Floods are one of the most devastating natural events that threaten social and economic conditions more than any other natural disaster. Therefore, the aim of this study was to map the Iso-severity flood patterns and determining the seasonality of flood events over different return periods in in Ardabil province, Iran with diverse relief and climatic regions. Thus, the data of 32 River Gauge Stations (RGS) were used in the spatiotemporal analysis in a 40-year recorded period. EasyFit software was used to determine the most appropriate statistical distribution for the discharge data at 2-, 5-, 10-, 25-, 50- and 100-year return periods. Then, the frequency of monthly and seasonal flood events was quantified in each RGS. Then, the significant trend of flood frequency in each RGS was tested, and the seasonality of flood events was explored. According to the results, it can be said that in the studied RGS, about 63.63% of the floods are occurred in April. On the other hand, the seasonality of flood events showed that the maximum number and percentage of flood events are assigned to spring season over different return periods. As in some stations such as Yamchi, Nouran, Naneh-Karan, Mashiran, Amouqin, Doroud and Pole- Almas, almost 100% of the floods are related to spring and this shows that the floods in Ardabil province are spring floods. Iso-severity flood mapping in different return periods showed that the frequency of floods was higher than the central and northern regions in the southern and highlands of the study area. It is necessary for the southern and Iso-severity flood areas of the province, while conducting the necessary studies, proper planning should be considered with the focus on Integrated Watershed Management (IWM) in order to reduce the flood occurrence in the future and minimize its human and economic losses.
Research Article
Iso-severity flood hazard patterns in diverse relief and climatic regions to support flood management
https://doi.org/10.21203/rs.3.rs-2991117/v1
This work is licensed under a CC BY 4.0 License
You are reading this latest preprint version
Flood Management
Flood Zoning
Natural Disasters
Precipitation Regime
Watershed Management
Sustainable and integrated water resources in watersheds is one of the important issues around the world that requires integrated approach and scientific research (Khiavi et al. 2022; Li et al. 2022). Flood events are among the most important geo-hydrological and water resources-related disasters that also threatens the economic and social systems at watershed scale (Jothibasu and Anbazhagan 2016; Abdo 2020; Mukherjee and Singh 2020; Arora et al. 2021; Costache et al. 2021). Flood is a very important phenomenon that causes human and economic losses and is one of the most complex and destructive natural events that endanger, environment, human life, property, social and economic conditions more than any other natural disaster (Jongman et al. 2015; Diakakis 2017; Ogie et al. 2020). In the last 50 years, about 3,700 floods have been reported in Iran, of which about 53% have occurred in recent years which emphasizes on flood intensification due to human and climate related causes (Shabani et al. 2016).
Due to the prevalence of floods in the world’s rivers and the role of humans in their occurrence, floods are a very important environmental hazard for aquatic plant and animal species (Smith 2013). Identification of seasonal flood distribution and their spatial pattern by hydrologists (Jeneiová et al. 2016) has been studied in many practical applications of hydrological engineering and water resources (Nasiri Khiavi et al. 2019; Esfandyari Darabad et al. 2020). Determining the frequency of floods and surveying flood-prone areas is a common hydrologists and water resources management (Papaioannou et al. 2015; Rahmati and Pourghasemi 2017). Mapping flood-prone areas is also essential for organizing and coordinating emergency response measures in natural disasters based on different management scenarios (Mostafazadeh et al. 2017). It is also important to carefully study the frequency and spatial distribution of flood events in urban and rural areas (Tango et al. 2022).
Numerous studies have been conducted on floods and their spatial distribution patterns, including regionalization of flood estimation historical flood events (Barriendos and Rodrigo 2006; Arshia et al. 2018); statistical distribution to analyze and estimate flood frequency with different return periods (Lin et al. 2016); seasonal flood peak (Chen et al. 2010; Fischer et al. 2016; Bartiko et al. 2019); flood seasonality and its temporal shifts (Ye et al. 2017); seasonal distribution of flooding (Diakakis 2017); climate change impacts on seasonal floods (Muttarak and Dimitrova 2019); river flood timing and seasonality (Dhakal and Palmer 2020); flood frequency analysis (Engeland et al. 2020); taking account of seasonality in a regional flood frequency (Ding and Arnaud 2022) and flood seasonality assessment (Ruiz et al. 2014; Alfieri et al. 2020; Couasnon et al. 2022).
In Ardabil province, flood producing precipitation regimes fluctuates due to climatic and relief diversity, in different regions. The average rainfall in the west of the province, which has the highest contribution of precipitation, is about 400 to 500 mm. On the other hand, the average annual rainfall in the southern regions is 350 mm and in the north of the province is 210 to 240 mm. Also, occurrence of flood events in some parts of the Ardabil province, causes significant damage to different areas due to climate change and spatial and temporal heterogeneity of rainfall and different climatic conditions throughout the year (Azizi et al. 2023). On the other hand, the increasing trend of floods and the resulting damages affect many parts of Ardabil province every year (Mostafazadeh and Mehri 2018; Ghorbani et al. 2023). Therefore, determining the frequency of floods and surveying flood-prone areas should be a priority in sound management of watershed resources. In this regard, mapping flood areas is necessary to carefully study floods and prevent its negative effects on the economy, environment, plant and animal species. Therefore, the aim of this study was to analyze the Iso-severity flood patterns and occurrence of flood events. In addition, the current research aimed to investigate the frequency of monthly and seasonal floods in different return periods in diverse relief and climatic regions of Ardabil province, Iran.
2.1 Description of the study area
Ardabil Province located in the northwest Iran in the geographical coordinates of 37° 45' to 39° 42' north latitude and 47° 30' to 48° 55' east longitude with an area of 17953 Km2. Iran is a neighbor of the Republic of Azerbaijan to the north, and is adjacent to East-Azarbaijan province from the west and Gilan province from the east. It is adjacent to Zanjan province in the southern part. In Ardabil province, with various topography, there are several rivers, and most of the rivers are exposed to exploitation and flow through population centers (Nasiri Khiavi et al. 2019). Despite the numerous rivers in the province and the quantity and quality diminish of water resources; it is necessary to analyze the condition and changes in water resources to develop appropriate management plans. The location of the study area in Iran is shown in Fig. 1.
