Succession planning (SP) and employee retention (ER) are mutually reinforcing. Meaning ineffective succession planning leads to turnover, and that would, in turn, make the succession plan ineffective. Hence the big challenge is to find how SP affects ER. For this, we proposed a model explaining the mediation effect of various factors on SP-ER nexus. We hypothesize that proper Succession planning produces a positive effect on Performance Goal Orientation, Supervisor Support, Working Environment, Rewards, Work-life Policies, Career Development, and Job Security. And these factors, in turn, lead to employee retention. We further assumed that the ER would lead to Organizational Effectiveness. To establish its empirical validity, we conducted a survey using a close-ended questionnaire. Data was gathered from 300 respondents who are serving in the middle and lower level of management in the private organizations in Pakistan. Data analysis was done through the descriptive statistics, partial least square (PLS), and Structural Equation Modeling (SEM) with the help of SmartPLS3. The findings indicated that effective succession planning practices had a meaningful, favorable connection with employee retention and out of seven mediators, only three mediators i.e. job security, rewards, and supervisor support significantly mediated the association between effective succession planning practices and employee retention. Succession planning also seems to significantly affect the working environment, work-life policies, and career development. Results also exhibited that there’s an insignificant link between effective succession planning practices and organizational effectiveness and also there is no positive relationship between employee retention and organizational effectiveness.

The landslide inventory helps to develop landslide susceptibility maps which further assist to minimize economic and human losses as well as towards hazard management. This work investigates landslide hazard mapping in the rugged mountain terrain vis-a-vis highly economically significant route between China and Pakistan i.e. Karakuram Highway (KKH). KKH is passing through the Karakorum mountainous region where landslides events occur frequently and prone to serious risk to local travelers, tourists, and to trading caravans. In this work, landslide inventory was developed (302 landslides) along KKH by visual interpretation of Sentinel and google images. Field survey was also carried to validate landslide datasets. Traditional knowledge-based model i.e. Analytic Hierarchy Process (AHP) and data-based models that include Frequency Ratio (FR) and weight of evidence were applied and compared to develop landslide susceptibility maps (LSM). The landslide dataset was divided into modelling/training (70%) and testing/validation (30%) datasets. LSMs are validated by Area Under Curve (AUC) criterion. The results show that weight of evidence, AHP and FR have success rate curves of 61%, 72% and 84%, respectively. In addition, most highly accurate models are validated for their prediction power using testing landslide datasets. The results for prediction capacity for weight of evidence, AHP and FR are 72%, 58%, and 64%, respectively. Further, landslide susceptibility index (LSI) maps are classified into susceptibility zones. The validation and prediction results show that FR model is the most reliable and accurate model for our study area. Our results will be helpful to minimize landslide hazard losses along KKH, ultimately assisting in successful implementation of CPEC idea between China and Pakistan.

Mountains regions like Gilgit-Baltistan (GB) province of Pakistan are solely dependent on seasonal snow and glacier melt. In Indus basin which forms in GB, there is a need to manage water in a sustainable way for the livelihood and economic activities of the downstream population. It is important to monitor water resources that include glaciers, snow-covered area, lakes, etc., besides traditional hydrological (point-based measurements by using the gauging station) and remote sensing-based studies (traditional satellite-based observations provide terrestrial water storage (TWS) change within few centimeters from the earth’s surface); the TWS anomalies (TWSA) for the GB region are not investigated. In this study, the TWSA in GB region is considered for the period of 13 years (from January 2003 to December 2016). Gravity Recovery and Climate Experiment (GRACE) level 2 monthly data from three processing centers, namely Centre for Space Research (CSR), German Research Center for Geosciences (GFZ), and Jet Propulsion Laboratory (JPL), System Global Land Data Assimilation System (GLDAS)-driven Noah model, and in situ precipitation data from weather stations, were used for the study investigation. GRACE can help to forecast the possible trends of increasing or decreasing TWS with high accuracy as compared to the past studies, which do not use satellite gravity data. Our results indicate that TWS shows a decreasing trend estimated by GRACE (CSR, GFZ, and JPL) and GLDAS-Noah model, but the trend is not significant statistically. The annual amplitude of GLDAS-Noah is greater than GRACE signal. Mean monthly analysis of TWSA indicates that TWS reaches its maximum in April, while it reaches its minimum in October. Furthermore, Spearman’s rank correlation is determined between GRACE estimated TWS with precipitation, soil moisture (SM) and snow water equivalent (SWE). We also assess the factors, SM and SWE which are the most efficient parameters producing GRACE TWS signal in the study area. In future, our results with the support of more in situ data can be helpful for conservation of natural resources and to manage flood hazards, droughts, and water distribution for the mountain regions.

