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Soil moisture can be obtained from in-situ measurements, satellite observations, and model simulations. This study evaluates different methods of combining model, satellite, and in-situ soil moisture data to provide an accurate and spatially-continuous soil moisture product. Three independent soil moisture datasets are used, including an in situ-based product that uses regression kriging (RK) with precipitation, SMAP L4 soil moisture, and model-simulated soil moisture from the Noah model as part of the North American Land Data Assimilation System. Triple collocation (TC), relative error variance (REV), and RK were used to estimate the error variance of each parent dataset, based on which the least squares weighting (LSW) was applied to blend the parent datasets. These results were also compared with that using simple average (AVE). The results indicated no significant differences between blended soil moisture datasets using errors estimated from TC, REV or RK. Moreover, the LSW did not outperform AVE. The SMAP L4 data have a significant negative bias (-18 %) comparing with in-situ measurements, and in-situ measurements are valuable for improving the accuracy of hybrid results. In addition, datasets using anomalies and percentiles have smaller errors than using volumetric water content, mainly due to the reduced bias. Finally, the in situ-based soil moisture and the simple-averaged product from in situ-based and Noah soil moisture are the two optimal datasets for soil moisture mapping. The in situ-based product performs better when the sample density is high, while the simple-averaged product performs better when the station density is low, or measurement sites are less representative. [ABSTRACT FROM AUTHOR]

ABSTRACT Regions of strong land-atmosphere coupling are often depicted as static in time. However, the mechanisms through which the land surface impacts atmospheric conditions vary on sub-daily to multi-decadal timescales. Therefore, characterizing the long-term variability of land-atmosphere interactions may provide a means of predicting when surface-induced extreme temperature events will be more or less likely to occur. We evaluate the coupling strength between soil moisture, as represented by in situ observations and 1-month Standardized Precipitation Index ( SPI), and subsequent monthly maximum temperature ( TMAX) over the contiguous United States. We find that the utility of SPI as a proxy for soil moisture anomalies is limited to the summer, as the correlations between SPI and TMAX are not significantly related in the other seasons. We examine the variability in summer SPI-TMAX coupling in four regions of the United States. In general, we find the strongest relationships between SPI and TMAX in the Southern Great Plains. However, our results demonstrate that the coupling strength varies considerably over time in most regions of the United States. The long-term variability in SPI-TMAX coupling strength is strongly related to the Pacific Decadal Oscillation in the northwest and midwest United States, and the Atlantic Multidecadal Oscillation in the southeast United States The results of this study suggest that land-atmosphere coupling in the contiguous United States is modulated by multi-decadal oscillations in Pacific and Atlantic sea surface temperatures. [ABSTRACT FROM AUTHOR]

ABSTRACT East China ( EC) has experienced a number of severe droughts in recent decades. Understanding the characteristics of droughts and their persistence will provide operational guidelines for water resource management and agricultural production. This study uses a logistic regression model to measure the probability of drought occurrence in the current season given the previous season's Standardized Precipitation Index ( SPI) and Southern Oscillation Index ( SOI) as well as drought persistence. Results reveal large spatial and seasonal variations in the relationship between the previous season's SPI and the drought occurrence probability in a given season. The drought persistence averaged over the entire study area for all the four seasons is approximately 34% with large variations from season to season and from region to region. The East and Northeast regions have the largest summer drought persistence (∼40%) and lowest fall drought persistence (∼28%). The spatial pattern in winter and spring drought persistence is dissimilar with stronger winter and weaker spring drought persistence in the Southwest and Northeast relative to other regions. Logistic regression analysis indicates a stronger negative relationship in summer-to-fall (or between fall drought occurrence and summer SPI) than other inter-season relationships. Most of the stations with significant relationships in summer-to-fall lie in the East and the southern part of the North region. Significant negative relationships in winter-spring and fall-winter are primarily located on the western portion of the study area. A significant negative spring-summer relationship exists in the North region. Further, spring drought occurrence has a statistically significant positive relationship with winter SOI in the Southeast. Fall drought occurrence and summer SOI are negatively correlated in the northwest portion of EC (i.e. the border of North and Southwest regions). Overall, this study demonstrates that the impact of previous season SPI and SOI on current season drought varies substantially from region to region and from season to season. This study also shows stronger drought persistence in summer than in other seasons. In other words, the probability of fall drought occurrence is closely related to summer moisture conditions in the EC. [ABSTRACT FROM AUTHOR]

