The highest long-term values were concentrated in northern Kerala, while the smallest erosion-potential domain covered much of southern and parts of central Kerala. Rainfall erosivity describes the capacity of rainfall to detach and transport soil, while erosivity density expresses that erosive potential relative to the amount of rainfall. The two measures help distinguish the effect of how much rain falls from the effect of how intensely it falls.

For the analysis, the researchers converted daily gridded rainfall observations into monthly totals for each year and applied the Arnoldus empirical approach, a monthly-rainfall method widely used for long-term regional assessments. They calculated annual erosivity density as annual rainfall erosivity divided by annual rainfall. Trend significance was evaluated with the modified Mann–Kendall test, which adjusts for serial correlation, and trend magnitude was estimated with Sen’s slope method.

Agglomerative hierarchical clustering with Ward’s linkage then grouped grid cells with similar 120-year mean rainfall erosivity and erosivity density. The analysis identified a pronounced north–south gradient. Northern Kerala contained the highest minimum, maximum and mean rainfall erosivity, as well as the highest erosivity-density values.

The largest values occurred in and around Kasaragod, Kannur, Wayanad, Kozhikode and parts of eastern Palakkad, with additional high-erosivity areas in parts of Thrissur, Ernakulam and Kottayam. Most of central and southern Kerala had low to moderate values. The mapped patterns broadly followed rainfall totals, but rainfall erosivity had a higher coefficient of variation than rainfall, indicating that the erosive potential of Kerala’s rainfall was more variable across space and time than precipitation amount alone.

Four statistically distinct erosivity domains emerged from the clustering. The largest, C1, contained 23 cells and represented comparatively low conditions, mainly across southern and parts of central Kerala. Its mean rainfall erosivity ranged from 1,291 to 7,044 MJ mm ha⁻¹ h⁻¹ y⁻¹, and its erosivity density ranged from 1.34 to 3.38 MJ ha⁻¹ h⁻¹.

The intermediate C2 domain comprised 17 cells across much of central Kerala, with rainfall erosivity from 8,375 to 12,685 MJ mm ha⁻¹ h⁻¹ y⁻¹ and erosivity density from 2.45 to 3.99 MJ ha⁻¹ h⁻¹. Eight northern cells formed the high-erosivity C3 domain, reaching 21,643 MJ mm ha⁻¹ h⁻¹ y⁻¹ for rainfall erosivity and 5.76 MJ ha⁻¹ h⁻¹ for erosivity density. Two northern cells formed C4, the extreme domain, with rainfall erosivity of 26,974 to 29,987 MJ mm ha⁻¹ h⁻¹ y⁻¹ and erosivity density of 6.54 to 7.38 MJ ha⁻¹ h⁻¹.

Long-term averages nevertheless concealed substantial temporal variability. The 1991–2000 decade showed the widest spread in rainfall erosivity, from 1,269 to 38,824 MJ mm ha⁻¹ h⁻¹ y⁻¹, and the widest spread in erosivity density, from 1.26 to 10.53 MJ ha⁻¹ h⁻¹. Those ranges did not coincide with the highest average values: the decade with the greatest mean rainfall erosivity was 1921–1930, at 12,224 MJ mm ha⁻¹ h⁻¹ y⁻¹, and the highest mean erosivity density was 3.74 MJ ha⁻¹ h⁻¹.

Inter-decadal comparisons showed alternating periods of increase and decrease rather than a smooth progression. Rainfall erosivity rose most strongly between the second and third decades, while pronounced decreases occurred in several later transitions, particularly between 1991–2000 and 2001–2010. Comparing the first and last decades likewise showed that declines predominated statewide.

Statewide linear analysis confirmed the general direction of change. Mean annual rainfall decreased at a rate of 3.53 mm per year, with an R² of 0.1119. Mean rainfall erosivity declined by 34.34 MJ mm ha⁻¹ h⁻¹ y⁻¹ per year, with an R² of 0.1302, while erosivity density fell by 0.0074 MJ ha⁻¹ h⁻¹ per year, with an R² of 0.0947.

Grid-level results were more varied. For rainfall erosivity, 39 of 50 cells showed decreasing trends, including 33 statistically significant declines; 10 showed increases, five of them significant, and one showed no trend. Sen’s slopes ranged from −171.72 to 38.79 MJ mm ha⁻¹ h⁻¹ y⁻¹ per year.

For erosivity density, 40 cells declined, including 34 significant decreases, nine increased, including four significant increases, and one showed no trend. The strongest declines occurred in northern districts, whereas increases were confined to a few eastern highland locations. Elevation did not explain the regional pattern by itself.

In an analysis using 300 spatially distributed points sampled from interpolated surfaces, elevation had a moderate negative relationship with mean rainfall, with r = −0.49 and R² = 0.2418. Its relationships with rainfall erosivity and erosivity density were much weaker: r = −0.24 and R² = 0.0589 for erosivity, and r = −0.18 and R² = 0.0325 for erosivity density. Low-elevation locations generally received more than 3,000 mm of annual rainfall and often had greater erosivity, whereas high-elevation sites commonly received 1,000–1,500 mm, but considerable variation occurred within every elevation band.

The results indicate that monsoon circulation, rainfall intensity, seasonal distribution, topographic position and local atmospheric conditions jointly shaped erosivity rather than altitude acting as a simple control. The coexistence of declining long-term averages and episodic high-erosivity periods is an important feature of the record. A reduction in mean rainfall erosivity does not necessarily imply a proportional reduction in erosion hazard, because average conditions and extreme events represent different parts of the rainfall regime.

The monthly-rainfall method also cannot resolve the kinetic energy, duration or within-storm intensity of individual short-duration storms. Although this approach provides a consistent century-scale comparison, it may under- or overestimate local annual erosivity where extreme storms make a disproportionate contribution. The estimates describe climatic potential for soil detachment, not observed soil loss, which also depends on soil properties, vegetation, land use, slope, infiltration and sediment connectivity.

