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GeoWRSI for Crop Monitoring and Early Warning in Tanzania

Explore the use of GeoWRSI in estimating water balance model parameters across Tanzania for enhanced crop monitoring and early warning systems. Access GeoWRSI functions to analyze rainfall estimates, climatological data, yield regression models, and more.

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GeoWRSI for Crop Monitoring and Early Warning in Tanzania

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  1. The Use of GeoWRSI in Crop Monitoring and Early Warning in Tanzania Isack B. Yonah Research and Applied Meteorology

  2. Introduction

  3. GeoWRSI Training at MAFC GeoWRSI Training workshop at MAFC , conducted by Mr Tamuka Magadzire, a GeoWRSI expert from FEWSNet (SADC). Organizers: SUA & MAFC Installation of GeoWRSI at TMA (Agromet Office) TMA Research & Applied Meteorology

  4. What is the GeoWRSI • A spatially explicit version of the water requirements satisfaction index (WRSI) attempting to estimate values of WRSI and related water-balance model parameters everywhere on a map. • A stand-alone, portable, installable version of the operational GeoWRSI run at USGS for the FEWSNET activity. (http://earlywarning.usgs.gov/fews) • Focuses on using raster datasets • Download: ftp://hollywood.geog.ucsb.edu/pub/geowrsi TMA Research & Applied Meteorology

  5. GeoWRSI ToolBar Import Precip and PET Data into GeoWRSI Download RFE and PET data Define options for WRSI outputs View list of available data Modify settings for WRSI calculation Merge rain gauge with RFE to improve Unbias rainfall estimates View Intra-seasonal rainfall summaries Calculate WRSI & other WB parameters Climatological Rainfall Analysis (mean, trend, percentile etc Climatological Water Balance Analysis Develop Yield Regression Models Batch Assistant For Easily Developing Automation Scripts Batch Editor For Editing Automation Scripts Display Spatial Data TMA Research & Applied Meteorology Extract Statistics

  6. GeoWRSI Schematic Historical Analysis Coarse Resolution and Global Datasets Current and Long Term Rainfall Analysis Historical Water Balance Parameters Time Series Crop WB Parameters Rainfall Combine Global and Local Planting Run Water Balance Crop Cycle Length Historical Crop Statistics Regression Modeling Ground Data and Local knowledge Yield & Prodn Estimates TMA Research & Applied Meteorology

  7. Water Requirement Satisfaction Index (WRSI) Implementation by USGS WRSI = f (ppt, pet, WHC, Crop Type, SOS, EOS, LGP) data from NOAA, generated at EDC RFE (NOAA) FAO soils map of the world Kc (FAO) TMA Research & Applied Meteorology

  8. AET WRSI = * 100 WR WR = ETo * Kc WRSI computation It is calculated as the ratio of seasonal actual evapotranspiration (AET) to the seasonal crop water requirement (WR): WR = Crop Water Requirement from Penman-Monteith Equation ( Shuttleworth, 1992) PET = Potential Evapotranspiration Kc = Crop coefficient (Doorenboos & Pruitt 1977, Gommes, 1993) AET = Actual Evapotranspiration, representing the actual amount of water withdrawn form the soil water reservoir TMA Research & Applied Meteorology

  9. GeoWRSI functions • The available functions in GeoWRSI can be classified as follows: • Water Balance Modeling • Modifying water balance model parameters • Incorporating field data • Updating data inputs • Viewing data inputs and outputs • Specifying and accessing data products • Analyzing Rainfall Estimates • Climatological analysis of rainfall and water balance products • Batch processing • Yield and production estimation • Training • GIS tools • These functions are as follows: TMA Research & Applied Meteorology

  10. GeoWRSI functions • Water Balance Modeling • Running the water balance model dekad by dekad, or automatically, producing, among various products, • WRSI, • SOS estimates • Soil water index • Water deficits • Surplus water • … and other water balance parameters TMA Research & Applied Meteorology

  11. Example TMA Research & Applied Meteorology

  12. Potentials of the GeoWRSI? • to give the user a value for water balance parameters everywhere • Inherently recognizes the need for informed users to be able to input their own parameters and improved datasets where available, as opposed to centralized model run • Recognizes the problem of often unavailable datasets, thereby allowing the program to be run with often-readily available datasets TMA Research & Applied Meteorology

  13. Water balance model PPT WR = Kc * ETo ET Runoff Surplus WR WHC Deficit Drainage SW = SWi-1 + PPT - ET TMA Research & Applied Meteorology

  14. What is WRSI • The WRSI is an index originally developed by FAO that is based on a crop-specific water balance model • By keeping track of the relationship between available soil water and the amount of water the crop required throughout the growing season, the WRSI can help to show the extent to which the crop has been negatively affected by any water deficits it experiences • Other products related to water balance can also be calculated TMA Research & Applied Meteorology

