Suitability Analysis of Groundwater for Eco-friendly Agricultural Growths in Food Basket of Pakistan

Water is an important component of earth’s atmosphere and it sustains ecosystems, agriculture and human settlements on earth (Samson et al., 2010). Salinity, sodicity and toxicity generally need to be considered for the evaluation of suitable quality of groundwater for irrigation (Khan et al., 2014; Cobbina et al., 2012; Todd and Mays, 2005). In Thal Doab Aquifer (TDA) groundwater occurs as a layer of fresh water over saline water and its availability is subjected to recharging potency of the Indus and Chenab rivers (Hussain et al., 2017 a, 2016 a)

Development of a GIS based hazard, exposure, and vulnerability analyzing method for monitoring drought risk at Karachi, Pakistan

peer reviewedDroughts have an adverse influence on agriculture, the environment, water supplies, and the global economy. The drought risk was computed using an integrated prospective approach: drought hazard, exposure, and vulnerability based on biophysical and socio-economic conditions over Karachi, Pakistan during 2000–2019. Drought hazard map (DHM) was created using annual Palmer drought severity Index (PDSI). Drought exposure map (DEM) was derived using population density and gross domestic product (GDP), as well as land surface temperature (LST), Normal difference vegetation index (NDVI), Night light images (NTL), land use land cover (LULC), and Distance to water were used for drought vulnerability map (DVM). An estimation of drought Risk (EDR) was derived by integrating layers of DHM, DEM, and DVM. Results showed that Central, South, and East regions of Karachi were at high risk, whereas the North East and North were less affected by the drought. The estimated average drought hazard (EDH) was 0.84, with minimum (maximum) value of 0.68 (1). Similarly, the average estimated drought exposure (estimated drought vulnerability) for EDE (EDV) was 0.27 (0.42), with the maximum value of 0.55 (0.84) and the minimum value of 0 (0). The drought risk assessment map (DRAM) shows that the average risk values is 0.18 while highest value is 0.36

Spatiotemporal shifts in thermal climate in responses to urban cover changes: a-case analysis of major cities in Punjab, Pakistan

peer reviewedThis study investigates the relationship of urban thermal environment (UTE) with various influential factors as well as ecological conditions. The relation between LST and land use and land cover (LULC) changes was explored in terms of remote-sensing (RS) based indices; heat effect contribution index (HECI), Urban thermal field variance index (UTFVI), Surface urban heat island intensity (SUHII), Normal Difference Built-up Index (NDBI), and Normal Difference Vegetation Index (NDVI). LULC maps were classified using the unsupervised classification technique and made error matrix to determine the accuracy. Results revealed that the vegetated area in Faisalabad decreased by 230km2 due to an expansion in the urban area of 124-320km2 during the period 1992-2014. An average LST in the rural buffers is increasing rapidly as compare to urban buffer and varied over the eight years with a range of 0.68-2.57 (°C). After 2007, SUHII's linear trend was negative because rural temperatures were still rising. Based on HECI, we found that urban expansion mainly led to increase in LST. UTFVI has shown poor ecological conditions in all urban buffers. In addition, there is a positive correlation between LST and NDBI, while NDVI indicates a negative correlation with LST

Evaluation the WRF Model with Different Land Surface Schemes: Heat Wave Event Simulations and Its Relation to Pacific Variability over Coastal Region, Karachi, Pakistan

This study investigates the relative role of land surface schemes (LSS) in the Weather Research and Forecasting (WRF) model, Version 4, to simulate the heat wave events in Karachi, Pakistan during 16–23 May 2018. The efficiency of the WRF model was evaluated in forecasting heat wave events over Karachi using the three different LSS, namely NOAH, NOAH-MP, and RUC. In addition to this we have used the longwave (RRTM) and shortwave (Dudhia) in all schemes. Three simulating setups were designed with a combination of shortwave, longwave, and LSS: E1 (Dudhia, RRTM, and Noah), E2 (Dudhia, RRTM, and Noah-MP), and E3 (Dudhia, RRTM, and RUC). All setups were carried out with a finer resolution of 1 km × 1 km. Findings of current study depicted that E2 produces a more realistic simulation of daily maximum temperature T(max) at 2 m, sensible heat (SH), and latent heat (LH) because it has higher R2 and lower errors (BIAS, RMSE, MAE) compared to other schemes. Consequently, Noah-MP (LSS) accurately estimates T(max) and land surface heat fluxes (SH&LH) because uses multiple physics options for land atmosphere interaction processes. According to statistical analyses, E2 setup outperforms other setups in term of T(max) and (LH&SH) forecasting with the higher Nash-Sutcliffe efficiency (NSE) agreement is 0.84 (0.89). This research emphasizes that the selection of LSS is of vital importance in the best simulation of T(max) and SH (LH) over Karachi. Further, it is resulted that the SH flux is taking a higher part to trigger the heat wave event intensity during May 2018 due to dense urban canopy and less vegetated area. El Niño-Southern Oscillation (ENSO) event played role to prolong and strengthen the heat wave period by effecting the Indian Ocean Dipole (IOD) through walker circulation extension

