Estimation of Soil Erosion Dynamics in the Koshi Basin Using GIS and Remote Sensing to Assess Priority Areas for Conservation

Assessing soil erosion risk in a peri-urban catchment of the Lake Victoria basin

Soil erosion and sedimentation contribute to deteriorating water quality, adverse alterations in basin hydrology and overall ecosystem biogeochemistry. Thus, understanding soil erosion patterns in catchments is critical for conservation planning. This study was conducted in a peri-urban Inner Murchison Bay (IMB) catchment on the northern shores of Lake Victoria since most soil erosion studies in Sub-Saharan Africa have been focused on rural landscapes. The study sought to identify sediment sources by mapping erosion hotspots using the revised universal soil loss equation (RUSLE) model in appendage with field walks. RUSLE model was built in ArcGIS 10.5 software with factors including: rainfall erosivity, soil erodibility, slope length and steepness, land cover and support practices. The model was run, producing an erosion risk map and field assessments conducted to ground-truth findings and identify other hotspots. The percentage areas for RUSLE modelled erosion rates were: 66.8% for 0–2 t ha⁻¹ year⁻¹; 10.8% for 2–5 t ha⁻¹ year⁻¹; 10.1% for 5–10 t ha⁻¹ year⁻¹; 9% for 10–50 t ha⁻¹ year⁻¹ and 3.3% for 50–100 t ha⁻¹ year⁻¹. Average erosion risk was 7 t ha⁻¹ year⁻¹ and the total watershed erosion risk was 197,400 t year⁻¹, with croplands and steep areas (slope factor > 20) as the major hotspots (> 5 t ha⁻¹ year⁻¹). Field walks revealed exposed soils, marrum (gravel) roads and unlined drainage channels as other sediment sources. This study provided the first assessment of erosion risk in this peri-urban catchment, to serve as a basis for identifying mitigation priorities. It is recommended that tailored soil and water conservation measures be integrated into physical planning, focusing on identified non-conventional hotspots to ameliorate sediment pollution in Lake Victoria.

Impact of Land Use/Land Cover Change on Soil Retention Service:

Globally, urbanization changes land use/land cover (LULC) and alters ecosystem functions and services. Soil retention (SR) is a critical ecological service that is strongly related to LULC change. The topic of this study is assessment of LULC change on soil retention service (SRS) in a fragile seminatural-urbanized landscape of the Jajrood basin in Northern Tehran, Iran, from 2000 to 2020. To achieve the goal, the LULC maps and the other relevant datasets were imported into the Integrated Valuation of Ecosystem Services and Trade-offs tool (InVEST) using the Universal Soil Loss Equation (USLE). Calibration and validation were performed using Goodness-of-fit test for observational and modeled data. The results revealed that LULC change had both negative and positive effects on SR. The built-up area increased dramatically by about 133 percent, while the rangeland shrunk by approximately 5 % during the twenty-year, leading to an increase in soil erosion and reducing SR. On the other hand, the agricultural and gardening activities expanded by 41 %, which caused an increment in SR. Due to the outgrowth of man-made areas compared to the other land uses, the overall SR decreased by about 17,000 tons. Moreover, the result indicated that slope, elevation, and land management factors, respectively, had the highest correlation with SRS. The finding of this research can provide insight to land use planners to protect the areas with high soil erosion.

