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Introduction: Runoff measurement and monitoring is a laborious, time-consuming, and costly task. Additionally, common runoff monitoring usually primarily provide water level, requiring information on the stage-discharge relation. Automatic equipment such as flow meter tipping bucket (TB) is a potential option to simplify and provide continuous runoff monitoring in small catchments. However, a proper description of how to size and adapt the design under different flow conditions is still lacking.Methodology: In this paper we present a novel standardized framework for the design of TB that can be used for low-cost and real-time runoff monitoring under many different conditions. The framework consists of an estimation of the runoff peak rate using the rational equation and a volumetric capacity estimate of the cavity based on runoff rate, operation speed, and inclination angle of TB when at resting position. The proposed framework was implemented in a case study where four TBs were designed for continuous runoff monitoring from experimental plots (100 m2) with different land use (sugarcane, soybean, and bare soil).Results: During field tests (five months), the designed TBs had a recovery rate of actual runoff ranging from 61% to 81% and were able to capture features poorly studied (starting/ending time and peak flow) that have potential importance in hydrological models.Discussion: The proposed framework is flexible and can be used for different environmental conditions to provide continuous runoff data records.

Abstract General Circulation and Earth System Models are the most advanced tools for investigating climate responses to future scenarios of greenhouse gas emissions, playing the role of projecting the climate throughout the century. Nevertheless, climate projections are model-dependent and may show systematic biases, requiring a bias correction for any further application. Here, we provide a dataset based on an ensemble of 19 bias-corrected CMIP6 climate models projections for the Brazilian territory based on the SSP2-4.5 and SSP5-8.5 scenarios. We used the Quantile Delta Mapping approach to bias-correct daily time-series of precipitation, maximum and minimum temperature, solar net radiation, near-surface wind speed, and relative humidity. The bias-corrected dataset is available for both historical (1980–2013) and future (2015–2100) simulations at a 0.25° × 0.25° spatial resolution. Besides the gridded product, we provide area-averaged projections for 735 catchments included in the Catchments Attributes for Brazil (CABra) dataset. The dataset provides important variables commonly used in environmental and hydroclimatological studies, paving the way for the development of high-quality research on climate change impacts in Brazil.

The assessment of flood vulnerability is a complex task that involves numerous uncertainties. Within this context, sensitivity analyses are crucial to better understand the variability of vulnerability index outcomes according to different input parameters. The present study sheds light on the importance of assessing the sensitivity of different criteria weights to construct flood vulnerability indexes using the Maquiné basin (Brazil) as a case study. Specifically, we compared vulnerability scores based on weights derived from a participatory survey with 44 stakeholders to those based on an equal weighting scheme. Results helped us identify areas with low and high uncertainty and the variables contributing to this. Overall, the preference for indicator weights did not vary significantly among stakeholders with distinct socioeconomic characteristics. Furthermore, the choice of weights only had an impact on the spatial distribution of flood vulnerability in certain regions. Compared to equal weights, the flood vulnerability outcomes obtained by averaging the stakeholder scenarios were similar, indicating that the results were robust and not highly sensitive to the choice of weights. By adopting a participatory approach, we were able to consider multiple stakeholders' views, which helped to provide a more comprehensive perspective of flood vulnerability and potentially increased the acceptance of the results. Based on our findings, end-users can better understand the relative importance of each indicator and how they contribute to vulnerability. Furthermore, our results can help identify points where stakeholders disagree, which can be used to facilitate dialogue and consensus building. The methodology applied is straightforward and could be easily adapted to other multi-criteria decision-making problems.

Automated soil moisture systems are commonly used in precision agriculture. Using low-cost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 tested under lab and field conditions. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor response in dry soil. During the second stage of testing, the sensors were coupled to a low-cost monitoring station and installed in the field. The sensors were capable of measuring daily and seasonal oscillations in soil moisture resulting from solar radiation and precipitation. The low-cost sensor performance was compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors provide single-point information with high reliability but at a high acquisition cost, while low-cost sensors can be acquired in larger numbers at a lower cost, allowing for more detailed spatial and temporal observations, but with medium accuracy. The use of SKU sensors is then indicated for short-term and limited-budget projects in which high accuracy of the collected data is not required.

The uncertainties present in hydrological models have made them difficult and often impossible to apply. This work evaluated the sensitivity of input parameters in the SWAT model used for the modeling of average monthly flow-discharge in the Jucu River Basin, located in the southeast portion of the state of Espírito Santo, Brazil. Sensitivity analysis was performed using the SWAT-CUP program, which uses the SUFI-2 algorithm. Four sensitivity analysis tests differing by the length of hydrological series, number of iteration and fluviometric station locations resulted in different sensitivity levels of input parameters used in the model. In this way, the present study emphasizes the need for a greater detailing of methodological processes used in the sensitivity analysis, so that different hierarchies of parameters can be obtained through the same tool.

Modelos hidrológicos, como o Soil and Water Assessment Tools (SWAT), são poderosas ferramentas para o gerenciamento de recursos hídricos. Entretanto, seu uso depende de muitos dados de entrada, comumente oriundos de bases internacionais e nacionais não realísticas. Dentre os dados de entrada, informações pedológicas em níveis regionais e locais são tidas como as mais críticas de obtenção e manuseio, especialmente em regiões em desenvolvimento como o Brasil, e a carência dessas informações leva a incertezas em simulações. O presente artigo tem como objetivo apresentar os procedimentos necessários para a obtenção dos valores de cada parâmetro pedológico exigido pelo modelo SWAT para estimativa de vazões em bacias hidrográficas. Ao longo trabalho são elencados os procedimentos para obtenção dos parâmetros pedológicos para cada classe de solo e ao seguir tal metodologia, foram identificados os valores para as classes presentes na área de estudo. O estudo se aplica à bacia do rio Jucu, localizada no estado do Espírito Santo, sendo referência e base de dados a futuros estudos hidrológicos da mesma região ou de outras; em vez de usar valores pouco coerentes com a realidade local.