Brazilian Cerrado biome is the largest and richest tropical savanna in the world. In order to understand the effects of climate changes on the hydrology of the Cerrado basins, this paper investigates the hydrological impacts of climate change throughout the 21st century under different emissions scenarios on the streamflow and on the droughts in the Sono, Manuel Alves da Natividade and Palma basins, located in the Brazilian Cerrado. For this purpose, the SWAT hydrological model driven by the downscaling of the HadGEM2‐ES and MIROC5 global climate models associated with RCP4.5 and RCP8.5 scenarios were used in three time slices (2011–2040, 2041–2070 and 2071–2099). The Standardized Precipitation Index (SPI) and the Standardized Streamflow Index (SSFI) were used to characterize droughts. In general, the results showed that the duration, intensity and frequency of the meteorological and hydrological droughts are expected to increase during the future periods. However, the hydrological droughts are projected to be larger than the meteorological droughts. Reductions in the streamflow are indicated in all future time slices and under both RCPs, especially, during dry periods, which may cause negative impacts in the ecological functions of the Cerrado biome, risk for reduction of the recharge of aquifers and risk for the electric energy production in northern Brazil.
Hydraulic projects and water management require reliable hydrological data. The Araguaia-Tocantins River basin, in addition to agricultural use, has great potential for hydroelectric exploitation. However, the streamflow monitoring network in the Araguaia River basin is composed of only a few stations, resulting in a lack of hydrological data. The regionalization of the reference streamflows is a technique that can help circumvent this lack of data, enabling the estimation of streamflows from easily obtainable explanatory variables. In this context, the objective of this study was to develop regional functions for the maximum streamflow (Qmax) applicable to different Return Periods (RP), the long-term mean streamflow (Qmlt) and the 95% streamflow permanence (Q95) of the upper and middle Araguaia River sub-basins. The dimensionless streamflow methodology was adopted with the drainage area as an explanatory variable. The tested regressive models were the linear, potential and quotient models. Leave-one-out cross-validation was used to assess the quality of the regional models. Ten statistical distributions of 2 to 5 parameters were used. (i) Satisfactory results were obtained for all reference streamflows. (ii) The cross-validation technique proved to be essential for the selection of the most robust model. (iii) The quotient model was shown to be superior to the potential linear model in most cases.
Understanding the spatiotemporal behaviour of soil moisture in tropical forests is fundamental because it mediates processes such as infiltration, groundwater recharge, runoff and evapotranspiration. This study aims to model the spatiotemporal dynamics of soil moisture in an Atlantic forest remnant (AFR) through four machine learning algorithms, as these dynamics represent an important knowledge gap under tropical conditions. Random forest (RF), support vector machine, average neural network and weighted k‐nearest neighbour were studied. The abilities of the models were evaluated by means of root mean square error, mean absolute error, coefficient of determination (R2) and Nash‐Sutcliffe efficiency (NS) for two calibration approaches: (a) chronological and (b) randomized. The models were further compared with a multilinear regression (MLR). The study period spans from September 2012 to November 2019 and relies on variables representing the weather, geographical location, forest structure, soil physics and morphology. RF was the best algorithm for modelling the spatiotemporal dynamics of the soil moisture with an NS of 0.77 and R2 of 0.51 in the randomized approach. This finding highlights the ability of RF to generalize a dataset with contrasting weather conditions. Kriging maps highlighted the suitability of RF to track the spatial distribution of soil moisture in the AFR. Throughfall (TF), potential evapotranspiration (ETo), longitude (Long), diameter at breast height (DBH) and species diversity (H) were the most important variables controlling soil moisture. MLR performed poorly in modelling the spatiotemporal dynamics of soil moisture due to the highly nonlinear condition of this process. Highlights Modelling soil moisture in an Atlantic forest through machine learning. Machine learning algorithms are powerful tools to address the spatiotemporal dynamics of soil moisture. Climate, position and forest variables drive the spatiotemporal pattern of soil moisture. Random forest is the best algorithm to simulate soil moisture dynamics.
Objetivou-se no presente trabalho caracterizar o regime hidrológico da bacia hidrográfica do Rio Manuel Alves da Natividade, TO, com seção de controle no posto fluviométrico denominado Fazenda Lobeira. Para tanto, foram quantificados indicadores hidrológicos regionais associados a vazões médias, mínimas e máximas, precipitação média na bacia, balanço hídrico anual, além de suas principais características morfométricas. Foram utilizadas cenas do modelo digital de elevação ASTER em ambiente SIG e séries históricas pluviométricas e fluviométricas disponibilizadas pela Agência Nacional de Águas (ANA). Os resultados mostraram que o regime de chuvas apresenta forte concentração no verão, com 50,4% do total anual entre os meses de dezembro, janeiro e fevereiro. Os indicadores de eventos hidrológicos extremos máximos mostraram que em um ano hidrológico típico a vazão média de cheia supera em 7,1 vezes a vazão média, e que o evento com recorrência de 100 anos, que é um indicativo do vulto da cheia associada com a inundação da zona ribeirinha supera em 2,2 vezes a vazão média de cheia na bacia. O período de vazante é fortemente influenciado pelo longo período de estiagem, que apresenta 5 meses consecutivos com precipitação inferior a 50 mm (maio a setembro). A análise de indicadores hidrológicos associados com vazões mínimas de referência identificou alta vulnerabilidade natural dos recursos hídricos superficiais, permitindo concluir que o incentivo à adoção de práticas conservacionistas de manejo de bacias hidrográficas e de obras de regularização são essenciais para a otimização dos recursos hídricos superficiais nesta bacia.Palavras-chave: modelo digital de elevação, morfometria, hidrologia, rendimento específico.
The Brazilian Cerrado biome is the largest and richest tropical savanna in the world and is among the 25 biodiversity hotspots identified worldwide. However, the lack of adequate hydrological monitoring in this region has led to problems in the management of water resources. In order to provide tools for the adequate management of water resources in the Brazilian Cerrado biome region, this paper develops the regionalization of maximum, mean and minimum streamflows in the Tocantins River Basin (287,405.5 km2), fully located in the Brazilian Cerrado biome. The streamflow records of 32 gauging stations in the Tocantins River Basin are examined using the Mann-Kendall test and the hydrological homogeneity non-parametric index-flood method. One homogeneous region was identified for the estimate of the streamflows Qltm (long-term mean streamflow), Q90% (streamflow with 90% of exceeding time), Q95% (streamflow with 95% of exceeding time) and Q7,10 (minimum annual streamflow over 7 days and return period of 10 years). Two homogeneous regions were identified for maximum annual streamflow estimation and the Generalized Extreme Value distribution is found to describe the distribution of maximus events appropriately within the both regions. Regional models were developed for each streamflow of each region and evaluated by cross-validation. These models can be used for the estimation of maximum, mean and minimum streamflows in ungauged basins within the Tocantins River Basin within the area boundaries identified. Therefore, the results provided in this paper are valuable tools for practicing water-resource managers in the Brazilian Cerrado biome. Keywords: l-moments, statistical hydrology, water use rights concessions.
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