Abstract
Insufficient rainfall has an impact on a variety of natural resources. This work aims to determine the variability of rainfall and drought in Hai town depending on the standardized rainfall index (SRI), rainfall concentration index (RCI), index of wetness (IW), and coefficient of variation (CV). Rainfall series were taken from the Meteorological Station Directorate of Hai Town, Iraq for a period of 30 years (1989–2018). The results indicated that the years 1996 and 2014 had high SRI and were under extremely wet conditions (IW = 195.93 and 165.93, respectively). However, the lowest SRI value was in 2004, with a wetness index of 35.15, whereas the RCI was strongly irregular in rainfall distribution. Also, the CV was highly variable that ranged between 113.78 and 244.01. Mathematical models were created and confirmed for predicting the wetness index using data-fitting software. Model 1 generated best outcomes (R2 = 99.99%, relative error (RE) = 0.221, root mean square error (RMSE = 0.253) and standard error of estimates (SEE = 0.28). The results demonstrated that rain indicators have significant differences and alteration throughout the study period. Hence, the best model for estimating wetness and droughts in Hai town is recommended.
HIGHLIGHTS
Variability of rainfall and drought monitoring in Hai town were eveluated.
SRI, RCI, IW, and CV were used.
DataFit program (statistically) was used to predict value index of wetness.
Make statistical models for index of wetness by using the DataFit program, and then the best model was chosen depending on R2, RMSE, SEE, RE.
Differences between predicted value and measured value for index of wetness were recorded.
INTRODUCTION
Rainwater is a significant atmospheric factor that is impacted by both floods and droughts. Many researchers are interested in planning management of water resources, hydrological simulation, analysis of flooding frequency, flood risk assessment, agricultural scheduling, effects of climate change, evaluations of water resources, as well as other environmental evaluations. These evaluations were done to understand the variability of temporal rainfall. Numerous studies have focused on how rainfall varies depending on precipitation indices (Zakwan & Ara 2019), and analyzed the temporal changes of rainfall in India depending on seasonal, monthly, and yearly rainfall series for the period 1950–2016. They demonstrated the result dependability of 90% from rainfall depth greater than 180 mm for the months of July and 160 mm for the months of August. Also, they noticed a reduction in overall rainfall for the years 1986–2016. Mahfouz et al. (2016) studied drought intervals for the duration of 1950–2014 in Lebanon based on the Standardized Precipitation Index (SPI) and found that annual precipitation increased in September–October and decreased in February, while drought conditions increased primarily in the winter–spring season. Moazzam et al. (2022) used the standard rainfall indicator to evaluate the wet and dry conditions for the period 1980–2020 and their results showed that extreme droughts occurred in Pakistan during the years (1982–1983, 1985, 1990–1991, 2000–2001) and moderate droughts occurred during the years (1977, 1984, 2007, 2014–2015, and 2017–2018). According to data from rain gauges at Ghanaian meteorological stations, Nkrumah et al. (2014) evaluated the annual and seasonal variability of rainfall in three zones that distribute rainfall in Ghana for a period of 1990–2008 and compared the results to data obtained from the regional climate model. Al-Shamarti (2017) examined the rainfall seasonality index using rainfall data from 28 stations in three zones of Iraq. His study was based on the amount of rainfall, and found a significant variability of the seasonality index for rainfall between zones and between years as well.
Saha (2020) studied the Precipitation Concentration Index (seasonal and annual) to assess the distribution of rainwater, in Bangladesh, and showed an equally distributed rainfall during the summer monsoon. The winter rain displayed a strong predominance of irregular precipitation distribution. The annual indicator of the concentration of rainwater ranged between 14.96 and 43.82 in 2006 and 2000, respectively, and the average of 37 years was 32.22, according to research by Rawat et al. (2021) using the Precipitation Concentration Index to study rainfall variability (annual and seasonal) in India for the period (1982–2018).
Pathak & Dodamani (2020) investigated annual and seasonal rainfall trends and also investigated the Ghataprabha River Basin meteorological dryness trends in India using non-parametric Mann–Kendall and standardized rain water indicators.
