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Australian inland riparian wetlands located east of the Great Dividing Range exhibit unique, hydroecological characteristics. These flood-dependent aquatic systems located in water-limited regions are declining rapidly due to the competitive demand for water for human activities, as well as climate change and variability. However, there exist very few reliable data to characterize inundation change conditions and quantify the impacts of the loss and deterioration of wetlands. A long-term time record of wetland inundation maps can provide a crucial baseline to monitor, assess, and assist the management and conservation of wetland ecosystems. This study presents a random forest-based multi-index classification algorithm (RaFMIC) on the Google Earth Engine (GEE) platform to efficiently construct a temporally dense, three-decadal time record of inundation maps of the southeast Australian riparian inland wetlands. The method was tested over the Macquarie Marshes located in the semiarid region of NSW, Australia. The results showed a good accuracy when compared against high-spatial resolution imagery. The total inundated area was consistent with precipitation and streamflow patterns, and the temporal dynamics of vegetation showed good agreement with the inundation maps. The inundation time record was analysed to generate inundation probability maps, which were in a good agreement with frequently flooded areas simulated by a hydrodynamic model and the distribution of flood-dependent vegetation species. The long-term, time-dense inundation maps derived from the RaFMIC method can provide key information to assess the condition and health of wetland ecosystems and have the potential to improve wetland inventory with spatially explicit water regime information. RaFMIC can be adapted over other dryland wetlands, as an effective semiautomated method of mapping long-term inundation dynamics.

Wetland ecosystems are experiencing rapid degradation due to human activities, particularly the diversion of natural flows for various purposes, leading to significant alterations in wetland hydrology and their ecological functions. However, understanding and quantifying these eco-hydrological changes, especially concerning inundation dynamics, presents a formidable challenge due to the lack of long-term, observation-based spatiotemporal inundation information. In this study, we classified wetland areas into ten equal-interval classes based on inundation probability derived from a dense, 30-year time series of Landsat-based inundation maps over an Australian dryland riparian wetland, Macquarie Marshes. These maps were then compared with three simplified vegetation patches in the area: river red gum forest, river red gum woodland, and shrubland. Our findings reveal a higher inundation probability over a small area covered by river red gum forest, exhibiting persistent inundation over time. In contrast, river red gum woodland and shrubland areas show fluctuating inundation patterns. When comparing percentage inundation with the Normalized Difference Vegetation Index (NDVI), we observed a notable agreement in peaks, with a lag time in NDVI response. A strong correlation between NDVI and the percentage of inundated area was found in the river red gum woodland patch. During dry, wet, and intermediate years, the shrubland patch consistently demonstrated similar inundation probabilities, while river red gum patches exhibited variable probabilities. During drying events, the shrubland patch dried faster, likely due to higher evaporation rates driven by exposure to solar radiation. However, long-term inundation probability exhibited agreement with the SAGA wetness index, highlighting the influence of topography on inundation probability. These findings provide crucial insights into the complex interactions between hydrological processes and vegetation dynamics in wetland ecosystems, underscoring the need for comprehensive monitoring and management strategies to mitigate degradation and preserve these vital ecosystems.

Wildfires are pivotal to the functioning of many ecosystems globally, including the magnitude of surface erosion rates. This study aims to investigate the relationships between surface erosion rates and wildfire intensity in the tropical north savanna of Australia. The occurrence of fires in western Arnhem Land, Northern Territory, Australia was determined with remotely sensed digital datasets as well as analogue erosion measurement methods. Analysis was performed using satellite imagery to quantify burn severity via a monthly delta normalised burn ratio (dNBR). This was compared and correlated against on-ground erosion measurements (erosion pins) for 13 years. The dNBR for each year (up to +0.4) displayed no relationship with subsequent erosion (up to ±4 mm of erosion/deposition per year). Poor correlation was attributed to low fire severity, patchy burning, significant time between fires and erosion-inducing rainfall. Other influences included surface roughness from disturbances from feral pigs and cyclone impacts. The findings here oppose many other studies that have found that fires increase surface erosion. This accentuates the unique ecosystem characteristics and fire regime properties found in the tropical Northern Territory. Scenarios of late dry season fires with high severity were not observed in this study and require more investigations. Ecosystems such as the one examined here require specialised management practices acknowledging the specific ecosystem functions and processes. The methods employed here combine both analogue and digital sensors to improve understandings of a unique environmental system.

