Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling
Abstract
:1. Introduction
2. Material and Methods
2.1. Study Area
2.2. Datasets
2.3. Verification Strategy
2.4. Hydrological Model
3. Model Development and Evaluation
4. Results and Discussion
4.1. Comparison of Satellite Data and Reanalysis Data with Ground-Based IMD Data
4.2. Hydrological Model-Based Evaluation of the Precipitation Products
4.2.1. Scenario A: SWAT Model Calibrated with IMD Data
4.2.2. Calibration of the SWAT Model with IMD Data
4.2.3. Scenario B: SWAT Model Calibrated Individually with Precipitation Products
4.2.4. Sensitive Parameters
4.2.5. Model Calibration
4.2.6. Parameter Uncertainty
4.2.7. Uncertainty in Streamflow Simulation
4.3. Comparison of the Annual Water Balance
4.3.1. Precipitation
4.3.2. Streamflow
4.3.3. Evapotranspiration
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Name and Type | Spatial Resolution/Coverage | Temporal Resolution/Period | Remarks, References and Data Sources |
---|---|---|---|
IMD (gridded rainfall dataset) | 0.25°/Pan India | Daily/1901–2013 | The gridded data were generated from the observed data of 6995 gauging stations across India using spatial interpolation for the period 1901–2013 [14] Details about these data can be obtained from http://imd.gov.in (homepage/rainfall information). |
TRMM 3B42V7 (satellite product) | 0.25°/Global (50 N-S) | Daily/1998–2014 | Joint mission between the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) to monitor tropical precipitation [6] one type of the TRMM multi-satellite precipitation analysis (TMPA) products) https://giovanni.gsfc.nasa.gov |
Bias-corrected TRMM (satellite product) | 0.25°/Global (50 N-S) | Daily/1998–2014) | The bias of TRMM rainfall was corrected by applying monthly multiplicative correction coefficients (MCC) of IMD rainfall [56] https://giovanni.gsfc.nasa.gov |
CFSR (reanalysis dataset) | 0.31°/Global (50 N-S) | Daily/1979–2014 | Global, high resolution, coupled atmosphere–ocean–land surface-sea ice system started in 1979 by the National Centers for Environmental Prediction [22] https://globalweather.tamu.edu/ |
S. No | Statistical Metrics | Equation | Description |
---|---|---|---|
1 | Probability of detection (POD) | POD = H/(H + M) | It represents the detected rainfall events correctly by satellite out of rainfall events by reference data. Its ranges from 0 to 1 (1 is the best value) |
2 | False alarm ratio (FAR) | FAR = F/(F + H) | It represents the false, detected as rainfall event by satellite when reference data showed no rainfall. Its ranges from 0 to 1 (0 is the best value) |
3 | Critical success index (CSI) | CSI = H/(H + M + F) | It is more accurate when correct negative values are not considered. Its ranges from 0 to 1 (1 is the perfect value) |
Data Type | Scale | Source |
---|---|---|
Digital elevation model (DEM) | 30 m × 30 m | Shuttle radar topography mission (SRTM) of USGS (https://earthexplorer.usgs.gov/) |
Land use/land cover (LULC) | 500 m | GIAM-IWMI (http://www.iwmi.cgiar.org) |
Soil | 1:1,500,000 | http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/ Food and Agriculture Organization (FAO) |
Rainfall, temperature | 0.25° (~32 km) | http://imdpune.gov.in/Clim_Pred_LRF_New/Grided_Data_Download.html Indian Metrological Department (IMD), PUNE |
