Local surface warming assessment in response to vegetation shifts over arid lands of Central Asia (2001-2020).

The Northern Eurasia Earth Science Partnership Initiative (NEESPI) was established to address the large-scale environmental change across this region. Regardless of the increasingly insightful literature addressing vegetation change across Central Asia, the biogeophysical warming effects of vegetation shifts still need to be clarified. To contribute, the utility of robust satellite observation is explored to evaluate the surface warming effects of vegetation shifts across Central Asia, which is among NEEPSI's hotspots. We estimated an average increase of +1.9 °C in daytime local surface temperature and + 1.5 °C in the nighttime due to vegetation shift (2001-2020). Meanwhile, the mean local latent heat increased by 4.65Wm-2, following the mild reduction of emitted longwave radiation (-0.8Wm-2). We found that vegetation shifts led to local surface warming with a bright surface, noting that the average air surface temperature was revealed to have increased significantly (2001-2020). This signal was driven mainly by agricultural expansion in western Kazakhstan stretching to Tajikistan and Xinjiang, then deforestation confined in Tajikistan, southeast Kazakhstan, and the northwestern edge of Xinjiang, and finally, grassland encroachment occurred massively in the west to central Kazakhstan. These findings address the latest information on Central Asia's vegetation shifts that may be substantial in landscape change mitigation plans.

A comparative analysis of feature selection models for spatial analysis of floods using hybrid metaheuristic and machine learning models.

The research aims to propose a feature selection model for hydraulic analysis as such a model has not been proposed previously. For this purpose, hybrids of three metaheuristic algorithms, particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) with two machine learning models which are support vector machine (SVM) and K-nearest neighbor (KNN) are employed. The dataset considered was hydraulic having an association with flood and possessed topographic, geo-environmental, and human-induced variables. The dataset considered had multicollinearity heteroscedasticity and autocorrelation problems. The metaheuristic algorithms were evaluated by varying the number of population size. Among them, PSO performed better by providing an appropriate number of features with a lower number of iterations. We have analyzed the performance of SVM with different kernels; linear, radial basis function (RBF), sigmoid, and polynomial, as the original SVM is designed only for linear datasets but the hydraulic dataset possesses non-linear characteristics as well. The performance of different kernels in terms of their accuracies is evaluated and recorded. This study showed that RBF performed the best and sigmoid showed the least accuracy for GA, PSO, and ACO algorithms. The performance of KNN is evaluated in terms of accuracies by varying the K-values. It was found that KNN shows low accuracy with a small K-value which then attained a maximum level by increasing K-values, and it finally started decreasing, explicitly, by further enhancing K-values. While comparing the performance of hybrids of GA, PSO, and ACO with SVM and KNN, it was analyzed that KNN performed better with these meta-heuristics with PSO-KNN which performed the best among the baseline models. Thus, the study proposes that PSO-KNN can be utilized as a feature selection technique to obtain optimal data subsets for hydraulic modeling and analysis.

Hydrological and ecological impacts of run off river scheme; a case study of Ghazi Barotha hydropower project on Indus River, Pakistan.

Run off river schemes are getting widespread importance as they are considered environmentally safe. However, number of studies and the consequent information regarding impacts of run off river schemes is very limited worldwide. Present study attempted to analyze impacts of Ghazi Barotha Hydropower Plant, which is a run off river scheme situated in Khyber Pakhtunkhwa province of Pakistan. This study attempted to analyze impacts of this run off river scheme on hydrological and ecological conditions of downstream areas. Data on river discharge, groundwater levels, agriculture area, vegetation and bare soil was utilized for this study. All data sets between the year 1990 till 2020 were analyzed. Hydrological impacts were analyzed through secondary data analysis, whereas ecological impacts were studied through remote sensing technique. Statistical methods were applied to further draw conclusions between hydrological and ecological interrelationships. Results showed that after functioning of Ghazi Barotha, there was 47% and 91% reduction of river discharge, in summer and winter seasons respectively. Groundwater level dropped by 50%. Agriculture area reduced by 1.69% and 9.11% during summer and winter respectively, whereas land under bare soil increased. River water diversion was considered to be responsible for groundwater reduction, as strong correlation was found between both. Agriculture land recovery, in post Ghazi Barotha period, was premised at intense groundwater mining, as groundwater level and agriculture area were significantly related (p < 0.05). Governments' groundwater development schemes, and a shift into motorized groundwater mining were major factors behind further groundwater exploitation in study area. This study came to the conclusion that Ghazi Barotha Hydropower Plant had impacted flow regime of Indus River, as well as groundwater levels and land use of downstream area along the river. These effects were triggered by inappropriate compensatory measures and uncontrolled water resource exploitation.