Figure 1. Geographical location of the study area in Ardabil Province, Iran
2.2 Research methodology
In the present study, data from 32 River Gauge Stations (RGS) in Ardabil province were used, which was the statistical period of the data used in a 40-years recorded period. First, the maximum annual discharge data at each RGS was estimated. Return periods of 2, 5, 10, 25, 50, and 100-year were used. Then, EasyFit software and Kolmogorov-Smirnov test (K-S) were used to select the most appropriate statistical distribution for maximum discharge data (Romali and Yusop 2017a; Farooq et al. 2018; Ahmad et al. 2023). Some important specifications of the studied RGS in Ardabil province were listed in Table 1.
|
No. |
Study range |
River |
Station |
Established year |
Geographical cordinates |
Area (Km2) |
||
|---|---|---|---|---|---|---|---|---|
|
Lon |
Lat |
Altitude (m) |
||||||
|
1 |
Ardabil |
Balkhlou-Chay |
Pol-e-Almas |
1348 |
48-11-19 |
38-08-16 |
1440 |
1070 |
|
2 |
Qareh-Sou |
Samian |
1350 |
48-14-48 |
38-22-53 |
1290 |
4004 |
|
|
3 |
Nouran-Chay |
Atashgah |
1376 |
48-03-23 |
38-13-05 |
1773 |
44 |
|
|
4 |
Balkhlou-Chay |
Yamchi |
1376 |
48-02-11 |
38-02-05 |
1584 |
710 |
|
|
5 |
Nir-Chay |
Nir |
1351 |
47-59-38 |
38-02-02 |
1624 |
256 |
|
|
6 |
Qareh-Sou |
Aladizgeh |
1371 |
48-35-22 |
38-17-00 |
1347 |
22 |
|
|
7 |
Nouran-Chay |
Nouran |
1376 |
48-11-37 |
38-13-59 |
1423 |
126 |
|
|
8 |
Namin-Chay |
Namin |
1367 |
48-29-06 |
38-25-45 |
1459 |
44 |
|
|
9 |
Meshginshahr |
Qareh-Sou |
Doost-Beiglou |
1351 |
47-32-18 |
38-33-02 |
780 |
7311 |
|
10 |
Qareh-Sou |
Arbab-Kandi |
1377 |
48-01-58 |
38-29-41 |
1116 |
4800 |
|
|
11 |
Moghan |
Darreh-Roud |
Boran |
1337 |
47-31-8 |
39-17-41 |
244 |
13670 |
|
12 |
Darreh-Roud |
Mashiran |
1328 |
47-32-01 |
38-41-10 |
705 |
11267 |
|
|
13 |
Khalkhal |
Firoozabad-Chay |
Firooz-Abad |
1340 |
48-13-35 |
37-35-08 |
1153 |
1515 |
|
14 |
Ardabil |
Qouri-Chay |
Koozeh-Topraqi |
1350 |
48-22-01 |
38-07-28 |
1394 |
812.5 |
|
15 |
Balkhlou-Chay |
Gilandeh |
1350 |
48-21-43 |
38-18-26 |
1332 |
1638 |
|
|
16 |
Hir-Chay |
Hir |
1373 |
48-30-28 |
38-04-55 |
1575 |
178 |
|
|
17 |
Ardabil |
Lay-Chay |
Lay |
1359 |
47-54-03 |
38-07-00 |
2068 |
36 |
|
18 |
Viladaraq-Chay |
Viladarq |
1369 |
48-03-19 |
38-10-38 |
1800 |
8 |
|
|
19 |
Shahrivar-Chay |
Barogh |
1371 |
48-10-06 |
38-18-17 |
1410 |
94 |
|
|
20 |
Saqez-Chay |
Aeril |
1371 |
48-34-30 |
38-13-23 |
1375 |
76 |
|
|
21 |
Narges-Chay |
Naneh-Karan |
1371 |
48-31-32 |
38-22-17 |
1350 |
8 |
|
|
22 |
Sola-Chay |
Sola |
1371 |
48-29-04 |
38-23-14 |
1352 |
40 |
|
|
23 |
Yedibooliq-Chay |
Amouqin |
1374 |
48-10-40 |
38-15-07 |
1385 |
110 |
|
|
24 |
Agh-Chay |
Shams-Abad |
1366 |
48-15-14 |
37-59-53 |
1493 |
128 |
|
|
25 |
Anbaran-Chay |
Anbaran |
1382 |
48-29-06 |
38-29-40 |
1550 |
36 |
|
|
26 |
Meshginshahr |
Khiav-Chay |
Pol-e-Soltani |
1348 |
47-41-39 |
38-23-56 |
1420 |
98 |
|
27 |
Hendalou-Chay |
Ahl-e-Iman |
1378 |
47-28-05 |
38-24-02 |
1107 |
- |
|
|
28 |
Moghan |
Barzand-Chay |
Haji-Ahmadkandi |
1374 |
47-56-17 |
39-02-10 |
820 |
157 |
|
29 |
Sariqamish |
Akbar-Davoud |
1368 |
48-05-31 |
39-11-45 |
334 |
710 |
|
|
30 |
Khalkhal |
Shahroud |
Doroud |
1370 |
48-41-48 |
37-24-38 |
1651 |
158 |
|
31 |
Hero-Chay |
Abgharm |
1373 |
48-24-25 |
37-41-45 |
1535 |
590 |
|
|
32 |
Bafrajerd-Chay |
Bafrajerd |
1373 |
48-34-37 |
37-32-15 |
2120 |
36 |
|
2.2.1 Fitting the best distribution for maximum flow data
To fit the distributions on the data in EasyFit software, the fitting options and input data were set and finally the distribution fitting process was performed. EasyFit software includes classical statistical analysis methods and various data analysis techniques (Sharifi Garmdareh et al. 2018). The main features of this software included having more than 50 continuous and discrete distributions, very strong automatic fitting of statistical distributions on data and also the possibility of manual fitting of the desired distribution on the data, very useful diagrams for comparing the results of different distributions, good fit tests, generating stochastically coefficients, very simple user space (Mehrannia and Pakgohar 2014; Hossain and Rahman 2018; Samantaray and Sahoo 2020).
First, the options for fitting the distribution to the data were identified. Then the Fit Distributions item was selected from the analyze menu and the input data were specified. After specifying the distribution fitting process, EasyFit presents probability density function, summary and goodness of fit. In the goodness of fit section, the results of the distributions were displayed. In the present study, the appropriate statistical distributions were chosen based on Kolmogorov-Smirnov tests (Acharya and Joshi 2020; Lee et al. 2020; Samantaray and Sahoo 2020; Unami et al. 2021). After determining the best rank for the statistical distribution, the output flow rate was calculated for different return periods and the probability of occurrence in the selected statistical distribution (Romali and Yusop 2017b).
2.2.2 Application of Annual Maximum Method (AMM) to determine flood frequency
The probability distribution of maximum river flows was assessed by determining the frequency of annual maximum occurrence. Accordingly, the maximum annual return period was the average interval between years, which included flood flow with a specified minimum. Therefore, based on AMM, it was determined that after determining the best distribution and also determining the maximum flow discharge in different return periods, the flood probability values was selected based on the probability values (Romali and Yusop 2017a; Acharya and Joshi 2020; Unami et al. 2021). In this way, the threshold for selecting the number of floods in each RGS was the maximum discharge values in the same return period that in RGS, values higher than this value were considered as the flood threshold.