Understanding hydrogeomorphic and climatic controls on soil erosion and sediment dynamics in large Himalayan basins

The Himalayan basins are characterised by severe soil erosion rates and several basins are among the largest sediment dispersal systems in the world. Unsustainable agricultural activities increase the soil erosion rates and influence the overall hydro-geomorphic regime of river basins. Consequently, the water holding capacity of soil reduces, which enhances the flood risk in the lowland regions. In addition, excessive sediment flux severely affects the reservoir capacity in the mountainous regions, thus amplifying the flood hazard in the upland regions. Here, we have analysed two large and hydro-geomorphically diverse Himalayan River basins, namely, the Ganga Basin (GBA) from source to Allahabad in northern India and the Kosi Basin (KB) draining through Nepal and north Bihar plains in eastern India. Based on RULSE and region-specific SDR modelling framework, which includes model calibration, validation and uncertainty assessment, we demonstrate that spatial variation in rainfall, hydrogeomorphic conditions, the presence of hydraulic structures, and large-scale agricultural activities influence the overall pattern of sediment production and transport in these two large river basins. Total soil erosion in GBA and KB are estimated to be ~404 × 10⁶ t/y and ~724 × 10⁶ t/y respectively, a large part of which comes from the mountainous regions in both basins. Sediment yield at the mountain exits of the GBA and KB are computed as 14.1 × 10⁶ t/y and 86.4 × 10⁶ t/y respectively, which work out to be ~5% and ~15% of total soil erosion from the respective contributing areas of the KB and GBA respectively. Similarly, sediment yields at outlets in the alluvial plains are estimated to be 32.2 × 10⁶ t/y and 37.3 × 10⁶ t/y in the GBA and the KB, respectively suggesting that a large part of sediments are accommodated in the alluvial plains of KB. These results have significant implications for sediment management in the Himalayan River basins.

Using big data for evaluating development outcomes: A systematic map

BACKGROUND

Policy makers need access to reliable data to monitor and evaluate the progress of development outcomes and targets such as sustainable development outcomes (SDGs). However, significant data and evidence gaps remain. Lack of resources, limited capacity within governments and logistical difficulties in collecting data are some of the reasons for the data gaps. Big data-that is digitally generated, passively produced and automatically collected-offers a great potential for answering some of the data needs. Satellite and sensors, mobile phone call detail records, online transactions and search data, and social media are some of the examples of big data. Integrating big data with the traditional household surveys and administrative data can complement data availability, quality, granularity, accuracy and frequency, and help measure development outcomes temporally and spatially in a number of new ways.The study maps different sources of big data onto development outcomes (based on SDGs) to identify current evidence base, use and the gaps. The map provides a visual overview of existing and ongoing studies. This study also discusses the risks, biases and ethical challenges in using big data for measuring and evaluating development outcomes. The study is a valuable resource for evaluators, researchers, funders, policymakers and practitioners in their effort to contributing to evidence informed policy making and in achieving the SDGs.

OBJECTIVES

Identify and appraise rigorous impact evaluations (IEs), systematic reviews and the studies that have innovatively used big data to measure any development outcomes with special reference to difficult contexts.

SEARCH METHODS

A number of general and specialised data bases and reporsitories of organisations were searched using keywords related to big data by an information specialist.

SELECTION CRITERIA

The studies were selected on basis of whether they used big data sources to measure or evaluate development outcomes.

DATA COLLECTION AND ANALYSIS

Data collection was conducted using a data extraction tool and all extracted data was entered into excel and then analysed using Stata. The data analysis involved looking at trends and descriptive statistics only.

MAIN RESULTS

The search yielded over 17,000 records, which we then screened down to 437 studies which became the foundation of our systematic map. We found that overall, there is a sizable and rapidly growing number of measurement studies using big data but a much smaller number of IEs. We also see that the bulk of the big data sources are machine-generated (mostly satellites) represented in the light blue. We find that satellite data was used in over 70% of the measurement studies and in over 80% of the IEs.

AUTHORS' CONCLUSIONS

This map gives us a sense that there is a lot of work being done to develop appropriate measures using big data which could subsequently be used in IEs. Information on costs, ethics, transparency is lacking in the studies and more work is needed in this area to understand the efficacies related to the use of big data. There are a number of outcomes which are not being studied using big data, either due to the lack to applicability such as education or due to lack of awareness about the new methods and data sources. The map points to a number of gaps as well as opportunities where future researchers can conduct research.