Soil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM retrievals, especially L-band microwave remote sensing, has emerged as a feasible solution to offer spatially continuous global-scale SM information. However, the coarse spatial resolution of these L-band microwave SM retrievals poses uncertainties in many regional- and local-scale SM applications which require a high amount of spatial details. Numerous studies have been conducted to develop downscaling algorithms to enhance the spatial resolution of coarse-resolution satellite-derived SM datasets. Machine Learning (ML)-based downscaling models have gained prominence recently due to their ability to capture non-linear, complex relationships between SM and its driving factors, such as vegetation, surface temperature, topography, and climatic conditions. This review paper presents a comprehensive review of the ML-based approaches used in SM downscaling. The usage of classical, ensemble, neural nets, and deep learning methods to downscale SM products and the comparison of multiple algorithms are detailed in this paper. Insights into the significance of surface ancillary variables for model accuracy and the improvements made to ML-based SM downscaling approaches are also discussed. Overall, this paper provides useful insights for future studies on developing reliable, high-spatial-resolution SM datasets using ML-based algorithms.

The adoption rate of winter cover crops (WCCs) as an effective conservation management practice to help reduce agricultural nutrient loads in the Chesapeake Bay (CB) is increasing. However, the WCC potential for water quality improvement has not been fully realized at the watershed scale. This study was conducted to evaluate the long-term impact of WCCs on hydrology and NO3-N loads in two adjacent watersheds and to identify key management factors that affect the effectiveness of WCCs using the Soil and Water Assessment Tool (SWAT) and statistical methods. Simulation results indicated that WCCs are effective for reducing NO3-N loads and their performance varied based on planting date, species, soil characteristics, and crop rotations. Early-planted WCCs outperformed late-planted WCCs on the reduction of NO3-N loads and early-planted rye (RE) reduced NO3-N loads by ~49.3% compared to the baseline (no WCC). The WCCs were more effective in a watershed dominated by well-drained soils with increased reductions in NO3-N fluxes of ~2.5 kg N·ha-1 delivered to streams and ~10.1 kg N·ha-1 leached into groundwater compared to poorly-drained soils. Well-drained agricultural lands had higher transport of NO3-N in the soil profile and groundwater due to increased N leaching. Poorly-drained agricultural lands had lower NO3-N due to extensive drainage ditches and anaerobic soil conditions promoting denitrification. The performance of WCCs varied by crop rotations (i.e., continuous corn and corn-soybean), with increased N uptake following soybean crops due to the increased soil mineral N availability by mineralization of soybean residue compared to corn residue. The WCCs can reduce N leaching where baseline NO3-N loads are high in well-drained soils and/or when residual and mineralized N availability is high due to the cropping practices. The findings suggested that WCC implementation plans should be established in watersheds according to local edaphic and agronomic characteristics for reducing N leaching.

To best conserve wetlands and manage associated ecosystem services in the face of climate and land-use change, wetlands must be routinely monitored to assess their extent and function. Wetland extent and function are largely driven by spatial and temporal patterns in inundation and soil moisture, which to date have been challenging to map, especially within forested wetlands. The objective of this paper is to investigate the different, but often interacting effects, of evergreen vegetation and inundation on leaf-off bare earth return lidar intensity within mixed deciduous-evergreen forests in the Coastal Plain of Maryland, and to develop an inundation mapping approach that is robust in areas of varying levels of evergreen influence. This was achieved through statistical comparison of field derived metrics, and development of a simple yet robust normalization process, based on first of many, and bare earth lidar intensity returns. Results demonstrate the confounding influence of forest canopy gap fraction and inundation, and the effectiveness of the normalization process. After normalization, inundated deciduous forest could be distinguished from non-inundated evergreen forest. Inundation was mapped with an overall accuracy between 99.4% and 100%. Inundation maps created using this approach provide insights into physical processes in support of environmental decision-making, and a vital link between fine-scale physical conditions and moderate resolution satellite imagery through enhanced calibration and validation.

An accurate estimation of soil moisture and groundwater is essential for monitoring the availability of water supply in domestic and agricultural sectors. In order to improve the water storage estimates, previous studies assimilated terrestrial water storage variation (ΔTWS) derived from the Gravity Recovery and Climate Experiment (GRACE) into land surface models (LSMs). However, the GRACE-derived ΔTWS was generally computed from the high-level products (e.g. time-variable gravity fields, i.e. level 2, and land grid from the level 3 product). The gridded data products are subjected to several drawbacks such as signal attenuation and/or distortion caused by a posteriori filters and a lack of error covariance information. The post-processing of GRACE data might lead to the undesired alteration of the signal and its statistical property. This study uses the GRACE least-squares normal equation data to exploit the GRACE information rigorously and negate these limitations. Our approach combines GRACE's least-squares normal equation (obtained from ITSG-Grace2016 product) with the results from the Community Atmosphere Biosphere Land Exchange (CABLE) model to improve soil moisture and groundwater estimates. This study demonstrates, for the first time, an importance of using the GRACE raw data. The GRACE-combined (GC) approach is developed for optimal least-squares combination and the approach is applied to estimate the soil moisture and groundwater over 10 Australian river basins. The results are validated against the satellite soil moisture observation and the in situ groundwater data. Comparing to CABLE, we demonstrate the GC approach delivers evident improvement of water storage estimates, consistently from all basins, yielding better agreement on seasonal and inter-annual timescales. Significant improvement is found in groundwater storage while marginal improvement is observed in surface soil moisture estimates.

