The impact of gully filling on soil quality in agricultural crops remains a topic rarely explored in the literature. In this study, soil quality and crop yield assessments were conducted in the Campiña of Southern Spain, near the city of Córdoba, to evaluate the effects of filling operations associated with a gully rehabilitation project. Soil and crop yield samples were collected from both disturbed and undisturbed sites within and around two gullies that were filled five years before the study and another two gullies filled one year before. A single soil quality indicator (SQI) was developed, and its spatial and temporal dynamics were analyzed to quantify the impact of gully filling. Elevation maps revealed cut depths up to 0.9 m and fill depths of up to 2 m between 2014 and 2020. The depth of filling operations and proximity to the gully were found to reduce soil quality at the sampling locations. The deviation of observed SQI values from those expected based on their topographic setting indicated a reduction in soil quality, down to 60

Soil erosion computation technology plays an important role in planning to prevent and mitigate soil loss and non-point source pollution from agricultural fields. In the US, the RUSLE2 erosion model is extensively used by conservationists to support efforts for adoption of new farm management practices and implementation of conservation alternatives. Within RUSLE2, the impact of precipitation is described by average annual rainfall erosivity (R) which is represented by a smoothly and spatially varying surface that covers the entire US, assuring consistency in erosion predictions for conservation planning. In the current RUSLE2 erosivity database, these surfaces were developed by a laborious process of analyzing and processing data by hand, so this had not been updated since 2001. In this study, a protocol to generate isoerodent surfaces for the continental US is proposed and evaluated. The methodology describes steps that integrate the official RUSLE2 calculations with proposed new methods. The newly generated surfaces were compared to official RUSLE2 erosivity surfaces and evaluated for smoothness. Results indicate agreement with RUSLE2 surfaces for absolute values but with slightly higher spatial and temporal smoothness. Further refinements include the inclusion of small events, determination of spatially varying recurrence intervals, and consideration of two-axis trend interpolation enhanced with additional weighting accounting for data gaps, which gives more weight to weather stations that have more complete datasets. The protocol provides the means for capturing long-term climatic variations impacting soil erosion in a consistent way. This protocol supports forthcoming updates to the RUSLE2 climate database and serves as a baseline for future enhancements in the characterization of changing climatological drivers impacting soil erosion.

Soil erosion is one of the major processes of land degradation. Climate change, marked by alterations in the precipitation spatial and temporal patterns as well as rainfall amounts projected to increase, is expected to exacerbate soil erosion and loss of soil in the agricultural landscape. Understanding soil loss using physically-based water erosion prediction models and improving knowledge of soil erosion of agricultural lands under future climate change scenarios is critical to developing best management practices for the conservation of soil resources as well as to inform decision and policy makers. This study aims at investigating the impacts of future climate changes on soil erosion in the United States. By integrating up-to-date climate datasets this study characterized differences and current trends in precipitation with respect to climate change and applied a climate model ensemble based on the CMIP6 climate scenarios to predict the future climate. These data are downscaled with machine learning algorithms. It also estimates soil erosion in different soil-climate-agricultural management systems from predicted precipitation under future climate change scenarios using the Revised Universal Soil Loss Equation, Version 2 (RUSLE2). Research findings on the impacts of future climate change scenarios on soil erosion in agricultural landscapes will allow the development of climate-driven best management practices and conservation agriculture techniques as well as inform decision and policy makers to reduce soil loss, therefore protecting the limited soil and water resources, and contributing to a sustainable agricultural production and food security.

