School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan, China.
Institute of International Rivers and Eco-security, Yunnan University, Kunming, Yunnan, China.
PeerJ. 2023 Mar 14;11:e14940. doi: 10.7717/peerj.14940. eCollection 2023.
Flood prediction for ungauged karst wetland is facing a great challenge. How to build a wetland hydrological model when there is a lack of basic hydrological data is the key to dealing with the above challenge. Napahai wetland is a typical ungauged karst wetland. In ungauged wetland/condition, this article used the wetland open water area (OWA) extracted from Landsat remote sensing images during 1987-2018 to characterize the hydrological characteristics of Napahai wetland. The local daily precipitation in the 1987-2018 rainy season (June-October) was used to set the variables. Based on the following hypothesis: in the rainy season, the OWA of the Napahai wetland rises when there is an increase in accumulated precipitation (AP), two data-driven models were established. The study took the area difference (AD) between two adjacent OWAs as the dependent variable, the accumulated precipitation (AP) within the acquisition time of two adjacent OWAs, and the corresponding time interval (TI) of the OWA as explanatory variables. Two data-driven models (a piecewise linear regression model and a decision tree model) were established to carry out flood forecasting simulations. The decision tree provided higher goodness of fit while the piecewise linear regression could offer a better interpretability between the variables which offset the decision tree. The results showed that: (1) the goodness of fit of the decision tree is higher than that of the piecewise linear regression model (2) the piecewise linear model has a better interpretation. When AP increased by 1 mm, the average AD increased by 2.41 ha; when TI exceeded 182 d and increased by 1 d, the average AD decreased to 3.66 ha. This article proposed an easy decision plan to help the local Napahai water managers forecast floods based on the results from the two models above. In addition, the modelling method proposed in this article, based on the idea of difference for non-equidistant time series, can be applied to karst wetland hydrological simulation problems with data acquisition difficulty.
对无测站岩溶湿地进行洪水预测面临着巨大的挑战。在缺乏基本水文数据的情况下，如何建立湿地水文模型是应对上述挑战的关键。纳帕海湿地是一个典型的无测站岩溶湿地。在无测站湿地/条件下，本文利用 1987 年至 2018 年期间从 Landsat 遥感图像中提取的湿地开阔水面（OWA）来描述纳帕海湿地的水文特征。利用 1987 年至 2018 年雨季（6 月至 10 月）的当地逐日降水来设置变量。基于以下假设：在雨季，当累积降水量（AP）增加时，纳帕海湿地的 OWA 会上升，建立了两个数据驱动模型。该研究以两个相邻 OWA 之间的面积差（AD）为因变量，以两个相邻 OWA 的采集时间内的累积降水量（AP）和 OWA 的相应时间间隔（TI）为解释变量。建立了两个数据驱动模型（分段线性回归模型和决策树模型）来进行洪水预测模拟。决策树提供了更高的拟合优度，而分段线性回归可以更好地解释变量之间的关系，从而弥补了决策树的不足。结果表明：（1）决策树的拟合优度高于分段线性回归模型；（2）分段线性模型具有更好的解释性。当 AP 增加 1mm 时，平均 AD 增加 2.41ha；当 TI 超过 182d 并增加 1d 时，平均 AD 减少到 3.66ha。本文提出了一个简单的决策方案，根据上述两个模型的结果，帮助当地纳帕海的水资源管理者进行洪水预测。此外，本文提出的建模方法，基于非等间距时间序列差值的思想，可以应用于数据采集困难的岩溶湿地水文模拟问题。
