The Effects of Check Dams on the Amount and Pattern of Flood using Hydrological Modeling

doi:10.21203/rs.3.rs-1992312/v1

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The Effects of Check Dams on the Amount and Pattern of Flood using Hydrological Modeling

https://doi.org/10.21203/rs.3.rs-1992312/v1

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In this study, field measurement, spatial information, and HEC-RAS modeling were used to determine changes in the amount and pattern of flow in a waterway due to the construction of check dams. The location map of the structures and their upstream area was prepared in the GIS environment. The flow rate with different return periods was calculated from empirical methods. To simulate the changes in the flow pattern, the HEC-RAS model was calibrated. Flows and corresponding water levels were measured and data at 70% and 30% ratios were used for calibration and validation, respectively. The accuracy of the hydrological model in predicting water elevation was assessed using statistical indicators. The effect of check dams on the flow pattern and time in different discharges was quantitatively calculated and compared with the conditions before construction. The simulation results by HEC-RAS model showed a high correlation between observed and calculated values of flow and water level in the waterway (R² = 0.96 and RMSE = 0.029). In most structures, the flow velocity increased after passing the structures. In a 2-yr flood, the flow time increased by 82.8 minutes due to the check dams. This time is lower in floods with higher return periods.

Erosion

HEC-RAS

Watershed Management

Mechanical Operations

Flood Control

Understanding physical processes, water, biota, hydrological components, and their effect on basin response to rainfall, and other resources for ecological, social, and economic purposes are among the basic principles in watershed management (Wang et al. 2016). Watershed management is a critical concern for both present and future generations. It is a complex and dynamic phenomenon based on natural and human changes in the basin. This underscores the importance of paying attention to evaluating the results of management to achieve better practices and apply adaptive management. Adaptive management can be coupled with integrated management and increase the ability to deal with the inherent uncertainties of managing watersheds by learning the results of management implementation and setting future methods accordingly (Porzecanski et al. 2012). Each year, rivers carry thousands of tons of sediment in a variety of sizes and types of materials, and the quantity with the size of these deposits are determined by hydraulic parameters such as water velocity, flow depth, and other aspects of controls (Mustafa et al. 2017). Various structural and non-structural methods have been used around the world to reduce runoff, promote water infiltration, and reduce flooding. However, the effectiveness of these strategies must be quantified by credible reviews. The negative effects of such measures, especially mechanical structures, on the intensification of erosion and flooding have also been reported (Ulfiana et al. 2020; Amini 2022). In order to better understand how a watershed will respond to different management strategies, hydrological models are quite useful. In the planning of integrated methods for watershed management, water resources engineers can benefit from this (Alaghmand et al. 2011).

Environmental and basin factors are commonly used to define the basin's sensitivity to flood and sediment transport in most hydrologic models (Katra 2020). In the last few decades, numerical models have been developed in order to accurately reproduce the real-world occurrences that have been observed. Empirical models appear to be suitable instruments for producing necessary data for basin management due to the paucity of hydrometry stations at the outlets of basins (Khaledian et al. 2017). An effective tool for simulating runoff is Hydraulic Engineering Center-River Analysis System, HEC-RAS, which uses channel morphology to model runoff (Thakur et al. 2017). Hydraulic structures of all kinds can be simulated using HEC-RAS, from simple rivers to complicated river systems. Despite the uncertainty associated with extreme occurrences, the numerical analysis method, HEC-RAS, is suitable for flood characterization (Chen et al., 2017). Joshi et al. (2019) evaluated sediment flow and riverbed parameters in order to limit the effects of river morphological changes. For a section of the Euphrates River, Mustafa et al. (2017) studied sediment transport and flow depth. The HEC-RAS model with optimal Manning Roughness coefficient (n) was used to distribute 196 cross-sections around the study area, which resulted in the lowest error ratio between observed and calculated water surface heights.

Aslam and Lasminto (2020) used HEC-RAS numerical model to simulate flow depth, velocity, and inundation area caused by the overflow of discharge from a dam spillway. They used the upstream flow hydrograph and downstream rating curve data of the spillway during a flood event as upstream and downstream boundary conditions respectively. Their results indicated a high variation of flood characteristics over the spill way so that the flow velocity was between 0.57 to 4.84 m/s, the inundation area was 1.7 km², and the flow depth was between 1.70 m and 7.38 m. To attenuate flood peak control and sediment transport, check dams are used as watershed management measures. Yazdi et al. (2018) linked HEC-HMS with a multi-objective evolutionary algorithm and found that check dams decrease peak discharge up to 54% for a 10-yr flood return period and decrease the time to peak up to 88% for a 2-yr flood. Ulfiana et al. (2020) by adopting the sediment trap concept to deposit sediments simulated the waterway width changes downstream of the check dam using the HEC-RAS model. They found that the check dam constructed for controlling the suspended sediment going into the Mrica reservoir is less effective and if the width of the structure is more than the width of the waterway, the volume of sediment will increase. Hydraulic construction and other watershed management measures can help conserve soil and water. Check dams have been utilized in many watersheds to prevent soil erosion and flood control (Bellin et al. 2011; Borja et al. 2018).

