Performance Assessment of Spatial Interpolation of Precipitation for Hydrological Process Simulation in the Three Gorges Basin

Accurate assessment of spatial and temporal precipitation is crucial for simulating hydrological processes in basins, but is challenging due to insufficient rain gauges. Our study aims to analyze different precipitation interpolation schemes and their performances in runoff simulation during light and heavy rain periods. In particular, combinations of different interpolation estimates are explored and their performances in runoff simulation are discussed. The study was carried out in the Pengxi River basin of the Three Gorges Basin. Precipitation data from 16 rain gauges were interpolated using the Thiessen Polygon (TP), Inverse Distance Weighted (IDW), and Co-Kriging (CK) methods. Results showed that streamflow predictions employing CK inputs demonstrated the best performance in the whole process, in terms of the Nash–Sutcliffe Coefficient (NSE), the coefficient of determination (R2), and the Root Mean Square Error (RMSE) indices. The TP, IDW, and CK methods showed good performance in the heavy rain period but poor performance in the light rain period compared with the default method (least sophisticated nearest neighbor technique) in Soil and Water Assessment Tool (SWAT). Furthermore, the correlation between the dynamic weight of one method and its performance during runoff simulation followed a parabolic function. The combination of CK and TP achieved a better performance in decreasing the largest and lowest absolute errors compared to any single method, but the IDW method outperformed all methods in terms of the median absolute error. However, it is clear from our findings that interpolation methods should be chosen depending on the amount of precipitation, adaptability of the method, and accuracy of the estimate in different rain periods.

[1]  Bernard A. Engel,et al.  Marginal land suitability for switchgrass, Miscanthus and hybrid poplar in the Upper Mississippi River Basin (UMRB) , 2017, Environ. Model. Softw..

[2]  Nitin Muttil,et al.  Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments , 2017 .

[3]  F. Yuan,et al.  Comparison of Spatial Interpolation Schemes for Rainfall Data and Application in Hydrological Modeling , 2017 .

[4]  B. Engel,et al.  Modelling hydrology and water quality processes in the Pengxi River basin of the Three Gorges Reservoir using the soil and water assessment tool , 2017 .

[5]  B. D. Dasanto,et al.  Application of hourly radar-gauge merging method for quantitative precipitation estimates , 2017 .

[6]  Mohammad Mahadi Hasan,et al.  Improving radar rainfall estimation by merging point rainfall measurements within a model combination framework , 2016 .

[7]  N. Giesen,et al.  Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas - A review , 2016 .

[8]  Mohammad Mahadi Hasan,et al.  Merging radar and in situ rainfall measurements: An assessment of different combination algorithms , 2016 .

[9]  E. Bean,et al.  Field Evaluation of Nitrogen Treatment by Conventional and Single-Pass Sand Filter Onsite Wastewater Systems in the North Carolina Piedmont , 2016, Water, Air, & Soil Pollution.

[10]  David G. Tarboton,et al.  An overview of current applications, challenges, and future trends in distributed process-based models in hydrology , 2016 .

[11]  H. Safavi,et al.  A modified regionalization weighting approach for climate change impact assessment at watershed scale , 2015, Theoretical and Applied Climatology.

[12]  Guoping Zhang,et al.  A comparison among spatial interpolation techniques for daily rainfall data in Sichuan Province, China , 2015 .

[13]  Nitin Muttil,et al.  Optimal design of rain gauge network in the Middle Yarra River catchment, Australia , 2015 .

[14]  Dmitri Kavetski,et al.  A unified approach for process‐based hydrologic modeling: 2. Model implementation and case studies , 2015 .

[15]  M. Piniewski,et al.  Improvement of Hydrological Simulations by Applying Daily Precipitation Interpolation Schemes in Meso-Scale Catchments , 2015 .

[16]  Elena Volpi,et al.  Hydrological effects of within-catchment heterogeneity of drainage density , 2015 .

[17]  Lijing Wang,et al.  Evaluation of Gridded Precipitation Data for Driving SWAT Model in Area Upstream of Three Gorges Reservoir , 2014, PloS one.

[18]  Min-kyeong Kim,et al.  Transferability of SWAT Models between SWAT2009 and SWAT2012. , 2014, Journal of environmental quality.

[19]  Jin Li,et al.  Spatial interpolation methods applied in the environmental sciences: A review , 2014, Environ. Model. Softw..

[20]  E. Arnone,et al.  Influence of Spatial Precipitation Sampling on Hydrological Response at the Catchment Scale , 2014 .

[21]  L. Galván,et al.  Rainfall estimation in SWAT: An alternative method to simulate orographic precipitation , 2014 .

[22]  Muhammad A. Al-Zahrani,et al.  Estimation of rainfall distribution for the southwestern region of Saudi Arabia , 2014 .

[23]  Christian Berndt,et al.  Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios , 2014 .

