Scientific and operational value of alternative datasets in hydrological science

应用水文科学在很大程度上依赖于水文气象信息，特别是降水和末端温度的测量。然而，相关水文气象变量的时间序列存在视界短、数据缺失、误差和各种偏差等问题。这有时严重地限制了在流域尺度上充分表现水文过程的时空变异性的能力。已经提出了新的数据集，以缓解缺乏直接观测和时间和空间上不理想的覆盖范围。可用的数据集包括传统的基于站点的数据和三种替代数据集：网格数据集、观测数据的再分析和动态生成的数据。参与该项目的科学家和工业合作伙伴建议：调查水文科学中替代数据集的科学和操作价值。更确切地说，这个项目打算处理下列具体目标：

Applied hydrological science strongly relies on hydrometeorological information, and especially on measurements of precipitation end temperature. However, time series of relevant hydrometeorological variables are plagued with problems such as short temporal horizons, missing data, errors, and various biases. This limits, sometimes severely, the ability to adequately represent the spatial and temporal variability of the hydrological processes at the basin scale. New datasets have been proposed to palliate the lack of direct observations and the less than optimal coverage in time and space. Available datasets consists of the traditional station-based data and of three alternative datasets: gridded datasets, reanalyses of observations and dynamically generated data. The scientists and industrial partners involved in this project propose to: Investigate the scientific and operational value of alternative datasets in hydrological science. More precisely, this project intends to address the following specific objectives: 1-Compare traditional and new datasets with respect to their ability at providing useful information for key hydrological applications and research. and, for given hydrologic problems: 2-Further explore the advantages and drawbacks of observationally-based datasets 3-Estimate the added-value of dynamically generated datasets in hydrological studies The methodology is centered around five research themes that will compare the different datasets with respect to their ability at providing relevant information for hydrological modelling, extreme analysis, as well as provide insights on the optimization of observation networks and on the added-value of complex physically-based hydrology models.