Soil Moisture Memory and Its Effect on the Surface Water and Heat Fluxes on Seasonal and Interannual Time Scales

2019 ◽  
Vol 124 (20) ◽  
pp. 10730-10741
Author(s):  
Y. M. Song ◽  
Z. F. Wang ◽  
L. L. Qi ◽  
A. N. Huang
Keyword(s):  
Soil Moisture ◽  
Surface Water ◽  
Time Scales ◽  
Heat Fluxes

scholarly journals Verification and Intercomparison of Multimodel Simulated Land Surface Hydrological Datasets over the United States

2011 ◽  
Vol 12 (4) ◽  
pp. 531-555 ◽  
Author(s):  
Yun Fan ◽  
Huug M. van den Dool ◽  
Wanru Wu
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Surface Water ◽  
Water Balance ◽  
Time Scales ◽  
Land Surface ◽  
The United States ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Water Balance Components

Abstract Several land surface datasets, such as the observed Illinois soil moisture dataset; three retrospective offline run datasets from the Noah land surface model (LSM), Variable Infiltration Capacity (VIC) LSM, and Climate Prediction Center leaky bucket soil model; and three reanalysis datasets (North American Regional Reanalysis, NCEP/Department of Energy Global Reanalysis, and 40-yr ECMWF Re-Analysis), are used to study the spatial and temporal variability of soil moisture and its response to the major components of land surface hydrologic cycles: precipitation, evaporation, and runoff. Detailed analysis was performed on the evolution of the soil moisture vertical profile. Over Illinois, model simulations are compared to observations, but for the United States as a whole some impressions can be gained by comparing the multiple soil moisture–precipitation–evaporation–runoff datasets to one another. The magnitudes and partitioning of major land surface water balance components on seasonal–interannual time scales have been explored. It appears that evaporation has the most prominent annual cycle but its interannual variability is relatively small. For other water balance components, such as precipitation, runoff, and surface water storage change, the amplitudes of their annual cycles and interannual variations are comparable. This study indicates that all models have a certain capability to reproduce observed soil moisture variability on seasonal–interannual time scales, but offline runs are decidedly better than reanalyses (in terms of validation against observations) and more highly correlated to one another (in terms of intercomparison) in general. However, noticeable differences are also observed, such as the degree of simulated drought severity and the locations affected—this is due to the uncertainty in model physics, input forcing, and mode of running (interactive or offline), which continue to be major issues for land surface modeling.

scholarly journals Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97

2007 ◽  
Vol 8 (1) ◽  
pp. 68-87 ◽  
Author(s):  
Margaret A. LeMone ◽  
Fei Chen ◽  
Joseph G. Alfieri ◽  
Mukul Tewari ◽  
Bart Geerts ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Latent Heat ◽  
Land Surface ◽  
Field Experiments ◽  
Time Of Day ◽  
Horizontal Distribution ◽  
Heat Fluxes ◽  
Surface Model ◽  
Green Vegetation ◽  
The Impact

Abstract Analyses of daytime fair-weather aircraft and surface-flux tower data from the May–June 2002 International H2O Project (IHOP_2002) and the April–May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in southeast Kansas. Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation. If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope ΔxyLE/ΔxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area. Based on the surface energy balance, H + LE = Rnet − Gsfc, where Rnet is the net radiation and Gsfc is the flux into the soil; Rnet − Gsfc ∼ constant over the area implies an approximately −1 slope. Right after rainfall, H and LE vary too little horizontally to define a slope. After sufficient drying to produce enough horizontal variation to define a slope, a steep (∼−2) slope emerges. The slope becomes shallower and better defined with time as H and LE horizontal variability increases. Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE. These trends are based on land surface model (LSM) runs and observations collected under nearly clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

scholarly journals The Evaporative Demand Drought Index. Part II: CONUS-Wide Assessment against Common Drought Indicators

2016 ◽  
Vol 17 (6) ◽  
pp. 1763-1779 ◽  
Author(s):  
Daniel J. McEvoy ◽  
Justin L. Huntington ◽  
Michael T. Hobbins ◽  
Andrew Wood ◽  
Charles Morton ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Time Scales ◽  
Drought Index ◽  
Standardized Precipitation Index ◽  
Stress Index ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Evaporative Demand ◽  
Hydrologic Drought

Abstract Precipitation, soil moisture, and air temperature are the most commonly used climate variables to monitor drought; however, other climatic factors such as solar radiation, wind speed, and humidity can be important drivers in the depletion of soil moisture and evolution and persistence of drought. This work assesses the Evaporative Demand Drought Index (EDDI) at multiple time scales for several hydroclimates as the second part of a two-part study. EDDI and individual evaporative demand components were examined as they relate to the dynamic evolution of flash drought over the central United States, characterization of hydrologic drought over the western United States, and comparison to commonly used drought metrics of the U.S. Drought Monitor (USDM), Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI), and the evaporative stress index (ESI). Two main advantages of EDDI over other drought indices are that it is independent of precipitation (similar to ESI) and it can be decomposed to identify the role individual evaporative drivers have on drought onset and persistence. At short time scales, spatial distributions and time series results illustrate that EDDI often indicates drought onset well in advance of the USDM, SPI, and SSI. Results illustrate the benefits of physically based evaporative demand estimates and demonstrate EDDI’s utility and effectiveness in an easy-to-implement agricultural early warning and long-term hydrologic drought–monitoring tool with potential applications in seasonal forecasting and fire-weather monitoring.

