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The amount (soil moisture) and state (freeze-thaw) of the water in soil plays a pivotal role in global water, energy, and carbon cycles. The water content of the top few [...]

Soil moisture (SM) and evapotranspiration (ET) are key variables of the terrestrial water cycle with a strong relationship. This study examines remotely sensed soil moisture and evapotranspiration data assimilation (DA) with the aim of improving drought monitoring. Although numerous efforts have gone into assimilating satellite soil moisture observations into land surface models to improve their predictive skills, little attention has been given to the combined use of soil moisture and evapotranspiration to better characterize hydrologic fluxes. In this study, we assimilate two remotely sensed datasets, namely, Soil Moisture Operational Product System (SMOPS) and MODIS evapotranspiration (MODIS16 ET), at 1-km spatial resolution, into the VIC land surface model by means of an evolutionary particle filter method. To achieve this, a fully parallelized framework based on model and domain decomposition using a parallel divide-and-conquer algorithm was implemented. The findings show improvement in soil moisture predictions by multivariate assimilation of both ET and SM as compared to univariate scenarios. In addition, monthly and weekly drought maps are produced using the updated root-zone soil moisture percentiles over the Apalachicola-Chattahoochee-Flint basin in the southeastern United States. The model-based estimates are then compared against the corresponding U.S. Drought Monitor (USDM) archive maps. The results are consistent with the USDM maps during the winter and spring season considering the drought extents; however, the drought severity was found to be slightly higher according to DA method. Comparing different assimilation scenarios showed that ET assimilation results in wetter conditions comparing to open-loop and univariate SM DA. The multivariate DA then combines the effects of the two variables and provides an in-between condition. [ABSTRACT FROM AUTHOR]

Abstract: This research aims to present a general framework by which the most appropriate wavelet parameters including mother wavelet, vanishing moment, and decomposition level can be chosen for a joint wavelet transform and machine learning model. This study is organized in 2 parts: the first part presents an evolutionary Levenberg‐Marquardt neural network (ELMNN) model as the most effective machine learning configuration, and the second part describes how the wavelet transform can be effectively embedded with the developed ELMNN model. In this research, the rainfall and runoff time series data of 2 distinct watersheds at 2 different time scales (daily and monthly) were used to build the proposed hybrid wavelet transform and ELMNN model. The conclusions of this study showed that the Daubechies wavelet more than other wavelet families is capable to extract the informative features of hydrologic series. The vanishing moment and decomposition level of this mother wavelet should be selected based on the watershed behavior and the time resolution of rainfall and runoff time series, respectively. The verification results for both watersheds at daily and monthly time scales indicated root mean square error, peak value criterion, low value criterion, and Kling‐Gupta efficiency as about 0.017, 0.021, 0.023, and 0.91, respectively. [ABSTRACT FROM AUTHOR]

AbstractTo separate the contributions of climate changes and human activities to alterations in flow for the Ahar Chay watershed in Iran, change points of Normalized Difference Vegetation Index (NDVI) and evapotranspiration (ET) were distinguished before and after significant alterations. The Budyko and Soil and Water Assessment Tool (SWAT) were used to find the reduction in runoff. Newly developed Penman-Monteith-Leuning (PML) ET data was used to consider plant types. Results revealed increase in NDVI, coinciding with the change point in 1998, as well as ET in 1999. The significance of the Sattarkhan Dam within watershed became evident when distinguishing the impacts in upstream and downstream areas of the dam. Results also indicated that climate changes contributed significantly to upstream flow reduction (83.5%), whereas downstream reductions were primarily linked to human-related impacts (59.7-82.5%). Assuming no dam interception, 2007 became the change point, signifying 37.5% reduction in average flow before and after this point due to the increased vegetation cover. [ABSTRACT FROM AUTHOR]