How Are Spring Snow Conditions in Central Canada Related to Early Warm-Season Precipitation?

Abstract The response of the warm-season atmosphere to antecedent snow anomalies has long been an area of study. This paper explores how the spring snow depth relates to subsequent precipitation in central Canada using ground observations, reanalysis datasets, and offline land surface model estimates. After removal of low-frequency ocean influences, April snow depth is found to correlate negatively with early warm-season (May–June) precipitation across a large portion of the study area. A chain of mechanisms is hypothesized to account for this observed negative relation: 1) a snow depth anomaly leads to a soil moisture anomaly, 2) the subsequent soil moisture anomaly affects ground turbulent fluxes, and 3) the atmospheric vertical structure allows dry soil to promote local convection. A detailed analysis supports this chain of mechanisms for those portions of the domain manifesting a statistically significant negative snow–precipitation correlation. For a portion of the study area, large-scale atmospheric circulation patterns also affect the early warm-season rainfall, indicating that the snow–precipitation feedback may depend on large-scale atmospheric dynamical features. This analysis suggests that spring snow conditions can contribute to warm-season precipitation predictability on a subseasonal to seasonal scale, but that the strength of such predictability varies geographically as it depends on the interplay of hydroclimatological conditions across multiple spatial scales.

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Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

Remote Sensing ◽

10.3390/rs13020228 ◽

2021 ◽

Vol 13 (2) ◽

pp. 228

Author(s):

Jian Kang ◽

Rui Jin ◽

Xin Li ◽

Yang Zhang

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Spatial Scales ◽

Microwave Remote Sensing ◽

In Situ Measurements ◽

Spatial Density ◽

Linear Regression Method ◽

Spatiotemporal Resolution ◽

Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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Spatial variability and its scale dependency of observed and modeled soil moisture over different climate regions

Hydrology and Earth System Sciences ◽

10.5194/hess-17-1177-2013 ◽

2013 ◽

Vol 17 (3) ◽

pp. 1177-1188 ◽

Cited By ~ 45

Author(s):

B. Li ◽

M. Rodell

Keyword(s):

Soil Moisture ◽

Spatial Variability ◽

Land Surface ◽

Large Scale ◽

Warm Season ◽

Scale Dependency ◽

Convex Shape ◽

Spatial Moments ◽

Environmental Controls

Abstract. Past studies on soil moisture spatial variability have been mainly conducted at catchment scales where soil moisture is often sampled over a short time period; as a result, the observed soil moisture often exhibited smaller dynamic ranges, which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness) of in situ soil moisture, modeled and satellite-retrieved soil moisture obtained in a warm season (198 days) were examined over three large climate regions in the US. The study found that spatial moments of in situ measurements strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean when statistics from dry, intermediate, and wet climates were combined. This upward convex shape was vaguely or partially observable in modeled and satellite-retrieved soil moisture estimates due to their smaller dynamic ranges. Despite different environmental controls on large-scale soil moisture spatial variability, the correlation between spatial variability and mean soil moisture remained similar to that observed at small scales, which is attributed to the boundedness of soil moisture. From the smaller support (effective area or volume represented by a measurement or estimate) to larger ones, soil moisture spatial variability decreased in each climate region. The scale dependency of spatial variability all followed the power law, but data with large supports showed stronger scale dependency than those with smaller supports. The scale dependency of soil moisture variability also varied with climates, which may be linked to the scale dependency of precipitation spatial variability. Influences of environmental controls on soil moisture spatial variability at large scales are discussed. The results of this study should be useful for diagnosing large scale soil moisture estimates and for improving the estimation of land surface processes.

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Drought Relief and Reversal over North America from 1500 to 2016

Earth Interactions ◽

10.1175/ei-d-20-0020.1 ◽

2021 ◽

Vol 25 (1) ◽

pp. 94-107

Author(s):

M. C. A. Torbenson ◽

D. W. Stahle ◽

I. M. Howard ◽

D. J. Burnette ◽

D. Griffin ◽

...

