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Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations—next to data from airborne or unmanned aerial vehicle (UAV)-borne campaigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster–Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success.

Sustainable transboundary water governance in Central Asia faces significant challenges, including political tensions, ecological issues, such as the drying Aral Sea, and seasonal hydropower disruptions impacting downstream countries. This study aims to address these problems by examining the complexities of water resource governance in the region, emphasizing the interplay between national interests and regional cooperation. We analyze how social, economic, environmental, and political factors influence water diplomacy among Central Asian states. Key challenges include water scarcity, climate change impacts and the growing tensions over transboundary river basins, particularly in the Aral Sea basin (i.e., the development of the Kushtepa Canal in Afghanistan). The intricate linkages between water, energy, and agriculture further complicate decision-making processes among riparian nations. While recent diplomatic efforts signal a shift towards enhanced regional cooperation, existing agreements remain fragmented, and a sustainable, long-term governance framework is still lacking. Our findings highlight the importance of an integrated, basin-wide approach to transboundary water management. We argue that a cohesive regional water strategy—grounded in international legal frameworks and supported by collaborative governance mechanisms—can mitigate conflicts and promote water security in Central Asia. The significance of this study lies in its potential to inform policy decisions and promote sustainable practices in transboundary water governance, ultimately contributing to the broader goals of sustainable development and regional cooperation.

Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

Gully erosion is a major driver of land degradation globally, particularly in semi-arid regions where it is fundamentally controlled by rainfall and runoff dynamics. Understanding how rainfall translates into runoff in gullied landscapes is crucial for predicting erosion processes and modelling runoff to inform land management strategies. In this study, rainfall-runoff analysis was conducted using high-resolution rainfall and runoff data from intensely monitored alluvial gullies in the semi-arid regions of northern Australia. Runoff responses were strongly seasonal, with flashy but low-volume flows during the early wet season (October–November) and prolonged, high-discharge events during peak rainfall months (December–March). Antecedent soil moisture had a limited influence on runoff generation, likely due to rapid wetting–drying cycles and shallow infiltration depths. Notably, rainfall-runoff behavior diverged with catchment-to-gully area ratio (Aca): linear runoff to rainfall responses were observed where gullies were eroded to the catchment limit (Aca ≈ 1) whereas high-Aca systems (Aca > 5) exhibited threshold, stepwise behavior with upslope contributions activating at ~26 mm event rainfall. Field infiltration tests showed upslope catchment infiltration capacity was ~70% higher than on gully floors (~36 vs. 21 mm h−1). This indicates greater near-surface storage and delayed upslope runoff, consistent with an activation threshold for upslope contributions. Mean rainfall–runoff ratios were higher in low-Aca gullies (≈0.52–0.68) than in high-Aca systems (≈0.40–0.46). These findings have implications for rainfall-runoff modelling, process-based understanding of gully erosion and gully management in semi-arid environments.

Existing wet tropical urban drainage systems often fail to accommodate runoff generated during extreme rainfall. Water-sensitive urban design (WSUD) systems have the potential to retrofit the existing urban drainage system by enhancing infiltration and retention functions. However, studies supporting this assumption were based on temperate or arid climatic conditions, raising questions about its relevance in wet tropical catchments. To answer these questions, in this study a comprehensive modelling study of WSUD effectiveness in a tropical environment was implemented. Engineers Park, a small sub-catchment of 0.27 km2 at Saltwater Creek, Cairns, Queensland, Australia was the study site in which the flood mitigation capabilities of grey and WSUD systems under major (1% Annual Exceedance Probability—AEP), moderate (20% AEP), and minor (63.2% AEP) magnitudes of rainfall were evaluated. A detailed one-dimensional (1D) and coupled 1D2D hydrodynamic model in MIKE+ were developed and deployed for this study. The results highlighted that the existing grey infrastructure within the catchment underperformed during major events resulting in high peak flows and overland flow, while minor rainfall events increased channel flow and shifted the location of flooding. However, the integration of WSUD with grey infrastructure reduced peak flow by 0% to 42%, total runoff volume by 0.9% to 46%, and the flood extent ratio to catchment area from 0.3% to 1.1%. Overall, the WSUD integration positively contributed to reduced flooding in this catchment, highlighting its potential applicability in tropical catchments subject to intense rainfall events. However, careful consideration is required before over-generalization of these results, since the study area is small. The results of this study can be used in similar study sites by decision-makers for planning and catchment management purposes, but with careful interpretation.

Existing drainage systems have limited capacity to mitigate future climate change-induced flooding problems effectively. However, some studies have evaluated the effectiveness of integrating Water-Sensitive Urban Design (WSUD) with existing drainage systems in mitigating flooding in tropical regions. This study examined the performance of drainage systems and integrated WSUD options under current and future climate scenarios in a sub-catchment of Saltwater Creek, a tropical catchment located in Cairns, Australia. A combination of one-dimensional (1D) and two-dimensional (1D2D) runoff generation and routing models (RORB, storm injector, and MIKE+) is used for simulating runoff and inundation. Several types of WSUDs are tested alongside different climate change scenarios to assess the impact of WSUD in flood mitigation. The results indicate that the existing grey infrastructure is insufficient to address the anticipated increase in precipitation intensity and the resulting flooding caused by climate change in the Engineers Park sub-catchment. Under future climate change scenarios, moderate rainfall events contribute to a 25% increase in peak flow (95% confidence interval = [1.5%, 0.8%]) and total runoff volume (95% confidence interval = [1.05%, 6.5%]), as per the Representative Concentration Pathway 8.5 in the 2090 scenario. Integrating WSUD with existing grey infrastructure positively contributed to reducing the flooded area by 18–54% under RCP 8.5 in 2090. However, the efficiency of these combined systems is governed by several factors such as rainfall characteristics, the climate change scenario, rain barrel and porous pavement systems, and the size and physical characteristics of the study area. In the tropics, the flooding problem is estimated to increase under future climatic conditions, and the integration of WSUD with grey infrastructure can play a positive role in reducing floods and their impacts. However, careful interpretation of results is required with an additional assessment clarifying how these systems perform in large catchments and their economic viability for extensive applications.

