How can flood modelling advance in the “big data” age?

Maskey, Shreedhar

doi:10.1111/jfr3.12560

Cited by 6 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the last few decades, developments in remote sensing earth observations have improved data availability for hydrological modelling and water resources applications such as drought monitoring [3,4], flood modelling and risk management [5,6], water level monitoring [7][8][9], and glaciers and snow cover estimations [10,11]. Such data includes topographic, land cover, and hydro-meteorological products with different spatial (1/3600 • × 1/3600 • to 1 • × 1 • ) and temporal (three hourly to daily, monthly or annual) resolutions.…”

Section: Introductionmentioning

confidence: 99%

Hydrological Model Calibration with Streamflow and Remote Sensing Based Evapotranspiration Data in a Data Poor Basin

2020

Self Cite

View full text Add to dashboard Cite

Conventional calibration methods adopted in hydrological modelling are based on streamflow data measured at certain river sections. However, streamflow measurements are usually sparse and, in such instances, remote-sensing-based products may be used as an additional dataset(s) in hydrological model calibration. This study compares two main calibration approaches: (a) single variable calibration with streamflow and evapotranspiration separately, and (b) multi-variable calibration with both variables together. Here, we used remote sensing-based evapotranspiration data from Global Land Evaporation: the Amsterdam Model (GLEAM ET), and measured streamflow at four stations to calibrate a Soil and Water Assessment Tool (SWAT) and evaluate the performances for Chindwin Basin, Myanmar. Our results showed that when one variable (either streamflow or evapotranspiration) is used for calibration, it led to good performance with respect to the calibration variable but resulted in reduced performance in the other variable. In the multi-variable calibration using both streamflow and evapotranspiration, reasonable results were obtained for both variables. For example, at the basin outlet, the best NSEs (Nash-Sutcliffe Efficiencies) of streamflow and evapotranspiration on monthly time series are, respectively, 0.98 and 0.59 in the calibration with streamflow alone, and 0.69 and 0.73 in the calibration with evapotranspiration alone. Whereas, in the multi-variable calibration, the NSEs at the basin outlet are 0.97 and 0.64 for streamflow and evapotranspiration, respectively. The results suggest that the GLEAM ET data, together with streamflow data, can be used for model calibration in the study region as the simulation results show reasonable performance for streamflow with an NSE > 0.85. Results also show that many different sets of parameter values (‘good parameter sets’) can produce results comparable to the best parameter set.

show abstract

Section: Introductionmentioning

confidence: 99%

Hydrological Model Calibration with Streamflow and Remote Sensing Based Evapotranspiration Data in a Data Poor Basin

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since the 1990s, exponential growth in computer storage and computational capacity is clearly evident, allowing for the use of more complex algorithms and methods for flood risk mapping (Maskey, 2019). Initially, such applications were limited to small areas where detailed topographic data could be obtained, but nowadays, the availability of global digital elevation model data and satellite information allows us to apply inundation models to large areas.…”

mentioning

confidence: 99%