Salman Khan

Climate change is altering the hydrological cycle, with implications for riverine ecosystems that are expected to vary over space and time. This study assesses projected changes in streamflow regimes for 27 Irish rivers across three future periods (2006–2035, 2036–2065, and 2066–2095) relative to a baseline period (1976–2005). Changes in hydrological behaviour are quantified using seven flow regime metrics that represent the magnitude, variability, frequency, and duration of the flow. These metrics include the 5th percentile daily streamflow (Q5), 99th percentile daily streamflow (Q99), maximum monthly mean streamflow (MaxMonthQ), Richards–Baker Flashiness Index (RBI), interdecile range ratio (QmaxIDR), low-flow event duration (LowDur), and the number of high-flow events (HighNum), capturing changes in both low- and high-flow conditions. The metrics are derived from daily streamflow simulated using the SMART model driven by five regional climate models under both RCP4.5 and RCP8.5 scenarios.The results, based on the ensemble median, indicate a consistent decline in low flow magnitude across all catchments and periods, with larger reductions under RCP8.5. In contrast, LowDur exhibits both increases and decreases depending on catchment and period relative to the baseline. High-flow magnitude increases at all but two stations, while changes in the frequency of high-flow events (HighNum) are mixed across periods and catchments. MaxMonthQ shows an overall increasing trend. QmaxIDR increases across most rivers indicating greater flow variability. The result show a widespread moderate increase in RBI, suggesting a progressively flashier flow regimes across Irish rivers under future climate scenarios. These findings help identify rivers and future periods that are the most vulnerable to alternation of the hydrological, highlighting the increased risks of both drying and flooding and their potential effects on aquatic ecosystems under climate change.

Urbanisation has a significant impact on the hydrological cycle, primarily through the replacement of natural, permeable surfaces with impermeable, constructed materials. This rapid increase in imperviousness, coupled with ongoing changes in precipitation patterns, has led to both an increased frequency and severity of urban flooding, especially in densely populated areas with limited green spaces. Residential gardens account for a significant proportion of a city’s total urban green spaces. These gardens can offer a promising opportunity for integrating nature-based solutions (NBS) to mitigate urban flooding by enhancing infiltration and reducing surface runoff. Therefore, this study evaluates the role of domestic gardens as NBS for flood mitigation within the highly urbanised Drimnagh neighbourhood in Dublin, Ireland. In particular, we consider diverse vegetation types, permeable and impermeable yard surfaces, and roof structures to assess their effects on flood dynamics during an extreme rainfall event on June 20, 2023. The neighbourhood covers an area of 69 hectares with 260 residential buildings. Using a hydrodynamic model, we simulate inundation depths, streamflow, runoff, and infiltration under four different scenarios for the same neighbourhood during this storm. Our results show that the best-case scenario, with improved garden vegetation and green roofs, could facilitate up to 1650 m³ of water infiltration water compared to the worst-case scenario, where all surfaces are paved. This scenarios with improved permeable surfaces can also reduce flood extents (up to 30%), mean inundation depths (8.3%), and flood peaks (5.6%) in the neighbourhood. This analysis highlights the potential of NBS implementation in domestic gardens in building urban flood resilience.

Phosphate (PO₄) is often the limiting nutrient driving eutrophication and algal blooms in freshwater ecosystems. Accurate estimation of PO₄ concentrations is crucial for assessing the impacts of agriculture and urban emissions on water bodies and evaluating the effectiveness of catchment mitigation measures. However, measuring PO₄, especially at low concentrations, is technically complex, and modelling its dynamics is challenging due to the interplay between emissions, absorption, and transport processes in the natural environment. This study develops a Random Forest model to predict PO₄ concentrations in Irish water bodies, using catchment descriptors related to climate, land use, geology, topography, and anthropogenic activities. The model achieves an R² of 0.35 and an RMSE of 0.03 mg/L on an independent validation dataset, demonstrating moderate predictive accuracy. The developed model is applied to current and future climate and land use change scenarios to evaluate the influence of catchment-level mitigation measures. The findings highlights the significant roles of soil texture and permeability in influencing downstream PO₄ concentrations. Moreover, descriptors which are associated with low-intensity land use, such as peatlands and forests, were identified as having a positive effect in reducing PO₄ concentrations in water bodies. This underscoring the importance of sustainable land management practices in maintaining healthier ecosystems.

Accurate rainfall measurement, particularly at high spatiotemporal resolution, is crucial for urban flood monitoring. However, traditional methods of obtaining rainfall data are often inaccessible, costly, or inadequate for capturing localised flooding events. Low-cost weather stations (LCWS) can provide a viable solution, promoting public awareness and engagement with climate-related issues, including flooding, amidst growing urbanisation. This study shows the important role individual citizens can play in monitoring rainfall and contributing to flood mitigation measures. A total of 40 LCWS were deployed across Dublin to monitor rainfall at 5-minute intervals. The recorded rainfall data were compared with measurements from three nearby reference stations (RefS) operated by Met Éireann, as well as satellite rainfall data from the Global Satellite Mapping of Precipitation (GSMaP), focusing on extreme events and hourly scales. Various performance indicators, were used to evaluate the accuracy of LCWS relative to the RefS. Overall, LCWS demonstrated closer alignment with the RefS, achieving higher CC (0.43 vs 0.26) and Probability of Detection (POD) (0.49 vs 0.23) values, along with lower Percent Bias (14.7 vs -48.3%) and False Alarm Ratio (FAR) (0.27 vs 0.38) values, compared to the GSMaP data. Moreover, POD values (FAR values) showed a decreasing (increasing) trend with distance from the RefS, representing the spatial variability of rainfall. Additionally, citizens’ engagement was assessed through a survey with preliminary results revealing that nearly 60% of homeowners observed intense rainfall events being recorded by their stations during the study period. 78.6% of respondents reported an increased interest in climate change and urban flooding, while 57% expressed a growing interest in advocating for climate action and urban sustainability due to their participation in this project. These findings underscore the potential of LCWS in participatory monitoring and their ability to drive advocacy for climate action and urban flood mitigation.

Accurate information related to soil texture is essential for understanding key biological, chemical, and hydrological processes. However, soil data is scarce and unevenly distributed, especially in tropical regions, and global soil information products lack regional assessments, leading to high uncertainty. Here, we leveraged an unprecedented dataset of soil observations in a particularly poorly documented region, French Guiana, to identify the drivers of soil texture in such territory, develop digital soil maps of textural components, and provide an independent assessment of existing soil products at a regional scale. Specifically, we employed the random forest (RF) model to predict sand, clay and silt contents from multiple environmental variables describing geology, climate, and geomorphology. Results were evaluated through k-fold random and spatial cross-validation. We used our model to derive a soil texture map for French Guiana. We tested our map and global soil texture maps from the Harmonized World Soil Database (HWSD) and SoilGrids against 72 independent soil profiles collected in the region. Geomorphology and geology were the most important predictors of sand, clay, and silt contents in our model, yielding relatively good predictions (random cross-validation: R2 = 0.54, 0.76, and 0.10; spatial cross-validation: R2=0.26, 0.64 and 0.05; RMSE=10.92%, 6.38%, and 6.50%, for sand, clay, and silt contents, respectively). When evaluated on the independent dataset, both SoilGrids and HWSD exhibited poor performance, characterised by lower R2 ( 13%). Furthermore, HWSD and SoilGrids failed to capture the spatial heterogeneity of soil texture in the region, calling for caution when using such global products at local scale. Overall, our study emphasises the need for sustained effort in assembling distributed soil information, as well as meaningful soil predictors at local and regional scales.

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.

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.