Prioritization of Watershed Using Remote Sensing and Geographic Information System

Soil erosion is becoming a major concern at the watershed scale for the environment, natural resources, and sustainable resource management. Therefore, the estimation of soil loss through this phenomenon and the identification of critical soil erosion-prone areas are considered to be key tasks in the soil conservation programme for the design and implementation of best management practices for specific regions or areas. In the present study, revised universal soil loss equation (RUSLE) modelling is combined with remote sensing (RS) and geographical information system (GIS) techniques and used to predict soil erosion and the prioritization of watersheds in Nainital district Uttarakhand, India. For the estimation of soil loss, different factors, namely, rainfall-runoff erosivity (R) factor, soil erodability (K) factor, slope length steepness (LS) factor, cover management (C) factor, and the erosion control practices (P) factor were computed. The data on various other aspects such as land use/land cover (LU/LC), the digital elevation model (DEM), slope, contours, drainage network, soil texture, organic matter, and rainfall were integrated to prepare a database for the RUSLE equation by employing ENVI & QGIS software. The results showed that a major portion (70.26%) of Nainital district is covered with forest, followed by area under fallow and agricultural land. Annual average soil loss ranged between 20 to 80 t ha−1 yr−1 in the study area. Out of 50 watersheds in the study area, 7 watersheds were given top priority for conserving natural resources, while 11 watersheds, mostly in the east-central part of Nainital, were kept under the next priority category. Only 4 watersheds of the total were given lowest priority. Moreover, it was concluded that major portions of Nainital district were in a severely prone category of soil erosion, and therefore required immediate action plans to check soil erosion and evade the possibility of landslides.

Sensitivity Assessment of Spatial Resolution Difference in DEM for Soil Erosion Estimation Based on UAV Observations: An Experiment on Agriculture Terraces in the Middle Hill of Nepal

Soil erosion in the agricultural area of a hill slope is a fundamental issue for crop productivity and environmental sustainability. Building terrace is a very popular way to control soil erosion, and accurate assessment of the soil erosion rate is important for sustainable agriculture and environmental management. Currently, many soil erosion estimations are mainly based on the freely available medium or coarse resolution digital elevation model (DEM) data that neglect micro topographic modification of the agriculture terraces. The development of unmanned aerial vehicle (UAV) technology enables the development of high-resolution (centimeter level) DEM to present accurate topographic features. To demonstrate the sensitivity of soil erosion estimates to DEM resolution at this high-resolution level, this study tries to evaluate soil erosion estimation in the Middle Hill agriculture terraces in Nepal based on UAV derived high-resolution (5 × 5 cm) DEM data and make a comparative study for the estimates by using the DEM data aggregated into different spatial resolutions (5 × 5 cm to 10 × 10 m). Firstly, slope gradient, slope length, and topographic factors were calculated at different resolutions. Then, the revised universal soil loss estimation (RUSLE) model was applied to estimate soil erosion rates with the derived LS factor at different resolutions. The results indicated that there was higher change rate in slope gradient, slope length, LS factor, and soil erosion rate when using DEM data with resolution from 5 × 5 cm to 2 × 2 m than using coarser DEM data. A power trend line was effectively used to present the relationship between soil erosion rate and DEM resolution. The findings indicated that soil erosion estimates are highly sensitive to DEM resolution (from 5 × 5 cm to 2 × 2 m), and the changes become relatively stable from 2 × 2 m. The use of DEM data with pixel size larger than 2 × 2 m cannot detect the micro topography. With the insights about the influencing mechanism of DEM resolution on soil erosion estimates, this study provides important suggestions for appropriate DEM data selection that should be investigated first for accurate soil erosion estimation.

