Land susceptibility to water and wind erosion risks in the East Africa region

An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniques

Spatial accurate mapping of land susceptibility to wind erosion is necessary to mitigate its destructive consequences. In this research, for the first time, we developed a novel methodology based on deep learning (DL) and active learning (AL) models, their combination (e.g., recurrent neural network (RNN), RNN-AL, gated recurrent units (GRU), and GRU-AL) and three interpretation techniques (e.g., synergy matrix, SHapley Additive exPlanations (SHAP) decision plot, and accumulated local effects (ALE) plot) to map global land susceptibility to wind erosion. In this respect, 13 variables were explored as controlling factors to wind erosion, and eight of them (e.g., wind speed, topsoil carbon content, topsoil clay content, elevation, topsoil gravel fragment, precipitation, topsoil sand content and soil moisture) were selected as important factors via the Harris Hawk Optimization (HHO) feature selection algorithm. The four models were applied to map land susceptibility to wind erosion, and their performance was assessed by three measures consisting of area under of receiver operating characteristic (AUROC) curve, cumulative gain and Kolmogorov Smirnov (KS) statistic plots. The results revealed that GRU-AL model was considered as the most accurate, revealing that 38.5%, 12.6%, 10.3%, 12.5% and 26.1% of the global lands are grouped at very low, low, moderate, high and very high susceptibility classes to wind erosion hazard, respectively. Interpretation techniques were applied to interpret the contribution and impact of the eight input variables on the model's output. Synergy plot revealed that the soil carbon content exhibited high synergy with DEM and soil moisture on the model's predictions. ALE plot showed that soil carbon content and precipitation had negative feedback on the prediction of land susceptibility to wind erosion. Based on SHAP decision plot, soil moisture and DEM presented the highest contribution on the model's output. Results highlighted new regions at high latitudes (southern Greenland coast, hotspots in Alaska and Siberia), which exhibited high and very high land susceptibility to wind erosion.

Soil erosion assessment and identification of erosion hotspot areas in the upper Tekeze Basin, Northern Ethiopia

Soil erosion is a major environmental problem in Ethiopia, reducing topsoil and agricultural land productivity. Soil loss estimation is a critical component of sustainable land management practices because it provides important information about soil erosion hotspot areas and prioritizes areas that require immediate management interventions. This study integrates the Revised Universal Soil Loss Equation (RUSLE) with Google Earth Engine (GEE) to estimate soil erosion rates and map soil erosion in the Upper Tekeze Basin, Northern Ethiopia. SoilGrids250 m, CHIRPS-V2, SRTM-V3, MERIT Hydrograph, NDVI from sentinel collections and land use land cover (LULC) data were accessed and processed in the GEE Platform. LULC was classified using Random forest (RF) classification algorithm in the GEE platform. Landsat surface reflectance images from Landsat 8 Operational land imager (OLI) sensors (2021) was used for LULC classification. Besides, different auxiliary data were utilized to improve the classification accuracy. Using the RUSLE-GEE framework, we analyzed the soil loss rate in different agroecologies and LULC types in the upper Tekeze basin in Waghimra zone. The results showed that the average soil loss rate in the Upper Tekeze basin is 25.5 t ha-1 yr-1. About 63 % of the basin is experiencing soil erosion above the maximum tolerable rate, which should be targeted for land management interventions. Specifically, 55 % of the study area, which is covered by unprotected shrubland is experiencing mean annual soil loss of 34.75 t ha-1 yr-1 indicating the need for immediate soil conservation intervention. The study also revealed evidence that this high mean soil loss rate of the basin can be reduced to a tolerable rate by implementing integrative watershed management and exclosures. Furthermore, this study demonstrated that GEE could be a good source of datasets and a computing platform for RUSLE, in particular for data scarce semi-arid and arid environments. The results from this study are reliable for decision-making for rapid soil erosion assessment and intervention prioritization.

Quantification of soil erosion and sediment yield using the RUSLE model in Boyo watershed, central Rift Valley Basin of Ethiopia

Changes in land use and land cover (LULC) are becoming recognized as critical to sustainability research, particularly in the context of changing landscapes. Soil erosion is one of the most important environmental challenges today, particularly in developing countries like Ethiopia. The objective of this study was evaluating the dynamics of soil loss, quantifying sediment yield, and detecting soil erosion hotspot fields in the Boyo watershed. To quantify the soil erosion risks, the Revised Universal Soil Loss Equation (RUSLE) model was used combined with remote sensing (RS) and geographic information system (GIS) technology, with land use/land cover, rainfall, soil, and management approaches as input variables. The sediment yield was estimated using the sediment delivery ratio (SDR) method. In contrast to a loss in forest land (1.7 %), water bodies (3.0 %), wetlands (1.5 %), and grassland (1.7 %), the analysis of LULC change (1991-2020) showed a yearly increase in the area of cultivated land (1.4 %), built-up land (0.8 %), and bare land (3.5 %). In 1991, 2000, and 2020, respectively, the watershed's mean annual soil loss increases by 15.5, 35.9, and 38.3 t/ha/y. Approximately 36 cm of the watershed's economically productive topsoil was lost throughout the study's twenty-nine-year period (1991-2020). According to the degree of erosion, 16 % of the watershed was deemed seriously damaged, while 70 % was deemed slightly degraded. Additionally, it is estimated for the year 2020 that 74,147.25 t/y of sediment (8.52 % of the total annual soil loss of 870,763.12 t) reach the Boyo watershed outlet. SW4 and SW5 were the two sub-watersheds with the highest erosion rates, requiring immediate conservation intervention to restore the ecology of the Boyo watershed.

An integrated modeling approach for estimating monthly global rainfall erosivity

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001-2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission's Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha-1 h-1 month-1 in June-August over the Northern Hemisphere and ~ 700 MJ mm ha-1 h-1 month-1 in December-February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December-February and June-August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10-30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

Modelling of soil erosion susceptibility incorporating sediment connectivity and export at landscape scale using integrated machine learning, InVEST-SDR and Fragstats

Evaluating the linkage between soil erosion and sediment connectivity for export assessment in different landscape patterns at catchment scale is valuable for optimization of soil and water conservation (SWC) practices. Present research attempts to identify the soil erosion susceptible (SES) sites in Kangsabati River Basin (KRB) using machine learning algorithm (decision trees, decision trees cross validation, CV, Extreme Gradient Boosting, XGB CV and bagging CV) taken thirty five variables, for investigating the linkage between erosion rates and sediment connectivity to assess the sediment export at sub-basin level employing connectivity index (IC) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) sediment delivery ratio (SDR) model. Based on AUC of receiving operating curve in validation test, excellent capacity of extreme Gradient Boosting, XGB CV and bagging CV (0.95, 0.90) than decision tree and decision tree CV (0.78, 0.82), exhibits about 18.58 % of basin areas facing susceptible to very high erosion. Conversely, considering universal soil loss equation (RUSLE) parameters, InVEST-SDR model estimated about 64.24 % of soil loss rate occurred from high SES in where sediment export rate become very high (136.995 t/ha-1/y-1). The IC result show that high sediment connectivity (<-4.4) measured in high SES of laterite and bare land in upper catchment, and double crop agricultural areas in lower catchment, while least connectivity (>-7.1) observed in low SES of dense forest, vegetation cover and settlement built-up areas. Pearson correlation matrix revealed that four landscape indices category i.e. edge metrics (p < 0.01), aggregation metrics (p < 0.001), shape metrics (p < 0.01-0.001) and diversity metrics (p < 0.01) signified the influence of landscape patterns on IC and SES. Accordingly, RUSLE, SDR and landscape matrices reveals that maximum sediment export rate associated with high connective delivery outlet and high SES in laterite, double crop and bare land due to simple landscape and greater homogeneity, whilst minimum export rate related with low connectivity and low SES in dense forest, vegetation cover and settlement built up area causes of fragmented landscape and spatial heterogeneity. Finally, findings could immense useful for formulating the optimizing measures of SWC in the watershed.

