Perceived and actual risks of drought: household and expert views from the lower Teesta River Basin of northern Bangladesh

Disaster risk perception and risk appraisal are essential in formulating an appropriate disaster risk reduction policy. This study examines the actual vs perceived drought risks by constructing risk indices at the household and expert levels using survey data from the lower Teesta River Basin in northern Bangladesh. The survey data were collected from 450 farmers using a structured questionnaire conducted between August and September 2019. A composite drought risk index was developed to understand households’ perceived and actual risks in the designated areas. The results show that the actual and perceived risk values differ significantly among the three case study sites locally known as Ganai, Ismail, and Par Sekh Sundar. The risk levels also differ significantly across the households’ gender, income, occupation, and educational attainment. People with insolvent socioeconomic status are more prone to drought risk compared to others. Results also reveal that the mean level of perceived risk agrees well with the actual risk, whereas females perceive comparatively higher risk than their male counterparts. Expert views on drought risk are similar to the individual household level perceived risk. The outcomes of this study would assist the policymakers and disaster managers to understand the concrete risk scenarios and take timely disaster risk reduction actions for ensuring a drought-resistant society

A Novel Technique for Modeling Ecosystem Health Condition: A Case Study in Saudi Arabia

The present paper proposes a novel fuzzy-VORS (vigor, organization, resilience, ecosystem services) model by integrating fuzzy logic and a VORS model to predict ecosystem health conditions in Abha city of Saudi Arabia from the past to the future. In this study, a support vector machine (SVM) classifier was utilized to classify the land use land cover (LULC) maps for 1990, 2000, and 2018. The LULCs dynamics in 1990–2000, 2000–2018, and 1990–2018 were computed using delta (Δ) change and Markovian transitional probability matrix. The future LULC map for 2028 was predicted using the artificial neural network-cellular automata model (ANN-CA). The machine learning algorithms, such as random forest (RF), classification and regression tree (CART), and probability distribution function (PDF) were utilized to perform sensitivity analysis. Pearson’s correlation technique was used to explore the correlation between the predicted models and their driving variables. The ecosystem health conditions for 1990–2028 were predicted by integrating the fuzzy inference system with the VORS model. The results of LULC maps showed that urban areas increased by 334.4% between 1990 and 2018. Except for dense vegetation, all the natural resources and generated ecosystem services have been decreased significantly due to the rapid and continuous urbanization process. A future LULC map (2028) showed that the built-up area would be 343.72 km2. The new urban area in 2028 would be 169 km2. All techniques for sensitivity analysis showed that proximity to urban areas, vegetation, and scrubland are highly sensitive to land suitability models to simulate and predict LULC maps of 2018 and 2028. Global sensitivity analysis showed that fragmentation or organization was the most sensitive parameter for ecosystem health conditions. View Full-Tex

Comparative Evaluation of Operational Land Imager sensor on board Landsat 8 and Landsat 9 for Land use Land Cover Mapping over a Heterogeneous Landscape

Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor characteristics, which have significantly improved accuracy. In this study, a comparison of the accuracy of Landsat OLI and OLI-2 satellites in land use land cover (LULC) mapping has been made. For this, image fusion techniques have been applied to enhance the spatial resolution of both OLI and OLI-2 multispectral images, and then a support vector machine (SVM) classifier has been used for LULC mapping. The results show that LULC classification from OLI-2 has better accuracy (83.4%) than OLI (92.4%). The validation of classified LULC maps shows that the OLI-2 data is more accurate in distinguishing dense and sparse vegetation as well as darker and lighter objects. The relationship between LULC maps and surface biophysical parameters using Local Moran’s I also shows better performance of the OLI-2 sensor in LULC mapping than the OLI sensor

Predicting long term regional drought pattern in Northeast India using advanced statistical technique and wavelet-machine learning approach

Understanding drought and its multifaceted challenges is crucial for safeguarding food security, promoting environmental sustainability, and fostering socio-economic well-being across the globe. As a consequence of climate change and anthropogenic factors, the occurrence and severity of drought has risen globally. In India, droughts are regular phenomenon afecting about 16% area of country each year which leads to a loss of about 0.5–1% of country’s annual GDP. Hence, the study aims to analyse and predict the meteorological drought in northeast India during 1901 to 2015 using standardised precipitation index (SPI) and analytical techniques such as Mann–Kendall test (MK), innovative trend analysis (ITA), and wavelet approach. In addition, the periodicity of the drought was estimated using Morlet wavelet technique, while discrete wavelet transform (DWT) was applied for decomposing the time series SPI-6 & SPI-12. Study shows that the northeast India experienced moderate drought conditions (SPI-6) in short term and two signifcant severe droughts (SPI-12) in long term between 1901 and 2015. The trend analysis shows a signifcant increase in SPI-6 & SPI-12 (p-value 0.01). Further, the combination of parameters i.e. approximation and levels result in the best drought prediction model with higher correlation coefcient and lower error. By using PSO-REPTtree, this study pioneers the use of decomposed parameters to detect trends and develop a drought prediction model. The study is the frst step towards establishing drought early warning system that will help decision-makers and farmers to mitigate the impact of drought at the regional level