Land atmosphere atmosphere coupling: How satellite LST data contribute to drought assessment and yield prediction

Transcription

1 Land atmosphere atmosphere coupling: How satellite LST data contribute to drought assessment and yield prediction Julia Stoyanova & Christo Georgiev National Institute of Meteorology and Hydrology (NIMH), Bulgaria Bulgarian Academy of Sciences 4 th ESA DUE GlobTemperature User Consultation Meeting 7 8 June 216, Lisbon, Portugal

2 Outline Background Drought problem Land atmosphere coupling Land surface skin temperature as a key parameter Objective and Scientific approach To propose a view how satellite data for skin temperature can contribute to terrestrial drought assessment and to prediction of its effect (yield) Drought Warning System Concept Meteorological products & applications Advanced products of Drought Warning System based on satellite Ts data (from IR measurements by geostationary MSG satellites) First tests of using data from Microwave (SSM/I) Radiometer for the region of SE Europe Concluding remarks/ Prospective/ Challenges 4 th ESA DUE GlobTemperature User Consultation Meeting, 7 8 June 216, Lisbon, Portugal

3 Background Drought is a more complex event driven climate extreme Land use and land cover change can alter hydrological and land surface atmosphere processes drastically. These signify the importance of understanding the relationships between water supply and water demand. Three universally recognized physically based forms of drought: meteorological, hydrological and agricultural, agricultural drought is measured by a shortage in water available for plant growth or sufficient soil moisture to replace evapotranspirative loss The method for quantifying drought is highly dependent both on the type of physical drought examined as well as the particular drought characteristic of interest: drought severity, magnitude, frequency, persistence or spatial extent

4 Complexity of Drought problem Drought as a result of Land Atmosphere coupling The importance of land atmosphere interactions and all processes they involve for the climate system is increasingly being recognized. Land atmosphere coupling in terms of the degree of consistent impact of soil moisture on surface temperature has been extensively studied and mechanism of this impact is well understood as seen in models and observation (Koster et. al, 29, 21, 211). A problem that is of increasing interest is the assessment of the role of land atmosphere interactions and Land Surface Processes /LSPs/ related to climate dynamics/extremes on a regional scale. Needs from a complex Drought Warning System To meet the need from operational assessments Possibilities for drought extent and impacts to be monitored routinely by numerous indicators, reflecting drought characteristics: severity, frequency, persistence, or spatial extend. Accounting for specific regional/local drought reveals. Tair (2 m) Tskin Root zone depth 5 cm 2 cm 5 cm 1 cm

5 Land surface skin temperature as a key parameter Land Surface Temperature (LST) is one of the key variables in the physics of land surface processes on regional/global scales; As a functional parameter in main biogeophysical processes of energy water carbon balances it combines the effects of all surface atmosphere interactions. Surface skin temperature knowledge on regional and global scales are of importance for Global Circulation Models, for estimation and parametrization of surface fluxes (e.g. Brutsaert et al, 1993), to monitor drought and estimate soil moisture (e.g. Feldhake et al., 1996) and water budget components (Park et al., 25), fire risk (e.g. Stoyanova and Georgiev, 21), and many others. Being related to the energetic and water cycles, LST derived by satellite data can be used to adequately respond to the demands of environmental monitoring and drought risk management.

6 Objectives Based on our (NIMH) experimental/modeling experience of studying energy watercarbon cycles and their coupling, the main objective of this presentation is to propose a view how satellite data for skin temperature Ts can contribute to terrestrial drought assessment and to prediction of its effect (yield), more specifically: To identify up to date solution for operational assessments of agricultural drought in terms of main biogeophysical processes, and its impact accounting for the water energy food nexus. To demonstrate the contribution of satellite information based on LST data from IR measurements by geostationary MSG satellites, and Environmental satellites (first test of Special Sensor Microwave/Imager (SSM/I) Radiometer on Defense Meteorological Satellite Program (DMSP) platforms for the target region of SEE. Work has been initiated in the frame of EUMETSAT SALGEE Project 212/213 & 214/215. It is planned to be continued during the next SALGEE phase Satellite Applications for Land surface analyses Group for south Eastern & eastern Europe (SALGEE) is EUMETSAT Project in support LSA SAF activities in the target region, started since 21 (after the 1 st workshop MSG land Surface Applications: Drought & Fires, 7 1 September 29, Sofia, Bulgaria.