Likewise, the annual erosivity-density calculation describes long-term rainfall aggressiveness per unit rainfall rather than seasonal concentration or event-scale behavior. The spatial divergence also carries implications for erosion modelling. Because Kerala’s rainfall erosivity varies sharply over a comparatively small and topographically diverse area, applying one statewide R-factor could obscure localized high-erosivity conditions.

The four-domain classification provides an alternative framework for parameterizing Revised Universal Soil Loss Equation models, prioritizing watersheds and supporting spatially differentiated conservation planning. It does not by itself establish current erosion rates, however, and the four-cluster solution was selected through dendrogram interpretation rather than quantitative validation with indices such as silhouette width. High-temporal-resolution rainfall observations, climate projections and further analysis of extreme events will be needed to clarify future erosion susceptibility, but the current evidence shows that Kerala’s erosive rainfall has been highly variable despite its broad long-term decline.

Subject of Research: Century-scale analysis of rainfall erosivity and erosivity density across Kerala, India

Article Title: Century-scale spatiotemporal variability and trends of rainfall erosivity and erosivity density in an ecologically sensitive tropical monsoon region of India (1901–2020)

Article References: Valappil, N. K. M., Yusup, Y., Dewi, J. R., Huda, N., & Hamza, V. (2026). Century-scale spatiotemporal variability and trends of rainfall erosivity and erosivity density in an ecologically sensitive tropical monsoon region of India (1901–2020). Environmental Earth Sciences, 85(17), Article 437. https://doi.org/10.1007/s12665-026-13119-5

Image Credits: AI Generated

DOI: 10.1007/s12665-026-13119-5

Keywords: rainfall erosivity, erosivity density, Kerala, monsoon rainfall, soil erosion, Western Ghats, climate variability, Mann–Kendall test, hydroclimatology, RUSLE, spatial analysis, long-term trends

The results are model estimates for the catchment, rather than direct measurements of sediment exported from every slope. RUSLE estimates mean annual soil loss as the product of five factors: rainfall erosivity, soil erodibility, slope length, slope steepness, cover and management, and support practices. Rainfall erosivity, denoted R, was derived from 20 years of daily rainfall data in the NASA database and calculated with an empirical equation calibrated for East African highland conditions.

The resulting R values ranged from 527.507 to 888.624 megajoules per millimetre per hectare per hour per year, with the highest values in the eastern and southern catchment and lower values in central and northwestern areas. Station values were distributed across the landscape through inverse-distance weighting in ArcGIS, producing a continuous raster of rainfall erosivity. Soil erodibility, or K, was calculated from global FAO soil-texture and organic-carbon data.

It ranged from 0.239 to 0.688 tonnes per hectare-hour per hectare-megajoule per millimetre, with the highest values generally found in the south and northeast. These light-textured sandy and silty soils with relatively little organic matter were modeled as more susceptible to detachment and transport than clay-rich or organically enriched soils in the central and northwest. The K layer was constructed pixel by pixel with a raster calculator, while a 30-metre digital elevation model supplied the terrain information needed to estimate the combined slope-length and slope-steepness factor.

The study therefore combined regional climate records, global soil information and digital topography rather than relying on a single erosion gauge. The topographic factor, LS, ranged from zero to 1.678. It increased on long, steep slopes, where runoff has more opportunity to gather speed and transport detached particles, particularly in the northeastern and central catchment.

The cover-management factor, C, was derived from Landsat 8 near-infrared and red reflectance through the Normalized Difference Vegetation Index, or NDVI. Values ranged from 0.0004 in dense forest to 0.5237 in bare or degraded areas. This more than 1,300-fold range made vegetation cover one of the model’s most spatially variable controls.

The support-practice factor was fixed at 1.0 because the study found few formal soil-conservation structures in the catchment, so the mapped losses represent conditions without effective terracing, contour farming or comparable erosion-control practices. Multiplying the five raster layers produced the study’s estimated soil-loss range of zero to 51.4391 tonnes per hectare per year. The highest values occurred where the model combined high rainfall erosivity with high C and LS values, rather than in a single uniform zone.

In the northeastern Akanyaru Hills, annual cropland occupied steep, elongated slopes with locally more than 30-degree gradients. C values of roughly 0.25 to 0.52 represented limited protection during land preparation and early crop development, while R values reached the catchment’s upper range. The most degraded slope units in this zone had estimated losses approaching or exceeding 40 to 51.4 tonnes per hectare per year.

The model thus identifies the intersection of intense rainfall, topographic amplification and incomplete vegetation cover as a defining combination in the northeast. A second major hotspot lay in the southwest, where persistently bare and severely degraded land coincided with C values of approximately 0.45 to 0.524 and K values reaching 0.55 to 0.688. In model terms, this zone lacked even the partial canopy protection present in annual cropland, allowing rainfall and runoff to act directly on exposed mineral soil.

The southern forest margin formed a third vulnerable landscape: a shifting mosaic of remnant forest, newly cleared plots and pioneer grassland. Here, R values approached 888 megajoules per millimetre per hectare per hour per year, and clearing abruptly removed the very low C values associated with intact forest. These are spatial model patterns, not a measured time series of erosion before and after forest clearing.

Not every high-loss location depended on the same combination of conditions. Smallholder fields in the Gisagara and Nyaruguru southern uplands sat on moderately sloping terrain, with LS values around 0.5 to 1.0, but underlain by the catchment’s most erodible sandy and silty soils. The study attributed this pattern to depleted organic carbon and weak aggregate stability, showing why land cover alone cannot provide a complete erosion assessment.

In the Bugesera and Cyohoha transitional zone, modeled per-hectare losses were generally lower, often 5 to 20 tonnes per hectare per year, because slopes were gentler. However, drainage networks connected this mixed agricultural and expanding residential landscape to Cyohoha Lake, increasing the potential delivery of detached sediment to wetlands and a transboundary lake system. The distinction between erosion on a slope and sediment delivery downstream is important: the mapped estimate concerns soil loss across the landscape, while the lake connection identifies a pathway of potential transport rather than a measured sediment load.