One of the primary concerns of the design engineers in the construction of hydraulic structures such as a check dam is the characteristics of sediment transported nearby and flow pattern (Joshi et al., 2019). Hence, it's critical to comprehend the influencing factors and mechanisms that lead to alterations in soil erosion and flow pattern. Understanding these changes is also a prerequisite for sustainable watershed management (Montgomery 2007).

Changes in hydrological processes due to changes in waterway profile will have significant effects on the quantity of runoff from the basin. The purpose of this study is to investigate the changes caused by the construction of runoff and sediment control structures on the amount and pattern of flow in a waterway using field measurements, spatial information, and hydrological modeling. In addition to validating the HEC-RAS model in estimating the flow pattern in waterways, the results of this research can be used by researchers and watershed management engineers to determine the effectiveness of mechanical operations in the waterways.

2.1. Hydrological Characteristics of the Study Sub-Basin

This research was carried out in a part of Khorkhoreh sub-basin of Urmia Lake basin which is located in Kurdistan province, Iran. The study area is one of the main sub-basins of Lake Urmia and despite having a small area compared to the entire lake basin (42769.8 of 5180000 ha), it provides more than one-third of the runoff to Lake Urmia. Lake Urmia, located in Iran's East Azerbaijan province, was previously regarded as the second-largest hypersaline lake in the world. Due to human interference, Lake Urmia has lost 90 percent of its surface area in the previous few decades (Shadkam et al. 2016). Khalebazeh waterway was selected for this research due to having the largest number of structures and variety in the type of structures. The average slope of the lands of Khorkhoreh watershed is 46%, which indicates that the lands of this basin are mountainous. The average annual rainfall of the basin at the meteorological station of Saghez city is equal to 254 mm (Hesami and Amini 2016). To describe the relationship between rainfall intensity, duration, and return period Intensity-Duration-Frequency (IDF) curves for the basin were derived and shown in Fig. 1a. The ambrothermic curve of the basin is also shown in Fig. 1b. In Bast station, which is the only rainfall station within the study area, 70.4 and 83 mm of rainfall occur in April and March, respectively, which is equivalent to 35% of the total rainfall. In September and August, less than one percent of the annual rainfall occurs. The waterway under study is located in the Khalebazeh sub-basin in a village of the same name and its basin area is 965 ha. This waterway is located at the head of the Khorkhoreh watershed and the implementation of the watershed management operation proposed by the General Administration of Watershed Management of Kurdistan Province has started from this watershed. Many parts of the Khalebazeh sub-basin are mountainous with a maximum height of 2656 m in the northern areas and the minimum height is 1838 m in the southern area.

The average slope of the lands in the studied hydrological unit was 45.9%. The density of the waterway was 4.35 km/km² and the length of the main waterway was 4.362 km. Therefore, a 3 km length waterway was selected in which the largest and most diverse number of mechanical structures were constructed.

2.2. Research Methods

In the flow path of waterways, mechanical structures are used to reduce the slope, diminish flow velocity, create suitable conditions for sedimentation and stabilize the streambed. Flow pattern on overflows in check dams depends on the type and height of the structure and its design (Amini et al. 2014). In this study, changes in flow characteristics at the construction site of structures on the Khalehbazeh waterway were investigated using Hydraulic Engineering Center-River Analysis System, HEC-RAS, model. In this research, gabion and mortar structures were emphasized.

2.2.1 Field Surveys

To conduct field surveys, spatial information from the study basin, including the type, volume, and geographical location of the structures were recorded by GPS and mapping. Relevant data were analyzed using ArcGIS software. In 2016, 16 structures were studied along the waterway. In the spring and summer of 2017, several floods occurred in the watershed, and the data related to these floods were measured and used in HEC-RAS modeling when crossing the waterway and on the structures.

