# Appraisal of Satellite Rainfall Products for Malwathu, Deduru, and Kalu River Basins, Sri Lanka

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## Abstract

**:**

## 1. Introduction

^{2}area); however, some areas are not comprehensively covered and represent the spatial distribution of rainfall. In addition, some of the rain gauges were not functioned well in the past due to the war environment in northern and eastern Sri Lanka. In addition, some of the rain gauges were not maintained well due to various other reasons, such as financial restrictions and a lack of human resources.

## 2. Study Area and Data Sets

#### 2.1. River Basins

^{2}. The lower river basin of the Malwathu River lies in the Vavuniya and Mannar districts, while the upper river basin lies in the Anuradhapura district. Nuwara Wewa and Tissa Wewa reservoirs of this basin are already used as drinking water sources for public water supply. The upper part of the catchment is in the dry zone receiving less than 1500 mm; however, the bottom part of the catchment is very dry as it is in a semi-arid area receiving an average rainfall of 1000 mm (refer to Figure 1) [19].

^{2}in total, with 3% in the central province and the rest in the northwestern province of the country. It is about 115 km long, and it consists of 9 tributaries. This basin water mostly contributes to the irrigational purposes in the areas of Northwestern Province. The river basin mostly falls into the intermediate zone of the country, as shown in Figure 1 [21]. However, it receives a monthly rainfall of 108 mm to 280 mm from September to December, which is the rainy season for the river basin.

^{2}with a large part of the catchment in the highest rainfall area of the country. The average annual rainfall of the basin is 4000 mm and leads to 4000 million m3 of annual flow, thus, it falls in the wet zone of the country (refer to Figure 1). Kalu River basin is home to a large population and facilitates the area with many valuable services, such as the provision of water for drinking, agriculture, etc. [20].

#### 2.2. Rain Gauge Data

^{2,}whereas the Deduru River basin has a rain gauge density of 2.7 gauges per 1000 km

^{2}. However, the Kalu River basin is denser in rain gauges and has a density of 3.6 gauges per 1000 km

^{2}which is higher than the rain gauge density for most of the research studies that incorporate evaluating SRPs for different areas of interest [16,22,23,24]. However, these densities clearly showcased their non-homogeneous spatial distribution in a particular river basin. Missing data for each basin were less than 10%, and the missing data were filled either by using rainfall data from nearby stations or by obtaining APHRODITE data which contain rainfall data for the whole of Asia [25].

#### 2.3. Satellite Rainfall Products

## 3. Methodology

#### 3.1. Evaluation Indices

#### 3.2. Non-Parametric Tests for Rainfall Trends

_{0}for no trend and H

_{1}when a trend is present). Initially, the Mann–Kendall Statistic (S) finds out whether the trend is increasing or decreasing or whether there is no significant trend. Then to find the probability of obtaining a significant trend, a normalized test statistic, Z, was computed which incorporates the computed S value as given in Equation (1) below [30]. A 95% significance level was used to determine whether the trend observed is increasing, decreasing, or no trend [16].

_{i}was calculated using Equation (2) below.

#### 3.3. Overall Methodology

## 4. Results and Discussion

#### 4.1. Accuracy of SRPs with Respect to Rain Gauge Data

#### 4.2. Detection Accuracy of SRPs

#### 4.3. Trend Analysis

## 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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**Figure 4.**Elevation maps with rain gauges: (

**a**) For Kurunegala; (

**b**) For Eheliyagoda; (

**c**) For Anuradhapura.

**Figure 6.**Variation of evaluation indices across the river basins (units are mm/day): (

**a**) r; (

**b**) RMSE; (

**c**) PBias; (

**d**) NSE.

River Basin | Identification Number | Station Name | Latitude | Longitude |
---|---|---|---|---|

