical-empirical models for prediction of seasonal rainfall extremes of peninsular Malaysia

Reliable prediction of rainfall extremes is vital for disaster management, particularly in the context of increasing rainfall extremes due to global climate change. Physical-empirical models have been developed in this study using three widely used Machine Learning (ML) methods namely, Support Vector Machines (SVM), Random Forests (RF), Bayesian Artificial Neural Networks (BANN) for the prediction of rainfall and rainfall related extremes during Northeast Monsoon (NEM) in Peninsular Malaysia from synoptic predictors. The gridded daily rainfall data of Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) was used to estimate four rainfall indices namely, rainfall amount, average rainfall intensity, days having >95-th percentile rainfall, and total number of dry days in Peninsular Malaysia during NEM for the period 1951–2015. The National Centers for Environmental Prediction (NCEP) reanalysis sea level pressure (SLP) data was used for the prediction of rainfall indices with different lead periods. The recursive feature elimination (RFE) method was used to select the SLP at different NCEP grid points which were found significantly correlated with NEM rainfall indices. The results showed superior performance of BANN among the ML models with normalised root mean square error of 0.04–0.14, Nash-Sutcliff Efficiency of 0.98–1.0, and modified agreement index of 0.97–0.99 and Kling-Gupta efficient index 0.65–0.96 for one-month lead period prediction. The 95% confidence interval (CI) band for BANN was found narrower than the other ML models. Almost all the forecasted values by BANN were also found with 95% CI, and therefore, the p-factor and the r-factor for BANN in predicting rainfall indices were found in the range of 0.95–1.0 and 0.25–0.49 respectively. Application of BANN in prediction of rainfall indices with higher lead time was also found excellent. The synoptic pattern revealed that SLP over the north of South China Sea is the major driver of NEM rainfall and rainfall extremes in Peninsular Malaysia

Spatial Pattern of the Unidirectional Trends in Thermal Bioclimatic Indicators in Iran

Changes in bioclimatic indicators can provide valuable information on how global warming induced climate change can affect humans, ecology and the environment. Trends in thermal bioclimatic indicators over the diverse climate of Iran were assessed in this study to comprehend their spatio-temporal changes in different climates. The gridded temperature data of Princeton Global Meteorological Forcing with a spatial resolution of 0.25° and temporal extent of 1948–2010 was used for this purpose. Autocorrelation and wavelets analyses were conducted to assess the presence of self-similarity and cycles in the data series. The modified version of the Mann–Kendall (MMK) test was employed to estimate unidirectional trends in 11 thermal bioclimatic indicators through removing the influence of natural cycles on trend significance. A large decrease in the number of grid points showing significant trends was noticed for the MMK in respect to the classical Mann–Kendall (MK) test which indicates that the natural variability of the climate should be taken into consideration in bioclimatic trend analyses in Iran. The unidirectional trends obtained using the MMK test revealed changes in almost all of the bioclimatic indicators in different parts of Iran, which indicates rising temperature have significantly affected the bioclimate of the country. The semi-dry region along the Persian Gulf in the south and mountainous region in the northeast were found to be more affected in terms of the changes in a number of bioclimatic indicators

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

Changes in reference evapotranspiration and its driving factors in peninsular Malaysia

Trends in reference evapotranspiration (ETo) have been found highly diverse in different regions of the globe due to the contradictory changes in the meteorological variables that define ETo. Despite a significant impact of ETo in water resources and ecology, knowledge on the changes and the cause of the changes in ETo is very limited in tropical regions. The trends in ETo, the factors influencing the changes in ETo and the change point (year) that made the trend significant were evaluated in this study for tropical peninsular Malaysia. The modified version of Mann-Kendall (MK) test was used for the assessment of unidirectional changes in ETo and the driving meteorological variables. The innovative trend analysis (ITA) was conducted to understand the variations in change with time. Sobol's method was used to measure the sensitivity of ETo to different meteorological factors and the Sequential MK test was employed to identify the change point. The study revealed an increase in annual (0.009–0.026 mm/year) and seasonal (0.014–0.027 mm/year during southwest monsoon and 0.015–0.074 during northeast monsoon) ETo in peninsular Malaysia which contradicts to evapotranspiration paradox found in many regions. The minimum temperature (31.5–48.2%) was found as the most influencing factor followed by wind speed (15.1–32.8%.) in defining ETo in peninsular Malaysia. Analysis of ITA and sequential MK test results revealed that the rise in minimum temperature is the major cause of the increase in ETo in peninsular Malaysia. A faster rise in minimum temperature after 1981–1985 caused an increase in ETo after 1993–1996 in most of the locations. The minimum temperature in the region was noticed to rise much faster compared to the global average which indicates a large and continuous increase in ETo due to global warming and thus, reduction in atmospheric water balance in peninsular Malaysia

Low impact development techniques to mitigate the impacts of climate-change-induced urban floods: current trends, issues and challenges

The severity and frequency of short-duration, but damaging, urban area floods have increased in recent years across the world. Alteration to the urban micro-climate due to global climate change impacts may also exacerbate the situation in future. Sustainable urban stormwater management using low impact development (LID) techniques, along with conventional urban stormwater management systems, can be implemented to mitigate climate-change-induced flood impacts. In this study, the effectiveness of LIDs in the mitigation of urban flood are analyzed to identify their limitations. Further research on the success of these techniques in urban flood mitigation planning is also recommended. The results revealed that LIDs can be an efficient method for mitigating urban flood impacts. Most of the LID methods developed so far, however, are found to be effective only for small flood peaks. They also often fail due to non-optimization of the site-specific and time-varying climatic conditions. Major challenges include identification of the best LID practices for the region of interest, efficiency improvements in technical areas, and site-specific optimization of LID parameters. Improvements in these areas will allow better mitigation of climate-change-induced urban floods in a cost-effective manner and will also assist in the achievement of sustainable development goals for cities

Streamflow prediction in ungauged catchments in the east coast of peninsular Malaysia using multivariate statistical techniques

The east coast of Peninsular Malaysia is one of the most vulnerable regions of Malaysia to hydrological disasters, which is believed to become more vulnerable due to climate change. Studies to have better understandings of the hydrological processes in the region are therefore, of paramount importance for disaster risk mitigation. However, unavailability of long-term river discharge data is one of the major constraints of hydrologic studies in the area. The major objective of this study is to predict river discharge in ungauged river basins in the study area. For this purpose, a set of multiple linear regression equations and exponential functions have been developed, which are expressed in the forms of multivariate equations. Available streamflow data along with other catchment characteristics from gauged catchments were used to develop the equations and were subsequently applied to the poorly gauged or ungauged catchments within the study area for prediction of streamflow. In this present study, 4 to 7 explanatory variables were selected as the input variables, which comprise of climatic, geomorphologic, geographic characteristics, soil properties, land use pattern and land cover of the area. Ten flow metrics as maximum, 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, and 0.95, mean and minimum were therefore predicted. Thus, the results of the developed multivariate equations revealed the model to be capable of predicting the desired flow metrics at ungauged catchments in the area under consideration with reasonable accuracy