2.2.2. Discharge with Different Return Periods

In waterways that do not have hydrometric stations and discharge data, models and experimental methods based on climatic characteristics and physiography of the basin can be used to determine the peak discharge in different return periods. Among the most common of these formulas are Krieger, Deacon, and Fuller (Taghavi 2017), which were used in this study. The Krieger method is one of the experimental methods that has been widely used to determine the flood discharge in various return periods at large and small watersheds and is expressed as Eq. 1 (Nicholson et al. 2020).

$${Q_p}=46C.{A^{\left( {0.894{A^{ - 0.48}}} \right)}}$$

Where Q_p is the maximum probability discharge(m³/s), C is a constant depending on the watershed characteristics and A (km²) is the watershed area. In the Deacon method, the maximum flood is obtained using the watershed area and the regional coefficient as Eq. 2.

$${Q_p}=C.{A^{0.75}}$$

Fuller method is the most important and widely accepted due to its simplicity for determining the maximum flood and is expressed as (Jimeno-Sáez et al. 2017),

$${Q_p}=C.{A^{0.8}}(1+B\log T)(1+2.67{A^{ - 0.3}})$$

where B is the flood area coefficient, which is usually considered equal to 0.8, and T is the return period (year). To determine C, the maximum instantaneous discharge values have been used and corresponding values of C are given in tables for different return periods (Zeraatkar et al. 2014). Then, using this table and extracting the area of sub-basins leading to each structure in the waterway in the GIS environment, the value of Q_P was calculated.

2.3. HEC-RAS model

Hydrological modeling of the flow pattern in the studied waterway was performed using the HEC-RAS model. The HEC-RAS software is a runoff simulator model that solves St. Venent equations in steady and unsteady flows and calculates flow characteristics (Amini et al. 2021). For simulation, it is necessary to define three main data sets, including geometric and flow data, and the type of modeling. Preliminary physiographic data of sub-basins were extracted in a GIS environment.

2.3.1. Geometric Data

To implement the HEC-RAS model, for importing geometric data in addition to schematic drawing of the subbasin and its elements, the waterway cross-sections, the length of the downstream interval as distance between two cross-sections, the roughness/Manning coefficient, and "inline" or "lateral" structures were specified. The distance between the cross-sections depends on the condition of the waterway curvature and the importance of the sections in influencing the flow simulation. In areas where the river is straight and uniform, long-distance sections can be used. Otherwise, the sections need to be close together. In this study, in the vicinity of the structure at distances of 2–10 m, the cross-sections of the waterway were created using field measuring and mapping. Between two consecutive structures, at larger distances of the waterwayو the transverse profiles were created, by digital DEM and a triangulated irregular network (TIN). Using the topographic map of the area, the TIN layer was extracted in the ArcGIS environment and the input data layers to HEC-RAS were prepared Fig. 2. HEC-GeoRas extension was used to connect spatial data with the HEC-RAS model. In the ArcGIS environment, different layers of the waterway were extracted in Geo Data Base format. River flow layers, cross-sections along the river path, and Manning roughness layer were imported into the software using the HEC-GeoRAS.

The longitudinal slope is another effective factor in the concentration of floods in the basin and affects the output hydrograph. The greater the slope of the main streamline, the shorter the concentration-time of the basin and, consequently, the longer the base time of the hydrograph and the peak discharge. The longitudinal profile of the river in the Khalehbazeh waterway was collected from the Khorkhoreh basin using accurate mapping and is shown in Fig. 2. This profile was used to verify different layers of information related to waterway cross-sections in the GIS. A schematic of the Geometric data at the studied waterway in the HEC-RAS environment is given in Fig. 2.

Figure 2a) Longitudinal profile of the main waterway and b) Geometric data drawing of Khalehbazeh waterway in HEC-RAS

The condition of each cross-section in terms of the presence of embankment, levee, and obstacle in the flow path is effective in simulating the runoff. The inline structure was used to create mechanical structures in the HEC-RAS model. In some structures, elements such as "Ineffective Flow Area" and "Abstractions" were used in the cross-sections upstream of the structure. To perform flood routing calculations, it is necessary to generate at least two cross-sections inside the check dam reservoir (Amini et al. 2021). These two cross-sections were selected immediately above the structure and at the beginning of the reservoir.

In order to determine the amount of flow penetration from the body of the structure, "Pilot flow" in HEC-RAS software was used for gabion structures. This flow indicates the amount of flow passing through the structures "Inline structure " (HEC-RAS Manual, 2016). With the flood subsiding and the flow cut off from the spillway, the amount of flow through the waterway streambed and the banks of the waterway in the vicinity of the structure was estimated.