The study recommends geographically targeted measures because its model identifies different dominant controls in different parts of the catchment. For steep agricultural slopes, it calls for bench terracing, grass and legume waterways and contour practices that interrupt runoff. Bare and degraded southwestern areas require rapid revegetation, including pioneer grasses, shrubs and agroforestry systems, while agricultural intervals with exposed soil could be protected with fast-establishing cover crops.

In the erodible southern uplands, the authors emphasize sustained organic-matter inputs, residue retention, reduced disturbance of soil structure and cross-slope contour bunds. Around Cyohoha Lake, riparian buffers, sediment-retention structures and controls on bare construction surfaces are proposed to reduce downstream sediment connectivity. These are conservation proposals based on the modeled vulnerability patterns; the research did not test their effectiveness in field experiments.

These findings should be read with the model’s limitations in mind. The analysis also relies on interpolated rainfall erosivity, global soil-texture information, satellite-derived vegetation cover and a constant support-practice factor; the accuracy of individual pixel estimates therefore depends on those inputs. The catchment’s different sub-catchments, including the Akanyaru Hills, Akanyaru Valley Left Bank and Akanyaru Cyohoha, also have different topographic and land-cover conditions, so the relative contribution of each factor varies across the landscape.

The 30-metre output provides a common spatial reference for identifying where modeled risk is concentrated, but it does not establish the amount of soil physically measured at a particular farm, nor does it demonstrate that land-cover change alone caused a measured increase in erosion. It offers a detailed baseline for comparing future conservation outcomes and targeting monitoring where steep slopes, exposed soil, erodible materials and intense rainfall overlap.

Subject of Research: Spatial modeling of soil loss across four land-cover types in Rwanda’s Akanyaru Catchment

Article Title: Quantification of soil loss by using RUSLE model under different land use and land cover conditions in AKANYARU catchment in Rwanda

Article References: Ntirenganya, J. B., Bugenimana, E. D., Niyonkuru, R., Iragena, A., Uwamahoro, P. C., Bosco, T. J., & Uwihirwe, J. (2026). Quantification of soil loss by using RUSLE model under different land use and land cover conditions in AKANYARU catchment in Rwanda. Discover Soil, 3(1), Article 191. https://doi.org/10.1007/s44378-026-00347-1

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00347-1

Keywords: soil erosion, RUSLE, Akanyaru Catchment, land cover, rainfall erosivity, soil erodibility, agricultural slopes, GIS, Rwanda, erosion hotspots, Quantification, soil

The Jonk drains roughly 3,454 square kilometres across five districts in Chhattisgarh and Odisha, spanning the Sunabeda plateau and gently sloping central plains. The National Bureau of Soil Survey and Land Use Planning has long classified the basin as severely to very severely vulnerable to erosion, yet no previous study had quantified erosion across its eight sub-basins. The region is also climatically paradoxical: although it sits under a tropical monsoon regime receiving 1,221 to 1,257 millimetres of rain annually, about 80 percent falls within the southwest monsoon, and parts of the basin were placed under the Drought Prone Area Development Programme after the 2002 drought. Prolonged dry spells harden and crust the soil, so when intense rains finally arrive, runoff surges over impermeable ground and strips away topsoil with unusual efficiency.

To model this, the team assembled five RUSLE input layers. Rainfall erosivity was computed from 39 years of NASA POWER precipitation data retrieved through an application programming interface and interpolated with kriging, using Arnoldus’s empirical formula because India lacks the high-temporal-resolution records needed for the original EI30 index. Soil erodibility came from the FAO global soil database, with texture and organic carbon converted into K values using Williams’s equations. The topographic LS factor was derived from a 30-metre SRTM digital elevation model in SAGA GIS, applying the Desmet and Govers method with a multiple-flow-direction algorithm to capture runoff dispersion across the dissected plateau terrain. Vegetation cover was expressed through NDVI calculated from Landsat 9 imagery, and the conservation practice factor was assigned by combining a 2022 land-use map, classified in Google Earth Engine with K-means clustering, against slope classes.

The land-cover classification itself was carefully validated. Using 210 field GPS points and high-resolution Google Earth imagery, the team achieved an overall accuracy of 95.71 percent with a kappa coefficient of 0.94, strong agreement for a heterogeneous landscape where open forest, cropland, and settlements can be spectrally confusable. Once all five factors were multiplied pixel by pixel, the model produced annual soil-loss estimates ranging from negligible values to 137.5 tonnes per hectare per year in the worst zones.

The headline finding is that 4.81 percent of the basin falls into moderate to very high erosion risk classes, with losses between 55 and 137.5 tonnes per hectare per year. Most of the basin, about 86 percent, sits in the very low category, but the hotspots are strikingly concentrated. Sub-basin SB-8, in the south near the Sunabeda plateau, emerges as the most erosion-prone area, contributing the largest shares of every high-loss category, a consequence of its moderately to highly dissected plateau terrain of hills and valleys that accelerates runoff and sediment transport. Sub-basin SB-3 follows with roughly 3 percent of its area under high erosion, while SB-5, with stable terrain and better vegetation cover, records the lowest risk, with 96.75 percent of its area losing less than 27.5 tonnes per hectare per year.

Perhaps the most revealing part of the study is its statistical dissection of which factors actually drive the erosion. Pearson correlation analysis showed the slope length and steepness factor had by far the strongest positive relationship with soil loss, a coefficient of 0.705, while rainfall erosivity, despite its high magnitude, correlated only weakly at minus 0.065. A Random Forest regression trained on 10,000 sampled pixels confirmed this hierarchy: the LS factor accounted for about 31.37 percent of model importance, followed by the conservation practice factor at 25.07 percent, soil erodibility at 17.61 percent, and rainfall erosivity and cover management trailing at roughly 13 percent each. In other words, it is not how much rain falls but where it falls on steep, unprotected slopes that determines whether soil stays or goes.

