Growing population, increasing urbanization, and rural to urban migration, coupled with the ongoing climate change, threaten the sustainability of cities, particularly in developing countries. Previous studies indicate numerous deficiencies in the water supply and sewage systems of Islamabad; however, a comprehensive insight into the water security assessment has not been carried out. Therefore, this study is aimed at assessing the urban water security of Islamabad by taking both human and environmental aspects into consideration. In principle, we achieve this objective by implementing the Water Security Assessment Framework, using five distinct parameters to calculate an urban water security index. The water supply dimension incorporates availability, accessibility, affordability, and the quality of drinking water in the city, whereas, sanitation and health dimension measures access to improved drainage systems as well as the state of overall hygiene of the city inhabitants. Furthermore, the water economy dimension includes water productivity and investment aspects in the study area, while the environment and ecosystem dimension looks into the current state of natural water bodies. Similarly, overall management and public support for freshwater resources are measured in the society and governance dimension. In general, we attempt to better comprehend water-security nexus in the federal capital considering it as a prerequisite to ensure a sustainable future for the city dwellers.

HIGHLIGHTS. Examining the applicability of TRMM and CFSR daily precipitation products in simulating runoff from a large watershed.Calibrating a hydrological mod

Conventional urbanization transforms natural into paved landscapes, posing a significant environmental challenge. Detecting the changes in (im)pervious surfaces in cities, where patches are small and intermingled, is particularly challenging. This study introduces a novel approach to these changes by integrating Coupled Non-negative Matrix Factorization (CNMF) image fusion with an automatic pixel purification algorithm. By fusing low-resolution hyperspectral (30m) with high-resolution panchromatic (5m) PRISMA imagery, we achieved enhanced spatial resolution, crucial for accurate land use and land cover (LULC) classification. We introduced automatic pixel purification as a key innovation method to improve LULC mapping accuracy, sensitive to training pixel selection and mixed pixels. This method, which is tested in Dublin City area, enhanced/ refined spectral signatures and clarity across major LULC classes including bare soil, industrial roofs, grasslands, trees, residential roofs/asphalts, and water bodies, significantly improving classification accuracy by removing outliers and ensuring spectral consistency. The Random Forest (RF) algorithm, applied before and after pixel purification, showed substantial increases in overall accuracy (from 94.04% to 96.69%,) and Kappa coefficient (from 92.60% to 95.91%) for 2021, with similar improvements in 2022. This method enabled accurate differential analysis of (im)pervious surfaces, revealing a 4.08% decrease in pervious (from 33.29 km2 to 28.08 km2) and a 4.09% increase in impervious surfaces (from 79.96 km2 to 82.92 km2) over one year, highlighting the rapid urbanization’s impact on Dublin’s landscape permeability. This study significantly advances LULC classification and urban monitoring, offering valuable insights for sustainable urban development and advocating for its integration into future remote sensing and urban planning initiatives.

Green roofs are essential for urban greening and climate adaptation, especially in densely populated areas. Analyzing runoff reduction parameters is crucial for effectively designing and implementing these systems. This study enhances traditional assessments using advanced sensors to gather meteorological and hydrological data from four green roof installations at University College Dublin (UCD) in Dublin, Ireland. The comprehensive dataset enabled detailed modeling of runoff hydrograph parameters using rainfall hyetographs, which were subsequently analyzed through sophisticated machine learning algorithms. This research introduces an innovative approach by identifying the optimal combination of variables for modeling key runoff characteristics, including Water Retention Amount (WRA), Total RUnoff Volume (TRUV), Peak Runoff Discharge (PRD), and Peak Flow Reduction (PFR). The findings are compelling, with Support Vector Regression (SVR) achieving R² values ranging from 0.67 to 0.82 and RMSE values ranging from 0.37 to 1.51 millimeters for WRA, TRUV, PRD, and PFR. XGBoost (XGB) demonstrated superior performance, with R² values ranging from 0.77 to 0.84 and RMSE values ranging from 0.28 to 1.26 millimeters for the same parameters. Random Forest Regression (RF) also showed robust results, with R² values ranging from 0.76 to 0.84 and RMSE values ranging from 0.31 to 1.29 millimeters. Overall, the green roof system demonstrated a water retention rate of 55.69% for the studied events. The study identifies Cumulative Rainfall Volume (CRV) and Peak Rainfall Intensity (PRI) as crucial for modeling runoff, highlighting green roofs’ potential as sustainable urban infrastructure and offering key insights for their design and optimization.

The effective management of disturbed forests requires adequate knowledge of forest dynamics. In this study, we assessed the changes in a managed forest using 18 permanent 1 ha plots located in a ‘terra firme’ tropical rainforest in the Eastern Amazon (Paragominas, Brazil). All individuals with a diameter at breast height (DBH) ≥ 20 cm were evaluated in two separate assessments conducted in 2006 and 2016. The results show that, ten years after logging, the managed forest exhibits an imbalance between recruitment (1.54% per year) and mortality (2.23% per year) rates, indicating that it is still in the process of recovering its structure. Nevertheless, biomass tended to increase after logging (28.49 tons per hectare). The characteristics of these changes suggest that the forest is undergoing a silvigenetic process driven by the effects of logging. Furthermore, our observations indicate that the forest remains active and has sufficient potential for new timber production at the end of the cutting cycle, considering the same species and tree sizes.

We compared sampling techniques in a managed native forest in Paragominas, Pará, Brazil. Our goal in this study was to evaluate the feasibility of using Double Sampling (DS) and Sampling with Partial Replacement (SPR), when compared to Continuous Forest Inventory (CFI), to estimate the wood stock for trees with DBH ≥ 20 cm in a managed forest. In our results, DS had the best volume prediction, generating a sampling error of 5.20% (11.48 m³ ha^-1) on the second occasion 3.86% (8.78 m³ ha^-1). The average volume increment, estimated for the forest in the monitored period (2014-2016) was 6.88 m³ ha^-1, with a relative sampling error of 63.09%. Therefore, as an alternative and of low cost, we suggest using DS in successive forest inventories in monitoring areas of forest resources in the Brazilian Amazon.