Spatial-temporal characterization of rainfall in Pakistan during the past half-century (1961-2020)

Aerosol-PM2.5 Dynamics: In-situ and satellite observations under the influence of regional crop residue burning in post-monsoon over Delhi-NCR, India

The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 μg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 μg/m³ (24 h) by 27-34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention.

Evolutionary analysis of rainstorm momentum and non-stationary variating patterns in response to climatic changes across diverse terrains

This study aims to analyze time-series measurements encompassing rainstorm events with over a century of datasets to identify rainstorm evolution and dimensional transitions in non-stationarity. Rainstorm events are identified using partial duration series (PDS) to extract changes in rainstorm characteristics, namely maximum intensity (MAXI), duration (D), total rainfall (TR), and average rainfall intensity (ARI), in response to climate change. Ensemble empirical mode decomposition is used for trend filtering and non-stationary identification to explore spatiotemporal insight patterns. Trend models for the first-second-order moments of rainstorm characteristics are used to formulate the identified mean-variance trends using combined multi-dimensional linear-parabolic regression. Best-fitting combinations of various distributions (probability density functions) and trend models for multiple characteristic series are identified based on the Akaike information criterion. We analyze the dimensional transition in rainfall non-stationarity based on sensitivity analysis using PDS to determine its natural geophysical causes. The integrated methodology was applied to the data retrieved from nine weather stations in Taiwan. Our findings reveal rainstorm characteristics of "short D but high rainfall intensity" or "lower MAXI but high TR" across multiple stations. The parabolic trend of the first-order moment (i.e., mean) of ARI, D, and TR appears at the endpoint of the mountain ranges. Areas receiving monsoons and those on the windward plain show a rising parabolic trend in the first- and second-order moments (i.e., mean-variance) characterizing MAXI, implying that the occurrence frequency and magnitude of extreme MAXI increases. Non-stationary transitions in MAXI appear for mountain ranges exposed to the monsoon co-movement effect on both windward and leeward sides. Stations in the plains and rift valleys show upgraded and downgraded transitions in the non-stationary dimensions for D, respectively.

Sustainable livelihood capital and climate change adaptation in Pakistan's agriculture: Structural equation modeling analysis in the VIABLE framework

This study aims to assess the role of sustainable livelihood capital, the mediation of investments and farming purposes, and the moderation of climatic and non-climatic factors in the adaptation process, particularly in the aspects of Crop, Farm, Irrigation, and Economic Management. Moreover, guided by the VIABLE (Values and Investments for Agent-Based Interaction and Learning in Environmental Systems) framework, we analyze stakeholders' actions, priorities, and goals in the climate change adaptation process. A structured questionnaire was designed based on a five-point Likert scale covering the concepts of livelihood capital, climate change adaptation, investment priorities, farming constraints, and farmers' decision-making factors. Field data were collected from 800 farmers during December 2021 to February 2022 in the irrigated agricultural regions in the Indus Plain of the Punjab and Sindh provinces, Pakistan. We employed the Partial Least Square Structural Equation Modeling approach to the VIABLE framework (VIABLE-SEM) to analyze the collected data. The results confirm livelihood capital as the most significant determinant (beta = 0.57, effect size = 0.503) for farmers' adaptation strategies in the Indus plain. Other variables, such as the principal purpose of farming, available investment options, natural and human constraints, appear less important. We identified 13 significant viability pathways that show investment priorities, farming purposes, and constraints faced by the farmers in climate change adaptation. The study also found that non-climatic factors negatively influence (beta = -0.156) the relationship between capital and adaptation, while climatic factors positively influence (beta = 0.050) this relationship. Interestingly, the presence of these influencing factors increases the adaptive capacity of farmers. These findings have important implications for policymakers and researchers in designing and implementing effective climate change adaptation strategies in the agricultural sector of Pakistan.

Indicators of Climate Change, Geospatial and Analytical Mapping of Trends in India, Pakistan and Bangladesh: An Observational Study

The year 2022 has served as a recall for the impact that climate change has in the South Asian region, which is one of the most vulnerable regions to climate shock. With a paucity of climate-based and geospatial observational studies in South Asia, this paper (i) links power sectors and carbon dioxide emissions, (ii) maps nitrogen dioxide density across three countries (Pakistan, India, and Bangladesh), (iii) understands electricity generation trends and projects weather changes through 2100. We monitored data monitored between 1995 and 2021. The following databases were used: the International Energy Agency, the World Bank, the UN Food and Agricultural Organization. Raw data was obtained for climate indicators, which were entered into Microsoft Excel. Geospatial trends were generated in the ArcGIS geostatistical tool by adopting the ordinary kriging method to interpolate and create continuous surfaces depicting the concentration of nitrogen dioxide in the three countries. We found increased usage of coal and fossil fuels in three countries (Pakistan, India, and Bangladesh). Both were significant contributors to carbon dioxide emissions. The geographic localities in South Asia were densely clouded with nitrogen dioxide as reported with the tropospheric column mapping. There are expected to be increased days with a heat index >35 °C, and consecutive dry days from 2020 and 2100. We also found increased chances of flooding in certain regions across the three countries. This study monitored climate change indicators and projects between 1995 and 2100. Lastly, we make recommendations to improve the relationship of the environment and living beings.