Validation was unusually rigorous for a RUSLE study. The team surveyed 24 field-verified erosion sites, identified by rills, gullies, sheet-erosion features, sediment deposits, and exposed topsoil, and overlaid them on the predicted risk map. A Receiver Operating Characteristic analysis yielded an Area Under the Curve of 0.97, placing the model in the excellent discrimination category and confirming that it reliably separates erosion-prone from stable ground. Field photographs documented hardened lateritic Murrum soil and exposed roots in the upper reaches, matching the modelled hotspots in SB-8 and SB-1.

The study also exposes management gaps that statistics alone cannot capture. Field observations revealed widespread paddy and cotton monocropping in a drought-prone basin, a practice that depletes nutrients, and puddling for paddy that compacts soil and forms hardpans, reducing infiltration so that monsoon downpours generate rapid runoff and erosion. Cross-border dynamics compound the problem: Chhattisgarh’s rice-bowl economy shapes cropping calendars, seed supply, and procurement policies across the state line in western Odisha, locking farmers into water-intensive rice even where millets, pulses, and oilseeds would be more resilient. Exposed slopes lack gabion walls, slope netting, or vegetative stabilization, leaving them open to rill and gully formation.

The authors propose a layered response: jute geotextiles and coir logs on steep slopes, deep-rooted grasses such as vetiver and Bermuda to bind topsoil, mulching to shield bare ground, and micro-watershed structures including check dams, farm ponds, percolation tanks, and contour bunding, some of which already exist in the basin. They also recommend shifting toward drought-tolerant, diversified cropping with efficient irrigation such as alternate wetting and drying in rice. The researchers are candid about limitations: no sediment-discharge data exist at the basin’s gauge station for numerical calibration, the FAO soil data are coarser than the DEM, satellite rainfall products can miss localized extremes, and the P factor relies on generalized slope-land-use assignments. They therefore present the estimates as indicative risk rather than absolute values. Even so, with an AUC of 0.97 and field-confirmed hotspots, the framework offers a reproducible, scalable template for prioritizing conservation in the many semi-arid basins where every tonne of topsoil lost is a tonne of future harvest gone.

Subject of Research: Spatial assessment of soil erosion using RUSLE, GIS and machine learning in the drought-prone Jonk River Basin of eastern India

Article Title: Integration of geospatial techniques and soil loss modelling for soil conservation in a drought prone river basin of eastern India

Article References: Shiwani, K., Maury, C., Rawat, R., & Singh, A. (2026). Integration of geospatial techniques and soil loss modelling for soil conservation in a drought prone river basin of eastern India. Discover Soil, 3(1), Article 101. https://doi.org/10.1007/s44378-026-00259-0

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00259-0

Keywords: soil erosion, RUSLE, GIS, remote sensing, Jonk River Basin, Mahanadi, Random Forest, drought, soil conservation, watershed management, NDVI, ROC analysis

The research, published in the journal Discover Sustainability, was led by Yilkal Gebeyehu Mekonnen of Debre Markos University together with colleagues from the Water and Land Resource Centre, Addis Ababa University, Royal Holloway University of London and the Chinese Academy of Sciences. Rather than relying on sparse field measurements alone, the team applied the Revised Universal Soil Loss Equation, widely known as RUSLE, on the Google Earth Engine cloud computing platform. This approach allowed them to integrate satellite-derived rainfall estimates, soil type data, a digital elevation model, flow accumulation grids, Dynamic World land-cover classifications and datasets describing the distribution of existing soil and water conservation structures into a single, coherent model of erosion risk across the landscape.

RUSLE works by multiplying together a series of factors that each capture one dimension of the erosion process. Rainfall erosivity describes how much kinetic energy storm water delivers to the ground; soil erodibility captures how easily particles detach; the slope length and steepness factor accounts for the accelerating power of water running downhill; the cover management factor reflects how much vegetation protects the surface; and the support practice factor represents interventions such as terraces and bunds that interrupt overland flow. By deriving each of these factors from remote sensing and openly available geospatial datasets, the researchers could generate wall-to-wall erosion estimates at a resolution and coverage that would be impossible to achieve through ground surveys alone.

The baseline results paint a sobering picture of the current state of the land. With a mean soil loss of 32.05 tonnes per hectare per year across the sub-basins, and 44.66 percent of the area classified as experiencing moderate to very severe erosion, the study confirms that land degradation is not a marginal problem confined to fragile hillsides. It is a widespread, landscape-scale phenomenon. The concentration of severe erosion on cultivated land is particularly significant, because it is precisely the fields that farmers depend on for teff, maize, wheat and barley that are losing their fertile topsoil fastest. Topsoil is where organic matter, nutrients and the microbial communities that sustain crop growth reside, and once it is gone, productivity declines in ways that are extremely difficult and expensive to reverse.

What makes the study especially valuable, however, is that it did not stop at diagnosis. The team used their model to ask a forward-looking question: what would happen to soil loss under different land management futures? They constructed scenarios by adjusting the two RUSLE factors that human decisions directly control. In a best management scenario, they assumed widespread adoption of effective soil and water conservation measures, reflected in improved support practice and cover management values. In the opposite extreme, a no-management scenario assumed that existing conservation structures were absent or abandoned. Because the model framework was already in place, running these alternative futures required only recalculating the relevant factor layers.

The contrast between the scenarios is striking. Under best management, mean soil loss across the sub-basins falls to 16.62 tonnes per hectare per year, roughly a 48 percent reduction from the current baseline. Under the no-management scenario, mean soil loss climbs to 47 tonnes per hectare per year, an increase of nearly 47 percent. In other words, the choices that farmers, communities and government agencies make about terraces, vegetation cover and field management could swing annual soil loss across the region by a factor of nearly three. The study does not merely document degradation; it quantifies the stakes of action versus inaction in a way that planners can use directly.

These findings carry particular weight in the Ethiopian highlands, one of the most severely eroded agricultural regions on Earth. Steep slopes, intense seasonal rainfall concentrated in a few months, centuries of cultivation and historically limited conservation investment have combined to make the Abbay basin, which feeds the Blue Nile, a hotspot of land degradation. Soil washed from highland fields does not simply vanish; it clogs rivers and reservoirs downstream, reduces the storage capacity of dams, and degrades aquatic ecosystems. The costs of erosion therefore ripple far beyond the farm gate, affecting hydropower, irrigation and water supply across national and even international boundaries.