With growing urbanization in mountainous landscapes, the built-up areas dominate other land use classesresulting in increased land surface temperature (LST). Gilgit city in northern Pakistan has witnessed tremendousurban growth in the recent past decades. It is anticipated that this growth will exponentially increase in the nearfuture because of the China-Pakistan Economic Corridor (CPEC) initiatives, as this city happens to be thecommercial hub of the northern region of Pakistan. The objective of present study is to explore the influence ofland use and land cover variations on LST and to evaluate the relationship between LST with normalizeddifference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built -up index (NDBI) values. This study is carried out on data from Google earth and three Landsat images (Landsat 5-TM, Landsat 7-ETM, and Landsat OLI_TIRS-8) during the period from 1992, 2004 and 2016. Land use/coverclasses are determined through supervised classification and LST maps are created using the Mono -windowalgorithm. The accuracy assessment of land use/cover classes is carried out comparing Google Earth digitizedvector for the periods of 2004 and 2016 with Landsat classified images. Further, NDVI, NDBI, and NDWI mapsare computed from images for years 1992, 2004, and 2016. The relationships of LST with NDVI, NDBI, andNDWI are computed using Linear Regression analysis. The results reveal that the variations in land use and landcover play a substantial role in LST variability. The maximum temperatures are connected with built -up areas andbarren land, ranging from 48.4°C, 50.7°C, 51.6°C, in 1992, 2004, and 2016, respectively. Inversely, minimumtemperatures are linked to forests and water bodies, ranging from 15.1°C, 16°C, 21.6°C, in 1992, 2004, and 2016respectively. This paper also results that NDBI correlates positively with high temperatures, whereas NDVI andNDWI associate negatively with lesser temperatures. The study will support to policymakers and urban planners tostrategize the initiatives for eco-friendly and climate-resilient urban development in fragile mountainouslandscapes.

No abstract

With growing urbanization in mountainous landscapes, the built-up areas dominate other land use classesresulting in increased land surface temperature (LST). Gilgit city in northern Pakistan has witnessed tremendousurban growth in the recent past decades. It is anticipated that this growth will exponentially increase in the nearfuture because of the China-Pakistan Economic Corridor (CPEC) initiatives, as this city happens to be thecommercial hub of the northern region of Pakistan. The objective of present study is to explore the influence ofland use and land cover variations on LST and to evaluate the relationship between LST with normalizeddifference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built -up index (NDBI) values. This study is carried out on data from Google earth and three Landsat images (Landsat 5-TM, Landsat 7-ETM, and Landsat OLI_TIRS-8) during the period from 1992, 2004 and 2016. Land use/coverclasses are determined through supervised classification and LST maps are created using the Mono -windowalgorithm. The accuracy assessment of land use/cover classes is carried out comparing Google Earth digitizedvector for the periods of 2004 and 2016 with Landsat classified images. Further, NDVI, NDBI, and NDWI mapsare computed from images for years 1992, 2004, and 2016. The relationships of LST with NDVI, NDBI, andNDWI are computed using Linear Regression analysis. The results reveal that the variations in land use and landcover play a substantial role in LST variability. The maximum temperatures are connected with built -up areas andbarren land, ranging from 48.4°C, 50.7°C, 51.6°C, in 1992, 2004, and 2016, respectively. Inversely, minimumtemperatures are linked to forests and water bodies, ranging from 15.1°C, 16°C, 21.6°C, in 1992, 2004, and 2016respectively. This paper also results that NDBI correlates positively with high temperatures, whereas NDVI andNDWI associate negatively with lesser temperatures. The study will support to policymakers and urban planners tostrategize the initiatives for eco-friendly and climate-resilient urban development in fragile mountainouslandscapes.

No abstract

In the Karakoram Mountain range, glacial lakes are essential elements of the cryosphere. As a function of climate change and increasing temperature, these glacial lakes threaten downstream existence and the ecosystem by short time glacial lake outburst floods (GLOF). Therefore, the Glacial Lake mapping technique is a vital task to observe GLOF hazards. In this study, microwave Sentinel-1 Ground Range Detected (GRD) data used. It has the dual-polarization capability (HH + HV or VV + VH) and the ability to penetrate even through clouds or any weather condition. The study objective is to explore the application of GRD data and evaluate the efficiency and accuracy of machine learning algorithms for the extraction of water bodies. The study method is based on two main procedures, GRD backscattering analysis and supervised Machine Learning classifiers. The most commonly used machine learning classifiers are Random Forest (RF), K-nearest neighbor (KNN), and Maximum Likelihood. Although both procedures show better results for glacial lakes mapping in the study area, the mean backscatter parameter has the best accuracy rate than others in the total backscattering analysis. Likewise, in the classification approach, accuracy assessment was executed by comparing the results obtained for each classifier with the reference data. For all experiments, KNN performed the best at given training samples (Accuracy = 93%, Error rate = 0.06%) for both classes, compared to RF (Accuracy = 92%, Error rate = 0.07) and Maximum Likelihood (Accuracy = 90%, Error rate = 0.09%). The high classification accuracy obtained to extract glacial lakes using our approach will be useful to determine the short time flood outburst and take future precautionary measurements.