Recent evidence of regional climate change associated with the intensification of human activities has led hydrologists to study a flood regime in a non-stationarity context. This study utilized a Bayesian framework with informed priors on shape parameter for a generalized extreme value (GEV) model for the estimation of design flood quantiles for “at site analysis” in a changing environment, and discussed its implications for flood management in the Kabul River basin (KRB), Pakistan. Initially, 29 study sites in the KRB were used to evaluate the annual maximum flood regime by applying the Mann–Kendall test. Stationary (without trend) and a non-stationary (with trend) Bayesian models for flood frequency estimation were used, and their results were compared using the corresponding flood frequency curves (FFCs), along with their uncertainty bounds. The results of trend analysis revealed significant positive trends for 27.6% of the gauges, and 10% showed significant negative trends at the significance level of 0.05. In addition to these, 6.9% of the gauges also represented significant positive trends at the significance level of 0.1, while the remaining stations displayed insignificant trends. The non-stationary Bayesian model was found to be reliable for study sites possessing a statistically significant trend at the significance level of 0.05, while the stationary Bayesian model overestimated or underestimated the flood hazard for these sites. Therefore, it is vital to consider the presence of non-stationarity for sustainable flood management under a changing environment in the KRB, which has a rich history of flooding. Furthermore, this study also states a regional shape parameter value of 0.26 for the KRB, which can be further used as an informed prior on shape parameter if the study site under consideration possesses the flood type “flash”. The synchronized appearance of a significant increase and decrease of trends within very close gauge stations is worth paying attention to. The present study, which considers non-stationarity in the flood regime, will provide a reference for hydrologists, water resource managers, planners, and decision makers.
During humanitarian emergencies, well-timed information on affected populations is central in planning humanitarian responses and the optimum allocation of available resources. However, this is usually only available following an on-ground assessment which, in most of the cases, comes too late to contribute to the initial decision-making process that informs the first wave of humanitarian response. To address this problem, a spatial model was developed for the assessment of the flood-affected population in a near real-time scenario. A flood extent vector, extracted from MODerate resolution Imaging Spectroradiometer daily images, was superimposed on a LandScan population grid to estimate the population count living in the flooded area, aggregated by their respective administrative level. The methodology was found to be both timeand cost-efficient for riverine floods. The model was tested for its accuracy using an on-ground initial vulnerability assessment and the figures matched to within 80-90%. This model can be used with a confidence level of ±10% for riverine floods.
The urban land administration system (LAS) of any country serves as a key pillar for good governance, resource planning, service delivery, infrastructure development, and revenue collection. To reform their LASs, countries need a thorough understanding of their existing context and global relevance. The goal of this paper is to examine the status and challenges of urban LASs in Pakistan using the United Nations Framework for Effective Land Administration (FELA). The exploratory case study method used in the paper employs a mixed approach, which includes FELA-based questionnaire surveys, group discussions, and desk reviews. A total of 525 urban LAS stakeholders, including owner-buyers, real estate agents, bankers, lawyers, and LAS organizations, participated in the activity. The results show that more than half of the stakeholders are not satisfied with existing urban LASs, their governance and accountability, laws, and policies. Corruption is prevalent mostly in government organizations. Fraud and joint ownership are the most common sources of dispute, with 67 percent of the respondents stating that the cases take more than two years to resolve in court. The financial aspect of urban LASs is suffering due to property undervaluation and low revenue collection. Manual data and record keeping in LASs further complicate the system, with 87 percent of all respondents interested in innovating the urban LAS using modern technologies. Furthermore, 92 percent of all respondents expressed the need to standardize the existing LASs. There is a lack of capacity and skills, and 89 percent of organizations’ respondents believe that human resources skilled in Geographical Information Systems (GIS) and Remote Sensing (RS) can improve the efficiency of urban LASs. There is a lack of partnership among LAS organizations and a gap in the accessibility of LAS-related quality information. The country’s vision of building smart cities can be realized through LAS standardization and 3D and GIS innovation.
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