scholarly journals Meteorological, soil moisture, surface water, and groundwater data from the St Denis National Wildlife Area, Saskatchewan, Canada

2018 ◽  
Author(s):  
Edward K. P. Bam ◽  
Rosa Brannen ◽  
Sujata Budhathoki ◽  
Andrew M. Ireson ◽  
Chris Spence ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Surface Water ◽  
Climate Models ◽  
Multiple Scales ◽  
Full Range ◽  
Research Network ◽  
Groundwater Levels ◽  
Groundwater Availability ◽  
Surface Water And Groundwater

Abstract. Long-term meteorological, soil moisture, surface water, and groundwater data provide information on past climate change, most notably information that can be used to analyze past changes in precipitation and groundwater availability in a region. These data are also valuable to test, calibrate and validate hydrological and climate models. CCRN (Changing Cold Regions Network) is a collaborative research network that brought together a team of over 40 experts from 8 universities and 4 federal government agencies in Canada for 5 years (2013–18) through the Climate Change and Atmospheric Research (CCAR) Initiative of the Natural Sciences and Engineering Research Council of Canada (NSERC). The working group aimed to integrate existing and new data with improved predictive and observational tools to understand, diagnose and predict interactions amongst the cryospheric, ecological, hydrological, and climatic components of the changing Earth system at multiple scales, with a geographic focus on the rapidly changing cold interior of Western Canada. The St Denis National Wildlife Area database contains data for the prairie research site, St Denis National Wildlife Research Area, and includes atmosphere, soil, and groundwater. The meteorological measurements are observed every 5 seconds, and half-hourly averages (or totals) are logged. Soil moisture data comprise volumetric water content, soil temperature, electrical conductivity and matric potential for probes installed at depths of 5 cm, 20 cm, 50 cm, 100 cm, 200 cm and 300 cm in all soil profiles. Additional data on snow surveys, pond and groundwater levels, and water isotope isotopes collected on an intermittent basis between 1968 and 2018 are also presented including information on the dates and ground elevations (datum) used to construct hydraulic heads. The metadata table provides location information, information about the full range of measurements carried out on each parameter and GPS locations that are relevant to the interpretation of the records, as well as citations for both publications and archived data. The compiled data are available at https://doi.org/10.20383/101.0115.

scholarly journals Soil Moisture-Boundary Layer Feedbacks on the Loess Plateau in China Using Radiosonde Data with 1-D Atmospheric Boundary Layer Model

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1619
Author(s):  
Yingsai Ma ◽  
Xianhong Meng ◽  
Yinhuan Ao ◽  
Ye Yu ◽  
Guangwei Li ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Atmospheric Boundary Layer ◽  
Loess Plateau ◽  
Atmospheric Stability ◽  
Heat Fluxes ◽  
Radiosonde Data ◽  
Atmospheric Conditions ◽  
The Loess Plateau ◽  
The Impact

The Loess Plateau is one land-atmosphere coupling hotspot. Soil moisture has an influence on atmospheric boundary layer development under specific early-morning atmospheric thermodynamic structures. This paper investigates the sensitivity of atmospheric convection to soil moisture conditions over the Loess Plateau in China by using the convective triggering potential (CTP)—humidity index (HIlow) framework. The CTP indicates atmospheric stability and the HIlow indicates atmospheric humidity in the low-level atmosphere. By comparing the model outcomes with the observations, the one-dimensional model achieves realistic daily behavior of the radiation and surface heat fluxes and the mixed layer properties with appropriate modifications. New CTP-HIlow thresholds for soil moisture-atmosphere feedbacks are found in the Loess Plateau area. By applying the new thresholds with long-time scales sounding data, we conclude that negative feedback is dominant in the north and west portion of the Loess Plateau; positive feedback is predominant in the south and east portion. In general, this framework has predictive significance for the impact of soil moisture on precipitation. By using this new CTP-HIlow framework, we can determine under what atmospheric conditions soil moisture can affect the triggering of precipitation and under what atmospheric conditions soil moisture has no influence on the triggering of precipitation.

scholarly journals Evaluating a land surface model at a water-limited site: implications for land surface contributions to droughts and heatwaves

2021 ◽  
Author(s):  
Mengyuan Mu ◽  
Martin De Kauwe ◽  
Anna Ukkola ◽  
Andy Pitman ◽  
Teresa Gimeno ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Heat Fluxes ◽  
Surface Model ◽  
Strong Impact ◽  
Water Fluxes ◽  
Land Surface Models ◽  
Groundwater Aquifer ◽  
Surface Models