The technical approach adopted by the team is as noteworthy as the results. Google Earth Engine provides free access to vast archives of satellite imagery and planetary-scale computing power, which means that analyses that once required expensive software, high-performance hardware and months of data processing can now be executed in the cloud in a fraction of the time. The authors emphasize that their framework can be adapted with minimal effort to customize soil loss estimates for a specific region or watershed. That portability matters for a country like Ethiopia, where thousands of watersheds need prioritization for conservation spending and where local decision-makers often lack the technical resources to build erosion models from scratch.

Dynamic World, the near-real-time land-cover product used in the analysis, deserves particular mention. Traditional land-cover maps are updated infrequently and quickly go out of date in landscapes where cropping patterns change annually. By drawing on a continuously updated, machine-learning-based classification, the researchers ensured that the cover management factor reflected the actual state of vegetation protection on the ground. Combined with satellite rainfall data and a soil and water conservation distribution dataset, this produced a picture of erosion dynamics that is both current and spatially detailed, allowing hotspots to be identified at a scale meaningful for intervention planning.

For stakeholders in the Abbay basin and beyond, the study provides a baseline and a decision-support tool in one package. Watersheds with the highest erosion rates can be identified and targeted for terracing, bund construction, vegetative barriers and other conservation practices, while the scenario results offer a quantified estimate of the returns those investments could deliver. The message from the numbers is unambiguous: conservation works, and its absence is costly. If the best management scenario were achieved, the region could cut its soil losses nearly in half, protecting the cereal fields that feed the nation and the waterways that sustain millions downstream. As climate variability intensifies rainfall extremes across the Horn of Africa, the window for acting on that evidence is narrowing, and studies like this one make it increasingly difficult to claim that the scale of the problem, or the size of the opportunity, is unknown.

Subject of Research: Water erosion modeling and land management scenarios in the northwestern Ethiopian highlands

Article Title: Soil loss dynamics under different land management scenarios in the Northwestern Ethiopian highlands

Article References: Mekonnen, Y. G., Kidane, T. A., Bantider, A., Abate, S. G., Molla, A., Birhanu, L., Teferi, E., Alamirew, T., & Zeleke, G. (2026). Soil loss dynamics under different land management scenarios in the Northwestern Ethiopian highlands. Discover Sustainability. https://doi.org/10.1007/s43621-026-04917-9

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04917-9

Keywords: soil erosion, RUSLE, Ethiopian highlands, Google Earth Engine, soil and water conservation, Abbay basin, land degradation, remote sensing, land management scenarios, food security, Dynamic World land cover, cloud computing

The stakes extend well beyond eroding hillsides. The Upper Omo Gibe Basin is a lifeline for millions of farmers and a critical source of water for the Gibe III hydropower project, one of Ethiopia’s flagship energy developments. Sediment washing off degraded slopes flows downstream into reservoirs, gradually stealing storage capacity, degrading water quality, and undermining the efficiency of turbines. Globally, soil erosion strips an estimated 75 billion metric tons of fertile soil from the land each year, and Ethiopia is among the hardest-hit nations. In severely degraded parts of the country, erosion rates can exceed 300 tons per hectare per year, and the nation’s rivers collectively carry more than 1.3 billion tons of sediment to neighboring countries annually, with the Blue Nile alone transporting roughly 131 million tons.

The research team, led by Muse Wldmchel Shomre of Wachemo University together with colleagues from Wolaita Sodo University, built their assessment on the Revised Universal Soil Loss Equation, or RUSLE, a widely used empirical model that estimates average annual soil loss from five factors: rainfall erosivity, soil erodibility, slope length and steepness, land cover management, and conservation support practices. Working at a 30-by-30-meter grid resolution within a geographic information system, the team computed each factor from field and satellite data. Rainfall erosivity was derived using Hurni’s regression equation adapted for the Ethiopian highlands, soil erodibility came from the Wischmeier and Smith nomograph applied to the FAO Harmonized World Soil Database, and topographic factors were extracted from a digital elevation model.

A crucial innovation of the study lies in its pairing of RUSLE with the Sub-Watershed Prioritization Tool, or SWPT, which automates the calculation of morphometric and topo-hydrological parameters such as drainage density, bifurcation ratio, form factor, circularity ratio, the topographic wetness index, the stream power index, and the sediment transport index. These descriptors capture how the shape and structure of each sub-watershed govern runoff behavior and sediment movement. By combining the two approaches through a weighted sum analysis, the researchers could rank nine sub-watersheds by vulnerability, moving beyond the broad-brush assessments that have historically obscured localized erosion hotspots in data-scarce regions.

The land cover story that emerged from the satellite record is stark. Between 1991 and 2020, agricultural land expanded from 4,751 square kilometers, about 30 percent of the basin, to 6,922 square kilometers, or nearly 44 percent, a net gain of 2,570 square kilometers. Settlement areas more than doubled, growing from 1,089 to 2,862 square kilometers as urbanization accelerated. Shrubland bore the brunt of this conversion, collapsing from 4,234 square kilometers to just 2,041, while bare land fell from 3,267 to 1,342 square kilometers. Forest cover dipped from 15 percent of the basin in 1991 to about 10.5 percent in 2006 before partially recovering to 15.6 percent by 2020, a rebound the authors attribute to afforestation programs, watershed rehabilitation, and natural regeneration. Classification accuracies ranged from 72 to 86.4 percent, with kappa coefficients between 0.67 and 0.84, indicating statistically reliable maps.

Climate projections add a troubling dimension. The team evaluated five regional climate model combinations from the CORDEX-Africa ensemble under two emission scenarios, RCP 4.5 and RCP 8.5, after bias-correcting the raw outputs with distribution mapping for precipitation and variance scaling for temperature. The ensemble mean consistently outperformed individual models, achieving correlations of 0.54 to 0.67 with observed rainfall and the lowest root mean square errors across six weather stations. Under the medium emission scenario, ensemble maximum temperatures are projected to rise by 0.54 degrees Celsius in the near future and 1.3 degrees Celsius by mid-century, while under the high emission scenario the far-future increase reaches 2.23 degrees Celsius. Rainfall, meanwhile, shows reductions of up to 31.8 percent in some model projections, a paradoxical combination in which less total rain may arrive in more intense, erosive bursts.