The Revised Universal Soil Loss Equation, Version 2 (RUSLE2) is the water erosion prediction tool for use by the USDA National Resources Conservation Service (NRCS) for all conservation planning in the United States. USDA NRCS utilizes the Integrated Erosion Tool (IET) that combines RUSLE2 with USDA data sets for soil, climate, and agricultural management. The Agricultural Research Service (ARS) is the USDA’s research agency charged with the development of the RUSLE2 model. RUSLE2 is an advanced computer model that estimates rill and interrill erosion by water, combining empirical and process-based science, for use on personal computers. This research aims at improving RUSLE2 science components, including the development of a web-based user interface for RUSLE2, for use by USDA NRCS. Advanced science components will be developed to quantify rainfall and land management effects on spatial and temporal variability of dynamic soil properties in agricultural watersheds in the United States, with emphasis on the assessment of soil erodibility and the risk of soil erosion under climate change. State-of-the-art technologies needed to measure, identify, and link dynamic soil erodibility to soil loss in the agricultural landscape, such as machine learning algorithms, remote sensing, and non-intrusive visualization and imaging technologies will provide advanced science components for RUSLE2. For the development of a web-based RUSLE2 modeling system, a new cloud based infrastructure is being deployed using the Amazon Web Services (AWS) platform, which will support online databases for climate, soil, and agricultural management data. A new database structure is being designed for RUSLE2, to support server based erosion calculations. AWS services will also provide web servers, spatial databases, geoprocessing capabilities, cooperative source code development and all compute and storage resources. These research findings and products will help understand how climate change and modern management practices impact soil erodibility dynamics. Improvements to RUSLE2 technology will lead to advances in determining soil loss across agricultural landscapes through improved physically based water erosion models. New web-based tools will provide best management practices for soil and water resources conservation under changing environments, contributing to sustainable agriculture and food security, while ensuring environmental health.

Concentrated runoff increases erosion and moves fine sediment and associated agrichemicals from upland areas to stream channels. Ephemeral gully erosion on croplands in the U.S. may contribute more of the sediment delivered to the edge of the field then from sheet and rill erosion. Typically, conservation practices developed for sheet and rill erosion are also expected to treat ephemeral gully erosion, but science and technology are needed to account for the separate benefits and effects of practices on each of the various sediment sources.Watershed modeling technology has been widely developed to aid in evaluating conservation practices implemented as part of a management plan, but typically lacks the capability to identify how a source, such as sheet and rill erosion, ephemeral gully erosion, or channel erosion, is specifically controlled by a practice or integrated practices. The U.S. Department of Agriculture’s Annualized Agricultural Non-Point Source pollutant loading model, AnnAGNPS, has been developed to determine the effects of conservation management plans on erosion and provide sediment tracking from all sources within the watershed, including sheet and rill, ephemeral gully, and channel erosion. This study describes the ephemeral gully erosion capabilities within the AnnAGNPS model and discusses research needs to further improve these components for integrated conservation management planning. Conservation management planning by agencies within the U.S. and by international organizations requires a systematic approach when determining the extent of ephemeral gully erosion impacts on a field, watershed, or national basis, and/or to predict recurring or new locations of ephemeral gullies prior to their development. This technology provides the capability to separate the impact of ephemeral gullies on erosion from other sources and then evaluate the impact of targeted practices to control erosion at the source and subsequent downstream resources.

Research and action agencies in the US work collaboratively to develop and use soil erosion technology to support the development of field-specific conservation plans. These tools and accompanying databases are applied in all counties throughout the country covering a wide range of natural and anthropogenic physical conditions. Climate, particularly precipitation, constitutes one of the key drivers directly related to soil detachment and transport. Observations spanning over 30 years have demonstrated that estimated long-term average annual soil loss in agricultural fields is the result of the cumulative effect of many small and moderate-sized storms along with the impact of occasional severe ones. In the Revised Universal Soil Loss Equation version 2 (RUSLE2) technology, the effect of rainfall is represented by the rainfall runoff erosivity index R. This index is designed to serve as an estimation of the potential storm energy specific to each location. In this study, we propose and evaluate a methodology to generate continuous surfaces of monthly R for the continental US from discrete 15-min precipitation data. Over 2000 stations covering more than 50 years of 15-min precipitation data were used. Storm identification algorithms were implemented and evaluated through comparison with existing tools. Outlier events were identified and removed using a 50-year recurrent interval calculated for each station. Using 30-years of recorded data, a custom universal kriging algorithm was employed to generate a smooth continuous surface as a raster grid. This step included a boxcox transformation of the station data, directional variogram fitting, and the removing of external trends using elevation, long-term annual precipitation totals, and distance to the coast. Predicted surfaces were compared with existing RUSLE2 surfaces for the same time period with great level of agreement. The proposed methodology is intended to be comprehensive and reproductible such that it can serve as a template for future updates of erosivity maps for the entire continent at a county-scale. This methodology provides the means for future systematic updates to the RUSLE2 climate database to account for climatic changes and to support continued national efforts in reducing soil erosion and conserving natural resources.

