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首页> 外文期刊>Regional Environmental Change >Assessment of the utility of dynamically -downscaled regional reanalysis data to predict streamflow in west central Florida using an integrated hydrologic model
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Assessment of the utility of dynamically -downscaled regional reanalysis data to predict streamflow in west central Florida using an integrated hydrologic model

机译：使用综合水文模型评估动态缩减的区域再分析数据在佛罗里达州中西部预测水流的实用性

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The goal of this study was to evaluate the ability of dynamically downscaled reanalysis data to reproduce local-scale spatiotemporal precipitation and temperature data needed to accurately predict streamflow in the Tampa Bay region of west central Florida. In particular, the Florida State University Center for Ocean-Atmospheric Prediction Studies CLARReS10 data (NCEP DOE 2 reanalysis data (R2) downscaled to 10-km over the Southeast USA using the Regional Spectral Model (RSM) were evaluated against locally available observed precipitation and temperature data and then used to drive an integrated hydrologic model that was previously calibrated for the Tampa Bay region. Resulting streamflow simulations were evaluated against observed data and previously calibrated model results. Results showed that the raw downscaled reanalysis predictions accurately reproduced the seasonal trends of mean daily minimum temperature, maximum temperature and precipitation, but generally overestimated the monthly mean and standard deviation of daily precipitation. Biases in the temporal mean and standard deviation of daily precipitation and temperature predictions were effectively removed using a CDF-mapping approach; however, errors in monthly precipitation totals remained after bias correction. Monthly streamflow simulation error statistics indicated that the accuracy of the streamflow produced by the bias-corrected downscaled reanalysis data was satisfactory (i.e., sufficient for seasonal to decadal planning), but that the accuracy of the stream-flow produced by the raw downscaled reanalysis data was unsatisfactory for water resource planning purposes. The findings of this study thus indicate that further improvement in large-scale reanalysis data and regional climate models is needed before dynamically downscaled reanalysis data can be used directly (i.e. without bias correction with local data) to drive hydrologic models. However, bias-corrected dynamically downscaled data show promise for extending local historic climate observation records for hydrologic simulations. Furthermore, results of this study indicate that similarly bias-corrected dynamically down-scaled retrospective and future GCM projections should be suitable for assessing potential hydrologic impacts of future climate change in the Tampa Bay region.
机译：这项研究的目的是评估动态缩减规模的再分析数据再现准确预测佛罗里达州中西部坦帕湾地区水流所需的局部时空降水和温度数据的能力。特别是，针对佛罗里达州州立大学海洋大气预测研究中心的CLARReS10数据（使用区域光谱模型（RSM）在美国东南部缩小为10公里的NCEP DOE 2再分析数据（R2）），针对当地可观测的降水量和温度数据，然后用于驱动先前针对坦帕湾地区校准的综合水文模型，并根据观测数据和先前校准的模型结果对所产生的水流模拟进行了评估，结果表明原始的按比例缩小的再分析预测可以准确地再现均值的季节性趋势每日最低温度，最高温度和降水量，但通常高估了每日降水的月均值和标准差；使用CDF映射方法可有效消除每日降水的时间均值和标准差以及温度预测中的偏差；但是，每月降水偏差校正后，总数仍然保持不变。每月流量模拟误差统计数据表明，由偏差校正后的缩减后的再分析数据产生的流量精度是令人满意的（即足以用于季节性至十年计划），但原始缩减后的再分析数据产生的流量精度却令人满意。对于水资源规划而言，是不令人满意的。因此，这项研究的结果表明，在直接将动态缩减规模的再分析数据直接使用（即无需使用本地数据进行偏差校正）来驱动水文模型之前，需要进一步改进大规模再分析数据和区域气候模型。但是，经过偏差校正的动态缩减数据显示出有望将当地历史气候观测记录扩展到水文模拟中。此外，这项研究的结果表明，类似的偏差校正的动态缩小的回顾性和未来的GCM预测应适合评估坦帕湾地区未来气候变化的潜在水文影响。

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