The effect of vegetation patterns on Aeolian mass flux at regional scale: a wind tunnel study

Although insight on the effect of vegetation pattern on Aeolian mass transport is essential for re-planting degraded land, only limited knowledge on this effect is available. The objective of this research was to understand the effect of vegetation design on the Aeolian mass flux inside a single land unit and at the borders among land units. A simulation of Atriplex halimus shrubs inside a wind tunnel was made, and sand redistribution was measured after the application of 200-230 seconds wind at a speed of 11 ms-1. The study showed that: 1) sediment maximum transport inside a single land unit is related to the neighboring land units and to the vegetation pattern within both the unit itself and the neighboring land units; 2) the effect of neighboring land units includes the protection effect and the ruling of sediment crossing from one land unit to the neighboring land units; 3) for the designing of re-planting of degraded land the ‘streets’ (zones of erosion areas similar to streets) effect need to be considered; and 4) in addition to the general knowledge needed on the effect of vegetation pattern on the erosion and deposition within an area, it is important to have insight on the redistribution of sediment at small scales upon the aim of the project

Developing a coastal analysis system: the Guyana coastal analysis system (G-CAS) as an example for small island developing states (SIDS)

Small Island Developing States (SIDS) face significant challenges due to coastal hazards, climate change impacts, and data limitations that hinder effective coastal management. The Guyana Coastal Analysis System (G-CAS) was developed as a web-based geospatial tool to address these challenges by integrating remote sensing, machine learning, and cloud computing technologies. This study presents G-CAS as a replicable framework that enhances coastal monitoring and decision-making processes in Guyana and similar SIDS. The system consists of four core analytical modules: Shoreline Analysis, Coastal Squeeze Assessment, Bathymetric Change Detection, and Flood Detection and Modelling. These modules provide near real-time, data-driven insights into shoreline erosion, wetland compression, underwater depth variations, and flood risk exposure. Results from the application of the Shoreline Analysis module indicate spatially variable shoreline retreat rates, with critically eroded sections requiring urgent intervention. The Coastal Squeeze Assessment highlights areas where infrastructure restricts landward migration, increasing vulnerability to habitat loss. Bathymetric mapping reveals dynamic sediment transport patterns, essential for understanding coastal stability and marine ecosystem health. The Flood Detection and Modelling module assists in identifying high-risk zones, particularly in low-lying coastal settlements, supporting early warning systems and disaster mitigation planning. The offering of a cost-effective, scalable, and accessible coastal monitoring tool like G-CAS provides a data-driven foundation for coastal adaptation strategies in Guyana and beyond. The findings show the importance of integrating geospatial technologies into national coastal management frameworks to support climate resilience, disaster risk reduction, and sustainable development. This study highlights the potential for similar coastal analysis systems to be adopted across SIDS, ensuring evidence-based decision-making and enhanced environmental stewardship in response to climate change

Developing a coastal analysis system: the Guyana coastal analysis system (G-CAS) as an example for small island developing states (SIDS)

Small Island Developing States (SIDS) face significant challenges due to coastal hazards, climate change impacts, and data limitations that hinder effective coastal management. The Guyana Coastal Analysis System (G-CAS) was developed as a web-based geospatial tool to address these challenges by integrating remote sensing, machine learning, and cloud computing technologies. This study presents G-CAS as a replicable framework that enhances coastal monitoring and decision-making processes in Guyana and similar SIDS. The system consists of four core analytical modules: Shoreline Analysis, Coastal Squeeze Assessment, Bathymetric Change Detection, and Flood Detection and Modelling. These modules provide near real-time, data-driven insights into shoreline erosion, wetland compression, underwater depth variations, and flood risk exposure. Results from the application of the Shoreline Analysis module indicate spatially variable shoreline retreat rates, with critically eroded sections requiring urgent intervention. The Coastal Squeeze Assessment highlights areas where infrastructure restricts landward migration, increasing vulnerability to habitat loss. Bathymetric mapping reveals dynamic sediment transport patterns, essential for understanding coastal stability and marine ecosystem health. The Flood Detection and Modelling module assists in identifying high-risk zones, particularly in low-lying coastal settlements, supporting early warning systems and disaster mitigation planning. The offering of a cost-effective, scalable, and accessible coastal monitoring tool like G-CAS provides a data-driven foundation for coastal adaptation strategies in Guyana and beyond. The findings show the importance of integrating geospatial technologies into national coastal management frameworks to support climate resilience, disaster risk reduction, and sustainable development. This study highlights the potential for similar coastal analysis systems to be adopted across SIDS, ensuring evidence-based decision-making and enhanced environmental stewardship in response to climate change

Measuring and modeling the effect of surface moisture on the spectral reflectance of coastal beach sand

