Determining water yield and flood discharges in catchments is a vital aspect of hydrology. This entails considering precipitation and runoff as key hydrological parameters. Constructing infrastructure like hydroelectric plants and regulators over streams necessitates continuous, accurate flow, and meteorological observations spanning at least 25 years. However, in developing nations, economic factors often impede such observations. This study proposes a method to estimate peak rainfall and flow values for ungauged basins with varying return periods by utilizing from gauged basins and spatial variables. Flood calculations were carried out for the ungauged Rabat River basin. In this study, regional flood frequency analysis was carried out using the flow values of the flow gauging stations neighboring the basin. In addition, maximum flow values were calculated using Moscus and the DSI synthetic method. Two- and three-parameter distributions were used to estimate 50-, 100-, 200- and 500-year flood return values at stations with observation periods ranging from 15 to 64 years. Kolmogorov–Smirnov and Probability Line Correlation coefficient (Chi-square) tests were applied to check the suitability of these distributions and the most appropriate distributions were found. This yielded an estimation for the flow values of the Rabat River, indicating the method's reliability for forecasting runoff-rainfall in the ungauged basin.

  • Essential hydrological estimates in catchments are vital in Engineering Hydrology.

  • Our study presents an innovative, flexible model system for precise parameter estimation.

  • We propose cost-effective alternatives for catchments lacking flow gaugings.

Information on stream flows is a fundamental element in many aspects of water resources, watershed planning, and water quality management. Runoff should be obtained from actual measurements of runoff at catchment outlets. In many parts of the world, there are no observed flow data or only short records of runoff data are available. Therefore, hydrologic forecasting in ungauged catchments is recognized as one of the most fundamental challenges and unsolved problems for the hydrological community (Blöschl et al. 2019; Brunner et al. 2021).

The majority of stream sections that necessitate flow information do not have gauges. For the precise anticipation of stream flow in an ungauged valley, close-by watercourse activity records are necessary. Presently, to acquire historical time series of watercourse discharges at ungauged basins, the most economical approach is to transfer data from gauged catchments that are similar and nearby, which are often referred to as analogous catchments.

The runoff process, which is highly dependent on precipitation, is often simulated by hydrological models. In hydrology, several types of models have been developed over the years for the prediction of surface runoff or stream flow (Sharma & Machiwal 2021), which fall into three categories: (i) physical or process-based models, (ii) conceptual models, and (iii) black box models. Physical or distributed/semi-distributed parameter models based on analytical solutions of differential equations describing the physical laws of conservation of mass, energy, and momentum can accurately simulate surface runoff. However, these models require extensive field data, which are often not available for mountainous watersheds (Beven & Freer 2001).

Regionalization techniques transfer model parameters from catchments with known parameters to ungauged catchments with similar hydrological characteristics. Commonly used regionalization techniques include the model averaging framework (McIntyre et al. 2005; Reichl et al. 2006), as well as the use of parameter sets from the closest upstream and downstream catchments, and the parameter regression approach (Peel et al. 2000; Merz & Bloschl 2004; Vogel 2005). Three approaches are utilized for assigning parameter values to ungauged catchments: parameter values from the nearest gauged catchment (nearest-neighbor approach) (Merz & Bloschl 2004; Chiew & Siriwardena 2005), regionalization of parameters (Kapangaziwiri & Hughes 2008). Zhang & Chiew (2009) outlined three commonly employed regionalization methods for selecting donor-gauged catchments whose optimized parameter values are utilized to simulate runoff for the target ungauged catchments. These methods comprise regression, spatial proximity (nearest-neighbor approach), and physical similarity methods. Parajka et al. (2005) offer a comprehensive overview of the uses of these regionalization methods in several studies, including a discussion of their achievements and shortcomings. Tuna (2013) indicated the hydroelectric potential in an ungauged basin with his study.

The hydrological response of ungauged basins can be modeled both spatially and temporally. Many researchers have reported the importance of these topics, especially for flood forecasting, dam safety, water resource management, climate change impact assessment, and watershed management (Javadinejad et al. 2019; KC et al. 2022; Athira et al. 2023; El Yousfi et al. 2023; Kaberia et al. 2023; Sahu et al. 2023).

