Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change
Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change
复制标题
计量不良流域分布式水文模型的校准方法：对气候变化下径流预测的影响
DOI：
10.5194/hess-19-857-2015
复制
发表时间：
2015
影响因子：
6.3
通讯作者：
C. Brown
中科院分区：
文献类型：
--
作者：
S. Wi;Y. Yang;S. Steinschneider;A. Khalil;C. Brown
本研究测试了空间分布式水文模型校准策略的性能和不确定性，以提高模型模拟精度并了解稀疏测量流域内部未监测地点的预测不确定性。该研究是使用应用于喀布尔河流域的分布式 HYMOD 水文模型 (HYMOD_DS) 进行的。进行了几次校准实验，以了解与不同校准选择相关的收益和成本，包括（1）在模型拟合过程中是否应同时使用多站点计量数据还是逐步使用多点计量数据，（2）参数复杂性增加的影响，以及（3）仅使用流域出口处的计量数据来估计内部流域流量的潜力。在气候变化下的水文预测的背景下考虑不同校准策略的影响。为了解决研究问题，利用高性能计算来管理高维优化问题产生的计算负担。这项研究得出了一些有趣的结果。多站点数据的同时使用被证明可以比逐步方法改进校准，并且两种多站点方法都远远超过仅基于流域出口的校准。流域出口校准可能导致对 21 世纪中叶水流的预测与多站点校准策略下的预测存在很大偏差，这支持在数据稀缺地区使用分布式模型进行气候变化影响评估时谨慎使用。令人惊讶的是，尽管确实出现了参数等效性，但参数复杂性的增加并没有显着增加水流预测的不确定性。结果表明，如果存在结构不确定性，则增加（过度）参数复杂性并不总是会导致预测不确定性增加。未来水流的最大不确定性是由于气候模型之间预测气候的变化造成的，这大大超过了校准不确定性。
This study tests the performance and uncertainty of calibration strategies for a spatially distributed hydrologic model in order to improve model simulation accuracy and understand prediction uncertainty at interior ungaged sites of a sparsely gaged watershed. The study is conducted using a distributed version of the HYMOD hydrologic model (HYMOD_DS) applied to the Kabul River basin. Several calibration experiments are conducted to understand the benefits and costs associated with different calibration choices, including (1) whether multisite gaged data should be used simultaneously or in a stepwise manner during model fitting, (2) the effects of increasing parameter complexity, and (3) the potential to estimate interior watershed flows using only gaged data at the basin outlet. The implications of the different calibration strategies are considered in the context of hydrologic projections under climate change. To address the research questions, high-performance computing is utilized to manage the computational burden that results from high-dimensional optimization problems. Several interesting results emerge from the study. The simultaneous use of multisite data is shown to improve the calibration over a stepwise approach, and both multisite approaches far exceed a calibration based on only the basin outlet. The basin outlet calibration can lead to projections of mid-21st century streamflow that deviate substantially from projections under multisite calibration strategies, supporting the use of caution when using distributed models in data-scarce regions for climate change impact assessments. Surprisingly, increased parameter complexity does not substantially increase the uncertainty in streamflow projections, even though parameter equifinality does emerge. The results suggest that increased (excessive) parameter complexity does not always lead to increased predictive uncertainty if structural uncertainties are present. The largest uncertainty in future streamflow results from variations in projected climate between climate models, which substantially outweighs the calibration uncertainty.