Contour mapping method was used to map the spatial distribution of flood attributes in ArcGIS 10.1 software (Demir and Kisi 2016; Lee et al. 2020). Then, the frequency of monthly and seasonal floods in each RGS was quantified. In addition, the significant trend of flood frequency in each RGS was tested according to the Man-Kendal trend test.
Different parametric and non-parametric tests are used to detect trends in water and meteorological data. Mann-Kendall and Pearson correlation tests are non-parametric tests. The World Meteorological Organization (WMO) strongly recommends the Mann-Kendall test to investigate and detect trends in climate time series. In the Mann-Kendall method, the data were arranged in the order of occurrence time and the process was determined based on the following steps and formulas. First, the S statistic was obtained for the Mann-Kendall test, and then it was determined that it was a function of the sign.
Where n is the number of series observations and Xi and Xj are the ith and jth data of the series, respectively. For independent random variables with a uniform distribution and no nodes (two or more data with equal numerical values), the mean and variance of S were calculated as follows:
If there is a node in the data series, the value of the variance will be calculated from the following equation:
Where n is the number of observational data, m represents the number of series in which there is at least one repeated data, and t represents the frequency of data with the same value. For example, if there are only two numbers with equal values in a data series, we will have a node with a capacity of two (t2 = 1). If the number of data in a series is more than 10, S will follow the normal distribution and the value of the standard statistical criterion (Z) will be as follows:
To assess the significance trend of the data, if the absolute MK values obtained from the Mann-Kendall test are greater than 1.96, then the data trend will be significant at the 5% level, and if the MK value is greater than 2.56 is greater, then they will be significant at the 1% level. If the MK statistic is positive, the trend of the data series is considered to be upward, and if it is negative, the trend is considered to be downward.
Therefore, in a two-way test to detect a trend at the confidence level α, the null hypothesis will be rejected if the value of Zs is greater than or equal to Z in the table. In these tests, the null hypothesis (H0) is the uniformity of the distribution and the independence of the data in the time series, and the opposite hypothesis (H1) is the increasing or decreasing trend. In this method, the data were sorted according to the time of occurrence and each data was compared with all the data after it. Finally, the box plot related to the frequency of seasonal floods in the Ardabil Province was drawn and analyzed.
Quantitative values of flow discharge with different return periods in the studied river gauge stations were presented in Table 2. Also, the frequency and spatial arrangement of monthly and seasonal flood events were presented in Table 3 and Figs. 2 and 3, respectively.
|
RGS |
Return periods (CMS) |
|||||
|---|---|---|---|---|---|---|
|
2 |
5 |
10 |
25 |
50 |
100 |
|
|
Abgarm |
5.35 |
37.65 |
48.90 |
150.32 |
341.70 |
763.84 |
|
Ahl-e-Iman |
3.37 |
5.30 |
6.71 |
7.24 |
7.46 |
7.61 |
|
Ahmad Kandi |
1.46 |
9.97 |
20.02 |
42.68 |
66.50 |
96.50 |
|
Akbar Davoud |
1.86 |
9.60 |
16.56 |
31.04 |
44.78 |
60.88 |
|
Aladizgeh |
3.53 |
5.70 |
7.25 |
9.17 |
10.58 |
11.95 |
|
Amooghin |
2.60 |
4.09 |
5.30 |
6.04 |
6.40 |
6.64 |
|
Anbaran |
6.84 |
13.56 |
20.70 |
27.57 |
32.45 |
37.11 |
|
Arbab Kandi |
16.80 |
31.56 |
40.90 |
47.08 |
49.66 |
51.20 |
|
Atashgah |
1.20 |
1.92 |
2.45 |
3.13 |
3.64 |
4.16 |
|
Bafrajerd |
2.82 |
3.27 |
3.44 |
4.10 |
5.05 |
6.70 |
|
Barough |
2.10 |
3.54 |
4.63 |
5.46 |
5.99 |
6.57 |
|
Bouran |
94.50 |
159.91 |
203.28 |
255.48 |
292.44 |
327.77 |
|
Doroud |
5.04 |
8.84 |
11.46 |
14.70 |
17.08 |
19.46 |
|
Doostbeiglou |
43.27 |
83.31 |
104.87 |
128.40 |
146.57 |
170.68 |
|
Firooz Abad |
26.08 |
33.12 |
55.47 |
