Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2023
Authors
Ruchiru D. Herath Uttam Pawar Dushyantha M. Aththanayake Kushan D. Siriwardhana Dimantha I. Jayaneththi Chatura Palliyaguru Miyuru Gunathilake Upaka RathnayakeAbstract
Climate change, urbanization, and many anthropogenic activities have intensified the floods in today’s world. However, poor attention was given to mitigation strategies for floods in the developing world due to funding and technical limitations. Developing flood inundation maps from historical flood records would be an important task in mitigating any future flood damages. Therefore, this study presents the predictive capability of the Rainfall-Runoff-Inundation (RRI) model, a 2D coupled hydrology-inundation model, and to build flood inundation maps utilizing available ground observation and satellite remote sensing data for Kalu River, Sri Lanka. Despite the lack of studies in predicting flood levels, Kalu River is an annually flooded river basin in Sri Lanka. The comparative results between ground-based rainfall (GBR) measurement and satellite rainfall products (SRPs) from the IMERG satellite have shown that SRPs underestimate peak discharges compared to GBR data. The accuracy and the reliability of the model were assessed using ground-measured discharges with a high coefficient of determination (R2 = 0.89) and Nash–Sutcliffe model efficiency coefficient (NSE = 0.86). Therefore, the developed RRI model can successfully be used to simulate the inundation of flood events in the KRB. The findings can directly be applied to the stakeholders.
Abstract
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.
Authors
Ravindu Panditharathne Miyuru Gunathilake Imiya M. Chathuranika Upaka Rathnayake Mukand S. Babel Manoj K. JhaAbstract
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.
Abstract
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.
Abstract
Light penetration plays a vital role in lakes and drinking water reservoirs, influencing fundamental processes such as primary production and thermal budgets. The Secchi depth (ZSD) and the compensation depth (ZCD) are commonly used measurements in this context. ZSD is determined through visual inspection using a Secchi disc, while ZCD represents the depth at which photosynthetic activity balances respiration and can be measured using a quantum irradiance sensor. Through in situ water-core samples from 23 lakes within a lake district in Southeastern Norway, we observed that DNOM exerts a diverse influence on these light irradiance measurements. If DNOM concentrations are reduced to half or a quarter of the current concentration, similar to what was measured during the 1980s, the median ZCD:ZSD ratios are estimated to have decreased by approximately 30 and 60% since then, respectively. Conversely, a plausible future climate-driven doubling or quadrupling of the present DNOM concentrations are estimated to further decrease the median ZCD:ZSD ratios in the lake district with approximately 10 and 25%, respectively. From this, the ZCD:ZSD ratios seem to have experienced a considerable long-term decline attributed to both climate change and the recovery from acid rain, and a further climate-driven decrease is expected.
Abstract
Levels of dissolved natural organic matter (DNOM) are increasing in our boreal watercourses. This is manifested by an apparent increase in its yellow to brown colour of the water, i.e., browning. Sound predictions of future changes in colour of our freshwaters is a prerequisite for predicting effects on aquatic fauna and a sustainable operation of drinking water facilities using surface waters as raw water sources. A model for the effect of climate on colour (mg Pt L-1) has been developed for two surface raw water sources in Scotland, i.e., at Bracadale and Port Charlotte. Both sites are situated far out on the Scottish west coast, without major impact of acid rain, with limited amounts of frost, and with limited recent land-use changes. The model was fitted to 15 years long data-series on colour measurements, provided by Scottish Water, at the two sites. Meteorological data were provided by UK Met. The models perform well for both sites in simulating the variation in monthly measured colour, explaining 89 and 90% of the variation at Bracadale and Port Charlotte, respectively. These well fitted models were used to predict future changes in colour due to changes in temperature and precipitation based on median climate data from a high emission climate RCP8.5 scenario from the HadCM3 climate model (UKCP18). The model predicted an increase in monthly average colour during growing season at both sites from about 150 mg Pt L-1 to about 200 mg Pt L-1 in 2050–2079. Temperature is found to be the most important positively driver for colour development at both sites.
Authors
Rolf David Vogt Petr Porcal Josef Hejzlar Ma. Cristina Paule‐Mercado Ståle Haaland Cathrine Brecke Gundersen Geir Orderud Bjørnar EikebrokkAbstract
Increasing levels of dissolved organic matter (DOM) in watercourses in the northern hemisphere are mainly due to reduced acid rain, climate change, and changes in agricultural practices. However, their impacts vary in time and space. To predict how DOM responds to changes in environmental pressures, we need to differentiate between allochthonous and autochthonous sources as well as identify anthropogenic DOM. In this study we distinguish between allochthonous, autochthonous, and anthropogenic sources of DOM in a diverse watercourse network by assessing effects of land cover on water quality and using DOM characterization tools. The main sources of DOM at the studied site are forests discharging allochthonous humic DOM, autochthonous fulvic DOM, and runoff from urban sites and fish farms with high levels of anthropogenic DOM rich in protein‐like material. Specific UV absorbency (sUVa) distinguishes allochthonous DOM from autochthonous and anthropogenic DOM. Anthropogenic DOM differs from autochthonous fulvic DOM by containing elevated levels of protein‐like material. DOM from fishponds is distinguished from autochthonous and sewage DOM by having high sUVa. DOM characteristics are thus valuable tools for deconvoluting the various sources of DOM, enabling water resource managers to identify anthropogenic sources of DOM and predict future trends in DOM
Authors
Geir Orderud Petr Porcal Bjørnar Eikebrokk Jiří Sláma Rolf David Vogt Josef Hejzlar Ståle HaalandAbstract
Several actors have an impact on the quality of drinking water, but ultimately drinking water treatment plants (DWTPs) play a decisive role in ensuring that water quality complies with public regulations. Several developing technologies are combined in water treatment processes. In this paper, we are analysing the technological development of DWTPs in the South Bohemian region of the Czech Republic. The empirical basis is five DWTPs of varying size, and data are gathered through semi-structured interviews with relevant staff inside and outside of the five DWTPs. This study identifies the interplay of factors driving technological development: public regulations, the economic capacity of local DWTP owners together with subsidies from the European Union and national authorities, political priorities by local authorities, and the knowledge network. The paper addressess learning–knowledge–change processes of DWTPs, thereby contributing to our understanding of developing competence in producing drinking water. Generally, large DWTPs are front-runners in introducing new technologies while the smaller ones are lagging. Still, private companies operating small plants on behalf of municipal owners ensure that those DWTPs are part of a wider knowledge network, aiding to introduce a necessary and cost-effective upgrade to treatment steps.
Abstract
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Abstract
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