  15. AET WRSI = * 100 WR WR = ETo * Kc WRSI computation It is calculated as the ratio of seasonal actual evapotranspiration (AET) to the seasonal crop water requirement (WR): WR = Crop Water Requirement from Penman-Monteith Equation ( Shuttleworth, 1992) PET = Potential Evapotranspiration Kc = Crop coefficient (Doorenboos & Pruitt 1977, Gommes, 1993) AET = Actual Evapotranspiration, representing the actual amount of water withdrawn form the soil water reservoir TMA Research & Applied Meteorology

  16. Why Use WRSI ? When Water is a limiting factor, then WRSI can be used to estimate: Production (tons) = f (Yield) Yield (tons/ha) = f (WRSI) WRSI = f(Water Available to the crop) TMA Research & Applied Meteorology

  17. GeoWRSI_WB products • Water Balance (WB) Modeling • Running the water balance model dekad by dekad, or automatically, producing, among various products, • WRSI, • SOS estimates • Soil water index • Water deficits • Surplus water • … and other water balance parameters TMA Research & Applied Meteorology

  18. Sample products for Tanzania TMA Research & Applied Meteorology

  19. Objectives • To produce the IRE for yield estimates • To Validate the RFE • To monitor crop development (WRSI) • Develop and run crop model for yield estimation • issue an early warning on impact of weather on crop performance and anticipated yields TMA Research & Applied Meteorology

  20. Data • Gauge data-Dekadal rainfall data (2001-2012) 18 Stations- TMA • Potential Evapotranspiration (Global)- NOAA (EDC) • Satellite Rainfall Estimates(RFE) 201-2012 (Dek 2 Nov, 2012) - NOAA • Yield data for maize(2001-2010) - MAFC • Water Holding Capacity (WHC) – FAO TMA Research & Applied Meteorology

  21. Rainfall amounts Dekadal Rainfall distribution (mm) Source: TMA Dekadal Weather Review-November 11-20, 2012 TMA Research & Applied Meteorology

  22. 20-31 October 2012 rainfall total 20-31 October 2012 rainfall anomaly TMA Research & Applied Meteorology

  23. Rainfall – actual & estimates Rainfall gauge stations Improved RFE TMA Research & Applied Meteorology

  24. SOS TMA Research & Applied Meteorology

  25. Soil water index %LGP (Crop phenology) TMA Research & Applied Meteorology

  26. WRSI TMA Research & Applied Meteorology

  27. WRSI WRSI for maize 2011/12 WRSI anomaly 2011/12 TMA Research & Applied Meteorology

  28. Yield estimates TMA Research & Applied Meteorology

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  31. Model performance Mbeya Rukwa Model output for Rukwa - Maize Multiple Regression of Yield on _WRSI_Index_EOS_ F-value: 15.0911 Prob>F : 0.0046 R-sqd : 0.6535 Model: Yield = -8.3927 + _WRSI_Index_EOS_ * 0.1097 • Model output for Mbeya - Maize • Multiple Regression of Yield on _WRSI_Index_EOS_ • F-value: 33.6848 • Prob>F : 0.0004 • R-sqd : 0.8081 • Model: Yield = -8.3235 + _WRSI_Index_EOS_ * 0.107 TMA Research & Applied Meteorology

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  34. Model performance Ruvuma Iringa Model output for Iringa - Maize Multiple Regression of Yield on _ActualEvap_VEG_ F-value: 7.8972 Prob>F : 0.0228 R-sqd : 0.4968 Model: Yield = 4.4005 + _ActualEvap_VEG_ * -0.0212 • Model output for Ruvuma - Maize • Multiple Regression of Yield on _ActualEvap_RPE_ • F-value: 3.9023 • Prob>F : 0.0836 • R-sqd : 0.3279 • Model: Yield = 0.456 + _ActualEvap_RPE_ * 0.0121 TMA Research & Applied Meteorology

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  38. Model performance Dodoma Kagera F-value: 5.8431 Prob>F : 0.0322 R-sqd : 0.6254 Model: Yield = -2.5137 + _ActualEvap_INI_ * 0.1593 + _ActualEvap_VEG_ * 0.0328 • F-value: 17.2114 • Prob>F : 0.0032 • R-sqd : 0.6827 • Model: Yield = -2.0117 + _WRSI_Index_EOS_ * 0.0331 TMA Research & Applied Meteorology

  39. Shinyanga – model performance • F-value: 34.7735 • Prob>F : 0.0004 • R-sqd : 0.813 • Model: Yield = 1.7328 + CropArea * 0 TMA Research & Applied Meteorology

  40. Conclusion • The tool could be used for crop modeling (monitoring and yield estimation); • with limited number of stations, the software will help to produce improved rainfall estimates from satellite rainfall estimates useful for monitoring, research and forecast verification; • wide range of products will be made available to various users and are likely to improve the current products such as bulletins that are issued by TMA and MAFC on dekadal, monthly and seasonal basis for local and international users. • The ability to produce extended time series of rainfall surfaces over the past 50 years or more also facilitates long term analysis of rainfall, start of season and crop suitability, among others. • However, the successful operations of GeoWRSI project in Tanzania will be determined by the commitment of TMA and MAFC to update data and generate products for various uses. TMA Research & Applied Meteorology

  41. The End THANK YOU FOR LISTENING TMA Research & Applied Meteorology

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