Climate change impact assessment, flood management, and mitigation strategies in Pakistan for sustainable future

Flood vulnerability assessment of Thailand's flood-prone Pathum Thani province and vulnerability mitigation strategies

This research assesses the flood vulnerability of Thailand's flood-prone province of Pathum Thani using a comprehensive vulnerability assessment framework. The assessment framework incorporates three key components: exposure, sensitivity, and adaptive capacity, consisting of 10, 12 and 11 flood vulnerability indicators, respectively. The flood vulnerability components and the flood vulnerability indicators are statistically validated by confirmatory factor analysis to determine the factor loadings and reliability of the components and indicators. The flood vulnerability questionnaire corresponding to the flood vulnerability indicators is subsequently developed and applied to the flood-prone districts of the province. The results show that proximity to rivers (with an indicator score of 0.685), household debt levels (0.612), land use patterns (0.617), and the proportion of low-income households (0.621) significantly contribute to the flood exposure of the province (with an exposure index score of 0.531). Larger household size (with an indicator score of 0.901), disruptions in public utility services (0.747), and workplace absenteeism due to flooding (0.741) contribute to the province's higher flood sensitivity (with a sensitivity index score of 0.633). Drainage capacity of natural and man-made waterways (0.571) contributes to low to moderate levels of flood adaptive capacity. The flood vulnerability of seven administrative districts of Pathum Thani, as measured by the flood vulnerability index scores (0.454-0.608), range from moderate to high. Local authorities need to invest in flood warning and response systems, prioritize infrastructure development and encourage community engagement to reduce the flood vulnerability.

Characterizing the level of urban Flood vulnerability using the social-ecological-technological systems framework, the case of Adama city, Ethiopia

This study characterizes the flood vulnerability of Adama City, Ethiopia, where the city faces high flood vulnerability due to its unplanned urbanization in low-lying floodplain areas surrounding deforested mountains and ridges. The study applied an interlinked Social-Ecological-Technological-Systems (SETS) vulnerability framework using a GIS-based Multi-Criteria Decision-Making and Analytical Hierarchy Process (MCDM-AHP). The framework analyzed exposure, sensitivity, and adaptive capacity to flooding for each of the three SETS domains. The study analyzed 18 variables at the city level within each SETS domain. The result revealed that clusters of flood-vulnerable areas were identified by each SETS domain showing the concentration of flood vulnerability in the study area and the need to consider prompt adaptive mechanisms to severe and recurring flooding. The finding has significant implications for holistic approaches to sustainable cities. Moreover, the reduction of complex urban flood vulnerabilities according to their priority as individual or combined solutions for decision-makers and professionals in early warning and flood management systems is the other contribution of the study.

Recovery and utilisation of waste heat from flue/exhaust gases: a bibliometric analysis (2010-2022)

The recovery and utilisation of waste heat from flue/exhaust gases (RU/WHFG) could potentially provide sustainable energy while curbing pollutant emissions. Over time, the RU/WHFG research landscape has gained significant traction and yielded innovative technologies, sustainable strategies, and publications. However, critical studies highlighting current advancements, publication trends, research hotspots, major stakeholders, and future research directions on RU/WHFG research remain lacking. Therefore, this paper presents a comprehensive bibliometric analysis and literature review of the RU/WHFG research landscape based on publications indexed in Scopus. Results showed that 123 publications and 2191 citations were recovered between 2010 and 2022. Publication trends revealed that the growing interest in RU/WHFG is mainly due to environmental concerns (e.g. pollution, global warming, and climate change), research collaborations, and funding availability. Stakeholder analysis revealed that numerous researchers, affiliations, and countries have actively contributed to the growth and development of RU/WHFG. Lin Fu and Tsinghua University (China) are the most prolific researchers and affiliations, whereas the National Natural Science Foundation of China (NSFC) and China are the most prolific funder and country, respectively. Funding availability from influential schemes such as NSFC has accounted for China's dominance. Keyword co-occurrence identified three major research hotspots, namely, thermal energy utilisation and management (cluster 1), integrated energy and resource recovery (cluster 2), and system analysis and optimisation (cluster 3). Literature review revealed that researchers are currently focused on maximising thermodynamic/energy efficiency, fuel minimisation, and emission reduction. Despite progress, research gaps remain in low-temperature/low-grade waste heat recovery, utilisation, storage, life cycle, and environmental impact analysis.

