Transient Storage Model Parameter Optimization Using the Simulated Annealing Method
Transient Storage Model Parameter Optimization Using the Simulated Annealing Method
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DOI：
10.1029/2022wr032018
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发表时间：
2022-06
影响因子：
5.4
通讯作者：
C. Tsai;D. Rucker;S. Brooks;T. Ginn;K. Carroll
中科院分区：
文献类型：
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作者：
C. Tsai;D. Rucker;S. Brooks;T. Ginn;K. Carroll
河流的潜流交换对生态系统功能至关重要，如河流廊道的营养循环，特别是对于缓慢移动或小型河流系统。暂态存储模型（TSM）已被广泛用于虚拟交换的建模。对于潜流交换，TSM校准通常用于估计四个参数，包括质量交换系数、色散系数、流横截面积和潜流区横截面积。先前的研究对TSM逆问题的非唯一性提出了担忧，即出现不同的参数向量，导致TSM解以相同的误差再现了观察到的流中示踪剂突破曲线（BTC）。这导致在确定未知参数矢量值时，即使存在全局最优值，也具有实际的不可辨识性，并且参数优化实际上变得非唯一。为了解决这个问题，我们应用模拟退火方法校准TSM到btc，因为它不容易受到局部最小值引起的不可识别性的影响。开发了一个具有已知参数的假设（或合成）示踪测试数据集，以证明模拟退火方法找到全局最小参数向量的能力，并且即使输入数据被高达10%的噪声修改而不增加收敛所需的迭代次数，它也能识别出“假设真实”的全局最小参数向量。然后，在田纳西州东叉Poplar Creek进行了两次流中示踪剂测试，对模拟退火TSM进行了校准。由于模拟退火对全局最小参数向量的搜索能力，因此确定模拟退火是量化TSM参数向量的合适方法。
Hyporheic exchange in streams is critical to ecosystem functions such as nutrient cycling along river corridors, especially for slowly moving or small stream systems. The transient storage model (TSM) has been widely used for modeling of hyporheic exchange. TSM calibration, for hyporheic exchange, is typically used to estimate four parameters, including the mass exchange rate coefficient, the dispersion coefficient, stream cross‐sectional area, and hyporheic zone cross‐sectional area. Prior studies have raised concerns regarding the non‐uniqueness of the inverse problem for the TSM, that is, the occurrence of different parameter vectors resulting in TSM solution that reproduces the observed in‐stream tracer break through curve (BTC) with the same error. This leads to practical non‐identifiability in determining the unknown parameter vector values even when global‐optimal values exist, and the parameter optimization becomes practically non‐unique. To address this problem, we applied the simulated annealing method to calibrate the TSM to BTCs, because it is less susceptible to local minima‐induced non‐identifiability. A hypothetical (or synthetic) tracer test data set with known parameters was developed to demonstrate the capability of the simulated annealing method to find the global minimum parameter vector, and it identified the “hypothetically‐true” global minimum parameter vector even with input data that were modified with up to 10% noise without increasing the number of iterations required for convergence. The simulated annealing TSM was then calibrated using two in‐stream tracer tests conducted in East Fork Poplar Creek, Tennessee. Simulated annealing was determined to be appropriate for quantifying the TSM parameter vector because of its search capability for the global minimum parameter vector.