Assessment of rainfall erosivity (R-factor) during 1986-2015 across Nepal: a step towards soil loss estimation

Rainfall is a main cause of soil erosion which varies spatially and temporarily. R-factor is an erosive power of the rainfall that is responsible for soil detachment and subsequent displacement. Mathematically, it is expressed as a sum of the product of kinetic energy and maximum 30-min rain intensity. A precise assessment of R-factor needs higher temporal resolution rainfall data (sub-hourly) for a period of several years, which is rarely available. Many empirical approaches are used to predict R-factor as a function of mean monthly and annual rainfall amount. In this study, we used Loureiro and Countinho (Journal of Hydrology 250:12-18, 2001) approximation approach to estimate R-factor and explore its intra-annual variability using 30 years (1986-2015) of daily rainfall data from 280 stations distributed across Nepal. This study employs different intra-annual variability indices and calculates erosivity density (ED) and weighted erosivity density (WED). The country average mean annual R-factor (MAR), annual ED, and WED are found to be 9434.8 MJ mm ha^-1 h^-1 year^-1, 4.39 MJ ha^-1 h^-1,and 1.61 MJ ha^-1 h^-1, respectively. On a monthly scale, July is the highest erosive month followed by August (> 2000 MJ mm ha^-1 h^-1 month^-1). Likewise, November is the lowest erosive month followed by December (~ 50 MJ mm ha^-1 h^-1 month^-1). Spatial distributions of these indices show clear delineations of areas with different erosivity patterns at different time of the year. In addition, this study explores inter-annual variation, temporal evolution, and trend estimation of R-factors over the country (for the first time). Significant rising trends are observed in the western region of the country. We found that the mean soil erosion for Nepal is estimated at 21.01 ton ha^-1 year^-1. The smallest R-factors are observed in the north-western region of the country and the maximum values are observed at mid hills and southern plains of the country. Our study could be an initial but important step for effective soil conservation, land use planning, and agricultural production.

Land use change affects water erosion in the Nepal Himalayas

Soil erosion is a global environmental threat, and Land Use Land Cover Changes (LUCC) have significant impacts on it. Nepal, being a mountainous country, has significant soil erosion issues. To examine the effects of LUCC on water erosion, we studied the LUCC in Sarada, Rapti and Thuli Bheri river basins of Nepal during the 1995-2015 period using the Remote Sensing. We calculated the average annual soil loss using the Revised Universal Soil Loss Equation and Geographical Information System. Our results suggest that an increase in the agricultural lands at the expense of bare lands and forests escalated the soil erosion through the years; rates being 5.35, 5.47 and 6.03 t/ha/year in 1995, 2007 and 2015, respectively. Of the different land uses, agricultural land experienced the most erosion, whereas the forests experienced the least erosion. Agricultural lands, particularly those on the steeper slopes, were severely degraded and needed urgent soil and water conservation measures. Our study confirms that the long term LUCC has considerable impacts on soil loss, and these results can be implemented in similar river basins in other parts of the country.

Sensitivity of source apportionment predicted by a Bayesian tracer mixing model to the inclusion of a sediment connectivity index as an informative prior: Illustration using the Kharka catchment (Nepal)

Long-chain saturated fatty acid (LCSFA) isotopic composition in tandem with Bayesian isotope mixing models (BIMM) can provide insight into land use-based sediment sources in catchment systems. Apportioning sediment sources robustly, however, requires careful consideration of how additional factors including topography, surface cover and land use practices interact to influence contributions from individual sources. Prior knowledge can be used in BIMM; however, the full capacity of this functionality has not been thoroughly exploited yet in conjunction with sediment fingerprinting. In response, we propose an approach for applying a state-of-the-art BIMM incorporating a sediment connectivity index (SCI) as an informative prior for sediment source apportionment in a highly hydrodynamic catchment in Nepal. A library of LCSFA carbon isotopic composition was constructed for surface soils collected from mixed forest, upland and lowland terraces in the Kharka micro-catchment. δ¹³C values of LCSFA of time-integrated suspended bulk (<2 mm) sediment were depleted by 4‰ compared to the fine (<0.063 mm) sediment fraction. Conventional source apportionment for fine sediment samples without the SCI informative prior suggested that 66% of the sediment is derived from forest soils followed by lowland (19%) and upland (15%) terraces. Incorporation of the SCI as an informative prior in BIMM, however, modified the original source apportionment estimates to 90%, 9% and 1% respectively. The lower contributions from agricultural terraces are explained by landscape complexity comprising small levelled terraces that reduce hillslope-to-channel sediment connectivity. This study demonstrates the sensitivity of BIMM posterior distributions to incorporation of an informative prior based on a SCI. Inclusion of SCI linked to land use and management can provide a more physically-grounded approach to estimating sediment source contributions from biogeochemical tracers, and critically one which generates results better reflecting what makes good environmental sense in the context of land management and visual evidence of sediment mobilisation and delivery.