An integrated framework for improving watershed management planning

Proper land use and management (LUM) planning is pivotal to curbing land degradation and ensuring sustainable use of limited watershed resources. Despite decades of research and development efforts, land degradation remains a serious environmental problem in many parts of the world. Issues regarding the sustainability of current LUM initiatives are due to poor linkages between the ecological and socio-economic dimensions of LUM decisions, and an integrated framework allowing LUM interventions to be properly planned and implemented is lacking. In this study, we developed an integrated framework to identify, evaluate, and propose LUM alternatives with ecological and socio-economic benefits. The framework comprises six components: (i) identification of land use problems and setting of objectives, (ii) identification of the best-performing land use-based integrated solutions, (iii) formulation of LUM alternatives and modeling of key indicators, (iv) cost-benefit analysis, (v) evaluation of the LUM alternatives with stakeholders engagement, and (vi) communication of the LUM alternatives to relevant stakeholders to obtain institutional and financial support for implementation. To demonstrate the use of this framework, we conducted a case study in the Aba Gerima watershed of the Upper Blue Nile basin in Ethiopia. This study used extensive plot- and watershed-scale observations (2015-2019) obtained under both conventional and improved sustainable land management practices. We analyzed changes in runoff, soil loss, soil organic carbon (SOC) stock, and land productivity of five LUM alternatives as compared to a baseline scenario (existing farming practices). The results showed that the LUM alternatives reduced runoff by 11-71% and soil loss by 66-95%, and SOC stock and watershed-scale land productivity were improved by 36-104% and 48-134%, respectively. Evaluation of LUM alternatives by stakeholders, including land users, policy makers, and researchers, produced divergent results. In particular, land users prioritized implementation of sustainable land management practices without altering existing land uses. The integrated framework developed in this study can serve as a valuable tool for identifying, evaluating, and proposing LUM alternatives and facilitating decision-making in planning and implementation of LUM practices in watersheds experiencing land degradation.

Upscaling plot-based measurements of RUSLE C-factor of different leaf-angled crops in semi-arid agroecosystems

Several models have been used to assess temporal cover change trends by using remote and proximal sensing tools. Particularly, from the point of hydrologic and erosional processes and sustainable land and soil management, it is crucial to determine and understand the variation of protective canopy cover change within a development period. Concordantly, leaf angle distribution (LAD) is a crucial parameter when using the vegetation indices (VIs) to define the radiation reflected by the canopy when estimating the cover-management factor (C-factor). This research aims to assess the C-factor of cultivated lands with sunflower and wheat that have different leaf orientations (planophile and erectophile, respectively) with the help of reduced models of NDVI and LAI for estimating crop-stage SLR values with the help of a stepwise linear regression. Those equations with R-squared values of 0.85 and 0.93 were obtained for sunflower and wheat-planted areas, respectively. The Normalized Difference Vegetation Index (NDVI), one of the two plant indices used in this study, was measured by remote and proximal sensing tools. At the same time, the Leaf Area Index (LAI) was obtained by a proximal hand-held crop sensor alone. Soil loss ratio (SLR) was upscaled for the establishment period (1P) of sunflower and the maturing period (3P) of wheat to present different growth stages simultaneously with plant-specific equations that can be easily adapted to those aforementioned crops instead of doing field measurements with conventional techniques in semi-arid cropping systems.

Agricultural practices drive elevated rates of topsoil decline across Kenya, but terracing and reduced tillage can reverse this

As agricultural land area increases to feed an expanding global population, soil erosion will likely accelerate, generating unsustainable losses of soil and nutrients. This is critical for Kenya where cropland expansion and nutrient loading from runoff and erosion is contributing to eutrophication of freshwater ecosystems and desertification. We used the Revised Universal Soil Loss Equation (RUSLE) to predict soil erosion rates under present land cover and potential natural vegetation nationally across Kenya. Simulating natural vegetation conditions allows the degree to which erosion rates are elevated under current land use practices to be determined. This methodology exploits new digital soil maps and two vegetation cover maps to model topsoil (top 20 cm) erosion rates, lifespans (the mass of topsoil divided by erosion rate), and lateral nutrient fluxes (nutrient concentration times erosion rate) under both scenarios. We estimated the mean soil erosion rate under current land cover at ~5.5 t ha^-1 yr^-1, ~3 times the rate estimated for natural vegetation cover (~1.8 t ha^-1 yr^-1), and equivalent to ~320 Mt yr^-1 of topsoil lost nationwide. Under present erosion rates, ~8.8 Mt, ~315 Kt, and ~ 110 Kt of soil organic carbon, nitrogen and phosphorous are lost from soil every year, respectively. Further, 5.3 % of topsoils (~3.1 Mha), including at >25 % of croplands, have short lifespans (<100 years). Additional scenarios were tested that assume combinations of terracing and reduced tillage practices were adopted on croplands to mitigate erosion. Establishing bench terraces with zoned tillage could reduce soil losses by ≥75 %; up to 87.1 t ha^-1 yr^-1. These reductions are comparable to converting croplands to natural vegetation, demonstrating most agricultural soils can be conserved successfully. Extensive long-term monitoring of croplands with terraces and reduced tillage established is required to verify the efficacy of these agricultural support practices as indicated by our modelling.

Progress and challenges in sustainable land management initiatives: A global review

Sustainable land management (SLM) is widely recognized as the key to reducing rates of land degradation, and preventing desertification. Many efforts have been made worldwide by various stakeholders to adopt and/or develop various SLM practices. Nevertheless, a comprehensive review on the spatial distribution, prospects, and challenges of SLM practices and research is lacking. To address this gap, we gathered information from a global SLM database provided by the World Overview of Conservation Approaches and Technologies (WOCAT) and two bibliographic databases of academic research. Over 1900 SLM practices and 1181 academic research papers from 129 and 90 countries were compiled and analyzed. Relatively better SLM dissemination was observed in dry subhumid countries and countries with medium scores on the Human Development Index (HDI), whereas dissemination and research were both lower in humid countries with low HDI values. Cropland was the main land use type targeted in both dissemination and research; degradation caused by water erosion and mitigation aimed at water erosion were also the main focus areas. Other dominant land use types (e.g., grazing) and SLM purposes (e.g., economic benefits) have received relatively less research attention compared to their dissemination. Overall, over 75 % of the 60 countries experiencing high soil erosion rates (>10 t ha^-1 yr^-1) also have low HDI scores, as well as poor SLM dissemination and research implying the limited evidence-based SLM dissemination in these countries. The limitation of research evidence can be addressed in the short term through integrating existing scientific research and SLM databases by adopting the proposed Research Evidence for SLM framework. There is, however, a great need for additional detailed studies of country-specific SLM challenges and prospects to create appropriate evidence-based SLM dissemination strategies to achieve multiple SLM benefits.

