Addressing soil protection concerns in forest ecosystem management under climate change

Spatial assessment of soil erosion is an important indicator of ecological soil change and global environmental changes. This is especially true for countries with rich forest cover such as Bosnia and Herzegovina. In this study, the risk of soil erosion was assessed using the Revised Universal Soil Loss Equation (RUSLE) model and the impact of changes in the forest ecosystem, current conditions were compared with possible future forest management scenarios, and measures and solutions were proposed to reduce soil erodibility in vulnerable areas of the Pale Municipality in Bosnia and Herzegovina. The studied area is at increased risk of soil erosion due to natural conditions (mountain relief, climate change, and the frequency of extreme climatic events—drought and heavy rains, which occur more and more frequently in a short period of time) and due to anthropogenic factors, such as large-scale deforestation and conversion of mountain areas for tourism purposes, tracing and construction of ski slopes and ski resorts in general, and expansion of settlements. All this leads to threats to water conservation areas, landslides, floods, forest fires, and additional reduction of forest areas due to drying of forests and expansion of settlements. GIS as a tool provides us with a quick and accurate way to find possible solutions to problems resulting from the intensive use and inadequate monitoring. In this study, we have tried to offer possible solutions and show the benefits that can be obtained by varying the factors that affect soil erodibility and depend on vegetation cover, that is, land use (C-factor). This study presents the application of RUSLE methods in combination with GIS for the purpose of planning economic activities, such as winter tourism development in the community of Pale. An increase in soil loss due to inappropriate land use was found, with the average annual soil loss due to deforestation in the ski area increasing to 909.43 t ha−1 year−1.

Soil erosion is considered a very critical environmental issue that has repercussions for almost every aspect of the world. In developing countries, such as India, soil erosion continues to be a major limitation. A prediction and assessment of erosion prone areas is of utmost importance for soil fertility and water management. Recent technological advancements have provided useful models through which remotely-sensed data for a large scale area can be analyzed and interpreted. This study aims to adopt an erosion model that is unique to the physiography, biological and climatic conditions of the Indian Himalayan Region. The Revised Universal Soil Loss Equation (RUSLE) model estimates the average annual soil loss A in tonnes ha-1 year-1. Recognizing the conditions of the region, the RUSLE developed by Renard et al., (1997) was applied in conjunction with Geographic Information System (GIS) for estimating soil loss. All parameters of the model were thoroughly studied, starting from reviews and research papers on soil erosion assessment at national and catchment levels. The study follows the RUSLE soil model in estimating the rate of soil erosion at state and district level. The model was developed around coarse resolution data requirements, with practicality in providing annual soil loss rate for a large study area. It provides a means to describe specific districts that are vulnerable to soil erosion, rendering immediate action for soil conservation practices. To determine the spatial distribution of average annual soil erosion within the study area, cell-based parameters of the RUSLE were multiplied in the specified 500m x 500m spatial resolution using the raster calculator function in ArcGIS 10.0 software. The spatial pattern of soil erosion indicates that maximum erosion takes place in the north, north-western and eastern regions of the study area while the areas with low erosion rates are located in the eastern-most part of the study area. Keywords: Soil erosion, Remote sensing, Geographic information system, Soil erodibility

Trade-offs between grain supply and soil conservation in the Grain for Green Program under changing climate: A case study in the Three Gorges Reservoir region

Mountain forest managers face the challenge to anticipate climate change (CC) impacts across large elevational ranges. For management planning, information on site-specific long-term responses to CC as well as the consequences for protection functions is particularly crucial. We used the process-based model ForClim to provide projections of forest development and their protective function as decision support for a large forest enterprise in the Northern Pre-Alps. Specifically, we investigated the impact of three climate scenarios (present climate, low- and high-impact CC) at five representative sites along an elevational gradient (700–1450 m a.s.l.). Relatively small changes to current forest structure and composition were evident under present climate, but divergent trajectories occurred under CC: while the low-elevation sites (≤ 1000 m) were affected by drought-related mortality, high-elevation sites benefited from the warming. Changes at low-elevation sites were accompanied by shifts in species composition, favouring in particular Tilia (‘low-impact’ CC) and Pinus sylvestris (‘high-impact’ CC). Forest management accelerated the shift towards climate-adapted tree species, thereby reducing detrimental effects of the ‘low-impact’ CC scenario. Under the ‘high-impact’ scenario, however, drastic decreases in protective function occurred for the late twenty-first century at low elevations. A set of exemplary disturbance scenarios (windthrow and bark beetle) demonstrated the importance of forest management and low browsing for the resilience of mountain forests. Overall, our results underline the potential of process-based forest models as decision support tools for forest enterprises, providing local projections of CC impacts across large elevational ranges at the site-specific resolution required by forest managers.

