PPT-Run analysis of potential evapotranspiration and soil moisture for investigating flash

Tagele Mossie Aschale 1 David J Peres 1 Nunziarita Palazzolo 1 Guido Scuito 2 Antonino Cancelliere 1 University of Catania Department of Civil Engineering and Architecture Catania Italy tageleaschalephdunictit . R. Bindlish, T. Jackson, M. Cosh. November 2014. Overview. Soil moisture algorithm. Soil moisture product. Validation. Linkage between Soil Moisture and SSS. Aquarius Soil Moisture Algorithm. The baseline soil moisture algorithm uses the . “Water Indicators”. Hiroko Beaudoing. Jordan Borak. Mike Jasinski. Sujay Kumar. Bailing Li. Yuqiong Liu. David Mocko. Matthew Rodell. Meeting w/Dr. Allison Leidner. NASA GSFC. Sept 23, 2014. NCA-LDAS. Colin Pettinati. Winter Ecology . Mountain Research Station 2014. University of Colorado, Boulder. Factors Controlling soil pH. Parent Material. Precipitation (moisture). Native Vegetation. Crops grown. The Hydrologic Cycle. General Topics. Evaporation. Transpiration. Condensation. Precipitation – interception, infiltration, depression storage, Overland Flow. Stream Hydrographs. Rainfal. -Runoff. Storm Duration Curves. . . Kingtse. C. Mo . . Climate Prediction Center. . &. . Dennis P. . Nicole Rogers. Department of Forest Engineering, Resources & Management . Oregon State University. Western Mensurationists Meeting 2013. Background. Productive forests. Douglas-fir. Site index and basal area. By . Kingtse. C. Mo and Dennis P. . Lettenmaier. . A brief synthesis presented by Josh Walston . ATMS Seminar Spring 2017. California’s Central Valley . Orville Lake, California . Humboldt River, Nevada . Hydrology and Water Resources . RSGIS. Institute of Space Technology. October . 11 & 23, . 2013. Infiltration, Percolation, Seepage. Infiltration. Water moving into soil by crossing the soil surface. Mississippi State University. Geosystems. Research Institute. NASA RPC Review (3/2/09). Aquarius Evaluation Team & Collaborators. MSU Team. Robert Moorhead. Xingang. Fan. Valentine . Anantharaj. Jacopo . Dari. . (1). , Pere Quintana-. Seguí. . (2). , Maria José . Escorihuela. . (3). , Luca Brocca . (4). , Renato Morbidelli . (1. ). , and Vivien Stefan . (3). (1) . University. . of Perugia, . University of Wisconsin-Madison, Cooperative Institute for Meteorological Satellite Studies. Jeff . Basara. and Jordan Christian. University of Oklahoma. Trent Ford. Southern Illinois University . –. from . combined LST, SM, precipitation observations. Y. Zhao. , . F. . Cheruy. LMD/IPSL. . LST_cci. User Workshop . 2020.  . 24-26 . June 2020  . Can we use combined LST, SM, PRECIP data to detect the contribution of soil thermal inertia which is strongly dependent on soil moisture to daily variations in nighttime minimum temperature?. Feldman, . Gianotti. , Konings, McColl, Akbar, . Salvucci. and . Entekhabi. , 2018. . Nature Plants. . https://doi.org/10.1038/s41477-018-0304-9. Background: . In deserts, plants have been shown to display ‘pulse-reserve behavior’ whereby rapid vegetation growth occurs immediately after a pulse of resources, such as a rain event. . 1. Motivation. Calculating potential Evapotranspiration (PET) is important especially for long term of water management on a regional scale.. Penman-Monteith (PM) is the best method to calculate PET as recommended by Food and Agriculture Association (FAO). However it requires a large number of meteorological input (not available for a long time), also uncertainty of the meteorological input can amplify the error of the calculated PET..