Surface moisture is an important supply limiting factor for aeolian sand transport, which is the primary driver of coastal dune development. As such, it is critical to account for the control of surface moisture on available sand for dune building. Optical remote sensing has the potential to measure surface moisture at a high spatio-temporal resolution. It is based on the principle that wet sand appears darker than dry sand: it is less reflective. The goals of this study are (1) to measure and model reflectance under controlled laboratory conditions as function of wavelength () and surface moisture () over the optical domain of 350–2500 nm, and (2) to explore the implications of our laboratory findings for accurately mapping the distribution of surface moisture under natural conditions. A laboratory spectroscopy experiment was conducted to measure spectral reflectance (1 nm interval) under different surface moisture conditions using beach sand. A non-linear increase of reflectance upon drying was observed over the full range of wavelengths. Two models were developed and tested. The first model is grounded in optics and describes the proportional contribution of scattering and absorption of light by pore water in an unsaturated sand matrix. The second model is grounded in soil physics and links the hydraulic behaviour of pore water in an unsaturated sand matrix to its optical properties. The optical model performed well for volumetric moisture content 24% ( 0.97), but underestimated reflectance for between 24–30% ( 0.92), most notable around the 1940 nm water absorption peak. The soil-physical model performed very well ( 0.99) but is limited to 4% 24%. Results from a field experiment show that a short-wave infrared terrestrial laser scanner ( = 1550 nm) can accurately relate surface moisture to reflectance (standard error 2.6%), demonstrating its potential to derive spatially extensive surface moisture maps of a natural coastal beach

Strategies, Practices, and Challenges for Interagency Co-Authorship in an International Science and Development Program

No abstract availabl

A Self-Calibrating Runoff and Streamflow Remote Sensing Model for Ungauged Basins Using Open-Access Earth Observation Data

Due to increasing pressures on water resources, there is a need to monitor regional water resource availability in a spatially and temporally explicit manner. However, for many parts of the world, there is insufficient data to quantify stream flow or ground water infiltration rates. We present the results of a pixel-based water balance formulation to partition rainfall into evapotranspiration, surface water runoff and potential ground water infiltration. The method leverages remote sensing derived estimates of precipitation, evapotranspiration, soil moisture, Leaf Area Index, and a single F coefficient to distinguish between runoff and storage changes. The study produced significant correlations between the remote sensing method and field based measurements of river flow in two Vietnamese river basins. For the Ca basin, we found R2 values ranging from 0.88–0.97 and Nash–Sutcliffe efficiency (NSE) values varying between 0.44–0.88. The R2 for the Red River varied between 0.87–0.93 and NSE values between 0.61 and 0.79. Based on these findings, we conclude that the method allows for a fast and cost-effective way to map water resource availability in basins with no gauges or monitoring infrastructure, without the need for application of sophisticated hydrological models or resource-intensive data

Measuring aeolian sand transport using acoustic sensors

Acoustic sensors are frequently used to measure aeolian saltation. Different approaches are used to process the signals from these instruments. The goal of this paper is to describe and discuss a method to measure aeolian saltation with acoustic sensors. In a laboratory experiment, we measured the output from an advanced signal processing scheme on the circuit board of the saltiphone. We use a software implementation of this processing scheme to re-analyse data from four miniphones obtained during a field experiment. It is shown that a set of filters remove background noise outside the frequency spectrum of aeolian saltation (at 8. kHz), whereas signals within this frequency spectrum are amplified. The resulting analogue signal is a proxy of the energy. Using an AC pulse convertor, this signal can be converted into a digital and analogue count signal or an analogue energy signal, using a rectifier and integrator. Spatio-temporal correlation between field deployed miniphones increases by using longer integration times for signal processing. To quantify aeolian grain impact, it is suggested to use the analogue energy output, as this mode is able to detect changes in frequency and amplitude. The analogue and digital count signals are able to detect an increase in frequency, but are not able to detect an increase in signal amplitude. We propose a two-stage calibration scheme consisting of (1) a factory calibration, to set the frequency spectrum of the sensor and (2) a standardized drop-test conducted before and after the experiment to evaluate the response of the sensor

Aeolian mass flux characterization: uncertainties from a wind-tunnel perspective and implications for field studies

Aeolian sediment traps are widely used to estimate the total volume of wind-driven sediment transport, but also to study the vertical mass distribution of a saltating sand cloud. The reliability of sediment flux estimations from this data are dependent upon the specific configuration of the measurement compartments and the analysis approach used. In this study, we analyse the uncertainty of these measurements by investigating the vertical cumulative probability distribution and relative sediment flux derived from both wind-tunnel and field studies. Three existing datasets were used in combination with a newly acquired meteorological dataset, which was collected in combination with sediment fluxes from six different events, using three customized catchers at one of the beaches of Ameland in the north of The Netherlands. Fast-temporal data collected in a wind-tunnel shows that eq has a scattered pattern between impact and fluid threshold, but increases linearly with shear velocities above the fluid threshold. For finer sediment fractions, a larger portion of the sediment was transported closer to the surface compared to coarser sediment fractions. It was also shown that errors originating from the the distribution of the sampling compartments, specifically the location of the lowest sediment trap relative to the surface, can be identified using the relative sediment flux. In the field, surface conditions such as surface moisture, surface crusts or frozen surfaces have a more pronounced, but localized effect, than shear velocity. Uncertainty in aeolian mass flux estimates can be reduced by placing multiple compartments in closer proximity to the surface.</jats:p