The thickness of Australia's regolith - the weathered mantle overlying bedrock - varies markedly, from near-zero on residual uplands to over a kilometre in palaeovalleys. Although knowledge of regolith thickness is critical for land and water management, it remains poorly constrained at decision-making scales. Predicting regolith depth is challenging due to spatial heterogeneity, legacy surface-process imprints, subsurface weathering histories and limited high-resolution subsurface data. However, machine learning approaches applied to increasingly available terrain, lithology, geochemistry, vegetation and climate datasets offer new opportunities to estimate regolith depth. This study used Gradient Boosted Machine (GBM) to model regolith thickness across similar to 28,800 km(2) of the Southern Zone of Rejuvenated Drainage (SZRD) in Western Australia. A compilation of 1568 regolith thickness observations from drillholes and passive seismic surveys was used to train and test the model, generating a spatially explicit prediction. The model incorporated 37 environmental covariates spanning geological, climatic and terrain-related domains. Predicted regolith thickness varied from 0.00 to 27.53 m, with the deepest profiles concentrated in broad palaeovalleys in the east. Regolith thickness was most strongly influenced by radiometric potassium, lithological age, rainfall, slope height and Height Above Nearest Drainage. Model accuracy was weak (R-2 = 0.27; RMSE = 11.76 m), reflecting the region's complex geomorphic and weathering history. Nonetheless, the map delineates major zones of regolith deposition and stripping, identifies key environmental drivers and provides a first-order framework for groundwater recharge modelling, mineral exploration and land suitability assessment. Future work should aim to obtain more spatially balanced regolith depth samples to address persistent challenges posed by biased and correlated ground data.

The Earth’s Critical Zone (CZ) encompasses the near-surface layer where rock, soil, water, air and living organisms interact to regulate the delivery of ecosystem services. Central to CZ functioning is its subsurface architecture, defined as the spatial distribution and connectivity of soil, sediment and weathered bedrock. This review examines the principal factors governing soil and regolith development, including climate, organisms, relief, parent material, time and anthropogenic disturbance, and their role in generating subsurface heterogeneity across landscapes. We then briefly explore the implications of subsurface architecture for rooting depth, water partitioning and landscape response to natural hazards. A brief overview of the principal tools used to characterise CZ subsurface architecture is also presented, including traditional approaches and more contemporary geophysical and remote sensing techniques that enable advanced understanding of subsurface structure and process interactions. Finally, we consider the opportunities and challenges associated with extending CZ science and subsurface architectural studies into lateritic landscapes. These deeply weathered systems remain comparatively underrepresented despite their hydrological and geomorphological importance and present distinct challenges related to their substantial depths and long-term chemical weathering. Greater integration of CZ frameworks into lateritic environments offers an important direction for future research aimed at improving landscape process understanding.

Urban greening is increasingly recognized as a key strategy for climate adaptation in cities, particularly in regions with variable climates. This paper explores how Australian cities are pioneering strategies that integrate urban greening with water-sensitive urban design (WSUD), offering valuable lessons for other areas facing similar challenges. Drawing on examples from diverse climate zones, from Darwin’s tropics to Melbourne’s temperate and Perth’s Mediterranean conditions, we analyse how local climate and water availability shape greening strategies and WSUD implementation. The study evaluates technologies such as bioretention basins, tree pits, green roofs, and vertical greening, highlighting successes, limitations, and locally adapted solutions. Key challenges include mismatches between water supply and demand, high maintenance requirements, and the need for community engagement. By reviewing modelling tools and metrics used in Australia to assess the benefits of urban greening, we assess their applicability and limitations. The paper offers valuable lessons from the Australian experience for other regions facing similar climate and water challenges, emphasising that integrating water management is essential for the long-term success of urban greening in building climate-resilient cities.

