Search Results (28)

Extreme hydrological events fundamentally alter sediment transport dynamics across grain, reach, and watershed scales, rendering classical equilibrium-based transport formulations inadequate. This review synthesizes recent advances in multiscale sediment transport modeling under highly unsteady and high-magnitude forcing conditions. At the grain scale, particle-resolved simulations demonstrate that sediment entrainment is governed by turbulence intermittency and transient force exceedance rather than mean bed shear stress thresholds, particularly when the hydrograph rise timescale (T_h) becomes comparable to particle response times (T_p). At the reach scale, non-equilibrium transport emerges when the unsteadiness ratio T_h/T_a∼O(1), where T_a is the sediment adaptation timescale representing the time required for sediment flux to adjust toward transport capacity. Under these conditions, pronounced hysteresis between discharge and sediment flux is observed, requiring relaxation-based transport formulations instead of instantaneous equilibrium laws. At the watershed scale, the sediment delivery ratio (SDR), defined as the ratio of sediment yield at the basin outlet to total hillslope erosion, becomes highly time-dependent. Extreme precipitation events can activate hillslope-channel connectivity, increasing SDR by orders of magnitude relative to baseline conditions. A unified dimensionless scaling framework is presented based on mobility intensity (θ/θ_c, where θ is the Shields parameter and θ_c is its critical value for incipient motion), unsteadiness ratio (T_h/T_a), and morphodynamic coupling (T_f/T_m, where T_f is the hydraulic advection timescale and T_m is the morphodynamic adjustment timescale). This framework enables classification of sediment transport regimes ranging from quasi-equilibrium to cascade-dominated states. The synthesis demonstrates that predictive uncertainty increases nonlinearly across scales due to timescale compression, threshold activation, and feedback between flow hydraulics and evolving morphology. Recent developments in hybrid physics-AI approaches show promise in improving predictive capability by enabling dynamic transport closures, surrogate modeling of computationally expensive microscale processes, and data assimilation for real-time forecasting. However, these approaches remain limited by extrapolation uncertainty and the need to enforce physical constraints. Overall, this review concludes that regime-aware multiscale coupling, combined with uncertainty quantification and adaptive modeling strategies, is essential for robust sediment hazard prediction and climate-resilient infrastructure design under intensifying hydrological extremes. Full article

This study systematically analyzes the spatio-temporal evolution, trade-offs, synergies and driving mechanisms of five ecosystem services (ESs) in Fujian Province (carbon storage, CS; habitat quality, HQ; sediment delivery ratio, SDR; water yield, WY; food provision, FP) based on multi-source data from 2003, 2013 and 2023 by adopting the InVEST model, Spearman correlation analysis, geographically weighted regression (GWR), self-organizing maps (SOM) and geographic detectors. Results show that: (1) ESs present a spatial pattern of “high in northwest and low in southeast” in Fujian; CS, HQ and FP show an overall decline, while SDR and WY increase significantly. (2) ES trade-offs and synergies have obvious scale effects and spatial heterogeneity, with stronger relationship intensity at the county level than the grid level, and FP generally shows a trade-off relationship with other services. (3) Land use is the key driving factor for CS, FP and HQ; precipitation dominates the changes in WY and SDR; and dual-factor interactions generally enhance the explanatory power of ES changes. The findings enrich the theoretical system of multi-scale ES trade-off and synergy research under rapid urbanization and provide a scientific basis for sustainable territorial spatial planning and differentiated ecological governance in Fujian. Meanwhile, the research framework can serve as a reference for ES management in other coastal mountainous regions worldwide, contributing to the realization of regional sustainable development goals (SDGs). Full article