Ungauged catchments are widespread in rural and remote locations. In Turkey, there are more than 3,000 streams, but daily gauging by the DSI (General Directorate of State Hydraulic Works) and EIE (General Directorate of Electrical Power Resources Survey and Development Administration) is limited to less than 500 (<15%). The study area of this research is the Rabat River Catchment in the Erzincan Province of Turkey. In the case of an ungauged site, stream flow parameter analysis is commonly used and typically consists of two steps: the initial step involves identifying a cluster of calibrated drainage basins that display a hydrological regime that is comparable enough to the uncalibrated site. The ensuing step is to make an estimation of flow quantiles at the uncalibrated site by transferring information from the sites identified in the former step.

The objective of the present study is to demonstrate the practicability of evaluating a stream flow parameter at an ungauged location. In order to achieve this, the paper examines the application of correlation analysis in order to measure the similarity between an ungauged site and gauged drainage basins and to establish hydrological neighborhoods for regional transfer. Three types of descriptors are employed, including hydrologic, meteorological, and physiographic variables. Furthermore, this paper presents a technique to forecast unmeasured stream flow parameters by employing historical data and publicly accessible climate and hydrologic data. The structure of this article is as follows: Section 1 provides an objective overview of the literature review and outlines the paper's research objectives clearly. Section 2 summarizes the data utilized. Section 3 explains the methodology used, specifically detailing the computational techniques applied. Finally, Section 4 presents the conclusions drawn from the study.

Study area

Seven stream gauging stations and three meteorology stations situated in eastern Turkey are utilized for this study. Table 1 provides an overview of their general characteristics. The Rabat basin, which covers an area of 141.2 km2 and has a main branch length of 26 km, originates from the Mercan Mountains and receives flow from one major tributary before discharging into the Keban Dam reservoir. The basin experiences warm, humid summers and cold winters, with substantial snowfall occurring in November, December, January, and February. The driest months are July and August. The average annual rainfall is around 650 mm. Basin land cover is mainly forested at higher elevations, while moderate development is primarily located in the stream valleys. Elevations range from 845 to 3,463 m.

Table 1

Stream flow gauging and meteorology stations

Station nameOperate institutionsObservation
RangePeriod (years)
Cemisgezek DMI 1938–2001 64 
Baspinar DMI 1965–1984 20 
Yesilyazi DMI 1965–1988 24 
2133 EIE 1969–2001 33 
2147 EIE 1964–1997 34 
2149 EIE 1964–2001 38 
2172 EIE 1979–2001 23 
21-99 DSI 1966–1987 22 
21-156 DSI 1978–1992 15 
Station nameOperate institutionsObservation
RangePeriod (years)
Cemisgezek DMI 1938–2001 64 
Baspinar DMI 1965–1984 20 
Yesilyazi DMI 1965–1988 24 
2133 EIE 1969–2001 33 
2147 EIE 1964–1997 34 
2149 EIE 1964–2001 38 
2172 EIE 1979–2001 23 
21-99 DSI 1966–1987 22 
21-156 DSI 1978–1992 15 

Weather and stream gauging stations

The meteorological observation map of stations both in and surrounding the study area can be found in Figure 1. These stations are monitored by DMI (Turkish State Meteorological Service), DSI, and EIE, and primarily measure rainfall and temperature. Furthermore, certain stations also record observations on snow depth, humidity, and wind.
Figure 1

Study area.

In the rainfall area of the study region, the Cemisgezek (DMI) meteorological station observes temperature. Between 1969 and 2010, the annual average temperature within the observation range of this station was 13.3 °C. Parameters of the basin are given in Table 2.

Table 2

Rabat catchment characteristics

Catchment area (A141.20 km2 
Catchment perimeter (P58.10 km 
Catchment length (Lh28.10 km 
Catchment width (Wh4.01 km 
Direction of the catchment Northwest-Southeast 
Catchment maximum height (hmax3,463 m 
Catchment minimum height (hmin845 m 
The average elevation of the catchment (hor2,184 m 
The median height of the catchment (hm2,207 m 
Mean catchment slope (SH5.9% 
Index-linked to the main waterway (S13.49 
Stemming length of the catchment index (S23.09 
The proportion of circular (S30.60 
Congestion index (Kc1.98 
Hydrologic soil cover number (CN) 73 
Length of the main watercourse (Ls26.35 km 
The total length of waterways (Lu45.90 km 
The outflow of the catchment to the basin center weight the distance (Lc11.30 km 
The main waterway slope profile (Ss3.12% 
The main waterway of harmonic pitch (S2.89% 
Bifurcation ratio (Rb9.8 
Drainage density (Dd2,125 m/km 
Frequency of waterways (Fr7.99 
Catchment area (A141.20 km2 
Catchment perimeter (P58.10 km 
Catchment length (Lh28.10 km 
Catchment width (Wh4.01 km 
Direction of the catchment Northwest-Southeast 
Catchment maximum height (hmax3,463 m 
Catchment minimum height (hmin845 m 
The average elevation of the catchment (hor2,184 m 
The median height of the catchment (hm2,207 m 
Mean catchment slope (SH5.9% 
Index-linked to the main waterway (S13.49 
Stemming length of the catchment index (S23.09 
The proportion of circular (S30.60 
Congestion index (Kc1.98 
Hydrologic soil cover number (CN) 73 
Length of the main watercourse (Ls26.35 km 
The total length of waterways (Lu45.90 km 
The outflow of the catchment to the basin center weight the distance (Lc11.30 km 
The main waterway slope profile (Ss3.12% 
The main waterway of harmonic pitch (S2.89% 
Bifurcation ratio (Rb9.8 
Drainage density (Dd2,125 m/km 
Frequency of waterways (Fr7.99 

