| Session Details | |
| Section | HS - Hydrological Sciences |
| Session Title | Hydrologic Prediction in Data-scarce Situations |
| Main Convener | Dr. Basudev Biswal (Indian Institute of Technology Hyderabad, India) |
| Co-convener(s) | Dr. Guangyao Gao (Chinese Academy of Sciences, China) Prof. Dawen Yang (Tsinghua University, China) Prof. Bellie Sivakumar (University of New South Wales, Australia) |
| Session Description | Planning and designing of water resources infrastructure require adequate amount of high-quality streamflow, evapotranspiration and ground-water data. Unfortunately, most streams and rivers in the world are either ungauged or poorly gauged. In most places, ground-water data is inadequate for sound policy decision-making. Similarly, direct measurement of evapotranspiration is a rarity. Moreover, existing data may not be adequate for projecting response of hydrologic systems under climate change. Water resources engineers and scientists have, therefore, adopted several approaches to address the problem of data scarcity, such as by transferring streamflow time series and flow duration curves from gauged locations to ungauged locations, which is known as regionalization. The list of regionalization methods include statistical as well as process-based methods. Models based on the concept of dryness-index, such as the Budyko model, are also found to be useful for estimation of hydrologic variables in data-scarce regions, as they require only little or even no calibration. Several approaches can be found in the hydrologic literature to model ground-water availability in data-scarce regions by performing baseflow recession analysis. In recent years, numerous attempts have been made to improve accuracy of prediction in data-scarce regions using data products obtained through satellite remote sensing like radar-based rainfall data and GRACE based terrestrial storage anomaly (TWSA) data. This session aims to serve as a platform for research subjects related to prediction in data-scarce situations including but not limited to the following topics: regionalization methods; methods for prediction under changing climatic conditions; usefulness of dryness-index based models like the Budyko model; and applications of satellite based data like radar rainfall data and GRACE based TWSA data. |