Linking hydrological connectivity to gully erosion in savanna rangelands tributary to the Great Barrier Reef using structure‐from‐motion photogrammetry

Gully erosion is a major land management challenge globally and a particularly important issue in dry tropical savanna rangelands tributary to the Great Barrier Reef, Australia. This study investigated linkages between hillslope hydrological connectivity pathways and gully geomorphic change in the Burdekin River Basin. High‐resolution (0.1 m) topographic and land cover data derived from low‐cost aerial (via unmanned aircraft system) structure‐from‐motion with multiview stereo photogrammetry (SfM) were used to map fine‐scale connectivity patterns and quantify headcut retreat at the hillslope scale (~150,000 m2). Very high resolution (0.01 m) topographic models derived from ground‐based (via handheld digital camera) SfM were used to quantify the morphology and geomorphic change of several gully arms (300–700 m2) between 2016 and 2018. Median linear, areal, and volumetric headcut (n = 21) retreat rates were 0.2 m, 0.8 m2, and 0.3 m3 yr−1, respectively. At all study sites, the points where modelled hydrological flow lines intersected gullies corresponded to observed geomorphic change, enabling spatially explicit identification of gully extension pathways as a result of overland flow. Application of an index of connectivity demarcated parts of the hillslope most connected to the gully network. Bare areas, roads, and cattle trails were identified as important runoff source areas and hydrological conduits driving gully extension. Ground‐based SfM accurately reconstructed complex morphologic features including undercuts, overhangs, rills, and flutes, providing insights into within‐channel erosion processes. This study contributes to an improved understanding and modelling of hydrogeomorphic drivers of gully erosion in degraded savanna rangelands, ultimately benefiting gully management

Flood modeling and prediction using Earth Observation data

The ability to map floods from satellites has been known for over 40 years. Early images of floods were rather difficult to obtain, and flood mapping from satellites was thus rather opportunistic and limited to only a few case studies. However, over the last decade, with a proliferation of open-access EO data, there has been much progress in the development of Earth Observation products and services tailored to various end-user needs, as well as its integration with flood modeling and prediction efforts. This article provides an overview of the use of satellite remote sensing of floods and outlines recent advances in its application for flood mapping, monitoring and its integration with flood models. Strengths and limita- tions are discussed throughput, and the article concludes by looking at new developments

Measuring, modelling and managing gully erosion at large scales: A state of the art

Soil erosion is generally recognized as the dominant process of land degradation. The formation and expansion of gullies is often a highly significant process of soil erosion. However, our ability to assess and simulate gully erosion and its impacts remains very limited. This is especially so at regional to continental scales. As a result, gullying is often overlooked in policies and land and catchment management strategies. Nevertheless, significant progress has been made over the past decades. Based on a review of >590 scientific articles and policy documents, we provide a state-of-the-art on our ability to monitor, model and manage gully erosion at regional to continental scales. In this review we discuss the relevance and need of assessing gully erosion at regional to continental scales (Section 1); current methods to monitor gully erosion as well as pitfalls and opportunities to apply them at larger scales (section 2); field-based gully erosion research conducted in Europe and European Russia (section 3); model approaches to simulate gully erosion and its contribution to catchment sediment yields at large scales (section 4); data products that can be used for such simulations (section 5); and currently existing policy tools and needs to address the problem of gully erosion (section 6). Section 7 formulates a series of recommendations for further research and policy development, based on this review. While several of these sections have a strong focus on Europe, most of our findings and recommendations are of global significance.info:eu-repo/semantics/publishedVersio

Twenty-three unsolved problems in hydrology (UPH) – a community perspective

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through on-line media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focussed on process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come

Evaluation of multiple satellite altimetry data for studying inland water bodies and river floods

Satellite altimeters have been launched with the objective to monitor changes in sea level and glacial ice sheet topography. More recently, their potential to monitor inland water bodies such as lakes, rivers and wetlands has been recognised. The objective of this study was to assess the accuracy of measuring surface water elevation changes of large multi-channel inland river systems using data from multiple altimetry satellite sensors and different retracking methods. We initially validated satellite altimetry data with in situ gauge data on Lake Argyle and Lake Eildon (Australia), and then investigated data of the only presently operational altimeter, Jason-2/Ocean Surface Topography Mission (OSTM), data at six locations on large inland rivers where temporary gauges were installed during 2011-2012 floods.Our analysis on Lake Argyle showed that the application of an alternative retracking algorithm significantly improved the agreement of altimeter and gauge data. We also found that the altimeter with the smallest footprint (50-90. m) and the highest along-track resolution (40. Hz, ~170. m), ICESat, provided more accurate lake water surface elevation measurements (mean. = 0.00. m, RMSE. = 0.04. m) than other altimeters, Jason-2 (mean. = -0.04. m, RMSE. = 0.28. m), Envisat (mean. = 0.25. m, RMSE. = 0.42. m), Jason-1 (mean. = -0.04. m, RMSE. = 1.07. m), GFO (mean. = 0.5. m, RMSE. = 0.89. m) and T/P (mean. = 0.77. m, RMSE. = 1.5. m).This study also investigated altimetry satellite data accuracy at six Jason-2/OSTM ground track sites crossing the Cooper/Diamantina Rivers where water level loggers were installed to collect data during the 2011-2012 floods (N.B. ICESat ceased operations in 2009). It is shown that satellite altimetry data is able to simulate moderate to major flood events in these large multi-channel inland river systems (R=0.90-0.98). Altimetry data further revealed a variation of water height in the channels across the river system. The general usefulness of satellite altimetry in hydrological applications in remote data sparse regions is confirmed, more specifically to study large multi-channel anabranching river systems such as the Cooper/Diamantina Rivers of the Lake Eyre Basin in Australia and similar systems worldwide where conventional gauging methods are difficult to use. This study also highlights the potential operational applications for monitoring inland flood wave characteristics and hydrodynamic behaviour of remote and multi-channel floodplain systems particularly using the still-operational Jason-2 platform