The infiltration flow was selected so that the ratio of critical depth to step height was less than 0.3 (Wüthrich and Chanson 2014). Using the relation of crest overflows, the infiltration flow from gabion structures was calculated. According to the HEC-RAS Manual (2016), the flow rate through the structure (Pilot flow) was considered in the runoff simulation process. The maximum amount of infiltration discharge in this study was equal to 80 lit/s. An illustration of the waterway cross-section the construction site of the mechanical structure and the geometric characteristics of the structure are given in Fig. 3.

2.3.2. Flow data

After importing the geometric data based on various floods, the simulation was performed in HEC-RAS software. In this study, due to the lack of hydrometric stations in the waterway, empirical methods were used to calculate the flow rate through the structures, and floods with various return periods were determined. In addition, flows measured in the waterway through field observation were used to calibrate and validate the HEC-RAS model.

2.3.3. Waterway Manning Coefficient

In hydraulic and hydrologic applications including flood modelling, determining a precise representation of river bathymetry is virtual (Dey et al. 2019). Therefore, in the GIS environment and using ArcMap software, zones of waterway that were with similar hydrological and morphology conditions were identified based on field observations along the waterway. According to the slope, the condition of material of the streambed and banks, the waterway was divided into five zones with similar characteristics and the Manning coefficient was estimated for each zone. The HEC-RAS software online guide was the basis for engineering judgment in field visits.

2.3.4. Preliminarily and Boundary Conditions

In the HEC-RAS model, it is essential to determine the boundary and initial conditions for upstream and downstream the structures as well as the level of the initial and final cross-sections of the waterway. The slope of water surface profiles and river slope were used as boundary conditions. The stage-discharge curve at the highest cross-section of the waterway was used as the boundary condition in the calibration and validation stages of the model as recommended by (Aslam and Lasminto 2020). The same method was used for the lowest cross-section of the river. The imported boundary conditions to the HEC-RAS software in the initial and end structures of the waterway are shown in Fig. 4a and Fig. 4b respectively. These hydrographs were extracted from two measured floods in the waterway.

2.3.5. Model Calibration and Validation

Model calibration and validation is a key step in any modeling process to ensure model accuracy. In the case of flood modeling, one of the most frequent approaches for model calibration and validation is varying the value of the Manning coefficient (Ardıçlıoglu and Kuriqui 2019; Joshi et al. 2019). In this study, the Manning coefficient was used as an independent variable and the water level on the overflow of two structures in the waterway was used as the main calibration and validation variables of the model. For this purpose, two floods that occurred on April 15, 2017, with rainfall of 19.2 mm, and on April 25, 2017, with rainfall of 10.6 mm in the basin were used to calibrate and validate the HEC-RAS model. In these two rainfall events, the amount of water height at the overflow site was measured. The flow rate equivalent to the height on the overflow was obtained using the ogee overflow equations (Yildiz et al. 2020) in the form of Eq. 4.

$${Q}_{i}={C}_{0}L{{H}_{i}}^{1.5}$$

where Q_i, discharge; i, the number of cross-sections on the river route; L, crest width; C₀, discharge coefficient; and H_i, total head over ogee spillway. The relationship of discharge coefficient, C₀, versus various values of Hi and spillway height was obtained from graphs presented by (USBR 1987). To validate the HEC-RAS model at the outlet of the study basin, in a section of the waterway with an almost uniform cross-section, the depth of flow in both floods was measured. The cross-section of the waterway was accurately measured by mapping. Moreover, the upstream and downstream cross-sections in this area were measured at the close interval and the data were entered into HEC-RAS software. Measured data at 70% and 30% ratios were used for calibration and validation, respectively (Bui et al. 2020).

2.3.6. Model Accuracy

The results of water height simulation were evaluated using coefficients of determination (R²), Mean Absolute Deviation (MSE), Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Eqs. (5) to (9) show these statistical indicators (Amini et al. 2019).

$${R^2}=\frac{{{{\left[ {\sum\nolimits_{{i=1}}^{n} {({A_i} - \overline {A} )({F_i} - \overline {F} )} } \right]}^2}}}{{{{\sum\nolimits_{{i=1}}^{n} {{{({A_i} - \overline {A} )}^2}({F_i} - \overline {F} )} }^2}}}$$

$$MSE=\frac{{\sum\nolimits_{{i=1}}^{n} {{{({A_i} - {F_i})}^2}} }}{n}$$

$$MAD=\frac{{\mathop \sum \nolimits_{{i=1}}^{n} \left| {{A_i} - {F_i}} \right|}}{n}$$

$$RMSE=\sqrt {\frac{{\mathop \sum \nolimits_{{i=1}}^{n} {{\left( {{A_i} - {F_i}} \right)}^2}}}{n}}$$

$$MAPE=\frac{{\mathop \sum \nolimits_{{i=1}}^{n} \left| {\frac{{{A_i} - {F_i}}}{{{A_i}}}} \right|}}{n} \times 100$$

In these Equations, n, A_i, F_i, $\stackrel{-}{A}$, and $\stackrel{-}{F}$ are the number of data, the computed, observed and average values of water height in the model and the average value of observed data, respectively.

