Spatial modeling of the highest daily maximum temperature in Korea via max-stable processes

This paper examines the annual highest daily maximum temperature （DMT） in Korea by using data from 56 weather stations and employing spatial extreme modeling. Our approach is based on max-stable processes （MSP） with Schlather＇s characterization. We divide the country into four regions for a better model fit and identify the best model for each region. We show that regional MSP modeling is more suitable than MSP modeling for the entire region and the pointwise generalized extreme value distribution approach. The advantage of spatial extreme modeling is that more precise and robust return levels and some indices of the highest temperatures can be obtained for observation stations and for locations with no observed data, and so help to determine the effects and assessment of vulnerability as well as to downscale extreme events.     

North Atlantic forcing of autumn drought in Southwest China

干旱持续时间长,因此往往和缓慢变化的海温异常相关.本文利用观测降水和NCEP-DOE再分析资料,探讨了北大西洋中高纬地区东北-西南走向的偶极子海温异常对我国西南地区秋季干旱的影响.结果 显示该偶极子海温异常可激发向东南方向传播的Rossby波,在印度北部和青藏高原西部引起正压气旋性环流和上升运动异常,并在我国西南地区引...     

Big data and remote sensing for multi-decadal drought impact assessment on Shorea robusta

Theoretical and Applied Climatology - Intense droughts in recent years are a global concern. The duration and timing of drought for forests have not been researched as much as crops. This study...     

Groundwater level estimation in northern region of Bangladesh using hybrid locally weighted linear regression and Gaussian process regression modeling

Theoretical and Applied Climatology - Urban groundwater resources (GWRs) have declined substantially in recent decades, due to rapid urbanization, population growth, groundwater exploitation, land...     

Trend analysis of maximum,minimum, and average temperatures in Bangladesh: 1961–2008

The present study is about the analysis of mean maximum and mean minimum temperatures carried out on annual, seasonal, and monthly timescales examining the data from 15 meteorological stations in Bangladesh for the period 1961–2008. Various spatial and statistical tools were used to display and analyze trends in temperature data. ArcGIS was used to produce the spatially distributed temperature data by using Thiessen polygon method. The nonparametric Mann–Kendall test was used to determine whether there is a positive or negative trend in data with their statistical significance. Sen’s method was also used to determine the magnitude of the trends. The results reveal positive trends in annual mean and mean maximum temperatures with 95 % significance. Trend test reveals that the significant positive trend is found in June to November in case of mean maximum temperature, but according to the mean minimum temperature, the situation is different and a significant positive trend was found from November to February. The analysis of the whole record reveals a tendency toward warmer years, with significantly warmer summer periods and slightly colder winters. These warming patterns may have important impacts on energy consumption, water supply, human health, and natural environment in Bangladesh.     

Migrating to tackle climate variability and change? Insights from coastal fishing communities in Bangladesh

There is an on-going debate about climate-induced migration but little empirical evidence. We examine how climate-induced migration has impacted vulnerability and adaptation of a coastal fishing community in Bangladesh. We used household surveys, interviews and focus group discussions to compare fishery dependent households who migrated from Kutubdia Island to mainland with those who stayed behind. Our results suggest that the resettled households are less exposed to floods, sea-level-rise and land erosion than those who stayed behind. They also have more livelihood assets, higher incomes and better access to water supply, health and educational services, technology and markets. In our case study migration has thus been a viable strategy to respond to climate variability and change.     

Rainfall variability and seasonality in northern Bangladesh

This paper aimed at the analysis of rainfall seasonality and variability for the northern part of South-Asian country, Bangladesh. The coefficient of variability was used to determine the variability of rainfall. While rainfall seasonality index (SI ) and mean individual seasonality index (\( \overline{SI_i} \)) were used to identify seasonal contrast. We also applied Mann-Kendall trend test and sequential Mann-Kendall test to determine the trend in seasonality. The lowest variability was found for monsoon among the four seasons whereas winter has the highest variability. Observed variability has a decreasing tendency from the northwest region towards the northeast region. The mean individual seasonality index (0.815378 to 0.977228) indicates that rainfall in Bangladesh is “markedly seasonal with a long dry season.” It was found that the length of the dry period is lower at the northeastern part of northern Bangladesh. Trend analysis results show no significant change in the seasonality of rainfall in this region. Regression analysis of \( \overline{SI_i} \) and SI, and longitude and mean individual seasonality index show a significant linear correlation for this area.     