Soil moisture observations have been collected since the late 1950s and are relatively abundant in the northern hemisphere. These readings are generally taken at shallow depths with sensors rarely installed more than 2 metres below the surface. However, deep soil moisture dynamics can play a crucial role in determining ecosystem services, land-atmosphere water fluxes, plant water use, nutrient cycle and, eventually, groundwater recharge. In thick unsaturated zones, shallow soil moisture observations are likely to fail to capture important hydrological processes, and their feedback with the atmosphere, generating significant uncertainties. Here we present the results from a soil moisture monitoring network established as part of the Recharge in a Changing Climate (RiCC) project. The network aims to capture soil moisture dynamics in deep sandy profiles of a Mediterranean-like zone in Western Australia, where traditional shallow and surface soil moisture observations fall short of detecting significant hydrological processes. The monitoring network, deployed since 2022, comprises over 75 sensors strategically distributed across 7 locations over the Swan Coastal Plain at depths of up to 9 m to provide continuous high-frequency soil moisture data. These soil moisture sensors are complemented by 14 access tubes where neutron moisture probe readings are taken to characterize the spatial heterogeneity.Findings reveal complex patterns of moisture movement through the profile, with significant temporal variations in wetting front depths and propagation patterns, improving the representation of soil water/vegetation interaction, and providing unique insights into groundwater recharge processes in sandy aquifer systems. These observations challenge existing assumptions about soil water movement in sandy soils and provide crucial validation data for improving ecohydrological models and recharge quantification. Information from the RiCC monitoring campaign can significantly reduce uncertainties in water resources management and, by including transpiration from deeper soil moisture pools, enhance the accuracy of modelled land-atmosphere feedback. These insights are also beneficial for understanding the resilience of ecosystems and agroecosystems under transient climate conditions.

Ecosystem evapotranspiration (ET) varies through space and time in response to environmental gradients and disturbances like fire. Field-based techniques (e.g. eddy covariance) can directly measure how ET responds to fire; however, these measurements are localised and only represent small areas. Remotely sensed ET products can potentially evaluate fire impacts on ecosystem ET, but their performance for this task remains largely unassessed. This paper uses three widely available remote sensing products: the CSIRO MODIS ReScaled ET (CMRSET), MODIS ET (MOD16), and Penman-Monteith-Leuning (PML), to assess ET changes caused by a fuel reduction burn in a Mediterranean woodland in comparison to the changes detected using eddy covariance measurements. All remotely sensed ET products were first compared to ET measured at the Gingin OzFlux eddy covariance tower at annual and monthly timescales. Then, we tested the ability of these products to identify fire-induced changes by comparing ET between burned and unburned areas. At the annual timescale, CMRSET, MOD16, and PML agreed well with eddy covariance with R 2 ranging from 0.44 to 0.56 and RMSE equalling 3.0 to 4.0% of the annual eddy covariance estimate. However, the agreement was worse at the monthly timescale with R 2 ranging from 0.14 to 0.54 and RMSE equalling 10.0 to 18.0% of the monthly eddy covariance estimate. In addition, CMRSET exhibited a two-month phase lag with measured ET. Eddy covariance suggested that the fuel reduction burn decreased ET by 56 mm over the first year post-fire (i.e. 10% of the annual pre-fire ET); however, ET quickly returned to pre-fire conditions as vegetation regrew. All remote sensing products could detect the direction, timing, and duration of the fire disturbance, but PML showed the greatest potential in quantifying ET changes over the first year following the fire, with an error of 9%, or approximately 5 mm/year. Based on these results, we are confident in PML capability to assess fire impacts on ecosystem ET in Mediterranean drylands.