[24]  A. Jacquin,et al.  Interpolation of monthly precipitation amounts in mountainous catchments with sparse precipitation networks , 2013 .

[25]  Ashish Sharma,et al.  Merging gauge and satellite rainfall with specification of associated uncertainty across Australia , 2013 .

[26]  B. Zahraie,et al.  Evaluation of spatial and spatiotemporal estimation methods in simulation of precipitation variability patterns , 2013, Theoretical and Applied Climatology.

[27]  Kenneth J. Tobin,et al.  Temporal analysis of Soil and Water Assessment Tool (SWAT) performance based on remotely sensed precipitation products , 2013 .

[28]  Lei Chen,et al.  Impact of spatial rainfall variability on hydrology and nonpoint source pollution modeling , 2012 .

[29]  P. Fiener,et al.  Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions , 2012 .

[30]  S. Liong,et al.  SWAT use of gridded observations for simulating runoff - a Vietnam river basin study , 2011 .

[31]  Murugesu Sivapalan,et al.  Spatiotemporal scaling of hydrological and agrochemical export dynamics in a tile‐drained Midwestern watershed , 2011 .

[32]  Jin Li,et al.  A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors , 2011, Ecol. Informatics.

[33]  Ashish Sharma,et al.  Global Sea Surface Temperature Forecasts Using a Pairwise Dynamic Combination Approach , 2011 .

[34]  A. Fares,et al.  Comparison of Rainfall Interpolation Methods in a Mountainous Region of a Tropical Island , 2011 .

[35]  Vladimir U. Smakhtin,et al.  Assessing the Impact of Areal Precipitation Input on Streamflow Simulations Using the SWAT Model 1 , 2011 .

[36]  Indrajeet Chaubey,et al.  Regionalization of SWAT Model Parameters for Use in Ungauged Watersheds , 2010 .

[37]  Efrat Morin,et al.  Improving interpolation of daily precipitation for hydrologic modelling: spatial patterns of preferred interpolators , 2009 .

[38]  Laurent Delobbe,et al.  Evaluation of radar-gauge merging methods for quantitative precipitation estimates , 2009 .

[39]  Denis Ruelland,et al.  Sensitivity of a lumped and semi-distributed hydrological model to several methods of rainfall interpolation on a large basin in West Africa , 2008 .

[40]  Bernard A. Engel,et al.  Fitting of Time Series Models to Forecast Streamflow and Groundwater Using Simulated Data from Swat , 2008 .

[41]  Gilles Boulet,et al.  Understanding hydrological processes with scarce data in a mountain environment , 2008 .

[42]  Jun Wang,et al.  An object oriented approach to the description and simulation of watershed scale hydrologic processes , 2005, Comput. Geosci..

[43]  D. Tetzlaff,et al.  Effects of spatial variability of precipitation for process-orientated hydrological modelling: results from two nested catchments , 2005 .

[44]  Upmanu Lall,et al.  Improved Combination of Multiple Atmospheric GCM Ensembles for Seasonal Prediction , 2004 .

[45]  S. Uhlenbrook,et al.  Hydrological process representation at the meso-scale: the potential of a distributed, conceptual catchment model , 2004 .

[46]  C. M. Kishtawal,et al.  Multimodel Ensemble Forecasts for Weather and Seasonal Climate , 2000 .

[47]  A. H. Thiessen PRECIPITATION AVERAGES FOR LARGE AREAS , 1911 .

[48]  C. Bernhofer,et al.  Comparison of spatial interpolation methods for the estimation of precipitation distribution in Distrito Federal, Brazil , 2015, Theoretical and Applied Climatology.

[49]  Zhongbo Yu,et al.  Spatial and Temporal Scale Effect in Simulating Hydrologic Processes in a Watershed , 2014 .

[50]  A. Dégre,et al.  Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale: a review , 2013 .

[51]  Neil R. Viney,et al.  The effect of soil data resolution on hydrological processes modelling in a large humid watershed , 2011 .

[52]  D. G. Sullivan,et al.  Evaluation of SWAT Manual Calibration and Input Parameter Sensitivity in the Little River Watershed , 2007 .

[53]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[54]  J. Yamamoto Correcting the Smoothing Effect of Ordinary Kriging Estimates , 2005 .

[55]  G. Pegram,et al.  Combining radar and rain gauge rainfall estimates using conditional merging , 2005 .

[56]  David D. Bosch,et al.  PROBLEMS AND POTENTIAL OF AUTOCALIBRATING A HYDROLOGIC MODEL , 2005 .

[57]  M. Sálek The radar and raingauge merge precipitation estimate of daily rainfall — First results in the Czech Republic , 2000 .

[58]  Adel Shirmohammadi,et al.  Hydrology of Alluvial Stream Channels in Southern Coastal Plain Watersheds , 1986 .