Spatiotemporal Variation in Gross Primary Productivity and Their Responses to Climate in the Great Lakes Region of Sub-Saharan Africa during 2001–2020

The impacts of climate on spatiotemporal variations of eco-physiological and bio-physical factors have been widely explored in previous research, especially in dry areas. However, the understanding of gross primary productivity (GPP) variations and its interactions with climate in humid and semi-humid areas remains unclear. Based on hyperspectral satellite remotely sensed vegetation phenology processes and related indices and the re-analysed climate datasets, we investigated the seasonal and inter-annual variability of GPP by using different light-use efficiency (LUE) models including the Carnegie-Ames-Stanford Approaches (CASA) model, vegetation photosynthesis models (VPMChl and VPMCanopy) and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products (MOD17A2H) during 2001–2020 over the Great Lakes region of Sub-Saharan Africa (GLR-SSA). The models’ validation against the in situ GPP-based upscaled observations (GPP-EC) indicated that these three models can explain 82%, 79% and 80% of GPP variations with root mean square error (RMSE) values of 5.7, 8.82 and 10.12 g C·m−2·yr−1, respectively. The spatiotemporal variations of GPP showed that the GLR-SSA experienced: (i) high GPP values during December-May; (ii) high annual GPP increase during 2002–2003, 2011–2013 and 2015–2016 and annual decreasing with a marked alternation in other years; (iii) evergreen broadleaf forests having the highest GPP values while grasslands and croplands showing lower GPP values. The spatial correlation between GPP and climate factors indicated 60% relative correlation between precipitation and GPP and 65% correction between surface air temperature and GPP. The results also showed high GPP values under wet conditions (in rainy seasons and humid areas) that significantly fell by the rise of dry conditions (in long dry season and arid areas). Therefore, these results showed that climate factors have potential impact on GPP variability in this region. However, these findings may provide a better understanding of climate implications on GPP variability in the GLR-SSA and other tropical climate zones

Spatiotemporal Variation in Gross Primary Productivity and Their Responses to Climate in the Great Lakes Region of Sub-Saharan Africa during 2001–2020

The impacts of climate on spatiotemporal variations of eco-physiological and bio-physical factors have been widely explored in previous research, especially in dry areas. However, the understanding of gross primary productivity (GPP) variations and its interactions with climate in humid and semi-humid areas remains unclear. Based on hyperspectral satellite remotely sensed vegetation phenology processes and related indices and the re-analysed climate datasets, we investigated the seasonal and inter-annual variability of GPP by using different light-use efficiency (LUE) models including the Carnegie-Ames-Stanford Approaches (CASA) model, vegetation photosynthesis models (VPMChl and VPMCanopy) and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products (MOD17A2H) during 2001–2020 over the Great Lakes region of Sub-Saharan Africa (GLR-SSA). The models’ validation against the in situ GPP-based upscaled observations (GPP-EC) indicated that these three models can explain 82%, 79% and 80% of GPP variations with root mean square error (RMSE) values of 5.7, 8.82 and 10.12 g C·m−2·yr−1, respectively. The spatiotemporal variations of GPP showed that the GLR-SSA experienced: (i) high GPP values during December-May; (ii) high annual GPP increase during 2002–2003, 2011–2013 and 2015–2016 and annual decreasing with a marked alternation in other years; (iii) evergreen broadleaf forests having the highest GPP values while grasslands and croplands showing lower GPP values. The spatial correlation between GPP and climate factors indicated 60% relative correlation between precipitation and GPP and 65% correction between surface air temperature and GPP. The results also showed high GPP values under wet conditions (in rainy seasons and humid areas) that significantly fell by the rise of dry conditions (in long dry season and arid areas). Therefore, these results showed that climate factors have potential impact on GPP variability in this region. However, these findings may provide a better understanding of climate implications on GPP variability in the GLR-SSA and other tropical climate zones

