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.
2023
Sammendrag
Climate change can have an influence on rainfall that significantly affects the magnitude frequency of floods and droughts. Therefore, the analysis of the spatiotemporal distribution, variability, and trends of rainfall over the Mahi Basin in India is an important objective of the present work. Accordingly, a serial autocorrelation, coefficient of variation, Mann–Kendall (MK) and Sen’s slope test, innovative trend analysis (ITA), and Pettitt’s test were used in the rainfall analysis. The outcomes were derived from the monthly precipitation data (1901–2012) of 14 meteorology stations in the Mahi Basin. The serial autocorrelation results showed that there is no autocorrelation in the data series. The rainfall statistics denoted that the Mahi Basin receives 94.8% of its rainfall (821 mm) in the monsoon period (June–September). The normalized accumulated departure from the mean reveals that the annual and monsoon rainfall of the Mahi Basin were below average from 1901 to 1930 and above average from 1930 to 1990, followed by a period of fluctuating conditions. Annual and monsoon rainfall variations increase in the lower catchment of the basin. The annual and monsoon rainfall trend analysis specified a significant declining tendency for four stations and an increasing tendency for 3 stations, respectively. A significant declining trend in winter rainfall was observed for 9 stations under review. Likewise, out of 14 stations, 9 stations denote a significant decrease in pre-monsoon rainfall. Nevertheless, there is no significant increasing or decreasing tendency in annual, monsoon, and post-monsoon rainfall in the Mahi Basin. The Mann–Kendall test and innovative trend analysis indicate identical tendencies of annual and seasonal rainfall on the basin scale. The annual and monsoon rainfall of the basin showed a positive shift in rainfall after 1926. The rainfall analysis confirms that despite spatiotemporal variations in rainfall, there are no significant positive or negative trends of annual and monsoon rainfall on the basin scale. It suggests that the Mahi Basin received average rainfall (867 mm) annually and in the monsoon season (821 mm) from 1901 to 2012, except for a few years of high and low rainfall. Therefore, this study is important for flood and drought management, agriculture, and water management in the Mahi Basin.
Forfattere
Ravindu Panditharathne Miyuru Gunathilake Imiya M. Chathuranika Upaka Rathnayake Mukand S. Babel Manoj K. JhaSammendrag
Rainfall is one of the dominating climatic parameters that affect water availability. Trend analysis is of paramount significance to understand the behavior of hydrological and climatic variables over a long timescale. The main aim of the present study was to identify trends and analyze existing linkages between rainfall and streamflow in the Nilwala River Basin (NRB) of Southern Sri Lanka. An investigation of the trends, detection of change points and streamflow alteration, and linkage between rainfall and streamflow were carried out using the Mann–Kendall test, Sen’s slope test, Pettitt’s test, indicators of hydrological alteration (IHA), and Pearson’s correlation test. Selected rainfall-related extreme climatic indices, namely, CDD, CWD, PRCPTOT, R25, and Rx5, were calculated using the RClimdex software. Trend analysis of rainfall data and extreme rainfall indices demonstrated few statistically significant trends at the monthly, seasonal, and annual scales, while streamflow data showed non-significant trends, except for December. Pettitt’s test showed that Dampahala had a higher number of statistically significant change points among the six rainfall stations. The Pearson coefficient correlation showed a strong-to–very-strong positive relationship between rainfall and streamflow. Generally, both rainfall and streamflow showed non-significant trend patterns in the NRB, suggesting that rainfall had a higher impact on streamflow patterns in the basin. The historical trends of extreme climatic indices suggested that the NRB did not experience extreme climates. The results of the present study will provide valuable information for water resource planning, flood and disaster mitigation, agricultural operations planning, and hydropower generation in the NRB.
