Hydrological Modeling in the Upper Lancang-Mekong River Basin Using Global and Regional Gridded Meteorological Re-Analyses
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. Evaluation Index
2.4. Hydrological Models
2.5. SWAT+ Parameter Sensitivity Analysis and Calibration Tool
3. Results
3.1. Spatial Annual Average Distribution of CFSR Dataset and the Difference for LMRB
3.2. Evaluating the Meteorological Variables of CFSR in the Upper LMRB
3.3. Comparison of the Hydrological Features between CFSR-Based SWAT+ and CMADS-Based SWAT+
3.3.1. Model Parameter Sensitivity Based on CFSR and CMADS
3.3.2. Model Calibration and Validation Based on CFSR and CMADS
4. Discussion
4.1. Differences in Meteorological Forcings
4.2. Differences in Hydrological Model Response
4.3. Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
NCEP | National Centers for Environmental Prediction |
HRU | Hydrological response unit |
DEMs | Digital elevation models |
LULC | Land use/land cover |
LAPS/STMAS | Local Analysis and Prediction System/Space-Time Multiscale Analysis System |
CMORPH | Climate Prediction Center morphing |
USGS | United States Geological Survey |
ET | Evaporation and transpiration |
PET | Potential evapotranspiration |
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Paper | Meteorological Datasets | Meteorological Variables | Time Step | The Number of Parameters Used for Calibration and Calibration Method | Calibration Strategy | Country | Good Performance | Reasons |
---|---|---|---|---|---|---|---|---|
Dile and Srinivasan (2014) [27] | CFSR vs. conventional observed weather data | Precipitation and temperature | Monthly | No parameters to calibrate | Uncalibrated SWAT model | Ethiopia | Conventional observed weather data | CFSR underestimated streamflow |
Fuka et al. (2014) [2] | CFSR vs. conventional observed weather data | Precipitation and temperature | Daily | 20 parameters; differential evolution optimization | Separate SWAT model calibrations | United States and Ethiopia | CFSR | CFSR represented the watershed area better than the weather station |
Lauri et al. (2014) [11] | CFSR vs. remotely sensed precipitation (ERA-Interim) | Precipitation and temperature | Daily and monthly | Unclear | VMod model, unclear | Mekong, China, Burma, Thailand, Cambodia, Laos PDR, and Vietnam | Remotely sensed precipitation | CFSR dataset included an area of high annual precipitation |
Gao et al. (2018) [25] | CFSR vs. CMADS | Precipitation | Daily and monthly | Unclear | Separate SWAT model calibrations for each meteorological dataset | Yangtze River, China | CMADS | CFSR overestimated precipitation |
Wang et al. (2020) [30] | CFSR vs. CMADS | Precipitation | Monthly | 12 parameters; Latin hypercube and one-factor-at-a-time sampling methods with sequential uncertainty fitting ver. 2 algorithm (SUFI-2) | SWAT model was calibrated only by gauge-observed meteorological elements | Yellow River, China | Gauge-based precipitation data | CFSR overestimated precipitation; CMADS underestimated precipitation |
Liu et al. (2018) [3] | CFSR vs. CMADS | Precipitation, temperature, wind speed, and relative humidity | Daily and monthly | 12 parameters; unclear | Separate SWAT model calibrations for each meteorological dataset | Yellow River Source Basin, China | CMADS | Gauge-observed meteorological stations were not representative |
Zhang et al. (2020) [26] | CFSR vs. CMADS | Precipitation and temperature | Monthly | 14 parameters; SWAT calibration uncertainty program (SWAT-CUP) | Separate SWAT model calibrations for each meteorological dataset | Hunhe River Basin, Northeast China | CMADS | CFSR overestimated and underestimated precipitation |
Guo et al. (2022) [5] | CMADS vs. TRMM 3B42 version 7 | Precipitation | Daily and monthly | 17 parameters; SUFI-2 algorithm in SWAT-CUP | Separate SWAT model calibrations for each meteorological dataset | Yangtze River, China | CMADS | Gauge SWAT data were overestimated |