Response of enhanced vegetation index changes to latent/sensible heat flux and precipitation over Pakistan using remote sensing

For a sustainable development and ecological integrity, it is of worth importance to monitor land use/ land cover (LULC) changes and related land-atmosphere fluxes. To serve this purpose, we have used moderate resolution imaging spectroradiometer (MODIS) retrieved-enhanced vegetation index (EVI), MERRA-2 re-analysis surface heat fluxes (latent heat flux, sensible heat flux and specific humidity), TRMM rainfall data, and OMI retrieved aerosol index (AI) over Pakistan during 2000 to 2021. High EVI (0.66) is observed in May 2021 as compared to May 2000 over Muzaffarabad, Srinagar, north and northwest of Khyber Pakhtunkhwa, east of Punjab and along the Indus River in Sindh. The highest increase in vegetative area is observed in Baluchistan (~ 366%), followed by Manavadar (~ 60%), Khyber Pakhtunkhwa (~ 41%), Sindh (~ 37%), and Punjab (~ 20%) whereas Gilgit-Baltistan and Jammu and Kashmir show reduction in vegetative area by 21% and 11% respectively. The coefficient of determination (R²) is found to be highest between rainfall and latent heat flux (R² = 0.59) followed by rainfall and specific humidity (R² = 0.35), and rainfall and sensible heat flux (R² = 0.06). The latent heat flux shows increasing trend at the rate of 0.003 Wm^-2 winter^-1, 0.0065 Wm^-2 pre-monsoon^-1 and 0.0272 Wm^-2 post-monsoon^-1 during 1980-2021 whereas sensible heat flux shows decreasing trend at the rate of 0.00056 Wm^-2 winter^-1, 0.00249 Wm^-2 pre-monsoon^-1 and 0.0037 Wm^-2 post-monsoon^-1 during 1980-2021. Specific humidity depicts increasing trend at the rate of 0.0002 Wm^-2 winter^-1, 0.0038 Wm^-2 pre-monsoon^-1 and decreasing trend at the rate of 0.0080 Wm^-2 post-monsoon^-1 during 1980-2021. The interannual variations in AI show highest AI of 2.28 in 2021 with maximum positive percentage anomaly of 28.06% during 2007.

A Two-Step Approach to Blending GSMaP Satellite Rainfall Estimates with Gauge Observations over Australia

An approach to developing a blended satellite-rainfall dataset over Australia that could be suitable for operational use is presented. In this study, Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates were blended with station-based rain gauge data over Australia, using operational station data that has not been harnessed by other blended products. A two-step method was utilized. First, GSMaP satellite precipitation estimates were adjusted using rain gauge data through multiplicative ratios that were gridded using ordinary kriging. This step resulted in reducing dry biases, especially over topography. The adjusted GSMaP data was then blended with the Australian Gridded Climate Dataset (AGCD) rainfall analysis, an operational station-based gridded rain gauge dataset, using an inverse error variance weighting method to further remove biases. A validation that was performed using a 20-year range (2001 to 2020) showed the proposed approach was successful; the resulting blended dataset displayed superior performance compared to other non-gauge-based datasets with respect to stations as well as displaying more realistic patterns of rainfall than the AGCD in areas with no rain gauges. The average mean absolute error (MAE) against station data was reduced from 0.89 to 0.31. The greatest bias reductions were obtained for extreme precipitation totals and over mountainous regions, provided sufficient rain gauge availability. The newly produced dataset supported the identification of a general positive bias in the AGCD over the north-west interior of Australia.

Analysis of PM2.5 and Meteorological Variables Using Enhanced Geospatial Techniques in Developing Countries: A Case Study of Cartagena de Indias City (Colombia)

The dispersion of air pollutants and the spatial representation of meteorological variables are subject to complex atmospheric local parameters. To reduce the impact of particulate matter (PM2.5) on human health, it is of great significance to know its concentration at high spatial resolution. In order to monitor its effects on an exposed population, geostatistical analysis offers great potential to obtain high-quality spatial representation mapping of PM2.5 and meteorological variables. The purpose of this study was to define the optimal spatial representation of PM2.5, relative humidity, temperature and wind speed in the urban district in Cartagena, Colombia. The lack of data due to the scarcity of stations called for an ad hoc methodology, which included the interpolation implementing an ordinary kriging (OK) model, which was fed by data obtained through the inverse distance weighting (IDW) model. To consider wind effects, empirical Bayesian kriging regression prediction (EBK) was implemented. The application of these interpolation methods clarified the areas across the city that exceed the recommended limits of PM2.5 concentrations (Zona Franca, Base Naval and Centro district), and described in a continuous way, on the surface, three main weather variables. Positive correlations were obtained for relative humidity (R2 of 0.47), wind speed (R2 of 0.59) and temperature (R2 of 0.64).

A Comparison of Various Correction and Blending Techniques for Creating an Improved Satellite-Gauge Rainfall Dataset over Australia

Satellites offer a way of estimating rainfall away from rain gauges which can be utilised to overcome the limitations imposed by gauge density on traditional rain gauge analyses. In this study, Australian station data along with the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) and the Bureau of Meteorology’s (BOM) Australian Gridded Climate Dataset (AGCD) rainfall analysis are combined to develop an improved satellite-gauge rainfall analysis over Australia that uses the strengths of the respective data sources. We investigated a variety of correction and blending methods with the aim of identifying the optimal blended dataset. The correction methods investigated were linear corrections to totals and anomalies, in addition to quantile-to-quantile matching. The blending methods tested used weights based on the error variance to MSWEP (Multi-Source Weighted Ensemble Product), distance to the closest gauge, and the error from a triple collocation analysis to ERA5 and Soil Moisture to Rain. A trade-off between away-from- and at-station performances was found, meaning there was a complementary nature between specific correction and blending methods. The most high-performance dataset was one corrected linearly to totals and subsequently blended to AGCD using an inverse error variance technique. This dataset demonstrated improved accuracy over its previous version, largely rectifying erroneous patches of excessive rainfall. Its modular use of individual datasets leads to potential applicability in other regions of the world.