The Standardized Precipitation indicators (SPI-C) were adjusted in a study by Cerpa Reyes et al. (2022) for the evolution of drought under conditions of zero monthly precipitation. The findings revealed that the SPI-C improved dryness detection in the Colombian Arroyo Pecheln Basin. Mehta & Yadav (2022) utilized Sen's slope estimator testing and the Mann–Kendall rule to analyze trends in rainfall across the Jalore region of South-West Rajasthan in the Luni river basin from 1901 to 2021, the findings indicated an increase in pre- and post-monsoon rainfall, and decline in annual rainfall, which is reflected in lower winter and S–W monsoon rainfall. Mahrokh et al. (2023) investigated the impact of climate change on dryness circumstances in Iran's Dez Basin by utilizing the hydro-meteorological dryness indices, which integrates the Standardized Rainfall Evapotranspiration Indicator and Standardized Runoff Factor, and their findings indicated that normal drought levels are going to continue, and in future mild and severe droughts will increase. Mahdavi & Ghorbanizadeh (2023) examined the effects of changing climates on droughts in Iran's Zard River Basin and demonstrated that upcoming droughts will be much more severe than they have ever been.
In order to identify the nature of variance and trends of rainwater in the West African Sahel, the researchers (Nouaceur & Murarescu 2020) utilized continuous wavelets transform and the ‘Bertin Matrix’ method of processing information chronologically in graphics. Their findings showed that rain had resumed recently over the Sahelian region and a significant association with the surface temperature of the Atlantic Ocean had been noticed.
Aryal et al. (2022) investigated the characteristics of droughts in Nepalese river basin that depend on the SPI, Rainwater Anomaly Index (RAI), and assessed their effects on yearly crop production. They demonstrated that the SPI and RAI could be utilized equally to evaluate the severity of the dryness. Additionally, the severity of the drought had a direct impact on crop yield, which included wheat, millet, barley, and paddy. Thus, planning irrigation and water resource management systems may benefit from these findings.
The SPI's temporal versatility is helpful in determining the beginning and finish of drought events and it permits research of the effects of dryness at various time scales by comparing them with different indicators (Gherissi et al. 2021, Rahman et al. 2021, and Bi et al. 2020).
STUDY REGION AND SOURCE OF DATA
METHODOLOGY OF WORK
The Excel program was used to calculate the rain indicators, and to find out the cases of drought and the time variation of rainfall annually, seasonally, and monthly. Many parameters and indicators were used to analyze the characteristics and rainfall analyses. Steps involved in Excel program: first, open the program and the worksheet, after that enter the data into the cells, then choose the empty cell to write all Equations (1)–(8). Next, press on the fill handle option to copy the equation to the rest of the cells.
Standardized Rainfall Index
SRI value . | Category . |
---|---|
≥2 | Extreme wet situation |
1.5–1.99 | Severe wet situation |
1.0–1.49 | Moderate wet situation |
−0.99 to 0.99 | Near normal situation |
−1.0 to (−1.49) | Moderate dryness situation |
−1.5 to (−1.99) | Severe dryness situation |
Less or equal −2.0 | Extreme drought condition |
SRI value . | Category . |
---|---|
≥2 | Extreme wet situation |
1.5–1.99 | Severe wet situation |
1.0–1.49 | Moderate wet situation |
−0.99 to 0.99 | Near normal situation |
−1.0 to (−1.49) | Moderate dryness situation |
−1.5 to (−1.99) | Severe dryness situation |
Less or equal −2.0 | Extreme drought condition |
Rainwater concentration indicators
RCI value . | Category . |