Zhang et al. (2020) [13] | CFSR vs. CMADS | Precipitation and temperature | Monthly | 13 parameters; SUFI-2 algorithm in SWAT-CUP | Separate SWAT model calibrations for each meteorological dataset | Muda River Basin, Malaysia | CMADS | CFSR overestimated the low flows and included a time lag in peak flow estimation |
Dao et al. (2021) [12] | Cau River Basin (CRB), northern Vietnam | Precipitation and temperature | Daily and monthly | 14 parameters; SUFI-2 algorithm in SWAT-CUP | Separate SWAT model calibrations for each meteorological dataset | Cau River Basin (CRB), northern Vietnam | CMADS | CFSR overestimated actual precipitation values |
Meteorological Variables | R | PBIAS | RMSE |
---|---|---|---|
Precipitation | 0.35 | 85.47 | 6.68 |
Maximum temperature | 0.68 | −18.45 | 9.39 |
Minimum temperature | 0.65 | −127.71 | 15.71 |
Relative humidity | 0.65 | 20.01 | 0.18 |
Wind speed | 0.47 | 103.36 | 1.52 |
Solar radiation | 0.72 | −12.11 | 5.45 |
Parameter | Description | Min | Max | Change Type * | Parameter Group | Optimal Value | |
---|---|---|---|---|---|---|---|
CFSR | CMADS | ||||||
esco | Soil evaporation compensation factor (-) | 0 | 1 | replace | HRU | 0.274 | 0.072 |
slope | Average slope steepness in HRU (m/m) | −50 | 50 | relative | HRU | 2.377 | 47.689 |
revap_co | Fraction of pet to calculate revap (-) | 0.02 | 0.2 | replace | Aquifer | 0.184 | 0.198 |
epco | Plant water uptake compensation (-) | 0 | 1 | replace | HRU | 0.046 | 0.717 |
canmx | Maximum canopy storage (mm_H2O) | 0 | 100 | replace | HRU | 46.584 | 30.641 |
surlag | Surface runoff lag time (days) | 0.05 | 24 | replace | Basin | 2.738 | 13.966 |
alpha | Alpha factor for gw recession curve (1/days) | 0 | 1 | replace | Aquifer | 0.075 | 0.264 |
awc | Available water capacity of soil layer (mm_H2O/mm_soil) | −25 | 25 | relative | Soil | 15.516 | −15.443 |
cn2 | SCS runoff curve number adjustment factor SCS (%) | −20 | 20 | relative | HRU | −19.682 | −18.928 |
flo_min | Minimum aquifer storage to allow return flow (mm) | 0 | 5000 | replace | Aquifer | 1704.311 | 3329.009 |
k | Saturated hydraulic conductivity of soil layer (mm/hr) | −80 | 80 | relative | Soil | 78.688 | 42.922 |
lat_ttime | Lateral flow travel time (days) | 0.5 | 180 | replace | HRU | 118.878 | 126.252 |
revap_min | Threshold depth of water in shallow aquifer required to allow revap to occur (mm) | 0 | 500 | replace | Aquifer | 451.913 | 231.766 |
snofall_tmp | Snow fall temperature (℃) | −5 | 5 | replace | HRU | 3.482 | 4.602 |
snomelt_lag | Snowmelt lag factor (-) | 0 | 1 | replace | HRU | 0.591 | 0.976 |
snomelt_max | Maximum snow melt factor (mm/(d·℃) | 0 | 10 | replace | HRU | 3.314 | 0.235 |
snomelt_min | Minimum snow melt factor (mm/(d·℃) | 0 | 10 | replace | HRU | 8.177 | 4.773 |
snomelt_tmp | Snow melt temperature (℃) | −5 | 5 | replace | HRU | −2.919 | −3.637 |
perco | Percolation coefficient | 0 | 1 | replace | HRU | 0.901 | 0.773 |
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Zhang, S.; Lang, Y.; Yang, F.; Qiao, X.; Li, X.; Gu, Y.; Yi, Q.; Luo, L.; Duan, Q. Hydrological Modeling in the Upper Lancang-Mekong River Basin Using Global and Regional Gridded Meteorological Re-Analyses. Water 2023, 15, 2209. https://doi.org/10.3390/w15122209
Zhang S, Lang Y, Yang F, Qiao X, Li X, Gu Y, Yi Q, Luo L, Duan Q. Hydrological Modeling in the Upper Lancang-Mekong River Basin Using Global and Regional Gridded Meteorological Re-Analyses. Water. 2023; 15(12):2209. https://doi.org/10.3390/w15122209
Chicago/Turabian StyleZhang, Shixiao, Yang Lang, Furong Yang, Xinran Qiao, Xiuni Li, Yuefei Gu, Qi Yi, Lifeng Luo, and Qingyun Duan. 2023. "Hydrological Modeling in the Upper Lancang-Mekong River Basin Using Global and Regional Gridded Meteorological Re-Analyses" Water 15, no. 12: 2209. https://doi.org/10.3390/w15122209