Soil and Soil Moisture From Measurement to Mesoscale

Soil and Soil Moisture: From Measurement to Mesoscale Benjamin Hatchett Division of Atmospheric Sciences Desert Research Institute Reno, Nevada

Overview • Soils 101 • A ‘Deeper View’ of Soil Moisture • Surface Energy Budget and Implications from Micro to Mesoscale • Measurement Methods

An Introduction to Soils “In the structure and functioning of landscapes, soils are the matrix through which energy, water, biomass, and nutrients flow…the interface in the cycling of water between the atmosphere and land…the location of large transformations of energy. ” Bonan, 2002

Soil Formation • Two processes form soil – Chemical Weathering – Physical Weathering Reactions! Disintegration! • Soil type influenced by various factors: – Climate – Geology – Topography – Time

Physical Weathering… …Is the actual disintegration of rocks due to SCOURING by wind, water, and/or ice In simple terms… Melt/Freeze, Wet/Dry = Expansion/Contraction (cracks in sidewalk) time Water and Wind in Death Valley Plants help too!!!!

Chemical Weathering • Climate important: Kinetic rates increase with temp. • Rocks dissolve due to reactions between rock minerals and water, acid, or other chemicals – Hydrolysis – Dissolution – Oxidation Mg 2 Si. O 4 + 4 H+ + 4 OH- ⇌ 2 Mg 2+ + 4 OH- + H 4 Si. O 4 CO 2 + H 2 O -> H 2 CO 3 then H 2 CO 3 + Ca. CO 3 -> Ca(HCO 3)2 4 Fe + 3 O 2 → 2 Fe 2 O 3

Soil Structure • Soils Composed of: – Organic Matter (>80% organic soil, <10% mineral soil) – Minerals (From parent geology, ~55% in mineral soil) – Air – Water • Type, abundance, arrangement of particles govern heat flow, water flow, nutrient availability

5 General Soil Structure Profiles Place matters!!!

Soil Texture • Relative abundance of sand, silt, and clay determines soil texture • Irregular shapes create voids, called pore spaces • Porosity = Volume of soil occupied by air and water

Implications of Porosity • Close packing: How much space? • Sand: Low porosity, large pore space, fast water movement • Clay: High Porosity, small pore space, very slow water movement So, porosity has strong influence on spatial and temporal presence and patterns of soil moisture presence. Has implications for remote sensing and modeling applications

General Patterns? • Soil Type – Don’t worry about something-sols, think agriculture and place… • Soil Moisture – Green = Wet – Red/Yellow = Dry

Soil Thermodynamics • Soils are repository of heat – Moderates diurnal and seasonal range in Tsurf – Gain heat during day/warm months – Lose heat during night/cold months

Soil Temperature Equation C 1 = Thermal Conductivity CV = Volumetric Heat Capacity K = Thermal Diffusivity Constant • Thermal conductivity and heat capacity depend on: • Mineral Composition (e. g. quartz) • Porosity (less pores = higher conductivity) • Organic Matter Content (very porous, low C 1, insulate) • Water Content (C 1 =20 x air, CV = 3500 x air)

Thermodynamic Responses to Soil Moisture Warner, 2004 • Note nonlinearities… – Implications for modeling

Soil Water • Richards Equation (from Darcy’s Law): K = Hydraulic conductivity ψ = Pressure head θ = Water Content • Influence of time and place…

The Surface Energy Budget

Simple Model of the Surface Energy Budget Rn = Total Radiation H = Surface Sensible Heat Flux LE = Latent Energy Heat Flux G = Ground (Soil) Heat Flux • Role of Soil in Each Term: • H: Heat from soil warms (-)/cools air (+) • LE: Heat used to evaporate water/freeze water • G: Heat stored in soil (remember C 1 and CV terms from thermodynamic equation)

Evaporation Rates and Model Initialization Warner, 2004 • Nonlinear evaporation rate – Limit = hydraulic diffusivity/moisture threshold (remember soil structure!) • How will model initialization runs vary as a result?

Linked In: Evapotranspiration Etot=Edir+Et+Ec Etot = Total Evaportranspiration from Soil and Vegetation Edir = Direct Evaporation from Soil Et = Transpiration from Plant Canopy Ec = Evaporation from Canopy Intercepted Rainfall Represents a moisture flux that can be approximated by comparing resistances to potential flux (Ohm’s Law: Flux=P/R) • Resistances include: • Available Soil Moisture • Canopy (Stomatal) Resistance (Vegetation type, ‘Greeness’) • Atmospheric Winds, Stability Bottom Line: Many Interacting Factors in Soil Moisture/Energy Budget !!!

Microscale • Effect Varies with Topography – Slope – Aspect – Topographic Convergence • Vegetation Growth – Crops have ideal growth temperature • Heat stress (out of LE to evaporate, increases H) – Plant diseases due to condensation • Local Surface Temperatures – Moderated by Soil Moisture • Wet soils = cold, Dry soils = warm (heat capacity) • Diurnal and seasonal flux of sensible heat • Latent heat use (evaporation cools, condensation warms)

Influence on Mesoscale Convection • Soil Moisture linked to Mesoscale Convection (e. g. Betts and Ball 1998, Sullivan et al. 2000) – Remains open research question due to many feedbacks/complicating factors – Sometimes wet soils suppress convection, dry soils aid propagation (Taylor and Ellis, 2006) • Role of Evaporation • Patchiness of wet/dry, creating gradients (Sahel, Central Plains US) that force surface PBL BUT! Not always true… Findell and Eltahir 2003 found that antecedent wet soils aided convection in SE US

Soil Moisture, Soil Temperature, ABL Heat Flux • Dry soil heats quickly with afternoon insolation, results in very high sensible heat flux to boundary layer Soil Moisture Soil Temperature 2 m Air Temperature

Large-eddy simulation of a coupled land-atmosphere system Sullivan et al. 2000 Response of the atmospheric boundary layer to heterogeneous soil moisture. The dramatic changes in boundary layer structure result from the non-linear dependence of soil properties on soil moisture.