Following extreme drought during the 2019–2020 bushfire summer, the eastern part of Australia suffered from a week‐long intense rainfall and extensive flooding in March 2021. Understanding how much water storage changes in response to these climate extremes is critical for developing timely water management strategies. To quantify prompt water storage changes associated with the 2021 March flooding, we processed the low‐latency (1–3 days), high‐precision intersatellite laser ranging measurements from GRACE Follow‐On spacecraft and determined instantaneous gravity changes along spacecraft orbital passes. Such new data processing detected an abrupt surge of water storage approaching 60–70 trillion liters (km3 of water) over a week in the region, which concurrently caused land subsidence of ∼5 mm measured by a network of ground GPS stations. This was the highest speed of ground water recharge ever recorded in the region over the last two decades. Compared to the condition in February 2020, the amount of recharged water was similar but the recharge speed was much faster in March 2021. While these two events together replenished the region up to ∼80% of the maximum storage over the last two decades, the wet antecedent condition of soils in 2021 was distinctly different from the dry conditions in 2020 and led to generating extensive runoff and flooding in 2021. Plain Language Summary The monthly mean snapshots of global gravity field and surface mass variation (“mascon”) from the GRACE and GRACE Follow‐On spacecraft missions are problematic to accurately quantify abrupt water storage changes and flooding by intense rainfall. This study demonstrates a new use of the GRACE Follow‐On data to measure immediate water storage changes by computing instantaneous gravity change along spacecraft orbital passes. This new application is also shown for low‐latency (a few days) data processing to assess surface mass changes immediately after extreme events. The results found that the eastern parts of Australia experienced the highest speed of ground water recharge ever recorded in the region and the wet antecedent condition of soils yielded extensive flooding in 2021. Key Points New GRACE Follow‐On gravity data processing quantified prompt changes in water storage by the heavy rainfall and flooding in March 2021 The eastern Australia experienced the highest speed of ground water recharge and wet antecedent soils were responsible for extensive flooding in 2021 The study demonstrated the feasibility of rapid processing (1–3 days) for immediate assessment of mass changes from extreme events

For almost 30 years, the Soil and Water Assessment Tool (SWAT) has been successfully implemented to address issues around various scientific subjects in the world. On the other hand, it has been reaching to the limit of potential flexibility in further development by the current structure. The new generation SWAT, dubbed SWAT+, was released recently with entirely new coding features. SWAT+ is designed to have far more advanced functions and capacities to handle challenging watershed modeling tasks for hydrologic and water quality processes. However, it is still inevitable to conduct model calibration before the SWAT+ model is applied to engineering projects and research programs. The primary goal of this study is to develop an open-source, easy-to-operate automatic calibration tool for SWAT+, dubbed IPEAT+ (Integrated Parameter Estimation and Uncertainty Analysis Tool Plus). There are four major advantages: (i) Open-source code to general users; (ii) compiled and integrated directly with SWAT+ source code as a single executable; (iii) supported by the SWAT developer group; and, (iv) built with efficient optimization technique. The coupling work between IPEAT+ and SWAT+ is fairly simple, which can be conducted by users with minor efforts. IPEAT+ will be regularly updated with the latest SWAT+ revision. If users would like to integrate IPEAT+ with various versions of SWAT+, only few lines in the SWAT+ source code are required to be updated. IPEAT+ is the first automatic calibration tool integrated with SWAT+ source code. Users can take advantage of the tool to pursue more cutting-edge and forward-thinking scientific questions.

Terrestrial water storage in the Amazon basin and its surrounding areas is studied by exploring the instantaneous measurements of distance changes between two satellites from the GRACE mission. The surface water in the channels and floodplains can be significant in weighing total water storage. Its magnitude can be as large as soil moisture perturbing the motions of the satellites to a detectable amount by the on‐board instrument. The river runoff routing simulations indicate the effective velocity throughout the Amazon basin over the years is about 30 cm/s with significant seasonal change. The lower velocity, during rising stages and peak water season, and the faster velocity, during falling stages, are delineated from the observations. The backwater effects may impact such seasonal change on the overall flow velocity. Direct assimilation of GRACE tracking data can contribute to land surface dynamic processes by resolving the time scale of transport in rivers and streams.