ABSTRACT Understanding flash floods in watersheds of the New York City water supply system ( NYCWSS) is important, as turbidity associated with flooding degrades water quality in the unfiltered portions of this water supply system. We examined synoptic-scale atmospheric conditions most frequently associated with flash flooding in this region. Flash floods between 1987 and 2013 were identified in two small watersheds of the NYCWSS using USGS 15-min discharge data at the Esopus Creek near Allaben, NY and Neversink River at Claryville, NY gauges, both located in the Catskill Mountains. A total of 25 flash floods were detected in these watersheds and there were 17 separate flash flood days. The Spatial Synoptic Typer Tools 4.0 were used to characterize the synoptic-scale atmospheric patterns influencing the study area based on NCEP/ NCAR 500-mb geopotential height reanalysis data. Through this procedure, 17 unique synoptic patterns were identified. Three of these types were found to be strongly associated with flash flooding events. Composites of these types show southwesterly flow which suggests advection of moisture from the Gulf of Mexico and Atlantic Ocean. The flash flood days were compared to the National Weather Service flash flood warnings. The flash flood warnings issued for Ulster County compared to the flash floods in the study watersheds highlight the highly localized nature of flash flooding in this region. [ABSTRACT FROM AUTHOR]

ABSTRACT Thirty years (1954-1983) of soil moisture data from 60 sites in Iowa are used to identify the dominant modes of spatial and temporal variability. Both soil moisture and precipitation exhibit a northwest to southeast gradient across Iowa, with wetter conditions in the southeast and drier conditions in the northwest. Climate and soil characteristics are responsible for the spatial variations in soil moisture. There is a pronounced annual soil moisture cycle that has three distinct phases: a moist phase (April-June), a drying phase (June-September), and a recharge phase (September-November). Spring soil moisture is strongly controlled by the amount of recharge during the previous fall. There is no evidence of a long-term trend in soil moisture; however there is significant interannual variability and about 20% of the variance is attributable to El Niño-Southern Oscillation (ENSO). The warm (cold) phase of ENSO is associated with above (below) average soil moisture in Iowa. The multi-decadal soil moisture record is used to analyse drought frequency and magnitude in Iowa, and to place the 2012 Midwest drought in historical context. In general, Iowa drought is more common during the latter part of the warm season (August through November), but tends to be more severe during the early part of the warm season (April-July). We find that the 2012 drought was unusual because it persisted and intensified throughout the growing season. [ABSTRACT FROM AUTHOR]

Soil moisture monitoring networks can provide real-time and accurate soil moisture measurements; however, missing values and the lack of unified measurement depths across different networks impedes soil moisture applications at regional and national scales. Therefore, methods for vertical extrapolation of soil moisture, i.e., using shallow soil moisture measurements to estimate deeper soil moisture, are needed for standardizing measurements to a set of common depths. This study compared three methods, artificial neural network (ANN), linear regression (LR), and exponential filter (ExpF), for vertical extrapolation of soil moisture using data from the Oklahoma Mesonet. Based on our analysis of intra-annual variations in soil moisture, we divided each year into two seasons, warm and cool. Our results demonstrate that all methods perform better in the warm season than in the cool season, especially at deeper depths. The Kling-Gupta efficiency was used to assess the performance of each method. All methods had similar performance for near-surface extrapolation of soil moisture (top 25 cm). Although the accuracy of all models tended to decrease with depth, the ExpF outperformed the other methods at deeper depths. The soil water index (SWI) is preferred over volumetric water content as input to the ExpF. Incorporating air temperature and an antecedent precipitation index into the ANN and LR methods did not significantly improve their accuracy. We demonstrated that both ExpF and general LR can be used for SWI extrapolation at sites where only surface soil moisture data are available. Soil properties may be useful for further improving the accuracy of the general LR method. [ABSTRACT FROM AUTHOR]