Keyword(s):

Soil Moisture ◽

North America ◽

Land Surface ◽

Large Scale ◽

Temporal Stability ◽

Warm Season ◽

Cool Season ◽

Continental Scale ◽

The North ◽

Drought Relief

Abstract Season-to-season persistence of soil moisture drought varies across North America. Such interseasonal autocorrelation can have modest skill in forecasting future conditions several months in advance. Because robust instrumental observations of precipitation span less than 100 years, the temporal stability of the relationship between seasonal moisture anomalies is uncertain. The North American Seasonal Precipitation Atlas (NASPA) is a gridded network of separately reconstructed cool-season (December–April) and warm-season (May–July) precipitation series and offers new insights on the intra-annual changes in drought for up to 2000 years. Here, the NASPA precipitation reconstructions are rescaled to represent the long-term soil moisture balance during the cool season and 3-month-long atmospheric moisture during the warm season. These rescaled seasonal reconstructions are then used to quantify the frequency, magnitude, and spatial extent of cool-season drought that was relieved or reversed during the following summer months. The adjusted seasonal reconstructions reproduce the general patterns of large-scale drought amelioration and termination in the instrumental record during the twentieth century and are used to estimate relief and reversals for the most skillfully reconstructed past 500 years. Subcontinental-to-continental-scale reversals of cool-season drought in the following warm season have been rare, but the reconstructions display periods prior to the instrumental data of increased reversal probabilities for the mid-Atlantic region and the U.S. Southwest. Drought relief at the continental scale may arise in part from macroscale ocean–atmosphere processes, whereas the smaller-scale regional reversals may reflect land surface feedbacks and stochastic variability.

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Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

10.5194/hess-2020-359 ◽

2020 ◽

Author(s):

Elizabeth Cooper ◽

Eleanor Blyth ◽

Hollie Cooper ◽

Rich Ellis ◽

Ewan Pinnington ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Large Scale ◽

Pedotransfer Functions ◽

Surface Model ◽

Climate Projections ◽

Land Surface Models ◽

Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

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Spatial variation of biogeochemical properties of landfast sea ice in the Gulf of Bothnia, Baltic Sea

Annals of Glaciology ◽

10.3189/172756406781811169 ◽

2006 ◽

Vol 44 ◽

pp. 80-87 ◽

Cited By ~ 15

Author(s):

M. Steffens ◽

M.A. Granskog ◽

H. Kaartokallio ◽

H. Kuosa ◽

K. Luodekari ◽

...

Keyword(s):

Sea Ice ◽

Baltic Sea ◽

Snow Depth ◽

Large Scale ◽

Spatial Scales ◽

Thick Layer ◽

Ice Cores ◽

Ice Thickness ◽

Gulf Of Bothnia ◽

Landfast Sea Ice

AbstractHorizontal variation of landfast sea-ice properties was studied in the Gulf of Bothnia, Baltic Sea, during March 2004. In order to estimate their variability among and within different spatial levels, 72 ice cores were sampled on five spatial scales (with spacings of 10 cm, 2.5 m, 25 m, 250m and 2.5 km) using a hierarchical sampling design. Entire cores were melted, and bulk-ice salinity, concentrations of chlorophylla(Chla), phaeophytin (Phaeo), dissolved nitrate plus nitrite (DIN) as well as dissolved organic carbon (DOC) and nitrogen (DON) were determined. All sampling sites were covered by a 5.5–23 cm thick layer of snow. Ice thicknesses of cores varied from 26 to 58 cm, with bulk-ice salinities ranging between 0.2 and 0.7 as is typical for Baltic Sea ice. Observed values for Chla(range: 0.8–6.0 mg ChlaL–1; median: 2.9 mg ChlaL–1) and DOC (range: 37–397 μM; median: 95 μM) were comparable to values reported by previous sea-ice studies from the Baltic Sea. Analysis of variance among different spatial levels revealed significant differences on the 2.5km scale for ice thickness, DOC and Phaeo (with the latter two being positively correlated with ice thickness). For salinity and Chla, the 250 m scale was found to be the largest scale where significant differences could be detected, while snow depth only varied significantly on the 25 m scale. Variability on the 2.5 m scale contributed significantly to the total variation for ice thickness, salinity, Chlaand DIN. In the case of DON, none of the investigated levels exhibited variation that was significantly different from the considerable amount of variation found between replicate cores. Results from a principal component analysis suggest that ice thickness is one of the main elements structuring the investigated ice habitat on a large scale, while snow depth, nutrients and salinity seem to be of secondary importance.

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Toward a Surface Soil Moisture Product at High Spatiotemporal Resolution: Temporally Interpolated, Spatially Disaggregated SMOS Data

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0280.1 ◽

2018 ◽

Vol 19 (1) ◽

pp. 183-200 ◽

Cited By ~ 15

Author(s):

Y. Malbéteau ◽

O. Merlin ◽

G. Balsamo ◽

S. Er-Raki ◽

S. Khabba ◽

...