Ephemeral gullies (EGs) are channels that form in low parts of the field where runoff concentrates, and are often responsible for considerable soil loss from agriculture fields. Unfortunately, predicting the gradual development of gullies in response to storm events remains challenging. The United States Department of Agriculture has developed several modeling tools to predict the location and dimensions of ephemeral gullies and the resulting soil loss. The tool described herein combines precise geospatial determination of EG pathways with soil erosion and delivery calculations for both those pathways and the contributing hillslopes.Considering the vital importance of determining runoff concentration for EG development, high-resolution (0.5 ~ 3 m) terrain elevation data are processed with specialized geospatial tools to determine topography-driven surface runoff patterns and define swales where concentrated flows occur. This creates integrated surface drainage descriptions defining hillslope areas where sheet-and-rill erosion predominates, and swales where EG gullies may develop. This results in detailed flow maps covering entire fields, optionally considering oriented roughness created by crop rows on flow distribution. These data, along with topography-derived parameters and spatial distributions of soil types and vegetation cover, form the digital landscape description for erosion modeling.The magnitude, frequency, and seasonal distribution of storms are represented by a synthetic series of events derived from long-term climate databases created for the RUSLE2 (Revised Universal Soil Loss Equation, version 2) model. Runoff for each storm event is estimated with RUSLER (RUSLE2-Raster), a two-dimensional (2D) raster implementation of RUSLE2 technology that calculates runoff and sheet-and-rill soil loss for all flow paths covering a field. The RUSLER calculation provides the spatial and temporal distribution of incoming runoff and sediment loads necessary for the calculation of erosion and deposition in the EG channels.The channel flow and sediment transport model EphGEE (Ephemeral Gully Erosion Estimator) employs an excess shear stress approach to determine where flow erosive forces cause soil detachment and transport, and where deposition occurs. EphGEE also calculates the rates at which channels locally deepen and widen, thus predicting how channel geometry evolves during each storm, which depends strongly on knowledge of soil erodibility parameters. EphGEE attempts to estimate how erodibility parameters vary in time and with depth using management operations data available from RUSLE2 databases. In most cases, however, field data and parameter calibration are still necessary.This modeling approach has been applied to monitored fields in the United States. It was successful in determining runoff and concentrated flow paths, resulting in good predictions of locations where gullies form and how they connect to runoff-generating areas. For tilled fields where a less erodible soil layer exists, the model provides good approximations of gully depths and widths. For no-till and pasture-to-crop transitions, where EG cross-sectional shapes may be dependent on how erodibility varies with soil depth, better data or prediction methods are needed to improve model performance.

Conservation practices have been recognized as an important mitigation tool to reduce soil loss and sediment transport from agricultural fields. Multiple conservation structures and farming practices have been proposed to target erosional processes with varying results of sediment trapping efficiency. The quantification of their performance at the watershed scale when multiple integrated and spatiotemporal varying processes occur, remains a challenge. In this study, the impact of conservation practices on erosion from sheet/rill and ephemeral gully sources at field and watershed scales were investigated on a USDA Conservation Effects Assessment Project (CEAP) watershed draining into the Chesapeake Bay. Three periods were identified: 1990-2000, 2010-2020, and 2000-2010 representing before, after, and a transition period of conservation practices implementation, respectively. Ephemeral gullies and conservation practices were characterized at raster grid scale and evaluated at field and watershed scales. The AnnAGNPS watershed pollution model was used to quantify sediment loads before and after actual conservation practices were implemented. Simulation results indicated a potential reduction from implemented conservation practices of approximately 10% from ephemeral gully sources and 30% from sheet/rill sources in annual average sediment loads, or by 60% overall when comparing annual averages expressed as sediment concentration. Analysis of sediment loads indicates that the implemented practices are less effective in deterring sediments from ephemeral gully sources. Two significant precipitation events with an estimated recurrence interval greater than 250 years were estimated to be responsible for adding 25% (151.73 Mg) to the 11-year annual average sediment load prompting the need for future research on the impact of extreme events on soil loss. Using customized soil erosion prediction technology designed for the quantification of diverse source and sink of sediment is a valuable resource for stakeholders seeking to develop erosion mitigation strategies.