Remote Sensing-Based Research for Monitoring Progress towards SDG 15 in Bangladesh: A Review

The Sustainable Development Goals (SDGs) have been in effect since 2015 to continue the progress of the Millennium Development Goals. Some of the SDGs are expected to be achieved by 2020, while others by 2030. Among the 17 SDGs, SDG 15 is particularly dedicated to environmental resources (e.g., forest, wetland, land). These resources are gravely threatened by human-induced climate change and intense anthropogenic activities. In Bangladesh, one of the most climate-vulnerable countries, climate change and human interventions are taking a heavy toll on environmental resources. Ensuring the sustainability of these resources requires regular monitoring and evaluation to identify challenges, concerns, and progress of environmental management. Remote sensing has been used as an effective tool to monitor and evaluate these resources. As such, many studies on Bangladesh used various remote-sensing approaches to conduct research on the issues related to SDG 15, particularly on forest, wetland, erosion, and landslides. However, we lack a comprehensive view of the progress, challenges, concerns, and future outlook of the goal and its targets. In this study, we sought to systematically review the remote-sensing studies related to SDG 15 (targets 15.1–15.3) to present developments, analyze trends and limitations, and provide future directions to ensure sustainability. We developed several search keywords and finally selected 53 articles for review. We discussed the topical and methodological trends of current remote-sensing works. In addition, limitations were identified and future research directions were provided.

Spatio-Temporal Analysis of Drought Variability Using CWSI in the Koshi River Basin (KRB)

Drought is one of the most frequent meteorological disasters, and has exerted significant impacts on the livelihoods and economy of the Koshi River Basin (KRB). In this study, we assessed drought patterns using the Crop Water Shortage Index (CWSI) based on the MOD16 product for the period between 2000 and 2014. The results revealed that the CWSI based on the MOD16 product can be act as an indicator to monitor the characteristics of the drought. Significant spatial heterogeneity of drought was observed in the basin, with higher CWSI values downstream and upstream than in the midstream. The midstream of the KRB was dominated by light drought, moderate drought occurred in the upstream, and the downstream was characterized by severe drought. The monthly CWSI during one year in KRB showed the higher CWSI between March to May (pre-monsoon) and October to December (post-monsoon) rather than June to September (monsoon), and the highest was observed in the month of April, suggesting that precipitation plays the most important role in the mitigation of CWSI. Additionally, the downstream and midstream showed a higher variation of drought compared to the upstream in the basin. This research indicates that the downstream suffered severe drought due to seasonal water shortages, especially during the pre-monsoon, and water-related infrastructure should be implemented to mitigate losses caused by drought.

