The researchers divided the approximately 2,093 km² Pampa basin into 31 sub-watersheds and evaluated 15 morphometric, hydrological, and hazard-related criteria. These included drainage-network properties, basin shape, relief, slope, ruggedness, overland-flow length, rainfall variability, and other indicators intended to describe the terrain’s response to water and the occurrence of floods, droughts, and landslides. A 30 m Shuttle Radar Topography Mission digital elevation model was processed in ArcGIS through sink filling, flow-direction and flow-accumulation analysis, stream extraction, stream ordering, and watershed delineation. Daily rainfall records from 2015–2025 were also interpolated in GIS to characterize long-term annual and seasonal variability across the basin.

Relative importance was assigned with AHP, which uses pairwise comparisons to derive criterion weights. Five specialists with more than 10 years of experience in watershed assessment and natural-hazard analysis independently compared the 15 parameters on Saaty’s nine-point scale. Their matrices were combined by geometric averaging, and the resulting consensus matrix was normalized to obtain weights. The consistency ratio was 0.08, below the study’s threshold of 0.10 for acceptable consistency. Each criterion was normalized to a dimensionless scale, then combined through a weighted linear equation to generate a priority score for every sub-watershed. Jenks natural-breaks classification was used to divide the scores into relative priority classes, but the numerical boundaries are specific to this basin and cannot be transferred unchanged to other watersheds.

The terrain analysis described a highly dissected basin with 560 identified stream segments and a Strahler stream-order system extending to sixth order. The drainage network included 405 first-order streams, 103 second-order streams, 37 third-order streams, 13 fourth-order streams, two fifth-order streams, and one sixth-order stream. Mean stream length ranged from 1.5 to 3.8 km, while the basin-wide bifurcation ratio averaged 2.38. Drainage density was below 5.0 km/km² and drainage texture was 8.10, placing the network in an intermediate texture class. Most Rho coefficient values were below 0.2, and more than 60 percent of the basin fell below 0.15, indicating relatively limited storage and a tendency toward rapid overland flow according to the study’s interpretation of the index.

Basin geometry also varied substantially. Pampa’s circularity ratio was 0.36, consistent with an elongated form, while its elongation ratio was 0.65. The highest terrain occurred in sub-watershed W31, and the lowest points appeared in W8 and W9. Sub-watersheds W5 and W18 had comparatively high ruggedness numbers, whereas W31 had a lower value. At the sub-basin scale, infiltration-number values were lowest in W31 and highest in W20, W21, and W24. These contrasts helped differentiate terrain controls on runoff, erosion, slope instability, and water retention, although the researchers cautioned that the AHP model represents these relationships through independent weighted criteria rather than fully coupled physical processes.

The uncertainty experiment began from the baseline AHP weight vector. In each of 10,000 iterations, all 15 weights were randomly perturbed by as much as plus or minus 10 percent using a uniform distribution, then renormalized to sum to one. Priority scores and rankings were recalculated for all 31 sub-watersheds after every perturbation. W10, W14, W7, W4, and W17 repeatedly occupied the highest ranks and were therefore classified as robust. W1, W2, and W22 showed moderate sensitivity. The simulation was designed specifically to examine variability in expert weights; its uniform perturbation range was not intended as a complete probability distribution of uncertainty, and it did not quantify errors in the elevation model, stream-extraction choices, spatial resolution, threshold selection, or model structure.

Household-level records were used as an independent check on the modeled priorities. District Disaster Management Authority data covered flood damage in 2018 and 2019, landslide damage in 2018, and drought impacts reported for the study area. About 75.0 percent of households affected by floods were located in sub-watersheds classified as moderate to very high priority. All 76 households affected by landslides were in moderate- or high-priority areas, while 67.9 percent of the 321 drought-affected households were in moderate- to very-high-priority classes. These results show concentration of reported impacts in relatively high-priority areas, but they do not amount to direct proof of physical susceptibility because household damage also reflects population density, settlement patterns, housing quality, land use, infrastructure, reporting practices, and community resilience.

The quantitative correlation also requires caution. Spearman’s rank correlation between the AHP priority scores and the number of flood-affected households was weak and statistically nonsignificant, with ρ = −0.011 and p = 0.952. This means the composite ranking did not closely track flood damage counts in individual sub-watersheds. The authors attributed the mismatch partly to the framework’s emphasis on intrinsic physical vulnerability, whereas damage records reflect hazard intensity together with exposure, socioeconomic conditions, infrastructure resilience, and post-disaster reporting. The household records were therefore treated as evidence of consistency and practical utility rather than as a direct proxy for susceptibility or a causal validation of the model.

The study’s principal contribution is therefore methodological as much as geographic. Integrating morphometric analysis with AHP creates a transparent way to combine terrain, drainage, rainfall, and hazard indicators when detailed process-model inputs are limited, while Monte Carlo analysis reveals whether the resulting priorities depend heavily on a particular set of expert weights. Even so, the framework remains a first-order regional screening tool. It does not simulate event-scale rainfall intensity, antecedent soil moisture, groundwater behavior, changing land use, cascading hazards, or the full mechanics of floods and slope failures. Transfer to other basins would require recalibrating the natural-breaks classes and examining alternative elevation products, raster resolutions, watershed boundaries, and preprocessing choices. The authors also identify population and infrastructure layers, dynamic land-cover data, climate and land-use scenarios, and process-based flood, drought, and slope-stability models as necessary additions if the approach is to evolve from relative susceptibility mapping into comprehensive risk assessment. The present results provide a reproducible screening tool for comparing 31 tropical monsoon watersheds while explicitly revealing which priorities remain stable and which depend on judgment-based weighting.

Subject of Research: Uncertainty-tested prioritization of 31 Kerala sub-watersheds for flood, drought, and landslide susceptibility

Article Title: Integrating morphometric analysis and AHP–Monte Carlo framework for multi-hazard risk reduction

Article References: Sekar, R., Natarajan, S., & Joseph, N. (2026). Integrating morphometric analysis and AHP–Monte Carlo framework for multi-hazard risk reduction. Natural Hazards, 122(22), Article 689. https://doi.org/10.1007/s11069-026-08468-0

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08468-0

Keywords: multi-hazard susceptibility, watershed prioritization, Monte Carlo simulation, Analytical Hierarchy Process, morphometric analysis, Pampa River basin, Kerala, flood risk, drought risk, landslide risk, GIS, uncertainty analysis

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