Do farmers' risk perception, adaptation strategies, and their determinants benefit towards climate change? Implications for agriculture sector of Punjab, Pakistan

Due to global and regional climatic dynamics for a couple of decades, agricultural productivity, rural livelihood, and food security have been badly affected in Pakistan. This study was conducted in Punjab, Pakistan, to explore the farmers' understanding of the impacts of climate change, adaptation strategies, determinants, and benefits on agriculture using data from 1080 respondents. Perceived risks by the farmers in the rice-wheat cropping system and the cotton-wheat cropping system were weed infestation, seed rate augmented, low-quality seeds, infestation of crop diseases and pests, change of cropping pattern, increase of input use, decrease of cropping intensity and productivity, decreasing soil fertility, increasing irrigation frequency, and increase of harvesting time. To alleviate the adverse influences of climate change, the adaptation strategies used by farmers were management of crop and variety, soil and irrigation water, diversification of agriculture production systems and livelihood sources, management of fertilizer and farm operations time, spatial adaptation, access to risk reduction measures and financial assets, adoption of new technologies, institutional support, and indigenous knowledge. Moreover, the results of Binary Logistic Regression indicate that adaptation strategies are affected by different factors like age, education, household family size, off-farm income, remittances, credit access, information on climatic and natural hazards, information on weather forecasting, land acreage, the experience of growing crops and rearing of livestock, tenancy status, tube well ownership, livestock inventory, access to market information, agricultural extension services, and distance from agricultural input/output market. There is a significant difference between adapters and nonadapters. The risk management system may be created to protect crops against failures caused by extreme weather events. There is a need to develop crop varieties that are both high yielding and resistant to climate change. Moreover, cropping patterns should be revised to combat the effects of climate change. To enhance farmers' standard of living, it is necessary to provide adequate extension services and a more significant number of investment facilities. These measures will assist farmers in maintaining their standard of living and food security over the long term to adapt to the effects of climate change based on various cropping zones.

Extreme flood in Pakistan: Is Pakistan paying the cost of climate change? A short communication

Global warming is one of the foremost causes of changes in climate patterns around the world. Pakistan is among the top ten countries affected by global warming. Today, Pakistan is facing severe consequences of global warming in the form of an extreme flood. It affected 33 million people, destroyed 1.5 million homes, and caused $2.3 billion in crop damage. It has also damaged more than 2000 km of roads, cutting off connectivity to provinces and major cities. Thus, inflation in Pakistan has reached its highest level, i.e. 26 % - 27 %, and a severe food crisis is not far away. Recently, Pakistan noted a record temperature of 40 °C in several territories, notably 51 °C in Jacobabad. The study reported that high temperatures, melting glaciers, heavy monsoon rains, government inattention, and poor governance are the key reasons of this severe flood. Moreover, in 2080, the average temperature in Pakistan is predicted to increase by 4.38 degrees Celsius. The study suggested that Supply of cheap seeds and fertilizers to farmers, maintenance of water supply infrastructure, availability of food and medicines through domestic and foreign assistance, and reduction of electricity rates and taxes in flood-affected areas can be the solution to stop this crisis. Similarly, building dams, investing in technology and training, and educating the general public about environmental change should be included in the long-term goals to avoid future disasters.

Evaluation of the water pollution risk of dam and dike-break floods in the inundated area

The inundated area of dam and dike-break floods includes various types of land and factories that release considerable amounts of pollutants into floods, causing serious water pollution and further endangering human health. Many pollution sources and factors affect the water pollution risk in inundated areas. Accurate assessment of the water pollution risk for dam and dike-break floods enables people to take measures in advance to reduce public health problems. The existing evaluation methods cannot effectively analyze the water pollution risk for dam and dike-break floods because partial or all pollution sources and influencing factors are ignored. The main factors affecting flood water quality were summarized into point source (PS), non-point source (NPS), flood depth, velocity, duration, and temperature. The water pollution risk caused by NPSs and PSs were quantified, as well as the impact of all main factors on water pollution risk. The evaluation model proposed for water pollution risk in inundated areas of dam and dike-break floods considers all pollution sources and influencing factors. The WPR was proposed to represent the water pollution risk value. The dam-break flood of Luhun Reservoir was simulated to verify the feasibility of the evaluation model. We concluded that (1) WPR varied with space and time in the inundated area and was seriously affected by PS in local areas; (2) the annual average WPR of different land use types from high to low were construction land, cropland, urban, water, rural area, woodland, and grassland. The evaluation model can be used to evaluate the water pollution risk for dam and dike-break floods at macro and micro scales. People can use this method to evaluate the impact, range, and degree of specific pollution sources or pollutants in the inundated area, thus allowing for measures to be taken in advance to reduce associated damages.