To estimate runoff from unmonitored catchments under present or projected future situations, an observer can employ a methodology that takes into account solely parameters that can be visibly or indirectly determined or extends parameters from parameters identified for monitored catchments within the same region. This methodology should ideally use catchment characteristics that affect flow characteristics. This allows the identification of whether an unmeasured catchment is a member of a region based on its catchment characteristics, in relation to a region with an established correlation between flow and catchment characteristics.

There is no flow observation station currently operating on the Rabat River. To study the water potential in the area, data from the No. 21-99 (DSI) Mercan River – Birik SGS (stream gauging station), which is located in the same basin, were used. Daily flow observation data from the No. 21-99 SGS between 1966 and 1987 are available. The EIE operates the No. 2176 Tacik River – Muti Strait flow observation station in the same river basin as the Rabat River. The daily average flow values that were missing for 21-99 No. SGS within the observation range of 1988–2001 have now been replaced with those of 21-76 No. SGS.

The rainfall area of the Rabat River was determined to be 141.20 km2. To estimate the river's flows, we used daily flow values ranging from 21 to 99 (DSI, rainfall area = 127.8 km2) SGS. During the ungauged years of flow observation from 1988 to 2001, we relied on flow observation values ranging from 21 to 76 (EIE, rainfall area = 94.4 km2) Tacik River – Muti Strait SGS. We completed the missing data using the correlation and regression method. Operations were carried out between the years 1984 and 1987, during which measurements were taken at both stations. A significant correlation Equation (1) and a correlation coefficient of R = 0.8072 were found between these two stations.
formula
(1)
Using this correlation equation, the missing data for the year 2004 have been completed. Daily flow values for location No. 21-99 SGS were found within the observation range of 1966–2001 by transferring them to the Rabat River based on the respective area ratio. The regression equation chart is presented in Figure 2.
Figure 2

The regression analysis of the gauged stations in the catchment.

Figure 2

The regression analysis of the gauged stations in the catchment.

Close modal
Figure 3 shows the calculated monthly average flow values of the Rabat River in m3/s.
Figure 3

Average monthly discharge of the Rabat Stream (m3/s).

Figure 3

Average monthly discharge of the Rabat Stream (m3/s).

Close modal

Flood situation

To conduct a catchment study in the No. 21 river basin, we identified the annual maximum flow values operated by the DSI and the EIE with the scan of flow observation yearbooks. We subjected these values to Kolmogorov–Smirnov and Chi-Square distribution tests to determine their statistical probability distribution and density functions using the appropriate software.

The annual peak flow values of the Flow Observation Stations considered for the No. 21 river basin and the regional flood recurrence discharge values determined through probability distribution and density function are presented in Table 3 for each of the SGS stations.