We present the detection of three exoplanets orbiting the early M dwarf TOI-663 (TIC 54962195; V = 13.7 mag, J = 10.4 mag, R-star = 0.512 +/- 0.015 R circle dot, M-star = 0.514 +/- 0.012 M-circle dot, d = 64 pc). TOI-663 b, c, and d, with respective radii of 2.27 +/- 0.10 R-circle plus, 2.26 +/- 0.10 R-circle plus, and 1.92 +/- 0.13 R-circle plus and masses of 4.45 +/- 0.65 M-circle plus, 3.65 +/- 0.97 M-circle plus, and <5.2 M-circle plus at 99%, are located just above the radius valley that separates rocky and volatile-rich exoplanets. The planet candidates are identified in two TESS sectors and are validated with ground-based photometric follow-up, precise radial-velocity measurements, and high-resolution imaging. We used the software package juliet to jointly model the photometric and radial-velocity datasets, with Gaussian processes applied to correct for systematics. The three planets discovered in the TOI-663 system are low-mass mini-Neptunes with radii significantly larger than those of rocky analogs, implying that volatiles, such as water, must predominate. In addition to this internal structure analysis, we also performed a dynamical analysis that confirmed the stability of the system. The three exoplanets in the TOI-663 system, similarly to other sub-Neptunes orbiting M dwarfs, have been found to have lower densities than planets of similar sizes orbiting stars of different spectral types.

Linear erosion channel (LEC) devastates arable land and significantly contributes to soil loss in agricultural watersheds. In the presence of a less- or non-erodible layer, channel widening governs the erosion process once the channel bed incises to this layer, accompanied by failure block generation and transport. Current knowledge on channel widening, however, is limited due to the lack of robust and efficient methods to capture the rapid sidewall expansion process. Laboratory experiments were designed to simulate the channel widening process with an initial channel width of 10 cm. Two packed soil beds with a non-erodible layer and two slope gradients (5 % and 11 %) were subjected to the inflow rate of 0.67 L/s. Images were captured by mounted digital cameras and automatically transformed into orthophotos. Channel edges and failure blocks were automatically detected by deep learning algorithm in a newly developed Channel-DeepLab network model based upon DeepLabv3+ platform. The procedure includes learning samples labelling, data augmentation, model construction, training, and validation. Sediment discharge and changes in channel width, geometry of channel edges, and failure blocks were measured. The results indicate that initial period is critical for erosion prediction and remediation due to its small sidewall failure interval, high channel expansion rate and sediment discharge. Channel surface area has great potential on accumulated sediment discharge prediction. The slope section that witnessed the fastest channel widening rate migrated downwards when slope gradient increased from 5 % to 11 %. The total number and area of the failure blocks increased with time, while the collapse frequency of the sidewalls decreased. Upstream reach experienced the highest sidewall collapse frequency and rate of disaggregation and transport, while the downstream reach experienced the highest total number of failure blocks. A time lag was found between sidewall collapse and sediment discharge, which increased as time progressed, attributing to decreased runoff erosivity as the flow velocity decreased. Results of this study will provide methodological support for channel sidewall and streambank retreat monitoring, realizing the automatic detection of channel edges and efficient output of rapid sidewall expansion process with high temporal and spatial precision. Future work can be focused on broadening the applicability of the Channel-DeepLab network model and quantifying the delayed response process between sidewall failure and sediment discharge.

CHEOPS is a space telescope specifically designed to monitor transiting exoplanets orbiting bright stars. In September 2023, CHEOPS completed its nominal mission and remains in excellent operational conditions. The mission has been extended until the end of 2026. Scientific and instrumental data have been collected throughout in-orbit commissioning and nominal operations, enabling a comprehensive analysis of the mission's performance. In this article, we present the results of this analysis with a twofold goal. First, we aim to inform the scientific community about the present status of the mission and what can be expected as the instrument ages. Secondly, we intend for this publication to serve as a legacy document for future missions, providing insights and lessons learned from the successful operation of CHEOPS. To evaluate the instrument performance in flight, we developed a comprehensive monitoring and characterisation programme. It consists of dedicated observations that allow us to characterise the instrument's response. In addition to the standard collection of nominal science and housekeeping data, these observations provide input for detecting, modelling, and correcting instrument systematics, discovering and addressing anomalies, and comparing the instrument's actual performance with expectations. The precision of the CHEOPS measurements has enabled the mission objectives to be met and exceeded. Careful modelling of the instrumental systematics allows the data quality to be significantly improved during the light curve analysis phase, resulting in more precise scientific measurements. CHEOPS is compliant with the driving scientific requirements of the mission. Although visible, the ageing of the instrument has not affected the mission's performance.