83.08 |
112.85 |
153.56 |
|
Gilandeh |
13.58 |
31.91 |
32.93 |
39.38 |
43.95 |
48.63 |
|
Hir |
1.35 |
3.13 |
4.75 |
8.50 |
12.86 |
19.20 |
|
Aeril |
3.40 |
5.07 |
6.52 |
8.74 |
10.95 |
13.76 |
|
Koozeh Topraqi |
6.96 |
18.21 |
24.10 |
39.18 |
55.16 |
76.78 |
|
Lay |
0.32 |
0.84 |
1.08 |
1.78 |
2.48 |
3.35 |
|
Mashiran |
75.75 |
132.29 |
162.13 |
187.81 |
200.75 |
209.90 |
|
Namin |
1.00 |
1.72 |
2.09 |
2.35 |
2.46 |
2.54 |
|
Nanehkaran |
2.01 |
2.34 |
2.78 |
3.17 |
3.45 |
3.71 |
|
Nir |
2.77 |
6.04 |
6.32 |
10.00 |
14.14 |
20.00 |
|
Nouran |
2.12 |
3.90 |
6.19 |
10.80 |
16.45 |
25.13 |
|
Pol-e-Almas |
18.42 |
38.21 |
53.18 |
72.97 |
87.94 |
102.91 |
|
Pol-e-Soltani |
3.76 |
11.29 |
20.98 |
40.02 |
58.65 |
80.38 |
|
Sad Neur |
0.86 |
1.16 |
1.34 |
1.67 |
2.08 |
2.76 |
|
Samian |
42.13 |
79.21 |
104.76 |
152.55 |
210.35 |
301.42 |
|
Shams Abad |
3.94 |
6.89 |
8.98 |
10.15 |
10.66 |
11.00 |
|
Sola |
2.21 |
3.71 |
5.08 |
7.30 |
9.45 |
12.18 |
|
Viladarq |
0.26 |
0.51 |
0.66 |
0.83 |
0.93 |
1.01 |
|
Yamchi |
10.15 |
15.78 |
19.05 |
21.33 |
22.32 |
22.96 |
|
RGS |
Month |
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
Oct |
Nov |
Dec |
Jan |
Feb |
Mar |
Apr |
May |
Jun |
Jul |
Aug |
Sept |
Tot |
|
|
Abgarm |
0 |
2 |
0 |
2 |
3 |
42 |
177 |
224 |
38 |
9 |
6 |
0 |
503 |
|
Ahl-e-Iman |
8 |
3 |
0 |
1 |
0 |
2 |
5 |
0 |
0 |
0 |
0 |
5 |
24 |
|
Ahmad Kandi |
1 |
0 |
0 |
0 |
0 |
5 |
5 |
6 |
0 |
0 |
0 |
0 |
17 |
|
Akbar Davoud |
4 |
0 |
0 |
0 |
0 |
0 |
0 |
5 |
10 |
1 |
0 |
5 |
25 |
|
Aladizgeh |
5 |
3 |
0 |
4 |
4 |
8 |
0 |
0 |
0 |
0 |
0 |
10 |
34 |
|
Amooghin |
0 |
0 |
0 |
0 |
0 |
2 |
17 |
12 |
0 |
0 |
0 |
0 |
31 |
|
Anbaran |
0 |
1 |
0 |
0 |
0 |
1 |
1 |
2 |
0 |
0 |
0 |
0 |
5 |
|
Arbab Kandi |
0 |
2 |
4 |
31 |
29 |
13 |
49 |
19 |
3 |
3 |
0 |
0 |
153 |
|
Atashgah |
1 |
2 |
0 |
0 |
1 |
4 |
27 |
9 |
1 |
0 |
0 |
0 |
45 |
|
Bafrajerd |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
16 |
4 |
1 |
0 |
1 |
25 |
|
Barough |
0 |
0 |
0 |
3 |
5 |
0 |
56 |
11 |
2 |
0 |
1 |
0 |
78 |
|
Bouran |
2 |
2 |
0 |
1 |
0 |
34 |
184 |
91 |
20 |
1 |
0 |
0 |
335 |
|
Doroud |
8 |
0 |
2 |
2 |
0 |
16 |
137 |
38 |
25 |
1 |
0 |
0 |
229 |
|
Doostbeiglou |
0 |
0 |
1 |
0 |
0 |
4 |
72 |
74 |
11 |
2 |
1 |
0 |
165 |
|
Firooz Abad |
0 |
0 |
0 |
0 |
0 |
8 |
42 |
31 |
2 |
0 |
11 |
0 |
94 |
|
Gilandeh |
0 |
6 |
2 |
9 |
3 |
14 |
192 |
37 |
2 |
0 |
0 |
0 |
265 |
|
Hir |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
11 |
28 |
32 |
32 |
31 |
137 |
|
Aeril |
0 |
0 |
1 |
0 |
0 |
6 |
47 |
46 |
33 |
34 |
27 |
1 |
195 |
|
Koozeh Topraqi |
0 |
6 |
6 |
3 |
0 |
22 |
178 |
70 |
10 |
0 |
0 |
1 |
296 |
|
Lay |
16 |
0 |
22 |
36 |
46 |
41 |
60 |
57 |
6 |
5 |
1 |
25 |
315 |
|
Mashiran |
3 |
0 |
0 |
1 |
0 |
17 |
224 |
85 |
22 |
2 |
0 |
1 |
355 |
|
Namin |
6 |
0 |
0 |
0 |
9 |
12 |
43 |
4 |
0 |
0 |
0 |
2 |
76 |
|
Nanehkaran |
0 |
0 |
0 |
0 |
0 |
1 |
28 |
1 |
0 |
0 |
0 |
0 |
30 |
|
Nir |
5 |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
62 |
85 |
43 |
0 |
198 |
|
Nouran |
1 |
14 |
6 |
0 |
1 |
33 |
206 |
131 |
21 |
0 |
0 |
0 |
413 |
|
Pol-e-Almas |
0 |
0 |
0 |
0 |
0 |
2 |
39 |
11 |
3 |
0 |
0 |
0 |
55 |
|
Pol-e-Soltani |
0 |
0 |
0 |
0 |
0 |
6 |
64 |
18 |
1 |
0 |
1 |
0 |
90 |
|
Sad Neur |
6 |
13 |
0 |
25 |
13 |
0 |
19 |
51 |
86 |
28 |
11 |
8 |
260 |
|
Samian |
3 |
0 |
3 |
0 |
2 |
19 |
138 |
23 |
4 |
0 |
0 |
0 |
192 |
|
Shams Abad |
0 |
0 |
1 |
0 |
8 |
2 |
36 |
18 |
0 |
0 |
0 |
0 |
65 |
|
Sola |
7 |
2 |
0 |
1 |
12 |
29 |
19 |
7 |
0 |
0 |
0 |
6 |
83 |
|
Viladarq |
0 |
19 |
8 |
0 |
0 |
19 |
56 |
59 |
21 |
8 |
10 |
0 |
200 |
|
Yamchi |
0 |
0 |
0 |
0 |
0 |
2 |
30 |
22 |
0 |
0 |
0 |
0 |
54 |
Figure 2. Spatial arrangement of monthly flood events based on different return periods in diverse relief and climatic regions
Figure 3. The seasonal flood frequency in each RGS, Ardabil Province
Next, the flood frequency in Ardabil Province was analyzed based on significance using Mann-Kendall test, and the quantitative results as well as the spatial arrangement of significant trend changes in five classes (significant increasing, increasing trend, no trend, decreasing trend and significant decreasing) were presented in Table 4 and Fig. 4.