Monitoring of vegetation condition using the NDVI/ENSO anomalies in Central Asia and their relationships with ONI (very strong) phases

An investigation of temporal dynamics of El Niño–Southern Oscillation (ENSO) and spatial patterns of dryness/wetness period over arid and semi-arid zones of Central Asia and their relationship with Normalized Difference Vegetation Index (NDVI) values (1982-2011) have explored in this article. For identifying periodical oscillations and their relationship with NDVI values have selected El Nino 3.4 index and thirty years of new generation bi-weekly NDVI 3g acquired by the Advanced Very High Resolution Radiometer (AVHRR) satellites time-series data. Based on identification ONI (Oceanic Nino Index) is a very strong El Nino (warm) anomalies observed during 1982-1983, 1997-1998 and very strong La Nino (cool) period events have observed 1988-1989 years. For correlation these two factors and seeking positive and negative trends it has extracted from NDVI time series data as “low productivity period” following years: 1982-1983, 1997 -1998; and as “high productivity period” following years: 1988 -1989. Linear regression observed warm events as moderate phase period selected between moderate El Nino (ME) and NDVI with following eriods:1986-1987; 1987-1988; 1991-1992; 2002-2003; 2009-2010; and moderate La Niña (ML) periods and NDVI (1998-1999; 1999-2000; 2007-2008) which has investigated a spatial patterns of wetness conditions. The results indicated that an inverse relationship between very strong El Nino and NDVI, decreased vegetation response with larger positive ONI value; and direct relationship between very strong La Niña and NDVI, increased vegetation response with smaller negative ONI value. Results assumed that significant impact of these anomalies influenced on vegetation productivity. These results will be a beneficial for efficient rangeland/grassland management and to propose drought periods for assessment and reducing quantity of flocks’ due to a lack of fodder biomass for surviving livestock flocks on upcoming years in rangelands. Also results demonstrate that a non-anthropogenic drivers of variability effected to land surface vegetation signals, nderstanding of which will be beneficial for efficient rangeland and agriculture management and establish ecosystem services in precipitation-driven drylands of Central Asia

Rainfall-Runoff Modelling Using Hydrological Connectivity Index and Artificial Neural Network Approach

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

A semi-automated object-based gully networks detection using different machine learning models : a case study of Bowen catchment, Queensland, Australia

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 models 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.(VLID)459265

Characterisation of Hydrological Response to Rainfall at Multi Spatio-Temporal Scales in Savannas of Semi-Arid Australia

Rainfall is the main driver of hydrological processes in dryland environments and characterising the rainfall variability and processes of runoff generation are critical for understanding ecosystem function of catchments. Using remote sensing and in situ data sets, we assess the spatial and temporal variability of the rainfall, rainfall–runoff response, and effects on runoff coefficients of antecedent soil moisture and ground cover at different spatial scales. This analysis was undertaken in the Upper Burdekin catchment, northeast Australia, which is a major contributor of sediment and nutrients to the Great Barrier Reef. The high temporal and spatial variability of rainfall are found to exert significant controls on runoff generation processes. Rainfall amount and intensity are the primary runoff controls, and runoff coefficients for wet antecedent conditions were higher than for dry conditions. The majority of runoff occurred via surface runoff generation mechanisms, with subsurface runoff likely contributing little runoff due to the intense nature of rainfall events. MODIS monthly ground cover data showed better results in distinguishing effects of ground cover on runoff that Landsat-derived seasonal ground cover data. We conclude that in the range of moderate to large catchments (193–36,260 km2) runoff generation processes are sensitive to both antecedent soil moisture and ground cover. A higher runoff–ground cover correlation in drier months with sparse ground cover highlighted the critical role of cover at the onset of the wet season (driest period) and how runoff generation is more sensitive to cover in drier months than in wetter months. The monthly water balance analysis indicates that runoff generation in wetter months (January and February) is partially influenced by saturation overland flow, most likely confined to saturated soils in riparian corridors, swales, and areas of shallow soil. By March and continuing through October, the soil “bucket” progressively empties by evapotranspiration, and Hortonian overland flow becomes the dominant, if not exclusive, flow generation process. The results of this study can be used to better understand the rainfall–runoff relationships in dryland environments and subsequent exposure of coral reef ecosystems in Australia and elsewhere to terrestrial runoff

Hydrogeomorphic processes affecting dryland gully erosion: implications for modelling

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