The importance of parameterization when simulating the hydrologic response of vegetative land-cover change
The importance of parameterization when simulating the hydrologic response of vegetative land-cover change
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模拟植被土地覆盖变化的水文响应时参数化的重要性
DOI：
10.5194/hess-21-3975-2017
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发表时间：
2017
影响因子：
6.3
通讯作者：
J. R. Banta
中科院分区：
文献类型：
--
作者：
J. White;Victoria Stengel;Samuel H. Rendon;J. R. Banta
抽象的。水文系统的计算机模型经常用于研究土地覆盖变化的水文响应。如果建模结果用于为资源管理决策提供信息，那么提供模拟响应中的不确定性的可靠估计是一个重要的考虑因素。在这里，我们研究了参数化（必然是主观过程）对土地覆盖变化模拟水文响应的不确定性估计的重要性。具体来说，我们将土壤水评估工具 (SWAT) 模型应用于德克萨斯州南部 1.4 平方公里的流域，以研究灌木丛管理（机械去除木本植物）的模拟水文响应，这是一种离散的土地覆盖变化。在进行灌丛管理活动之前和之后，对流域进行了仪器仪表测量，并且可以获得降水量、水流和蒸散量 (ET) 的估计值；这些数据用于调节和验证模型。通过构建两个模型来评估参数化在刷子管理模拟中的作用，一个模型有 12 个可调参数（减少参数化），另一个模型有 1305 个可调参数（完全参数化）。这两个模型均经过全局敏感性分析以及蒙特卡罗和广义似然不确定性估计 (GLUE) 调节，以识别重要的模型输入并估计与灌木管理相关的多个利益量的不确定性。这两种参数化的许多实现都被认为是行为性的，因为它们根据纳什-萨特克利夫模型效率系数、百分比偏差和决定系数很好地再现了每日平均水流。然而，在对日平均水流进行调节后，模拟灌丛管理产生的总体积蒸散量差异仍然高度不确定，这表明单独的水流数据不足以告知对灌丛管理模拟结果影响最大的模型输入。此外，与全参数化模型相比，简化参数化模型严重低估了总体积 ET 差异的不确定性；总体积 ET 差异是评估刷子管理结果的主要指标。简化参数化模型未能提供稳健的不确定性估计，这表明在尝试量化土地覆盖变化模拟中的不确定性时参数化的重要性。
Abstract. Computer models of hydrologic systems are frequently used to investigate the hydrologic response of land-cover change. If the modeling results are used to inform resource-management decisions, then providing robust estimates of uncertainty in the simulated response is an important consideration. Here we examine the importance of parameterization, a necessarily subjective process, on uncertainty estimates of the simulated hydrologic response of land-cover change. Specifically, we applied the soil water assessment tool (SWAT) model to a 1.4 km2 watershed in southern Texas to investigate the simulated hydrologic response of brush management (the mechanical removal of woody plants), a discrete land-cover change. The watershed was instrumented before and after brush-management activities were undertaken, and estimates of precipitation, streamflow, and evapotranspiration (ET) are available; these data were used to condition and verify the model. The role of parameterization in brush-management simulation was evaluated by constructing two models, one with 12 adjustable parameters (reduced parameterization) and one with 1305 adjustable parameters (full parameterization). Both models were subjected to global sensitivity analysis as well as Monte Carlo and generalized likelihood uncertainty estimation (GLUE) conditioning to identify important model inputs and to estimate uncertainty in several quantities of interest related to brush management. Many realizations from both parameterizations were identified as behavioral in that they reproduce daily mean streamflow acceptably well according to Nash–Sutcliffe model efficiency coefficient, percent bias, and coefficient of determination. However, the total volumetric ET difference resulting from simulated brush management remains highly uncertain after conditioning to daily mean streamflow, indicating that streamflow data alone are not sufficient to inform the model inputs that influence the simulated outcomes of brush management the most. Additionally, the reduced-parameterization model grossly underestimates uncertainty in the total volumetric ET difference compared to the full-parameterization model; total volumetric ET difference is a primary metric for evaluating the outcomes of brush management. The failure of the reduced-parameterization model to provide robust uncertainty estimates demonstrates the importance of parameterization when attempting to quantify uncertainty in land-cover change simulations.