Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2024
Forfattere
Yvonne RognanSammendrag
På vegne av Tinfos AS har NIBIO gjennomført overvåking av vannkvalitet i Hovlandsåna i forbindelse med etableringen av nye Flateland kraftverk i Vegusdal. I 4. kvartal 2023 (Q4) har mesteparten av arbeidene vært tilknyttet ferdigstilling av utløpet til overføringstunnelen fra Myklebostad til Kjetevatn, ferdigstilling av dammen i Kjetevatn og trykktunnelen fra Kjetevatn til Flateland. Dette er aktiviteter som har hatt liten grad av påvirkning på vannkjemien i Hovlandsåna. I oktober var det lite nedbør, men høy fyllingsgrad i magasinene oppstrøms Lislevatn bidro til en jevn og relativt høy vannføring med svært lav partikkeltransport i elva. I forbindelse med svært mye nedbør i begynnelsen av november ble det registrert kraftig økende turbiditet ved begge loggerstasjonene og turbiditetssondene målte tidvis svært høye verdier som følge av opphopning av sand, grus og organisk materiale i stålrørene der målesondene er plassert. Loggerne ble tatt inn 09.11.2023 og både kvartalsprøvene tatt samme dag, samt ukesprøvene som ble tatt frem til juleferien, viste jevnt lave konsentrasjoner av nitrogen og fosfor ved de tre stasjonene. Bunndyrprøver ble tatt 13.10.2023 og tilstandsvurdering basert på eutrofieringsindeksen ASPT viste «god» tilstand ved samtlige stasjoner.
Forfattere
Linn SolliSammendrag
Det er ikke registrert sammendrag
Forfattere
Sanjana De Zoysa Jeewanthi Sirisena Helani Perera Shalinda Fernando Miyuru Gunathilake Upaka RathnayakeSammendrag
Rainfall Intensity Duration Frequency (IDF) curves can be identified as a major role in the planning of urban drainage infrastructure. Sri Lanka, which is an island surrounded by the Indian Ocean, is frequently exposed to various climatic alterations. Sri Lanka has specific region-wise IDF relationships for the entire country, however, these IDF curves were developed more than 30 years ago. Many in-situ rainfall observations in Sri Lanka have insufficient record lengths and the absence of finer time scale records (e.g. 15 min, hourly) leading to unreliable IDF curve developments. Given this importance, the present paper demonstrates the application of Satellite-based Precipitation Product (SbPP) daily rainfall in developing IDF curves for Sri Lanka. Rainfall satellite estimates derived from Integrated Multi-satellite Retrievals for GPM (IMERG), Tropical Rainfall Measuring Mission (TRMM-3B42), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR) have been used to evaluate the ability of application of remote sensing data to develop IDF curves for Sri Lanka against rain gauge (RG) data. Our study breaks new ground by generating 50 IDF curves tailored to specific locations covering the whole county Sri Lanka, using daily rainfall data from RGs and three SbPPs during the period between 1990 and 2019. This marks a significant departure from the conventional approach, offering a more granular understanding of rainfall patterns in the country. By providing IDF curves for individual locations, our research presents a novel contribution to Sri Lanka's IDF history. At first, to evaluate the accuracy of SbPPs, statistical analysis was conducted using continuous and categorical evaluation indices. Second, IDF curves were developed and compared with the presently available IDF curves. Results showcased that IMERG outperformed all SbPPs, while PERSIANN-CDR showed dire performances. The IMERG and TRMM-3B42 products tend to overestimate light precipitation regions in high elevations and overestimate heavy rainfall in low elevations compared to rain gauge data. Rainfall intensities derived by rain gauge data depicted relative changes within ±30% for shorter durations and ±20% for longer durations while SbPPs showed beyond ±30% difference concerning the previously developed IDFs. It was apparent that these products have significant inaccuracies which cannot be neglected when utilizing them in developing IDF curves. This study will be beneficial in solving design problems associated with urban runoff control and disposal where knowing the rainfall intensities of different return periods with different durations is vital.
Sammendrag
Climate change stands as one of the most pressing challenges confronting global ecosystems and human livelihoods. The agriculture sector of Anuradhapura district, Sri Lanka, well renowned for its pivotal role in the nation’s food production, faces an increasing threat from the changing climate. This study aims to incorporate the indicator system method which uses a set of indicators to assess the agricultural vulnerability (AV) to climate change in Anuradhapura district. The AV assessment used in this study involves three principal components exposure, sensitivity, and adaptive capacity. The indicators are normalized to allow spatial analysis and smooth integration within a geographic information system (GIS) framework. The AV of Anuradhapura district ranged from 0.32 to 0.67 and was divided among five levels very low, low, moderate, high, and very high. The findings suggest that Anuradhapura’s agricultural sector was significantly impacted by climate change as the majority of the total area was found to have very high, high, and moderate levels of AV with 25, 28, and 24%, respectively. The results obtained from this study are essential for formulating plans to improve Anuradhapura’s agricultural sector resilience and adaptability to ensure food security and livelihood sustainability considering the ever-changing climate.
