ABSTRACT Precipitation associated with landfalling tropical cyclones (TCs) poses a significant flood risk to vast regions along and inland of the coasts. Quantifying spatial characteristics of tropical cyclone precipitation (TCP) and defining homogeneous rainfall regions can benefit forecasts and hazard mitigation of TCs. This work aims to evaluate the application of spatial metrics and satellite precipitation data in characterizing precipitation associated with landfalling TCs over the North Atlantic. This study applied an object-based Geographic Information System method to measure rainfall fields associated with North Atlantic landfalling TCs from the satellite-based rain rate estimates from 1998–2014. Eleven spatial metrics measuring the entire rainfall field and the largest rainfall polygon within the rain field were evaluated using a set of non-parametric tests to determine if they could distinguish rainfall patterns among four storm intensity categories. A multivariate clustering method with hotspot analysis was utilized to investigate spatial variations and regionalize the rainfall patterns into nine clusters using selected metrics. Five spatial metrics, namely area, solidity, dispersion, closure, and roundness, were selected since they meet three criteria: 1) having a relatively full range of 0– 1 (besides area); 2) having a significant increasing or decreasing trend with intensity, and 3) showing significant differences between any two storm categories. The clustering results indicate that TC rainfall patterns exhibit significant regional variations when storms are over land as well as over sub-basins, including the Caribbean Sea, Gulf of Mexico, and the North Atlantic. The clustering results reveal that intensity is one of the key factors in determining rainfall patterns and reflect the impact of other factors, such as wind shear, moisture content, and interaction with land.

ABSTRACT Examining the suitability of landscape patches for endangered species enhances critical insights and indicators into the processes of population structure, community dynamics, and functioning in ecosystems particularly in protected areas (PAs). While PAs are the cornerstone in biodiversity conservation, there is debate on their efficacy to retain their conservation superiority over unprotected areas under climate change. In the present study, we examined the spatial and temporal effectiveness of PAs at maintaining suitable habitat for the “vulnerable” Southern Ground-hornbill (SGH), Bucorvus leadbeateri compared with the unprotected areas in Zimbabwe. We used a landscape-scale analysis of 182 PAs, their surrounding buffer zones, and unprotected areas coupled with three machine learning models (maximum entropy: MaxEnt, random forest, and support vector machines) to simulate SGH habitat suitability. Bioclimatic, vegetation seasonality and terrain variables were used as predictors against SGH “presence-only” observations and the models were projected for 2050 as future climatic scenarios (i.e. representative concentration pathways: RCP2.6 and RCP8.5). The true skill statistic (TSS) and area under the curve (AUC) were used to evaluate the performance of the modeling framework. Our results show that the PAs network in Zimbabwe is extremely relevant for the conservation of SGH, with 8% of the suitable habitat within PAs projected to become unsuitable by 2050. Higher levels of protection status resulted in higher levels of suitable habitat for the SGH while the suitability of eastern-based PAs showed a decrease and the western-based PAs will potentially increase in suitability. Thus, conservation strategies should take the eastern PAs range contraction and associated westward shift into account. The established potential increase in suitability outside the PAs network (23%–31%) might increase conflicts between agriculture and conservation. We, therefore, suggest an expanded cross-boundary institutional alliance and policy development with all stakeholders to implement a holistic conservation plan. Our work demonstrates the importance of combining multi-source remotely sensed data in predicting habitat suitability for endangered species such as the SGH as key indicators of biological conservation and PAs’ effectiveness.