Impacts of climate change on wheat phenology and yield in Indus Basin, Pakistan.

Aim of this study is to quantify the impacts of climate change on phenology and yield of winter wheat in rainfed and irrigated regions of Pakistan by using integration of two well-known crop models including STICS and APSIM with CORDEX-SA regional climate models (RCMs). A number of different adaptation strategies based on early sowing (i.e. S1:10 and S2:20 days), irrigation (I1:15% and I2:30% additional water) and a combination of sowing and irrigation adaptations were examined to recover the potential losses that would occur due to climate change. The data for the wheat phenology, biomass (t/ha) at different stages and yield (t/ha) was obtained from several experiments at national research institutes in Pakistan under both rainfed and irrigated conditions. After calibration and validation of both crop models (STICS and APSIM), the current climate data were replaced with the CORDEX-SA RCM-projections for climate change impact analysis. A significant rising and declining trends were observed in temperature and precipitation patterns, respectively, for the selected study regions. Consequently, a substantial impact of climate change on wheat phenology (anthesis stage, maturity stage, growing length), biomass (t/ha) and yield (t/ha) was observed under scenario periods for RCP4.5 and RCP8.5. Additionally, the adaptation strategies on wheat for rainfed regions showed a substantial improvement in wheat biomass and yield simulated by STICS model particularly for sowing-2 under RCP4.5. Irrigated regions showed more improvement for irrigation-2 (I2) and combination of sowing-1 + irrigation-2 (S1 + I2) using the STICS model under both RCPs. Overall, it was observed that changes in crop phenology had a stronger impact in terms of crop yield for RCP8.5 as compare to RCP4.5. This study provides a valuable understanding and way forward for the better wheat management under changes in precipitation and temperature patterns. The study also discuss in detail, the adaptation strategies to cope with potential damage, over two different irrigation zones (rainfed and irrigated) in Pakistan.

Towards Supply Chain Visibility Using Internet of Things: A Dyadic Analysis Review.

The Internet of Things (IoT) and its benefits and challenges are the most emergent research topics among academics and practitioners. With supply chains (SCs) gaining rapid complexity, having high supply chain visibility (SCV) would help companies ease the processes and reduce complexity by improving inaccuracies. Extant literature has given attention to the organisation's capability to collect and evaluate information to balance between strategy and goals. The majority of studies focus on investigating IoT's impact on different areas such as sustainability, organisational structure, lean manufacturing, product development, and strategic management. However, research investigating the relationships and impact of IoT on SCV is minimal. This study closes this gap using a structured literature review to critically analyse existing literature to synthesise the use of IoT applications in SCs to gain visibility, and the SC. We found key IoT technologies that help SCs gain visibility, and seven benefits and three key challenges of these technologies. We also found the concept of Supply 4.0 that grasps the element of Industry 4.0 within the SC context. This paper contributes by combining IoT application synthesis, enablers, and challenges in SCV by highlighting key IoT technologies used in the SCs to gain visibility. Finally, the authors propose an empirical research agenda to address the identified gaps.

Landfill site selection by integrating fuzzy logic, AHP, and WLC method based on multi-criteria decision analysis.

Rapid population growth integrated with poor governance and urban planning is highly challenging resulting key for the selection of unsuitable landfill sites, particularly in developing counties. Therefore, the aim of this study is to investigate the suitable solid waste landfill sites in the capital of the country as a case study, by the integration of Geographical Information System (GIS) with fuzzy logic, analytical hierarchy process (AHP), and weighted linear combination (WLC) method based on multi-criteria decision-making (MCDM). We chose thirteen (13) criteria (9 factors and 4 constraints) and grouped them into two main categories (environmental and socioeconomic) to achieve the objectives. The AHP was employed to evaluate the relative importance of the factors followed by standardization of criteria factors based on fuzzy set theory. Subsequently, all criteria factors were combined based on AHP and fuzzy logic-WLC method in order to obtain land suitability map. Finally, the sites were identified by the intersection of two combined suitability index layers. The obtained results depicted that the integration of fuzzy logic, AHP, and WLC technique with GIS can produce satisfactory results for the suitable locations of solid waste landfill sites over complex topographic regions. Overall, the land suitability obtained based on fuzzy-WLC is more refined and smooth because of its better segregation and its potential to consider full tradeoff between factors and average risk. The AHP was identified (47 km2) as high suitable while fuzzy-WLC generated 36 km2 as suitable area. Finally, the intersection of both suitability index map shows numerous suitable landfill sites available in Islamabad city; however, the surface areas of the sites are small at individual level (less than 15 ha).