Modeling the ABL Siquiera et. al 2008

Bowen Ratio and ABL Heights as Functions of Soil Moisture Siquiera et. al 2008

Measurement Methods • • Passive Remote Sensing Aircraft Towers Field Collection

Scales of Measurement • Satellite Data – 50 km resolution • Aircraft Data – 1 km resolution • Tower Data – 10 m resolution • Field Data – To <10 cm resolution • Problem with scale… – Spatial variation in SM at larger scales and application of same retrieval algorithms to all scales – Nonlinearities, once again!

Field Measurement Techniques • Used to calibrate/verify Remote Sensing Data • Neutron Depth Moisture Gauge – Single Radium-Berillium source probe – Number of neutrons deflected back to probe is proportional to H 20 in soil – Gives total water content in profile • Gamma Meter – Two probes, Cs 137 in one, detector in other – Intensity of radiation received proportional to density of material, density in soil constant except for changes in water content

Factors in Soil Reflectance • “A goal of remote sensing is to disentangle spectral response recorded and indentify proportions and influences of the characteristics within the instantaneous field of view of the sensor system” (Jensen, 2007) – Soil Texture – Soil Moisture Content – Organic Matter – Fe-Ox Content – Salinity – Surface Roughness – Vegetation

Soil Response • Note absorption bands • Why wet soils appear darker! • Implications of SM: • Precipitation • Measurement timing • Soil type!

Porosity Revisted Dry Soil Wet Soil

Microwave Remote Sensing • Use of RADAR -Pulse of microwave energy that interacts with Earth’s terrain -Measure of material’s electrical characteristics: -Complex Dielectic Constant “ability to conduct electrical energy” (why microwave!) -Dry surfaces = 3 -8 um -Water = 80 um -Therefore, amount of moisture on surface influence amount of backscattered energy

Jackson (1993) Inverse Soil Moisture Retrieval Model • Model is a summation of research since 1970 s that has established and verified use of passive microwave emission from land surfaces

Advanced Microwave Scanning Radiometer: Earth Observing System (AMSR-E) West Africa, June 2006 Note Moisture Gradient, Pattern Gantner et. al

Food for Thought… • Soil moisture is difficult phenomena to measure and model because… – Place matters! (Soil type, vegetation, topography) – Time matters! (For measurement, e. g. pre/post precip, initial conditions)

But Improving Our Understanding and Measurement Capabilities Will… • Improve Land Surface Component of Coupled Models • Increase abilities to forecast: – Convective Processes – Seasonal Climate – QPF

References • • • • • • Bonan, G. 2002 Ecological Climatology. Cambridge Univ. Press Betts, A. K. , and J. H. Ball, 1998 J. Atmos. Sci. , 55, 1091– 1108. Findell, K. L. , and E. A. B. Eltahir, 2003 J. Hydrometeorology, 4, 552 -569 Findell, K. L. , and E. A. B. Eltahir 2003 J. Hydrometeorology, 4, 570 -583 Findell, K. L. 2003 Journal of Geophysical Research 108(d 8): 8385 Harpstead, M. I. , T. J. Sauer, W. F. Bennett. 2001 Soil Science Simplified. Blackwell Publishing Jensen, J. R. 2007 Remote Sensing of the Environment. Prentice Hall. Marshall, C. 1999 COMAP Symposium 99 -1 Taylor C. M. , and Ellis R. J. 2006 Geophysical Research Letters 33(3) Siqueira, M. , K. Gabriel, Submitted 2008. J. Hydrometeorology Warner, T. T. 2004. Desert Meteorology. Cambridge Univ. Press https: //courseware. e-education. psu. edu/simsphere/workbook/figures/7. 3. gif http: //www. nrmsc. usgs. gov/files/norock/research/soil_moisture. gif http: //www. mmm. ucar. edu/modeling/les/images/les_lg. jpg http: //nature. berkeley. edu/biometlab/images/olive_apilles. GIF http: //grapevine. com. au/~pbeirwirth/images/bagoview. jpg http: //oceanworld. tamu. edu/resources/environment-book/groundwater. html http: //www. orcbs. msu. edu/environ/programs_guidelines/wellhead/glossary_faq/capillary_fringe. j pg http: //techalive. mtu. edu/meec/module 06/Packing. htm http: //research. eeescience. utoledo. edu/lees/papers_PDF/Saxton_1986_SSSAJ_files/Fig_6. gif http: //www. eol. ucar. edu/projects/cases/maps. html http: //gis. esri. com/library/userconf/proc 99/proceed/papers/pap 365/p 3654. gif http: //weather. msfc. nasa. gov/surface_hydrology_inverse_model. html

Questions? ?