---|---|
RCI ≤ 10 | Uniform distribution of precipitation (low concentration of precipitation) |
RCI > 10 ≤ 15 | Moderate rainfall distribution |
RCI > 15 ≤ 20 | Unequal distribution of rainwater |
RCI > 20 | Distribution of rain that is extremely irregular |
RCI value . | Category . |
---|---|
RCI ≤ 10 | Uniform distribution of precipitation (low concentration of precipitation) |
RCI > 10 ≤ 15 | Moderate rainfall distribution |
RCI > 15 ≤ 20 | Unequal distribution of rainwater |
RCI > 20 | Distribution of rain that is extremely irregular |
Normal annual rainfall
Index of wetness
Coefficient of variation
Statistical analysis and variability indices
Using the DataFit 9.1 (2014) program, statistical models were constructed to forecast (IW) based on RCI, SRI, CV, and N. There were four math models constructed that were non-linear (Tables 3–5) to evaluate the accuracy and effectiveness of the model. Equations (7)–(9) of the standard statistical measures (R2, SEE, RMSE, MAE, and RE) were employed, following the methodology of earlier researchers (Al-humairi et al. (2020, 2023); Pandey et al. (2020), Zakwan & Niazkar (2021), Rahal & Al-humairi (2019), Al-humairi & Rahal (2023)). Following are the building steps of the program:
Models . | ||
---|---|---|
IW models | 1 | IW =a0*SRI +a1*RCI +a2*N+a3*CV +a4 |
2 | IW =a0+a1* SRI +a2*ln(RCI)+a3*(SRI) 2+a4*ln(RCI) 2+a5* SRI *ln(RCI)+a6* (SRI)3+a7*ln(RCI) 3+a8* SRI *ln(RCI) 2+a9*(SRI)2 *ln(RCI) | |
3 | IW = EXP(a0* SRI +a1* RCI +a2* CV +a3) | |
4 | IW = EXP(a0* SRI +a1* CV +a2* N+a3) |
Models . | ||
---|---|---|
IW models | 1 | IW =a0*SRI +a1*RCI +a2*N+a3*CV +a4 |
2 | IW =a0+a1* SRI +a2*ln(RCI)+a3*(SRI) 2+a4*ln(RCI) 2+a5* SRI *ln(RCI)+a6* (SRI)3+a7*ln(RCI) 3+a8* SRI *ln(RCI) 2+a9*(SRI)2 *ln(RCI) | |
3 | IW = EXP(a0* SRI +a1* RCI +a2* CV +a3) | |
4 | IW = EXP(a0* SRI +a1* CV +a2* N+a3) |
Coefficient . | Model . | |||
---|---|---|---|---|
1 . | 2 . | 3 . | 4 . | |
a0 | 0.008 | −5244.322 | 0.123 | 0.056 |
a1 | −0.040 | 83.956 | −0.035 | 0.0004 |
a2 | 100.077 | 4621.977 | 0.010 | 0.496 |
a3 | 0.013 | −2.426 | 4.178 | 4.168 |
a4 | −0.852 | −1330.699 | – | – |
a5 | – | −52.724 | – | – |
a6 | – | 0.104 | – | – |
a7 | – | 127.518 | – | – |
a8 | – | 9.529 | – | – |
a9 | – | 1.328 | – | – |
R2 | 99.99 | 99.77 | 96.98 | 99.43 |
SEE | 0.28 | 2.55 | 8.23 | 3.55 |
RMSE | 0.253 | 2.084 | 7.670 | 3.308 |
MAE | 0.221 | 1.574 | 5.979 | 2.388 |
RE | 0.221 | 1.574 | 6.019 | 2.385 |
Coefficient . | Model . | |||
---|---|---|---|---|
1 . | 2 . | 3 . | 4 . | |
a0 | 0.008 | −5244.322 | 0.123 | 0.056 |
a1 | −0.040 | 83.956 | −0.035 | 0.0004 |
a2 | 100.077 | 4621.977 | 0.010 | 0.496 |
a3 | 0.013 | −2.426 | 4.178 | 4.168 |
a4 | −0.852 | −1330.699 | – | – |
a5 | – | −52.724 | – | – |
a6 | – | 0.104 | – | – |
a7 | – | 127.518 | – | – |
a8 | – | 9.529 | – | – |
a9 | – | 1.328 | – | – |
R2 | 99.99 | 99.77 | 96.98 | 99.43 |
SEE | 0.28 | 2.55 | 8.23 | 3.55 |
RMSE | 0.253 | 2.084 | 7.670 | 3.308 |
MAE | 0.221 | 1.574 | 5.979 | 2.388 |
RE | 0.221 | 1.574 | 6.019 | 2.385 |
Years . | Measured . | Predicted . | Residual . | %Error . |
---|---|---|---|---|
1989 | 107.28 | 107.10 | 0.18 | 0.17 |
1990 | 59.33 | 59.10 | 0.23 | 0.38 |
1991 | 152.37 | 152.21 | 0.16 | 0.11 |
1992 | 116.89 | 117.11 | −0.22 | −0.19 |
1993 | 152.20 | 152.19 | 0.01 | 0.01 |
1994 | 151.29 | 151.20 | 0.09 | 0.06 |
1995 | 59.91 | 59.94 | −0.03 | −0.04 |
1996 | 195.30 | 195.23 | 0.07 | 0.03 |
1997 | 133.38 | 133.18 | 0.20 | 0.15 |
1998 | 81.83 | 82.140 | −0.31 | −0.38 |
1999 | 109.45 | 109.11 | 0.34 | 0.31 |
2000 | 74.72 | 75.11 | −0.39 | −0.52 |
2001 | 55.64 | 55.95 | −0.31 | −0.55 |
2002 | 114.63 | 115.16 | −0.53 | −0.46 |
2003 | 45.66 | 46.09 | −0.43 | −0.95 |
2004 | 35.15 | 35.01 | 0.14 | 0.39 |
2005 | 88.86 | 89.10 | −0.24 | −0.27 |