This study aims to assess the evolution of land cover in the Lam Phra Phloeng (LPP) watershed and predict future land use patterns. By employing the Gray Level Co-occurrence Matrix (GLCM) and several spectral indices, high classification accuracy (>92%) was achieved using the Random Forest (RF) algorithm. Based on classified land use maps from 2003 and 2023, future land use predictions for 2030, and 2050 were generated using the CA-Markov chain model. The predictions suggest a gradual trend toward deforestation and the expansion of croplands, driven by population growth and increased anthropogenic activity in the region. The Sediment Delivery Ratio (SDR) model, part of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) suite, was used to simulate soil loss in the LPP watershed. The results indicate minimal soil loss in vegetated areas and significant erosion in regions adjacent to water bodies, primarily due to rainfall erosivity. This research highlights the social, ecological, and economic implications of land use change. Furthermore, best management practices (BMPs) are identified as effective strategies for land restoration and erosion reduction. The study also discusses three widely adopted soil erosion control techniques, providing recommendations for reforestation and erosion mitigation programmes. Full article

The Mun River Basin, the largest Mekong tributary in Northeast Thailand, has experienced extensive agricultural expansion and forest decline, raising concerns over increasing soil erosion and sediment transfer. This study provides an integrated assessment of soil loss, sediment yield (SY), and sediment delivery ratio (SDR) across 19 sub-watersheds using the Universal Soil Loss Equation (USLE), field-based SY data, and multivariate statistical analyses in 2024. Basinwide soil loss was estimated at ~35 million t y⁻¹ (mean 4.96 t ha⁻¹ y⁻¹), with more than 80% of the basin classified in the no erosion to very low erosion classes. Despite substantial hillslope erosion, only 402,405 t y⁻¹ of sediment reaches the river network, corresponding to a low SDR of 1.15%, which falls within the range reported for large tropical watersheds with significant reservoir infrastructure. Soil loss is most strongly influenced by slope and forested terrain, while SY responds primarily to rainfall and tree plantations; urban land, croplands, and reservoirs act as sediment sinks. Principal Component Analysis (PCA) resolved multicollinearity and produced six components explaining over 90% of predictor variance. A PCA-based regression model predicted SY per unit area with high accuracy (r = 0.81). The results highlight the dominant roles of hydroclimate and land-use structure in shaping sediment connectivity, supporting targeted soil and watershed-management strategies. Full article

Soil erosion threatens agricultural sustainability and water quality in Mediterranean watersheds, necessitating effective Nature-Based Solutions (NBSs) for mitigation. This study applied the InVEST Sediment Delivery Ratio (SDR) model to assess erosion patterns and evaluate NBS effectiveness in the Carapelle watershed (506 km²). The SDR model was calibrated and validated using measured sediment yield data from 2007 and 2008. Model validation achieved a 4.3% deviation from observed data after parameter optimization. Four NBS scenarios were evaluated: contour farming (CF), no-tillage (NT), cover crops (CCs), and combined practices (Comb). Baseline soil loss varied from 2.43 t ha⁻¹ yr⁻¹ (2007) to 3.88 t ha⁻¹ yr⁻¹ (2008), with sediment export ranging from 0.86 to 1.30 t ha⁻¹ yr⁻¹. NT demonstrated the highest individual effectiveness, reducing sediment export by 72.2% on average. The Comb approach (NT + CCs) achieved a superior performance with a 75.9% sediment export reduction and a 70.5% soil loss reduction. Spatial analysis revealed that high-retention zones were concentrated in forest and shrubland, while agricultural zones showed the greatest potential for NBS implementation. NBSs significantly enhance sediment retention services in Mediterranean agricultural watersheds. The InVEST SDR model proves to be effective for watershed-scale assessment. The results provide actionable guidance for sustainable land management and soil conservation policy in erosion-prone Mediterranean environments. Full article

