A Comparison and Ranking Study of Monthly Average Rainfall Datasets with IMD Gridded Data in India
Abstract
:1. Introduction
2. Study Area, Datasets Used and Methodology
2.1. Study Area
2.2. Datasets
- (a)
- IMD Gridded rainfall datasets
- (b)
- CHIRPS
- (c)
- CRU
- (d)
- GLDAS
- (e)
- GPM
- (f)
- PERSIANN-CDR
- (g)
- SM2RAIN
- (h)
- TerraClimate
2.3. Methodology
S.No | Skill Metrics | Equation | Range | Source |
---|---|---|---|---|
1 | Pearson Correlation Coefficient () | −1 and 1. Where 0 is no correlation, 1 is a total positive correlation, and −1 is a total negative correlation | (Pearson 1895) [54] | |
2 | Root Mean Square Error (RMSE) | A smaller value indicates good performance. | (Moriasi et al. 1983) [53] | |
3 | Nash–Sutcliffe Efficiencies (NSE) | -Infinity to 1. If this parameter is closer to 1, the model is further accurate. | (Nash and Sutcliffe 1970) [55] | |
4 | Percentage Bias | * 100 | A smaller percentage indicates good performance. | (Gupta et al. 1999) [56] |
5 | RMSE-observations standard deviation ratio (RSR) | 0 to ∞. ≤0.7 indicating a good-performing range. | (Chu and Shirmo-hammadi 2004) [57] |
3. Results
3.1. Dataset-Wise Suitability Analysis
3.1.1. CHIRPS
3.1.2. CRU
3.1.3. GLDAS
3.1.4. GPM
3.1.5. PERSIANN-CDR
3.1.6. SM2RAIN
3.1.7. TerraClimate
3.2. Rainfall Dataset Type Suitability for MSDs
3.3. Pixel Wise Analysis
India as a Whole
3.4. Temporal Analysis of Rainfall Datasets for India
3.5. Good-Performing Datasets at the Pixel Level for India
4. Discussion
5. Summary and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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1 | Andaman and Nicobar Islands (A and N I) | 13 | Haryana, Chandigarh and Delhi (HC and D) | 25 | Marathwada (MW) |
2 | Arunachal Pradesh (ARP) | 14 | Punjab (PN) | 26 | Vidarbha (VB) |
3 | Assam & Meghalaya (A and M) | 15 | Himachal Pradesh (HP) | 27 | Chhattisgarh (CG) |
4 | Nagaland, Manipur, Mizoram and Tripura (NMMT) | 16 | Jammu and Kashmir and Ladakh (JK and L) | 28 | Coastal Andhra Pradesh and Yanam (C-AP and Y) |
5 | Sub-Himalayan West Bengal and Sikkim (SHWB) | 17 | West Rajasthan (W R) | 29 | Telangana (TS) |
6 | Gangetic West Bengal (GWB) | 18 | East Rajasthan (E R) | 30 | Rayalaseema (RS) |
7 | Odisha (OD) | 19 | West Madhya Pradesh (W MP) | 31 | Tamil Nadu and Puducherry and Karaikal (TN and P) |
8 | Jharkhand (JH) | 20 | East Madhya Pradesh (E MP) | 32 | Coastal Karnataka (C-KA) |
9 | Bihar (BH) | 21 | Gujarat region (GJ) | 33 | N.I. Karnataka (NI KA) |
10 | East Uttar Pradesh (E UP) | 22 | Saurashtra and Kutch (S and K) | 34 | S.I. Karnataka (SI KA) |
11 | West Uttar Pradesh (W UP) | 23 | Konkan and Goa (K and G) | 35 | Kerala and Mahe (KL) |
12 | Uttarakhand (UK) | 24 | Madhya Maharashtra (MH) | 36 | Lakshadweep (L) |
Parameter | Dataset and Source | Spatial Resolution | Temporal Resolution | Data Availability |
---|---|---|---|---|
Rainfall | IMD Gridded Data https://www.imdpune.gov.in/Clim_Pred_LRF_New/Grided_Data_Download.html (accessed on 15 September 2020) | 0.25° | Daily | 1901–Present |
CHIRPS (Merged gauge + satellite) https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_monthly/tifs/ (accessed on 15 September 2020) | 0.05° | Monthly | 1981–Present | |
CRU (Gauged data) https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.05/cruts.2103051243.v4.05/ (accessed on 19 June 2021) | 0.5° | Monthly | 1901–Present | |
GLDAS 2.1 Rain Precipitation Rate (Combination of Model and Gauged data) https://giovanni.gsfc.nasa.gov/ (accessed on 15 September 2020) | 0.25° | Monthly | 2000–Present | |
GPM (Merged satellite-gauge precipitation estimate - Final Run) https://giovanni.gsfc.nasa.gov/ (accessed on 15 September 2020) | 0.1° | Monthly | 2000–Present | |
PERSIANN-CDR (Satellite) http://chrsdata.eng.uci.edu/ (accessed on 19 June 2021) | 0.25° | Monthly | 1983–Present | |
SM2RAIN (Satellite) https://zenodo.org/record/4570192#.YORPVOgzY2x (accessed on 19 June 2021) | 0.25° | Monthly | 2007–Present | |
TerraClimate (Gauged data) https://www.climatologylab.org/terraclimate.html (accessed on 19 June 2021) | 0.05° | Monthly | 1958–Present |
Skill Metrics | Good-Performing Range | Moderate-Performing Range | Low-Performing Range |
---|---|---|---|
γ | >0.8 | 0.4–0.80 | <0.4 |
NSE | >0.75 | 0.5–0.75 | <0.50 |
RMSE | <25 | 25–75 | >75 |
PBIAS | −10 to +10 | 10 to 25 or −10 to −25 | >25 or <−25 |
RSR | 0–0.5 | 0.5–0.7 | >0.7 |
Dataset | NSE | RMSE | RSR | PBIAS | Suitability | |
---|---|---|---|---|---|---|
CHIRPS | 0.84 | 0.52 | 69.44 | 0.62 | −14.48 | Moderate-Performing |
CRU | 0.84 | 0.48 | 73.38 | 0.64 | −10.45 | Moderate-Performing |
GLDAS | 0.83 | 0.36 | 75.99 | 0.67 | −12.42 | Low-Performing |
GPM | 0.89 | 0.64 | 57.98 | 0.51 | −8.36 | Good-Performing |
PERSIANN-CDR | 0.86 | 0.5 | 71.54 | 0.62 | −17.12 | Moderate-Performing |
SM2RAIN | 0.82 | 0.46 | 74.42 | 0.65 | −8.02 | Moderate-Performing |
Terraclimate | 0.84 | 0.51 | 74.04 | 0.63 | −7.33 | Moderate-Performing |
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Saicharan, V.; Rangaswamy, S.H. A Comparison and Ranking Study of Monthly Average Rainfall Datasets with IMD Gridded Data in India. Sustainability 2023, 15, 5758. https://doi.org/10.3390/su15075758
Saicharan V, Rangaswamy SH. A Comparison and Ranking Study of Monthly Average Rainfall Datasets with IMD Gridded Data in India. Sustainability. 2023; 15(7):5758. https://doi.org/10.3390/su15075758
Chicago/Turabian StyleSaicharan, Vasala, and Shwetha Hassan Rangaswamy. 2023. "A Comparison and Ranking Study of Monthly Average Rainfall Datasets with IMD Gridded Data in India" Sustainability 15, no. 7: 5758. https://doi.org/10.3390/su15075758