Land use, management and climate effects on runoff and soil loss responses in the highlands of Ethiopia

Soil erosion by water is a major cause of land degradation in the highlands of Ethiopia and anywhere else in the world, but its magnitude and variability are rarely documented across land uses and climatological conditions. The purpose of this study was to examine runoff and soil loss responses under cropland (CL) and grazing land (GL) management practices in three climatic regions of the Ethiopian highlands: semi-arid (Mayleba), dry sub-humid (Gumara), and humid (Guder). We measured runoff and soil loss using runoff plots with and without soil and water conservation (SWC) measures (trenches, stone/soil bunds [embankments] with trenches on the upslope side, and exclosure) during the rainy season (July-September). The results revealed significant variation in runoff and soil loss amounts across land uses, SWC measures, and climatic regions. At Mayleba, seasonal runoff and soil loss in control plot were far higher from GL (280 mm, 26.5 t ha^-1) than from CL (108 mm, 7.0 t ha^-1) largely due to lack of protective vegetation cover and soil disruption because of intense grazing. In contrast, at Gumara and Guder, seasonal soil loss values were much higher from CL (21.4-71.2 t ha^-1) than from GL (0.6-24.2 t ha^-1) irrespective of runoff values. This was attributed to the excessive tillage/weeding operations involved in cultivation of teff (cereal crop) at Gumara and potato at Guder. Although SWC measures (practices) substantially reduced runoff and soil loss (decreased by 23%-86%) relative to control plot, seasonal soil loss under GL uses with trenches at Mayleba (12.6 t ha^-1), CL with soil bunds and trenches at Gumara (22.1 t ha^-1), and Guder (21.4 t ha^-1) remained higher than the average tolerable soil loss rate (10 t ha^-1 year^-1) proposed for the Ethiopian highlands. This suggests that SWC measures should be carefully designed and evaluated specific to land use and climatic conditions. Overall, the results of this study can help improve SWC planning in regions where land use and climate impact on soil erosion vary across geographical areas, as they do in Ethiopia and anywhere else. However, further investigation is crucial with replication of measurements over years and locations to provide more accurate information on land use, management and climate controls on hydrological and soil erosion processes.

Soil Erosion Characteristics and Scenario Analysis in the Yellow River Basin Based on PLUS and RUSLE Models

Soil erosion is an important global environmental issue that severely affects regional ecological environment and socio-economic development. The Yellow River (YR) is China's second largest river and the fifth largest one worldwide. Its watershed is key to China's economic growth and environmental security. In this study, six impact factors, including rainfall erosivity (R), soil erosivity (K), slope length (L), slope steepness (S), cover management (C), and protective measures (P), were used. Based on the revised universal soil loss equation (RUSLE) model, and combined with a geographic information system (GIS), the temporal and spatial distribution of soil erosion (SE) in the YR from 2000 to 2020 was estimated. The patch-generating land use simulation (PLUS) model was used to simulate the land-use and land-cover change (LUCC) under two scenarios (natural development and ecological protection) in 2040; the RUSLE factor P was found to be associated with LUCC in 2040, and soil erosion in the Yellow River Basin (YRB) in 2040 under the two scenarios were predicted and evaluated. This method has great advantages in land-use simulation, but soil erosion is greatly affected by rainfall and slope, and it only focuses on the link between land-usage alteration and SE. Therefore, this method has certain limitations in assessing soil erosion by simulating and predicting land-use change. We found that there is generally slight soil erosivity in the YRB, with the most serious soil erosion occurring in 2000. Areas with serious SE are predominantly situated in the upper reaches (URs), followed by the middle reaches (MRs), and soil erosion is less severe in the lower reaches. Soil erosion in the YRB decreased 11.92% from 2000 to 2020; thus, soil erosion has gradually reduced in this area over time. Based on the GIS statistics, land-use change strongly influences SE, while an increase in woodland area has an important positive effect in reducing soil erosion. By predicting land-use changes in 2040, compared to the natural development scenario, woodland and grassland under the ecological protection scenario can be increased by 1978 km2 and 2407 km2, respectively. Soil erosion can be decreased by 6.24%, indicating the implementation of woodland and grassland protection will help reduce soil erosion. Policies such as forest protection and grassland restoration should be further developed and implemented on the MRs and URs of the YR. Our research results possess important trend-setting significance for soil erosion control protocols and ecological environmental protection in other large river basins worldwide.

Spatiotemporal characteristics of soil erosion in a typical watershed consisting of different landscape: A case study of the Qin River Basin

Soil erosion has a severe impact on habitat and productivity. It is considered to be a major environmental threat prevalent in ecosystems. However, few researchers have studied the spatial distribution of soil erosion intensity among different geographic environmental factors. The Qin River Basin is a geographical unit consisting of mountains, hills, and plains with significant regional characteristics, and it has a basin area of 14,810.91 km². This study uses the Geographical Information Systems, Revised Universal Soil Loss Equation model to analyze the spatiotemporal changes in the soil-erosion intensity in the Qin River Basin from 1990 to 2018. Different environmental factors of land use, slope and altitude on erosion intensities of 19 secondary land types were analyzed. It can better reflect the soil erosion under different environmental factors and different land use types. Results show that the soil erosion modulus of Qin River Basin were 10.25 t hm⁻² a⁻¹, and it belong to slight erosion from 1990 to 2018. Soil erosion intensity is greater in grassland and woodland than in cropland. The strongest soil erosion occurred in the sparse forestland, and the lowest was in beach land. Soil erosion was the highest for a slope of 15~25° and an altitude of 1200~1500 m. Rainfall and slope are important factors lead to soil erosion, indicating weak water and soil conservation implemented in these areas. Therefore, priority should be given to these geomorphic units to formulate and implement soil-erosion control and ecological restoration policies in the Qin River Basin. This study provides a good reference for preventing and controlling soil erosion in river basins.

Assessing soil system changes under climate-smart agriculture via farmers' observations and conventional soil testing

Soil degradation remains a challenge in African highlands, where land management lacks a strong context-specific evidence base. We investigated the impacts of recently implemented soil and water conservation (SWC) practices-farmyard manure addition, incorporation of crop residues in soil and fanya juu terracing under an agroforestry system on soil health indicators in the East Usambara Mountains of Tanzania. Farmers' observations of soil changes were combined with conventional soil testing to assess the initial impacts of SWC practices relative to conventional non-SWC practice. Majority of farmers (66%-83%) reported that combining fanya juu terracing with organic amendments led to soil colour change from red to black and an increase in crop yield. Despite the observed darkening of the soil, there was no significant increase in soil organic carbon stock and the contents of N, P, K. There were important changes in soil physical properties, including greater aggregate stability (mean weight diameter of 1.51-1.71 mm) in the SWC plots, a greater volume of transmission pores (>60 μm) and coarse storage pores (10-60 μm) in the surface soil layer (0-15 cm), and greater volume of fine storage pores (0.2-10 μm) and residual pores (0.2 μm) in the sub-surface layer (15-30 cm) of the SWC plots compared with the conventional plots. These changes indicate that SWC rapidly enhances infiltration and retention of water within the root zone, which are important for increasing crop yields and improving the resilience of the agro-ecosystem to environmental stress. Combining SWC with effective soil fertility management is needed for sustainable highland agriculture.