7 Scientific approach Studying the water energy food nexus based on using integrated and interdisciplinary modelling/observing approaches; Ts derived by satellite data is used as a parameter to assess land surface state in an integrated LSM and in a biothermodynamic model; The approach involves applications introduced as a result of multiple spatialtemporal scales studies in support to sustain the environment. Tair (2 m) Target region: Bulgaria, SE Europe, Eastern Mediterranean Tskin Root zone depth 5 cm 2 cm 5 cm 1 cm Work has been initiated in the frame of EUMETSAT SALGEE Project 212/213 & 214/215. It is planned to be continued during the next SALGEE phase Satellite Applications for Land surface analyses Group for south Eastern & eastern Europe (SALGEE) is EUMETSAT Project in support LSA SAF activities in the target region, started since 21 (after the 1st workshop MSG land Surface Applications: Drought & Fires, 7 1 September 29, Sofia, Bulgaria.

8 Drought Warning System The concept for an operational Drought Warning System /DWS/has been developed at NIMH, Bulgaria (Stoyanova et al., 214) three level classification scheme of warnings: watch, warn, alert Watch Watch Warn Warn Alert Alert Drought Warning System /DWS/ has been developed as a site scale modeling approach that applies quantitatively following principles: Drought is assessed as a dry bigeophysical cycle anomaly; Ecosystems are functioning as open thermodynamic systems; Biogeophysical and biogeochemical cycles on the land surface are coupled by functional cause consequence relations, correspondingly.

9 Drought Warning System three level classification scheme of warnings: watch, warn, alert Watch Watch Warn Warn Alert Alert Physical background of DWS components comprises Energetic description of soil vegetation atmosphere interface by a SVAT model, applying the first law of Thermodynamics, TD (principle of conservation of energy, Stoyanova & Georgiev 27; 213) TD description of functional relation between biogeophysical cycling (as a cause, after second law of TD, see Florov, 1978; 22 ) and the biogeochemical cycle (as a consequence, Stoyanova, 1985; 1996), and on this basis vegetation growth/yield response assessment. Transfer site scale assessments to a regional /national/ level applying satellite technology. For that purpose, Ts derived from satellite data is combined with air T2m to obtain a parameter as a proxy of the root zone soil moisture and of the entropy production in the main funtional units of vegetation systems (leaves).

10 Drought Warning System /DWS/ at NIMH of Bulgaria Approach for agricultural drought assessment on regional (national) level DWS has been developed based on the experience applying three specific approaches. leaf plant stand landscape Approach #1: Scaling of knowledge from controlled experiments to environmental conditions (long term experience) controlled microclimate/ biogeophysical cycle thermodynamic description of soil vegetation atmosphere interface/biophysical processes at natural environment Approach #2: SVAT modelling: Land Information System (site scale), SVAT_bg Rn = LE + H + G Energy balance estimates of: G Site scale output SMA LE H

11 Drought Warning System /DWS/ at NIMH of Bulgaria Approach for agricultural drought assessment on regional (national) level Approach #2: Meteorological products based on SVAT model output (site scale) Analyses of land surface state by a three level classification scheme of drought severity. Optimal Drought risk Drought Physical interpretation Biogeophysical reveals of drought Index of Soil Moisture Availability /SMA/ Site scale index of Soil Moisture Availability for monitoring soil moisture anomalies 4 levels of soil depth (5, 2, 5 1 cm) 6 level threshold scaheme SMA limited/ Evapotranspitarion restricted Watch Watch Meteorological Meteorological drought drought leading leading to to SMA SMA initial initial depletion depletion SMA exhausted/ Evapotranspitarion prevented Warn Warn SMA SMA is is exhausted exhausted SVAT_bg Assessment of Soil Moisture Availability