Soil erosion is a problem across the globe and, quite often, is accelerated within agricultural settings. Splash, sheet, rill, and gully erosion govern soil loss by water, and these erosion mechanisms increase in complexity as the contributing area increases. For example, splash erosion occurs throughout the field during a rainfall event but is hyper localized (i.e., single drop impact) and sheet erosion occurs within the field but is connected to flow shear and a uniform depth of soil loss. With rill and gully erosion the processes are more complex and interactive, i.e., progressing incrementally from splash to gully erosion. Here, we focus on rill and gully channel widening mechanics influenced by a fixed bed. Experiments were conducted with three soils, three slopes, and three constant flow discharges. Soil textures ranged from sandy to clayey; slopes were 1, 5, and 11%; and flow discharges were 2.5, 6.7, and 10.8 cm2/sec. As bed slope and flow discharge increased, channel widening rate increased. As texture shifted from sandy to clayey, channel widening rate decreased. Initial experimentation and widening algorithm development was based upon the same experimental protocols with one soil only (sandy texture) and an empirical equation was defined in terms of slope and discharge. The current work builds upon the initial widening equation, investigates a Bayesian hierarchical structure, and strives to unite the previous algorithm development with the new experimental data.

Sustainable agriculture begins with reliable conservation management planning. Conservation management planning addresses soil loss or erosion control while coupling productivity and profitability on the farm together with environmental stewardship. Traditional row crop agriculture utilizes soil loss prediction technology to estimate the impact of different management strategies to minimize soil loss and maximize soil conservation. In this present study, two current USDA water erosion prediction technologies used in the United States to prescribe conservation management plans (RUSLE2 and WEPP) were examined. The technologies were compared first as designed and intended for management plan implementation (17,525 simulations) and second for equivalent climate-specific conditions (18,480 simulations), using similar input parameters for management, soils, terrain (slope length and gradient), and crop yields. Results of the simulations generally show similar soil loss trends for managements, terrain characteristics, and crop yields. Simulated soil loss estimates disagree particularly for clay-textured soils and no-till management. Both studies show these trends independent of climate inputs evaluated. Though the comparison results provide important insight into model tendencies, there are still open questions remaining regarding climate.

A new generation of observatories is enabling detailed study of exoplanetary atmospheres and the diversity of alien climates, allowing us to seek evidence for extraterrestrial biological and geological processes. Now is therefore the time to identify the most unique planets to be characterised with these instruments. In this context, we report on the discovery and validation of TOI-715 b, a $R_{\rm b}=1.55\pm 0.06\rm R_{\oplus}$ planet orbiting its nearby ($42$ pc) M4 host (TOI-715/TIC 271971130) with a period $P_{\rm b} = 19.288004_{-0.000024}^{+0.000027}$ days. TOI-715 b was first identified by TESS and validated using ground-based photometry, high-resolution imaging and statistical validation. The planet's orbital period combined with the stellar effective temperature $T_{\rm eff}=3075\pm75~\rm K$ give this planet an instellation $S_{\rm b} = 0.67_{-0.20}^{+0.15}~\rm S_\oplus$, placing it within the most conservative definitions of the habitable zone for rocky planets. TOI-715 b's radius falls exactly between two measured locations of the M-dwarf radius valley; characterising its mass and composition will help understand the true nature of the radius valley for low-mass stars. We demonstrate TOI-715 b is amenable for characterisation using precise radial velocities and transmission spectroscopy. Additionally, we reveal a second candidate planet in the system, TIC 271971130.02, with a potential orbital period of $P_{02} = 25.60712_{-0.00036}^{+0.00031}$ days and a radius of $R_{02} = 1.066\pm0.092\,\rm R_{\oplus}$, just inside the outer boundary of the habitable zone, and near a 4:3 orbital period commensurability. Should this second planet be confirmed, it would represent the smallest habitable zone planet discovered by TESS to date.