Soil erosion is a significant challenge to the environment, ecology, and economy, and areas that undergo fast land use change and climate change are the most affected. This research evaluates the effects that climate change and Land-Use/Land-Cover (LULC) change have, separately and together, on soil loss and sediment retention in the Lam Phra Phloeng (LPP) watershed, Thailand. The InVEST Sediment Delivery Ratio (SDR) model was applied under the Shared from Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), using projected LULC for 2050 and 2100. The Cellular Automata–Markov (CA–Markov) model has been utilized to generate future land use/land cover (LULC) scenarios demonstrating how land changes over spatial and temporal scale. Results show a marked decline in sediment retention and a rise in soil loss, especially under high-emission scenarios and cropland expansion. By 2100, cropland soil loss increased by 57.35%, while forest cover—a key determinant of sediment retention—declined from 45.41% in 2020 to 22.19%. When climate and land-use changes are considered together, they have a much greater effect on sediment loss, especially in cropland and built-up areas. These results highlight the vital role that forest conservation and adaptive land management, e.g., afforestation and sustainable agriculture, play in ensuring the continued availability of clean water in watersheds and in erosion control. The research provides policy-makers with real-life scenarios to draw on when sketching integrated watershed management plans aimed at reducing the negative effects of land use and climate change on soil stability and water resources in the LPP watershed. Full article

The increasing rate of land use change, particularly deforestation and agricultural expansion, has intensified soil degradation, leading to reduced sediment retention and accelerated soil erosion. This study aims to analyze soil erosion and sediment retention in the Lam Phra Phloeng (LPP) watershed, Thailand, using a coupled modelling approach integrating the Revised Universal Soil Loss Equation (RUSLE) and the Sediment Delivery Ratio (SDR) model from the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) suite. Six land use classes (forest, cropland, rangeland, flooded vegetation, built-up areas, and water bodies) were identified using Sentinel-2 MSI satellite data, with a Random Forest (RF) classification algorithm achieving an overall accuracy of 91.3% (Kappa coefficient = 0.89). The results indicate that forested areas exhibit the highest sediment retention, whereas croplands and rangelands experience the most significant soil loss due to erosion. The RUSLE model estimated an average annual soil loss ranging between 50 and 90 tons/ha/year, with the highest erosion rates observed in agricultural lands with steep slopes and minimal vegetation cover. The InVEST SDR model further corroborates these findings, showing that sediment retention is predominantly concentrated in densely vegetated areas, reinforcing the crucial role of natural forests in preventing soil displacement. This complementary modelling approach identifies priority areas for soil conservation practices. This study is the first study to integrate the RUSLE and InVEST models for the Lam Phra Phloeng watershed, providing a coupled assessment of erosion risk and sediment retention capacity and offering a novel and transferable framework for watershed-scale conservation planning and soil management in tropical monsoonal environments. Full article

The operation of reservoirs has prompted rivers to transition from natural ecosystems to “natural–artificial” composite ecosystems, which has not only altered the water–sediment processes but has also affected river ecology in the downstream river channels. To reveal the impact of the Xiaolangdi Reservoir (China) on sediment transport and aquatic organisms in the Lower Yellow River (LYR), this article analyzes the changes in the water–sediment processes and sediment transport characteristics prior to and following the reservoir construction, based on measured water–sediment data of 688 floods from 1960 to 2023. It derives a theoretical formulation for the sediment delivery ratio (SDR) of flood events based on the sediment transport rate equation and evaluates the living environment of aquatic organisms in the LYR. The results indicate that after the construction of Xiaolangdi Reservoir, the frequency of floods with an average flow discharge below 1000 m³/s increased from 26.08% to 37.42%, and the frequency of floods with an average sediment concentration below 20 kg/m³ increased from 46.34% to 89.03%. The SDR of flood events significantly correlates positively with the average flow discharge and the water load variation coefficient. Conversely, it negatively correlates with the average sediment concentration and the incoming sediment coefficient. The sediment transport capacity of various river reaches in the LYR gradually increases along the direction of the river channel. The use of Xiaolangdi Reservoir has enhanced sediment transport in the upper LYR reach while decreasing it in the lower reach, aligning the overall sediment transport capacity of the downstream river channel. Additionally, the water–sediment process of the flood events following the completion of the Xiaolangdi Reservoir construction has improved the living environment for aquatic organisms, which is conducive to restoring biodiversity and improving the ecological environment of the river. The research results have enriched the understanding of the impact of reservoir construction on downstream water–sediment transport and aquatic organisms in sandy rivers, providing technical support for the health and sustainable development of rivers. Full article