Environmental fragility and vegetation cover dynamics in the Lapa Grande State Park, MG, Brazil

The environmental fragility analysis evaluates the susceptibility of an environment to degradation. Thus, the goals of this work are to analyze and map the environmental fragility of the Lapa Grande State Park (PELG), Brazil, and its buffer zone (ZA) and also quantify the changes in the vegetation cover before and after the implementation of the PELG. The environmental fragility was evaluated through multicriteria analysis using the factors: slope, geology, pedology and land use and land cover. The changes in the high vigor vegetation cover were determined from the normalized difference vegetation index (NDVI) for 1996, 2006 and 2016. It was verified that the central-east and northeast regions of the PELG presented the greatest environmental fragilities in 1996, and significant reductions in the areas with high and very high fragility were observed in 2006 and 2016 due to the increase in the vegetation cover after the implementation of the PELG, which was more expressive in the park area than in the ZA. The increase of 20.7% of 2006 to 2016 in the vegetation with greater vigor, proved the importance of the creation of a conservation unit.

Investigation of Runoff and Sediment Yields Under Different Crop and Tillage Conditions by Field Artificial Rainfall Experiments

Crop types and tillage measures on slopes have significant impacts on regional water and soil conservation. In this study, we investigated the influences of multiple crop types and tillage measures on water and sediment yields based on plot-scale experiments under artificial rainfall. The objective of the study is to find the best combination of crop type and tillage measure from the perspective of reducing soil erosion. We performed artificial rainfall experiments under eight slope treatments, which are the bare-land (BL, as a reference), peanut monoculture (PL), corn monoculture (CL), bare land (upper slope) mixed with peanut monoculture (lower slope) (BP), corn and peanut intercropping (TCP), corn and soybean intercropping (TCS), downslope ridge cultivation (BS) slope, and straw-mulched (SC), respectively. Under similar rainfall intensity and initial soil moisture conditions, these treatments except for BS efficiently reduced sediment yield compared to the BL slope. In comparison, the most effective slope treatment to reduce soil erosion is TCP, followed by PL and TCS. The amount of sediment yielded from the three treatments accounts for 0.4%, 2.0%, and 3.3% of the sediment yielded from BL. We recommend the three slope treatments as the preferred choices among eight treatments. Also, the lower sediment yield in the three slope treatments benefits from their higher vegetation coverage. Vegetation coverage plays a greater role in regulating sediment yield than the surface runoff at a plot scale.

Estimation of Sediment Yield and Maximum Outflow Using the IntErO Model in the Sarada River Basin of Nepal

Soil erosion is a severe environmental problem worldwide as it washes away the fertile topsoil and reduces agricultural production. Nepal, being a hilly country, has significant erosion disputes as well. It is important to cognise the soil erosion processes occurring in a river basin to manage the erosion severity and plan for better soil conservation programs. This paper seeks to calculate the sediment yield and maximum outflow from the Sarada river basin located in the western hills of Nepal using the computer-graphic Intensity of Erosion and Outflow (IntErO) model. Asymmetry coefficient of 0.63 was calculated, which suggests a possibility of large floods to come in the river basin in the future whereas the maximum outflow from the river basin was 1918 m³ s−1. An erosion coefficient value of 0.40 was obtained, which indicates surface erosion of medium strength prevails in the river basin. Similarly, the gross soil loss rate of 10.74 Mg ha−1 year−1 was obtained with the IntErO modeling which compares well with the soil loss from the erosion plot measurements. The IntErO model was used for the very first time to calculate soil erosion rates in the Nepalese hills and has a very good opportunity to be applied in similar river basins.

Towards the assessment of sediment connectivity in a large Himalayan river basin

Sediment connectivity, defined as the degree of linkage between the sediment sources to downstream areas, is one of the most important properties that control landscape evolution in river basins. The degree of linkages amongst different parts of a catchment depends mainly on the hinterland characteristics (e.g. catchment morphology, slope, shape, relief, and elevation), channel characteristics (e.g. slope, stream network density, valley confinement), and the combined effects of vegetation (e.g. land use changes and land abandonment). This paper evaluates the sediment connectivity of the upper Kosi basin covering an area of ~52,731 km² including Tibet and Nepal at different spatial scales. We have computed the index of connectivity (IC) using the equations originally proposed by Borselli et al. (2008) and modified by Cavalli et al. (2013) to evaluate the potential connection of sediment source areas to the primary channel network as well to the catchment outlet. Our results highlight significant spatial variability in sediment connectivity across the basin and provide important insights on structural sediment dynamics in a complex geological and geomorphological setting. We compare our results with the observed sediment load data at certain outlets and demonstrate that sediment flux in different sub-basins is controlled by variable slope distribution and land use and land cover that are strongly related to the structural connectivity. We argue that IC model can be extremely beneficial to understand sediment dynamics at catchment scale in a large river basin (~10³-10⁴ km² scale), where systematic field investigations for mapping hillslope-channel linkages are not feasible.