Compound flooding in coastal tropical cities is becoming increasingly prominent, driven by extreme rainfall events and sea level rise, under a changing climate. Quantifying the impact of these events is limited due to a lack of long-term data and funding and the need for advanced computational tools. To address this issue, this study employed a coupled one-dimensional (1D) and two-dimensional (2D) hydrodynamic model for the Saltwater Creek catchment in tropical north Queensland, Australia. In total, eight scenarios with compounding effects were assessed: four under the current climate (CC) and four under representative concentration pathway (RCP) 8.5. Under CC, the compound flooding event inundated almost 3% to 18% of the area conditions. This condition is further exacerbated under the RCP 8.5 climate change scenario, expanding the area flooded by 2% to 7% by 2090. The site experiences inundation up to 4.6 m at low-lying locations and extreme velocities up to 4 m/s at the upstream catchment with high flooding risk. The results suggest that this catchment requires an integrated approach to flood mitigation to meet the challenges posed by climate change, but careful consideration is required in interpreting the results. The results can be further improved by adopting higher-resolution and longer datasets for modelling, as well as considering land use change under the climate change scenarios.

The input selection process for data-driven rainfall-runoff models is critical because input vectors determine the structure of the model and, hence, can influence model results. Here, hydro-geomorphic and biophysical time series inputs, including Normalized Difference Vegetation Index (NDVI) and Index of Connectivity (IC; a type of hydrological connectivity index), in addition to climatic and hydrologic inputs were assessed. Selected inputs were used to develop Artificial Neural Networks (ANNs) in the Haughton River catchment and the Calliope River catchment, Queensland, Australia. Results show that incorporating IC as a hydro-geomorphic parameter and remote sensing NDVI as a biophysical parameter, together with rainfall and runoff as hydro-climatic parameters, can improve ANN model performance compared to ANN models using only hydro-climatic parameters. Comparisons amongst different input patterns showed that IC inputs can contribute to further improvement in model performance, than NDVI inputs. Overall, ANN model simulations showed that using IC along with hydro-climatic inputs noticeably improved model performance in both catchments, especially in the Calliope catchment. This improvement is indicated by a slight increase (9.77% and 11.25%) in the Nash–Sutcliffe efficiency and noticeable decrease (24.43% and 37.89%) in the root mean squared error of monthly runoff from Haughton River and Calliope River, respectively. Here, we demonstrate the significant effect of hydro-geomorphic and biophysical time series inputs for estimating monthly runoff using ANN data-driven models, which are valuable for water resources planning and management.

Structure from Motion with Multi-View Stereo photogrammetry (SfM-MVS) is increasingly used in geoscience investigations, but has not been thoroughly tested in gullied savanna systems. The aim of this study was to test the accuracy of topographic models derived from aerial (via Unmanned Aerial Vehicle, ‘UAV’) and ground-based (via handheld digital camera, ‘ground’) SfM-MVS in modelling hillslope gully systems in a dry-tropical savanna, and to assess the strengths and limitations of the approach at a hillslope scale and an individual gully scale. UAV surveys covered three separate hillslope gully systems (with areas of 0.412–0.715 km2), while ground surveys assessed individual gullies within the broader systems (with areas of 350–750 m2). SfM-MVS topographic models, including Digital Surface Models (DSM) and dense point clouds, were compared against RTK-GPS point data and a pre-existing airborne LiDAR Digital Elevation Model (DEM). Results indicate that UAV SfM-MVS can deliver topographic models with a resolution and accuracy suitable to define gully systems at a hillslope scale (e.g., approximately 0.1 m resolution with 0.4–1.2 m elevation error), while ground-based SfM-MVS is more capable of quantifying gully morphology (e.g., approximately 0.01 m resolution with 0.04–0.1 m elevation error). Despite difficulties in reconstructing vegetated surfaces, uncertainty as to optimal survey and processing designs, and high computational demands, this study has demonstrated great potential for SfM-MVS to be used as a cost-effective tool to aid in the mapping, modelling and management of hillslope gully systems at different scales, in savanna landscapes and elsewhere.

The Vakhsh River is tributary to the Amu Dayra, supporting numerous hydropower facilities as well as irrigation and community water supplies. High sediment loads are major concerns for these uses, yet little is known about the spatial distribution of the dominant sediment sources or their connectivity to fluvial systems. Here, we address this gap by combining findings from a series of field expeditions, remotely sensed climate and vegetation assessments, systematic sediment sampling, hydrograph analysis, and a review of local literature. Our preliminary findings show that various mass wasting processes (e.g., landslides, debris flows, rockfall, dry ravel, bank failures) constitute the major connected sources of sediment, particularly in the mid- to downriver reaches, many of which are unaffected by land use. Surface erosion, including the large gullies in loess deposits of the lower basin, are more affected by poor agricultural practices and road runoff, and can supply large loads of fine sediment into the river. Climate trends detected through remote sensing show an increase in rainfall in the lower half of the basin from spring to early summer while solid precipitation has increased in the eastern half in March. These trends may lead to more runoff and increases in sedimentation if they continue.