Context. Since the discovery of the first exoplanet almost three decades ago, the number of known exoplanets has increased dramatically. By beginning of the 2000s it was clear that dedicated facilities to advance our studies in this field were needed. The CHaracterising ExOPlanet Satellite (CHEOPS) is a space telescope specifically designed to monitor transiting exoplanets orbiting bright stars. In September 2023, CHEOPS completed its nominal mission duration of 3.5 yr and remains in excellent operational conditions. As a testament to this, the mission has been extended until the end of 2026. Aims. Scientific and instrumental data have been collected throughout in-orbit commissioning and nominal operations, enabling a comprehensive analysis of the mission’s performance. In this article, we present the results of this analysis with a twofold goal. First, we aim to inform the scientific community about the present status of the mission and what can be expected as the instrument ages. Secondly, we intend for this publication to serve as a legacy document for future missions, providing insights and lessons learned from the successful operation of CHEOPS. Methods. To evaluate the instrument performance in flight, we developed a comprehensive monitoring and characterisation (M&C) programme. It consists of dedicated observations that allow us to characterise the instrument’s response and continuously monitor its behaviour. In addition to the standard collection of nominal science and housekeeping data, these observations provide valuable input for detecting, modelling, and correcting instrument systematics, discovering and addressing anomalies, and comparing the instrument’s actual performance with expectations. Results. The precision of the CHEOPS measurements has enabled the mission objectives to be met and exceeded. The satellite’s performance remains stable and reliable, ensuring accurate data collection throughout its operational life. Careful modelling of the instrumental systematics allows the data quality to be significantly improved during the light curve analysis phase, resulting in more precise scientific measurements. Conclusions. CHEOPS is compliant with the driving scientific requirements of the mission. Although visible, the ageing of the instrument has not affected the mission’s performance. The satellite’s capabilities remain robust, and we are confident that we will continue to acquire high-quality data during the mission extension.

The Lower Mississippi River Basin Long-Term Agroecosystem Research Site (LMRB-LTAR) encompasses six states from Missouri to the Gulf of Mexico and is coordinated by the USDA-ARS National Sedimentation Laboratory, Oxford, MS. The overarching goal of LTAR is to assess regionally diverse and geographically scalable farming practices for enhanced sustainability of agroecosystem goods and services under changing environment and resource-use conditions. The LMRB-LTAR overall goal is to assess sustainable row crop agricultural production systems that integrate regional environmental and socioeconomic needs. Primary row crops in the region include soybeans, corn, cotton, rice, and sugarcane with crop rotations influenced by commodity crop price and other factors. The field-scale common experiment (CE) includes four row crop farms (26-101 ha) established in 2021 and 2023. Three fields are managed with alternative practices, including reduced tillage, cover crops, and automated prescription irrigation, and three fields are managed with prevailing farming practices, consisting of conventional tillage, no cover crop, and nonprescription irrigation. Treatment effects on crop productivity, soil quality, water use efficiency, water quality, and carbon storage are assessed. Research from the LMRB CE will deliver outcomes linked to overarching LTAR network goals, including innovative agricultural systems, strengthened partnerships, data management technologies, and precision environmental tools.

We report the discovery by the TESS mission of a super-Earth on a 4.8-d orbit around an inactive M4.5 dwarf (TOI-1680) validated by ground-based facilities. The host star is located 37.14 pc away, with a radius of 0.2100+/-0.0064 R_sun, mass of 0.1800+/-0.0044 M_sun and an effective temperature of 3211+/-100 K. We validated and characterized the planet using TESS data, ground-based multi-wavelength photometry from TRAPPIST, SPECULOOS, and LCO, as well as high-resolution AO observations from Keck/NIRC2 and Shane. Our analyses have determined the following parameters for the planet: a radius of 1.466+0.063/-0.049 R_earth and an equilibrium temperature of 404+/-14 K, assuming no albedo and perfect heat redistribution. Assuming a mass based on mass-radius relations, this planet is a promising target for atmospheric characterization with the James Webb Space Telescope (JWST).