Forfattere
C. Jayasuriya C. Palliyaguru V. Basnayake R. K. Makumbura Miyuru Gunathilake U. RathnayakeSammendrag
Soil erosion is a significant environmental issue in most mountainous areas and is further exacerbated due to ongoing climatic changes and anthropogenic activities. Soil erosion not only triggers natural disasters like landslides but also degrades the fertile topsoil layers. Therefore, modeling and evaluation of soil erosion in mountainous areas and river basins are highly important. The Uma Oya River Basin (UORB), Sri Lanka is an area with rich biodiversity and is also important for agricultural production. Moreover, this area is frequently discussed due to the ongoing developments of the Uma Oya Project. This paper presents a comprehensive evaluation of soil erosion in the UORB and results are compared for two decades from 2000 to 2020. The Revised Universal Soil Loss Equation (RUSLE) was used to determine the annual soil erosion rates. In addition, the spatial-temporal variation of land use and land cover was assessed in the UORB. Results revealed that extreme soil erosion scenarios occur when forests and other vegetation lands are converted to agricultural lands and farmlands. We found that soil loss in the area largely happened due to steep slopes, reduction of vegetation and forest covers, and growth of cultivation lands. Erosion-prone areas in the river basin are identified and conservation strategies are discussed. In addition, the impact of the ongoing climate change on the UORB is highlighted.
Forfattere
Zumry Niyas Charuni I. Madhushani Miyuru Gunathilake Vindhya Basnayaka Komali Kantamaneni Upaka RathnayakeSammendrag
This study evaluates the rainfall erosivity (RE) and erosivity density (ED) over the Kelani River basin, Sri Lanka for a period of 31 years (1990–2020). The river basin is well known for its annual floods during the southwestern monsoon season and severe erosion including landslides can be observed. The catchment was analyzed for its RE using the Wischmeier and Smith algorithm and for its ED using Kinnel's algorithm. The monthly rainfall data spreading over the river basin were used to analyze the monthly, seasonal, and annual RE and ED. Interestingly, the annual RE showed a linear increasing trend line over 31 years, and a maximum value of 2,831.41 MJ mm ha−1 h−1 yr−1 was able to be observed in the year 2016. The RE peaks in May which is in the southwestern monsoon season. This reveals that the risk of soil erosion in the basin is high in the southwestern monsoon season. In addition, land use and land cover changes over the years have adversely impacted the erosion rates. Therefore, it is highly recommended to investigate soil erosion in-depth and then implement relevant regulations to conserve the soil layers upstream of the river basin.
Forfattere
Randika K. Makumbura Prasad Dissanayake Miyuru Gunathilake Namal Rathnayake Komali Kantamaneni Upaka RathnayakeSammendrag
This study presents the first attempt in Sri Lanka to generate a forest fire risk map covering the entire country using a GIS-based forest fire index (FFI) model. The model utilized seven parameters: land use, temperature, slope, proximity to roads and settlements, elevation, and aspect. All these parameters were derived using GIS techniques with ArcGIS10.4 and QGIS3.16. Data from Remote Sensing sources, particularly the MODIS hotspot real-world dataset, were employed to gather fire count information for the year 2020. Validation was conducted through the merging hotspot technique and kernel density estimation (KDE). The research findings highlight the districts in the Central and Uva provinces, such as NuwaraEliya (10.3 km2), Kandy (2.74 km2), and Badulla (10.41 km2), as having a “very low risk" of forest fire potential. Conversely, districts like Hambanthota (0.1 km2), Kaluthara (0.04 km2), and Kurunegala (0.2 km2) exhibit a “very high risk" of forest fire potential, although it is negligible compared country's total area. Overall, the study suggests that Sri Lanka is not currently facing a significant threat of forest fires and is a “medium risk" of forest fires as 49.49% of land falls under this category. These results are of immense value to relevant authorities, including the Ministry of Wildlife and Forest Resources Conservation, in formulating effective strategies to manage and mitigate forest fire risks in the country.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Trygve S. Aamlid Karin Juul Hesselsøe Anette Sundbye Guttorm Ray Tuxen Martin Bjerke SvendsenSammendrag
Det er ikke registrert sammendrag
Forfattere
Daniel Ruiz Potma Gonçalves Thiago Inagaki Luis Gustavo Barioni Newton La Scala Junior Maurício Roberto Cherubin João Carlos de Moraes Sá Carlos Eduardo Pellegrino Cerri Adriano AnselmiSammendrag
Soils are the third largest carbon pool on Earth and play a crucial role in mitigating climate change. Therefore, understanding and predicting soil carbon sequestration is of major interest to mitigate climate change globally, especially in countries with strong agricultural backgrounds. In this study, we used a new database composed of 5029 samples collected up to 1-meter depth in three biomes that are most representative of agriculture, Pampas (Prairie), Cerrados (Savanna), and Atlantic Forest (Forest), to explore soil organic carbon (SOC) stocks and its environmental drivers. The Cerrado (Savanna) biome was the only one where croplands presented higher SOC stocks than native vegetation (Native vegetation 121.23 Mg/ha and croplands 127.85 Mg/ha or 5 % higher). From the tested models, the Random Forest outperformed the others, achieving an R2 of 0.64 for croplands and 0.56 for native vegetation. The accuracy of the models varied with soil depth, showing better predictions in shallow layers for croplands and deeper layers for native vegetation. Our results highlight the importance of clay content, precipitation, net primary production (NPP), and temperature as key predictors for soil carbon stocks in the studied biomes. The findings emphasize the importance of protecting the surface layers, especially in the Cerrado biome, to enhance SOC stocks and promote sustainable land management practices. Moreover, the results provide valuable insights for the development of nature-based carbon markets and suggest potential strategies for climate change mitigation. Enhancing our understanding of SOC dynamics and adopting precise environmental predictors will contribute to the formulation of targeted soil management strategies and accelerate progress toward achieving climate goals.