Regional Potential Wind Erosion Simulation Using Different Models in the Agro-Pastoral Ecotone of Northern China

Wind erosion is crucial for assessing regional ecosystem services and sustainable development. The Agro-Pastoral Ecotone of northern China (APEC) is a typical region undergoing wind erosion and soil degradation. In this study, the National Wind Erosion Survey Model of China, the Integrated Wind Erosion Modeling System, and the regional versions of the Revised Wind Erosion Equation and Wind Erosion Prediction System were used to evaluate the regional potential wind erosion of the APEC during 2000 and 2012. The results showed that the potential wind erosion predicted by National Wind Erosion Survey Model of China (NWESMC), Revised Wind Erosion Equation (RWEQ), Wind Erosion Prediction System (WEPS), and Integrated Wind Erosion Modeling System (IWEMS) were significantly related to the observed wind erosion collected from published literature, but the observed data were generally smaller than the predicted values. The average potential wind erosions were 12.58, 25.87, 52.63, and 58.72 t hm⁻² a⁻¹ for NWESMC, RWEQ, WEPS, and IWEMS, respectively, while the spatial pattern and temporal trend of annual potential wind erosion were similar for different wind erosion models. Wind speed, soil moisture, and vegetation coverage were the dominant factors affecting regional wind erosion estimation. These results highlight that it is necessary to comprehensively calibrate and validate the selected wind erosion models. A long-term standard wind erosion monitoring network is urgently required. This study can serve as a useful reference for improving wind erosion models.

Regionalization of the SWAT+ model for projecting climate change impacts on sediment yield: An application in the Nile basin

Study region

Nile basin.

Study focus

Several studies have shown a relationship between climate change and changes in sediment yield. However, there are limited modeling applications that study this relationship at regional scales mainly due to data availability and computational cost. This study proposes a methodological framework using the SWAT+ model to predict and project sediment yield at a regional scale in data-scarce regions using global datasets. We implement a framework that (a) incorporates topographic factors from high/medium resolution DEMs (b) incorporates crop phenology data (c) introduces an areal threshold to linearize sediment yield in large model units and (d) apply a hydrological mass balance calibration. We test this methodology in the Nile Basin using a model application with (revised) and without (default) the framework under historical and future climate projections.

New hydrological insights for the region

Results show improved sediment yield estimates in the revised model, both in absolute values and spatial distribution when compared to measured and reported estimates. The contemporary long term (1989 - 2019) annual mean sediment yield in the revised model was 1.79 t ha-1 yr-1 and projected to increase by 61 % (44 % more than the default estimates) in the future period (2071 - 2100), with the greatest sediment yield increase in the eastern part of the basin. Thus, the proposed framework improves and influences modeled and predicted sediment yield respectively.

Inferring Land Conditions in the Tumen River Basin by Trend Analysis Based on Satellite Imagery and Geoinformation

The aim of this study was to map the land condition within the area of the Tumen River Basin (TRB), located on the Sino–North Korean border, using trend analysis of environmental factors. The normalized difference vegetation index (NDVI) and land surface temperature (LST) trends over the past 30 years were analyzed to identify areas that have undergone degradation, restoration, and/or a transition. Landsat NDVI and LST were obtained using the Google Earth Engine (GEE) platform. Erosion was also gauged over the same period using the Revised Universal Soil Loss Equation (RUSLE). Our results showed that only 0.3% of the land within the TRB underwent change that can be characterized as statistically significant within the study period. We therefore infer that land degradation may not be a major concern in the study area. Areas with a significant upward trend of soil loss accounted for 0.8% of the basin’s footprint and were mainly distributed upstream of North Korea. However, more than 80% of the area was found to be suffering from water stress, 10% of these areas were statistically significant and most were located downstream.

A rare shrub species as flagship for conserving desert steppe in arid Inner Mongolia

The rare species Amygdalus pedunculata Pall. (Rosaceae) in arid northern China is endangered to the point of extinction. Determined to save it, the local government of Inner Mongolia Autonomous Region encouraged the herdsmen to limit grazing activities. Here, we are testing if this species could be considered as a conspicuous flagship for restoring and conserving wind-sensitive arid lands as desert steppe in northern China. We examined statistically the growing states and environmental roles of A. pedunculata populations under the comparative conditions of free and limited grazing in winter since the year 2001. This species was observed to play a critical role in preventing wind erosion and stabilising the lands, as was indicated by the formation of micro-dunes under the shrubs. This role can be attributed mainly to the crown diameters or cover from the shrubs. Under the grazing limitation condition, accompanying species and plants around the shrubs increased significantly. Regardless of free or limited grazing conditions, the shrubs were not observed to inhibit the occurrence or growth of other plants. The grazing limitation over a period of 20 years has caused the effective revival of the rare A. pedunculata species, with statistically larger and taller A. pedunculata individuals than under the free grazing condition, as well as a slightly higher population density and total crown cover. The grazing limitation policy for saving A. pedunculata is believed to be effective and the rare A. pedunculata shrub is a conspicuous flagship for helping to conserve wind-sensitive desert steppe in terms of ecosystem integrity and authenticity.

Evaluating spatio-temporal soil erosion dynamics in the Winam Gulf catchment, Kenya for enhanced decision making in the land-lake interface

Soil erosion accelerated by poor agricultural practices, land degradation, deprived infrastructure development and other anthropogenic activities has important implications for nutrient cycling, land and lake productivity, loss of livelihoods and ecosystem services, as well as socioeconomic disruption. Enhanced knowledge of dynamic factors influencing soil erosion is critical for policymakers engaged in land use decision-making. This study presents the first spatio-temporal assessment of soil erosion risk modelling in the Winam Gulf, Kenya using the Revised Universal Soil Loss Equation (RUSLE) within a geospatial framework at a monthly resolution between January 2017 and June 2020. Dynamic rainfall erosivity and land cover management factors were derived from existing datasets to determine their effect on average monthly soil loss by water erosion. By assessing soil erosion rates with enhanced temporal resolution, it is possible to provide greater knowledge regarding months that are particularly susceptible to soil erosion and can better inform future strategies for targeted mitigation measures. Whilst the pseudo monthly average soil loss was calculated (0.80 t ha^-1 month^-1), the application of this value would lead to misrepresentation of monthly soil loss throughout the year. Our results indicate that the highest erosion rates occur between February and April (average 0.95 t ha^-1 month^-1). In contrast, between May and August, there is a significantly reduced risk (average 0.72 t ha^-1 month^-1) due to the low rainfall erosivity and increased vegetation cover as a result of the long rainy season. The mean annual gross soil loss by water erosion in the Winam Gulf catchment amounts to 10.71 Mt year^-1, with a mean soil loss rate of 9.63 t ha^-1 year^-1. These findings highlight the need to consider dynamic factors within the RUSLE model and can prove vital for identifying areas of high erosion risk for future targeted investigation and conservation action.

Evaluation of Desertification Severity in El-Farafra Oasis, Western Desert of Egypt: Application of Modified MEDALUS Approach Using Wind Erosion Index and Factor Analysis

Desertification is a serious threat to human survival and to ecosystems, especially to inland desert oases. An assessment of desertification severity is essential to ensure national sustainable development for agricultural and land expansion processes in this region. In this study, Index of Land Susceptibility to Wind Erosion (ILSWE) was integrated with a Modified Mediterranean Desertification and Land Use (MEDALUS) method and factor analysis (FA) to develop a GIS-based model for mapping desertification severity. The model was then applied to 987.77 km2 in the El-Farafra Oasis, located in the Western Desert of Egypt, as a case study. Climate and field survey data together with remote sensing images were used to generate five quality indices (soil, climate, vegetation, land management and wind erosion). Based on the FA, a weighted value was assigned to each index. Five thematic layers representing the indices were created within the GIS environment and overlaid using the weighted sum model. The developed model showed that 59% of the total area was identified as high-critical and 38% as medium-critical. The results of an environmentally sensitive area index suggested by the original MEDALUS model indicated similar results: 18.37% of the total area was classified as high-critical and 78.73% as medium-critical. However, the sensitivity analysis indicated that weights derived from FA resulted in better performance of the developed spatial model than that derived from the original MEDALUS method. The proposed model would be a suitable tool for monitoring vulnerable zones, and could be a starting point for sustainable agricultural development in inland oases.

