Evaluation of Flux-PIHM, a physically-based land surface hydrologic model in an agricultural watershed

An agricultural watershed, Cole Farm, was established as the newest of the three subcatchments in the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO) in 2017. The catchment contains mostly pasture and crops, with a small portion of deciduous forest. The observations in Cole Farm afford an opportunity to test the spatially distributed land surface hydrologic model, Flux-PIHM, in farmland for the first time. In this study, we calibrated the model to only discharge and groundwater level observations at Cole Farm, but it’s able to capture the variations and magnitudes of soil moisture, latent heat (LE) and sensible heat (H) fluxes. Modeled soil moisture on the ridge top matched the observations well, but modeled soil moisture in the mid-slope differed from observations likely due to the existence of fragipan in the soil column. Flux-PIHM reproduced the seasonality and diurnal variations of watershed-average evapotranspiration (ET), sensible heat flux (H), though modeled ET in summer is about 25% greater than tower ET. To study the impact of land cover on hydrology, we imposed two different LAI forcings to the model: spatially distributed versus uniform LAI. Spatially distributed LAI produced higher ET and lower soil moisture in the forested part of the watershed due to higher LAI of deciduous forest in comparison to crops and pasture. But the impact of different LAI forcings on discharge was small. We further compared the water budget simulated by Flux-PIHM in the agricultural watershed (Cole Farm) to a forested watershed (Shale Hills). Flux-PIHM simulated less discharge and higher transpiration and bare soil evaporation in the Cole Farm watershed relative to Shale Hills watershed. Our work shows that with a few key observations, Flux-PIHM can be calibrated to simulate agricultural watershed hydrology, but spatially distributed LAI and soils data are needed to capture the spatial variations in soil moisture and ET.

Download Full-text

Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo forest, Puerto Rico

Hydrology and Earth System Sciences ◽

10.5194/hess-17-3371-2013 ◽

2013 ◽

Vol 17 (9) ◽

pp. 3371-3387 ◽

Cited By ~ 40

Author(s):

C. Lepore ◽

E. Arnone ◽

L. V. Noto ◽

G. Sivandran ◽

R. L. Bras

Keyword(s):

Soil Moisture ◽

Soil Column ◽

Hydrologic Model ◽

Richards Equation ◽

Landslide Risk ◽

Modeling Framework ◽

Soil Moisture Dynamics ◽

Physically Based ◽

Spatially Distributed ◽

Moisture Dynamics

Abstract. This paper presents the development of a rainfall-triggered landslide module within an existing physically based spatially distributed ecohydrologic model. The model, tRIBS-VEGGIE (Triangulated Irregular Networks-based Real-time Integrated Basin Simulator and Vegetation Generator for Interactive Evolution), is capable of a sophisticated description of many hydrological processes; in particular, the soil moisture dynamics are resolved at a temporal and spatial resolution required to examine the triggering mechanisms of rainfall-induced landslides. The validity of the tRIBS-VEGGIE model to a tropical environment is shown with an evaluation of its performance against direct observations made within the study area of Luquillo Forest. The newly developed landslide module builds upon the previous version of the tRIBS landslide component. This new module utilizes a numerical solution to the Richards' equation (present in tRIBS-VEGGIE but not in tRIBS), which better represents the time evolution of soil moisture transport through the soil column. Moreover, the new landslide module utilizes an extended formulation of the factor of safety (FS) to correctly quantify the role of matric suction in slope stability and to account for unsaturated conditions in the evaluation of FS. The new modeling framework couples the capabilities of the detailed hydrologic model to describe soil moisture dynamics with the infinite slope model, creating a powerful tool for the assessment of rainfall-triggered landslide risk.