|
RGS |
Return periods |
|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
2 |
5 |
10 |
25 |
50 |
100 |
|||||||
|
Sig. |
MKT |
Sig. |
MKT |
Sig. |
MKT |
Sig. |
MKT |
Sig. |
MKT |
Sig. |
MKT |
|
|
Abgarm |
0.03 |
- |
0.47 |
- |
0.47 |
- |
NS |
+ |
NS |
+ |
NS |
+ |
|
Ahl-e-Iman |
0.05 |
- |
0.02 |
- |
0.03 |
- |
0.06 |
- |
NS |
+ |
NS |
+ |
|
Ahmad Kandi |
0.08 |
- |
0.23 |
- |
NS |
+ |
NS |
+ |
NS |
+ |
NS |
+ |
|
Akbar Davoud |
0.02 |
+ |
0.27 |
+ |
0.26 |
+ |
0.26 |
+ |
0.26 |
+ |
0.26 |
+ |
|
Aladizgeh |
0.00 |
- |
0.02 |
- |
0.06 |
- |
NS |
+ |
NS |
+ |
NS |
+ |
|
Amooghin |
0.37 |
- |
0.18 |
- |
0.06 |
- |
NS |
+ |
NS |
+ |
NS |
+ |
|
Anbaran |
NS |
+ |
NS |
+ |
NS |
+ |
NS |
+ |
NS |
+ |
NS |
+ |
|
Arbab Kandi |
0.20 |
- |
0.13 |
- |
0.28 |
- |
0.28 |
- |
0.28 |
- |
0.28 |
- |
|
Atashgah |
0.05 |
+ |
0.13 |
+ |
0.10 |
+ |
NS |
+ |
NS |
+ |
NS |
+ |
|
Bafrajerd |
0.25 |
+ |
0.40 |
- |
0.40 |
- |
0.40 |
- |
NS |
+ |
NS |
+ |
|
Barough |
0.27 |
- |
0.44 |
+ |
0.15 |
- |
0.15 |
- |
NS |
+ |
NS |
+ |
|
Bouran |
0.00 |
- |
0.00 |
- |
0.00 |
- |
0.05 |
- |
0.05 |
- |
0.10 |
- |
|
Doroud |
0.00 |
- |
0.00 |
- |
0.02 |
- |
0.04 |
- |
0.04 |
- |
0.18 |
- |
|
Doostbeiglou |
0.04 |
- |
0.00 |
- |
0.06 |
- |
0.22 |
- |
0.22 |
- |
NS |
+ |
|
Firooz Abad |
0.36 |
- |
0.30 |
- |
0.50 |
+ |
NS |
+ |
NS |
+ |
NS |
+ |
|
Gilandeh |
0.00 |
- |
0.00 |
- |
0.00 |
- |
0.02 |
- |
0.03 |
- |
0.12 |
- |
|
Hir |
0.02 |
+ |
0.47 |
+ |
NS |
+ |
NS |
+ |
NS |
+ |
NS |
+ |
|
Aeril |
0.26 |
- |
0.17 |
- |
0.11 |
- |
0.11 |
- |
0.43 |
- |
0.43 |
- |
|
Koozeh Topraqi |
0.02 |
- |
0.10 |
- |
0.21 |
- |
0.23 |
- |
NS |
+ |
NS |
+ |
|
Lay |
0.17 |
+ |
0.43 |
+ |
0.23 |
- |
0.08 |
- |
0.08 |
- |
NS |
+ |
|
Mashiran |
0.00 |
- |
0.02 |
- |
0.09 |
- |
0.20 |
- |
0.37 |
+ |
0.37 |
+ |
|
Namin |
0.19 |
- |
0.36 |
- |
0.34 |
+ |
0.12 |
+ |
NS |
+ |
NS |
+ |
|
Nanehkaran |
0.01 |
+ |
NS |
+ |
NS |
+ |
NS |
+ |
NS |
+ |
NS |
+ |
|
Nir |
0.49 |
- |
0.28 |
- |
0.26 |
- |
0.05 |
- |
0.05 |
- |
0.05 |
- |
|
Nouran |
0.02 |
- |
0.05 |
- |
0.05 |
- |
0.10 |
- |
0.36 |
- |
0.36 |
- |
|
Pol-e-Almas |
NS |
+ |
0.12 |
- |
0.50 |
- |
NS |
+ |
NS |
+ |
NS |
+ |
|
Pol-e-Soltani |
0.00 |
- |
0.00 |
- |
0.01 |
- |
0.21 |
- |
0.21 |
- |
0.21 |
- |
|
Sad Neur |
0.35 |
- |
0.46 |
- |
0.15 |
- |
0.07 |
- |
0.07 |
- |
0.07 |
- |
|
Samian |
0.00 |
- |
0.00 |
- |
0.00 |
- |
0.00 |
- |
0.20 |
- |
0.20 |
- |
|
Shams Abad |
0.00 |
- |
0.05 |
- |
0.10 |
- |
NS |
+ |
NS |
+ |
NS |
+ |
|
Sola |
0.03 |
- |
0.00 |
- |
0.04 |
- |
0.07 |
- |
0.07 |
- |
NS |
+ |
|
Viladarq |
0.17 |
- |
0.06 |
- |
0.16 |
- |
NS |
+ |
NS |
+ |
NS |
+ |
|
Yamchi |
0.21 |
+ |
0.50 |
+ |
NS |
+ |
NS |
+ |
NS |
+ |
NS |
+ |
| * NS: Not Significant; MKA: Man-Kendal Trend | ||||||||||||
Figure 4. Spatial arrangement of Mann-Kendall values based on the annual flood frequency in different return periods, Ardabil Province
Finally, based on the flood discharge at each river gauge station, the Iso flood curves were drawn in Ardabil province, which is presented in Fig. 5. Also, in order to investigate the deviations in the seasonal flood frequency, a box plot was used in different return periods (2, 5, 10, 25, 50, and 100-year), which was included in Fig. 6.
Figure 5. Iso flood curves based on flood discharge (m3/s) in each RGS, Ardabil Province
Figure 6. Box plot of seasonal flood frequency in Ardabil Province; (a) 2-year, (b) 5- year, (c) 10- year, (d) 25- year, (e) 50- year and 100-year return periods
Understanding the seasonal causes of floods is important for safe flood management (Ye et al. 2017). Floods can be occurred in periodic and seasonal manner and may occur infrequently during different months and seasons. It may also be severe or short-lived and even have spatial effects (Yang et al. 2017; Marhaento et al. 2018; Pokhrel et al. 2018).
Considering the complex nature of flood and its spatial changes, mapping its intensity and frequency at the watershed scale can be vital for making correct decisions in flood management, as well as understanding changes and predicting its occurrence (Avand et al. 2021). The spatial mapping of flood events and its seasonal analysis of flood occurrence is useful and optimal tool for investigating the hydrological aspects of flooding (Ruiz et al. 2014; Fischer et al. 2016). Distinguishing floods in different seasons can help planners and managers determine the different source of floods.
According to the results (Table 3 and Fig. 6), it can be said that in the stations studied in Ardabil province, out of 32 stations studied in 21 stations, the monthly flood frequency was high in April, which showed that about 63.63% of the floods occurred in April, which was the same as the results of (Sivapalan et al. 2005; Shankman et al. 2006; Hall and Blöschl 2018). Meanwhile, (Diakakis 2017) in Greece analyzed the seasonal floods and according to their results, the autumn season had the largest share of floods. The highest percentage of flood frequency was related to Naneh-Karan and Samian stations, about 93.33 and 77.55%, respectively. Based on the results, it can be said that the maximum floods were related to spring and in all return periods studied (Jain and Lall 2000; Reihan et al. 2012; Nolin et al. 2021; Sadeghi et al. 2021), the maximum amount and percentage of floods occurred in this season, which was different from the results of (Hall and Blöschl 2018) that floods are spring.