Soil erosion is an important environmental problem worldwide. Climate change can affect soil erosion by changing the rainfall pattern; hence, it is essential to assess climate change and its effect on soil erosion in different regions. This study aimed to predict the effect of climate change on soil erosion risk using the RUSLE model in Anar Sheitan forest and Tal Siah area in Jiroft region. For this purpose, meteorological station data, remote sensing images, and GIS techniques were used to prepare the necessary model inputs. Three climate change scenarios, RCP2.6, RCP4.5 and RCP8.5, were simulated in three 2006-2025, 2046-2065, and 2080-2099 periods. The rainfall erosivity factor was estimated for these periods in order to assess the impact of climate change on soil erosion risk using existing meteorological data. Other factors of the RUSLE model were considered constant, and soil erosion risk was calculated for each scenario in each period using the ArcGIS software. The analysis of results under RCP8.5 scenario during 2006-2025 period and RCP8.5 scenario during 2080-2099 period indicated that the average soil erosion risk dropped from 0.8 to 1.2 ton ha-1 year-1 in Anar Sheitan forest, reflecting an surge of 0.4 ton ha-1year-1. Furthermore, soil erosion risk spiked from 0.23 to 0.35 ton ha-1 year-1 in Tal Siah, suggesting a surge of 0.1 ton ha-1year-1. Overall, the results suggest that the soil loss will be higher in the future, which may be partly prevented by watershed management and soil conservation practices.

Millar Western Forest Products Ltd. completed its 2007–2016 detailed forest management plan for its forest management agreement area by engaging multi-discipline expertise to better manage forest values. Disciplines were formed into impact assessment groups, which were charged with developing indicators and targets for identified values and objectives and with constructing models to predict the impacts of forest management activities. Three different modeling approaches were used in developing the plan and the best results were obtained by integrating indicators directly into the forecasting model even when the indicators were simple versions of complex models. Integrating indicators into the forecasting model reduced the penalty cost on other indicators, permitted the model to find better solutions, provided almost immediate feedback to the plan development team, permitted more scenarios to be investigated, and provided a better understanding of the dynamics and tradeoffs. The other two techniques used in the DFMP involved the assessment of forecasted scenarios with more complex discipline specific models, which provided more detailed information about the indicators. Key words: forecasting, forest management planning, Patchworks, forest management modeling, timber supply analysis, trade-off analysis, VOIT

Climate change poses a serious risk to sustainable forest management, particularly in boreal forests where natural disturbances have been projected to become more severe. In three Quebec boreal forest management units, biomass carbon storage under various climate change and management scenarios was projected over 300 years (2010–2310) with a process-based dynamic landscape model (PnET-succession for Landis-II). Several strategies varying in their use of partial cuts and clear cuts, including business as usual (BAU) (clear-cut applied on more than 95% of the managed area), were tested and compared to conservation scenarios (no-harvest). Based on simulation results at the landscape scale, the clearcut-based scenarios such as BAU could result in a decrease of biomass carbon stock by 10 tC ha−1 yr−1 compared to the natural scenario. However, this reduction in carbon stock could be offset in the long term through changes in composition, as clearcut systems promote the expansion of trembling aspen and white birch. In contrast, the use of strategies based on partial cuts on more than 75% or 50% of the managed area was closer to or better than the natural scenario and resulted in greater coniferous cover retention. These strategies seemed to be the best to maximize and stabilize biomass carbon storage and ensure wood supply under different climate change scenarios, yet they would require further access and appropriate infrastructure. Furthermore, these strategies could maintain species compositions and age structures similar to natural scenarios, and thus may consequently help achieve forest ecosystem-based management targets. This study presents promising strategies to guide sustainable forest management in Eastern Canada in the context of climate change.