Solar radiation, relative humidity, wind speed | 0.31° (~38 km) | https://rda.ucar.edu/datasets/ds093.1/ Climate Forecast System Reanalysis (CFSR) |
Hydrological data | Daily | https://indiawris.gov.in/wris, (Central Water Commission (CWC)_ |
Statistical Measures | IMD | TRMM | Bias Corrected TRMM | CFSR |
---|---|---|---|---|
Minimum rainfall (mm) | 2.5 | 2.5 | 2.5 | 2.5 |
Maximum rainfall (mm) | 140.2 | 157.7 | 119.4 | 176.4 |
Mean rainfall (mm) | 11.9 | 13.3 | 11.9 | 12.6 |
Standard deviation(mm) | 12.7 | 13.7 | 11.9 | 13.1 |
Skewness | 3.8 | 3.4 | 3.2 | 4.3 |
Event | IMD | Observed Event | TRMM | Bias Corrected TRMM | CFSR |
---|---|---|---|---|---|
Rainy days | 1591 | Hit (H) | 978 | 932 | 1161 |
Miss (M) | 613 | 659 | 430 | ||
Non-rainy days | 4253 | False alarm (F) | 718 | 658 | 712 |
Correct negative | 3535 | 3595 | 3541 |
Statistical Metrics | TRMM | Bias Corrected TRMM | CFSR | |
---|---|---|---|---|
Probability of detection | POD | 0.61 | 0.69 | 0.73 |
False alarm ratio | FAR | 0.42 | 0.39 | 0.38 |
Critical success index | CSI | 0.42 | 0.45 | 0.50 |
Statistical Metrics | Performance of Different Datasets under Scenario A | |||
---|---|---|---|---|
IMD | TRMM | Bias Corrected TRMM | CFSR | |
R2 | 0.87 | 0.60 | 0.68 | 0.54 |
NS | 0.85 | 0.55 | 0.62 | 0.52 |
PBIAS | −5.5 | −15.3 | −7.4 | −16.2 |
P-factor | 0.83 | 0.65 | 0.8 | 0.65 |
r-factor | 0.67 | 0.48 | 0.63 | 0.91 |
S. No | Parameter | Description | Min | Max |
---|---|---|---|---|
1 | CN2.mgt * | SCS runoff curve number for moisture condition II, which controls the surface runoff | −0.1 | 0.1 |
2 | ALPHA_BF.gw | Baseflow alpha factor for groundwater flow response to changes in recharge | 0 | 1 |
3 | GW_DELAY.gw | Groundwater delay represents the time taken by water move from the bottom soil layer to the upper shallow aquifer (days) | 30 | 450 |
4 | GWQMN.gw | Threshold depth of water in the shallow aquifer required for return flow to occur. If it is decreased, baseflow will be decreased (in mm) | 0 | 5000 |
5 | GW_REVAP.gw | Groundwater “revap” coefficient (Water movement from shallow aquifer to vegetation roots). If it is close to zero, the movement of water is restricted; if it approaches 1, the rate of water approaches the rate of evapotranspiration | 0.02 | 0.2 |
6 | RCHRG_DP.gw | Deep aquifer percolation fraction from the root zone which recharges in a deep aquifer | 0 | 1 |
7 | REVAPMN.gw | Threshold depth of water in the shallow aquifer for “revap” to occur (mm). Controls the water movement from a shallow aquifer to a deep aquifer | 0 | 1000 |
8 | SLSUBBSN.hru | Average slope length | 10 | 150 |
9 | HRU_SLP.hru | Average slope steepness (m/m) | 0 | 1 |
10 | OV_N.hru | Manning’s “n” value for overland flow | 0.01 | 30 |
11 | LAT_TTIME.hru | Lateral flow travel time (days) | 0 | 180 |
12 | ESCO.hru | Soil evaporation compensation factor (represents the process of evapotranspiration from soil moisture to atmosphere, if the ESCO is reduced, the evaporation is increased) | 0 | 1 |
13 | EPCO.hru | Plant uptake compensation factor (The amount of water uptake by a plant for transpiration, If EPCO value is 1, the model allows more of the water uptake demand) | 0 | 1 |
14 | SURLAG.bsn | Surface runoff lags time (days). It controls the fraction of water to allow to the reach on the day | 0.05 | 24 |