Download Full-text

Improvement of modelling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements

10.5194/gmd-2020-273 ◽

2020 ◽

Author(s):

Anna B. Harper ◽

Karina E. Williams ◽

Patrick C. McGuire ◽

Maria Carolina Duran Rojas ◽

Debbie Hemming ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Deciduous Forest ◽

Soil Depth ◽

Regional Climate ◽

Moisture Stress ◽

Plant Responses ◽

Soil Moisture Stress ◽

Forest Sites ◽

The Impact

Abstract. Drought is predicted to increase in the future due to climate change, bringing with it a myriad of impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance, in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local/regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales, and evaluated ten different representations of stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high latitudes/cold region sites, while LE was best simulated in temperate and high latitude/cold sites. Errors not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savannah and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14, and the soil depth from 3m to 10.8m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation, when the onset of stress was delayed, and when roots extended deeper into the soil. For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and made the simulation worse. Further evaluation into the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress.

Download Full-text

What’s the impact of improved soil representations in the ECMWF land surface model and how does it affect the extremes?

10.5194/egusphere-egu21-9740 ◽

2021 ◽

Author(s):

Souhail Boussetta ◽

Gabriele Arduini ◽

Gianpaolo Balsamo ◽

Emanuel Dutra ◽

Anna Agusti-Panareda ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Soil Depth ◽

Soil Column ◽

Carbon Fluxes ◽

Moisture Stress ◽

Rooting Depth ◽

Surface Model ◽

Soil Moisture Stress ◽

The Impact

<p>With increasingly higher spatial resolution and a broader applications, the importance of soil representation (e.g. soil depth, vertical discretisation, vegetation rooting) within land surface models is enhanced. Those modelling choices actually affects the way land surfaces store and regulate water, energy and also carbon fluxes. Heat and water vapour fluxes towards the atmosphere and deeper soil, exhibit variations spanning a range of time scales from minutes to months in the coupled land-atmosphere system. This is further modulated by the vertical roots' distribution, and soil moisture stress function, which control evapotranspiration under soil moisture stress conditions. Currently in the ECMWF land Surface Scheme the soil column is represented by a fixed 4 layers configuration with a total of approximately 3m depth.</p><p>In the present study we explore new configurations with increased soil depth (up to 8m) and higher vertical discretisation (up to 10 layers) including a dissociation between the treatment of water and heat fluxes. Associated with the soil vertical resolution, the vertical distribution of roots is also investigated. A new scheme that assumes a uniform root distribution with an associated maximum rooting depth is explored.&#160;The impact of these new configurations is assessed through surface offline simulations driven by the ERA5 meteorological forcing against in-situ and global products of energy, water and carbon fluxes with a particular focus on the diurnal cycle and extreme events in recent years.</p>

Download Full-text

Indications of Surface and Sub-Surface Hydrologic Properties from SMAP Soil Moisture Retrievals

Hydrology ◽

10.3390/hydrology5030036 ◽

2018 ◽

Vol 5 (3) ◽

pp. 36 ◽

Cited By ~ 5

Author(s):

Paul Dirmeyer ◽

Holly Norton

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Soil Column ◽

Remotely Sensed ◽

Subsurface Geology ◽

South Central ◽

Significant Relationships ◽

The Impact ◽

Soil Moisture Variability ◽

Moisture Variability

Variability and covariability of land properties (soil, vegetation and subsurface geology) and remotely sensed soil moisture over the southeast and south-central U.S. are assessed. The goal is to determine whether satellite soil moisture memory contains information regarding land properties, especially the distribution karst formations below the active soil column that have a bearing on land-atmosphere feedbacks. Local (within a few tens of km) statistics of land states and soil moisture are considered to minimize the impact of climatic variations, and the local statistics are then correlated across the domain to illuminate significant relationships. There is a clear correspondence between soil moisture memory and many land properties including karst distribution. This has implications for distributed land surface modeling, which has not considered preferential water flows through geologic formations. All correspondences are found to be strongest during spring and fall, and weak during summer, when atmospheric moisture demand appears to dominate soil moisture variability. While there are significant relationships between remotely-sensed soil moisture variability and land properties, it will be a challenge to use satellite data for terrestrial parameter estimation as there is often a great deal of correlation among soil, vegetation and karst property distributions.

