This work evaluated the simulation of streamflow using observed and estimated gridded meteorological datasets and the Soil and Water Assessment Tool (SWAT) model for a humid area with scarce data in northeastern Brazil. The coefficient of determination (R²), Nash-Sutcliffe efficiency (NS), root mean square error (RMSE), normalized root mean square error (NRMSE), and percent bias (PBIAS) were used to assess the SWAT results yielded by estimated and observed rainfall data. The hydrological modeling data from three streamflow stations were used (2000 to 2006 for calibration and 2007 to 2010 for validation). The results show that at daily scale, the estimated rainfall data show a poor agreement (R² ranging from 0.22 to 0.04) with the observed rainfall but good agreement at monthly (R² = 0.85) and annual scales (R² = 0.80). The results showed that estimated accumulated precipitation overestimated the observed data. The results showed that R² ranged from 0.51 to 0.55 at monthly scale and 0.44-0.52 at annual scale. However, the global data can represent well the variability of rainfall within the region. The results indicated a good correlation in the seasonal variability (R² ranged from 0.72 to 0.60). The modeling results using monthly TRMM data and observed rainfall data showed good values of NS and R² during calibration and validation, but PBIAS was unsatisfactory for the three streamflow gauges. The streamflow estimates from the SWAT model using data from the TRMM satellite showed that such data are capable of generating satisfactory results after calibration, although measured rainfall data presented better results; the data could support areas with scarce rainfall data and be applied to other river basins, for example, to analyze the hydrological potential of other basins in the coastal region of northeastern Brazil. Over the past three decades, considerable advances have been made in remote sensing with environmental satellites, increasing the amount of information available, including rainfall estimates. In this context, the use of TRMM data to estimate rainfall has ultimately been shown to be an interesting alternative for areas with scarce rainfall data.

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中文翻译：

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巴西东北部Pirapama流域的网格化气象数据评估及其在潮湿地区的潜在水文应用（缺乏数据）

这项工作使用观测和估计的网格气象数据集以及土壤和水评估工具（SWAT）模型对巴西东北部缺乏数据的潮湿地区评估了水流的模拟。使用确定系数（R ²），纳什-苏克利夫效率（NS），均方根误差（RMSE），归一化均方根误差（NRMSE）和偏差百分比（PBIAS）来评估通过估算得出的SWAT结果和观测的降雨数据。使用了三个水流站的水文模拟数据（2000年至2006年用于校准，2007年至2010年用于验证）。结果表明，在日尺度上，估计的降雨数据显示出较差的一致性（R ²观测的降雨范围在0.22至0.04之间，但在月度（R ² = 0.85）和年尺度（R ² = 0.80）上有很好的一致性。结果表明，估计的累积降水量高估了观测数据。结果表明，R ²在月度范围内为0.51至0.55，在年度范围内为0.44-0.52。但是，全球数据可以很好地代表该区域内降雨的变化性。结果表明季节变化具有良好的相关性（R ^2的范围为0.72至0.60）。使用每月TRMM数据和观测到的降雨数据的建模结果显示出良好的NS和R ²值在校准和验证过程中，但对于三个流量表而言，PBIAS不能令人满意。使用TRMM卫星的数据对SWAT模型进行的流量估算表明，这些数据能够在校准后产生令人满意的结果，尽管实测的降雨数据显示出更好的结果。这些数据可以为降雨稀少的地区提供支持，并可以应用于其他流域，例如，以分析巴西东北部沿海地区其他流域的水文潜力。在过去的三十年中，环境卫星遥感技术取得了长足的进步，增加了包括雨量估计在内的可用信息量。在这种情况下，

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