This matters for erosion because the relationship between rainfall and soil loss is nonlinear. Even as annual totals decline, shifts in rainfall intensity and distribution can amplify erosion during extreme events, while higher temperatures reduce soil moisture and vegetation cover, weakening soil structure. The study’s authors note that future erosion patterns may therefore diverge from historical trends, demanding adaptive rather than static conservation strategies. The RUSLE analysis itself showed the consequences of past changes: total soil loss across the basin rose from about 40.1 million tons per year in 1991 to 57.8 million tons per year in 2020, an intensification of nearly 44 percent over three decades.

The prioritization analysis pinpointed where intervention would pay off most. Sub-watersheds SW1 and SW3 emerged as the highest priorities, with compound parameter values of -1903 and 927 respectively, reflecting unfavorable morphometric characteristics and high sediment transport potential. SW3 recorded the highest sediment transport index in the basin at 19.72, while SW5 showed the greatest stream power index at 6.56, flagging it for channel erosion risk. Four sub-watersheds, SW1, SW3, SW4, and SW5, were identified as highly vulnerable overall. The authors recommend immediate soil and water conservation measures in the top-priority zones, including afforestation, check dam construction, and sustainable land management, moderate interventions in medium-priority areas such as SW5, and routine monitoring in lower-priority sub-watersheds like SW6 and SW9.

The findings also expose why past conservation efforts have struggled. Terracing, stone bunds, and mulching schemes across Ethiopia have often underperformed due to poor design, inadequate maintenance, and insufficient funding, and a major culprit has been the misallocation of resources to less vulnerable areas while critical hotspots went unaddressed. By fusing empirical soil loss estimates with geomorphometric prioritization, the RUSLE-SWPT framework offers a way to direct scarce conservation funding where it will do the most good, an approach consistent with recent multi-criteria decision analyses in India’s Teesta River Basin that combined morphometric parameters with techniques such as AHP and TOPSIS.

The study is candid about its limits. RUSLE captures only sheet and rill erosion, not the gully erosion and mass movements common on steep degraded slopes, and the researchers did not feed climate projections directly into the erosion model because of uncertainties in future land use, soil characteristics, and conservation practices. Limited field sediment data restricted validation, and uncertainties in rainfall records, soil properties, and digital elevation model resolution all propagate into the estimates. Still, the authors argue that the coupled framework provides a practical blueprint for data-scarce regions, and they call for future work integrating climate and land use scenarios into process-based models such as SWAT or WEPP. For a basin whose hillsides feed both farms and turbines, the message is clear: without targeted, evidence-based conservation, the soil that sustains Ethiopia’s Upper Omo Gibe will keep washing away, one storm at a time.

Subject of Research: Soil erosion assessment and sub-watershed prioritization using coupled RUSLE and SWPT modeling under land use change and climate projections in the Upper Omo Gibe Basin, Ethiopia

Article Title: Coupled RUSLE-SWPT morphometric characterization of soil erosion under land use change and climate projections in the Upper OMO Gibe Basin

Article References: Shomre, M. W., Anticho, L. A., & Meskele, D. Y. (2026). Coupled RUSLE-SWPT morphometric characterization of soil erosion under land use change and climate projections in the Upper OMO Gibe Basin. Discover Soil, 3(1), Article 117. https://doi.org/10.1007/s44378-026-00264-3

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00264-3

Keywords: soil erosion, RUSLE, SWPT, Upper Omo Gibe Basin, Ethiopia, land use change, climate projections, watershed prioritization, morphometric analysis, hydropower, GIS, remote sensing

The research team, led by Ninu Krishnan Modon Valappil of Universiti Sains Malaysia, together with Yusri Yusup and Vijith Hamza, drew on the India Meteorological Department’s high-resolution daily gridded rainfall dataset, which covers the subcontinent at a quarter-degree grid spacing and extends back to the beginning of the twentieth century. From these daily records, aggregated into monthly totals, the team computed two closely related quantities. The first is rainfall erosivity, often called the R-factor, a term in the Universal Soil Loss Equation family of models that quantifies the kinetic punch delivered by raindrops and the runoff they generate. The second is erosivity density, which normalizes that punch by the amount of rain, effectively asking how destructive each millimeter of rainfall is on average.

To estimate these quantities across the full century, the researchers employed the monthly rainfall-based empirical model introduced by Arnoldus in 1980, a widely used approach when sub-hourly rainfall intensity data are unavailable, as they are for most of the historical record. The resulting values reveal an extraordinary range. Annual rainfall across Kerala varied from as little as 134 millimeters to as much as 5,424 millimeters in individual grid cells and years. Rainfall erosivity ranged from 61 to 58,063 megajoule millimeters per hectare per hour per year, a spread of nearly three orders of magnitude, while erosivity density spanned 0.38 to 17.61 megajoules per hectare per hour. That enormous variability is precisely why the authors argue that averages and totals conceal more than they reveal about erosion risk.

Geographically, the study found a persistent north-south divide. Higher rainfall, higher erosivity, and higher erosivity density were consistently concentrated in northern Kerala, where the Western Ghats force moisture-laden monsoon winds upward and squeeze out intense orographic precipitation. Lower values predominated across much of the southern region. This spatial pattern matters because the Western Ghats are recognized as one of the world’s biodiversity hotspots, and previous work has documented substantial soil loss across the region, including dramatic erosion episodes following the severe Kerala floods of 2018. Knowing where the erosive energy of the climate is concentrated provides a scientific basis for targeting watershed management and soil conservation measures where they will do the most good.