The highest share of spring floods was related to the 100-year return period with 87% and the lowest was related to the 25-year return period with 73%. However, in most of the return periods after spring, it was winter, which, with a slight difference compared to autumn and summer, accounted for a high percentage of floods, and the highest amount (14%) was related to the 25-year return period (Fig. 3.). Also in some stations such as Yamchi, Nouran, Naneh-Karan, Mashiran, Amouqin, Droud and Pol-e-Almas, nearly 100% of the floods were related to spring (Therrell and Bialecki 2015; Nolin et al. 2022). The reason for the occurrence of most floods in the spring season can be related to the mountainous and high area of the above-mentioned stations. In this regard, due to the snowfall in the winter season and the melting of snowpacks in the spring season and the simultaneous occurrence of precipitation, the maximum flow rate of the rivers increases. The seasons of winter, summer and autumn, with a slight difference, have allocated a part of these floods in each river gauge station. The significant trend of decreasing the average of the studied components showed that in the long term, the average values of the components had undergone many changes, and unfortunately, these changes have been in the form of a decrease, which has had a negative effect on the water resources of the province (Mostafazadeh et al. 2023).
Based on the results of the diagrams in Fig. 5, which show the I-F curves in the return periods of 2-, 5-, 10-, 25, 50 and 100-year in the studied river in Ardabil province, it can be said that in 2-year return period, floods in the southern and upstream regions of the province compared to the northern and downstream regions had a high frequency. Also, during the 5-year return period, a decreasing trend was observed in the floods of Ardabil province from the southern regions to the central and northern regions. According to a study by (Diakakis 2017) in Greece, the eastern parts had the most floods in the fall. The results of (Mostafazadeh et al. 2023) showed that the reliability of climatic events has been disturbed in recent decades and the intense flow pulses had increased over time, which confirmed the occurrence of destructive Also, the evaluation of average values of the seasonal index showed that Pol-e-Soltani station had the lowest and Nair station had the highest amount of seasonal flow in the study area. The analysis of flow seasonality and its relationship with flood occurrences has been confirmed in different studies (Radecki-Pawlik et al. 2020; Mwinuka et al. 2021).
On the other hand, the decrease in the frequency of spring floods in the eastern part of Ardabil Province can be related to the precipitation system from the Caspian Sea. Also, based on the seasonal patterns of flood frequency, the west of the province had the most spring floods among other studied river gauge stations (Fig. 3.).
These changes in the seasonal pattern can be attributed to changes in climatic parameters (Nastos and Zerefos 2008; Llasat et al. 2010; Philandras et al. 2010). For example, in the 2-year return period at Bafrajerd RGS with an altitude of 2020 meters above sea level, the frequency of floods was 33; while in the same return period at Bouran RGS with an average altitude of 244 meters above sea level, the frequency of floods was 4. This can be due to the increase in altitude and consequently the increase in rainfall, which shows the impact of climatic components and was similar to the results of (Hall and Blöschl 2018), based on the impact of climatic components and altitude on the flood occurrence. This difference in flood frequency was also observed based on 10-, 25-, 50- and 100-year return periods diagrams, which based on the results in the upstream and highlands of the province, an increase in flood frequency was evident.
The present research was conducted with the aim of studying and analyzing flood pattern iso-severity and investigating the frequency of monthly and seasonal floods in a mountainous region in Iran with climatic and topographical diversity. Thus, in the current research, the data of 32 river gauging stations (RGS) in Ardabil province were used. After choosing the most suitable statistical distribution, the flow data has been calculated in different return periods of 2, 5, 10, 25, 50, and 100-years. Then, the frequency of monthly and seasonal floods in each RGS was quantified. Next, the significant trend of flood frequency in each RGS was tested. Finally, the box plot related to the frequency of seasonal floods in the Ardabil Province was drawn and analyzed. Finally, after examining the seasonal distribution of floods and I-F areas in terms of flooding and discharge changes in each return period in some watersheds of Ardabil province, ArcGIS 10.1 software was used to show their spatial changes. Based on the results of seasonal comparison charts of floods, it can be said that the maximum floods were related to spring and in all return periods studied, the maximum amount and percentage of floods occurred in this season. The iso-severity flood and areas with equal intensity in terms of flooding occurrence showed that the frequency of floods in the upstream and high areas of the study area is more than the central, northern and lower areas of the province. Human activities such as uncontrolled interventions, construction in the floodplain, urban development, land use change and vegetation removal in the upstream areas also played an important role in this. According to the results of the present study, the southern regions of the province had a very high frequency of floods, so management and preventive measures in these stations should be given priority. It is necessary to use structural and non-structural methods to prevent floods. It is also necessary for the southern and flood-prone areas of the province, while conducting the necessary studies, proper planning should be considered with a focus on Integrated Watershed Management (IWM) to reduce the likelihood of floods in the future and minimize its damage. Finally, it can be concluded that in order to control the floods, the river side riparian areas should be protected while strengthening its natural landscapes, and the manipulation and change of natural conditions, as well as the destruction of the vegetation in the riverside areas, must be prevented. It is suggested that in future studies, in addition to the use of RGS data, climate change will also be considered in the study of floods, as well as the views of local stakeholders in watersheds in relation to floods and its seasonal distribution. In general, it can be said that the present study provides an overview of the seasonal distribution of floods in most watersheds of Ardabil province, which can improve our understanding of their seasonal distribution and highlight the differences in different regions.
Authors Contribution All authors contributed to the conception and design of the study. Material preparation, data collection and analysis were performed by A Nasiri Khiavi, R Mostafazadeh and H Talebi Khaivi. The first draft of the manuscript was written by A Nasiri Khiavi and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding Not applicable
Availability of Data and Material We have no permission to release data and codes.
Ethics Approval We confirm that this manuscript has not been published elsewhere and is not under consideration by another journal.
Consent to Participate All authors have participated the manuscript and agree with submission to Water Resources Management.
Consent for Publication Authors allow publication if the research is accepted.
Competing Interests The authors declare no conflict of interest.