Novel mutant camelina and jatropha as valuable feedstocks for biodiesel production.

Novel mutant camelina has become a crop of interest inspired by its short growing season, low harvesting costs and high oil composition. Despite those advantages, limited research has been done on novel mutant lines to determine applicability for biodiesel production. Jatropha is an extremely hardy, frugal and high oil yielding plant species. The major aim of the present study was not only to compare biodiesel production from jatropha and camelina but was also to test the efficacy of camelina mutant lines (M6 progenies) as superior feedstock. The biodiesel yield from camelina oil and jatropha oil was 96% and 92%, respectively. The gas chromatographic analysis using flame ionization detector (GC-FID) showed that mutant camelina oil biodiesel sample contain major amount of oleic acid (46.54 wt%) followed by linolenic acid (20.41 wt%) and linoleic acid (16.55 wt%). Jatropha biodiesel found to contain major amount of oleic acid (45.03 wt%) followed by linoleic acid (25.07 wt%) and palmitic acid (19.31 wt%). The fuel properties of produced biodiesel were found in good agreement with EN14214 and ASTM D6751 standards. The mutant camelina lines biodiesel have shown comparatively better fuel properties than jatropha. It has shown low saponification value (120.87-149.35), high iodine value (130.2-157.9) and better cetane number (48.53-59.35) compared to jatropha biodiesel which have high saponification value (177.39-198.9), low iodine value (109.7-123.1) and lesser cetane number (47.76-51.26). The results of the present student of utilizing novel mutant camelina lines for biodiesel production are quite promising and are helpful in turning out the outcomes of the previous studies suggesting that C. sativa biodiesel presents serious drawbacks for biodiesel applications.

Climatic and hydrological projections to changing climate under CORDEX-South Asia experiments over the Karakoram-Hindukush-Himalayan water towers.

The complex snow and glacier (cryosphere) dynamics over the "third pole" mountainous regions of the Karakoram-Hindukush-Himalayas (HKH) makes this region challenging for accurate hydrological predictions. The objective of this study is to investigate the impacts of climate change on major hydrological components (precipitation-runoff, snow- and glacier-runoff, evapotranspiration and inter-annual change in streamflows) over the Hunza-, Gilgit- and Astore-River basins, located in HKH. For this purpose, three different hydrological models (snowmelt runoff (SRM), HEC-HMS and HBV are tested over snow- and glacier-covered river basins. These are subsequently integrated with the climate projections simulated from regional climate models (RCMs) developed under CORDEX-SA experiments. The basin-wide RCM-simulations for future scenarios exhibited an increase in precipitation but decline in intensity of rise over high-altitude zones. The temperature rise showed a maximum increase during monsoon by 4.18 °C, 4.37 °C and 4.34 °C over Hunza-, Gilgit- and Astore-River basins, respectively, for the period 2071-2099 (2090s) and a high emission scenario (RCP8.5). Further, in response to rise in precipitation and temperature, the SRM simulations showed a significant increase in snow- glacier-melt runoff (49%, 42% and 46% for SRM) and precipitation runoff (23.8%, 15.7% and 27% for HEC-HMS) in the Hunza-, Gilgit- and Astore-River basins, respectively, for the 2090s under RCP8.5. The streamflow projections for SRM showed a shift in hydrological regime with an increase by 369 (168.4%), 216.5 (74.8%) and 131.8 m3/s (82%) during pre-monsoon in the Hunza-, Gilgit- and Astore-River basins, respectively and then decline by -73.2 m3/s (-13.9%) and -45.4 m3/s (23.4%) during monsoon of the 2090s, in the Hunza- and Astore-River basins, respectively, under RCP8.5. Overall, the projections show that the pre-monsoon and monsoon seasons are expected to be strongly influenced by climate change, through alterations in snow- and glacier-accumulation, and melt regimes with substantial consequences for river runoff in the region.

