Reliability Analysis in Presence of Random Variables and Fuzzy Variables

For mixed uncertainties of random variables and fuzzy variables in engineering, three indices, that is, interval reliability index, mean reliability index, and numerical reliability index, are proposed to measure safety of structure. Comparing to the reliability membership function for measuring the safety in case of mixed uncertainties, the proposed indices are more intuitive and easier to represent the safety degree of the engineering structure, and they are more suitable for the reliability design in the case of the mixed uncertainties. The differences and relations among three proposed indices are investigated, and their applicability is compared. Furthermore, a technique based on the probability density function evolution method is employed to improve the computational efficiency of the proposed indices. At last, a numerical example and two engineering examples are illustrated to demonstrate the feasibility, reasonability, and efficiency of the computational technique of the proposed indices

Estimation and Assessment of the Root Zone Soil Moisture from Near-Surface Measurements over Huai River Basin

Root zone soil moisture (RZSM) is a vital variable for agricultural production, water resource management and runoff prediction. Satellites provide large-scale and long-term near-surface soil moisture retrievals, which can be used to estimate RZSM through various methods. In this study, we tested the utility of an exponential filter (ExpF) using in situ soil moisture by optimizing the optimal characteristic time length T_opt for different soil depths. Furthermore, the parameter analysis showed that T_opt correlated negatively with precipitation and had no significant correlation with selected soil properties. Two approaches were taken to obtain T_opt: (1) optimization of the Nash–Sutcliffe efficiency coefficient (NSE); (2) calculation based on annual average precipitation. The precipitation-based T_pre outperformed the station-specific T_opt and stations-averaged T_opt. To apply the ExpF on grid scale, the precipitation-based T_pre considering spatial variability was adopted in the ExpF to obtain RZSM from a new soil moisture dataset RF_SMAP_L3_P (Random Forest Soil Moisture Active Passive_L3_Passive) continuous in time and space over Huai River Basin. Finally, the performance of RF_SMAP_L3_P RZSM (0–100 cm) was evaluated using in situ measurements and compared with mainstream products, for instance, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity Level 4 (SMOS L4) RZSM. The results indicated that RF_SMAP_L3_P RZSM could captured the temporal variation of measured RZSM best with R value of 0.586, followed by SMAP L4, which had the lowest bias value of 0.03, and SMOS L4 significantly underestimated the measured RZSM with bias value of −0.048 in the basin. Higher accuracy of RF_SMAP_L3_P RZSM was found in the flood period compared with the non-flood period, which indicates a better application for ExpF in wetter weather conditions.This article is published as Liu E, Zhu Y, Lü H, Horton R, Gou Q, Wang X, Ding Z, Xu H, Pan Y. Estimation and Assessment of the Root Zone Soil Moisture from Near-Surface Measurements over Huai River Basin. Atmosphere. 2023; 14(1):124. https://doi.org/10.3390/atmos14010124. Posted with permission.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)

Estimation and Assessment of the Root Zone Soil Moisture from Near-Surface Measurements over Huai River Basin

Root zone soil moisture (RZSM) is a vital variable for agricultural production, water resource management and runoff prediction. Satellites provide large-scale and long-term near-surface soil moisture retrievals, which can be used to estimate RZSM through various methods. In this study, we tested the utility of an exponential filter (ExpF) using in situ soil moisture by optimizing the optimal characteristic time length T_opt for different soil depths. Furthermore, the parameter analysis showed that T_opt correlated negatively with precipitation and had no significant correlation with selected soil properties. Two approaches were taken to obtain T_opt: (1) optimization of the Nash–Sutcliffe efficiency coefficient (NSE); (2) calculation based on annual average precipitation. The precipitation-based T_pre outperformed the station-specific T_opt and stations-averaged T_opt. To apply the ExpF on grid scale, the precipitation-based T_pre considering spatial variability was adopted in the ExpF to obtain RZSM from a new soil moisture dataset RF_SMAP_L3_P (Random Forest Soil Moisture Active Passive_L3_Passive) continuous in time and space over Huai River Basin. Finally, the performance of RF_SMAP_L3_P RZSM (0–100 cm) was evaluated using in situ measurements and compared with mainstream products, for instance, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity Level 4 (SMOS L4) RZSM. The results indicated that RF_SMAP_L3_P RZSM could captured the temporal variation of measured RZSM best with R value of 0.586, followed by SMAP L4, which had the lowest bias value of 0.03, and SMOS L4 significantly underestimated the measured RZSM with bias value of −0.048 in the basin. Higher accuracy of RF_SMAP_L3_P RZSM was found in the flood period compared with the non-flood period, which indicates a better application for ExpF in wetter weather conditions