Keyword(s):

Soil Moisture ◽

Temporal Resolution ◽

Land Surface ◽

Large Scale ◽

Surface Soil ◽

Land Surface Model ◽

Open Loop ◽

Surface Model ◽

Surface Soil Moisture ◽

Spatiotemporal Resolution

Abstract High spatial and temporal resolution surface soil moisture is required for most hydrological and agricultural applications. The recently developed Disaggregation based on Physical and Theoretical Scale Change (DisPATCh) algorithm provides 1-km-resolution surface soil moisture by downscaling the 40-km Soil Moisture Ocean Salinity (SMOS) soil moisture using Moderate Resolution Imaging Spectroradiometer (MODIS) data. However, the temporal resolution of DisPATCh data is constrained by the temporal resolution of SMOS (a global coverage every 3 days) and further limited by gaps in MODIS images due to cloud cover. This paper proposes an approach to overcome these limitations based on the assimilation of the 1-km-resolution DisPATCh data into a simple dynamic soil model forced by (inaccurate) precipitation data. The performance of the approach was assessed using ground measurements of surface soil moisture in the Yanco area in Australia and the Tensift-Haouz region in Morocco during 2014. It was found that the analyzed daily 1-km-resolution surface soil moisture compared slightly better to in situ data for all sites than the original disaggregated soil moisture products. Over the entire year, assimilation increased the correlation coefficient between estimated soil moisture and ground measurements from 0.53 to 0.70, whereas the mean unbiased RMSE (ubRMSE) slightly decreased from 0.07 to 0.06 m3 m−3 compared to the open-loop force–restore model. The proposed assimilation scheme has significant potential for large-scale applications over semiarid areas, since the method is based on data available at the global scale together with a parsimonious land surface model.

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Assimilation of SMOS brightness temperature into a large-scale distributed conceptual hydrological model

10.5194/hess-2019-414 ◽

2019 ◽

Author(s):

Renaud Hostache ◽

Dominik Rains ◽

Kaniska Mallick ◽

Marco Chini ◽

Ramona Pelich ◽

...

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Land Surface ◽

Large Scale ◽

Hydrological Model ◽

Land Surface Model ◽

Surface Model ◽

Meteorological Model ◽

Temperature Observations

Abstract. The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help in reducing errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. In particular, we use as forcings the ERA-Interim public dataset and we couple the CMEM radiative transfer model with a hydro-meteorological model enabling therefore soil moisture and SMOS-like brightness temperature simulations. The hydro-meteorological model is configured using recent developments of the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application as well as to data availability and computational requirements. In this case, the model spatial resolution is adapted to the spatial grid of the satellite data, and the soil stratification is tailored to the satellite datasets to be assimilated and the forcing data. The hydrological model is first calibrated using a sample of SMOS brightness temperature observations (period 2010–2011). Next, SMOS-derived brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX-CMEM model (period 2010–2015). For this experiment, a Local Ensemble Transform Kalman Filter is used and the meteorological forcings (ERA interim-based rainfall, air and soil temperature) are perturbed to generate a background ensemble. Each time a SMOS observation is available, the SUPERFLEX state variables related to the water content in the various soil layers are updated and the model simulations are resumed until the next SMOS observation becomes available. Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set up using the CLM land surface model. This shows that a simple model, when carefully calibrated, can yield performance level similar to that of a much more complex model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72. The assimilation of SMOS brightness temperature observation into the SUPERFLEX-CMEM modelling chain improves the correlation between predicted and in situ observed soil moisture by 0.03 on average showing improvements similar to those obtained using the CLM land surface model.

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The Effect of the Dry Line and Convective Initiation on Drought Evolution over Oklahoma during the 2011 Drought

Advances in Meteorology ◽

10.1155/2017/8430743 ◽

2017 ◽

Vol 2017 ◽

pp. 1-16 ◽

Cited By ~ 3

Author(s):

Paul X. Flanagan ◽

Jeffrey B. Basara ◽

Bradley G. Illston ◽

Jason A. Otkin

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Warm Season ◽

Convective Initiation ◽

Model Simulations ◽

Variable Surface ◽

Oklahoma Mesonet ◽

Dry Conditions ◽

Moisture Conditions ◽

The Impact

Observations from the Oklahoma Mesonet and high resolution Weather Research and Forecasting model simulations were used to evaluate the effect that the dry line and large-scale atmospheric patterns had on drought evolution during 2011. Mesonet observations showed that a “dry” and “wet” pattern developed across Oklahoma due to anomalous atmospheric patterns. The location of the dry line varied due to this “dry” and “wet” pattern, with the average dry line location around 1.5° longitude further to the east than climatology. Model simulations were used to further quantify the impact of variable surface conditions on dry line evolution and convective initiation (CI) during April and May 2011. Specifically, soil moisture conditions were altered to depict “wet” and “dry” conditions across the domain by replacing the soil moisture values by each soil category’s porosity or wilting point value. Overall, the strength of the dry line boundary, its position, and subsequent CI were dependent on the modification of soil moisture. The simulations demonstrated that modifying soil moisture impacted the nature of the dry line and showed that soil moisture conditions during the first half of the warm season modified the dry line pattern and influenced the evolution and perpetuation of drought over Oklahoma.

