Evaluation of the impacts of climate change on streamflow through hydrological simulation and under downscaling scenarios: case study in a watershed in southeastern Brazil

Neves, Gabriela Leite; Barbosa, Mariana Abibi Guimarães Araujo; Anjinho, Phelipe da Silva; Guimarães, Tainá Thomassim; Filho, Jorim Sousa das Virgens; Mauad, Frederico Fábio

doi:10.1007/s10661-020-08671-x

Cited by 13 publications

(7 citation statements)

References 51 publications

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“…The increase in land use, especially for agriculture activities, and the effects of climate change are two major challenges for water security. The effects of climate change on water availability were evaluated in the "Ribeirão do Lobo" hydrographic basin (São Paulo, Brazil) [33] for five future scenarios, using hydrological and climatic models based on the report of the Intergovernmental Panel on Climate Change (IPCC). One of these scenarios demonstrated that the increase in air temperature and decrease in rainfall may reduce by up to 55.50%, 54.18%, and 38.17% the flows Q 90 , Q 95, and Q 7,10 , respectively, until the end of the 21st century.…”

Section: Water Security Analysismentioning

confidence: 99%

Comparison between Regionalized Minimum Reference Flow and On-Site Measurements in Hydrographic Basins of Rural Communities in the State of Goiás, Brazil

Basso

Honório

Costa

et al. 2022

Water

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Reference flows are important variables for assessing water availability in Brazil, as well as in rural communities in the state of Goiás (Brazil). However, as there is a lack of flowrate data and measurement points, regionalization methods have been used for forecasting the minimum reference flow (Qref) allowed for maintaining water uses. The present research covered 92 hydrographic basins within 46 selected rural communities in the state of Goiás, and 21 basins were selected for carrying out on-site flow measurements, as well as for Qref estimation following three regionalization methodologies. Results show a large variation between the values measured and estimated by the three methodologies, but the statistical analysis found regression equations of one of the methods more suitable for application in rural hydrograph basins of Goiás.

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Section: Water Security Analysismentioning

confidence: 99%

Comparison between Regionalized Minimum Reference Flow and On-Site Measurements in Hydrographic Basins of Rural Communities in the State of Goiás, Brazil

Basso

Honório

Costa

et al. 2022

Water

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“…Some HIs already existing in the literature were kept such as the annual drained water balance (Dayyani et al, 2012;Pease et al, 2017;Awad et al, 2020;Wang et al, 2021). To improve the accuracy of the analysis of subsurface drainage, new HIs were set-the length and beginning of the drainage season (Henine et al, 2022); the temporal flow dynamics class distinguishing each annual flow type (Molenat et al, 2008;Humbert et al, 2015;Strohmenger et al, 2020); HIs regarding the water content in the soil storage; HIs dealing with flood events and the discharge specific to an RP (QRP)-which is an approximate version of the indicators currently used to study the impact of climate change on conventional hydrology (Neves et al, 2020;Henriksen et al, 2021). The whole set of HIs allows us to provide farmers and decision-makers with potentially useful tools and information (Kobierska et al, 2020) on crop management, on the risk of pesticide and nitrate leaching and on the sustainability of drainage networks designs.…”

Section: Relevant Hydrological Subsurface Drainage Indicatorsmentioning

confidence: 99%

Effects of Climate Change on Hydrological Indicators of Subsurface Drainage for a Representative French Drainage Site

Jeantet

Thirel

Jeliazkov

et al. 2022

Front. Environ. Sci.

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The aim of this study is to evaluate from a hydrological perspective and in the context of climate change the future of subsurface drainage of the La Jaillière site (western France), which is representative of the pedology of the majority of French subsurface drainage. We used a uniquely large and comprehensive range of 17 hydrological indicators (HIs), describing the temporal dynamics of drainage season, soil saturation, drained water balance and flood events. The HI values are calculated from simulated discharges provided by a subsurface drainage model, the SIDRA-RU model, fed by 12 climate projections from 1975 to 2100 (CMIP5 Euro-Cordex project), with three climate change scenarios: Representative Concentration Pathways (RCP) 2.6, RCP4.5 and RCP8.5. We first verified that the HIs simulated using climate projections in the SIDRA-RU model over the historical period were not critically biased compared to the HIs obtained from the reference climatic reanalysis (SAFRAN). Second, we analysed and compared the HI evolution over different periods and under different scenarios. Our results showed that the number of significant changes in HI values increased under climate change by 2100, depending on the RCP: 2 HIs out of the 17 changed under RCP2.6; 6 HIs under RCP4.5; 10 HIs under RCP8.5. The intensity of drainage peak flows linked to flood events and the annual maximal discharge changed significantly under all RCPs. The temporality of the drainage season was substantially affected according to how pessimistic the RCP was. The worst changes were observed under RCP8.5, which exacerbated extreme events: The wet period was shorter while the dry period was longer by about 67%; the drought index increased by 100%; the summer drained water balance decreased by 9%. On the contrary, in winter, the duration of the wet period decreased while maintaining the same drained water balance, thus inducing stronger flood events leading to an earlier saturation of the drainage networks. The sustainability of the drainage system design at La Jaillière is therefore threatened, with the risk of fulfilling its function less effectively by 2100, exposing current crops to more important runoff and affecting water quality by increasing the leaching of agrochemical inputs.

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“…Indeed, several studies have pointed out the importance of having access to reliable stochastic time‐series of precipitation, since impact models tend to fail to reproduce the extreme events (e.g., floods) as a consequence of the underestimated variance typically exhibited by deterministic predictions, that collapse the uncertainty into a single value (Acharya et al., 2017; Mukundan et al., 2019; Nogueira & Barros, 2015). As suggested by several recent works, this limitation can be mitigated by introducing a meaningful stochastic component of uncertainty in the models (Farmer & Vogel, 2016; Haberlandt et al., 2015; Langousis & Kaleris, 2014; Neves et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

A Posteriori Random Forests for Stochastic Downscaling of Precipitation by Predicting Probability Distributions

Legasa

Calviño

Gutiérrez

2022

Water Resources Research

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Despite being the main tool used nowadays to simulate the evolution of the climate system, the spatial resolution of the current global climate models (GCMs)-typically up to around a hundred kilometers-is still insufficient for most practical applications (see e.g., Doblas-Reyes et al., 2013 and references therein). To alleviate this limitation, statistical downscaling (SD) methods aim to build models linking a set of key large-scale predictors (e.g., geopotential or winds) with the target predictand (e.g., precipitation or temperature) over the area of interest (see e.g., von Storch et al., 1993). Under the perfect prognosis (PP) approach (

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Evaluation of the impacts of climate change on streamflow through hydrological simulation and under downscaling scenarios: case study in a watershed in southeastern Brazil

Cited by 13 publications

References 51 publications

Comparison between Regionalized Minimum Reference Flow and On-Site Measurements in Hydrographic Basins of Rural Communities in the State of Goiás, Brazil

Comparison between Regionalized Minimum Reference Flow and On-Site Measurements in Hydrographic Basins of Rural Communities in the State of Goiás, Brazil

Effects of Climate Change on Hydrological Indicators of Subsurface Drainage for a Representative French Drainage Site

A Posteriori Random Forests for Stochastic Downscaling of Precipitation by Predicting Probability Distributions

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