2006 | 149.86 | 150.11 | −0.25 | −0.16 |
2007 | 53.97 | 53.96 | 0.01 | 0.01 |
2008 | 73.30 | 73.13 | 0.17 | 0.23 |
2009 | 71.38 | 71.07 | 0.31 | 0.43 |
2010 | 67.19 | 66.99 | 0.12 | 0.30 |
2011 | 104.43 | 104.12 | 0.31 | 0.30 |
2012 | 67.94 | 68.12 | −0.18 | −0.26 |
2013 | 157.48 | 157.17 | 0.31 | 0.20 |
2014 | 165.93 | 166.05 | −0.12 | −0.07 |
2015 | 162.83 | 163.19 | −0.36 | −0.22 |
2016 | 103.34 | 103.10 | 0.24 | 0.23 |
2017 | 39.08 | 39.01 | 0.07 | 0.18 |
2018 | 49.34 | 49.04 | 0.30 | 0.61 |
Years . | Measured . | Predicted . | Residual . | %Error . |
---|---|---|---|---|
1989 | 107.28 | 107.10 | 0.18 | 0.17 |
1990 | 59.33 | 59.10 | 0.23 | 0.38 |
1991 | 152.37 | 152.21 | 0.16 | 0.11 |
1992 | 116.89 | 117.11 | −0.22 | −0.19 |
1993 | 152.20 | 152.19 | 0.01 | 0.01 |
1994 | 151.29 | 151.20 | 0.09 | 0.06 |
1995 | 59.91 | 59.94 | −0.03 | −0.04 |
1996 | 195.30 | 195.23 | 0.07 | 0.03 |
1997 | 133.38 | 133.18 | 0.20 | 0.15 |
1998 | 81.83 | 82.140 | −0.31 | −0.38 |
1999 | 109.45 | 109.11 | 0.34 | 0.31 |
2000 | 74.72 | 75.11 | −0.39 | −0.52 |
2001 | 55.64 | 55.95 | −0.31 | −0.55 |
2002 | 114.63 | 115.16 | −0.53 | −0.46 |
2003 | 45.66 | 46.09 | −0.43 | −0.95 |
2004 | 35.15 | 35.01 | 0.14 | 0.39 |
2005 | 88.86 | 89.10 | −0.24 | −0.27 |
2006 | 149.86 | 150.11 | −0.25 | −0.16 |
2007 | 53.97 | 53.96 | 0.01 | 0.01 |
2008 | 73.30 | 73.13 | 0.17 | 0.23 |
2009 | 71.38 | 71.07 | 0.31 | 0.43 |
2010 | 67.19 | 66.99 | 0.12 | 0.30 |
2011 | 104.43 | 104.12 | 0.31 | 0.30 |
2012 | 67.94 | 68.12 | −0.18 | −0.26 |
2013 | 157.48 | 157.17 | 0.31 | 0.20 |
2014 | 165.93 | 166.05 | −0.12 | −0.07 |
2015 | 162.83 | 163.19 | −0.36 | −0.22 |
2016 | 103.34 | 103.10 | 0.24 | 0.23 |
2017 | 39.08 | 39.01 | 0.07 | 0.18 |
2018 | 49.34 | 49.04 | 0.30 | 0.61 |
RESULTS AND DISCUSSION
CONCLUSIONS
The amount of rainfall in a location has a significant impact on a number of aspects of managing water resources including monitoring water quality, distributing water, planning, and using water. The objective of this study was to evaluate the variation of rainwater and drought monitoring in Hai town by using Excel and DataFit software . The determination of rainfall variation was done based on the yearly, seasonal, and monthly rainfall series of data as well as the SRI, rainfall concentration index (RCI), IW (IW), and coefficient of variation (CV). The results showed that the years 2014 and 1996 had a high SRI yearly values and wetness indices of 165.93 and 195.3, respectively. Also, were regarded to be under very wet conditions. In contrast, the year 2004 had a low SRI value, which indicated an extreme drought condition with a wetness index of 35.15. Except for the RCI in the year 2014, which was an irregularity in rainfall distribution. The RCI value for yearly, seasonal, and for 6 months is characterized by strong irregularity in rainfall distribution. The CV showed that Hai town's rainfall was extremely variable. The mathematical models were constructed using DataFit software to forecast IW, and the results indicated that model 1 (IW = a0*SRI+ a1*RCI + a2*N + a3*CV+ a4) is the more appropriate model. Its values include RE = 0.221, SEE = 0.28, RMSE = 0.253.The better model for forecasting IW had positive proportions of errors between 0.01 and 0.61% and a negative error value between −0.04 and 0.95%.These models are crucial for managing irrigation water systems and water resources. The results show that rain indicators has important difference and alteration from time to time throughout the study period. It is suggested to establish water reservoirs to store surplus rainwater in wet times and to use the stored water in the dry season. Also, it is recommended to use a modern irrigation system such as sprinkler or drip irrigation during dry periods.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.