Deduru | 1 | Chilaw | 7.575 | 79.787 |

2 | Polontalawa | 7.72 | 80 | |

3 | Nikaweratiya | 7.75 | 80.12 | |

4 | Mediyaya Wewa | 7.88 | 80.28 | |

5 | Ridibendi ela | 7.729 | 80.262 | |

6 | Wariyapola | 7.62 | 80.24 | |

7 | Kurunegala | 7.48 | 80.36 | |

Kalu | 8 | St. Vincents Group | 6.52 | 80 |

9 | Bombuwala | 6.57 | 80.02 | |

10 | Geekiyan Kanda | 6.6 | 80.12 | |

11 | Usk Valley | 6.57 | 80.23 | |

12 | Halwathura | 6.72 | 80.2 | |

13 | Galathura | 6.7 | 80.28 | |

14 | Eheliyagoda | 6.85 | 80.27 | |

15 | Rathnapura | 6.68 | 80.4 | |

16 | Hapugasthenna | 6.72 | 80.52 | |

17 | Depedena | 6.47 | 80.55 | |

Malwathu | 18 | Mannar | 8.98 | 79.92 |

19 | Murungan | 8.83 | 80.05 | |

20 | Vavuniya | 8.75 | 80.5 | |

21 | Pawatakulam | 8.68 | 80.43 | |

22 | Anuradhapura | 8.335 | 80.415 | |

23 | Nachchaduwa | 8.25 | 80.47 |

Product | Finest Time Resolution | Data Extracted | Spatial Resolution | Spatial Coverage |
---|---|---|---|---|

PERSIANN | 1 h | March 2000–December/2019 | 0.25° × 0.25° | 60° N–60° S |

PERSIANN-CSS | 1 h | January 2003–December 2019 | 0.04° × 0.04° | 60° N–60° S |

PERSIANN-CDR | 1 day | January 1990–December 2019 | 0.25° × 0.25° | 60° N–60° S |

TRMM-3B42 V7 | 3 h | January 1998–December 2019 | 0.25° × 0.25° | 50° N–50° S |

TRMM-3B42RT V7 | 3 h | March 2000–December 2019 | 0.25° × 0.25° | 60° N–560° S |

IMERG V06 | 30 min | March 2000–December 2019 | 0.10° × 0.10° | 90° N–90° S |

Precipitation Product | Station | |||
---|---|---|---|---|

Kurunegala | Eheliyagoda | Anuradhapura | ||

Mean/(Standard deviation) (mm/year) | Observed | 1987.4/(363.8) | 4106.0/(590.9) | 1396.4/(352.2) |

PERSIANN | 2331.1/(371.6) | 2549.7/(328.1) | 1940.9/(362.9) | |

PERSIANN-CCS | 2579.5/(301.0) | 3031.5/(345.3) | 2347.5/(510.1) | |

PERSIANN-CDR | 2038.8/(305.0) | 2381.3/(288.0) | 1488.6/(211.7) | |

IMERG | 1880.7/(338.4) | 2478.7/(381.2) | 1511.1/(289.5) | |

TRMM_3B42 | 2037.6/(305.5) | 2673.5/(318.0) | 1568.0/(253.6) | |

TRMM_3B42-RT | 2054.5/(358.4) | 2420.4/(356.8) | 1734.4/(320.7) |

Statistical Indicators | Equation | Use of Indicator |
---|---|---|

r | $\mathrm{r}=\frac{{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{N}}\left({\mathrm{O}}_{\mathrm{i}}-\hat{\mathrm{O}}\right)\left({\mathrm{S}}_{\mathrm{i}}-\hat{\mathrm{O}}\right)}{\sqrt{{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{N}}{\left({\mathrm{O}}_{\mathrm{i}}-\hat{\mathrm{O}}\right)}^{2}}\sqrt{{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{N}}{\left({\mathrm{S}}_{\mathrm{i}}-\hat{\mathrm{O}}\right)}^{2}}}$ | Degree of correlation |

RMSE | $\mathrm{RMSE}=\sqrt{\frac{1}{\mathrm{N}}{\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{n}}({\mathrm{S}}_{\mathrm{i}-}{\mathrm{O}}_{\mathrm{i}})2}$ | The absolute average magnitude of error |

PBias | $\mathrm{PBias}=\frac{{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{N}}({\mathrm{S}}_{\mathrm{i}-}{\mathrm{O}}_{\mathrm{i}})}{{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{O}}_{\mathrm{i}}}$ | Degree of deviation |

NSE | $\mathrm{NSE}=1-\left(\frac{{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{n}}{({\mathrm{S}}_{\mathrm{i}}-{\mathrm{O}}_{\mathrm{i}})}^{2}}{{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{n}}{({\mathrm{O}}_{\mathrm{i}}-\mathrm{O}^)}^{2}}\right)$ | The relative magnitude of variance |

Categorical Statistics | Calculation Formula | Optimal Value [0, 1] |
---|---|---|

POD | $\mathrm{POD}=\raisebox{1ex}{$\left(\mathrm{correct}\mathrm{hits}\right)$}\!\left/ \!\raisebox{-1ex}{$\left(\mathrm{correct}\mathrm{hits}+\mathrm{misses}\right)$}\right.$ | 1 |

