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
Ji Liu Yi Wang Yong Li Josep Peñuelas Ying Zhao Jordi Sardans Doerthe Tetzlaff Jian Liu Xinliang Liu Hongzhao Yuan Yanyan Li Ji Chen Jinshui WuAbstract
Whether and how to synchronously regulate stream water nitrogen (N) and phosphorus (P) concentrations and ratios is a major challenge for sustainable aquatic functions. Soil carbon (C):N:P ratios influence soil N and P stocks and biogeochemical processes that elicit subsequent substantial impacts on stream water N and P concentrations and ratios. Therefore, bridging soil and stream water with ecological stoichiometry is one of the most promising technologies for improving stream water quality. Here, we quantified the ecological stoichiometry of soil and stream water relationships across nine catchments. Soil C:P ratio was the main driver of water quality, showing negative correlations with stream water N and P concentrations, and positive correlations with the N:P ratio in P-limited catchments. We revealed that soil C:P ratios higher than 97.8 mol mol−1 are required to achieve the simultaneous regulation of stream water N and P concentrations below the eutrophication threshold and make algal growth P-limited. Furthermore, we found that the relationships between catchment landscape and soil ecological stoichiometry likely provided practical options for regulating soil ecological stoichiometry. Our work highlights that soil ecological stoichiometry can effectively indicate the amount and proportion of soil N and P losses, and can be intervened through rational landscape planning to achieve sustainable aquatic ecosystems in catchments.
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.
Abstract
No abstract has been registered
Authors
Jian LiuAbstract
No abstract has been registered
Authors
Jian LiuAbstract
No abstract has been registered
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.
Authors
Jian LiuAbstract
No abstract has been registered
Authors
Karin Juul Hesselsøe Anne friederike Borchert Trygve S. Aamlid Bjarni Hannesson Karin Normann Per Rasmussen Jørgen Hornslien Pia Heltoft ThomsenAbstract
No abstract has been registered
Authors
Karin Juul Hesselsøe Anne friederike Borchert Karin Normann Trygve S. Aamlid Pia Heltoft ThomsenAbstract
No abstract has been registered