{"id":467,"date":"2024-01-17T21:31:46","date_gmt":"2024-01-17T21:31:46","guid":{"rendered":"https:\/\/research.reading.ac.uk\/tamsat\/?page_id=467"},"modified":"2024-09-14T20:37:48","modified_gmt":"2024-09-14T19:37:48","slug":"impact_tamsat_alert","status":"publish","type":"page","link":"https:\/\/research.reading.ac.uk\/tamsat\/impact_tamsat_alert\/","title":{"rendered":"TAMSAT-ALERT – forecasting of agricultural drought"},"content":{"rendered":"

Summary<\/strong><\/h3>\n

Early warning of drought is crucial for a range of decision makers to mitigate their exposure to risk. For small-scale farmers, reliable information on the likelihood of drought can inform decisions about when to prepare land, plant and apply topdressing, which variety of seeds to buy and plant, how best to manage grazing resources, and post-harvest decisions around selling and storage. Drought information is also important to a range of decision makers who require an understanding of drought risk to inform their activities to mitigate drought impacts (for example, in the humanitarian and financial sectors).<\/p>\n

Multiple sources of environmental information are available to support decision making around drought risk, including remotely-sensed and ground-based measures of rainfall and soil moisture, model reanalyses and meteorological forecasts.<\/p>\n

An exciting new development for TAMSAT is the capacity for forecast agricultural drought – defined as deficit in soil moisture that leads to plant water stress. These forecasts are undertaken using the TAMSAT-ALERT<\/strong> framework.\u00a0TAMSAT-ALERT<\/strong>\u00a0(TAMSAT<\/strong>–A<\/strong>griculturaL<\/strong>\u00a0E<\/strong>arly waR<\/strong>ning sysT<\/strong>em) is a monitoring and decision support tool that makes use of such data streams to assess and anticipate the risk of drought.<\/p>\n

How does TAMSAT-ALERT work?<\/strong><\/h3>\n

TAMSAT-ALERT provides reliable forecasts of land-surface conditions (namely, soil moisture) at timescales from weeks (for germination) to months (for crop yield). It does so by using meteorological variables to drive a land surface model.<\/p>\n

Historical<\/span>:\u00a0<\/em>Historical meteorological variables (including rainfall, wind and temperature) are used to drive the land-surface model to produce a historical timeseries (1983-present) of land-surface variables. The historical time series of land-surface metrics provides a climatology of land-surface metrics for the period of interest.<\/p>\n

Forecast<\/span>:\u00a0<\/em>Historical meteorological variables are used to construct the climatological ensemble of meteorological variables. The initial conditions of the land-surface model are estimated from the historical run. Using each of the climatological ensemble members, the land-surface model is driven forward in time to produce an ensemble time series of future land-surface conditions. Combining the historical timeseries and ensemble of future land-surface variables, an ensemble of the land-surface metric of interest can be derived for the whole period (incorporating the historic or observed period and the forecast period). The ensemble can be weighted based on a related meteorological forecast. Considering both the historical and ensemble time series, the likelihood of an adverse event (drought) can be estimated.<\/p>\n

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\nIn doing so, TAMSAT-ALERT considers the\u2026<\/p>\n