Understanding the impact of climate change on groundwater recharge is crucial for the sustainable management of groundwater systems, especially when regulatory agencies are managing aquifers already fully allocated. Recharge emerges as the outcome of Critical Zone (CZ) processes such as interception, runoff, or plant water uptake that use or store water from rainfall as it traverses the soil-plant-atmosphere continuum. Consequently, recharge is best understood and observed through multiple observations that can characterise storage, potentials and transport of water both in the soil and in the groundwater. Understanding how these CZ processes respond to a variable climate is essential for informing groundwater allocation management and decision-making.We present the results of field observations and a meta-analysis of recharge studies spanning the last 50 years in the Mediterranean climate area around Perth (Australia). This period coincides with a 15% reduction in winter rainfall, with the impacts on recharge partly revealed by the meta-analysis, but confounded by varying observation methods and sites. Seven field observation sites with consistent, multi-sensor instrumentation were therefore established to reveal recharge dynamics and estimate recharge fluxes. Electromagnetic soil moisture sensors provide vertical information across the soil profile (up to 10 meters below ground), complemented with soil water potential sensing at the surface and capillary fringe. ERT observations and manual soil moisture measurements in ancillary access tubes extend this information laterally (i.e. from 1D to 2D). Groundwater depth, meteorological and remotely sensed information enables contextualisation of the observations. Historical data analysis shows that rainfall reductions lead to nonlinear (3 to 4 times higher), decreases in recharge. The installed monitoring stations reveal how the dynamics of wetting fronts are influenced not only by the climatic variables but also by the types of vegetation and their response to a drying climate. This suggests the presence of distinct local recharge mechanisms operating within the transient systems of the area. The insights obtained from these monitoring sites can be benchmarked against broader observations, such as data provided by remote sensing or borewell measurements, to generate databases of recharge estimates useful for models.

Understanding and predicting plant water dynamics during and after water stress is increasingly important but challenging because the high-dimensional nature of the soil-plant-atmosphere system makes it difficult to identify mechanisms and constrain behaviour. Datasets that capture hydrological, physiological and meteorological variation during changing water availability are relatively rare but offer a potentially valuable resource to constrain plant water dynamics. This study reports on a drydown and re-wetting experiment of potted Populus trichocarpa, which intensively characterised plant water fluxes, water status and water sources. We synthesised the data qualitatively to assess the ability to better identify possible mechanisms and quantitatively, using information theory metrics, to measure the value of different measurements in constraining plant water fluxes and water status. Transpiration rates declined during the drydown and then showed a delayed and partial recovery following rewatering. After rewatering, plant water potentials also became decoupled from transpiration rates and the canopies experienced significant yellowing and leaf loss. Hormonal mechanisms were identified as a likely driver, demonstrating a mechanism with sustained impacts on plant water fluxes in the absence of xylem hydraulic damage. Quantitatively, the constraints offered by different measurements varied with the dynamic of interest, and temporally, with behaviour during recovery more difficult to constrain than during water stress. The study provides a uniquely diverse dataset offering insight into mechanisms of plant water stress response and approaches for studying these responses.

Mining is a major driver of dryland disturbance and degradation, and there is a growing need for effective and resilient methods for restoration of former mine sites. An important restoration goal is preventing water from accessing mine waste, thus avoiding mobilization and transport of contaminants. Evapotranspiration (ET) covers are soil covers where vegetation manages the water balance to minimize leakage into underlying waste, with potential co-benefits of restoring ecological function and fixing carbon. However, cover designs often overlook potentially complex interactions between plant physiology and physical design parameters (cover depth, soil properties, etc.) that affect plant water fluxes, particularly in water-limited environments. To better understand how physiologically mediated dynamics impact cover performance, we develop an ET cover model that mechanistically describes plant-environment interactions through a plant hydraulics framework. We use the model to determine how soil cover depth, a fundamental design parameter, interacts with physiology to impact leakage, plant stress/mortality, and carbon sequestration. The model is parameterized using data from a prior study of plant water relations in engineered cover systems of varying depths. When run under historical rainfall trajectories, the model shows that significant plant water stress was ubiquitous across cover depths and was most frequent in shallower covers, where it was accompanied by higher leakage and lower net carbon assimilation. Precipitation variation had an important role in driving outcomes, and hydraulic impairment of vegetation played a role in higher leakage and lower net carbon assimilation. Design approaches that account for plant physiological processes have the potential to yield more effective and resilient systems, and we present a framework for incorporating these critical feedbacks into the design process.