Table 3

2133 (EIE) station-iterative values

Distribution name25102550100200500
Normal 543.55 733.72 833.15 939.24 1,007.65 1,069.24 1,125.42 1,193.21 
Log-Normal 2 501.90 702.35 837.25 1,009.87 1,139.61 1,270.65 1,403.25 1,581.82 
Log-Normal 3 504.64 705.94 838.83 1,006.54 1,131.11 1,255.83 1,381.03 1,548.22 
Gama 2 Par. 498.22 706.10 844.29 1,016.56 1,142.15 1,265.54 1,386.92 1,545.08 
Log-Pearson 3 518.03 719.67 838.96 974.55 1,066.06 1,150.39 1,228.35 — 
Gumbel 508.86 737.03 888.10 1,078.98 1,220.58 1,361.14 1,501.19 1,685.95 
Distribution name25102550100200500
Normal 543.55 733.72 833.15 939.24 1,007.65 1,069.24 1,125.42 1,193.21 
Log-Normal 2 501.90 702.35 837.25 1,009.87 1,139.61 1,270.65 1,403.25 1,581.82 
Log-Normal 3 504.64 705.94 838.83 1,006.54 1,131.11 1,255.83 1,381.03 1,548.22 
Gama 2 Par. 498.22 706.10 844.29 1,016.56 1,142.15 1,265.54 1,386.92 1,545.08 
Log-Pearson 3 518.03 719.67 838.96 974.55 1,066.06 1,150.39 1,228.35 — 
Gumbel 508.86 737.03 888.10 1,078.98 1,220.58 1,361.14 1,501.19 1,685.95 

Recursive rainfalls

By conducting the Simirnov–Kolmogorov and Chi-Square tests on the extreme distributions of the daily maximum rainfall of the rainfall stations in the study area, we have calculated recursive rainfalls according to the relevant type of distribution. The results are presented in Table 4.

Table 4

Type of distribution and Kolmogorov–Simirnov results

TheoreticalEmpiricalMaximum DistributionSignificant percentage
Distribution functionDistributionDistributionDifferenceChikare0.800.850.900.950.99
Normal 0.660 0.765 0.104 5.8 OK. OK. OK. OK. OK. 
Log-Normal 2 par. 0.319 0.235 0.084 2.8 OK. OK. OK. OK. OK. 
Log-Normal 3 par. 0.318 0.235 0.083 3.3 OK. OK. OK. OK. OK. 
Gama 2 par. 0.330 0.235 0.095 3.3 OK. OK. OK. OK. OK. 
Log-Pearson tip-III 0.326 0.235 0.091 5.8 OK. OK. OK. OK. OK. 
Gumbel 0.333 0.235 0.098 4.1 OK. OK. OK. OK. OK. 
TheoreticalEmpiricalMaximum DistributionSignificant percentage
Distribution functionDistributionDistributionDifferenceChikare0.800.850.900.950.99
Normal 0.660 0.765 0.104 5.8 OK. OK. OK. OK. OK. 
Log-Normal 2 par. 0.319 0.235 0.084 2.8 OK. OK. OK. OK. OK. 
Log-Normal 3 par. 0.318 0.235 0.083 3.3 OK. OK. OK. OK. OK. 
Gama 2 par. 0.330 0.235 0.095 3.3 OK. OK. OK. OK. OK. 
Log-Pearson tip-III 0.326 0.235 0.091 5.8 OK. OK. OK. OK. OK. 
Gumbel 0.333 0.235 0.098 4.1 OK. OK. OK. OK. OK. 

Regional flood analysis

The flood recurrence values, which have been calculated using the probability distribution function that best fits the annual instantaneous peak discharge series of the Flow Observation Stations in the No. 21 river basin, are presented in Table 5.

Table 5

(a) Regional flood frequency analysis and (b) dimensionless regional flood frequency analysis