Soil erosion is one of the major issues in the tropics. The erosion is highly affected by the changes in climate and land cover. Future changes in tropical climate, particularly precipitation are expected to influence the potential risks of soil erosion. In the face of rapid changes in rural land cover for agricultural purposes, the combined forcings of land cover and climate changes have been to be a major threat to the soil conservation due to soil erosion. In this study, climate change scenarios at the northern part of Borneo were developed based on the RCP 4.5 and RCP 8.5 climate scenarios using Weather Research Forecast Model (WRF). The future climate projection scenarios of the total precipitation were used to simulate the potential erosion risks in varying land covers in a rural area of Sabah, Malaysia. The RUSLE model was used for soil erosion modelling, which was integrated with IDRISI Selva that allow the analysis and assessment of erosion risk. The variability of future total precipitations in the area of varying land cover types have resulted in varying degree of potential soil erosion risk The average soil loss at the studied area has increased by 262 t/ha/yr with 35.94 % increment in annual precipitation under RCP 8.5 emission scenario. However, under RCP 4.5, 26.65 % decrement in precipitation has reduced the soil loss by 315.1 t/ha/yr. In this rural area, exceptionally high soil erosion was found at steep slopes and thin vegetation covers. Therefore, an appropriate land use planning, soil conservation practices, and strategic adaptation options plan should be created and developed to ensure the sustainability of the soil conservation and enhance rural agricultural productivity.

Land degradation is a pervasive environmental and “economic challenge of the present time, especially in developing countries. Soil erosion caused by water is considered as one of the major types of land degradation processes. Soil loss estimation and detection of soil erosion-prone areas are the most important for agricultural planning and various other land management plannings in the recent era. The amount of annual average soil loss was calculated by using the Revised Universal Soil Loss Equation (RUSLE) model. This model was popularized for identifying soil loss zone areas or zones and soil erosion risk areas. This study provided a creditable prediction of soil erosion and the probable soil erosion severity zones of Jalpaiguri Sadar Block in Jalpaiguri District, West Bengal. GIS Environment was used to create the raster layer in the RUSLE factors. RUSLE data layers including rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and conservation practice (P) factors were calculated to account for the average annual soil loss of the study area. The results of this work show that the range of soil erosion is >100.00 tons hac-1year-1 to <25.00 tons hac-1year-1 while the amount of soil erosion is more in the upper part of the Jalpaiguri Sadar block, mainly along the right bank of the Tista river and the bank of the Karala river, because the soil texture is very loose in nature and the area also receive heavy rainfall. Therefore, the high level of risk of soil erosion can be checked through the processes of various soil conservation techniques.

Land use is one of the vital factors in estimating soil erosion over an area. Significant changes in spatio-temporal land use distribution might lead to water runoff which causes soil erosion events. It is important to assess the soil erosion risk for hydropower reservoir as part of sediment management activities. This study aims to assess the impact of land use and land cover (LULC) changes on soil erosion risk at Chenderoh catchment in Perak. LULC distribution within the study area was mapped for 2008, 2010 and 2015 by using land use maps obtained from Department of Agriculture, Malaysia. The soil loss rate was measured using the Revised Universal Soil Loss Equation (RUSLE) utilizing spatial data of rainfall factor, soil erodibility, topographical factor, conservation factor, and support practice factor. Average soil loss was calculated according to sub-catchment of Chenderoh, and it is found that the soil loss trend marginally increases from year 2008 to 2015. Based on soil erosion risk map results, it is found that conversion of forest land to agriculture is the main contributor to soil loss, which might be due to land clearance and agriculture practice activities.