Estimation of Soil Erosion in Nepal Using a RUSLE Modeling and Geospatial Tool

Soil erosion is a major issue, causing the loss of topsoil and fertility in agricultural land in mountainous terrain. Estimation of soil erosion in Nepal is essential because of its agriculture-dependent economy (contributing 36% to national GDP) and for preparing erosion control plans. The present study, for the first time, attempts to estimate the soil loss of Nepal through the application of the Revised Universal Soil Loss Equation (RUSLE) model. In addition, it analyzes the effect of Land Use and Land Cover (LULC) and slope ( β ) exposition on soil erosion. Nation-wide mean annual soil loss of Nepal is estimated at 25 t ha−1 yr−1 with a total of 369 million tonnes (mT) of potential soil loss. Soil erosion based on the physiographic region of the country shows that the Middle Mountains, High Mountains, High Himal, Chure, and Terai have mean erosion rates of 38.0, 32.0, 28.0, 7.0, and 0.1 t ha−1 yr−1. The soil erosion rate by basins showed that the annual erosions of the Karnali, Gandaki, Koshi, and Mahakali River basins are 135, 96, 79, and 15 mT, respectively. The mean soil erosion rate was significantly high (34 t ha−1 yr−1) for steep slopes (β > 26.8%) and the low (3 t ha−1 yr−1) for gentle slopes (β < 5%). Based on LULC, the mean erosion rate for barren land was the highest (40 t ha−1 yr−1), followed by agricultural land (29 t ha−1 yr−1), shrubland (25 t ha−1 yr−1), grassland (23 t ha−1 yr−1), and forests (22 t ha−1 yr−1). The entire area had been categorized into 6 erosion classes based on the erosion severity, and 11% of the area was found to be under a very severe erosion risk (> 80 t ha−1 yr−1) that urgently required reducing the risk of erosion.

Land Degradation by Soil Erosion in Nepal: A Review

Land degradation, particularly soil erosion, is currently a major challenge for Nepal. With a high rate of population growth, subsistence-based rural economy, and increasingly intense rainfall events in the monsoon season, Nepal is prone to several forms of land degradation, such as floods, landslides, and soil erosion. To understand the causes, impacts, and possible management options for soil erosion, a review on the causal factors, status, and amelioration measures for land degradation in Nepal was conducted based on recent information available in national and international journals and grey literature. Intense rainfall and conventional tillage practices coupled with poor soil structure and steep slopes are the main drivers of soil erosion. Soil erosion leads to losses in soil and crop productivity, pollution of land and water resources, and a loss of farm income. Strategies to manage erosion include mulching, cover cropping, contour farming, strip cropping, and conservation agriculture practices, along with bioengineering techniques. Land degradation issues are a prime policy focus in Nepal, including national three- and five-year plans. However, these policies have been generally ineffective in reducing soil erosion, landslides, and floods in relation to the set targets. Realistic plans need to be formulated in Nepal focusing more on capacity enhancement and local participation to actively influence land-degradation processes.