12 Drought Warning System /DWS/ at NIMH of Bulgaria Satellites in support to agricultural drought assessment at a regional scale (Ts T2m) temperature difference is used as an Index to approximate SMA in the root zone (1 cm) Tair (2 m) Data sources Air temperature at 2m /T2m/ from ECMWF operational NWP model LSA SAF LST data set Threshold based approach for root zone SMA assessment at specific site/lc type: A set of different thresholds for local site scale environments (n = 26). Aggregation according soil type. Reference data: root zone SMA (within 1 cm depth) derived by physically standartized SMA ( SVAT_bg ) Tskin Root zone depth 5 cm 2 cm 5 cm 1 cm (Ts T2m) temperature difference Index is calculated (as a relative quantity of root zone SMA (ratio between different soil depths, designating warm ( drought ), moderately warm ( drought risk ), and cold ( optimal ) MSG pixels. Optimal Drought risk Drought

13 Application example # 1: Spatiotemporal distribution of agricultural drought risk Limitations: cloudy pixels The (LST T2m) Index is a meteorological product based on a set of thresholds, derived by ground measurements and model assessments, and is used as a measure of Soil Moisture Availability deficit, i.e as a Drought Risk Index. Maps based on satellite derived LST and air temperature from ECMWF numerical model data with resolution of 9 km.

14 Drought Warning System /DWS/ at NIMH of Bulgaria Approach for agricultural drought assessment on regional (national) level Question: What is the physical meaning of Alert in DWS? Biogeochemical nature of drought Alert Alert Risk Risk of of yield yield reduction reduction Accumulated Accumulated SMA SMAdeficit in in critical critical periods periods leads leads to to increase increase of of vegetation vegetation water water stress, stress, destroys destroys energy /water exchange energy /water exchange leading leading to to decreased decreased capacity capacity of of agroecosystems agroecosystemsto to utilize utilize solar solar energy energy in in biochemical biochemical energy energy and and enhanced enhanced energy energy looses, looses, quantified quantified via via the the entropy entropy production production in in canopy canopy leaves leaves (at (at specific specific phenological phenologicalstage). These These all all reduce reduce the the crop crop yield yield at at the the specific specific site site environment. environment. For quantification of energy water food nexus: Approach #3: Thermodynamic model of ecosystems: Ecosystems as open thermodynamic systems Gibbs free energy balance of living systems (Florov, 1983; 1988; 22) Entropy production in canopy leaves Φ = S H T q = + LE t T t t T i S - entropy production; E evapotranspiration rate; H leaf-air heat exchange rate; T mean leaf-air absolute temperature; T- leaf-air temperature difference; q is the difference between specific air humidity at the transpiring leaf level and the surrounding air, L latent heat of vaporization of water. Answer: The coupling between biogeophysical (as a cause) & biogeochemical (as a consequence) cycling (Stoyanova, 1985;1996; 213). Biogeochemical nature of drought Climatic bioenergetic resources (Φ) Potential productivity (crop yield) Yield Accumulated solar energy= f(dissipated energy like heat, Φ)

15 Thermodynamic approach: to formalize the relationship between heat, work, and energy Alert Alert Risk Risk of of yield yield reduction reduction Second law of thermodynamics & Ecosystem functioning (Surface-Boundary approach in biothermodynamics, Florov, 1983; 1988; 22). Climatic (bioenergetic) resources and entropy production quantification: Φ S H T q = T i = + LE t T t t (1) Analytical description of the relation between bioenergetic resources & yield production. Scaling climatic bioenergetic resources towards potential yield prediction: High, Moderate, Low Mean wheat yield, kg/dka Accumulated entropy production during the critical for yield formation period vs. mean inter annual yield(27 213) Yield ranking: Low Moderate High Function of dissipation, Φ x 1 MJ Yield Forecast 214 Moderate Yield Assessed 214 Moderate /Ministry of Food and Agriculture/ Climatic bioenergetic recourses versus mean country yield (winter wheat)* production in Bulgaria, *NIMH agro database yield estimates. Thermodynamic model: Climatic bioenergetic resources & Yield prediction (Stoyanova, 1985; 1996; 214)