Sammendrag
Wetlands are simply areas that are fully or partially saturated with water. Not much attention has been given to wetlands in the past, due to the unawareness of their value to the general public. However, wetlands have numerous hydrological, ecological, and social values. They play an important role in interactions among soil, water, plants, and animals. The rich biodiversity in the vicinity of wetlands makes them invaluable. Therefore, the conservation of wetlands is highly important in today’s world. Many anthropogenic activities damage wetlands. Climate change has adversely impacted wetlands and their biodiversity. The shrinking of wetland areas and reducing wetland water levels can therefore be frequently seen. However, the opposite can be seen during stormy seasons. Since wetlands have permissible water levels, the prediction of wetland water levels is important. Flooding and many other severe environmental damage can happen when these water levels are exceeded. Therefore, the prediction of wetland water level is an important task to identify potential environmental damage. However, the monitoring of water levels in wetlands all over the world has been limited due to many difficulties. A Scopus-based search and a bibliometric analysis showcased the limited research work that has been carried out in the prediction of wetland water level using machine-learning techniques. Therefore, there is a clear need to assess what is available in the literature and then present it in a comprehensive review. Therefore, this review paper focuses on the state of the art of water-level prediction techniques of wetlands using machine-learning techniques. Nonlinear climatic parameters such as precipitation, evaporation, and inflows are some of the main factors deciding water levels; therefore, identifying the relationships between these parameters is complex. Therefore, machine-learning techniques are widely used to present nonlinear relationships and to predict water levels. The state-of-the-art literature summarizes that artificial neural networks (ANNs) are some of the most effective tools in wetland water-level prediction. This review can be effectively used in any future research work on wetland water-level prediction.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Karin Juul Hesselsøe Anne friederike Borchert Trygve S. Aamlid Bjarni Hannesson Karin Normann Per Rasmussen Jørgen Hornslien Pia Heltoft ThomsenSammendrag
Det er ikke registrert sammendrag
Forfattere
Karin Juul Hesselsøe Anne friederike Borchert Karin Normann Trygve S. Aamlid Pia Heltoft ThomsenSammendrag
Det er ikke registrert sammendrag
Forfattere
Thiago InagakiSammendrag
The Norwegian Institute of Bioeconomy Research (NIBIO) has been working on many fronts to promote sustainable agriculture. As part of the Department of Biogeochemistry and Soil Quality, I will present initiatives and progress made by the NIBIO Institute in promoting soil organic matter persistence and sustainable agriculture in Norway and worldwide. Two major challenges have been targeted with a focus on Norway: waste generation by several industries (e.g., agriculture, forestry, and fishery) and the short time of the cropping season in the country due to climatic constraints. To solve these issues, we are working on several projects focused on re-utilizing waste products by producing organic fertilizers, optimizing these fertilizers (e.g., biochar N-enrichment), and improving current cropping systems with crop diversification. Our main objective is to investigate the benefits of these practices in improving soil quality and crop productivity and enhancing soil organic matter persistence. Our work on soil science also goes beyond Norwegian and Nordic conditions. Among our international collaborations, we are currently working on a multi-institution bilateral project between China and Norway to promote the restoration of a semi-arid ecosystem in Inner Mongolia. We are also often engaging in project proposals for promoting sustainable agriculture in tropical regions. To develop these ideas, we promote a combined approach of spectroscopy techniques in collaboration with other institutions, such as nanoscale secondary ion mass spectrometry (NanoSIMS) in partnership with the Technical University of Munich (TUM) and NMR spectroscopy in partnership with the National Research Council of Italy (CNR-Pisa). Also, our research facilities count on good infrastructure, focusing on incubations with 13C and 15N labeled amendments and 13C pulse labeling.