Understanding temporary reduction in atmospheric pollution and its impacts on coastal aquatic system during COVID-19 lockdown : a case study of South Asia

The strict lockdown measures not only contributed to curbing the spread of COVID-19 infection, but also improved the environmental conditions worldwide. The main goal of the current study was to investigate the co-benefits of COVID-19 lockdown on the atmosphere and aquatic ecological system under restricted anthropogenic activities in South Asia. The remote sensing data (a) NO2 emissions from the Ozone Monitoring Instrument (OMI), (b) Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and (c) chlorophyll (Chl-a) and turbidity data from MODIS-Aqua Level-3 during Jan–Oct (2020) were analyzed to assess the changes in air and water pollution compared to the last five years (2015–2019). The interactions between the air and water pollution were also investigated using overland runoff and precipitation in 2019 and 2020 at a monthly scale to investigate the anomalous events, which could affect the N loading to coastal regions. The results revealed a considerable drop in the air and water pollution (30–40% reduction in NO2 emissions, 45% in AOD, 50% decline in coastal Chl-a concentration, and 29% decline in turbidity) over South Asia. The rate of reduction in NO2 emissions was found the highest for Lahore (32%), New Delhi (31%), Ahmadabad (29%), Karachi (26%), Hyderabad (24%), and Chennai (17%) during the strict lockdown period from Apr–Jun, 2020. A positive correlation between AOD and NO2 emissions (0.23–0.50) implies that a decrease in AOD is attributed to a reduction in NO2. It was observed that during strict lockdown, the turbidity has decreased by 29%, 11%, 16%, and 17% along the coastal regions of Karachi, Mumbai, Calcutta, and Dhaka, respectively, while a 5–6% increase in turbidity was seen over the Madras during the same period. The findings stress the importance of reduced N emissions due to halted fossil fuel consumption and their relationships with the reduced air and water pollution. It is concluded that the atmospheric and hydrospheric environment can be improved by implementing smart restrictions on fossil fuel consumption with a minimum effect on socioeconomics in the region. Smart constraints on fossil fuel usage are recommended to control air and water pollution even after the social and economic activities resume business-as-usual scenario.Validerad;2021;Nivå 2;2021-02-15 (alebob)</p

Observed Changes in Crop Yield Associated with Droughts Propagation via Natural and Human-Disturbed Agro-Ecological Zones of Pakistan

Pakistan’s agriculture and food production account for 27% of its overall gross domestic product (GDP). Despite ongoing advances in technology and crop varieties, an imbalance between water availability and demand, combined with robust shifts in drought propagation has negatively affected the agro-ecosystem and environmental conditions. In this study, we examined hydro-meteorological drought propagation and its associated impacts on crop yield across natural and human-disturbed agro-ecological zones (AEZs) in Pakistan. Multisource datasets (i.e., ground observations, reanalysis, and satellites) were used to characterize the most extensive, intense drought episodes from 1981 to 2018 based on the standardized precipitation evaporation index (SPEI), standardized streamflow index (SSFI), standardized surface water storage index (SSWSI), and standardized groundwater storage index (SGWI). The most common and intense drought episodes characterized by SPEI, SSFI, SSWSI, and SGWI were observed in years 1981–1983, 2000–2003, 2005, and 2018. SPEI yielded the maximum number of drought months (90) followed by SSFI (85), SSWSI (75), and SGWI (35). Droughts were frequently longer and had a slower termination rate in the human-disturbed AEZs (e.g., North Irrigated Plain and South Irrigated Plain) compared to natural zones (e.g., Wet Mountains and Northern Dry Mountains). The historical droughts are likely caused by the anomalous large-scale patterns of geopotential height, near-surface air temperature, total precipitation, and prevailing soil moisture conditions. The negative values (<−2) of standardized drought severity index (DSI) observed during the drought episodes (1988, 2000, and 2002) indicated a decline in vegetation growth and yield of major crops such as sugarcane, maize, wheat, cotton, and rice. A large number of low-yield years (SYRI ≤ −1.5) were recorded for sugarcane and maize (10 years), followed by rice (9 years), wheat (8 years), and cotton (6 years). Maximum crop yield reductions relative to the historic mean (1981–2017) were recorded in 1983 (38% for cotton), 1985 (51% for maize), 1999 (15% for wheat), 2000 (29% for cotton), 2001 (37% for rice), 2002 (21% for rice), and 2004 (32% for maize). The percentage yield losses associated with shifts in SSFI and SSWSI were greater than those in SPEI, likely due to longer drought termination duration and a slower termination rate in the human-disturbed AEZs. The study’s findings will assist policymakers to adopt sustainable agricultural and water management practices, and make climate change adaptation plans to mitigate drought impacts in the study region