The temporal analysis was where the study broke new ground. Using linear trend analysis alongside seasonal, decadal, and inter-decadal comparisons, the team found that rainfall, erosivity, and erosivity density did not move in lockstep. Instead, the record alternated between phases of increasing and decreasing values on decadal timescales, and seasonal hotspot analysis, performed with the Getis-Ord Gi* statistic, a method for identifying statistically significant spatial clustering, revealed pronounced shifts between monsoon and non-monsoon periods. In other words, the places and times where erosive power concentrates are not fixed features of the landscape but migrate through the decades and across the calendar, responding to the shifting rhythms of the monsoon system.

The conceptual centerpiece of the paper is the decoupling index, a measure borrowed from economics, where decoupling analysis was developed to examine whether economic growth could be separated from environmental damage. Applied here, the index asks a simple question: when rainfall amount changes, does erosivity change proportionally? The answer, across most of Kerala’s twentieth century, was no. Weak coupling predominated throughout the study period, meaning that changes in how much rain fell were only loosely reflected in changes in how erosive that rain was. More strikingly, the results suggest that climatic rainfall erosivity became progressively less dependent on rainfall amount alone as the century wore on, hinting that the character of the rain itself, its intensity, concentration, and timing, has been changing in ways that totals cannot capture.

This decoupling has a physical explanation rooted in how raindrops transfer energy to the ground. Erosivity scales with the kinetic energy of falling drops and with rainfall intensity, not merely with volume. A season that delivers the same total rainfall as another, but in fewer, fiercer bursts, will strip far more soil. Climate change is widely expected to intensify precisely this pattern across the tropics, with warming seas and atmospheres loading more moisture into individual storm events even where total rainfall stagnates or declines. Related studies cited by the authors have documented intensifying erosivity in West Africa, and research along the Western Ghats and the southwest coast of India has documented changes in extreme rainfall, mesoscale convective systems, and moisture transport in recent decades, all consistent with a monsoon regime whose extremes are sharpening.

For Kerala, the practical stakes are considerable. The state’s steep slopes, lateritic soils, dense river networks, and reservoir-dependent agriculture make it acutely sensitive to sediment loss, which chokes reservoirs, degrades farmland, and compounds landslide and flood hazards. The study’s authors frame their results as a foundation for regional soil erosion assessment, watershed management, and climate adaptation in tropical monsoon environments. If planners continue to infer erosion risk from rainfall totals, they may systematically misjudge which decades and districts face the greatest threat. A decade of modest total rainfall punctuated by violent downpours could be more erosive than a wetter, gentler decade, and the decoupling index offers a way to detect exactly that divergence in the historical record.

Methodologically, the study also demonstrates the value of squeezing more from the data that exist. True erosivity calculations ideally require high-temporal-resolution rainfall intensity measurements, which global efforts such as the Global Rainfall Erosivity Database have assembled for recent decades. But century-scale assessment demands the long observational records that only monthly or daily data can provide, and the Arnoldus monthly model, though an approximation, allows researchers to extend erosion-relevant analysis back through periods when no rain gauge recorded intensity. The trade-off is acknowledged in the literature, and the authors’ use of trend analysis, hotspot statistics, and the decoupling index together provides a more robust picture than any single metric could, triangulating on the underlying behavior of the monsoon system.

The broader message extends well beyond Kerala. Rainfall-driven soil erosion is a major cause of land degradation in tropical monsoon regions, where hundreds of millions of people depend on rain-fed agriculture. As global assessments of rainfall erosivity grow more sophisticated, the Kerala study adds a century-scale caution: the relationship between the amount of water falling from the sky and the damage that water does is neither fixed nor guaranteed. In a warming world, that relationship appears to be loosening, and the erosion threat may be growing fastest precisely where rainfall statistics look unremarkable. For the steep, green slopes of the Western Ghats and for monsoon landscapes across Asia, Africa, and South America, the rain that matters most may be the rain that falls hardest, not the rain that falls most.

Subject of Research: Century-scale changes in rainfall erosivity and its decoupling from rainfall amount in Kerala, India

Article Title: Decoupling rainfall amount and rainfall erosivity: century-scale changes in climatic rainfall erosivity across Kerala, India

Article References: Valappil, N. K. M., Yusup, Y., & Hamza, V. (2026). Decoupling rainfall amount and rainfall erosivity: century-scale changes in climatic rainfall erosivity across Kerala, India. Theoretical and Applied Climatology, 157(10), Article 675. https://doi.org/10.1007/s00704-026-06587-z

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06587-z

Keywords: rainfall erosivity, soil erosion, Kerala, monsoon, Western Ghats, erosivity density, decoupling index, climate change, India Meteorological Department, watershed management, RUSLE, land degradation

The study, conducted by Prodip Mandal and Mayank Joshi of the Govind Ballabh Pant National Institute of Himalayan Environment and Siddharth Prizomwala of the Institute of Seismological Research, applied the Revised Universal Soil Loss Equation, widely known as RUSLE, to a 75.52 square kilometre area around the rapidly growing district of Namchi. Their results, published in the journal Discover Geoscience, estimate an average annual soil loss of 39.10 tonnes per hectare per year, with localized hotspots reaching up to 138.62 tonnes per hectare per year. While those figures far exceed the global average erosion rate of roughly 2.4 tonnes per hectare per year, they are modest compared with rates exceeding 800 tonnes per hectare per year recorded in parts of India’s Northeastern Himalaya, where shifting cultivation and deforested wastelands leave slopes fully exposed.

RUSLE is an empirical model originally developed by the United States Department of Agriculture that computes average annual soil loss as the product of five factors: rainfall erosivity, soil erodibility, slope length and steepness, land-cover management, and conservation practices. Its enduring appeal lies in its adaptability. Because each factor can be derived from remotely sensed data and processed within a geographic information system, the model works well in data-scarce mountain regions where rain gauges with sub-hourly intensity measurements and detailed field soil surveys simply do not exist. That is precisely the situation in much of the Sikkim Himalaya, a tectonically young fold mountain belt built of weakly consolidated rocks from the Daling Group, threaded with active faults and hammered by prolonged monsoonal rainfall that makes the region a natural hotspot for erosion and landslides alike.