Modeling and Assessing Potential Soil Erosion Hazards Using USLE and Wind Erosion Models in Integration with GIS Techniques: Dakhla Oasis, Egypt

Soil erosion modeling is becoming more significant in the development and implementation of soil management and conservation policies. For a better understanding of the geographical distribution of soil erosion, spatial-based models of soil erosion are required. The current study proposed a spatial-based model that integrated geographic information systems (GIS) techniques with both the universal soil loss equation (USLE) model and the Index of Land Susceptibility to Wind Erosion (ILSWE). The proposed Spatial Soil Loss Model (SSLM) was designed to generate the potential soil erosion maps based on water erosion and wind erosion by integrating factors of the USLE and ILSWE models into the GIS environment. Hence, the main objective of this study is to predict, quantify, and assess the soil erosion hazards using the SSLM in the Dakhla Oasis as a case study. The water soil loss values were computed by overlaying the values of five factors: the rainfall factor (R-Factor), soil erodibility (K-Factor), topography (LS-Factor), crop types (C-Factor), and conservation practice (P-Factor). The severity of wind-driven soil loss was calculated by overlaying the values of five factors: climatic erosivity (CE-Factor), soil erodibility (E-Factor), soil crust (SC-Factor), vegetation cover (VC-Factor), and surface roughness (SR-Factor). The proposed model was statistically validated by comparing its outputs to the results of USLE and ILSWE models. Soil loss values based on USLE and SSLM varied from 0.26 to 3.51 t ha−1 yr−1 with an average of 1.30 t ha−1 yr−1 and from 0.26 to 3.09 t ha−1 yr−1 with a mean of 1.33 t ha−1 yr−1, respectively. As a result, and according to the assessment of both the USLE and the SSLM, one soil erosion class, the very low class (<6.7 t ha−1 yr−1), has been reported to be the prevalent erosion class in the study area. These findings indicate that the Dakhla Oasis is slightly eroded and more tolerable against water erosion factors under current management conditions. Furthermore, the study area was classified into four classes of wind erosion severity: very slight, slight, moderate, and high, representing 1.0%, 25.2%, 41.5%, and 32.3% of the total study area, respectively, based on the ILSWE model and 0.9%, 25.4%, 43.9%, and 29.9%, respectively, according to the SSLM. Consequently, the Dakhla Oasis is qualified as a promising area for sustainable agriculture when appropriate management is applied. The USLE and ILSWE model rates had a strong positive correlation (r = 0.97 and 0.98, respectively), with the SSLM rates, as well as a strong relationship based on the average linear regression (R2 = 0.94 and 0.97, respectively). The present study is an attempt to adopt a spatial-based model to compute and map the potential soil erosion. It also pointed out that designing soil erosion spatial models using available data sources and the integration of USLE and ILSWE with GIS techniques is a viable option for calculating soil loss rates. Therefore, the proposed soil erosion spatial model is fit for calculating and assessing soil loss rates under this study and is valid for use in other studies under arid regions with the same conditions.

Soil erosion assessment in the Blue Nile Basin driven by a novel RUSLE-GEE framework

Assessment of soil loss and understanding its major drivers are essential to implement targeted management interventions. We have proposed and developed a Revised Universal Soil Loss Equation framework fully implemented in the Google Earth Engine cloud platform (RUSLE-GEE) for high spatial resolution (90 m) soil erosion assessment. Using RUSLE-GEE, we analyzed the soil loss rate for different erosion levels, land cover types, and slopes in the Blue Nile Basin. The results showed that the mean soil loss rate is 39.73, 57.98, and 6.40 t ha^-1 yr^-1 for the entire Blue Nile, Upper Blue Nile, and Lower Blue Nile Basins, respectively. Our results also indicated that soil protection measures should be implemented in approximately 27% of the Blue Nile Basin, as these areas face a moderate to high risk of erosion (>10 t ha^-1 yr^-1). In addition, downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 km to 1 km spatial resolution significantly impacts rainfall erosivity and soil loss rate. In terms of soil erosion assessment, the study showed the rapid characterization of soil loss rates that could be used to prioritize erosion mitigation plans to support sustainable land resources and tackle land degradation in the Blue Nile Basin.

Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya

Ongoing climate change poses a major threat to the soil resources of many African countries that mainly rely on an agricultural economy. While arid and semi-arid lands (ASALs) take up most of Kenya’s land mass, approximately 64% of its total croplands lie within mountainous areas with high rainfall, hence, areas highly vulnerable to water erosion. Flooding of the Great Lakes and increasing desertification of the ASALs are illustrative cases of the implications of recent precipitation dynamics in Kenya. This study applied the Revised Universal Soil Loss Equation (RUSLE) to estimate future soil erosion rates at the national level based on four Coupled Model Intercomparison Project v5 (CMIP5) models under two Representative Concentration Pathway (RCP) scenarios. Results showed the current soil loss rate to be at 4.76 t ha−1 yr−1 and projected an increase in average rainfall erosivity under the two scenarios, except for RCP-2.6 (2030s) and (2080s) for the MIROC-5 model. Future projections revealed an incremental change in rainfall erosivity from the baseline climate by a cumulative average of 39.9% and 61.1% for all scenarios by the 2030s and 2080s, respectively, while soil loss is likely to increase concomitantly by 29% and 60%, respectively. The CCCMA_CANESM2 model under the RCP 8.5 (2080s) scenario projected the highest erosion rate of 15 t ha−1 yr−1 over Kenya, which is a maximum increase of above 200%, with the Rift Valley region recording an increase of up to 100% from 7.05 to 14.66 t ha−1 yr−1. As a first countrywide future soil erosion study, this assessment provides a useful reference for preventing water erosion and improving ecosystem service security.

Drivers, Impacts and Mitigation of Increased Sedimentation in the Hydropower Reservoirs of East Africa

Hydropower reservoirs are essential for the climate-neutral development of East Africa. Hydropower production, however, is threatened by human activities that lead to a decrease in water storage capacity of reservoirs. Land use/land cover and climatic changes are driving accelerated soil erosion in semi-arid East Africa, which ultimately increases reservoir sedimentation and decreases energy production. Sediment delivery dynamics at the catchment scale are complex, involving the interaction of multiple factors and processes on different spatial and temporal scales. A lack of understanding of these processes and their interactions may impede the efficiency of sediment mitigation and control strategies. A deep understanding of the processes of erosion and connectivity of the land to river channel, as well as storage of eroded material within hillslopes and floodplains, and sediment accumulation in the reservoirs supports selection of future dam locations and sustainable management of reservoirs. The sediment budget approach can provide such a holistic perspective by accounting for the various sediment sources, transport, sinks, and redistribution when the sediment is routed through that catchment. Constructing sediment budgets is challenging, but the potential for integrating a number of different techniques offers new opportunities to collect the required information. In East Africa, the spatial planning of dams is mainly dominated by political and financial motives, and impacts of land use and climate on the sediment transport dynamics are not adequately considered. Production of sediment budgets under different scenarios of land use and climate change should be an essential step when deciding the location and management strategies for dams. Selection of new hydroelectric reservoir sites must consider long-term scientific data on climate change, and the sediment budget components for sustainable land management planning, hydropower sustainability.