This study assesses the projected impacts of climate change on sediment retention and soil loss in Savannakhet Province, Lao PDR, through the application of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Sediment Delivery Ratio (SDR) model. Using climate projections under SSP2-4.5 and SSP5-8.5 scenarios for the mid- and late-21st century (2050 and 2080), compared against a 2015 baseline, the analysis quantifies changes in sediment dynamics and ecosystem service provision. Results reveal a substantial increase in sediment retention, particularly in forested and flooded vegetation areas, under moderate and high-emission pathways. However, an overall rise in soil loss is observed across croplands and urbanized zones, driven by intensified high-risk areas, which requires conservative management. This study advocates for ecosystem-based adaptation (EbA) strategies—including afforestation, intercropping, and riparian restoration—to enhance watershed resilience. These nature-based solutions align with national adaptation goals and offer co-benefits for biodiversity, climate regulation, and rural livelihoods. Full article

The growing mismatch between ecosystem service (ES) supply and demand underscores the importance of thoroughly understanding their spatiotemporal patterns and key drivers to promote ecological civilization and sustainable development at the regional level in China. This study investigates six key ES indicators across mainland China—habitat quality (HQ), carbon sequestration (CS), water yield (WY), sediment delivery ratio (SDR), food production (FP), and nutrient delivery ratio (NDR)—by integrating a suite of analytical approaches. These include a spatiotemporal analysis of trade-offs and synergies in supply, demand, and their ratios; self-organizing maps (SOM) for bundle identification; and interpretable machine learning models. While prior research studies have typically examined ES at a single spatial scale, focusing on supply-side bundles or associated drivers, they have often overlooked demand dynamics and cross-scale interactions. In contrast, this study integrates SOM and SHAP-based machine learning into a dual-scale framework (grid and city levels), enabling more precise identification of scale-dependent drivers and a deeper understanding of the complex interrelationships between ES supply, demand, and their spatial mismatches. The results reveal pronounced spatiotemporal heterogeneity in ES supply and demand at both grid and city scales. Overall, the supply services display a spatial pattern of higher values in the east and south, and lower values in the west and north. High-value areas for multiple demand services are concentrated in the densely populated eastern regions. The grid scale better captures spatial clustering, enhancing the detection of trade-offs and synergies. For instance, the correlation between HQ and NDR supply increased from 0.62 (grid scale) to 0.92 (city scale), while the correlation between HQ and SDR demand decreased from −0.03 to −0.58, indicating that upscaling may highlight broader synergistic or conflicting trends missed at finer resolutions. In the spatiotemporal interaction network of supply–demand ratios, CS, WY, FP, and NDR persistently show low values (below −0.5) in western and northern regions, indicating ongoing mismatches and uneven development. Driver analysis demonstrates scale-dependent effects: at the grid scale, HQ and FP are predominantly influenced by socioeconomic factors, SDR and WY by ecological variables, and CS and NDR by climatic conditions. At the city level, socioeconomic drivers dominate most services. Based on these findings, nine distinct supply–demand bundles were identified at both scales. The largest bundle at the grid scale (B3) occupies 29.1% of the study area, while the largest city-scale bundle (B8) covers 26.5%. This study deepens the understanding of trade-offs, synergies, and driving mechanisms of ecosystem services across multiple spatial scales; reveals scale-sensitive patterns of spatial mismatch; and provides scientific support for tiered ecological compensation, integrated regional planning, and sustainable development strategies. Full article