The soil erosion risk on the slopes in Luong Son District, Hoa Binh Province, Vietnam, was determined to inform sustainable land management and conservation planning. Remote sensing and geographic information system (GIS) technologies were integrated with the universal soil loss equation (USLE) model to generate thematic maps of rainfall erosivity (R), soil erodibility (K), topographic factors (LS), and vegetation cover. These maps were combined to produce a comprehensive soil erosion risk map. The results showed that 65.09% of the district (23,747.61 hectares), mainly flat and midland areas, had no erosion risk. Light, moderate, and severe erosion affected 19.95%, 7.61%, and 7.35% of the region, respectively. Higher erosion risk is concentrated in mid-level mountainous and limestone regions, characterized by steep slopes and sparse vegetation. These findings highlight the influence of slope gradient and length on erosion severity and spatial patterns. Remote sensing, GIS, and USLE were integrated to spatially assess soil erosion, providing a scientific basis for targeted interventions, such as reforestation and terrace farming. This study contributes to gaps in the literature by comprehensively analyzing spatial soil erosion risk and providing practical recommendations for mitigating soil erosion in vulnerable landscapes and supporting sustainable land use planning under climate change pressures.

Forests and grasslands play an important role in water and air purification, prevention of the soil erosion, and in provision of habitat to wildlife. Internet of Things has a tremendous potential to play a vital role in the forest ecosystem management and stability. The conservation of species and habitats, timber production, prevention of forest soil degradation, forest fire prediction, mitigation, and control can be attained through forest management using Internet of Things. The use and adoption of IoT in forest ecosystem management is challenging due to many factors. Vast geographical areas and limited resources in terms of budget and equipment are some of the limiting factors. In digital forestry, IoT deployment offers effective operations, control, and forecasts for soil erosion, fires, and undesirable depositions. In this chapter, IoT sensing and communication applications are presented for digital forestry systems. Different IoT systems for digital forest monitoring applications are also discussed.

Soil erosion is a major environmental problem that threatens world food production; it can be described as the removal of organic as well as inorganic soil surface materials by wind and water. This (e present) study aims to identify the most erosion vulnerable sub-watersheds for planning appropriate conservation intervention and restoration measures in Jedeb watershed. Revised Universal Soil Loss Equation (RUSLE) integrated with satellite remote sensing (RS) and geographical information system (GIS) has been applied to calculate potential soil loss at sub-watershed level. The parameters of RUSLE model were estimated using remote sensing data and ArcGIS software. The rainfall erosivity R-factor was found from 3209.6 to 3896.69 MJ.mm/ (ha.h), soil erodibility K-factor varies from 0.1 to 0.25 t ha h/ (ha MJ mm), Topographic (LS factor) ranging from 0.19 to 6.7, C-factor varies 0.01 to 0.25 and support practice P-factors from 0.1 to 1. The total annual soil loss potential of the study area was found 1,383,158 t/ yr from the total watershed area of 29,633 ha. Average annual soil loss at sub-watersheds level was estimated from 29.275 to72.529 t/ha/year and mean annual soil loss of the entire watershed was 46t/ha/year. The study results indicated that potential soil erosion rate in the watershed was ranged into four priority categories from high to extremely severe. Three sub-watersheds covering 22.975% of the watershed shows very severe mean soil loss rate, two sub-watersheds was found severe mean soil rate which covered 19.014%, eight sub-watershed were existed under very high mean soil loss rate covers 47.59% and Three sub-watersheds were found in the high soil loss rate and covers 10.42%. Sever and extremely severe micro watersheds demand immediate attention in terms of management and planning perspective. Soil and water conservation structure was recommended and designed based on peak runoff rate, soil type, slope of the land, land use type and soil erosion hazard . Keywords : Soil Erosion; RUSLE; GIS; Soil Conservation; Jedeb watershed; Ethiopia DOI: 10.7176/JNSR/10-9-02 Publication date: May 31 st 2020

