Valladares-Castellanos Mariam, de Jesús Crespo Rebeca, Xu Y Jun, Douthat Thomas H
Department of Environmental Sciences, Louisiana State University, Baton Rouge, LA, USA.
Department of Renewable Natural Resources, Louisiana State University, Baton Rouge, LA, USA; Coastal Studies Institute, Louisiana State University, Baton Rouge, LA, USA.
Sci Total Environ. 2024 Nov 1;949:175111. doi: 10.1016/j.scitotenv.2024.175111. Epub 2024 Jul 28.
Modeling of watershed Ecosystem Services (ES) processes has increased greatly in recent years, potentially improving environmental management and decision-making by describing the value of nature. ES models may be sensitive to different conditions and, therefore, should ideally be validated against observed data for their use as a decision-support instrument. However, outcomes from such ES modeling are barely validated, making it difficult to assess uncertainties associated with the modeling and justify their actual usefulness to develop generalizable management recommendations. This study proposes a framework for the systematic validation of one of such tools, the InVEST Nutrient Delivery Model (NDR) for nutrient retention estimates. The framework is divided into three stages: 1) running the NDR model inputs, processes, and outputs; 2) building a long-term reference dataset from open access water quality observations; and 3) using the reference data for model calibration and validation. We applied this framework to twenty watersheds in the Commonwealth of Puerto Rico, where data availability resembles thar of watersheds across the United States. Long-term water quality data from monitoring stations facilitated model calibration and validation. Our framework provided a reproducible method to linking the vast monitoring network in the U.S. and its territories for evaluating the InVEST's NDR model performance. Beyond the framework development, this study found that the InVEST NDR model explained 62.7 % and 79.3 % of the variance in the total nitrogen and total phosphorus between 2000 and 2022, respectively, supporting the suitability of the model for watershed scale ecosystem services assessment. The findings can also serve as a reference to support the use of InVEST for other locations in the tropics without publically available monitoring data.
近年来，流域生态系统服务（ES）过程的建模有了大幅增加，通过描述自然价值，有望改善环境管理和决策。ES模型可能对不同条件敏感，因此，理想情况下应以观测数据进行验证，以便用作决策支持工具。然而，此类ES建模的结果几乎未经验证，难以评估与建模相关的不确定性，也难以证明其对制定可推广管理建议的实际有用性。本研究提出了一个框架，用于系统验证此类工具之一，即用于养分截留估算的InVEST养分输送模型（NDR）。该框架分为三个阶段：1）运行NDR模型的输入、过程和输出；2）根据开放获取的水质观测数据建立长期参考数据集；3）使用参考数据进行模型校准和验证。我们将此框架应用于波多黎各联邦的20个流域，那里的数据可用性与美国各地流域相似。监测站的长期水质数据有助于模型校准和验证。我们的框架提供了一种可重复的方法，将美国及其领土上庞大的监测网络联系起来，以评估InVEST的NDR模型性能。除了框架开发，本研究发现InVEST NDR模型分别解释了2000年至2022年总氮和总磷变化的62.7%和79.3%，支持该模型适用于流域尺度的生态系统服务评估。这些发现也可作为参考，支持在没有公开可用监测数据的热带其他地区使用InVEST。