Advanced machine learning algorithms for flood susceptibility modeling - performance comparison: Red Sea, Egypt

Floods are among the most devastating environmental hazards that directly and indirectly affect people's lives and activities. In many countries, sustainable environmental management requires the assessment of floods and the likely flood-prone areas to avoid potential hazards. In this study, the performance and capabilities of seven machine learning algorithms (MLAs) for flood susceptibility mapping were tested, evaluated, and compared. These MLAs, including support vector machine (SVM), random forest (RF), multivariate adaptive regression spline (MARS), boosted regression tree (BRT), functional data analysis (FDA), general linear model (GLM), and multivariate discriminant analysis (MDA), were tested for the area between Safaga and Ras Gharib cities, Red Sea, Egypt. A geospatial database was developed with eleven flood-related factors, namely altitude, slope aspect, lithology, land use/land cover (LULC), slope length (LS), topographic wetness index (TWI), slope angle, profile curvature, plan curvature, stream power index (SPI), and hydrolithology units. In addition, 420 actual flooded areas were recorded from the study area to create a flood inventory map. The inventory data were randomly divided into training group with 70% and validation group with 30%. The flood-related factors were tested with a multicollinearity test, the variance inflation factor (VIF) was less than 2.135, the tolerance (TOL) was more than 0.468, and their importance was evaluated with a partial least squares (PLS) method. The results show that RF performed the best with the highest AUC (area under curve) value of 0.813, followed by GLM with 0.802, MARS with 0.801, BRT with 0.777, MDA with 0.768%, FDA with 0.763, and SVM with 0.733. The results of this study and the flood susceptibility maps could be useful for environmental mitigation, future development activities in the area, and flood control areas.

A systematic PLS-SEM approach on assessment of indigenous knowledge in adapting to floods; A way forward to sustainable agriculture

The present study was conducted in one of the major agriculture areas to check farmers indigenous knowledge about the impacts of floods on their farming lives, food security, sustainable development, and risk assessment. In the current study, primary data was used to analyze the situation. A semi-structured questionnaire was distributed among farmers. We have collected a cross-sectional dataset and applied the PLS-SEM dual-stage hybrid model to test the proposed hypotheses and rank the social, economic, and technological factors according to their normalized importance. Results revealed that farmers' knowledge associated with adaption strategies, food security, risk assessment, and livelihood assets are the most significant predictors. Farmers need to have sufficient knowledge about floods, and it can help them to adopt proper measurements. A PLS-SEM dual-stage hybrid model was used to check the relationship among all variables, which showed a significant relationship among DV, IV, and control variables. PLS-SEM direct path analysis revealed that AS (b = -0.155; p 0.001), FS (b = 0.343; p 0.001), LA (b = 0.273; p 0.001), RA (b = 0.147; p 0.006), and for FKF have statistically significant values of beta, while SD (b = -0.079NS) is not significant. These results offer support to hypotheses H1 through H4 and H5 being rejected. On the other hand, age does not have any relationship with farmers' knowledge of floods. Our study results have important policy suggestions for governments and other stakeholders to consider in order to make useful policies for the ecosystem. The study will aid in the implementation of effective monitoring and public policies to promote integrated and sustainable development, as well as how to minimize the impacts of floods on farmers' lives and save the ecosystem and food.

The effect of climate patterns on rice productivity in Pakistan: an application of Driscoll and Kraay estimator

This study assesses climate change impact on rice productivity in 28 top rice-producing districts in Pakistan. Driscoll and Kraay estimation is applied on panel data from 1981 to 2019. The cross-sectional dependency test results reveals that climatic factors such as rainfall, humidity, and temperature have correlation issue in different selected regions. The study observes that temperature, rice productivity, and rainfall have inverted U-shape relationship. Rice productivity response is quadratic instead of linear towards average rainfall and temperature during the particular cultivation time, comprising of harvesting, flowering, and planting. The coefficient of temperature during planting time is positive and significant, while the square of temperature during planting time is negative and significant. Temperature during flowering and harvesting time is significant and positive, while the square of temperature during flowering and harvesting time is negative and significant. Rain fall during planting and flowering time are positive and significant; besides that, fertilizer usage stimulates and humidity hinders rice productivity in selected districts of Pakistan. Our empirical results considered the issues of spatial dependency, serial correlation, and heterogeneity.