- Abdo HG (2020) Evolving a total-evaluation map of flash flood hazard for hydro-prioritization based on geohydromorphometric parameters and GIS–RS manner in Al-Hussain river basin, Tartous, Syria. Nat Hazards 104:681–703. https://doi.org/10.1007/s11069-020-04186-3
- Acharya B, Joshi B (2020) Flood frequency analysis for an ungauged Himalayan river basin using different methods: a case study of Modi Khola, Parbat, Nepal. Meteorol Hydrol Water Manag 8:46–51. https://doi.org/10.26491/mhwm/131092
- Ahmad AM, Romali NS, Sulong S (2023) Flood frequency analysis of annual maximum stream flows for Kuantan River Basin. In: AIP Conference Proceedings
- Alfieri L, Dottori F, Salamon P, et al (2020) Global Modeling of Seasonal Mortality Rates From River Floods. Earth’s Futur 8:. https://doi.org/10.1029/2020EF001541
- Arora A, Pandey M, Siddiqui MA, et al (2021) Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models. Geocarto Int 36:2085–2116
- Arshia A, Haghizadeh A, Tahmasebipour N, Zeinivand H (2018) Prioritization of Sezar Subbasins in Terms of Flooding Potentian Using Game Theory. Iran J Ecohydrol 5:1219–1231
- Avand M, Khiavi AN, Khazaei M, Tiefenbacher JP (2021) Determination of flood probability and prioritization of sub-watersheds: A comparison of game theory to machine learning. J Environ Manage 295:113040. https://doi.org/10.1016/j.jenvman.2021.113040
- Azizi E, Nikoo MR, Mostafazadeh R, Hazbavi Z (2023) Flood vulnerability analysis using different aggregation frameworks across watersheds of Ardabil province, northwestern Iran. Int J Disaster Risk Reduct 91:103680
- Barriendos M, Rodrigo FS (2006) Study of historical flood events on Spanish rivers using documentary data. Hydrol Sci J 51:765–783. https://doi.org/10.1623/hysj.51.5.765
- Bartiko D, Oliveira DY, Bonumá NB, Chaffe PLB (2019) Spatial and seasonal patterns of flood change across Brazil. Hydrol Sci J 64:1071–1079
- Chen L, Guo S, Yan B, et al (2010) A new seasonal design flood method based on bivariate joint distribution of flood magnitude and date of occurrence. Hydrol Sci Journal--Journal des Sci Hydrol 55:1264–1280
- Costache R, Arabameri A, Blaschke T, et al (2021) Flash-flood potential mapping using deep learning, alternating decision trees and data provided by remote sensing sensors. Sensors 21:280
- Couasnon A, Scussolini P, Tran TVT, et al (2022) A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels—An Application to Ho Chi Minh City, Vietnam. Water Resour Res 58:. https://doi.org/10.1029/2021WR030002
- Demir V, Kisi O (2016) Flood Hazard Mapping by Using Geographic Information System and Hydraulic Model: Mert River, Samsun, Turkey. Adv Meteorol 2016:. https://doi.org/10.1155/2016/4891015
- Dhakal N, Palmer RN (2020) Changing river flood timing in the northeastern and upper Midwest United States: Weakening of seasonality over time? Water (Switzerland) 12:. https://doi.org/10.3390/w12071951
- Diakakis M (2017) Flood seasonality in Greece and its comparison to seasonal distribution of flooding in selected areas across southern Europe. J Flood Risk Manag 10:30–41. https://doi.org/10.1111/jfr3.12139
- Ding Q, Arnaud P (2022) Taking Account of Seasonality in a Regional Flood Frequency Estimation Approach Based on Event Simulations. Water (Switzerland) 14:. https://doi.org/10.3390/w14091376
- Engeland K, Aano A, Steffensen I, et al (2020) New flood frequency estimates for the largest river in Norway based on the combination of short and long time series. Hydrol Earth Syst Sci 24:5595–5619. https://doi.org/10.5194/hess-24-5595-2020
- Esfandyari Darabad F, Mostafazadeh R, Shahmoradi R, Nasiri Khiavi A (2020) The Effect of Dam Construction on flood and low flow Indices in South of Lake Urmia. J Nat Environ Hazards 9:1–14
- Farooq M, Shafique M, Khattak MS (2018) Flood frequency analysis of river swat using Log Pearson type 3, Generalized Extreme Value, Normal, and Gumbel Max distribution methods. Arab J Geosci 11:1–10
- Fischer S, Schumann A, Schulte M (2016) Characterisation of seasonal flood types according to timescales in mixed probability distributions. J Hydrol 539:38–56. https://doi.org/10.1016/j.jhydrol.2016.05.005
- Ghorbani A, Zabihi M, Mostafazadeh R (2023) Determining the Distribution Pattern of Spatial Correlation of Flood Occurrence in Ardabil Province Using Moran's Index in GIS. J RS GIS Nat Resour. https://doi.org/10.30495/girs.2023.1977963.2036
- Hall J, Blöschl G (2018) Spatial patterns and characteristics of flood seasonality in Europe. Hydrol Earth Syst Sci 22:3883–3901
- Hossain SMA, Rahman A (2018) Flood frequency analysis for the Brisbane River catchment. In: Hydrology and Water Resources Symposium (HWRS 2018): Water and Communities, 3-6 Dec 2018, Melbourne, Australia. pp 340–350
- Jain S, Lall U (2000) Magnitude and timing of annual maximum floods: Trends and large-scale climatic associations for the Blacksmith Fork River, Utah. Water Resour Res 36:3641–3651
- Jeneiová K, Kohnová S, Hall J, Parajka J (2016) Variability of seasonal floods in the Upper Danube River basin. J Hydrol Hydromechanics 64:357–366. https://doi.org/10.1515/johh-2016-0037
- Jongman B, Winsemius HC, Aerts JCJH, et al (2015) Declining vulnerability to river floods and the global benefits of adaptation. Proc Natl Acad Sci 112:E2271--E2280
- Jothibasu A, Anbazhagan S (2016) Flood susceptibility appraisal in Ponnaiyar River Basin, India using frequency ratio (FR) and Shannon’s Entropy (SE) models. Int J Adv Rem Sens GIS 5:1946–1962
- Khiavi AN, Vafakhah M, Sadeghi SH (2022) Comparative prioritization of sub-watersheds based on Flood Generation potential using physical, hydrological and co-managerial approaches. Water Resour Manag 36:1897–1917
- Lee S-M, Park K-D, Kim I-K (2020) Comparison of machine learning algorithms for Chl-a prediction in the middle of Nakdong River (focusing on water quality and quantity factors). J Korean Soc Water Wastewater 34:277–288
- Li H, Hui Y, Pan J (2022) Evolution and Influencing Factors of Social-Ecological System Vulnerability in the Wuling Mountains Area. Int J Environ Res Public Health 19:11688
- Lin N, Kopp RE, Horton BP, Donnelly JP (2016) Hurricane Sandy’s flood frequency increasing from year 1800 to 2100. Proc Natl Acad Sci 113:12071–12075
