Article
Environmental Sciences
N. Harishnaika, N. Shilpa, S. A. Ahmed
Summary: This study used non-parametric tests to evaluate rainfall data series from 2000 to 2020. The results show that the Dakshina Kannada district has the highest average rainfall while the Koppala district has the lowest. The findings of this research are important for agricultural and water resource management in the state of Karnataka.
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2023)
Article
Environmental Sciences
Sabyasachi Swain, Surendra Kumar Mishra, Ashish Pandey, Deen Dayal, Prashant Kumar Srivastava
Summary: This study analyzes the long-term trends in maximum temperature (T-MAX) and minimum temperature (T-MIN) over the Narmada river basin in India using observation data from the India Meteorological Department. The results show significant spatiotemporal variation in the temperature trends, with both T-MAX and T-MIN generally increasing. The hottest months have become hotter and the coldest month has become colder, indicating a higher probability of temperature extremes. The study also discusses the implications of these temperature trends on agriculture in the predominantly agricultural basin.
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2022)
Article
Engineering, Environmental
Manjie Li, Zhaowei Liu, Mingdong Zhang, Yongcan Chen
Summary: Multivariate statistical techniques are crucial in water chemical analysis, and constructing a reliable workflow is essential for practice and research. The selection of methods and paths depends on the data set structure and task requirements, and trial and adjustment can optimize the analysis strategy. Reasonable division of complex datasets contributes to data interpretation and pattern recognition.
Article
Environmental Sciences
Konthoujam Khelchandra Singh, Kshetrimayum Krishnakanta Singh, Khuraijam Usha, Subhasish Das, Salam Shantikumar Singh
Summary: This study elucidates the effective application of multivariate statistics in understanding the relations between water hydrochemistry and macrophyte productivity in a remote mountainous lake. The results show significant variations in lake hydrochemistry and the impact of seasons on macrophyte productivity. The importance of statistical tools in understanding aquatic ecosystems is highlighted.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Water Resources
Amit Kumar, Siddharth Kumar, Kuldeep Singh Rautela, Sulochana Shekhar, Tapas Ray, Mohanasundari Thangavel
Summary: This study analyzed the long-term spatio-temporal change in rainfall patterns of Madhya Pradesh, Central India, and found a significant shift in seasonal rainfall distribution after 1998. Pre-monsoon rainfall showed an increasing trend, while annual, monsoon, post-monsoon, and winter rainfall showed decreasing trends. The findings highlight the need for researchers and policymakers to address water availability and foster climate resilience for a sustainable future in the region.
JOURNAL OF WATER AND CLIMATE CHANGE
(2023)
Article
Marine & Freshwater Biology
B. Sridevi, V. Vaury, B. S. K. Kumar, V. V. S. S. Sarma, D. Cardinal, M. Sebilo
Summary: Indian estuaries have been found to export a significant amount of nitrogen nutrients to coastal waters due to high fertilizer usage. A study on the Godavari River estuary in India revealed that nitrate concentrations increase significantly during wet periods and decrease during dry periods. The isotopic composition of the nitrate indicates a probable source of nitrification of soil or sewage-borne nitrogen.
ESTUARINE COASTAL AND SHELF SCIENCE
(2023)
Article
Meteorology & Atmospheric Sciences
Sheila Kavwenje, Lin Zhao, Liang Chen, Cosmo Ngongondo, Evance Chaima, Moses Akintayo Aborisade, Belay Tafa Oba, Patsani Kumambala
Summary: This study integrates statistical methods with graphical methods to examine rainfall variations in the Shire River Basin in Malawi. The results show a decreasing trend in annual rainfall throughout the basin, with different trends observed in different seasons. The innovative trend analysis method identifies the most significant rainfall patterns and complements the results from other statistical methods.