Future climate and cryosphere impacts on the hydrology of a scarcely gauged catchment on the Jhelum river basin, Northern Pakistan.

Streamflow projections are fundamental sources for future water resources strategic planning and management, particularly in high-altitude scarcely-gauged basins located in high mountain Asia. Therefore, quantification of the climate change impacts on major hydrological components (evapotranspiration, soil water storage, snowmelt-runoff, rainfall-runoff and streamflow) is of high importance and remains a challenge. For this purpose, we analysed general circulation models (GCMs) using a multiple bias correction approach and two different hydrological models i.e. the Hydrological Modelling System (HEC-HMS) and the Snowmelt Runoff Model (SRM), to examine the impact of climate change on the hydrological behaviour of the Jhelum River basin. Based on scrutiny, climate projections using four best fit CMIP5 GCMs (i.e. BCC-CSM1.1, INMCM4, IPSL-CM5A-LR and CMCC-CMS) were chosen by evaluating linear scaling, local intensity scaling (LOCI) and distribution mapping (DM) approaches at twenty climate stations. Subsequently, after calibration and validation of HEC-HMS and SRM at five streamflow gauging stations, the bias corrected projected climate data was integrated with HEC-HMS and SRM to simulate projected streamflow. Results demonstrate that the DM approach fitted the projections best. The climate projections exhibited maximum intra-annual rises in precipitation by 183.2 mm (12.74%) during the monsoon for RCP4.5 and a rise in Tmin (Tmax) by 4.77 °C (4.42 °C) during pre-monsoon, for RCP8.5 during 2090s. The precipitation and temperature rise is expected to expedite and increase snowmelt-runoff up to 48% and evapotranspiration and soil water storage up to 45%. The projections exhibited significant increases in streamflows by 330 m3/s (22.6%) for HEC-HMS and 449 m3/s (30.7%) for SRM during the pre-monfaf0000soon season by the 2090s under RCP8.5. Overall, our results reveal that the pre-monsoon season is potentially utmost affected under scenario-periods, and consequently, which has the potential to alter the precipitation and flow regime of the Jhelum River basin due to significant early snow- and glacier-melt.

Comprehensive screening and selection of okra (Abelmoschus esculentus) germplasm for salinity tolerance at the seedling stage and during plant ontogeny.

The okra germplasm was screened for salinity tolerance at the seedling stage and during plant ontogeny. Substantial variation existed in okra for salinity tolerance at the seedling stage. An 80 mmol/L NaCl concentration was suitable for discriminating tolerant and non-tolerant okra genotypes. The pooled ranking of the genotypes, based on individual rankings for each trait (root and shoot length, germination percentage, and relative Na(+) and K(+)) in individual NaCl concentrations, was effective for selecting tolerant genotypes. Genotypes selected at the seedling stage maintained their tolerance to NaCl during plant ontogeny, suggesting that screening of the germplasm entries and advanced breeding materials for salt tolerance at the seedling stage is effective. Among 39 okra genotypes, five were identified as the most tolerant genotypes and showed potential for use in breeding programs that focus on the development of salt-tolerant, high-yield okra cultivars.

Extraction of DNA suitable for PCR applications from mature leaves of Mangifera indica L.

Good quality deoxyribonucleic acid (DNA) is the pre-requisite for its downstream applications. The presence of high concentrations of polysaccharides, polyphenols, proteins, and other secondary metabolites in mango leaves poses problem in getting good quality DNA fit for polymerase chain reaction (PCR) applications. The problem is exacerbated when DNA is extracted from mature mango leaves. A reliable and modified protocol based on the cetyltrimethylammonium bromide (CTAB) method for DNA extraction from mature mango leaves is described here. High concentrations of inert salt were used to remove polysaccharides; Polyvinylpyrrolidone (PVP) and ß-mercaptoethanol were employed to manage phenolic compounds. Extended chloroform-isoamyl alcohol treatment followed by RNase treatment yielded 950-1050 µg of good quality DNA, free of protein and RNA. The problems of DNA degradation, contamination, and low yield due to irreversible binding of phenolic compounds and coprecipitation of polysaccharides with DNA were avoided by this method. The DNA isolated by the modified method showed good PCR amplification using simple sequence repeat (SSR) primers. This modified protocol can also be used to extract DNA from other woody plants having similar problems.