Application of an improved spatio-temporal identification method of flash droughts

Flash droughts are regional phenomena that can manifest in large areas with rapid intensification for a period of time. Few studies have considered the spatial pathways of flash droughts or their drought period. This study uses the five criteria based on the standardized evaporative stress ratio (SESR) method to identify flash droughts, and when a SESR value recovers to the 20th percentile, the flash drought is considered to terminate. To define spatially continuous flash droughts accurately, the usual order of first calculating drought patches and then identifying flash droughts is reversed to first identify flash droughts on the grid and then determine flash drought patches. In addition, this study tracks the spatial paths of flash droughts via centroid transfers of flash drought patches. Using MOD16 data, the methodology is evaluated by analyzing the regional characteristics of flash droughts in the Huaibei Plain of China from 2001 to 2019. The flash droughts in this region most frequently tracked in the northeast and west. The average flash drought duration was 31 days, of which the rapid intensification period was 18 days and drought period was 14 days. Flash drought events in this region mostly occurred in May, August and October, and east to west transition and non-transitions, which accounted for 44% and 22%, respectively, were the main spatial track paths. Only 26% of flash drought events transitioned to long term drought events. This study expands our knowledge of the evolution process of flash droughts to space-time dimensions, which is essential for flash droughts early warning and agricultural water management.This is a manuscript of an article published as Gou, Qiqi, Yonghua Zhu, Haishen Lü, Robert Horton, Xiaohan Yu, Haoqiang Zhang, Xiaoyi Wang et al. "Application of an improved spatio-temporal identification method of flash droughts." Journal of Hydrology (2021): 127224. doi:10.1016/j.jhydrol.2021.127224. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License

Evaluation of root zone soil moisture products over the Huai River basin

International audienceAbstract. Root zone soil moisture (RZSM) is critical for water resource management, drought monitoring and sub-seasonal flood climate prediction. While RZSM is not directly observable from space, several RZSM products are available and widely used at global and continental scales. This study conducts a comprehensive and quantitative evaluation of eight RZSM products using observations from 58 in situ soil moisture stations over the Huai River basin (HRB) in China. Attention is drawn to the potential factors that contribute to the uncertainties of model-based RZSM, including the errors in atmospheric forcing, vegetation parameterizations, soil properties and spatial scale mismatch. The results show that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) outperforms the other RZSM products with the highest correlation coefficient (R= 0.69) and the lowest unbiased root mean square error (ubRMSE = 0.018 m3 m−3), while SMOS Level 4 (L4) RZSM shows the worst performance among eight RZSM products. The RZSM products based on land surface models generally perform better in the wet season than in the dry season due to the enhanced ability to capture of the temporal dynamics of in situ observations in the wet season and the inertia of remaining high soil moisture values even in the dry season, while the SMOS L4 RZSM product, derived from SMOS L3 surface moisture (SSM) combined with an exponential filter method, performs better in the dry season due to the attenuated ground microwave radiation signal caused by the increased water vapour absorption and scattering in the wet season. The underestimated SMOS L3 SSM triggers the underestimation of RZSM in SMOS L4. The overestimated RZSM products based on land surface models could be associated with the overestimated precipitation amounts and frequency, the underestimated air temperature, and the underestimated ratio of transpiration to the total terrestrial evapotranspiration. In addition, the biased soil properties and flawed vegetation parameterizations affect the hydrothermal transport processes represented in different land surface models (LSMs) and lead to inaccurate soil moisture simulation. The scale mismatch between point and footprint also introduces representative errors. The comparison of frequency of normalized soil moisture between RZSM products and in situ observations indicates that the LSMs should focus on reducing the frequency of wet soil moisture, increasing the frequency of dry soil moisture and the ability to capture the frequency peak of soil moisture. The study provides some insights into how to improve the ability of land surface models to simulate the land surface states and fluxes by taking into account the issues mentioned above. Finally, these results can be extrapolated to other regions located in similar climate zones, as they share similar precipitation patterns that dominate the terrestrial water cycle