THEORETICAL AND APPLIED CLIMATOLOGY
(2023)
Article
Meteorology & Atmospheric Sciences
Poria Mohit Isfahani, Saeid Soltani, Reza Modarres
Summary: The present study used Multivariate Standardized Drought Index (MSDI) to investigate Iran's agrometeorological drought history and its properties. The results showed that longer and more severe droughts are more dominant in certain regions of the country. Trend analysis revealed rising frequency and significant drying trend, particularly in winter in central plateau and western regions. The study also identified two main periods in which many stations experienced change-points in their MSDI time series, mainly due to climatic and anthropogenic causes.
THEORETICAL AND APPLIED CLIMATOLOGY
(2022)
Article
Environmental Sciences
Yulong Zhong, Hongbing Bai, Wei Feng, Jing Lu, Vincent Humphrey
Summary: This study analyzes the trends in terrestrial water storage (TWS) driven by precipitation and non-precipitation factors in the Chinese mainland from 2003 to 2016. The results show that TWS has increased in the Yangtze River basin, the northern part of the Tibetan Plateau, and part of Heilongjiang Province, while it has decreased in the Tien Shan Mountains, the southeastern part of the Tibetan Plateau, and the North China Plain. Precipitation and reservoir construction contribute to the rise of TWS, while anthropogenic activities and glacial melting due to global warming contribute to the decline. Long-term precipitation change has a significant impact on water storage in northern China.
WATER RESOURCES RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Carmen Jimenez-Mesa, Javier Ramirez, John Suckling, Jonathan Voeglein, Johannes Levin, Juan Manuel Gorriz
Summary: Deep learning predictions are often uncertain, and this study proposes a framework to assess the statistical significance of these predictions. The use of resubstitution with upper bound correction as a validation method improves the generalization ability of deep learning models, especially in low sample size scenarios.
INFORMATION FUSION
(2023)
Article
Water Resources
Nishtha Agrawal, Vivek K. Pandey, Shailendra K. Mishra, Vinay S. Pandey
Summary: This study evaluates temperature extremes in the troposphere over India during different seasons, finding significant trends in the lower troposphere but not the upper troposphere. The identification of these changes can be useful for analyzing coastal vulnerability and extreme weather conditions.
JOURNAL OF WATER AND CLIMATE CHANGE
(2021)
Article
Meteorology & Atmospheric Sciences
Armand Feudjio Tchinda, Romeo Steve Tanessong, Ossenatou Mamadou, Jean Bio Chabi Orou
Summary: The study found that the NMME performs well in seasonal rainfall forecasting in Central Africa within 0 to 2 months lead time, but shows poorer performance within 3 to 5 months lead time. Probabilities of detection are higher than 50% for normal seasons, but lower than 45% for below and above normal seasons.
THEORETICAL AND APPLIED CLIMATOLOGY
(2022)
Article
Environmental Sciences
Bikram Parajuli, Xiang Zhang, Sudip Deuja, Yingbing Liu
Summary: This study evaluated how regional and seasonal precipitation and drought patterns had changed in the Ganga-Brahmaputra Basin using PERSIANN-CDR precipitation data. Results showed a declining trend in annual precipitation at a rate of 5.8 mm/year, an increasing trend in pre-monsoon rainfall, and an increasing frequency and intensity of drought events indicated by decreasing trends of SPI.
Article
Environmental Sciences
Stergios Emmanouil, Andreas Langousis, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou
Summary: The study focuses on the importance of spatiotemporal characteristics of rainfall for hydrological modeling, hydroclimatic risk estimation, and impact assessment. By bias-correcting and downscaling the ERA5 rainfall dataset using the Stage IV precipitation product, the authors developed a high-resolution precipitation product over the CONUS on a 4-km grid with records back to 1979. The developed product demonstrates good performance and robust behavior, making it suitable for hydroclimatic risk applications and frequency analysis, as well as distributed hydrologic modeling.
WATER RESOURCES RESEARCH
(2021)
Article
Chemistry, Analytical
Tiziano Zarra, Mark Gino K. Galang, Vincenzo Belgiorno, Vincenzo Naddeo
Summary: This research discusses traditional and innovative techniques for effective odour quantification monitoring model (OQMM), aiming to optimize the accuracy of the measurements. Artificial neural network (ANN) was found to provide the most accurate results among the investigated techniques.