StationsWatershedQ (2)Q (5)Q (10)Q (25)Q (50)Q (100)
(a) 
21-99 127.8 18.1 24.1 28 32.9 36.4 40.0 
21-156 657.2 98.3 141.4 176.2 228.2 273.2 324.3 
2133 3,284.8 504.6 705.9 838.8 1,006.5 1,131.1 1,255.8 
2147 875.0 161.3 213.6 244.8 281.3 306.7 331.0 
2148 1,238.4 242.3 334.8 377.4 415.4 430.5 450.4 
2149 1,669.0 317.2 437.3 507.8 589 644.9 697.6 
2172 1,374.0 193.4 295.5 373.3 482.2 570.5 664.9 
(b) 
21-99 127.8 1.00 1.33 1.55 1.82 2.01 2.21 
21-156 657.2 1.00 1.44 1.79 2.32 2.78 3.30 
2133 3,284.8 1.00 1.40 1.66 1.99 2.24 2.49 
2147 875.0 1.00 1.32 1.52 1.74 1.90 2.05 
2148 1,238.4 1.00 1.38 1.56 1.71 1.78 1.86 
2149 1,669.0 1.00 1.38 1.60 1.86 2.03 2.20 
2172 1,374.0 1.00 1.53 1.93 2.49 2.95 3.44 
 Total 7.00 9.78 11.61 13.94 15.69 17.55 
 Average 1.00 1.40 1.66 1.99 2.24 2.51 
StationsWatershedQ (2)Q (5)Q (10)Q (25)Q (50)Q (100)
(a) 
21-99 127.8 18.1 24.1 28 32.9 36.4 40.0 
21-156 657.2 98.3 141.4 176.2 228.2 273.2 324.3 
2133 3,284.8 504.6 705.9 838.8 1,006.5 1,131.1 1,255.8 
2147 875.0 161.3 213.6 244.8 281.3 306.7 331.0 
2148 1,238.4 242.3 334.8 377.4 415.4 430.5 450.4 
2149 1,669.0 317.2 437.3 507.8 589 644.9 697.6 
2172 1,374.0 193.4 295.5 373.3 482.2 570.5 664.9 
(b) 
21-99 127.8 1.00 1.33 1.55 1.82 2.01 2.21 
21-156 657.2 1.00 1.44 1.79 2.32 2.78 3.30 
2133 3,284.8 1.00 1.40 1.66 1.99 2.24 2.49 
2147 875.0 1.00 1.32 1.52 1.74 1.90 2.05 
2148 1,238.4 1.00 1.38 1.56 1.71 1.78 1.86 
2149 1,669.0 1.00 1.38 1.60 1.86 2.03 2.20 
2172 1,374.0 1.00 1.53 1.93 2.49 2.95 3.44 
 Total 7.00 9.78 11.61 13.94 15.69 17.55 
 Average 1.00 1.40 1.66 1.99 2.24 2.51 

Using the table of point flood recurrence values (Table 5), estimated using the probability distribution function that best fits the series of annual instantaneous peak discharge at Flow Observation Stations in the No. 21 River basin, we have estimated the Q2 annual discharge values as shown in Figure 4 based on the log–log charts corresponding to the rainfall areas of the stations.
Figure 4

Regional flood frequency analysis of 21 catchments.

Figure 4

Regional flood frequency analysis of 21 catchments.

Close modal

The annual recurrent discharge value from Figure 4, corresponding to the rainfall areas of the section axis location, has been multiplied by the average dimensionless regional flood recurrence values. This produces the flood recurrence discharge values for each section axis location, as outlined in Table 6. Technical terms are explained upon first use.

Table 6

Flood flows according to different methods

MethodsQ (2)Q (5)Q (10)Q (25)Q (50)Q (100)
DSI synthetic 13.7 23.0 30.0 39.9 47.4 55.5 
Mockus 9.4 19.0 27.1 39.3 49.2 60.1 
Regional flood analysis 24.2 33.9 40.2 48.2 54.2 60.7 
MethodsQ (2)Q (5)Q (10)Q (25)Q (50)Q (100)
DSI synthetic 13.7 23.0 30.0 39.9 47.4 55.5 
Mockus 9.4 19.0 27.1 39.3 49.2 60.1 
Regional flood analysis 24.2 33.9 40.2 48.2 54.2 60.7 

Area distribution of rainfall

The distribution of rainfall across the area was determined by using values obtained from the rainfall area continuity curves of the ‘US Soil Conservation Service’ for different durations. The RAD (area distribution of rainfall) values corresponding to 0.5, 1, 3, 6, and 24 h were considered for the rainfall area of 141.2 km2 in the DSI synthetic method.

Rainfall-runoff

The relationship between rainfall and flow has been estimated using equations developed by the ‘US Soil Conservation Service’ as follows:
formula
(2)
formula
(3)
where S is the leak loss (mm); P is the rainfall (mm); h is the flow (mm); CN is the flow curve number.

By calculating the recursive rainfall of the meteorological stations using Thissen rates, we have determined the catchment area's recursive rainfall through a weighted mean (Equations (2) and (3)).

Using the hourly pluviograph rates from the Keban (DMI) meteorological station, recursive rainfalls were estimated. To estimate the recursive flood discharges, which were chosen based on the hydrological conditions of the catchment area, flow and increment flows were calculated with the CN II (73) condition curve number. These were superpositioned with the unit hydrograph to estimate the recursive flood hydrographs.

Optimization of synthetic unit hydrograph

The physical size of the Rabat River is as follows:

DSI synthetic method

This technique, devised by the DSI, is applicable to areas with rainfall up to 1,000 km2. The unit hydrograph, derived from 2 h of heavy rainfall, must have a rise time (Tp) of at least 2 h. Table 7 presents the physical parameters and calculation findings for the DSI synthetic technique.

