Calibration techniques were investigated on how to best optimize a 149 parameter, distributed hydrological model of the Lake of the Woods – Rainy Lake (LOWRL) watershed. Single objective calibrations based only on streamflow, only on reservoir inflows or average performance of both observation types, optimized using the Dynamically Dimensioned Search (DDS) algorithm, were compared with a multi-objective optimization approach with both observation types using the Pareto Archived DDS (PADDS) algorithm. Results from synthetic calibration tests against a known solution showed that PADDS was able to repeatedly find solutions with streamflow and reservoir inflow Nash-Sutcliffe coefficients of more than 0.95 and 0.99 using 2000 and 8000 model evaluations, respectively, demonstrating the effectiveness of PADDS on a limited calibration budget. When the LOWRL model was calibrated to actual observations with PADDS using 2000 evaluations, the algorithm repeatedly returned solutions with validation period streamflow and reservoir inflow Nash-Sutcliffe coefficients of approximately 0.71 and 0.87, respectively. Results demonstrate the capabilities of PADDS to reasonably calibrate a large dimensional hydrologic model on a restricted budget of 2000 model evaluations and highlight the importance of calibrating to both reservoir inflows and streamflows simultaneously. Considering the comparative results under multiple calibration trials, the multi-objective formulation solved by PADDS is shown to generate equivalent quality results as a weighted single objective approach solved by DDS (averaging reservoir inflow and streamflow calibration objectives).

研究了如何最佳优化149参数的伍兹湖-雨湖（LOWRL）分水岭的分布式水文模型的校准技术。使用动态尺寸搜索（DDS）算法优化的仅基于流量，仅基于储层流入量或两种观测类型的平均性能的单目标标定与使用帕累托归档DDS对两种观测类型的多目标优化方法进行了比较（ PADDS）算法。针对已知解决方案的综合校准测试的结果表明，PADDS能够分别使用2000和8000模型评估来反复找到流量和储层流入Nash-Sutcliffe系数分别大于0.95和0.99的解决方案，这证明了PADDS在有限范围内的有效性校准预算。当使用2000年评估将PADDS的LOWRL模型校准为实际观测值时，该算法反复返回具有有效期流量和储层流入Nash-Sutcliffe系数分别约为0.71和0.87的解。结果表明，PADDS能够在2000年模型评估的有限预算内合理地校准大型水文模型，并强调了同时校准水库入流和水流的重要性。考虑到多次校准试验下的比较结果，通过PADDS解决的多目标公式显示出与DDS解决的加权单目标方法（平均储层流入量和流量校准目标）一样的质量结果。