About

This page provides explanation on the maps, the method, the input data used, and references.

About the maps

Precipitation Estimated total precipitation (the sum of rainfall, snow, hail, etc)
Streamflow Estimated amount of overland runoff and groundwater that locally enters the streams
Catchment Water Storage Estimated total sum of water stored in the catchment, whether as snow, ponding water, soil water, groundwater or water in local streams.
Actual values Value expressed in millimetres (mm) per day: 1 mm water is equivalent to 1 litre per square meter. For periods of several days, values are usually the average daily flow (in mm per day) and should be multiplied with the number of days per period to estimate total amount.
Anomalies Difference between the actual value and the long-term (1950 onwards) average value for that same day or period.
Deciles Ranking that indicates how unusual the conditions are, when compared to conditions for the same day or period in previous years (1950 onwards). Classes include above/ below average and very much above/below average (meaning that, on average, such conditions occurred less than 3 and less than 1 out of every 10 years, respectively) and highest/lowest on record (meaning conditions have not occurred since at least 1950)
Percentage of Average Value expressed as a percentage of the long-term (1950 onwards) average value for that same day or period.

Method overview

The Asia-Pacific Water Monitor (‘the Monitor’) provides daily updated estimates of recent precipitation, streamflow and catchment water storage. These are provided as actual quantities, anomalies, deciles and percentage of average. The meaning of the different terms is given below.

The Monitor is based on a water balance model that is updated daily using weather data derived from a combination of on-ground and satellite measurements and weather forecasts. The model used in the Monitor is the World-Wide Water Resources Assessment (W3RA) model. It shares a common heritage with the Australian Water Resources Assessment Landscape (AWRA-L) (Van Dijk, 2010) but is applicable to a wider range of conditions. Processes such as evapotranspiration, soil and groundwater movement and streamflow are represented for two vegetation classes in each 1° grid cell (forest and non-forest cover). The climate data that are fed into the model are a combination of several available sources that are blended to obtain the best estimates of past and current conditions (see input data sources below).

Water balance estimates from the W3RA are produced globally, but the Monitor only shows results for the Asia-Pacific region. Global gridded estimates of several model-estimated variables are freely available for research purposes; to request these data, please contact us.

Input data

The climate data that are fed into the model include the following:

  • Near-surface meteorological data for the period 1948-2008, derived by blending weather forecast model reanalysis data with satellite and on-ground observations (Sheffield and others, 2006). Data is available from Princeton University (go to data)
  • 3-hourly rainfall estimates from observations by several satellites (code TMPA 3B42 RT; Huffman et al., 2007). The data used here were acquired as part of the activities of NASA’s Science Mission Directorate, and are archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC) (go to data)
  • Weather forecast model reanalysis data (called ERA-Interim) produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). (go to data)
  • Weather forecasts from the ECMWF made available through the TIGGE initiative (go to data)

In addition, a number of satellite and mapping data sources have been used in setting up the W3RA model.

References

Sheffield, J., G. Goteti, and E. F. Wood. Development of a 50-yr high-resolution global dataset of meteorological forcings for land surface modeling, Journal of Climate 19, 3088-3111, 2006. (link to publication)

Van Dijk, A.I.J.M. AWRA Technical Report 3: Landscape Model (version 0.5) Technical Description. WIRADA / CSIRO report, 2010 (link to publication)

Huffman, G. J., R. F. Adler, D. T. Bolvin, G. J. Gu, E. J. Nelkin, K. P. Bowman, Y. Hong, E. F. Stocker, and D. B. Wolff. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology 8, 38-55, 2007 (link to publication)