15 | SOL_AWC().sol | Available water capacity of the soil layer | 0 | 1 |
16 | SOL_K().sol | Saturated hydraulic conductivity (mm/h). This relates to soil water flow rate to the hydraulic gradient. | 0 | 2000 |
17 | CH_K2.rte | Effective hydraulic conductivity in main channel alluvium (mm/h). If streams have continuous groundwater contribution, the effective conductivity will be zero. | 0.1 | 500 |
18 | ALPHA_BNK.rte | Baseflow alpha factor for bank storage (Contributes flow to the reach within the subbasin) | 0 | 1 |
S. No | Parameter | IMD | TRMM | Bias Corrected TRMM | CFSR |
---|---|---|---|---|---|
1 | r__CN2.mgt | 6 | 1 | 2 | 1 |
2 | v__ALPHA_BF.gw | 18 | 18 | 17 | 14 |
3 | v__GW_DELAY.gw | 12 | 13 | 15 | 10 |
4 | v__GWQMN.gw | 3 | 6 | 8 | 3 |
5 | v__GW_REVAP.gw | 5 | 8 | 14 | 5 |
6 | v__RCHRG_DP.gw | 1 | 2 | 3 | 2 |
7 | v__REVAPMN.gw | 13 | 10 | 10 | 15 |
8 | v__SLSUBBSN.hru | 7 | 4 | 4 | 6 |
9 | v__HRU_SLP.hru | 8 | 5 | 6 | 7 |
10 | v__OV_N.hru | 16 | 11 | 12 | 11 |
11 | v__LAT_TTIME.hru | 17 | 7 | 7 | 9 |
12 | v__ESCO.hru | 2 | 3 | 1 | 4 |
13 | v__EPCO.hru | 11 | 12 | 11 | 17 |
14 | v__SURLAG.bsn | 15 | 16 | 18 | 18 |
15 | r__SOL_AWC().sol | 9 | 9 | 5 | 8 |
16 | r__SOL_K().sol | 14 | 14 | 9 | 13 |
17 | v__CH_K2.rte | 10 | 17 | 16 | 12 |
18 | v__ALPHA_BNK.rte | 4 | 15 | 13 | 16 |
Statistical Results during the Calibration and Validation Period | ||||||||
---|---|---|---|---|---|---|---|---|
Statistical Metrics | Calibration | Validation | ||||||
IMD | TRMM | TRMM-Bias | CFSR | IMD | TRMM | Bias Corrected TRMM | CFSR | |
R2 | 0.87 | 0.76 | 0.78 | 0.77 | 0.82 | 0.77 | 0.74 | 0.74 |
NS | 0.85 | 0.71 | 0.74 | 0.76 | 0.79 | 0.72 | 0.72 | 0.73 |
PBIAS | −5.5 | −13.4 | −7.2 | −14.3 | −6.2 | −15.2 | −8.1 | −17.2 |
P-factor | 0.83 | 0.68 | 0.72 | 0.64 | 0.85 | 0.67 | 0.69 | 0.61 |
r-factor | 0.67 | 0.46 | 0.59 | 0.40 | 0.79 | 0.44 | 0.61 | 0.41 |
S. No | Parameter | IMD | TRMM | Bias Corrected TRMM | CFSR |
---|---|---|---|---|---|
1 | v__ESCO.hru | 0.11 | 0.56 | 0.06 | 0.67 |
2 | v__SLSUBBSN.hru | 42.59 | 145.7 | 36.7 | 145.6 |
3 | v__HRU_SLP.hru | 0.28 | 0.06 | 1.0 | 0.11 |
4 | r__SOL_AWC().sol | −0.03 | 0.037 | 0.054 | 0.03 |
5 | r__CH_K2.rte | 367.1 | X | X | X |
6 | r__CN2.mgt | −0.152 | −0.09 | -0.15 | −0.19 |
7 | V__ALPHA_BNK.rte | 0.33 | X | X | X |
8 | V__RCHRG_DP.gw | 0.073 | 0.047 | 0.06 | 0.011 |
9 | V__GW_REVAP.gw | 0.13 | 0.079 | X | 0.115 |
10 | V__GWQMN.gw | 4577 | 3196 | 3234 | 3942 |
11 | V__OV_N.hru | X | 13.47 | X | 21.1 |
12 | V__LAT_TTIME.hru | X | 85 | 47 | 28.8 |
13 | V__GW_DELAY.gw | X | X | X | 72 |
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Setti, S.; Maheswaran, R.; Sridhar, V.; Barik, K.K.; Merz, B.; Agarwal, A. Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling. Atmosphere 2020, 11, 1252. https://doi.org/10.3390/atmos11111252
Setti S, Maheswaran R, Sridhar V, Barik KK, Merz B, Agarwal A. Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling. Atmosphere. 2020; 11(11):1252. https://doi.org/10.3390/atmos11111252
Chicago/Turabian StyleSetti, Sridhara, Rathinasamy Maheswaran, Venkataramana Sridhar, Kamal Kumar Barik, Bruno Merz, and Ankit Agarwal. 2020. "Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling" Atmosphere 11, no. 11: 1252. https://doi.org/10.3390/atmos11111252