Evaluation of time stability indices for soil water storage upscaling
Evaluation of time stability indices for soil water storage upscaling
复制标题
土壤储水放大时间稳定性指标评价
DOI：
10.1016/j.jhydrol.2012.09.050
复制
发表时间：
2012-12-19
影响因子：
6.4
通讯作者：
Si, Bing Cheng
中科院分区：
文献类型：
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作者：
Hu, Wei;Tallon, Lindsay K.;Si, Bing Cheng
时间稳定性指数（TSI）是评价点尺度土壤水储量时间稳定性的常用指标。本研究的目的是评价7个TSI，以估计平均SWS。包括用于间接估计的六个指数（即，相对差的标准偏差（SDRD）、平均绝对偏差误差（MABE）、相对含水量的90%经验容许区间的宽度（T）、卡方统计量（chi（2））、均方根差（D）和时间变异系数（CV）;以及用于直接估计的一个指标（即，均方根误差（RMSE））。选取均方根偏差（RMSD）、Nash-Sutcliffe效率系数（NSCE）、决定系数（R-2）、绝对平均差（BIAS）和相对偏差误差（RBIAS）等5个拟合优度指数（GFI）评价校准期和验证期的平均SWS估计质量。考虑到不同的取样开始日期，确定了确定时间最稳定地点所需的最低取样次数。利用加拿大草原景观和中国黄土高原0-1.0 m层SWS数据对TSIs进行了评价。结果表明，无论使用何种GFI，MABE、chi（2）、D和CVt均优于SDRD和T。若采用RMSD和NSCE，D为最佳TSI。如果采用BIAS和RBIAS，MABE是最好的TSI。间接法估算的平均SWS比直接法更准确。对于这两个研究领域，需要确定的最稳定的时间位置的最小采样次数随SWS测量的开始日期而变化，一般需要五到七个采样时间来确定最稳定的时间位置与D和MABE。(C)2012爱思唯尔有限公司版权所有。
Time stability index (TSI) is usually used to evaluate time stability of soil water storage (SWS) at point scales for SWS upscaling. The objective of this study was to evaluate seven TSIs for estimating mean SWS. Included were six indices used for indirect estimation (i.e., standard deviation of relative difference (SDRD), mean absolute bias error (MABE), width of the 90% empirical tolerance interval of relative water content (T), chi-squared statistic (chi(2)), root-mean-squared differences (D), and temporal coefficient of variability (CV,)); and one index used for direct estimation (i.e., root mean square error (RMSE)). Five goodness-of-fit indices (GFIs), including root mean squared deviations (RMSD), Nash-Sutcliffe coefficient of efficiency (NSCE), coefficient of determination (R-2), absolute mean difference (BIAS), and relative bias error (RBIAS) were selected for evaluating mean SWS estimation quality in both calibration and validation periods. The minimum number of sampling occasions needed to identify the most time-stable location was identified considering different starting dates of sampling. Evaluation of the TSIs was performed using SWS data of 0-1.0 m layer obtaining from the Canadian Prairie landscape and the Chinese Loess Plateau. The results showed that MABE, chi(2), D, and CVt outperformed SDRD and T irrespective of the GFI used. If RMSD and NSCE were used, D was the best TSI. If BIAS and RBIAS were adopted, MABE was the best TSI. Mean SWS estimation by the indirect method was more accurate than that by the direct method. For both study areas, the minimum number of sampling occasions needed to identify the most time-stable location varied with starting dates of SWS measurement, and generally five to seven sampling occasions was needed to identify the most time-stable location with D and MABE. (C) 2012 Elsevier B.V. All rights reserved.