Investigating the Potential Climatic Effects of Atmospheric Pollution across China under the National Clean Air Action Plan

To reduce air pollution, China adopted rigorous control mechanisms and announced the Air Pollution Prevention and Control Action Plan (APPCAP) in 2013. Here, using OMI satellite, the NASA Socioeconomic Data and Application Center (SEDAC), and Fifth ECMWF (ERA5) data at a 0.25° × 0.25° resolution, we explored changes in NO2, PM, SO2, and O3 and climatology over China in response to the Action Plan between 2004 and 2021. This study attempts to investigate the long term trend analysis of air pollution and climatic variations during two scenarios before (2004–2013) and after (2013–2021) APPCAP. We investigated the climatic effects of air pollution in China before and after APPCAP adoption using geographically weighted regression (GWR) and differential models to assess the contribution of air pollution. The spatial representation analysis demonstrated how air pollution affected climatic factors before and after the APPCAP. Several important findings were derived: (1) the APPCAP significantly influenced air pollution reduction in China post-scenario (2013–2021); (2) the Mann Kendall test investigated that all pollutants showed an increasing trend pre-APPCAP, while they showed a decreasing trend, except for O3, post-APPCAP; (3) for climatic factors, the MK test showed an increasing trend of precipitation and mean minimum air temperature tmin post-APPCAP; (4) innovative trend analysis (ITA) showed a reduction in NO2, SO2, and PM, although O3 showed no trend post-APPCAP; and (5) pre-scenario, NO2 contributed to an increase in the mean maximum air temperature (tmax) by 0.62 °C, PM contributed to raising tmin by 0.41 °C, while O3 reduced the tmax(tmin) by 0.15 °C (0.05 °C). PM increased tmax and precipitation with a magnitude 0.38 °C (7.38 mm), and NO2 contributed to increasing tmin by (0.35 °C), respectively, post-scenario. In particular, post-scenario led to an increase in tmin and precipitation across China. The results and discussion presented in this study can be beneficial for policymakers in China to establish long-term management plans for air pollution and climatological changes

Evaluation the WRF model with different land surface schemes: Heat wave event simulations and its relation to pacific variability over Coastal region, Karachi, Pakistan

peer reviewedThis study investigates the relative role of land surface schemes (LSS) in the Weather Research and Forecasting (WRF) model, Version 4, to simulate the heat wave events in Karachi, Pakistan during 16–23 May 2018. The efficiency of the WRF model was evaluated in forecasting heat wave events over Karachi using the three different LSS, namely NOAH, NOAH-MP, and RUC. In addition to this we have used the longwave (RRTM) and shortwave (Dudhia) in all schemes. Three simulating setups were designed with a combination of shortwave, longwave, and LSS: E1 (Dudhia, RRTM, and Noah), E2 (Dudhia, RRTM, and Noah-MP), and E3 (Dudhia, RRTM, and RUC). All setups were carried out with a finer resolution of 1 km × 1 km. Findings of current study depicted that E2 produces a more realistic simulation of daily maximum temperature T(max) at 2 m, sensible heat (SH), and latent heat (LH) because it has higher R2 and lower errors (BIAS, RMSE, MAE) compared to other schemes. Consequently, Noah-MP (LSS) accurately estimates T(max) and land surface heat fluxes (SH&LH) because uses multiple physics options for land atmosphere interaction processes. According to statistical analyses, E2 setup outperforms other setups in term of T(max) and (LH&SH) forecasting with the higher Nash-Sutcliffe efficiency (NSE) agreement is 0.84 (0.89). This research emphasizes that the selection of LSS is of vital importance in the best simulation of T(max) and SH (LH) over Karachi. Further, it is resulted that the SH flux is taking a higher part to trigger the heat wave event intensity during May 2018 due to dense urban canopy and less vegetated area. El Niño-Southern Oscillation (ENSO) event played role to prolong and strengthen the heat wave period by effecting the Indian Ocean Dipole (IOD) through walker circulation extension