16 Climatic (bioenergetic) resources and entropy production quantification: (1) Φ Satellites LST data in support to monitor water energy food nexus Second Law of Thermodynamics & Ecosystem functioning (Surface-Boundary approach in biothermodynamics, Florov, 1983; 1988; 22). S H T q = T i = + LE t T t t LST datasets from geostationary satellites (MSG LST T2m) Index is used as a first proxy of climatic bioenergetic resources & related entropy production, Φ (Eq. 1) Alert Alert Risk Risk of of yield yield reduction reduction Mean (MSG LST T2m) Index during critical for yield formation period vs. mean yield (27 213) of winter wheat * in Bulgaria Yield Forecast 214 Moderate Yield Assessed 214 Moderate /Ministry of Food and Agriculture/ Based on (LST T2m) approximation Wheat yield forecast for 214 (kg/dk) Mean wheat yield, kg/dka Mean temperature difference (12 LSA SAF LST T2m), deg *NIMH agro database yield estimates. Yield Yield ranking: ranking: Low Low Moderate Moderate High High Application example # 2: MSG LST data in support to assessment of climatic bioenergetic resources

17 First results from local scale applications of Ts from SSM/I microwave observations BULGARIA Needs from reliable estimates of all weather Ts to better characterize land surface states and dry / wet anomalies, and related processes First results using the available 23 dataset through the GlobT web portal This study at NIMH of Bulgaria started in February 216. The initial results were applied as a contribution to an improvement of generation of SSM/I Ts NetCDF files in GlobT data web portal: To fix the problem with script generating the Metadata in the file (as regards to maximum value of dtime /time difference from reference time/and the reference start time value).

18 First results from local scale applications of Ts from SSM/I microwave observations Needs from reliable estimates of all weather Ts to better characterize land surface states and dry / wet anomalies, and related processes Site scale Ts estimates for the region of synoptic station V.Turnovo (43.7;25.62) Example: comparison with synoptic air T2m and seasonality Example: (Ts T2m) evaluation as a drought stress factor Example: (Ts T2m) evaluation to reflect moisture state.

19 Example: Microwave from SSM/I Ts comparing with in situ air T2m day time (6: UTC) Ts vs. T2m night time (18: UTC) Ts vs. T2m 3 Scatterplot SSM/I Ts vs. 6 UTC air T2m V.Turnovo (43.7;25.62), April-Sep 23 R 2 =.5 35 Scatterplot of SSM/I Ts vs. 18 UTC air T2m V.Turnovo (43.7;25.62), April-Sep R 2 = Air T2m, deg Air T2m, deg SSM/I Ts, deg SSM/I Ts, deg Example: April Sep 23 Comparison of T2m measurements at synoptic station V.Turnovo vs. Ts satellite estimates from SSM/I overpasses of the satellites at nearest point For two data sets of air T2m from synoptic observations, morning/ evening time, 6: UTC/ 18: UTC, correspondingly. Satellite overpasses within ± 1 h. A strong correlation for two data sets is observed

20 3 25 Trend of 6: UTC temperature difference between SSM/I Ts and air T2m V.Turnovo (43.7;25.62), April-September 23 6 UTC (SSM/I LST-T2m) difference Morning Ts Tair > Example: Ts from microwave SSM/I: Behavior during heat waves Temperature difference, deg Temperature difference, deg April May June July Aug Sep DOY (April-Sep 23) Course of 18: UTC temperature difference between SSM/I LST and air T2m V.Turnovo (43.7;25.62), April-September UTC (SSM/I LST-T2m) difference DOY (April-Sep 23) Evening Ts Tair < April May June July Aug Sep Heat waves across SE Europe in 23 as characterized by (Ts T2m) difference: Morning time (9: LT) positive (as normal) and high temperature difference during whole season April Sep very high temperature difference of single cases are observed (further studies are needed for explanation) Evening time temperature difference (21: LT) negative(as normal) temperature difference steadily increasing from April to August due to the drought, and becoming even positive, when is the peak of drought in 23 (during August).

