Soil loss estimation using rusle model to prioritize erosion control in KELANI river basin in Sri Lanka

Fayas, Cassim Mohamed; Abeysingha, N. S.; Nirmanee, K. G. S.; Samaratunga, Dinithi; Mallawatantri, A. P.

doi:10.1016/j.iswcr.2019.01.003

Cited by 107 publications

(79 citation statements)

References 16 publications

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“…For instance, watershed-wide deforestation exacerbates soil erosion and subsequent sedimentation of the river channel, leading to flashfloods in the wet season [9]. Estimated soil erosion for Kelani Basin varied widely (0-103.69 t ha −1 yr −1 ) with a mean of 10.88 t ha −1 yr −1 , according to a Revised Universal Water 2020, 12, 26 7 of 23 Soil Loss Model [65]. The mid basin (25% of the entire basin) is likely to suffer the most severe erosion (12-25 t ha −1 yr −1 ), exceeding the maximum tolerable soil loss (9-13 t ha −1 yr −1 ).…”

Section: Non-point Source Pollutionmentioning

confidence: 99%

Challenges in Biodiversity Conservation in a Highly Modified Tropical River Basin in Sri Lanka

Surasinghe

Kariyawasam²,

Sudasinghe

et al. 2019

Water

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Kelani River is the fourth longest river in the South-Asian island, Sri Lanka. It originates from the central hills and flows through a diverse array of landscapes, including some of the most urbanized regions and intensive land uses. Kelani River suffers a multitude of environmental issues: illegal water diversions and extractions, impoundment for hydroelectricity generation, and pollution, mostly from agrochemicals, urban runoff, industrial discharges, and domestic waste. Moreover, loss of riparian forest cover, sand-mining, and unplanned development in floodplains have accentuated the environmental damage. In this study, based on Kelani River basin, we reviewed the status of biodiversity, threats encountered, conservation challenges, and provided guidance for science-based conservation planning. Kelani River basin is high in biodiversity and endemism, which includes 60 freshwater fish species of which 30 are endemic. Urbanization related threats are more severe in the middle and lower reaches while agriculture and impoundments peril the river in upper reaches. Documentation of these threats can be dated back to 1980, yet, Sri Lankan government has failed to take substantial actions for sustainable management of Kelani River basin, despite the presence of nearly 50 legislations pertaining to water and land management. Given high biodiversity richness, human dependency, and evident ecological deterioration, Kelani River basin should be prioritized for biodiversity conservation and sustainable resource management. Conservation and wise use of freshwater resources is a global concern, particularly for developing nations in Asia. Therefore, our review and guidance for scientifically informed conservation would serve as a prototype for basin-wide river management for Sri Lanka as well as for other developing nations of tropical Asia.

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Section: Non-point Source Pollutionmentioning

confidence: 99%

Challenges in Biodiversity Conservation in a Highly Modified Tropical River Basin in Sri Lanka

Surasinghe

Kariyawasam²,

Sudasinghe

et al. 2019

Water

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“…Recently, many studies have reported to predict soil erosion by combining the RUSLE and geographic information system (GIS) [12][13][14][15][16][17]. For example, Fayas et al [18] evaluated the maximum average annual soil erosion of 103.7 t•ha − 1 •yr − 1 in the Kelani River watershed in Sri Lanka. Panagos et al [19] evaluated the mean soil loss rate was 2.46 t•ha − 1 •yr − 1 in erosion-prone areas of the European Union.…”

Section: Introductionmentioning

confidence: 99%

Test of the RUSLE and Key Influencing Factors Using GIS and Probability Methods: A Case Study in Nanling National Nature Reserve, South China