FAR | $\mathrm{F}\mathrm{AR}=\raisebox{1ex}{$\left(\mathrm{false}\mathrm{alarms}\right)$}\!\left/ \!\raisebox{-1ex}{$\left(\mathrm{correct}\mathrm{hits}+\mathrm{false}\mathrm{alarms}\right)$}\right.$ | 0 |

CSI | $\mathrm{C}\mathrm{SI}=\raisebox{1ex}{$\left(\mathrm{correct}\mathrm{hits}\right)$}\!\left/ \!\raisebox{-1ex}{$\left(\mathrm{correct}\mathrm{hits}+\mathrm{misses}+\mathrm{false}\mathrm{alarms}\right)$}\right.$ | 1 |

PC | $\mathrm{PC}=\frac{\left(\mathrm{correct}\mathrm{hits}+\mathrm{correct}\mathrm{negatives}\right)}{\left(\mathrm{correct}\mathrm{hits}+\mathrm{misses}+\mathrm{false}\mathrm{alarms}+\mathrm{correct}\mathrm{negatives}\right)}$ | 1 |

Climatic Zone | Best Performer | |||
---|---|---|---|---|

POD | CSI | FAR | PC | |

Wet zone | PERSIANN-CDR | IMERG | TRMM-3B42_RT | IMERG |

Intermediate zone | IMERG | IMERG | IMERG | IMERG |

Dry zone | PERSIANN-CDR | TRMM-3B42 | TRMM-3B42 | TRMM-3B42 |

Station | p Value | Sen’s Slope (mm) | |
---|---|---|---|

Annual | |||

Nachchaduwa | Observed | 0.003 | 26.9 |

PERSIANN-CDR | 0.032 | 20.1 | |

Vavuniya | Observed | 0.001 | 14.2 |

PERSIANN-CDR | 0.013 | 9.8 | |

Usk Valley | Observed | 0.001 | 65.8 |

TRMM-3B42 | 0.005 | 42.3 | |

TRMM-3B42_RT | 0 | 79.7 | |

Geekiyan Kanda | Observed | 0.028 | 20.1 |

TRMM-3B42 | 0.015 | 37.1 | |

TRMM-3B42_RT | 0.001 | 65.9 | |

Monthly | |||

Depedena (December) | Observed | 0.032 | 3.7 |

TRMM-3B42_RT | 0.027 | 11.5 | |

Usk Valley (February) | Observed | 0.004 | 5.8 |

TRMM-3B42_RT | 0.048 | 6.6 | |

Usk Valley (December) | Observed | 0.040 | 6.5 |

PERSIANN-CCS | 0.033 | 21.0 | |

TRMM-3B42_RT | 0.013 | 11.8 | |

Seasonal | |||

Murungan (SWM) | Observed | 0.016 | −10.6 |

PERSIANN-CDR | 0.025 | −35.3 | |

Depedena (NEM) | Observed | 0.039 | 18.3 |

IMERG | 0.029 | 26.9 | |

TRMM-3B42_RT | 0.008 | 52.4 | |

Usk Valley (NEM) | Observed | 0.014 | 34.9 |

TRMM-3B42_RT | 0.023 | 48.5 | |

Usk Valley (SWM) | Observed | 0.025 | 54.2 |

IMERG | 0.047 | 50.4 | |

TRMM-3B42_RT | 0.045 | 74.3 |

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**MDPI and ACS Style**

Perera, H.; Senaratne, N.; Gunathilake, M.B.; Mutill, N.; Rathnayake, U.
Appraisal of Satellite Rainfall Products for Malwathu, Deduru, and Kalu River Basins, Sri Lanka. *Climate* **2022**, *10*, 156.
https://doi.org/10.3390/cli10100156

**AMA Style**

Perera H, Senaratne N, Gunathilake MB, Mutill N, Rathnayake U.
Appraisal of Satellite Rainfall Products for Malwathu, Deduru, and Kalu River Basins, Sri Lanka. *Climate*. 2022; 10(10):156.
https://doi.org/10.3390/cli10100156

**Chicago/Turabian Style**

Perera, Helani, Nipuna Senaratne, Miyuru B. Gunathilake, Nitin Mutill, and Upaka Rathnayake.
2022. "Appraisal of Satellite Rainfall Products for Malwathu, Deduru, and Kalu River Basins, Sri Lanka" *Climate* 10, no. 10: 156.
https://doi.org/10.3390/cli10100156