Abstract Twelve actual evaporation datasets are evaluated for their ability to improve the performance of the fully distributed mesoscale Hydrologic Model (mHM). The datasets consist of satellite-based diagnostic models (MOD16A2, SSEBop, ALEXI, CMRSET, SEBS), satellite-based prognostic models (GLEAM v3.2a, GLEAM v3.3a, GLEAM v3.2b, GLEAM v3.3b), and reanalysis (ERA5, MERRA-2, JRA-55). Four distinct multivariate calibration strategies (basin-average, pixel-wise, spatial bias-accounting and spatial bias-insensitive) using actual evaporation and streamflow are implemented, resulting in 48 scenarios whose results are compared with a benchmark model calibrated solely with streamflow data. A process-diagnostic approach is adopted to evaluate the model responses with in-situ data of streamflow and independent remotely sensed data of soil moisture from ESA-CCI and terrestrial water storage from GRACE. The method is implemented in the Volta River basin, which is a data scarce region in West Africa, for the period from 2003 to 2012. Results show that the evaporation datasets have a good potential for improving model calibration, but this is dependent on the calibration strategy. All the multivariate calibration strategies outperform the streamflow-only calibration. The highest improvement in the overall model performance is obtained with the spatial bias-accounting strategy (+29%), followed by the spatial bias-insensitive strategy (+26%) and the pixel-wise strategy (+24%), while the basin-average strategy (+20%) gives the lowest improvement. On average, using evaporation data in addition to streamflow for model calibration decreases the model performance for streamflow (-7%), which is counterbalance by the increase in the performance of the terrestrial water storage (+11%), temporal dynamics of soil moisture (+6%) and spatial patterns of soil moisture (+89%). In general, the top three best performing evaporation datasets are MERRA-2, GLEAM v3.3a and SSEBop, while the bottom three datasets are MOD16A2, SEBS and ERA5. However, performances of the evaporation products diverge according to model responses and across climatic zones. These findings open up avenues for improving process representation of hydrological models and advancing the spatiotemporal prediction of floods and droughts under climate and land use changes.

中文翻译：

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卫星和再分析蒸发数据集在各种模型校准策略下用于水文建模的潜力

摘要 评估了 12 个实际蒸发数据集提高完全分布式中尺度水文模型 (mHM) 性能的能力。数据集包括基于卫星的诊断模型（MOD16A2、SSEBop、ALEXI、CMRSET、SEBS）、基于卫星的预后模型（GLEAM v3.2a、GLEAM v3.3a、GLEAM v3.2b、GLEAM v3.3b）和再分析（ERA5、MERRA-2、JRA-55）。实施了使用实际蒸发和流量的四种不同的多元校准策略（流域平均、像素方式、空间偏差核算和空间偏差不敏感），产生了 48 个场景，其结果与仅使用流量数据校准的基准模型进行了比较。采用过程诊断方法来评估模型响应，其中包括来自 ESA-CCI 的原位流量数据和来自 ESA-CCI 的土壤水分和来自 GRACE 的陆地储水量的独立遥感数据。该方法在 2003 年至 2012 年期间在西非数据稀缺地区沃尔特河流域实施。 结果表明蒸发数据集具有改善模型校准的良好潜力，但这取决于校准战略。所有多元校准策略都优于仅流校准。使用空间偏差计算策略 (+29%) 获得了整体模型性能的最高改进，其次是空间偏差不敏感策略 (+26%) 和像素策略 (+24%)，而流域平均策略 (+20%) 的改进最低。平均而言，使用蒸发数据和水流进行模型校准会降低水流的模型性能 (-7%)，这与陆地蓄水性能的增加 (+11%)、土壤水分的时间动态相抵消(+6%) 和土壤水分的空间格局 (+89%)。一般来说，表现最好的前三个蒸发数据集是 MERRA-2、GLEAM v3.3a 和 SSEBop，而后三个数据集是 MOD16A2、SEBS 和 ERA5。然而，蒸发产物的性能根据模型响应和跨气候带而不同。