Download Full-text

Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo Forest, Puerto Rico

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-1333-2013 ◽

2013 ◽

Vol 10 (1) ◽

pp. 1333-1373 ◽

Cited By ~ 1

Author(s):

C. Lepore ◽

E. Arnone ◽

L. V. Noto ◽

G. Sivandran ◽

R. L. Bras

Keyword(s):

Soil Moisture ◽

Soil Column ◽

Hydrologic Model ◽

Richards Equation ◽

Landslide Risk ◽

Modeling Framework ◽

Soil Moisture Dynamics ◽

Physically Based ◽

Spatially Distributed ◽

Moisture Dynamics

Abstract. This paper presents the development of a rainfall-triggered landslide module within a physically based spatially distributed ecohydrologic model. The model, Triangulated Irregular Networks Real-time Integrated Basin Simulator and VEGetation Generator for Interactive Evolution (tRIBS-VEGGIE), is capable of a sophisticated description of many hydrological processes; in particular, the soil moisture dynamics is resolved at a temporal and spatial resolution required to examine the triggering mechanisms of rainfall-induced landslides. The validity of the tRIBS-VEGGIE model to a tropical environment is shown with an evaluation of its performance against direct observations made within the Luquillo Forest (the study area). The newly developed landslide module builds upon the previous version of the tRIBS landslide component. This new module utilizes a numerical solution to the Richards equation to better represent the time evolution of soil moisture transport through the soil column. Moreover, the new landslide module utilizes an extended formulation of the Factor of Safety (FS) to correctly quantify the role of matric suction in slope stability and to account for unsaturated conditions in the evaluation of FS. The new modeling framework couples the capabilities of the detailed hydrologic model to describe soil moisture dynamics with the Infinite Slope model creating a powerful tool for the assessment of landslide risk.

Download Full-text

Impacts of soil-aquifer heat and water fluxes on simulated global climate

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-1185-2013 ◽

2013 ◽

Vol 10 (1) ◽

pp. 1185-1212 ◽

Cited By ~ 1

Author(s):

N. Y. Krakauer ◽

M. J. Puma ◽

B. I. Cook

Keyword(s):

Soil Moisture ◽

Land Surface ◽

General Circulation ◽

Soil Column ◽

Circulation Model ◽

The Arctic ◽

Water Fluxes ◽

Limited Impact ◽

Mean Climate ◽

The Impact

Abstract. Climate models have traditionally only represented heat and water fluxes within relatively shallow soil layers, but there is increasing interest in the possible role of heat and water exchanges with the deeper subsurface. Here, we integrate an idealized 50 m deep aquifer into the land surface module of the GISS ModelE general circulation model to test the influence of aquifer-soil moisture and heat exchanges on climate variables. We evaluate the impact on the modeled climate of aquifer-soil heat and water fluxes separately, as well as in combination. The addition of the aquifer to ModelE has limited impact on annual-mean climate, with little change in global mean land temperature, precipitation, or evaporation. The seasonal amplitude of deep soil temperature is strongly damped by the soil-aquifer heat flux. This not only improves the model representation of permafrost area but propagates to the surface, resulting in an increase in the seasonal amplitude of surface air temperature of >1 K in the Arctic. The soil-aquifer water and heat fluxes both slightly decrease interannual variability in soil moisture and land-surface temperature, and decrease the soil moisture memory of the land surface on annual timescales. The results of this experiment suggest that deepening the modeled land surface, compared to modeling only a shallower soil column with a no-flux bottom boundary condition, has limited impact on mean climate but does affect seasonality and interannual persistence.