Spatiotemporal variability of drought/flood and its teleconnection with large-scale climate indices based on standard precipitation index: a case study of Taihu Basin, China

With the intensification of global warming, frequency of floods and droughts has been increasing. Understanding their long-term characteristics and possible relationship with large-scale meteorological factors is essential. In this study, we apply signal denoising, dimensionality reduction technique, and wavelet transform to study the spatiotemporal distribution pattern of drought/flood and its teleconnection with large-scale climate indices. Based on the precipitation data of 63 hydrological stations in the Taihu Lake Basin (TLB) for 54 years from 1965 to 2018, the standard precipitation index (SPI) was used as an indicator. The ensemble empirical mode decomposition (EEMD) and empirical orthogonal function (EOF) methods were used to explore the spatiotemporal evolution characteristics of droughts and floods. In addition, the cross-wavelet transform (XWT) method was used for teleconnection analysis. The results indicated that during 1965-2018, the SPI of the TLB showed quasiperiodic oscillations dominated by interannual oscillations (52.5%). Except for the trend of drought in spring, the basin showed a wetter trend at annual, summer, autumn, and winter scales. There were two main spatial modes (total 78.48% contribution) in the TLB, consistent across the region and reverse distributed from south to north. The dry areas were mainly in southern Zhexi and the northern Huxi sub-regions; the Hangjiahu and Yangchengdianmao sub-regions were prone to flooding. In addition, SPI was correlated with various large-scale meteorological factors, but the strength of the correlation had specific temporal and spatial heterogeneity. The research results can provide TLB reference values for water resource management and flood/drought disaster control.

Flood risk public perception in flash flood-prone areas of Punjab, Pakistan

Floods due to higher severity of destruction are considered the most destructive natural hazards in the world. Initiating the appropriate strategies of disaster risk reduction is necessary to understand risk perception. This study attempted to examine the flood risk public perception in flash flood-prone areas of Punjab, Pakistan. A simple random sampling technique was used for collecting the data of 560 household respondents, and a logistic regression model was employed to make out the factors of flood risk perception. In classification of low and high perceived risk of relevant indicators, flood risk perception index was constructed. Risk perception is significantly influenced by socioeconomic factors which have a direct impact on disaster preparedness and potential adaptive capacities. After that, potential correlation of risk perception with the demographic status of respondents was investigated in this study. Empirical estimates indicated as respondents' schooling, ownership of house, size of household, employment status and past flood experience significantly influence flood risk perception. Risk perception determinants also diverse among both communities portray spatial differences. Inadequate protection measures from public authorities and institutions, limited preparedness regarding actions of private mitigation, reduced intensity of reliance in institutions and authorities are major reasons for high risk and lower mitigation in these flash flood-prone areas. The outcomes of this research can facilitate to understand flood risk perception and its factors for conniving appropriate management plan of flood risk and communication strategies. Furthermore, this research can help consider multidimensional flood risks and its spatial vibrancy from the perspective of social science.

Regulation of Soil Microbial Community Structure and Biomass to Mitigate Soil Greenhouse Gas Emission

Sustainable reduction of fertilization with technology acquisition for improving soil quality and realizing green food production is a major strategic demand for global agricultural production. Introducing legume (LCCs) and/or non-legume cover crops (NLCCs) during the fallow period before planting main crops such as wheat and corn increases surface coverage, retains soil moisture content, and absorbs excess mineral nutrients, thus reducing pollution. In addition, the cover crops (CCs) supplement the soil nutrients upon decomposition and have a green manure effect. Compared to the traditional bare land, the introduction of CCs systems has multiple ecological benefits, such as improving soil structure, promoting nutrient cycling, improving soil fertility and microbial activity, controlling soil erosion, and inhibiting weed growth, pests, and diseases. The residual decomposition process of cultivated crops after being pressed into the soil will directly change the soil carbon (C) and nitrogen (N) cycle and greenhouse gas emissions (GHGs), and thus affect the soil microbial activities. This key ecological process determines the realization of various ecological and environmental benefits of the cultivated system. Understanding the mechanism of these ecological environmental benefits provides a scientific basis for the restoration and promotion of cultivated crops in dry farming areas of the world. These findings provide an important contribution for understanding the mutual interrelationships and the research in this area, as well as increasing the use of CCs in the soil for better soil fertility, GHGs mitigation, and improving soil microbial community structure. This literature review studies the effects of crop biomass and quality on soil GHGs emissions, microbial biomass, and community structure of the crop cultivation system, aiming to clarify crop cultivation in theory.