Table 7

DSI synthetic methods

Y (2)Y (5)Y (10)Y (25)Y (50)Y (100)
6 hourly rainfall data 27.8 33.3 36.9 41.3 44.5 47.8 
8 hourly rainfall data 30.1 36.1 40.0 44.8 48.3 51.8 
12 hourly rainfall data 33.0 39.5 43.7 49.1 52.8 56.7 
18 hourly rainfall data 36.2 43.4 48.0 53.9 58.0 62.3 
24 hourly rainfall data 41.2 49.3 54.6 61.2 65.9 70.7 
Y (2)Y (5)Y (10)Y (25)Y (50)Y (100)
6 hourly rainfall data 27.8 33.3 36.9 41.3 44.5 47.8 
8 hourly rainfall data 30.1 36.1 40.0 44.8 48.3 51.8 
12 hourly rainfall data 33.0 39.5 43.7 49.1 52.8 56.7 
18 hourly rainfall data 36.2 43.4 48.0 53.9 58.0 62.3 
24 hourly rainfall data 41.2 49.3 54.6 61.2 65.9 70.7 

Moscus method

The results obtained from studies conducted using the Mockus method are presented in Table 8. This data provide the Rabat River unit hydrograph, which is analyzed in Table 8 and represented in Figure 5.
Table 8

Mockus methods

Y (2)Y (5)Y (10)Y (25)Y (50)Y (100)
3.5 hourly rainfall data 27.5 32.9 36.4 40.9 44.0 47.2 
3.5 hourly runoff data 0.7 1.9 2.8 4.2 5.3 6.6 
Discharge iterations (m3/s) 6.4 16.0 24.1 36.3 46.2 57.1 
Y (2)Y (5)Y (10)Y (25)Y (50)Y (100)
3.5 hourly rainfall data 27.5 32.9 36.4 40.9 44.0 47.2 
3.5 hourly runoff data 0.7 1.9 2.8 4.2 5.3 6.6 
Discharge iterations (m3/s) 6.4 16.0 24.1 36.3 46.2 57.1 
Figure 5

Unit hydrograph.

Figure 5

Unit hydrograph.

Close modal
Table 6 outlines the recursive flood discharges, while Figure 6 showcases the project flood hydrographs.
Figure 6

Recurrence of flood hydrographs.

Figure 6

Recurrence of flood hydrographs.

Close modal

The estimation of hydrological parameters in unmonitored catchments poses a crucial challenge in hydrology. Stream flow data play a pivotal role in hydrological assessments pertaining to water resource project planning. To tackle this issue, one possible solution is to employ a method to a vast array of stream gauging and meteorology stations and to obtain statistical correlations between rainfall-runoff parameters and catchment attributes.

Considering the method presented in this paper, many of the regression models exhibit the same catchment characteristics or the interpretation of these characteristics. An alternative method to regression entails using a hydrological response unit-based model system that is flexible. Catchment descriptors of vegetation and soil type determine more intricate and specific model structures within each catchment than can be detected from stream flow data from a single catchment.

This paper presents a technique for estimating hydrological parameters at unmonitored locations. The approach was tested using a set of stream flow gauging and meteorology stations situated in eastern Turkey.

In this study, the hydrological potential parameters of the Rabat River basin were identified through the use of stream gauging stations in nearby catchments and meteorological observation stations. Additionally, the physical characteristics and the amount of rainfall in the region were used to identify the instantaneous maximum flood discharges and rainfall-flow parameters. It is noted that the hydrological potential parameters of an area are determined by its topographic parameters, including its area, flora, environment, minimum, maximum, and average height, while its physical parameters, such as slope and exposure, are influenced by hydro-meteorological factors, such as rainfall and temperature. The findings of this investigation indicate that for catchments lacking sufficient or non-existent flow gaugings, faster and more cost-effective alternatives to geodetic gaugings may be derived from topographic, physical, and hydro-meteorological parameters of the relevant catchment.

The hydrologist faces the challenge of estimating stream flow, as project sites often do not correspond with gauge and discharge sites. This is particularly evident in developing countries like Turkey, where river gauging sites are very scarce. In such circumstances, the hydrologist must devise creative approaches to utilize available hydro-meteorological data and produce a reasonably precise estimate of stream flow. This paper demonstrates an innovative method.

During feasibility and master planning stages, the project discharge can be easily identified by obtaining ‘stream rainfall-flow parameters’ using the method detailed in this study, particularly in catchments lacking or with inadequate flow gauging values. This will enable the design of water construction in a sound manner.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Athira
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D. S.
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