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.

2022

Abstract

Zoogenic faecal contamination of the environment is one of the indices included in the evaluation of ecological threats, health hazards and adverse impacts on various ecosystems. The risks and environmental concerns are associated with the fact that faeces of wild and domesticated animals constitute the largest source of environmental loading of enteropathogens associated with transmission of zoonotic diseases (enteric zoonoses). Although sick animals are more likely to transmit pathogens, healthy ones can also be the carriers and defecate them into the environment. This is of particular importance given the close human-animal interactions and health effects resulting from human and ecological exposures to faecal hazards from companion and farm animals. We have therefore set out to investigate whether healthy equines can carry and defecate human infectious pathogens. For this purpose, we set up a pilot study to examine the faecal DNA of horses using culture-independent molecular diagnostics – fluorescent probe-based quantitative real-time PCR. Our results revealed that among a total of 23 horses, 6 were found to carry Campylobacter jejuni (C. jejuni), and 5 had Salmonella enterica serovar Typhimurium (S. Typhimurium). Moreover, Enterococcus faecalis (E. faecalis) was found in 14 horses, while 19 were positive for Clostridium perfringens (C. perfringens). Furthermore, the frequently reported protozoan parasites in livestock, Cryptosporidium parvum (C. parvum) and Giardia lamblia (G. lamblia), were discovered in 8 and 7 samples, respectively. This pilot study shed new light on the phenomenon of healthy horses carrying C. jejuni and other human-health-related enteropathogens.

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Abstract

The application of numerical models to understand the behavioural pattern of a flood is widely found in the literature. However, the selection of an appropriate hydraulic model is highly essential to conduct reliable predictions. Predicting flood discharges and inundation extents are the two most important outcomes of flood simulations to stakeholders. Precise topographical data and channel geometries along a suitable hydraulic model are required to accurately predict floods. One-dimensional (1D) hydraulic models are now replaced by two-dimensional (2D) or combined 1D/2D models for higher performances. The Hydraulic Engineering Centre’s River Analysis System (HEC-RAS) has been widely used in all three forms for predicting flood characteristics. However, comparison studies among the 1D, 2D to 1D/2D models are limited in the literature to identify the better/best approach. Therefore, this research was carried out to identify the better approach using an example case study of the Kelani River basin in Sri Lanka. Two flood events (in 2016 and 2018) were separately simulated and tested for their accuracy using observed inundations and satellite-based inundations. It was found that the combined 1D/2D HEC-RAS hydraulic model outperforms other models for the prediction of flows and inundation for both flood events. Therefore, the combined model can be concluded as the better hydraulic model to predict flood characteristics of the Kelani River basin in Sri Lanka. With more flood studies, the conclusions can be more generalized.

Abstract

In the present study, the streamflow simulation capacities between the Soil and Water Assessment Tool (SWAT) and the Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) were compared for the Huai Bang Sai (HBS) watershed in northeastern Thailand. During calibration (2007–2010) and validation (2011–2014), the SWAT model demonstrated a Coefficient of Determination (R2) and a Nash Sutcliffe Efficiency (NSE) of 0.83 and 0.82, and 0.78 and 0.77, respectively. During the same periods, the HEC-HMS model demonstrated values of 0.80 and 0.79, and 0.84 and 0.82. The exceedance probabilities at 10%, 40%, and 90% were 144.5, 14.5, and 0.9 mm in the flow duration curves (FDCs) obtained for observed flow. From the HEC-HMS and SWAT models, these indices yielded 109.0, 15.0, and 0.02 mm, and 123.5, 16.95, and 0.02 mm. These results inferred those high flows were captured well by the SWAT model, while medium flows were captured well by the HEC-HMS model. It is noteworthy that the low flows were accurately simulated by both models. Furthermore, dry and wet seasonal flows were simulated reasonably well by the SWAT model with slight under-predictions of 2.12% and 13.52% compared to the observed values. The HEC-HMS model under-predicted the dry and wet seasonal flows by 10.76% and 18.54% compared to observed flows. The results of the present study will provide valuable recommendations for the stakeholders of the HBS watershed to improve water usage policies. In addition, the present study will be helpful to select the most appropriate hydrologic model for humid tropical watersheds in Thailand and elsewhere in the world.