This study addresses the challenges of monitoring urban development where sprawl, infill and soft densification are prevalent. To accurately identify the spatial distribution and characteristics of different development types, a multi-modal deep learning model was developed to predict parcel-level development types using very high-resolution land cover maps and tabular data representing parcel attributes of surface cover, built form and dwelling counts. This model was compared to a range of supervised and unsupervised classification models and evaluated in two contexts: (i) within Perth, where training data was abundant, and (ii) when generalising models to other cities across Australia. To undertake this analysis, a ground truth dataset of almost 70,000 labelled parcels were sampled from Perth, Western Australia, with fine-tuning and test datasets produced for other Australian cities (Melbourne, Sydney, Brisbane and Adelaide). The multi-modal deep learning model achieved an overall accuracy of 94% when evaluated in Perth and overall accuracy ranging from 91% to 94% when evaluated in other cities. The model was more accurate in classifying visually distinct development types that are relevant to monitoring infill and densification and performed well in transferring to other cities. These results indicate that deep learning models and transfer learning workflows can be leveraged to develop generalisable urban development type mapping models. This approach opens opportunities for multi-city analyses to further understand the spatial and temporal dynamics of urban growth and densification, and provides data to guide and evaluate the effects of policy that seeks to balance the need for housing our growing populations with maintaining healthy natural environments.

Describing flow resistance from the properties of an underlying surface is a challenge in surface hydrology. Runoff models must specify a resistance formulation or "roughness scheme"-describing the functional relationship between flow resistance and flow depth/velocity-and its parameters. Uncertainty in runoff predictions derives from both the selected roughness scheme (e.g., Darcy Weisbach, Manning's, or laminar flow equations), and its parameterization with a roughness coefficient (e.g., Manning's n $n$). Both choices are informed by model calibration to data, usually discharge, and, if available, velocity. In this study, a Saint Venant Equation-based runoff model is calibrated to discharge and velocity data from 112 rainfall simulator experiments. The results are used to identify the optimal roughness scheme among four widely-used options for each experiment, and to explore whether surface properties can be used to select the optimal roughness scheme and its coefficient. Among the tested roughness schemes, a transitional flow equation provided the best fit to the plurality of experiments. The most suitable roughness scheme for a given experiment was not related to measured surface properties. Regression models predicted the calibrated roughness coefficients with adjusted r2 ${r}<^>{2}$ values between 0.48 and 0.54, depending on the roughness scheme used. Litter cover was the best predictor of the roughness coefficient, followed by soil cover and average canopy gap size. The results suggest that selection of an optimal roughness scheme based on surface properties alone remains difficult, but that once a scheme is selected, roughness coefficients can be estimated from surface properties.

Farm dams are important water security features supporting both agricultural production and the natural environment. In Australia alone, over two million farm dams provide the water resources underpinning rural and regional primary industries with an annual export value of $80 billion. However, monitoring these water bodies to understand water security and vulnerability is challenging, primarily because of their large quantity, size and highly variable spectral signatures. These characteristics result in difficulty determining thresholds for index-based water detection methods and add to the difficulty of creating adequate training datasets for deep learning methods. We present an adaptive approach named OmniWaterMask (OWM) that uses existing mapped water features to optimise the combination of deep learning outputs and a common water index (Normalised Difference Water Index, NDWI) to achieve robust water detection, for both agricultural and other water resources. OWM demonstrates strong performance across multiple datasets and spatial scales, achieving Intersection over Union (IoU) scores of 96.9 % (Sentinel-2), 73.8 % (Landsat) and 90.9 % (National Agriculture Imagery Program, NAIP). When applied to farm dam monitoring in Western Australia using Sentinel-2 imagery, the approach successfully tracks water extent across a range of dam sizes, with Mean Absolute Error (MAE) of 587 m2 when using Sentinel-2 and 785 m2 when using PlanetScope. Our two case studies demonstrate the practicality and scalability of this approach by monitoring water levels in both a single dam and across 7,172 farm dams at monthly intervals over an 8-year period. This methodology enables reliable monitoring of small water bodies at scale, supporting rural water security assessment in increasingly uncertain climatic conditions. The open source OWM library is made available as a Python package on PyPI.