A new modeling framework has been developed to extend RUSLE2 erosion prediction technology for large areas with the use of machine learning and improved geoprocessing tools. RUSLE2 has been traditionally used to estimate soil loss over one-dimensional hillslopes that would be representative of the erosion for an area. The new approach calculates sheet-and-rill erosion in two horizontal dimensions, producing detailed maps of soil loss that can be used to identify critical areas and guide the design of soil conservation measures for entire watersheds. Geoprocessing algorithms developed for this application utilize high-resolution gridded digital elevation models to analyze overland flow paths and create a drainage network and corresponding hillslopes, ensuring the correct representation of topography and runoff distribution for RUSLE2 calculations. Topographic attributes, soil types, and land management parameters are extracted for each hillslope from GIS layers and associated databases. Soil erosion is then inferred from a machine learning procedure that uses a sequential, densely connected artificial neural network (ANN). A total of 12 input parameters describe climate, topography, soil properties, and vegetation and agricultural management operations for each hillslope. The ANN determines the corresponding long-term average soil loss of the area. To illustrate the methodology, a map of average annual soil loss for the entire state of Iowa, USA, was created covering all areas typically planted with corn and soybeans that were assumed to be managed as two-year rotations, with a conservation tillage practice, which cover about 96% of the state. Elevation data was derived from the state‘s Lidar survey, resampled to 10-meter resolution. Land use data was obtained from the USDA-NASS Crop Data Layer for 2018. A set of independent, simple-profile RUSLE2 calculations were used to train the ANN. The training set included five climate definitions for different regions of the state, 7 soil types from the SSURGO database of varying erodibility, and 10 typical RUSLE2 management descriptions for corn and soybean rotations with varying yields. Topography was represented by uniform hillslopes with lengths varying from 25 to 300 ft (7.6 to 91 m) and slopes between 1% and 18%. All input parameters were combined to create a training set of 459,900 simulation results. Validation tests when compared with average values calculated with the original RUSLE2 model showed that ANN computed erosion values were within 0.27 tons per acre (0.61 Mg/ha). Application of the geoprocessing algorithms for the entire state required 20 hours of processing time across 12 cores using Message Passing Interface parallelization. The calculation of soil loss through the trained ANN for about 186 million hillslopes in 1712 HUC-12 watersheds covering the entire state was computed in about 30 minutes on a single core. A similar calculation using RUSLER-Distributed on a 192-core server (websim.rusle2.org) would take about 43 days. The new methodology shows that the introduction of machine learning allows RUSLE2 to be easily extended to watershed scales while maintaining a high level of spatial detail, making it a useful tool for prioritization of areas for erosion control, evaluating the impact of best management practices, or in the design of soil conservation practices.

We present the discovery and validation of two TESS exoplanets orbiting nearby M dwarfs: TOI-2084b, and TOI-4184b. We characterized the host stars by combining spectra from Shane/Kast and Magellan/FIRE, SED (Spectral Energy Distribution) analysis, and stellar evolutionary models. In addition, we used Gemini-South/Zorro -North/Alopeke high-resolution imaging, archival science images, and statistical validation packages to support the planetary interpretation. We performed a global analysis of multi-colour photometric data from TESS and ground-based facilities in order to derive the stellar and planetary physical parameters for each system. We find that TOI-2084b and TOI-4184b are sub-Neptune-sized planets with radii of Rp = 2.47 +/- 0.13R_Earth and Rp = 2.43 +/- 0.21R_Earth, respectively. TOI-2084b completes an orbit around its host star every 6.08 days, has an equilibrium temperature of T_eq = 527 +/- 8K and an irradiation of S_p = 12.8 +/- 0.8 S_Earth. Its host star is a dwarf of spectral M2.0 +/- 0.5 at a distance of 114pc with an effective temperature of T_eff = 3550 +/- 50 K, and has a wide, co-moving M8 companion at a projected separation of 1400 au. TOI-4184b orbits around an M5.0 +/- 0.5 type dwarf star (Kmag = 11.87) each 4.9 days, and has an equilibrium temperature of T_eq = 412 +/- 8 K and an irradiation of S_p = 4.8 +/- 0.4 S_Earth. TOI-4184 is a metal poor star ([Fe/H] = -0.27 +/- 0.09 dex) at a distance of 69 pc with an effective temperature of T_eff = 3225 +/- 75 K. Both planets are located at the edge of the sub-Jovian desert in the radius-period plane. The combination of the small size and the large infrared brightness of their host stars make these new planets promising targets for future atmospheric exploration with JWST.

The variability induced by precipitable water vapour (PWV) can heavily affect the accuracy of time-series photometric measurements gathered from the ground, especially in the near-infrared. We present here a novel method of modelling and mitigating this variability, as well as open-sourcing the developed tool -- Umbrella. In this study, we evaluate the extent to which the photometry in three common bandpasses (r', i', z'), and SPECULOOS' primary bandpass (I+z'), are photometrically affected by PWV variability. In this selection of bandpasses, the I+z' bandpass was found to be most sensitive to PWV variability, followed by z', i', and r'. The correction was evaluated on global light curves of nearby late M- and L-type stars observed by SPECULOOS' Southern Observatory (SSO) with the I+z' bandpass, using PWV measurements from the LHATPRO and local temperature/humidity sensors. A median reduction in RMS of 1.1% was observed for variability shorter than the expected transit duration for SSO's targets. On timescales longer than the expected transit duration, where long-term variability may be induced, a median reduction in RMS of 53.8% was observed for the same method of correction.