Download Full-text

Assessing the Impact of Soil Layer Depth Specification on the Observability of Modeled Soil Moisture and Brightness Temperature

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0280.1 ◽

2020 ◽

Vol 21 (9) ◽

pp. 2041-2060

Author(s):

Peter J. Shellito ◽

Sujay V. Kumar ◽

Joseph A. Santanello ◽

Patricia Lawston-Parker ◽

John D. Bolten ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Soil Column ◽

Soil Layer ◽

Distribution Functions ◽

Cumulative Distribution ◽

Radiative Transfer Model ◽

Transfer Model ◽

L Band ◽

The Impact

AbstractThe utility of hydrologic land surface models (LSMs) can be enhanced by using information from observational platforms, but mismatches between the two are common. This study assesses the degree to which model agreement with observations is affected by two mechanisms in particular: 1) physical incongruities between the support volumes being characterized and 2) inadequate or inconsistent parameterizations of physical processes. The Noah and Noah-MP LSMs by default characterize surface soil moisture (SSM) in the top 10 cm of the soil column. This depth is notably different from the 5-cm (or less) sensing depth of L-band radiometers such as NASA’s Soil Moisture Active Passive (SMAP) satellite mission. These depth inconsistencies are examined by using thinner model layers in the Noah and Noah-MP LSMs and comparing resultant simulations to in situ and SMAP soil moisture. In addition, a forward radiative transfer model (RTM) is used to facilitate direct comparisons of LSM-based and SMAP-based L-band Tb retrievals. Agreement between models and observations is quantified using Kolmogorov–Smirnov distance values, calculated from empirical cumulative distribution functions of SSM and Tb time series. Results show that agreement of SSM and Tb with observations depends primarily on systematic biases, and the sign of those biases depends on the particular subspace being analyzed (SSM or Tb). This study concludes that the role of increased soil layer discretization on simulated soil moisture and Tb is secondary to the influence of component parameterizations, the effects of which dominate systematic differences with observations.

Download Full-text

Impacts of soil–aquifer heat and water fluxes on simulated global climate

Hydrology and Earth System Sciences ◽

10.5194/hess-17-1963-2013 ◽

2013 ◽

Vol 17 (5) ◽

pp. 1963-1974 ◽

Cited By ~ 11

Author(s):

N. Y. Krakauer ◽

M. J. Puma ◽

B. I. Cook

Keyword(s):

Soil Moisture ◽

Land Surface ◽

General Circulation ◽

Soil Column ◽

Circulation Model ◽

The Arctic ◽

Water Fluxes ◽

Limited Impact ◽

Mean Climate ◽

The Impact

Abstract. Climate models have traditionally only represented heat and water fluxes within relatively shallow soil layers, but there is increasing interest in the possible role of heat and water exchanges with the deeper subsurface. Here, we integrate an idealized 50 m deep aquifer into the land surface module of the GISS ModelE general circulation model to test the influence of aquifer–soil moisture and heat exchanges on climate variables. We evaluate the impact on the modeled climate of aquifer–soil heat and water fluxes separately, as well as in combination. The addition of the aquifer to ModelE has limited impact on annual-mean climate, with little change in global mean land temperature, precipitation, or evaporation. The seasonal amplitude of deep soil temperature is strongly damped by the soil–aquifer heat flux. This not only improves the model representation of permafrost area but propagates to the surface, resulting in an increase in the seasonal amplitude of surface air temperature of > 1 K in the Arctic. The soil–aquifer water and heat fluxes both slightly decrease interannual variability in soil moisture and in land-surface temperature, and decrease the soil moisture memory of the land surface on seasonal to annual timescales. The results of this experiment suggest that deepening the modeled land surface, compared to modeling only a shallower soil column with a no-flux bottom boundary condition, has limited impact on mean climate but does affect seasonality and interannual persistence.

Download Full-text

Simulating high-resolution soil moisture patterns in the Shale Hills watershed using a land surface hydrologic model

Hydrological Processes ◽

10.1002/hyp.10593 ◽

2015 ◽

Vol 29 (21) ◽

pp. 4624-4637 ◽

Cited By ~ 18

Author(s):

Yuning Shi ◽

Douglas C. Baldwin ◽

Kenneth J. Davis ◽

Xuan Yu ◽

Christopher J. Duffy ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

Hydrologic Model ◽

Shale Hills

Download Full-text

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

Journal of Hydrometeorology ◽

10.1175/jhm554.1 ◽

2007 ◽

Vol 8 (1) ◽

pp. 68-87 ◽

Cited By ~ 71

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.

Download Full-text