Soil erosion and sediment yield assessment using RUSLE and GIS-based approach in Anjeb watershed, Northwest Ethiopia

Soil erosion is a serious and continuous environmental problem in Ethiopia. Lack of land use planning, environmental protection, over-cultivation, and overgrazing are prominent causes of erosion and sedimentation. This study is conducted in Anjeb watershed located in the Upper Blue Nile Basin, Ethiopia. In this study, the quantity and distribution of soil erosion, sediment delivery ratio (SDR), and sediment yield of the watershed were assessed by employing remote sensing, geographic information system (GIS), and revised universal soil loss equation analysis capabilities. Important data sets of topography, soil, conservations practices, cover management, and rainfall factors were processed and superimposed in GIS analysis, and soil loss rate, SDR, and sediment yield of the watershed were derived. Based on the result found, the watershed was categorized into six classes of erosion: slight (0–5), moderate (5–10), high (10–15), very high (15–30), severe (30–50), and very severe (> 50) t ha−1 yr−1. The estimated average annual soil loss was 17.3 t ha−1 yr−1. The soil loss rate is higher in the steeper and topographically dissected part of the watershed. The average sediment delivery capacity was about 0.122. The result showed that the average sediment yield in the watershed was grouped into classes of low (< 2.5), moderate (2.5–7.5), high (7.5–12.5), very high (12.5–22.5), severe (22.5–40), and very severe (> 40) t ha−1 yr−1. It is found that from a total of 20,125.5 t yr−1 eroded soil over the whole watershed 2254.5 t yr−1 of sediment has been brought and deposited to the channels. Sediment accumulation from the watershed threatens the storage capacity and life span of Anjeb reservoir which is the source of irrigation water downstream. The study provides an insight to planners and resource managers to design and implement practices of watershed management to reduce erosion and enhance land productivity and to minimize the reservoir sediment accumulation.

The Road Map to Classify the Potential Risk of Wind Erosion

Environmental degradation, for example, by wind erosion, is a serious global problem. Despite the enormous research on this topic, complex methods considering all relevant factors remain unpublished. The main intent of our paper is to develop a methodological road map to identify key soil–climatic conditions that make soil vulnerable to wind and demonstrate the road map in a case study using a relevant data source. Potential wind erosion (PWE) results from soil erosivity and climate erosivity. Soil erosivity directly reflects the wind-erodible fraction and indirectly reflects the soil-crust factor, vegetation-cover factor and surface-roughness factor. The climatic erosivity directly reflects the drought in the surface layer, erosive wind occurrence and clay soil-specific winter regime, making these soils vulnerable to wind erosion. The novelty of our method lies in the following: (1) all relevant soil–climatic data of wind erosion are combined; (2) different soil types “sand” and “clay” are evaluated simultaneously with respect to the different mechanisms of wind erosion; and (3) a methodological road map enables its application for various conditions. Based on our method, it is possible to set threshold values that, when exceeded, trigger landscape adjustments, more detailed in situ measurements or indicate the need for specific management.

Agroecology-based soil erosion assessment for better conservation planning in Ethiopian river basins

Soil erosion by water is one of the main environmental concerns in Ethiopia. Several studies have examined this at plot and watershed scales, but no systematic study of soil erosion severity and management solutions at national scale is available. This study investigated soil erosion and the potential of land-cover- and agroecology-specific land management practices in reducing soil loss through employing the Revised Universal Soil Loss Equation and the best available datasets. The mean rate of soil loss by water erosion in Ethiopia was estimated as 16.5 t ha^-1 yr^-1, with an annual gross soil loss of ca. 1.9 × 10⁹ t, of which the net soil loss was estimated as ca. 410 × 10⁶ t (22% of the gross soil loss). Soil loss varied across land cover types, 15 agroecological zones, and 10 river basins, with the main contributors in the respective analyses being cropland (ca. 23% of Ethiopia; 50% of the soil loss; mean soil loss rate of 36.5 t ha^-1 yr^-1), Moist Weyna Dega (ca. 10%; 20%; 33.3 t ha^-1 yr^-1), and the Abay basin (ca. 15%; 30%; 32.8 t ha^-1 yr^-1). Our results show that ca. 25% of Ethiopia (28 × 10⁶ ha) has soil loss rates above 10 t ha^-1 yr^-1, which is higher than the tolerable soil loss limits estimated for Ethiopia. Ex-ante analysis revealed that implementation of land-cover- and agroecology-specific land management practices (level bunds, graded bunds, trenches, and exclosures combined with trenches and/or bunds) in such areas could reduce the mean soil loss rate from 16.5 t ha^-1 yr^-1 to 5.3 t ha^-1 yr^-1 (mean, by ca. 68%; range, 65-70%). Suitable land management practices in the Abay and Tekeze basins and Dega and Weyna Dega agroecologies, which experience particularly severe erosion, would account for ca. 50 and 70% of the estimated soil loss reduction, respectively. This study can help raise awareness among policy makers and land managers of the extent and severity of soil loss by water erosion for better conservation planning in river basins to support sustainable use of land and water resources.

Water Erosion Risk Assessment in the Kenya Great Rift Valley Region

The Kenya Great Rift Valley (KGRV) region unique landscape comprises of mountainous terrain, large valley-floor lakes, and agricultural lands bordered by extensive Arid and Semi-Arid Lands (ASALs). The East Africa (EA) region has received high amounts of rainfall in the recent past as evidenced by the rising lake levels in the GRV lakes. In Kenya, few studies have quantified soil loss at national scales and erosion rates information on these GRV lakes’ regional basins within the ASALs is lacking. This study used the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates between 1990 and 2015 in the Great Rift Valley region of Kenya which is approximately 84.5% ASAL. The mean erosion rates for both periods was estimated to be tolerable (6.26 t ha−1 yr−1 and 7.14 t ha−1 yr−1 in 1990 and 2015 respectively) resulting in total soil loss of 116 Mt yr−1 and 132 Mt yr−1 in 1990 and 2015 respectively. Approximately 83% and 81% of the erosive lands in KGRV fell under the low risk category (<10 t ha−1 yr−1) in 1990 and 2015 respectively while about 10% were classified under the top three conservation priority levels in 2015. Lake Nakuru basin had the highest erosion rate net change (4.19 t ha−1 yr−1) among the GRV lake basins with Lake Bogoria-Baringo recording annual soil loss rates >10 t ha−1 yr−1 in both years. The mountainous central parts of the KGRV with Andosol/Nitisols soils and high rainfall experienced a large change of land uses to croplands thus had highest soil loss net change (4.34 t ha−1 yr−1). In both years, forests recorded the lowest annual soil loss rates (<3.0 t ha−1 yr−1) while most of the ASAL districts presented erosion rates (<8 t ha−1 yr−1). Only 34% of all the protected areas were found to have erosion rates <10 t ha−1 yr−1 highlighting the need for effective anti-erosive measures.

Evaluating runoff and sediment responses to soil and water conservation practices by employing alternative modeling approaches

Evaluating runoff and sediment responses to human activities and climate variability is crucial for prioritizing erosion hotspots and implementing appropriate land management interventions. This study evaluated the separate and combined impacts of soil and water conservation (SWC) practices, land use/land cover, and climate variability, on runoff and sediment yield (SY) using two approaches in drought-prone watersheds of northwestern Ethiopia. In the first (paired watershed) approach, runoff and SY outputs of Kecha (treated) and Laguna (untreated) watersheds were compared. In the second approach, we compared data before and after the implementation of SWC practices in the Kecha watershed. The Soil and Water Assessment Tool (SWAT) model was adopted for both untreated and treated watersheds and used to evaluate runoff and SY responses in the two approaches. Paired watershed approach results revealed that the SWC practices reduced the surface runoff in Kecha by about 28-36% and SY by about 51-68% as compared to those in Laguna. Similarly, compared with the baseline data at Kecha, the SWC practices reduced the surface runoff and SY by about 40% and 43%, respectively, corresponding to about 65-78% of the total changes brought by changes in land use/land cover and climate variability. Hence, combining the two approaches helped reasonably estimate the reduction of surface runoff and SY due to SWC practices by about 28-40% and about 43-68%, respectively, implying that SWC practices had a considerably greater effect on SY than surface runoff. The study further revealed that the untreated Laguna watershed, where >86% of the total area is categorized as the very high soil erosion severity class, should be an immediate conservation priority. The findings of this study will be vital to devise future alternative land management scenarios in these watersheds and similar agro-ecological areas elsewhere.