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Abstract

Climate change is a serious and complex crisis that impacts humankind in different ways. It affects the availability of water resources, especially in the tropical regions of South Asia to a greater extent. However, the impact of climate change on water resources in Sri Lanka has been the least explored. Noteworthy, this is the first study in Sri Lanka that attempts to evaluate the impact of climate change in streamflow in a watershed located in the southern coastal belt of the island. The objective of this paper is to evaluate the climate change impact on streamflow of the Upper Nilwala River Basin (UNRB), Sri Lanka. In this study, the bias-corrected rainfall data from three Regional Climate Models (RCMs) under two Representative Concentration Pathways (RCPs): RCP4.5 and RCP8.5 were fed into the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model to obtain future streamflow. Bias correction of future rainfall data in the Nilwala River Basin (NRB) was conducted using the Linear Scaling Method (LSM). Future precipitation was projected under three timelines: 2020s (2021–2047), 2050s (2048–2073), and 2080s (2074–2099) and was compared against the baseline period from 1980 to 2020. The ensemble mean annual precipitation in the NRB is expected to rise by 3.63%, 16.49%, and 12.82% under the RCP 4.5 emission scenario during the 2020s, 2050s, and 2080s, and 4.26%, 8.94%, and 18.04% under RCP 8.5 emission scenario during 2020s, 2050s and 2080s, respectively. The future annual streamflow of the UNRB is projected to increase by 59.30% and 65.79% under the ensemble RCP4.5 and RCP8.5 climate scenarios, respectively, when compared to the baseline scenario. In addition, the seasonal flows are also expected to increase for both RCPs for all seasons with an exception during the southwest monsoon season in the 2015–2042 period under the RCP4.5 emission scenario. In general, the results of the present study demonstrate that climate and streamflow of the NRB are expected to experience changes when compared to current climatic conditions. The results of the present study will be of major importance for river basin planners and government agencies to develop sustainable water management strategies and adaptation options to offset the negative impacts of future changes in climate.

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Abstract

The availability of accurate spatiotemporal rainfall data is of utmost importance for reliable predictions from hydroclimatological studies. Challenges and limitations faced due to the absence of dense rain gauge (RG) networks are seen especially in the developing countries. Therefore, alternative rainfall measurements such as satellite rainfall products (SRPs) are used when RG networks are scarce or completely do not exist. Noteworthy, rainfall data retrieved from satellites also possess several uncertainties. Hence, these SRPs should essentially be validated beforehand. The Mahaweli River Basin (MRB), the largest river basin in Sri Lanka, is the heart of the country’s water resources contributing to a significant share of the hydropower production and agricultural sector. Given the importance of the MRB, this study explored the suitability of SRPs as an alternative for RG data for the basin. Daily rainfall data of six types of SRPs were extracted at 14 locations within the MRB. Thereafter, statistical analysis was carried out using continuous and categorical evaluation indices to evaluate the accuracy of SRPs. Nonparametric tests, including the Mann-Kendall and Sen’s slope estimator tests, were used to detect the possibility of trends and the magnitude, respectively. Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) outperformed among all SRPs, while Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products showed dire performances. However, IMERG also demonstrated underestimations when compared to RG data. Trend analysis results showcased that the IMERG product agreed more with RG data on monthly and annual time scales while Tropical Rainfall Measurement Mission Multisatellite Precipitation Analysis–3B42 (TRMM-3B42) agreed more on the seasonal scale. Overall, IMERG turned out to be the best alternative among the SRPs analyzed for MRB. However, it was clear that these products possess significant errors which cannot be ignored when using them in hydrological applications. The results of the study will be valuable for many parties including river basin authorities, agriculturists, meteorologists, hydrologists, and many other stakeholders.