Using Remote Sensing Data‐Based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments

Abstract

Because remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged catchments and poorly gauged regions, a challenging area of research in hydrology. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments in the Yalong River basin in China. To this end, seven RS data calibration schemes are explored and compared to direct calibration against observed runoff and traditional regionalization using spatial proximity to predict runoff in ungauged catchments. The results show that using bias‐corrected remotely sensed AET (bias‐corrected PML‐AET data) for constraining model calibration performs much better than using the raw remotely sensed AET data (nonbias‐corrected AET obtained from PML model estimate). Using the bias‐corrected PML‐AET data in a gridded way is much better than using lumped data and outperforms the traditional regionalization approach especially in headwater and large catchments. Combining the bias‐corrected PML‐AET and GRACE water storage data performs similarly to using the bias‐corrected PML‐AET data only. This study demonstrates that there is great potential in using bias‐corrected RS‐AET data to calibrating hydrological models (without the need for gauged streamflow data) to estimate daily and monthly runoff time series in ungauged catchments and sparsely gauged regions.

Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review

“…Remote sensing data can also be used directly to calibrate model parameters in ungauged catchments without streamflow observation (Kunnath-Poovakka, Ryu, Renzullo, & George, 2016) or constraining model parameters or hydrological signature (Kunnath-Poovakka, Ryu, Renzullo, & George, 2018). For example, Zhang et al (2020) proposed a new approach to calibrate the hydrological model using sorely remotely sensed evapotranspiration, and the calibrated parameters set, to predict the streamflow time series directly. Since this approach does not use observed streamflow for model calibration, it does not require regionalization.…”

“…Since this approach does not use observed streamflow for model calibration, it does not require regionalization. This approach performs very well in wet regions of Australia and has a large potential for data-sparse or ungauged regions (Huang et al, 2020). Apart from calibration using remote sensing data, physically based hydrological numerical models that do not need to calibrations are also developing fast these years due to recent advances in computing power and data availability (Lewis, 2016).…”

Responses of Precipitation and Runoff to Climate Warming and Implications for Future Drought Changes in China

“…Comparing to precipitation, the more scattered spatial patterns in runoff scaling rates can be attributed to varying terrestrial conditions across different catchments. For example, differences in catchment characteristics such as areas, elevation, slope, and vegetation index lead to divergent runoff regimes (Huang et al, 2020) and further varied sensitivity to rising temperatures. If a catchment owes high vegetation index, the associated large surface roughness and low albedo can weaken runoff sensitivity to future warming climates (Lean & Warrilow, 1989).…”

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

“…Therefore, attention should be paid when using global ET products (such as MOD16A2) at a watershed scale, particularly when the watershed involves snowmelt processes. Moreover, it is necessary to have bias corrections of the ET data before use for model parameterization [7].…”