Wang¹,

Ping³

et al. 2019

Advances in Civil Engineering

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The main purposes of the study were to test the performance of the Revised Universal Soil Loss Equation (RUSLE) and to understand the key factors responsible for generating soil erosion in the Nanling National Nature Reserve (NNNR), South China, where soil erosion has become a very serious ecological and environmental problem. By combining the RUSLE and geographic information system (GIS) data, we first produced a map of soil erosion risk at 30 m-resolution pixel level with predicted factors. We then used consecutive Landsat 8 satellite images to obtain the spatial distribution of four types of soil erosion and carried out ground truth checking of the RUSLE. On this basis, we innovatively developed a probability model to explore the relationship between four types of soil erosion and the key influencing factors, identify high erosion area, and analyze the reason for the differences derived from the RUSLE. The results showed that the overall accuracy of image interpretation was acceptable, which could be used to represent the currently actual spatial distribution of soil erosion. Ground truth checking indicated some differences between the spatial distribution and class of soil erosion derived from the RUSLE and the actual situation. The performance of the RUSLE was unsatisfactory, producing differences and even some errors when used to estimate the ecological risks posed by soil erosion within the NNNR. We finally produced a probability table revealing the degree of influence of each factor on different types of soil erosion and quantitatively elucidated the reason for generating these differences. We suggested that soil erosion type and the key influencing factors should be identified prior to soil erosion risk assessment in a region.

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“…For example, several studies used the USLE or RUSLE combined with GIS estimates of land use areas in order to estimate soil erosion losses from multiple land uses. Examples include Pacheco et al [66] in Portugal; Ganasri and Ramesh [67] and Prasannakumar et al [68] in India; Tadesse et al [69] in Ethiopia; Fayas et al [70] in Sri Lanka; and Pham et al [71] in Vietnam. However, it should be noted that these studies did not address the different level of BMPs that would modify potential erosion rates.…”

Section: Discussionmentioning

confidence: 99%

Estimated Sediment Protection Efficiences for Increasing Levels of Best Management Practices on Forest Harvests in the Piedmont, USA

Cristan

Aust

Bolding

et al. 2019

Forests

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In-stream watershed level evaluations confirm that application of recommended forestry best management practices (BMPs) can minimize sedimentation following management, while on-site erosion research shows that BMPs reduce erosion from individual forest operations, thus implying watershed-level sediment reductions. Assessments of forest operations and sediment have developed very few sediment delivery ratios (SDR). Linking BMP levels (low, standard recommendation, high) within specific forest operations to sedimentation could enable managers to evaluate BMP effects. Reported data regarding forest operations, erosion rates and SDR by forest operation, and BMP implementation levels were sufficient within the Piedmont region to allow approximations of sediment delivery and BMP efficiency. Existing United States Department of Agriculture (USDA) Forest Service reports and published erosion and sediment research were used to comprise the following method. For regional annual harvests, estimated sediment deliveries (Mg year−1) = annual harvest area (ha year−1) × weighted average erosion rate from all forest operations (Mg ha−1 year−1) × SDR (unitless ratio). Weighted average erosion rates for all forest operations were determined by applying areas in each operational activity (%) × estimated erosion per operation (Mg ha−1 year−1). In comparing published data, standard BMPs reduced estimated sedimentation by 75% compared to low BMP implementation levels. This supports forestry BMP efficiency findings reported for sediment removals in watershed studies. Higher levels of BMP implementation were estimated to potentially remove nearly all forest operation-produced sediment. Values of this pilot study should be viewed cautiously, as estimates were based on limited data, estimated operations, and limited SDRs; are based on BMP categories that vary between states; and address only one year following harvests. However, the approach provided approximations that facilitate BMP evaluations and can be improved with additional data. This methodology highlights the importance of accurate estimates of erosion rates, SDRs, sediment masses, and area for operations. This supports the importance of state programs, which have increased BMP implementation rates and compliance options with BMP program maturation.

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Soil loss estimation using rusle model to prioritize erosion control in KELANI river basin in Sri Lanka

Cited by 107 publications

References 16 publications

Challenges in Biodiversity Conservation in a Highly Modified Tropical River Basin in Sri Lanka

Challenges in Biodiversity Conservation in a Highly Modified Tropical River Basin in Sri Lanka

Test of the RUSLE and Key Influencing Factors Using GIS and Probability Methods: A Case Study in Nanling National Nature Reserve, South China

Estimated Sediment Protection Efficiences for Increasing Levels of Best Management Practices on Forest Harvests in the Piedmont, USA

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