BackgroundWater induced soil erosion is the most prevailing form of land resources deterioration in the highlands of Ethiopia, where huge amount of fertile soil is being lost annually. The purpose of this study was to estimate and map mean annual soil loss rates in the Gedalas watershed of the Blue Nile Basin, Northeastern Ethiopia. The estimation was carried out by using RUSLE model coupled with local perceptions. Soil, land use/cover, DEM, rainfall and support practice data were used as an input parameters. The raster layers were processed to present the required input parameters in ArcGIS platform and finally the inputs were multiplied together to quantify annual average soil loss rate and generate intensity maps of the watershed.ResultsThe estimated annual mean soil loss rate of the watershed was found to be 37 t ha−1 year−1, which is more than two times higher as compared to the maximum tolerable soil loss value (16 t ha−1 year−1) and the annual erosion rates range from 0 to above 935 ton ha−1 year−1. The annual mean soil loss values below 5 ton ha−1 year−1 were rated as very slight, while those above 50 ton ha−1 year−1 were categorized as very severe soil erosion risk. Areas experiencing values between these two were further classified into slight, moderate, and severe erosion zones. Soil loss in more than 1/3rd of the study area (36.4%) was below moderate (< 15 t ha−1 year−1). Nearly one-fourth (25.5%) of the watershed area experienced moderate (between 15 and 30 ton t ha−1 year−1) soil loss values. Only 14% of the watershed areas were classified under severe to extremely severe (> 30 t ha−1 year−1) soil erosion risk zones. While 62% of the watershed still undergoes from very slight to moderate levels of soil loss, yet 72.6% of soil erosion occurred in zones less than 3200 m in elevation which represents more than half (51%) of the total surface area of the watershed. The study also demonstrated that the northeastern parts of the watershed suffer more from high soil erosion risk due to steep slope and rugged landforms of the area. Similarly, 43% and 35% of soil erosion occurred on cropland and grasslands areas, respectively.ConclusionThe quantitative soil loss estimation results and farmers’ perception of soil erosion revealed that soil erosion has still remained significant problems in the watershed. The results underscore the urgent need for comprehensive and site-specific SLM practices in the watershed.

Socioeconomic factors and farmer's perception of soil erosion and conservation were examined with special reference to Wadi Kufranja catchment, northern Jordan. Field data were collected through a household field survey, and soil erosion loss was calculated and mapped using the Revised Universal Soil Loss Equation (RUSLE), within a GIS/RS environment. In-situ field measurements of soil erosion were also conducted to assess splash, sheet and runoff soil erosion. The estimated potential average annual soil loss is 10 ton·ha-1 year for the watershed. 42.1% (5317. 23 ha) of the watershed area was estimated to have moderate soil loss (5 - 25 ton·ha-1·years -1). Soil erosion risk is severe to extreme over 31.2% (3940.56 ha) of the catchment, whereas the calculated soil loss is 25 - 50 and >50 ton·ha-1·year-1. The measured sheet and splash soil erosion in W. Kufranja was 10 ton·ha-1·year-1 from tillage land, and 3 ton·ha-1·year-1 from the fallow land, with an average ranges from 8 to 10 ton·ha-1·year-1. Similarly, the maximum measured soil erosion on the eastern margin of W. Kufranja was 12.7 ton·ha-1·year-1, while the minimum soil erosion was 2.9 ton·ha-1·year-1. The collected household socioeconomic/conservation data have been subjected to multivariate statistical analysis. Through factor analysis, the twenty one variables were reduced into four significant factors which account for 69.7% of the variation in the original variable. Stepwise multiple regression analysis revealed that the total variance explained by three independent variables was 0.585 (R = 0.765, R2 = 0.585). Out of the total variance, forest clearance explained 34.7%, fallow land 7.7%, and land use/land cover 16.1% respectively. The F-value for forest clearance, fallow land, and land use/land cover are significant at 0.1% level. Most of the farmers aware that poor land management, deforestation, overgrazing, traditional cultivation (cultivation up-and-down the slope, and mono-cropping), and population pressure, are the major direct and indirect causes of soil erosion. By contrast, vegetative measures (i.e., afforestation and tree planting), adoption of structural soil and water conservation measures (terraced farming, check dams and gully control), and crop system management were recommended to control soil erosion.