This study assesses the performance of the new Global Precipitation Measurement (GPM)-based satellite precipitation estimates (SPEs) datasets in the Brazilian Central Plateau and compares it with the previous Tropical Rainfall Measurement Mission (TRMM)-era datasets. To do so, the Integrated Multi-satellitE Retrievals for GPM (IMERG)-v5 and the Global Satellite Mapping of Precipitation (GSMaP)-v7 were evaluated at their original 0.1° spatial resolution and for a 0.25° grid for comparison with TRMM Multi-satellite Precipitation Analysis (TMPA). The assessment was made on an annual, monthly, and daily basis for both wet and dry seasons. Overall, IMERG presents the best annual and monthly results. In both time steps, IMERG’s precipitation estimations present bias with lower magnitudes and smaller root-mean-square error. However, GSMaP performs slightly better for the daily time step based on categorical and quantitative statistical analysis. Both IMERG and GSMaP estimates are seasonally influenced, with the highest difficulty in estimating precipitation occurring during the dry season. Additionally, the study indicates that GPM-based SPEs products are capable of continuing TRMM-based precipitation monitoring with similar or even better accuracy than obtained previously with the widely used TMPA product.

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The recent and continuous development of unmanned aerial vehicles (UAV) and small cameras with different spectral resolutions and imaging systems promotes new remote sensing platforms that can supply ultra-high spatial and temporal resolution, filling the gap between ground-based surveys and orbital sensors. This work aimed to monitor siltation in two large rural and urban reservoirs by recording water color variations within a savanna biome in the central region of Brazil using a low cost and very light unmanned platform. Airborne surveys were conducted using a Parrot Sequoia camera (~0.15 kg) onboard a DJI Phantom 4 UAV (~1.4 kg) during dry and rainy seasons over inlet areas of both reservoirs. Field measurements of total suspended solids (TSS) and water clarity were made jointly with the airborne survey campaigns. Field hyperspectral radiometry data were also collected during two field surveys. Bio-optical models for TSS were tested for all spectral bands of the Sequoia camera. The near-infrared single band was found to perform the best (R2: 0.94; RMSE: 7.8 mg L−1) for a 0–180 mg L−1 TSS range and was used to produce time series of TSS concentration maps of the study areas. This flexible platform enabled monitoring of the increase of TSS concentration at a ~13 cm spatial resolution in urban and rural drainages in the rainy season. Aerial surveys allowed us to map TSS load fluctuations in a 1 week period during which no satellite images were available due to continuous cloud coverage in the rainy season. This work demonstrates that a low-cost configuration allows dense TSS monitoring at the inlet areas of reservoirs and thus enables mapping of the sources of sediment inputs, supporting the definition of mitigation plans to limit the siltation process.

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The purpose of this study was to analyze the soil database influence on streamflow simulation using the SWAT model in the experimental catchment of the Pipiripau river (235 km²), located in Brazilian savanna (Cerrado biome). To achieve this goal, two databases were used, one developed with data collected nearby the Pipiripau region (SDB1), and another one with data from literature (SDB2). The evaluation was performed using a streamflow historical time series of 10 years (1989-1998), on monthly and daily basis. The analysis was made without calibration, using only the results from SWATs first simulation, as it was applied in an ungagged basin. For daily simulations, the Nash & Sutcliffe model efficiency (NSE), the adapted Nash & Sutcliffe model efficiency (ANSE), and the Percent Bias (PBIAS) were respectively -11.88, -11.80, and -23.15% for SDB1, and -9.94, -9.88, and -84.72, for SDB2. For the monthly simulations, the NSE, ANSE, and PBIAS results were, respectively,-1.78, -2.98, and -24.53% for SDB1, and 6.51, 9.88, and -84.72 for SDB2. Even if the PBIAS analysis and the annual water budget results for the SDB1 simulations had better results, the negative values of NSE and ANSE indicates that, without calibration, the simulations failed to represent observed data. It is remarkable that the use of SDB1 visibly improved daily baseflow simulations during the rainy season. The results indicate the importance of developing specific soil databases for different regions, as well as research on other parameters in order to improve SWATs physical basis.

Resumo O crescimento da área irrigada no Cerrado afeta o volume de água captado nas bacias hidrográficas, influenciando diretamente na disponibilidade e alocação dos recursos hídricos. Em bacias com predominância da agricultura irrigada, torna-se fundamental a compreensão da variação espaço-temporal da evapotranspiração real de uma cultura (ETR), para o mais assertivo planejamento e gerenciamento dos reservatórios. Considerando o potencial de cultivo do trigo no Cerrado, o estudo tem por objetivo estimar a demanda hídrica da cultura nas safras de 2018 e 2019, por meio dos modelos SEBAL e SSEBop. Em comparação com o método da razão de Bowen, o SEBAL apresentou variações de R2 entre 0,86 e 0,72, tendo seu desempenho classificado como satisfatório. O RMSE determinado foi de 0,50 mm d−1 em 2018 e de 0,42 mm d−1 em 2019. O modelo SSEBop expressou melhor desempenho nas duas safras, com variabilidade de R2 entre 0,95 e 0,78, representando de forma mais adequada a ETR com RMSE menor, de 0,25 mm d−1 e 0,41 mm d−1, respectivos aos ciclos de 2018 e 2019. A configuração simplificada do SSEBop e o bom desempenho nas condições verificadas, tornam o modelo uma ferramenta apropriada, podendo contribuir para um planejamento hídrico eficiente na região.