Describing flow resistance using the physical properties of an underlying surface is a recalcitrant problem in overland flow models. If discharge measurements are available, an equivalent roughness (e.g., Manning's n ) can be calibrated to represent the effects of surface properties within the domain with a single numerical value. Alternatively, the flow resistance can be estimated from discharge and velocity measured at a point, typically a runoff plot outlet. However, such experimental estimates are often inconsistent with the equivalent roughness determined from calibration to discharge, even if both derive from the same dataset. For example, if Manning's equation is used to parameterize flow resistance, the Manning's n obtained by calibrating a model to discharge differs from the value of n calculated from measured flow and velocity at the hillslope outlet. Here, this discrepancy is resolved by deriving a correction factor relating experimentally-determined flow resistance to the equivalent roughness. The derived correction factor is tested for four commonly- used resistance formulations using 129 rainfall simulator experiments. The correction factor is necessary to reproduce measured velocities, and yields minor improvements in discharge prediction.

The growing global network of Critical Zone Observatories provides exciting insights into how terrestrial and subsurface environments are interconnected, emphasising the value of understanding the Critical Zone as a vertically integrated system. Yet this network is situated overwhelmingly in the frequently young and post-glacial or glacially-influenced landscapes of the Northern Hemisphere. The Southern Hemisphere offers diverse landscapes with geologic parent materials spanning the Archaean to the Cenozoic, which have experienced little glaciation relative to the Northern Hemisphere. The Australian Critical Zone Observatory Network was established in 2020 to provide insights into the structure and functioning of such landscapes on the ancient, chemically depleted, dry and diverse Australian continent. Five sites have been established with a common suite of instrumentation and operating principles, and are working collaboratively to develop Critical Zone datasets in landscapes ranging from rainforest to eucalyptus woodlands, dryland mallee, tropical savannah and rain-dependent agricultural lands.This talk will introduce the OzCZO – the Australian Critical Zone Observatory Network, the five sites, their instrumentation and opportunities for scientific research within and by making comparisons among the sites. It will then share some of the initial observations being collected at one of the observatories – the ancient lateritic landscape of the Avon Critical Zone Observatory. We will illustrate how CZ structure, illuminated by bore logs and geophysics, organises soil physical and chemical properties across the landscape, and reveal how these properties then feed into land management decisions, hydrological functioning, and large-scale ecological health. The Avon CZO is located within a biodiversity hotspot in the South-West of Australia, where the health of land and waters, and the ecosystems and agricultural production that depend on them, is threatened by both dryland salinity and a drying climate – with outcomes all mediated by the Critical Zone.All data from OzCZO will be publicly available for use, and the sites are intended to act as an open platform where researchers can develop and test their ideas. Given the scope for valuable cooperation and comparisons across these sites, we invite researchers at EGU to engage with OzCZO and keep progressing towards a global Critical Zone science.

On hillslopes with patchy vegetation cover, vegetation is a significant factor controlling surface hydraulic and hydrological properties. Soil permeability is often greater within vegetated areas than in surrounding bare soil areas, leading to the redistribution of rainfall from bare, runoff-generating areas to permeable, vegetated areas. While many studies have examined the hydrological consequences of permeability contrasts, the hydrodynamic effects of greater surface roughness in vegetated patches compared to bare areas remain under-investigated. The role of roughness is not obvious: greater roughness in vegetated patches provides greater resistance to flow, slowing water movement and thus extending the time frame over which infiltration can occur. However, greater roughness may also cause partial blocking and flow diversion, reducing the volume of water traversing vegetated areas, a mechanism that could reduce rainfall redistribution to these sites. To differentiate the roles of spatially-varying roughness and permeability on rainfall redistribution, the two-dimensional Saint Venant Equations are employed to model the hydrologic outcomes of permeability and roughness contrasts under varying rainfall intensities.The simulations consider the dynamically interesting case of an idealized vegetated patch surrounded by runoff-generating unvegetated areas. The model results indicate that greater resistance causes flow diversion around vegetation. However, vegetative resistance only reduces rainfall redistributed to the vegetation under the specific conditions of low rainfall intensity and high soil permeability. Otherwise, prolonged ponding during the recession period, due to greater vegetative resistance, creates additional time for infiltration, compensating for increased flow diversion around the vegetation.