Assessment of Land Cover Change and Its Impact on Changes in Soil Erosion Risk in Nepal

Land cover change is a critical driver for enhancing the soil erosion risk in Nepal. Loss of the topsoil has a direct and indirect effect on human life and livelihoods. The present study provides an assessment of the decadal land use and land cover (LULC) change and consequent changes in the distribution of soil erosion risk for the years, 1990, 2000, and 2010, for the entire country of Nepal. The study attempted to understand how different land cover types change over the three decades and how it has changed the distribution of soil erosion risks in Nepal that would help in the development of soil conservation priority. The land cover maps were produced using geographic object-based image analysis (GEOBIA) using Landsat images. Soil erosion patterns were assessed using the revised universal soil loss equation (RUSLE) with the land cover as the input. The study shows that the forest cover is the most dominant land cover in Nepal that comprises about 6,200,000 ha forest cover. The estimated annual erosion was 129.30 million tons in 1990 and 110.53 million tons in 2010. The assessment of soil erosion dynamics was presented at the national, provincial, and district level. District wise analysis revealed that Gulmi, Parbat, Syangja, and the Tanahu district require priority for soil conservation.

Assessment of Soil Fertility under Different Land-Use Systems in Dhading District of Nepal

Unscientific land use and cropping techniques have led high soil erosion and degradation of soil quality in the mid-hills of Nepal. To understand the effects of land use systems for selected soil chemical properties in mid-hills, composite soil samples at 0 cm to 20 cm depth were collected from five different land-use systems: Grassland, forest land, upland, lowland, and vegetable farms from Dhading district of Nepal in 2017. Soil samples were analyzed for soil fertility parameters: Soil pH, organic matter (OM), total nitrogen (N), available phosphorus (P), available potassium (K) and its effect due to different land use systems were compared. Results showed that soil pH was neutral in vegetable farms (6.61), whereas the rest of the land-use systems had acidic soils. Soil OM (3.55%) and N (0.18%) content was significantly higher in forest, but the lowest soil OM (1.26%) and N (0.06%) contents were recorded from upland and lowland farms, respectively. Available P was the highest in the vegetable farm (41.07 mg kg−1) and was the lowest in grazing land (2.89 mg kg−1). The upland farm had significantly higher P levels (39.89 mg kg−1) than the lowland farm (9.02 mg kg−1). Available K was the highest in the vegetable farm (130.2 mg kg−1) and lowest in grazing land (36.8 mg kg−1). These results indicated that the land under traditional mixed cereal-based farming had poor soil health compared with adjacent vegetable, grazing, and forest lands among the study area. The variations in soil fertility parameters suggest the immediate need for improvement in soil health of traditional farmlands.

Analyzing ecological restoration strategies for water and soil conservation

The choice of areas for nature conservation involves the attempt to maximize the benefits, whether by carrying out an economic activity or by the provision of Ecosystem Services. Studies are needed to improve the understanding of the effect of the extent and position along the watershed of restored areas on soil and water conservation. This study aimed to understand how different restoration strategies might reflect in soil conservation and sediment retention. Using InVEST tool, sediment transport was simulated in a small 12 km² watershed (Posses River, in Southeast Brazil), where one of first Brazilian Payment for Ecosystem Services (PES) projects is being carried out, comparing different hypothetical restoration strategies. With 25% of restoration, sediment export decreased by 78% for riparian restoration, and 27% for the steepest slopes restoration. On the other hand, the decrease in soil loss was lower for riparian restoration, with a 16% decrease, while the steepest slopes restoration reduced it by 21%. This mismatch between the reduction of sediment export and soil loss was explained by the fact that forest not only reduces soil loss locally but also traps sediment arriving from the upper parts of the watershed. While the first mechanism is important to provide soil stability, decreasing the risk of landslip, and to maintain agricultural productivity, the second can improve water quality and decrease the risk of silting, with positive effects on the water reservoirs at the outlet of the watershed. This suggests that Riparian and the Steepest Slopes restoration strategies are complementary in the sense of preventing sediments from reaching the water bodies as well as protecting them at their origin (with the reduction of erosion), so it will be advisable to consider the two types of restoration.