<p>Land surface models underpin coupled climate model projections of droughts and heatwaves. However, the lack of simultaneous observations of individual components of evapotranspiration, concurrent with root-zone soil moisture, has limited previous model evaluations. Here, we use a comprehensive set of observations from a water-limited site in southeastern Australia including both evapotranspiration and soil moisture to a depth of 4.5 m to evaluate the Community Atmosphere-Biosphere Land Exchange (CABLE) land surface model. We demonstrate that alternative process representations within CABLE had the capacity to improve simulated evapotranspiration, but not necessarily soil moisture dynamics - highlighting problems of model evaluations against water fluxes alone. Our best simulation was achieved by resolving a soil evaporation bias; a more realistic initialisation of the groundwater aquifer state; higher vertical soil resolution informed by observed soil properties; and further calibrating soil hydraulic conductivity. Despite these improvements, the role of the empirical soil moisture stress function in influencing the simulated water fluxes remained important: using a site calibrated function reduced the soil water stress on plants by 36 % during drought and 23 % at other times. These changes in CABLE not only improve the seasonal cycle of evapotranspiration, but also affect the latent and sensible heat fluxes during droughts and heatwaves. The range of parameterisations tested led to differences of ~150 W m<sup>-2</sup> in the simulated latent heat flux during a heatwave, implying a strong impact of parameterisations on the capacity for evaporative cooling and feedbacks to the boundary layer (when coupled). Overall, our results highlight the opportunity to advance the capability of land surface models to capture water cycle processes, particularly during meteorological extremes, when sufficient observations of both evapotranspiration fluxes and soil moisture profiles are available.</p>

scholarly journals Remotely sensed latent heat fluxes for improving model predictions of soil moisture: a case study

2010 ◽  
Vol 7 (4) ◽  
pp. 6179-6205
Author(s):  
J. M. Schuurmans ◽  
F. C. van Geer ◽  
M. F. P. Bierkens
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Latent Heat ◽  
Satellite Data ◽  
Hydrological Model ◽  
Remotely Sensed ◽  
Heat Fluxes ◽  
Model Errors ◽  
Small Catchment

Abstract. This paper investigates whether the use of remotely sensed latent heat fluxes improves the accuracy of spatially-distributed soil moisture predictions by a hydrological model. By using real data we aim to show the potential and limitations in practice. We use (i) satellite data of both ASTER and MODIS for the same two days in the summer of 2006 that, in association with the Surface Energy Balance Algorithm for Land (SEBAL), provides us the spatial distribution of daily ETact and (ii) an operational physically based distributed (25 m×25 m) hydrological model of a small catchment (70 km2) in The Netherlands that simulates the water flow in both the unsaturated and saturated zone. Firstly, model outcomes of ETact are compared to the processed satellite data. Secondly, we perform data assimilation that updates the modelled soil moisture. We show that remotely sensed ETact is useful in hydrological modelling for two reasons. Firstly, in the procedure of model calibration: comparison of modeled and remotely sensed ETact together with the outcomes of our data assimilation procedure points out potential model errors (both conceptual and flux-related). Secondly, assimilation of remotely sensed ETact results in a realistic spatial adjustment of soil moisture, except for the area with forest and deep groundwater levels. As both ASTER and MODIS images were available for the same days, this study provides also an excellent opportunity to compare the worth of these two satellite sources. It is shown that, although ASTER provides much better insight in the spatial distribution of ETact due to its higher spatial resolution than MODIS, they appeared in this study just as useful.

scholarly journals A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle

2020 ◽  
Author(s):  
Toby N. Carlson ◽  
George Petropoulos
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heat Fluxes ◽  
Surface Model ◽  
Landsat 8 ◽  
Simple Method ◽  
Practical Applications ◽  
Infrared Measurements

Earth Observation (EO) provides a promising approach towards deriving accurate spatiotemporal estimates of key parameters characterizing land surface interactions, such as latent (LE) and sensible (H) heat fluxes as well as soil moisture content. This paper proposes a very simple method to implement, yet reliable to calculate evapotranspiration fraction (EF) and surface moisture availability (Mo) from remotely sensed imagery of Normalized Difference Vegetation Index (NDVI) and surface radiometric temperature (Tir). The method is unique in that it derives all of its information solely from these two images. As such, it does not depend on knowing ancillary surface or atmospheric parameters, nor does it require the use of a land surface model. The procedure for computing spatiotemporal estimates of these important land surface parameters is outlined herein stepwise for practical application by the user. Moreover, as the newly developedscheme is not tied to any particular sensor, it can also beimplemented with technologically advanced EO sensors launched recently or planned to be launched such as Landsat 8 and Sentinel 3. The latter offers a number of key advantages in terms of future implementation of the method and wider use for research and practical applications alike.

Remote Sensing of Surface Water and Soil Moisture

2013 ◽  
pp. 119-142 ◽  
Author(s):  
Alain Pietroniro ◽  
Jessika Töyrö ◽  
Robert Leconte ◽  
Geoff Kite
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Water
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