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.

Paddy-rice cultivation using the traditional continuous flooding method requires much water, up to 2500 L, to produce 1 kg of rice. Decreasing water availability is being exacerbated by climate dynamics, i.e., droughts and rainfall variability negatively affecting food security in developing regions, particularly Africa. Alternate wetting and drying (AWD) practice is a climate-smart water management strategy that, together with puddling (a critical field preparation process), significantly affects soil hydrological and physicochemical regimes, such as soil water dynamics and oxidation states in paddy fields. However, there are limited reviews on the effects and interaction of the AWD duration on hydrological conditions in the paddy-rice rhizosphere continuum under AWD practice at different rice growth stages. Our review synthesizes key scientific literature to examine water management and hydrological properties of paddy soils under AWD practice with climate change and sheds light on why farmers are skeptical in adopting the practice. To develop this paper, we reviewed scientific information from published journal articles, reliable reports, and our knowledge on paddy-rice cultivation and water management with climate change in Asia and Sub-Saharan Africa. Several studies confirm that AWD practice increases water–rice–crop productivity, yields, and reduces methane emissions. Limitations and challenges of AWD irrigation, including changes in soil structure that influence irrigation water application, variations in hydraulic conductivity caused by the duration and frequency of irrigation cycles, and frequent manual water level (WL) monitoring, are discussed. Opportunities to improve the integration of AWD strategies within government policies, irrigation schemes, and farmer acceptance due to skepticism, limited knowledge, and fear of unreliable water hindering adoption are highlighted. Future research suggestions include the following: (i) long-term measurement of water stress indices using infrared thermometers; (ii) seasonal suitability mapping using NDVI, GIS, and remote sensing; and (iii) application of smart sensors based on the Internet of Things (IoT) to address AWD challenges for precision water management in paddy fields with climate change.

Hydrological models have proliferated in the past several decades prompting debates on the virtues and shortcomings of various modelling approaches. Rather than critiquing individual models or modelling approaches, the objective here is to address the critical issues of scaling and hydrological process representation in various types of models with suggestions for improving these attributes in a parsimonious manner that captures and explains their functionality as simply as possible. This discussion focuses mostly on conceptual and physical/process-based models where understanding the internal catchment processes and hydrologic pathways is important. Such hydrological models can be improved by using data from advanced remote sensing (both spatial and temporal) and derivatives, applications of machine learning, flexible structures, and informing models through nested catchment studies in which internal catchment processes are elucidated. Incorporating concepts of hydrological connectivity into flexible model structures is a promising approach for improving flow path representation. Also important is consideration of the scale dependency of hydrological parameters to avoid scale mismatch between measured and modelled parameters. Examples are presented from remote high-elevation regions where water sources and pathways differ from temperate and tropical environments where more attention has been focused. The challenge of incorporating spatially and temporally variable water inputs, hydrologically pathways, climate, and land use into hydrological models requires modellers to collaborate with catchment hydrologists to include important processes at relevant scales—i.e. develop smarter hydrological models.

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.

Gullies contribute high sediment loads to receiving waters and significantly degrade landscapes. In drylands, low annual rainfall and resultant poor ground cover, coupled with high-intensity storms and dispersive soils, predispose these landscapes to gully erosion. Land management, such as grazing, exacerbates gully-forming processes by degrading ground cover and compacting soils, thereby increasing and concentrating overland flow. Current surface erosion models do not adequately represent sediment export from gullied terrain due to lack of distributed data and complex hydrogeomorphic processes, such as overland flow concentration, waterfall erosion, soil pipe collapse, and mass wasting. Here, we outline the strengths and weaknesses of past modelling approaches in erodible terrain and focus on how gully erosion processes can be better simulated at appropriate scales using newly available remote-sensing techniques and databases, coupled with improved understanding of relevant hydrogeomorphic processes. We also discuss and present examples of challenges related to assessing land management practices in drylands that affect gully erosion.

The interception and smoothing effect of forest canopies on pulses of incident rainfall and its delivery to the soil has been suggested as a factor in moderating peak pore water pressure in soil mantles, thus reducing the risk of shallow landslides. Here we provide 3 years of rainfall and throughfall data in a tropical secondary dipterocarp forest characterized by few large trees in northern Thailand, along with selected soil moisture dynamics, to address this issue. Throughfall was an estimated 88 % of rainfall, varying from 86 to 90 % in individual years. Data from 167 events demonstrate that canopy interception was only weakly associated (via a nonlinear relationship) with total event rainfall, but not significantly correlated with duration, mean intensity, or antecedent 2-day precipitation (API₂). Mean interception during small events (≤ 35 mm) was 17 % (n = 135 events) compared with only 7 % for large events (> 35 mm; n = 32). Examining small temporal intervals within the largest and highest intensity events that would potentially trigger landslides revealed complex patterns of interception. The tropical forest canopy had little smoothing effect on incident rainfall during the largest events. During events with high peak intensities, high wind speeds, and/or moderate-to-high pre-event wetting, measured throughfall was occasionally higher than rainfall during large event peaks, demonstrating limited buffering. However, in events with little wetting and low-to-moderate wind speed, early event rainfall peaks were buffered by the canopy. As rainfall continued during most large events, there was little difference between rainfall and throughfall depths. A comparison of both rainfall and throughfall depths to conservative mean intensity–duration thresholds for landslide initiation revealed that throughfall exceeded the threshold in 75 % of the events in which rainfall exceeded the threshold for both wet and dry conditions. Throughfall intensity for the 11 largest events (rainfall = 65–116 mm) plotted near or above the intensity–duration threshold for landslide initiation during wet conditions; 5 of the events were near or above the threshold for dry conditions. Soil moisture responses during large events were heavily and progressively buffered at depths of 1 to 2 m, indicating that the timescale of any short-term smoothing of peak rainfall inputs (i.e., ≤ 1 h) has little influence on peak pore water pressure at depths where landslides would initiate in this area. Given these findings, we conclude that canopy interception would have little effect on mitigating shallow landslide initiation during the types of monsoon rainfall conditions in this and similar tropical secondary forest sites.