Forfattere
Thiago Inagaki Angela R. Possinger Steffen A. Schweizer Carsten W. Mueller Carmen Hoeschen Michael J. Zachman Lena F. Kourkoutis Ingrid Kögel-Knabner Johannes LehmannSammendrag
The spatial distribution of organic substrates and microscale soil heterogeneity significantly influence organic matter (OM) persistence as constraints on OM accessibility to microorganisms. However, it is unclear how changes in OM spatial heterogeneity driven by factors such as soil depth affect the relative importance of substrate spatial distribution on OM persistence. This work evaluated the decomposition and persistence of 13C and 15N labeled water-extractable OM inputs over 50 days as either hotspot (i.e., pelleted in 1 – 2 mm-size pieces) or distributed (i.e., added as OM < 0.07 µm suspended in water) forms in topsoil (0-0.2 m) and subsoil (0.8-0.9 m) samples of an Andisol. We observed greater persistence of added C in the subsoil with distributed OM inputs relative to hotspot OM, indicated by a 17% reduction in cumulative mineralization of the added C and a 10% higher conversion to mineral-associated OM. A lower substrate availability potentially reduced mineralization due to OM dispersion throughout the soil. NanoSIMS (nanoscale secondary ion mass spectrometry) analysis identified organo-mineral associations on cross-sectioned aggregate interiors in the subsoil. On the other hand, in the topsoil, we did not observe significant differences in the persistence of OM, suggesting that the large amounts of particulate OM already present in the soil outweighed the influence of added OM spatial distribution. Here, we demonstrated under laboratory conditions that the spatial distribution of fresh OM input alone significantly affected the decomposition and persistence of OM inputs in the subsoil. On the other hand, spatial distribution seems to play a lower role in topsoils rich in particulate OM. The divergence in the influence of OM spatial distribution between the top and subsoil is likely driven by differences in soil mineralogy and OM composition.
Forfattere
Luís F.J. Almeida Ivan F. Souza Luís C.C. Hurtarte Pedro P.C. Teixeira Thiago Inagaki Ivo R. Silva Carsten W. MuellerSammendrag
The molecular diversity of the source substrate has been regarded as a significant controller of the proportion of plant material that is either mineralized or incorporated into soil organic matter (SOM). However, quantitative parameters to express substrate molecular diversity remain elusive. In this research, we fractionated leaves, twigs, bark, and root tissues of 13C-enriched eucalypt seedlings into hot water extractables (HWE), total solvent (acetone) extractables (TSE), a cellulosic fraction (CF), and the acid unhydrolyzable residue (AUR). We used 13C NMR spectroscopy to obtain a molecular diversity index (MDI) based on the relative abundance of carbohydrate, protein, lignin, lipid, and carbonyl functional groups within the biochemical fractions. Subsequently, we obtained artificial plant organs containing fixed proportions (25%) of their respective biochemical fractions to be incubated with soil material obtained from a Haplic Ferralsol for 200-days, under controlled temperature (25 ± 1 ◦C) and moisture adjusted to 70–80% of the soil water holding capacity. Our experimental design was a randomized complete block design, arranged according to a factorial scheme including 4 plant organs, 4 biochemical fractions, and 3 blocks as replicates. During the incubation, we assessed the evolution of CO2 from the microcosms after 1, 2, 3, 4, 7, 10, 13, 21, 28, 38, 45, 70, 80, 92, 112, 148, 178 and 200 days from the start of the incubation. After the incubation, soil subsamples were submitted to a density fractionation to separate the light fraction of SOM (LFOM) i.e., with density <1.8 g cm 3. The heavy fraction remaining was submitted to wetsieving yielding the sand-sized SOM (SSOM) and the mineral-associated SOM (MAOM), with particle-size greater and smaller than 53 μm, respectively. We found that HWE and AUR exhibited comparatively higher MDIs than the TSE and CF. During the incubation, HWE and CF were the primary sources of 13C-CO2 from all plant organs and after 92 days, the respiration of the TSE of bark and roots increased. Otherwise, the AUR contributed the least for the release of 13C-CO2. There were no significant relationships between the MDI and the amount of 13C transferred into the LFOM or SSOM. Otherwise, the transfer of 13C into the MAOM increased as a linear-quadratic function of MDI, which in turn was negatively correlated with the total 13C-CO2 loss. Overall, the MDI exerted a stronger control on the 13C-labeled MAOM than on 13C-CO2 emissions, highlighting the need to improve our ability to distinguish and quantify direct plant inputs from those of microbial origin entering soil C pools.