Lateritic landscapes are structurally complex systems formed through intense chemical weathering under tropical paleoclimates. These profiles are found in stable, low-relief landscapes across tropical, subtropical, and Mediterranean climates, particularly between 35°N to 35°S. Their vertical structure reflects long-term shifts in climatic, hydrological, and tectonic conditions, offering a valuable "memory" of past environmental changes. Despite their environmental and economic significance, lateritic landscapes remain underrepresented in CZ research, a bias compounded by the concentration of Critical Zone Observatories in the Northern Hemisphere, where shallow, truncated profiles prevail due to glacial erosion. This underrepresentation limits our understanding of long-term CZ processes and how they have shaped subsurface architecture.This study investigates the subsurface architecture of a lateritic hillslope at the Avon River Critical Zone Observatory (AR-CZO) in Western Australia. Prolonged subaerial weathering since the Cretaceous, followed by mid-Miocene aridification, has created a stratigraphically complex regolith hillslope shaped by weathering, erosion, and colluvial deposition. To resolve the structural complexity of this hillslope, we applied a multi-method geophysical approach, combining electrical resistivity tomography (ERT), horizontal-to-vertical spectral ratio (HVSR) passive seismic methods, and borehole observations. ERT captured fine-scale stratigraphy, delineating the pallid zone, saprolite, and duricrust, while HVSR resolved broader interfaces, such as the duricrust-bedrock boundary and the base of the colluvial deposit.The results reveal how landscape position influences CZ structure. The hilltop is capped by a duricrust that transitions downslope into an erosional surface, where the pallid zone of the lateritic weathering profile is exposed at the surface. At the foot slope, approximately 11 m of colluvial sediment has accumulated from the erosion of the hillslope material. Bedrock depth estimates differed between methods, with ERT indicating depths of 23 m on the slope and 32 m at the foot slope, while HVSR revealed deeper depths of 31 m and 39 m, respectively. The discrepancy highlights the limitations of ERT in saline environments, where conductivity masks key interfaces, while HVSR’s broader resolution provides more reliable bedrock detection in such conditions. Together, these methods reveal a laterally variable weathering profile that responds to shifts in landscape position, erosion, and deposition.The complementarity of ERT and HVSR underscores the value of a multi-method geophysical approach for resolving the structural complexity of lateritic CZs. Our conceptual model demonstrates how weathering, erosion, and colluvial processes shape the structure of a deeply weathered hillslope, while also providing a transferable framework for characterizing saline, regolith-dominated systems. Given their depth, age, and capacity to preserve past climatic and tectonic conditions, lateritic CZs offer a vital opportunity to enhance global understanding of long-term CZ evolution. This research addresses the Northern Hemisphere bias in CZ science, highlights the underexplored role of stable, deeply weathered landscapes, and underscores the need for future comparative studies to understand the drivers of heterogeneity in subsurface architecture across CZs worldwide.

