Assessment of hydrological trends and changes in hydroclimatic and land use parameters in a river basin in northeast Brazil

Sousa, Lucas Farias de; Ferraz, Lorena Lima; Santos, Carlos Amilton Silva; Rocha, Felizardo Adenilson; Jesus, Raildo Mota de

doi:10.1016/j.jsames.2023.104464

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…Existing studies are usually conducted at the administrative scale, and few studies have been conducted at the large watershed scale where climatic conditions are complex [31,32]. In addition, most studies have utilized statistical methods, such as linear regression, multiple linear regression, or nonparametric tests, to identify correlations between climatic factors and surface runoff and to identify factors that influence surface runoff [27,[33][34][35]. However, these methods make it difficult to accurately capture the relationship between climatic factors and surface runoff.…”

Section: Introductionmentioning

confidence: 99%

Projection of Future Climate Change and Its Influence on Surface Runoff of the Upper Yangtze River Basin, China

Wan

2023

Atmosphere

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Global climate change will modify precipitation and temperatures’ temporal and spatial distribution, trigger more extreme weather events, and impact hydrological processes. The Yangtze River basin is one of the world’s largest basins, and understanding future climate changes is vital for water resource management and supply. Research on predicting future climate change in the upper Yangtze River basin (UYRB) and introducing machine learning algorithms to analyze the impact of climate factors, including extreme weather indicators, on surface runoff is urgently needed. In this study, a statistical downscaling model (SDSM) was used to forecast the future climate in the UYRB, and the Mann–Kendall (MK) or modified Mann–Kendall (MMK) trend test at a 5% level of significance was applied to analyze temporal trends. The Spearman rank correlation (SRC) test at a 5% level of significance and random forest regression (RFR) model were employed to identify the key climatic factors affecting surface runoff from annual precipitation, annual temperature, maximum 5-day precipitation (R×5Day), number of tropical nights (TR), and consecutive dry days (CDD), and the RFR model was also used to predict future runoff. Based on the results, we found that, compared to the selected historical period (1985–2014), the mean annual precipitation (temperature) during the mid-term (2036–2065) increased by 18.93% (12.77%), 17.78% (14.68%), 20.03% (17.03%), and 19.67% (19.29%) under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively, and during the long term (2071–2100), increased by 19.44% (12.95%), 22.01% (21.37%), 30.31% (30.32%), and 34.48% (37.97%), respectively. The warming and humidification characteristics of the northwestern UYRB were more pronounced. The key climatic factors influencing surface runoff were annual precipitation, maximum 5-day precipitation (R×5day), and annual temperature. Because of warming and humidification, surface runoff in the UYRB is expected to increase relative to the historical period. The surface runoff during the mid-term (long term) increased by 12.09% (12.58%), 8.15% (6.84%), 8.86% (8.87%), and 5.77% (6.21%) under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. The implementation of sustainable development pathways under the low radiative forcing scenario can be effective in mitigating climate change, but at the same time, it may increase the risk of floods in the UYRB.

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Section: Introductionmentioning

confidence: 99%

Projection of Future Climate Change and Its Influence on Surface Runoff of the Upper Yangtze River Basin, China

Wan

2023

Atmosphere

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“…Carvalho et al (2020) verified trends of reduction in rainfall, in the number of rainy days and in the number of rainy days in the coastal strip and in the semi-arid and arid region of the NEB. However, the analysis of frequency and trends of monthly and seasonal rainfall on a spatial scale are scarce in Brazil, especially for NEB (Sousa et al, 2023). One of the reasons is the deficiency in the monitoring and availability of time series with long observation periods, without data gaps and with appropriate spatial coverage (Abreu et al, 2020;Bouklikha et al, 2021).…”

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confidence: 99%

Long‐term trend analysis in annual, seasonal and monthly rainfall in East Northeast of Brazil and the influence of modes of climate variability

Abreu,

de Souza Fraga,

Lyra

et al. 2023

Intl Journal of Climatology

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The study of rainfall trends is crucial for food security and water availability in Alagoas state, Northeast of Brazil. In this work, monthly, seasonal and annual rainfall trends have been studied (1960–2016) for homogeneous rainfall regions over the eastern part of the Northeast Brazil (ENEB) and later related to climate variability. Cluster analysis was applied to identify homogeneous rainfall regions while the Mann–Kendall (MK), modified Mann–Kendall (MMK) and Pettitt tests were used in the analysis and identification of trends on a spatial and temporal scale. To relate rainfall and climate variability modes, Spearman's correlation was used in each homogeneous region. The rainfall series provided evidence of a general decrease in rainfall in the rainy period and an increase in the dry period, mainly over the driest region. The break points of time series occurred mostly in periods of great variations in values of modes of climate variability, especially the Monthly Niño3.4 Index and the Southern Oscillation Index (SOI), both having a robust influence across the region. Moreover, the probable rainfall in the time series with trends was different in most months before and after the breakpoint. After the breakpoint, probable rainfall was lower, influenced by the breakpoint year (size of the series before and after the breakpoint), which mainly occurred in the 1980s and 1990s and presented a warm phase and a greater number of El Niño events. The MK and MMK trend tests showed the ability to detect trends, although there is no established standard on which test or version to use due to self‐correlated, nonhomogeneous series with nonrandom or nonindependent data. Rainfall is an important variable for water and food security and in the monitoring of natural disasters. The changes detected in this study can be used as reference information for public policies on water resources and future studies for Alagoas and similar regions.

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Assessment of hydrological trends and changes in hydroclimatic and land use parameters in a river basin in northeast Brazil

Cited by 2 publications

References 41 publications

Projection of Future Climate Change and Its Influence on Surface Runoff of the Upper Yangtze River Basin, China

Projection of Future Climate Change and Its Influence on Surface Runoff of the Upper Yangtze River Basin, China

Long‐term trend analysis in annual, seasonal and monthly rainfall in East Northeast of Brazil and the influence of modes of climate variability

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