observations to generate 1- to 4-month drought predictions. The system helps
farmers, commodity investors, local governments, and global relief organizations
plan for and react to droughts [84].
GIDMaPS is designed as a cyber-infrastructure system to facilitate drought
analysis based on multiple indicators and input data sets. The system can support
advanced data management, acquisition, storage, and visualization. The system
integrates data from multiple institutions and provides historical and near-real-time
drought conditions as well as probabilistic future forecasts [83]. Currently the
monitoring and prediction information are based on three indicators: standardized
precipitation index (SPI) as a measure of meteorological drought, standardized soil
moisture index (SSI) as indicator of agricultural drought, and the multivariate
standardized drought index as a composite agro-meteorological drought index.
From the main interface, users can select the input data, drought indicator, year,
and month to visualize or download drought information [84]. Other investigators
have been able to predict agricultural drought through the prediction of agricultural
yield using models based on the SPI and the NDVI [137].
Recent advances in remote sensing from satellites and radar, as well as the use of
thousands of daily in situ precipitation measurements, have significantly improved
drought monitoring capabilities [138]. Significant advances in mitigating drought
impacts have also been made by forecasting the conditions that result in drought.
Meteorologists at the NOAA Climate Prediction Center (CPC) are using mediumrange forecast models to predict soil moisture 2 weeks into the future. For the
longer term, meteorologists are using statistical techniques and historical drought
information to construct analogues to current conditions. They then create forecasts
up to several seasons ahead of time based on past events. CPC is also using
sophisticated computer models that link ground and ocean conditions to the overlying atmosphere to create forecasts of temperature, precipitation, and soil moisture
months in advance [138].
8 Conclusions
In arid and semiarid environments, the exploration, detection, mapping, and monitoring of water resources are a prerequisite for freshwater availability, accessibility, fair utilization, and rational management. Arid lands, including soil degradation
and irrigated areas, have been classified based on vegetation and aridity indices
using remotely sensed data. The arid regions have been delimited on a global scale
by combining climate data, i.e., aridity index (AI), and vegetation data, i.e.,
vegetation index. Maps of the global distribution of arid regions have been produced using these unified criteria that have both physical and biological meaning.
To monitor and predict droughts, systems exist that use multiple drought indicators
to allow users to visualize and download drought information. Significant advances
in mitigating drought impacts have also been made by forecasting the conditions
that result in drought.
52
V. Klemas and A. Pieterse
farmers, commodity investors, local governments, and global relief organizations
plan for and react to droughts [84].
GIDMaPS is designed as a cyber-infrastructure system to facilitate drought
analysis based on multiple indicators and input data sets. The system can support
advanced data management, acquisition, storage, and visualization. The system
integrates data from multiple institutions and provides historical and near-real-time
drought conditions as well as probabilistic future forecasts [83]. Currently the
monitoring and prediction information are based on three indicators: standardized
precipitation index (SPI) as a measure of meteorological drought, standardized soil
moisture index (SSI) as indicator of agricultural drought, and the multivariate
standardized drought index as a composite agro-meteorological drought index.
From the main interface, users can select the input data, drought indicator, year,
and month to visualize or download drought information [84]. Other investigators
have been able to predict agricultural drought through the prediction of agricultural
yield using models based on the SPI and the NDVI [137].
Recent advances in remote sensing from satellites and radar, as well as the use of
thousands of daily in situ precipitation measurements, have significantly improved
drought monitoring capabilities [138]. Significant advances in mitigating drought
impacts have also been made by forecasting the conditions that result in drought.
Meteorologists at the NOAA Climate Prediction Center (CPC) are using mediumrange forecast models to predict soil moisture 2 weeks into the future. For the
longer term, meteorologists are using statistical techniques and historical drought
information to construct analogues to current conditions. They then create forecasts
up to several seasons ahead of time based on past events. CPC is also using
sophisticated computer models that link ground and ocean conditions to the overlying atmosphere to create forecasts of temperature, precipitation, and soil moisture
months in advance [138].
8 Conclusions
In arid and semiarid environments, the exploration, detection, mapping, and monitoring of water resources are a prerequisite for freshwater availability, accessibility, fair utilization, and rational management. Arid lands, including soil degradation
and irrigated areas, have been classified based on vegetation and aridity indices
using remotely sensed data. The arid regions have been delimited on a global scale
by combining climate data, i.e., aridity index (AI), and vegetation data, i.e.,
vegetation index. Maps of the global distribution of arid regions have been produced using these unified criteria that have both physical and biological meaning.
To monitor and predict droughts, systems exist that use multiple drought indicators
to allow users to visualize and download drought information. Significant advances
in mitigating drought impacts have also been made by forecasting the conditions
that result in drought.
52
V. Klemas and A. Pieterse