Deep learning models are widely used to extract features and insights from remotely sensed imagery. However, these models typically perform optimally when applied to the same sensor, resolution and imagery processing level as used during their training, and are rarely used or evaluated on out-of-domain data. This limitation results in duplication of efforts in collecting similar training datasets from different satellites to train sensor-specific models. Here, we introduce a range of techniques to train deep learning models that generalise across various sensors, resolutions, and processing levels. We applied this approach to train OmniCloudMask (OCM), a sensor- agnostic deep learning model that segments clouds and cloud shadow. OCM demonstrates robust state-of-the-art performance across various satellite platforms when classifying clear, cloud, and shadow classes, with balanced overall accuracy values across: Landsat (91.5 % clear, 91.5 % cloud, and 75.2 % shadow); Sentinel-2 (92.2 % clear, 91.2 % cloud, and 80.5 % shadow); and PlanetScope (96.9 % clear, 98.8 % cloud, and 97.4 % shadow). OCM achieves this accuracy while only being trained on a single Sentinel-2 dataset, employing spectral normalisation and mixed resolution training to address the spectral and spatial differences between satellite platforms. This approach allows the model to effectively handle imagery from different sensors within the 10 m to 50 m resolution range, as well as higher resolution imagery that has been resampled to 10 m. The OCM library is available as an open source Python package on PyPI.

The interception of rainfall by a surface, such as vegetation and soil, reduces the quantity of water participating in downstream processes. This rainfall interception loss is a non-negligible quantity that varies with the ecosystem, meteorological, and rainfall conditions. Rainfall interception models are needed to incorporate the three properties to estimate interception loss. However, the interception losses from these models are rarely validated directly. Rather their validation relies on the residual of the water balance. Eddy Covariance (EC) towers measure the fluxes of water vapour and present an opportunity to validate the modelled interception losses.We present a pioneering interception study that compares evaporation from physically calibrated interception models to the energy balance closure corrected water vapour fluxes recorded intermittently by an EC tower. We generate parameters for the canopy interception models by the hierarchical Bayesian treatment of the Rutter, Rutter sparse, Gash, and Calder models with the data from automatic throughfall and rain gauges over 271 rain events (177 for Gash). We use these models to estimate the extent of soil cover, throughfall to the soil underneath the canopy, and interception losses. A physically calibrated soil evaporative capacitor model was then used to model the evaporation from the bare soil and soil beneath the canopy. The canopy interception models recreated the event-wise throughfall well, however they did not represent a substantial improvement on the benchmark of a simple percentage estimate. The combined soil and canopy model is considerably better than a simple percentage in recreating the magnitude and time series of interception loss. The method developed can be applied to any EC tower that measure throughfall allowing for broader insights to be generated into the capability and limitations of interception loss modelling.

Reversal of land degradation in agricultural systems through a wide range of natural resources management tailored to the specific climate, soil, and topography is crucial. This is especially critical for subsistence farming systems such as the Ethiopian Highlands, subject to some of the highest soil erosion rates in the world. Although biodrainage is widely and successfully used for waterlogging and erosion control in arid regions, it is not widely applied in sub-humid places like the Ethiopian Highlands. Beginning with field observations of the complex vertical soil profiles and variations in lithology and soil hydraulic properties along a hillslope ridge-channel transect in the Debre Mawi watershed of the Abay (Upper Blue Nile) Basin in the Ethiopian Highlands, we construct a synthetic model hillslope in HYRDUS-2D. For the growing season coinciding with the rainy season, known as Kiremt, we calibrated the hillslope water storage using observations of water table elevation. The synthetic hillslope is then used to test the feasibility of biodrainage to mitigate saturation, a major driver of gullying and soil loss in the area, and to identify plant traits to which such mitigation is sensitive. The simulation results using bare soil, eucalyptus and maize land covers revealed that in spite of low potential evapotranspiration during the Kiremt season, biodrainage with Eucalyptus can reduce the occurrence and duration of saturation of the top 0.6 m surface soil profile of the hillslope. Of the different plant traits explored, the maximum soil water potential at which the crop can transpire at potential rates, and the LAI, which controls potential evaporation rates via the crop coefficient, provide the most important controls on the effectiveness of biodrainage in desaturating the hillslope. We conclude that biodrainage strategies to mitigate soil erosion in sub humid locales merits further investigation through empirical trials. However, implementation of biodrainage strategies should be subject to thoughtful consideration of the implications for socio-economic outcomes and the local environment.