21 Example: Ts from microwave SSM/I in parallel to precipitation Course of 6 UTC (Ts-T2m) temperature difference and accumulated precipitation for last 6 hours V.Turnovo (43.7;25.62), April-September Precipitation, mm (SSM/I LST-T2m) Temperature difference, deg April May June July Aug Sep Precipitation, mm -1 DOY (April-Sep 23) Precipitation reduce the (SSM/I Ts T2m) temperature difference V.Turnovo (43.7;25.62) Highly positive values in the periods of persistent drought

22 SSM/I Ts SMA at 5, 2 cm SSM/I Ts SMA 5, 2 cm UTC SSM/I Ts course along with SMA V. Turnovo (43.7;25.62), July UTC SSM/I Ts course along with SMA V.Turnovo, June DOY June 23 P, mm (SSM/I LST -T2m) SMA5 SMA2 SMA5 SMA P, mm Precipitation, mm SSM/I Ts SMA 5 cm depth 1 SMA 2 cm depth SMA5 cm depth 5 SMA 5, 1 cm SMA 5, 1 cm Example: Soil Moisture Availability & behaviour of Ts from microwave SSM/I 23; day time; selected months; SMA site scale estimate from SVAT_bg output Increasing values of temperature difference (SSM/I Ts T2m) are observed along with: Decreasing SMA, valid for all soil depths (5, 2, 5, 1 cm), and Valid for whole season (May, Aug, Sep) Rainfall leads to increase of SMA that corresponds to decreasing (Ts T2m) values -2 DOY July UTC SSM/I Ts course along with SMA V. Turnovo (43.7;25.62), August 23 SSM/I Ts SMA 5, 2 cm Precipitation, mm SSM/I Ts SMA 5 cm depth SMA 5 cm depth SMA 2 cm depth SMA 5 cm depth DOY Aug SMA 5, 1 cm

23 Example: Rainfall leads to increase of SMA and decreasing (Ts T2m) 4 July 23 5 July 23 6 July 23 7 July 23 dry spell wet spell wet spell dry spell (SSM/I Ts- T2m) index and precipitation courses for selected period of July 23 V. Turnovo (43.7;25.62) 1 8 Accumulated precipitation last 12 h, mm 6 UTC SSM/I LST 12 1 Example: 4 11 July 23, region of synoptic station V.Turnovo, Bulgaria (SSM/I Ts-T2m) Index dry spell wet spell dry spell wet spell dry spell Precipitation, mm Days with rainfall are associated with rising of SMA ( wet spells ) During wet spells temperature difference (Ts T2m) is decreasing

24 Concluding remarks/ Prospective/ Challenges Current study proposes an application of the land surface skin temperature Ts (derived by geostationary MSG, LSA SAF LST) in the concept of the functional relations between the water energy food nexus as a background for development of operational DWS on a regional (national) scale. (Ts T2m) Index is applied in terms of Soil Moisture Availability Drought Risk Index to: approximate root zone soil moisture as a measure of agricultural drought risk (severity, duration), characterize space/time variability of drought assess climatic bioenergetic resources and predict potential crop productivity. The use of Ts from microwave SSM/I observations is initiated. The initial results were applied as a contribution to an improvement of generation of SSM/I Ts NetCDF files in GlobT data web portal

25 Concluding remarks/ Prospective/ Challenges Next steps for satellite derived Ts application (in accordance to relevant Copernicus domains): operational DWS regime (generation of soil moisture deficit maps; assessment of crops bioclimatic energetic resources/yield forecast), to identify forest types, detect changes, harmonized information systems including satellite data for wildfire risks assessment and its forecast at a regional (national) level (under development at NIMH), potential implication in Net Primary Productivity (NPP) models. Needs for surface temperature Ts data at both medium (1 5 km) and higher (1 m) resolutions, reliable estimates for all weather conditions from microwave measurements or merged products with gostationary and microwaves, and in standard formats (netcdf, HDF). Link between Ts and T2m at a regional level. Acknowledgements: For the purposes of this study, new version of ECMWF model is introduced for use at NIMH, Bulgaria by Plamen Neytchev, Evgeni Vladimirov. SSM/I LST data sets 23 from the GlobTemperature Data Portal are used. These microwave LSTs have been produced with support from NASA, CNRS, and more recently by GlobTemperature.