To build each factor layer, the researchers assembled a mix of datasets. Rainfall erosivity, which captures the capacity of falling rain to detach and transport soil particles, was estimated from daily rainfall records at six stations in and around the study area using an empirical relationship suited to Indian conditions, yielding values between 717.47 and 826.03 megajoule millimetres per hectare per hour per year, highest in the wetter southern portion of the map. Soil erodibility was computed with the EPIC equation from sand, silt, clay and organic carbon contents drawn from the ISRIC Soil Grids database, producing values between roughly 0.0137 and 0.0156, with the more erodible soils at higher elevations where organic carbon is abundant. The topographic factor, combining slope length and steepness, was derived from a 12.5-metre ALOS PALSAR digital elevation model and reached values as high as 19.05 in dissected terrain near drainage channels.

The remaining two factors encode human influence on the land surface. The cover management factor, ranging from 0.004 to 1.0, was assigned to five land-use classes mapped from European Space Agency satellite imagery acquired in October 2024 and refined with high-resolution imagery from Google Earth: dense forest, moderately dense forest, sparse forest, agricultural land and built-up areas. Forested slopes, where canopy intercepts raindrops and roots bind the soil, received the lowest values, while bare and open land received the highest. The conservation support practice factor, spanning 0.55 to 1.0, reflects measures such as terracing, contour farming and retaining walls that locally slow runoff. When the five layers were multiplied together in raster format and the resulting erosion values classified with the Jenks natural breaks method, the picture that emerged was strikingly uneven: 89.52 percent of the study area falls in the low-risk class, 6.52 percent in the moderate class and only 3.96 percent in the high-risk class.

Where does the sediment actually come from? The answer, overwhelmingly, is the steepest farmland. Among land-use classes, agricultural land and open land showed the highest erosion rates, peaking at 138.62 tonnes per hectare per year, whereas dense forest lost only 21.53 tonnes and moderately dense forest 20.23 tonnes per hectare per year. Together, dense and moderately dense forest blanket more than 55 percent of the study area, and the analysis makes clear that this vegetation is the single most important brake on soil loss, absorbing raindrop kinetic energy and reducing runoff velocity before it can carve rills into hillsides. Interestingly, the urban core of Namchi itself recorded relatively low erosion of 85.76 tonnes per hectare per year, an unexpected finding the researchers attribute to impervious concrete surfaces and the retaining walls residents build to protect hillside properties, which shield the soil from direct raindrop impact even as construction churns the surrounding periphery.

Slope proved equally decisive. The highest erosion rates occurred on slopes between 35 and 50 degrees, where gravity accelerates runoff and shallow, weakly stable soils sit atop fragile Himalayan bedrock. Somewhat counterintuitively, the steepest slopes of all, above 50 degrees, showed the lowest measured rates at just 6.33 tonnes per hectare per year, because exposed hard rock and difficult access limit both soil availability and human interference there. The geographic pattern also follows the rain: the southern part of the study area, closer to the moisture-laden winds arriving from the Bay of Bengal, receives more rainfall and more sunshine on its south-facing slopes, and consequently loses more soil than the northern segment.

Seeking to understand what drives these patterns, the team extracted six topographic and hydrological factors from the digital elevation model, including elevation, slope, aspect, relative relief, topographic wetness index and drainage density, and examined their pairwise relationships with Pearson correlation coefficients after checking for multicollinearity. Elevation showed weak but statistically significant positive correlations with slope, relative relief and drainage density, and a weak negative correlation with the topographic wetness index, which is higher in low-lying zones where moisture accumulates and deposition rather than erosion dominates. Aspect correlated positively with slope and relief, and the strongest relationship in the matrix linked rainfall erosivity with drainage density, underscoring how climatic forcing and landscape dissection interact to concentrate erosion along densely channelled terrain. Overall, relative relief and slope emerged as the most influential contributors to soil loss in the region.

The implications reach well beyond geomorphology. The authors frame their findings against the United Nations Sustainable Development Goals, noting that erosion degrades spring catchments that mountain communities depend on for drinking water, undermining SDG 6 on clean water; strips fertile topsoil from farms, deepening rural inequality under SDG 10; and threatens the sustainability of fast-growing Himalayan towns under SDG 11. Because erosion here is most severe during the monsoon, targeted interventions such as contour farming, terracing and slope protection on the moderate-to-steep agricultural slopes of the southern study area could deliver outsized benefits. Prior studies cited in the work suggest that well-implemented conservation practices can cut erosion by a factor of 2.5 to 7, and the Namchi results hint that even informal measures by local residents already mute erosion around settlements.

The study is candid about its limits. Empirical erosivity equations built on daily rainfall cannot fully capture the short, violent cloudbursts that characterize early-monsoon events in the Eastern Himalaya and frequently trigger landslides, and globally generalized soil databases may smooth over local heterogeneity in the rugged terrain. Climate change, intensifying extreme precipitation and accelerating infrastructure development are all expected to shift erosion rates in ways the current model does not project. Even so, the team argues that RUSLE, paired with freely available satellite data, offers a practical blueprint for other data-scarce Himalayan districts: by identifying exactly which slopes, land uses and aspect zones bleed soil fastest, it gives planners a prioritized map for conservation investment in one of the most erosion-prone mountain belts on the planet.

Subject of Research: Quantitative assessment of monsoon-driven soil erosion using the RUSLE model in the Sikkim Himalaya

Article Title: RUSLE-based quantitative soil erosion assessment in the monsoon-dominated region of Sikkim Himalaya

Article References: Mandal, P., Joshi, M., & Prizomwala, S. (2026). RUSLE-based quantitative soil erosion assessment in the monsoon-dominated region of Sikkim Himalaya. Discover Geoscience, 4(1), Article 348. https://doi.org/10.1007/s44288-026-00706-z

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00706-z

Keywords: soil erosion, RUSLE, Sikkim Himalaya, monsoon rainfall, land use, slope classes, soil conservation, GIS and remote sensing, Namchi, rainfall erosivity, forest cover, sustainable development goals