Mapping of soil erosion hazard zones of Sri Lanka

Jayasekara, M. J. P. T. M.; Kadupitiya, H. K.; Vitharana, U.W.A.

doi:10.4038/tar.v29i2.8284

Cited by 29 publications

(27 citation statements)

References 8 publications

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“…e erosive power of rainfall can be estimated by calculating the erosivity factor for a particular location [31]. It depends on the amount and the intensity of rainfall [2]. e original equation proposed by Wischmeier and Smith [32] and Renard et al [33] was not used due to nonavailability of rainfall intensities.…”

Section: Study Areamentioning

confidence: 99%

“…Soil erosion is a natural process of removal of soil material and transportation through the action of erosive agents such as water, wind, gravity, and human disturbance [1], and it has been accelerated by human activities such as intensive agriculture, improper land management, deforestation, and cultivation on steep slopes [2]. It is a serious and continuous environmental problem when combined with climate-induced high-intensity rainfall [3].…”

Section: Introductionmentioning

confidence: 99%

“…ough soil erosion is a naturally occurring process, this has been accelerated by human activities such as intensive agriculture, improper land management, deforestation, and cultivation on steep slopes [2]. Removal of vegetation cover and shaping of surface topography induce or accelerate soil displacement and movement [7].…”

Section: Introductionmentioning

confidence: 99%

“…Several areas in Sri Lanka are subjected to severe soil erosion [2,8,9]. Soil loss in agricultural areas in steep central highlands of Sri Lanka is 10-100 times more compared to natural rates of erosion [8].…”

Section: Introductionmentioning

confidence: 99%

“…Soil erosion and degradation of lands in Sri Lanka are a ecting the national food production and sustainability of natural eco systems [2]. Erosion rates are high in highlands as well as in low lands due to land use changes including the removal of vegetative cover and urbanization [7].…”

Section: Introductionmentioning

confidence: 99%

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Application of Revised Universal Soil Loss Equation (Rusle) Model to Assess Soil Erosion in “Kalu Ganga” River Basin in Sri Lanka

Panditharathne

Abeysingha

Nirmanee

et al. 2019

Applied and Environmental Soil Science

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Soil erosion is one of the main forms of land degradation. Erosion contributes to loss of agricultural land productivity and ecological and esthetic values of natural environment, and it impairs the production of safe drinking water and hydroenergy production. Thus, assessment of soil erosion and identifying the lands more prone to erosion are vital for erosion management process. Revised Universal Soil Loss Equation (Rusle) model supported by a GIS system was used to assess the spatial variability of erosion occurring at Kalu Ganga river basin in Sri Lanka. Digital Elevation Model (30 × 30 m), twenty years’ rainfall data measured at 11 rain gauge stations across the basin, land use and soil maps, and published literature were used as inputs to the model. The average annual soil loss in Kalu Ganga river basin varied from 0 to 134 t ha−1 year−1 and mean annual soil loss was estimated at 0.63 t ha−1 year−1. Based on erosion estimates, the basin landscape was divided into four different erosion severity classes: very low, low, moderate, and high. About 1.68% of the areas (4714 ha) in the river basin were identified with moderate to high erosion severity (>5 t ha−1 year−1) class which urgently need measures to control soil erosion. Lands with moderate to high soil erosion classes were mostly found in Bulathsinghala, Kuruwita, and Rathnapura divisional secretarial divisions. Use of the erosion severity information coupled with basin wide individual RUSLE parameters can help to design the appropriate land use management practices and improved management based on the observations to minimize soil erosion in the basin.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Revised Universal Soil Loss Equation (Rusle) Model to Assess Soil Erosion in “Kalu Ganga” River Basin in Sri Lanka

Panditharathne

Abeysingha

Nirmanee

et al. 2019

Applied and Environmental Soil Science

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Using big data for evaluating development outcomes: A systematic map

Rathinam

Khatua²,

Siddiqui³

et al. 2021

Campbell Systematic Reviews

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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 efficaci...

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Post‐tsunami land degradation on a South Asian island: Implications for conservation priorities

2021

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In recent decades, land degradation has accelerated due to an increase in unsustainable human activities. In a case‐study of the Andaman Islands, located in the South Asian region, changes in land degradation types, soil erosion, and the effect of conservation measures for pre‐ (2000) and post‐2004 tsunami periods (2006, 2019) were recorded using temporal remote sensing data (LANDSAT, RESOURCESAT, CARTOSAT), field observations and a GIS‐based revised universal soil loss equation (RUSLE). The results showed that erosion, leaching, acidification, salinization, and drainage congestion were the major land degradation processes that could be traced to post‐tsunami human activities (R2 = 0.634). The study revealed significant forest cover loss during 2006–2019 (3.76%) compared to 2000–2006 (0.61%), while it revealed increased areas under settlement (14 km2) and mining (22 km2). The annual crop area exhibited a 0.42% loss, whereas the plantation crop area increased by 0.46%, indicating the abandonment of annual crops that resulted in an increased fallow area (21 km2) during the post‐tsunami period. As a consequence, degraded land increased by 2.5% of the total geographical area which was affaected by water erosion (2.3%) and chemical degradation (0.25%). The average erosion rate varied from 0.7 to 108 t ha−1yr−1 in 2000 but increased by 47% during the post‐tsunami period due to mining, settlement, and vegetable cultivation on slopes, as indicated by high cover management, support practice, and slope length factors. Practicing suitable conservation measures can provide a conservation benefit of 105,560 t yr−1 and a reduction in other forms of degradation without compromising the food production area.

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Mapping of soil erosion hazard zones of Sri Lanka

Cited by 29 publications

References 8 publications

Application of Revised Universal Soil Loss Equation (Rusle) Model to Assess Soil Erosion in “Kalu Ganga” River Basin in Sri Lanka

Application of Revised Universal Soil Loss Equation (Rusle) Model to Assess Soil Erosion in “Kalu Ganga” River Basin in Sri Lanka

Using big data for evaluating development outcomes: A systematic map

Post‐tsunami land degradation on a South Asian island: Implications for conservation priorities

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