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Understanding the Distinct Impacts of MCS and Non-MCS Rainfall on the Surface Water Balance in the Central United States Using a Numerical Water-Tagging Technique

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0081.1 ◽

2020 ◽

Vol 21 (10) ◽

pp. 2343-2357

Author(s):

Huancui Hu ◽

L. Ruby Leung ◽

Zhe Feng

Keyword(s):

United States ◽

Soil Moisture ◽

Surface Water ◽

Water Balance ◽

Surface Runoff ◽

Land Surface ◽

Warm Season ◽

Surface Model ◽

Central United States ◽

Infiltration Excess

ABSTRACTWarm-season rainfall associated with mesoscale convective systems (MCSs) in the central United States is characterized by higher intensity and nocturnal timing compared to rainfall from non-MCS systems, suggesting their potentially different footprints on the land surface. To differentiate the impacts of MCS and non-MCS rainfall on the surface water balance, a water tracer tool embedded in the Noah land surface model with multiparameterization options (WT-Noah-MP) is used to numerically “tag” water from MCS and non-MCS rainfall separately during April–August (1997–2018) and track their transit in the terrestrial system. From the water-tagging results, over 50% of warm-season rainfall leaves the surface–subsurface system through evapotranspiration by the end of August, but non-MCS rainfall contributes a larger fraction. However, MCS rainfall plays a more important role in generating surface runoff. These differences are mostly attributed to the rainfall intensity differences. The higher-intensity MCS rainfall tends to produce more surface runoff through infiltration excess flow and drives a deeper penetration of the rainwater into the soil. Over 70% of the top 10th percentile runoff is contributed by MCS rainfall, demonstrating its important contribution to local flooding. In contrast, lower-intensity non-MCS rainfall resides mostly in the top layer and contributes more to evapotranspiration through soil evaporation. Diurnal timing of rainfall has negligible effects on the flux partitioning for both MCS and non-MCS rainfall. Differences in soil moisture profiles for MCS and non-MCS rainfall and the resultant evapotranspiration suggest differences in their roles in soil moisture–precipitation feedbacks and ecohydrology.

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Role of Subsurface Physics in the Assimilation of Surface Soil Moisture Observations

Journal of Hydrometeorology ◽

10.1175/2009jhm1134.1 ◽

2009 ◽

Vol 10 (6) ◽

pp. 1534-1547 ◽

Cited By ~ 115

Author(s):

Sujay V. Kumar ◽

Rolf H. Reichle ◽

Randal D. Koster ◽

Wade T. Crow ◽

Christa D. Peters-Lidard

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Large Scale ◽

Surface Soil ◽

Root Zone ◽

Regional Climate ◽

Surface Model ◽

Surface Soil Moisture ◽

Community Land Model ◽

Assimilation System

Abstract Root-zone soil moisture controls the land–atmosphere exchange of water and energy, and exhibits memory that may be useful for climate prediction at monthly scales. Assimilation of satellite-based surface soil moisture observations into a land surface model is an effective way to estimate large-scale root-zone soil moisture. The propagation of surface information into deeper soil layers depends on the model-specific representation of subsurface physics that is used in the assimilation system. In a suite of experiments, synthetic surface soil moisture observations are assimilated into four different models [Catchment, Mosaic, Noah, and Community Land Model (CLM)] using the ensemble Kalman filter. The authors demonstrate that identical twin experiments significantly overestimate the information that can be obtained from the assimilation of surface soil moisture observations. The second key result indicates that the potential of surface soil moisture assimilation to improve root-zone information is higher when the surface–root zone coupling is stronger. The experiments also suggest that (faced with unknown true subsurface physics) overestimating surface–root zone coupling in the assimilation system provides more robust skill improvements in the root zone compared with underestimating the coupling. When CLM is excluded from the analysis, the skill improvements from using models with different vertical coupling strengths are comparable for different subsurface truths. Last, the skill improvements through assimilation were found to be sensitive to the regional climate and soil types.

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