Prediction of factors affecting activation of soil erosion by mathematical modeling at pedon scale under laboratory conditions

Soil degradation due to erosion is a significant worldwide problem at different spatial (from pedon to watershed) and temporal scales. All stages and factors in the erosion process must be detected and evaluated to reduce this environmental issue and protect existing fertile soils and natural ecosystems. Laboratory studies using rainfall simulators allow single factors and interactive effects to be investigated under controlled conditions during extreme rainfall events. In this study, three main factors (rainfall intensity, inclination, and rainfall duration) were assessed to obtain empirical data for modeling water erosion during single rainfall events. Each factor was divided into three levels (− 1, 0, + 1), which were applied in different combinations using a rainfall simulator on beds (6 × 1 m) filled with soil from a study plot located in the arid Sistan region, Iran. The rainfall duration levels tested were 3, 5, and 7 min, the rainfall intensity levels were 30, 60, and 90 mm/h, and the inclination levels were 5, 15, and 25%. The results showed that the highest rainfall intensity tested (90 mm/h) for the longest duration (7 min) caused the highest runoff (62 mm³/s) and soil loss (1580 g/m²/h). Based on the empirical results, a quadratic function was the best mathematical model (R² = 0.90) for predicting runoff (Q) and soil loss. Single-factor analysis revealed that rainfall intensity was more influential for runoff production than changes in time and inclination, while rainfall duration was the most influential single factor for soil loss. Modeling and three-dimensional depictions of the data revealed that sediment production was high and runoff production lower at the beginning of the experiment, but this trend was reversed over time as the soil became saturated. These results indicate that avoiding the initial stage of erosion is critical, so all soil protection measures should be taken to reduce the impact at this stage. The final stages of erosion appeared too complicated to be modeled, because different factors showed differing effects on erosion.

Quantitative Soil Wind Erosion Potential Mapping for Central Asia Using the Google Earth Engine Platform

A lack of long-term soil wind erosion data impedes sustainable land management in developing regions, especially in Central Asia (CA). Compared with large-scale field measurements, wind erosion modeling based on geospatial data is an efficient and effective method for quantitative soil wind erosion mapping. However, conventional local-based wind erosion modeling is time-consuming and labor-intensive, especially when processing large amounts of geospatial data. To address this issue, we developed a Google Earth Engine-based Revised Wind Erosion Equation (RWEQ) model, named GEE-RWEQ, to delineate the Soil Wind Erosion Potential (SWEP). Based on the GEE-RWEQ model, terabytes of Remote Sensing (RS) data, climate assimilation data, and some other geospatial data were applied to produce monthly SWEP with a high spatial resolution (500 m) across CA between 2000 and 2019. The results show that the mean SWEP is in good agreement with the ground observation-based dust storm index (DSI), satellite-based Aerosol Optical Depth (AOD), and Absorbing Aerosol Index (AAI), confirming that GEE-RWEQ is a robust wind erosion prediction model. Wind speed factors primarily determined the wind erosion in CA (r = 0.7, p < 0.001), and the SWEP has significantly increased since 2011 because of the reversal of global terrestrial stilling in recent years. The Aral Sea Dry Lakebed (ASDLB), formed by shrinkage of the Aral Sea, is the most severe wind erosion area in CA (47.29 kg/m2/y). Temporally, the wind erosion dominated by wind speed has the largest spatial extent of wind erosion in Spring (MAM). Meanwhile, affected by the spatial difference of the snowmelt period in CA, the wind erosion hazard center moved from the southwest (Karakum Desert) to the middle of CA (Kyzylkum Desert and Muyunkum Desert) during spring. According to the impacts of land cover change on the spatial dynamic of wind erosion, the SWEP of bareland was the highest, while that of forestland was the lowest.

Determining tributary sources of increased sedimentation in East-African Rift Lakes

Temporal and spatial sediment dynamics in an East-African Rift Lake (Lake Manyara, Tanzania), and its river inputs, have been evaluated via a combination of sediment tracing and radioactive dating. Changes in sedimentation rates were assessed using radioactive dating of sediment cores in combination with geochemical profile analysis of allogenic and autogenic elements. Geochemical fingerprinting of riverine and lake sediment was integrated within a Bayesian mixing model framework, including spatial factors, to establish which tributary sources were the main contributors to recent lake sedimentation. The novel application of Bayesian source attribution on sediment cores and subsequent integration with sedimentation data permitted the coupling of changes in the rate of lake sedimentation with variations in sediment delivery from the tributaries. These complimentary evidence bases demonstrated that Lake Manyara has experienced an overall upward trajectory in sedimentation rates over the last 120 years with distinct maxima between 0.80 and 0.85 g cm^-2 yr^-1 in the 1960s and between 0.84 and 1.81 g cm^-2 yr^-1 in 2010. Increased sedimentation rates are largely a result of a complex interaction between increased upstream sediment delivery following changes in land cover and natural rainfall fluctuations. Modelling results identified two specific tributaries as responsible for elevated sedimentation rates, contributing 58% and 38% of the recently deposited lake sediment. However, the effects of sedimentation were shown to be spatially distinct given the domination of different tributaries in various areas of Lake Manyara. The application of source-tracing techniques constrained sedimentation problems in Lake Manyara to specific tributary sources and established a link between upstream land degradation and downstream ecosystem health. This novel application provides a solid foundation for targeted land and water management strategies to safeguard water security and environmental health in Lake Manyara and has potential application to fill knowledge gaps on sediment dynamics in other East-African Rift Lakes.

Quantification of Soil Losses along the Coastal Protected Areas in Kenya

Monitoring of improper soil erosion empowered by water is constantly adding more risk to the natural resource mitigation scenarios, especially in developing countries. The demographical pattern and the rate of growth, in addition to the impairments of the rainfall pattern, are consequently disposed to adverse environmental disturbances. The current research goal is to evaluate soil erosion triggered by water in the coastal area of Kenya on the district level, and also in protected areas. The Revised Universal Soil Loss Equation (RUSLE) model was exercised to estimate the soil loss in the designated study area. RUSLE input parameters were functionally realized in terms of rainfall and runoff erosivity factor (R), soil erodibility factor (K), slope length and gradient factor (LS), land cover management factor (C) and slope factor (P). The realization of RUSLE input parameters was carried out using different dataset sources, including meteorological data, soil/geology maps, the Digital Elevation Model (DEM) and processing of satellite imagery. Out of 26 districts in coastal area, eight districts were projected to have mean annual soil loss rates of >10 t·ha−1·y−1: Kololenli (19.709 t·ha−1·y−1), Kubo (14.36 t·ha−1·y−1), Matuga (19.32 t·ha−1·y−1), Changamwe (26.7 t·ha−1·y−1), Kisauni (16.23 t·ha−1·y−1), Likoni (27.9 t·ha−1·y−1), Mwatate (15.9 t·ha−1·y−1) and Wundanyi (26.51 t·ha−1·y−1). Out of 34 protected areas at the coastal areas, only four were projected to have high soil loss estimation rates >10 t·ha−1·y−1: Taita Hills (11.12 t·ha−1·y−1), Gonja (18.52 t·ha−1·y−1), Mailuganji (13.75.74 t·ha−1·y−1), and Shimba Hills (15.06 t·ha−1·y−1). In order to mitigate soil erosion in Kenya’s coastal areas, it is crucial to regulate the anthropogenic disturbances embedded mainly in deforestation of the timberlands, in addition to the natural deforestation process caused by the wildfires.