- Llasat MC, Llasat-Botija M, Prat MA, et al (2010) High-impact floods and flash floods in Mediterranean countries: the FLASH preliminary database. Adv Geosci 23:47–55
- Marhaento H, Booij MJ, Hoekstra AY (2018) Hydrological response to future land-use change and climate change in a tropical catchment. Hydrol Sci J 63:1368–1385
- Mehrannia H, Pakgohar A (2014) Using EASY FIT Software for Goodness-Of-Fit Test and Data Generation. Int J Math Arch 5:118–124
- Mostafazadeh R, Mehri S (2018) Trends in variability of flood coefficient in river gauge stations of Ardabil province, Iran. J Watershed Manag Res 9:82–95
- Mostafazadeh R, Nasiri Khiavi A, Ghabelnezam E (2023) Temporal changes and flow pattern analysis using Colwell indices in mountainous rivers. Environ Dev Sustain. https://doi.org/10.1007/s10668-023-03033-2
- Mostafazadeh R, Sadoddin A, Bahremand A, et al (2017) Scenario analysis of flood control structures using a multi-criteria decision-making technique in Northeast Iran. Nat Hazards 87:1827–1846
- Mukherjee F, Singh D (2020) Detecting flood prone areas in Harris County: A GIS based analysis. GeoJournal 85:647–663
- Muttarak R, Dimitrova A (2019) Climate change and seasonal floods: Potential long-term nutritional consequences for children in Kerala, India. BMJ Glob Heal 4:1–4. https://doi.org/10.1136/bmjgh-2018-001215
- Mwinuka PJ, Uiso CBS, Chang LB, Mwingereza J (2021) Monthly and Seasonal Rainfall Concentrations and Predictability in. 47:243–257
- Nasiri Khiavi A, Mostafazadeh R, Esmali Ouri A, et al (2019) Alteration of hydrologic flow indicators in Ardabil Balikhlouchai River under combined effects of change in climatic variables and Yamchi Dam construction using range of variability approach. Watershed Eng Manag 11:851–865
- Nastos PT, Zerefos CS (2008) Decadal changes in extreme daily precipitation in Greece. Adv Geosci 16:55–62
- Nolin AF, Girardin MP, Tardif JC, et al (2022) A 247-year tree-ring reconstruction of spring temperature and relation to spring flooding in eastern boreal Canada. Int J Climatol 42:6479–6498
- Nolin AF, Tardif JC, Conciatori F, et al (2021) Multi-century tree-ring anatomical evidence reveals increasing frequency and magnitude of spring discharge and floods in eastern boreal Canada. Glob Planet Change 199:103444
- Ogie RI, Adam C, Perez P (2020) A review of structural approach to flood management in coastal megacities of developing nations: current research and future directions. J Environ Plan Manag 63:127–147. https://doi.org/10.1080/09640568.2018.1547693
- Papaioannou G, Vasiliades L, Loukas A (2015) Multi-criteria analysis framework for potential flood prone areas mapping. Water Resour Manag 29:399–418
- Philandras CM, Nastos PT, Paliatsos AG, Repapis CC (2010) Study of the rain intensity in Athens and Thessaloniki, Greece. Adv Geosci 23:37–45
- Pokhrel Y, Burbano M, Roush J, et al (2018) A review of the integrated effects of changing climate, land use, and dams on Mekong river hydrology. Water 10:266
- Radecki-Pawlik A, Wałęga A, Młyński D, et al (2020) Seasonality of mean flows as a potential tool for the assessment of ecological processes: Mountain rivers, Polish Carpathians. Sci Total Environ 716:. https://doi.org/10.1016/j.scitotenv.2020.136988
- Rahmati O, Pourghasemi HR (2017) Identification of critical flood prone areas in data-scarce and ungauged regions: a comparison of three data mining models. Water Resour Manag 31:1473–1487
- Reihan A, Kriauciuniene J, Meilutyte-Barauskiene D, Kolcova T (2012) Temporal variation of spring flood in rivers of the Baltic States. Hydrol Res 43:301–314
- Romali NS, Yusop Z (2017a) Frequency Analysis of Annual Maximum Flood for Segamat River. MATEC Web Conf 103:. https://doi.org/10.1051/matecconf/201710304003
- Romali NS, Yusop Z (2017b) Frequency analysis of annual maximum flood for Segamat River. In: MATEC Web of Conferences. p 4003
- Ruiz JM, Carmona P, Pérez Cueva A (2014) Flood frequency and seasonality of the Jucar and Turia Mediterranean rivers (Spain) During the “Little Ice Age.” Mediterranee 122:121–130. https://doi.org/10.4000/mediterranee.7208
- Sadeghi M, Shearer EJ, Mosaffa H, et al (2021) Application of remote sensing precipitation data and the CONNECT algorithm to investigate spatiotemporal variations of heavy precipitation: Case study of major floods across Iran (Spring 2019). J Hydrol 600:126569
- Samantaray S, Sahoo A (2020) Estimation of flood frequency using statistical method: Mahanadi River basin, India. H2Open J 3:189–207. https://doi.org/10.2166/h2oj.2020.004
- Shabani S, Yousefi P, Adamowski J, Naser G (2016) Intelligent soft computing models in water demand forecasting. Water Stress Plants 99–117
- Shankman D, Keim BD, Song J (2006) Flood frequency in China’s Poyang Lake region: trends and teleconnections. Int J Climatol A J R Meteorol Soc 26:1255–1266
- Sharifi Garmdareh E, Vafakhah M, Eslamian SS (2018) Regional flood frequency analysis using support vector regression in arid and semi-arid regions of Iran. Hydrol Sci J 63:426–440. https://doi.org/10.1080/02626667.2018.1432056
- Sivapalan M, Blöschl G, Merz R, Gutknecht D (2005) Linking flood frequency to long-term water balance: Incorporating effects of seasonality. Water Resour Res 41:
- Smith B (2013) The role of vegetation in catastrophic floods: A spatial analysis. Bachelor Thesis 136
- Tango M, Acomb J, Harrington A, et al (2022) milwaukee urban development: assessing the drivers of urban flooding vulnerability in milwaukee using nasa earth observations
- Therrell MD, Bialecki MB (2015) A multi-century tree-ring record of spring flooding on the Mississippi River. J Hydrol 529:490–498
- Unami K, Mohawesh O, Fadhil RM (2021) Statistical analysis of flash flood events for designing water harvesting systems in an extremely arid environment. Hydrol Process 35:1–16. https://doi.org/10.1002/hyp.14370
- Yang L, Feng Q, Yin Z, et al (2017) Identifying separate impacts of climate and land use/cover change on hydrological processes in upper stream of Heihe River, Northwest China. Hydrol Process 31:1100–1112
- Ye S, Li HY, Leung LR, et al (2017) Understanding flood seasonality and its temporal shifts within the contiguous United States. J Hydrometeorol 18:1997–2009. https://doi.org/10.1175/JHM-D-16-0207.1