Flood Exposure and Social Vulnerability Analysis in Rural Areas of Developing Countries: An Empirical Study of Charsadda District, Pakistan

In recent years, social vulnerability has gained much importance in academic studies. However, social indices are rarely combined and validated with exposure and resilience components. This study provides an integrated analysis of the flood exposure and social vulnerability of rural households in a case area of Charsadda District, Khyber Pakhtunkhwa, Pakistan. A conceptual framework was designed (based on the MOVE framework) as a guideline and key indicators were identified. For the exposure component, parameters such as elevation, flooded locations, and distance from the river were endorsed to understand flood mechanisms. For populating socioeconomic variables, questionnaire-based interviews were conducted with 210 households. The results were presented through ArcGIS-generated maps. The most significant indicators interplaying with high vulnerability were exposure-related indicators. The findings showed that the southern areas, including Agra, Daulat Pura, and Hisar Yasinzai were highly vulnerable due to having the highest number of flood locations, lowest elevations, and shortest distances from rivers, as well as larger household sizes, more elderly, children and women, illiteracy rates, and weak financial capacity. Understanding such dominant indicators and areas where high social vulnerability and high exposure converge can inform the authorities in mitigating both social and physical flood vulnerability.

Flood hazards and agricultural production risks management practices in flood-prone areas of Punjab, Pakistan

Climate induced disasters, more specifically floods, have caused severe damage to the agriculture sector in Pakistan. These climatic risks have constrained the farming community to adopt risk management strategies to overcome such climate change risks. This research work attempted to examine the association of risk management tools with farmers' perception of risk, risk averse attitude, and various socioeconomic factors. The study has employed the sample data of 398 farmers from two high-risk flood-prone districts of Punjab, Pakistan. The multivariate probit model was used in this study to investigate the association of dependent and independent variables. The findings of the study indicated that small farmers consider heavy rains and floods severe risks to their agricultural production and are more risk averse than large farmers. Estimates of a multivariate probit model interpreted as age of farmer (0.036), heavy rains risk perception (0.597), and landholding size (0.114) were positively related with the risk management tool of depletion of assets. Farmers' education (0.056), off-farm income (3.47), age (0.018), and risk averse attitude of farmer (0.687) were positive, whereas experience of farming (-0.037) was negatively linked with reduction of consumption. Furthermore, experience of farming (0.005), risk averse attitude (0.493), heavy rains (0.481), and flood risk perception (0.536) were positively related with diversification adoption. The flood-prone farming community is more vulnerable to these climatic risks and rely on traditional strategies for risk management. There is a need to adopt crop diversification based on developing research capacity for innovative crop varieties having resistance to floods and climate change affects. Some significant policy measures, such as a more resilient scenario of climate change and floods, need to stimulate activities of enterprise diversification, opportunities of diversifying employment, and strengthening activities of off-farm employment for the sound livelihood of flood-prone farmers and to minimize severe affects of climatic risks.

Straw and residual film management enhances crop yield and weakens CO2 emissions in wheat-maize intercropping system

Higher CO₂ emissions and lower crop productivity are becoming thorny problems and restricted sustainable development of agriculture in arid inland areas. Intercropping has been shown to enhance crop productivity. However, Intercropping generally requires more input that led to an increase in CO₂ emissions. It is unknown whether designing tillage and film mulching in reduction could decrease soil CO₂ emissions in intercropping. Therefore, we integrated no tillage combined with residual film mulching and straw returning into wheat–maize intercropping. The maximal soil CO₂ fluxes (F_s) with intercropping was decreased by 12–21% compared to sole maize. Residual film mulching combined with straw returning (NTSMI) significantly reduced average F_s during the entire period of crop growth by 14–15%, compared with the conventional tillage (CTI). Soil CO₂ emissions (CE) with intercropping was 18–20% less than that with sole maize and the NTSMI reduced CE by 12–16% compared to the CTI. The NTSMI boosted total grain yields (GY) by 14–17%, compared with the CTI. Wheat–maize intercropping significantly enhanced soil CO₂ emission efficiency (CEE) by 33–41% in comparison to sole maize, and CEE with NTSMI was increased by 29–40% than that of CTI. A quadratic function for aboveground biomass (BA) combined with two linear functions for soil temperature (T_s) and soil water-filled pore space (WFPS) was suitable for the monitored results. A multiple regression model composed of the above three factors can explain 73–91% of the F_s variation. Crop biomass accumulation at the time of maximal F_s was less with intercropping compared with sole maize. The structural equation indicated that the BA synergistic effect on CEE through combining negative effects on CE and positive effects on GY in intercropping. In conclusion, no tillage with straw returning and residual film mulching in wheat–maize intercropping was confirmed to be an optimum management practice to reducing soil CO₂ emissions and enhancing soil CO₂ emission efficiency in arid inland agroecosystem.