Effectiveness of Polyacrylamide in Reducing Runoff and Soil Loss under Consecutive Rainfall Storms

The use of anionic polyacrylamide (PAM) as a soil conditioner could help prevent soil loss by water. In this study, we determined the effective granular PAM rate that best reduces runoff and soil loss from Oxisols. Furthermore, the effectiveness of the selected PAM rate was tested by applying it in a mixture with gypsum (G) or lime (L). The study was conducted in two phases: (i) Dry PAM rates of 0 (C), 20 kg ha−1 (P20), 40 kg ha−1 (P40), and 60 kg ha−1 (P60) were applied onto soil surface and run for six consecutive rainfall storms of 70 mm h−1 intensity for 1 h duration, and the effective PAM rate was selected; and (ii) G (4 t ha−1) or L (2 t ha−1) were applied alone or mixed with the selected PAM rate. The P20 was found to be effective in reducing runoff in the beginning while P40 and P60 were more effective starting from the third storm through the end of the consecutive storms, but with no statistically significant difference between P40 and P60. Hence, P40 was selected as the most suitable rate for the given test soil and rainfall pattern. On the other hand, the mixed application of P40 with G or L increased infiltration rate (IR) in the first two storms through improving soil solution viscosity. However, effectiveness of the mixtures had diminished by various degrees as rain progressed, as compared to P40 alone, which could be attributed to the rate and properties of G and L. In conclusion, the variation in effectiveness of PAM rates in reducing runoff with storm duration could indicate that the effective rates shall be selected based on the climatic region in that lower rates for the short rains or higher rates for elongated rains. Moreover, combined application of PAM with L could offer a good option to both fairly reduce soil erosion and improve land productivity especially in acidic soils like Oxisols, which requires further field verification.

Seasonal Rainfall Variability in Ethiopia and Its Long-Term Link to Global Sea Surface Temperatures

Investigating the influence of sea surface temperatures (SSTs) on seasonal rainfall is a crucial factor for managing Ethiopian water resources. For this purpose, SST and rainfall data were used to study a wide range of inhomogeneous areas in Ethiopia with uneven distribution of rainfall for both summer (1951–2015) and spring (1951–2000) seasons. Firstly, a preliminary subdivision of rainfall grid points into zones was applied depending on spatial homogeneity and seasonality of rainfall. This introduced new clusters, including nine zones for summer rainfall peak (July/August) and five zones for spring rainfall peak (April/May). Afterward, the time series for each zone was derived by calculating the rainfall averaged over grid points within the zone. Secondly, the oceanic regions that significantly correlated with the Ethiopian rainfall were identified through cross-correlations between rainfalls averaged over every homogeneous zone and the monthly averaged SST. For summer rainfall as a main rainy season, the results indicated that the Gulf of Guinea and southern Pacific Ocean had a significant influence on rainfall zones at a lag time of 5–6 and 6–7 months. Besides, for summer rainfall zones 8 and 9 at lag time 5–6 months, the common SST regions of the southern Pacific Ocean showed the opposite sense of positive and negative correlations. Thus, the difference in SSTs between the two regions was more strongly correlated (r ≥ 0.46) with summer rainfall in both zones than others. For the spring season, the results indicated that SST of the northern Atlantic Ocean had a strong influence on spring rainfall zones (3 and 5) at a lag time 6–7 months, as indicated by a significant correlation (r ≥ −0.40). Therefore, this study suggests that SSTs of southern Pacific and northern Atlantic oceans can be used as effective inputs for prediction models of Ethiopian summer and spring rainfalls, respectively.

Land Use Change Impacts on Water Erosion in Rwanda

Rwanda has experienced accelerated soil erosion as a result of unsustainable human activities and changes in land use. Therefore, this study aimed at applying the RUSLE (Revised Universal Soil Loss Equation) model using GIS (Geographical Information System) and remote sensing to assess water erosion in Rwanda, focusing on the erosion-prone lands for the time span 2000 to 2015. The estimated mean annual soil losses were 48.6 t ha−1 y−1 and 39.2 t ha−1 y−1 in 2000 and 2015, respectively, resulting in total nationwide losses of approximately 110 and 89 million tons. Over the 15 years, 34.6% of the total area of evaluated LULC (land use/land cover) types have undergone changes. The highest mean soil loss of 91.6 t ha−1 y−1 occurred in the area changing from grassland to forestland (0.5%) while a mean soil loss of 10.0 t ha−1 y−1 was observed for grassland converting to cropland (4.4%). An attempt has been made to identify the embedded driving forces of soil erosion in Rwanda. As a result, we found that mean soil loss for Rwanda’s districts in 2015 was significantly correlated with poverty (r = 0.45, p = 0.013), increased use of chemical fertilizers (r = 0.77, p = 0.005), and especially was related to extreme poverty (r = 0.77, p = 0.000). The soil conservation scenario analysis for Rwanda’s cropland in 2015 revealed that terracing could reduce the soil loss by 24.8% (from 14.6 t ha−1 y−1 to 11.7 t ha−1 y−1). Most importantly, the study suggests that (1) terracing integrated with mulching and cover crops could effectively control water erosion while ameliorating soil quality and fertility, and (2) reforestation schemes targeting the rapid-growing tree species are therefore recommended as an important feature for erosion control in the study area.

Evaluation and Application of Multi-Source Satellite Rainfall Product CHIRPS to Assess Spatio-Temporal Rainfall Variability on Data-Sparse Western Margins of Ethiopian Highlands

The spatio-temporal characteristic of rainfall in the Beles Basin of Ethiopia is poorly understood, mainly due to lack of data. With recent advances in remote sensing, satellite derived rainfall products have become alternative sources of rainfall data for such poorly gauged areas. The objectives of this study were: (i) to evaluate a multi-source rainfall product (Climate Hazards Group Infrared Precipitation with Stations: CHIRPS) for the Beles Basin using gauge measurements and (ii) to assess the spatial and temporal variability of rainfall across the basin using validated CHIRPS data for the period 1981–2017. Categorical and continuous validation statistics were used to evaluate the performance, and time-space variability of rainfall was analyzed using GIS operations and statistical methods. Results showed a slight overestimation of rainfall occurrence by CHIRPS for the lowland region and underestimation for the highland region. CHIRPS underestimated the proportion of light daily rainfall events and overestimated the proportion of high intensity daily rainfall events. CHIRPS rainfall amount estimates were better in highland regions than in lowland regions, and became more accurate as the duration of the integration time increases from days to months. The annual spatio-temporal analysis result using CHIRPS revealed: a mean annual rainfall of the basin is 1490 mm (1050–2090 mm), a 50 mm increase of mean annual rainfall per 100 m elevation rise, periodical and persistent drought occurrence every 8 to 10 years, a significant increasing trend of rainfall (~5 mm year−1), high rainfall variability observed at the lowland and drier parts of the basin and high coefficient of variation of monthly rainfall in March and April (revealing occurrence of bimodal rainfall characteristics). This study shows that the performance of CHIRPS product can vary spatially within a small basin level, and CHIRPS can help for better decision making in poorly gauged areas by giving an option to understand the space-time variability of rainfall characteristics.