In this paper, the Revised Universal Soil Loss Equation (RUSLE) model has been employed as a critical analytical instrument to assess the likelihood of soil erosion and pinpoint the most appropriate locations for conservation initiatives in the Wadi Mina basin (Algeria). The compilation of thematic maps was accomplished through the integration of the Spatial Analyst module in ArcGIS, resulting in a comprehensive map depicting potential erosion. This process incorporated rainfall data collected over a four-decade period from 1971 to 2010. The findings of this study demonstrate that the intensity of soil erosion and the generation of sediment are influenced by the topographical characteristics of the region, and the steepness of the terrain. Soil erosion within the Wadi Mina basin presents notable fluctuations, spanning a spectrum from a low of 0 to a high of 772.16 tons per hectare annually, with the mean annual erosion rate calculated at 16.69 tons per hectare. The Sediment Delivery Ratio (SDR) for the basin is estimated to be around 19.20%. Understanding soil erosion patterns at different sub-basin levels can be valuable for designing effective conservation strategies. This information helps to implement erosion control measures and to improve overall environmental management within the basin. Full article

The Qin-Mang River Basin is an important biodiversity conservation area in the Yellow River Basin. Studying the spatiotemporal changes in its ecosystem services (ESs) and the trade-offs and synergies (TOSs) between them is crucial for regional ecological protection and high-quality development. This study, based on land use type (LUT), and meteorological and soil data from 1992 to 2022, combined with the InVEST model, correlation analysis, and spatial autocorrelation analysis, explores the impacts of land use/land cover changes (LUCCs) on ESs. The results show that: (1) driven by urbanization and economic development, the expansion of built-up areas has replaced cultivated land and forests, with 35,000 hectares of farmland lost, thereby increasing pressure on ESs; (2) ESs show an overall downward trend, habitat quality (HQ) has deteriorated, carbon storage (CS) remains stable but the area of low CS has expanded, and sediment delivery ratio (SDR) and water yield (WY) fluctuate due to human activities and climate influence; (3) the TOSs of ESs change dynamically, with strong synergies among HQ, CS, and SDR. However, in areas with water scarcity, the negative correlation between HQ and WY has strengthened; (4) spatial autocorrelation analysis reveals that in 1992, significant positive synergies existed between ESs in the northern and northwestern regions, with WY negatively correlated with other services. By 2022, accelerated urbanization has intensified trade-off effects in the southern and eastern regions, leading to significant ecological degradation. This study provides scientific support for the sustainable management and policymaking of watershed ecosystems. Full article

Estimating sediment yield in a river is a challenging task in the water resources field. Different methods are available for estimating sediment erosion and yield, but generally they are not spatially distributed in nature. This paper presents the application of the Revised Universal Soil Loss Equation (RUSLE) for estimating soil erosion and integrates it with spatially distributed Sediment Delivery Ratio (SDR) to calculate sediment yield in a Himalayan river. The study area is Kabeli sub-catchment, located upstream of the Koshi River Basin in the eastern part of Nepal. The Kabeli River is where numerous hydropower projects are envisaged, and sediment-related issues are of major concern. With the use of the RUSLE, the mean annual soil erosion is estimated at 35.96 tons/ha/yr. The estimated specific sediment yield (SSY) from the distributed SDR method is 6.74 tons/ha/yr, which is close to the observed SSY of 7.26 tons/ha/yr using the data records of ~8 years. Based on correlation analysis, the topographic factor ($LS$) is the most sensitive RUSLE parameter with respect to sediment erosion. The sloping areas near the river hillslope are particularly vulnerable to soil erosion. The results indicate that the approach employed in this study may be potentially applied in other catchments with similar physiographic characteristics for the estimation of sediment yield. Full article