Sorption Behaviour of Phenols on Natural Sandy Aquifer Material during Flow‐through Column Experiments: The Effect of pH

Flow‐through column experiments were carried out to investigate the influence of pH on the sorption of three phenols (2‐methyl‐4, 6‐dinitrophenol, 2, 4, 6‐trichlorophenol, pentachlorophenol) onto a natural sandy aquifer material collected from a bank filtration site of River Elbe, Germany. For the phenols investigated, an increase in sorption (retardation) with decreasing pH is observed indicating a stronger sorption of the neutral species in comparison to that of the anions formed by dissociation. The anions of 2‐methyl‐4, 6‐dinitrophenol and 2, 4, 6‐trichlorophenol do not show significant sorption. On the contrary, pentachlorophenol showed sorption not only in neutral form but also in ionic form significantly which should be taken into account while assessing the fate and transport of such compound. A linear model based on the degree of protonation (calculated from pH and pK_a) can be used to resolve the apparent (observed) sorption coefficient (K_{d, app}) into its neutral (K_{d, n}) and ionised (K_{d, i}) components. Knowing pK_a, K_{d, n}, and K_{d, i} the apparent sorption coefficient for pH values other than experimentally investigated can be predicted.     

Discretization of Complex Geometric Domain Through Computer-Based Stair-Step Representation Method for Estimating Water Levels Associated with A Storm

In this paper, an improvement has been made to the approximation technique of a complex domain through the stairstep approach to have a considerable accuracy, minimize computational cost, and avoid the hardship of manual work. A novel stair-step representation algorithm is used in this regard, where the entire procedure is carried out through our developed MATLAB routine. Arakawa C-grid is used in our approximation with (1/120)° grid resolution. As a test case, the method is applied to approximate the domain covering the area between 15°–23°N latitudes and 85°–95° E longitudes in the Bay of Bengal. Along with the approximation of the land-sea interface, coastal stations are also identified. Approximated land-sea interfaces and coastal stations are found to be in good agreement with the actual ones based on the similarity index, overlap fraction, and extra fraction criteria. The method can be used for approximating an irregular geometric domain to employ the finite difference method in solving problems related to long waves. As a test case, shallow water equations in Cartesian coordinates are solved on the domain of interest for simulating water levels due to the nonlinear tide-surge interaction associated with the storms April 1991 and AILA, 2009 along the coast of Bangladesh. The same input except for the discretized domain and bathymetry as that of Paul et al. (2016) is used in our simulation. The results are found to be in reasonable agreement with the observed data procured from Bangladesh Inland Water Transport Authority.

     

A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data

The main objectives of this paper are to design and evaluate a hybrid approach based on Gaussian mixture model (GMM) and random forest (RF) for detecting rockfall source areas using airborne laser scanning data. The former model was used to calculate automatically slope angle thresholds for different type of landslides such as shallow, translational, rotational, rotational-translational, complex, debris flow, and rockfalls. After calculating the slope angle thresholds, a homogenous morphometric land use area (HMLA) was constructed to improve the performance of the model computations and reduce the sensitivity of the model to the variations in different conditioning factors. After that, the support vector machine (SVM) was applied in addition to backward elimination (BE) to select and rank the conditioning factors considering the type of landslides. Then, different machine learning methods [artificial neural network (ANN), logistic regression (LR), and random forest (RF) were trained with the selected best factors and previously prepared inventory datasets. The best fit method (RF) was then used to generate the probability maps and then the source areas were detected by combining the slope raster (reclassified according to the thresholds found by the GMM model) and the probability maps. The accuracy assessment shows that the proposed hybrid model could detect the potential rockfalls with an accuracy of 0.92 based on training data and 0.96 on validation data. Overall, the proposed model is an efficient model for identifying rockfall source areas in the presence of other types of landslides with an accepted generalization performance.