Soil water storage prediction at high space–time resolution along an agricultural hillslope
Soil water storage prediction at high space–time resolution along an agricultural hillslope
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农业山坡高时空分辨率土壤储水量预测
DOI：
10.1016/j.agwat.2015.11.012
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发表时间：
2016-02
影响因子：
6.7
通讯作者：
Xinhua Peng
中科院分区：
文献类型：
--
作者：
Dongdong Wang;Tahir Muhammad;Asim Biswas;Xinhua Peng
高时空分辨率的土壤水储量信息对于理解众多的水文、生物和化学过程至关重要。然而，获得这样的信息是时间和成本密集型的，由于强可变性的SWS。我们假设可以使用时间稳定性（TS）概念以高时空分辨率和低成本准确预测SWS信息。从2013年7月至2015年7月，在4个位置使用自动测量系统和103个位置手动测量了沿着3.1公顷斜坡下至1.0 m深度的不同土层的含水量。将这些值乘以深度，将其转换为SWS（0-0.2、0.2-0.5、0.5-1.0和0-1.0 m）。SWS的空间分布具有时间稳定性。利用高时空分辨率和低时空分辨率资料以及高时空分辨率和低时空分辨率资料，在高时空分辨率条件下对SWS值进行了预测。第一种方法（M1）是基于四个自动测量的位置中的时间上最稳定的位置（MTSL）。第二种方法（M2）从107个位置，包括103个人工测量的四个土壤层的MTSL识别。M2的MTSL分配的高时间分辨率数据的基础上的MTSL之间的关系M1和M2在每个土壤层。一旦确定了这两种方法的MTSL和时间稳定性关系（TSR），一个自动测量位置（A4）的SWS数据就足以预测边坡在任何时间的空间平均和空间分布SWS。虽然M1的预测误差通常是可以接受的，但在大多数研究病例中，M2比M1更准确。M2的估计误差均小于10%，一般小于5%。在所调查的4个土层中，M2在0-0.2和0-1.0 m土层中的表现优于M1，而在0.5-1.0 m土层中，两种方法的结果相当。同时，尽管M1在0.2-0.5 m土层中的表现略优于M2，但两者都表现良好。该方法预测精度高，成本低，可提高干旱预警和农业水资源管理的预测精度。
Soil water storage (SWS) information at high space–time resolution is critical for understanding numerous hydrological, biological and chemical processes. However, obtaining such information is time- and cost-intensive due to the strong variability of SWS. We hypothesized that SWS information could be predicted accurately at high space–time resolution and low cost using the temporal stability (TS) concept. The water contents of different soil layers down to 1.0 m in depth were measured along a 3.1 ha slope from July 2013 to July 2015 at 4 locations using automatic measurement systems and at 103 locations manually. These values were multiplied by depth to convert them into SWS (0–0.2, 0.2–0.5, 0.5–1.0, and 0–1.0 m). The spatial patterns of SWS were temporally stable. The SWS values were predicted at high space–time resolution by combining high-space and low-time resolution data and high-time and low-space resolution data using two methods. The first method (M1) was based on the most temporally stable locations (MTSLs) among four auto-measured locations. The second method (M2) identified the MTSLs from 107 locations including 103 manually measured locations of the four soil layers. The MTSLs of M2 were assigned high-time resolution data based on the relationships between the MTSLs of M1 and M2 at each soil layer. Once the MTSL and temporal stability relationship (TSR) of these two methods were identified, the SWS data for one auto-measured location (A4) were sufficient to predict the spatially averaged and spatially distributed SWS for the slope at any time. Although the predictive errors for M1 were generally acceptable, M2 was more accurate than M1 in most of the cases studied. The estimation errors for M2 were all less than 10% and were generally less than 5%. Among the four investigated soil layers, M2 outperformed M1 for the 0–0.2 and 0–1.0 m soil layers, and the two methods yielded comparable results for the 0.5–1.0 m soil layer. Meanwhile, although M1 slightly outperformed M2 for the 0.2–0.5 m soil layer, both performed well. This method for predicting SWS at high accuracy and low cost could improve the prediction accuracy of early drought warnings and agricultural water resources management.
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