The serious problem of soil erosion not only has a profound impact on people’s lives but also results in a series of ecological and environmental challenges. To determine the impact of changes in land use type on soil erosion in the urban agglomeration on the northern slopes of the Tianshan Mountains, this study commences by employing the InVEST-SDR (integrated valuation of ecosystem services and tradeoffs–sediment delivery ratio) model to calculate soil erosion levels spanning from 2000 to 2020. Subsequently, it forecasts land use and land cover (LULC) conditions for the year 2030 under three scenarios: Q1 (natural development), Q2 (ecological protection), and Q3 (economic priority). This projection is accomplished through the integration of a coupled Markov chain and multi-objective planning model (MOP) alongside patch-generating land use simulation (PLUS) models. Ultimately, based on these outcomes, the study predicts soil erosion levels for the year 2030. There has been a consistent decline in soil erosion from 2000 to 2020 with high-intensity erosion concentrated in the Tianshan Mountain region. Grasslands, glaciers, and permafrost are identified as the most erosion-prone land types in the study area, with forests exhibiting the highest capacity for soil retention. Converting from grassland and barren land to forest within the same area results in a substantial reduction in soil erosion, specifically by 27.3% and 46.3%, respectively. Furthermore, the transformation from barren land to grassland also leads to a noteworthy 19% decrease in soil erosion. Over the past two decades, the study area has witnessed a significant decline in the area of grasslands, with a notable shift towards barren and impervious surfaces due to economic development and mining activities. The three predicted scenarios depict significant expansion towards barren land, grassland, and impervious area, respectively. Soil erosion decreases under different shared socio-economic pathway (SSP) scenarios relative to 2020. There is an increase in soil erosion in the Q1 scenario and in the Q3 scenario, whereas the amount of soil erosion in the Q2 scenario exhibits a continued decrease when only the effect of land change on soil erosion is considered. Persistently rapid economic development can exacerbate soil erosion problems, underscoring the need to find a balance between economic growth and ecological conservation. As economic expansion slows down, greater emphasis should be placed on environmental protection to maintain ecological stability. Full article

The empirical Revised Universal Soil Loss Equation (RUSLE) has been adapted to geographical information system (GIS) frameworks to study the spatial variability of soil erosion across landscapes and has also been used to estimate reservoir sedimentation. The literature presents contradictory results about the efficacy of using RUSLE in a GIS context for quantifying reservoir sedimentation, requiring further evaluation and validation of its estimates relative to measured reservoir sedimentation. Our primary objective was to determine if these contradictory results may be a function of the RUSLE’s inability to account for sediments derived from gullies, stream channels, or stream banks; the temporal variability of some of RUSLE’s empirically based factors such as the land cover/land management (C-) factor; and in some model renditions, the choice of value for the sediment delivery ratio (SDR). The usefulness of adjusting these estimates using a regional representative value of gully/stream bank sediment contributions was also assessed. High-spatial horizontal resolution (2 m) digital elevation models (DEMs) for 12 watersheds were used together with C-factor data for five representative years in a GIS-based RUSLE model that incorporates SDR within a sediment routing routine to study the impacts of choice of C-factor and SDR on reservoir sedimentation estimates. Choice of image date for developing C-factors was found to impact reservoir estimates. We also found that the value of SDR for some of the study watersheds would have to be unrealistically small to produce sedimentation estimates comparable to measured values. Estimates of reservoir sedimentation were comparable to measured data for 5 of the 12 watersheds, when the regionally based adjustment for gully/stream bank contributions was applied. However, differences remained large for the remaining seven watersheds. Statistical analysis revealed that certain combinations of geomorphic, pedologic, or topographic variables could be used to predict the degree of sediment underestimation with a significant and high level of correlation (0.72 < R² ≤ 0.99; p-value < 0.05). Our findings indicate that the level of agreement between GIS-based RUSLE estimates of reservoir sedimentation and measured values is a function of watershed characteristics; for example, the area-weighted soil erodibility (K-) factor of the soils within the watershed and stream channels, the stream entrenchment ratio and bank full depth, the percentage of the stream corridor having slopes ≥ 21°, and the width of the stream flood way as a percentage of the watershed area. Within the context of GIS, these metrics are easily obtained from digital elevation models and publicly available soils data and may be useful in prioritizing reservoirs’ assessments for function and safety. Full article