3.1. Peak Flood Discharge

In this study, the maximum flood discharge was calculated from empirical methods. The results of the maximum probability flood discharge in different return periods are given in Table 1.

Table 1

Maximum probability flood
Return period (yr)		2	3	10	25	50	100
Krieger Method
	Discharge Coefficient	9.80	7.74	5.99	4.08	2.90	1.63
	Q_max (m³/s)	38.5	30.41	23.53	16.03	11.39	6.40
Deacon Method
	Discharge Coefficient	4.29	3.32	2.57	1.75	1.24	0.70
	Q_max (m³/s)	23.00	18018	14.07	9.58	6.79	3.83
Fuller Method
	Discharge Coefficient	0.85	0.74	0.64	0.51	0.42	0.30
	Q_max (m³/s)	31.81	25.19	19.56	13.24	9.45	5.37

Table 1 shows that the maximum amount of flood in the study basin using Krieger method is equal to 38.5 m³/s. The Krieger method estimated greater floods, and the Deacon method estimates the lowest amount of floods in the same return period. In this study, data obtained from Krieger method were used.

3.2. Manning Coefficient

To determine the Manning/roughness coefficient of Khorkhoreh river in Khalehbazeh waterway, according to the morphology of the waterway, bed materials, length of waterway interval, condition of banks, cross-section changes and density vegetation, the studied waterway was divided into five zones using the aerial map and multiple local visits. Then the Manning coefficient was estimated for each zone separately. It should be noted that the Manning coefficients obtained from this stage were partially adjusted in the model calibration process. The amount of manning coefficient on the right and left banks of the river in proportion to the vegetation was higher than the main waterway. This finding is consistent with the results obtained from (Shafaei et al. 2019).

3.3. Flood Discharge Measurement

Recorded data from events of April 15 and 25, 2017 with 19.2 and 10.6 mm rainfall were used to calibrate the HEC-RAS model. Observed and simulated water levels at the upstream and downstream of the waterway and on the overflow of structures constructed in the Khalebazeh waterway were used to calibrate and validate the model. These data are given in Table 2, in which H_i is the height of water measured on spillway. The H_i/H_D value is extracted from the standard tables, where H_D is design height of the structural overflow. The discharge coefficient (C_D) is obtained based on H_i/H_D and H_i values (Roushangar et al. 2018). The value of the discharge coefficient was obtained as C_D = 0.48 and 0.49 for downstream (Kh19) and upstream (KH6) structure respectively.

Table 2

Comparison of measured flood characteristics and model simulations
i	Water Elevation	Water Head H_i (m)	H_i /H_D	C_i /C_D	C_i	Discharge (m³/s)		Water Level (m)
i	Water Elevation	Water Head H_i (m)	H_i /H_D	C_i /C_D	C_i	Flood	Total Runoff	Measured	Calculated
April 15, 2017 (19.2 mm rainfall)
0	1911.00	0.00	0.0	0.78	0.385	0.00	0.500	0.11	0.09
1	1911.02	0.02	0.1	0.811	0.400	0.04	0.540	0.11	0.09
2	1911.04	0.04	0.2	0.842	0.415	0.118	0.618	0.12	0.12
3	1911.06	0.06	0.3	0.869	0.428	0.223	0.723	0.15	0.16
4	1911.08	0.08	0.4	0.895	0.441	0.354	0.854	0.18	0.2
5	1911.1	0.10	0.5	0.915	0.451	0.505	1.005	0.19	0.21
6	1911.12	0.12	0.6	0.935	0.461	0.679	1.179	0.19	0.21
7	1911.14	0.14	0.7	0.951	0.469	0.87	1.370	0.21	0.23
8	1911.16	0.16	0.8	0.967	0.477	1.081	1.581	0.22	0.23
9	1911.18	0.18	0.9	0.984	0.485	1.312	1.812	0.25	0.24
10	1911.2	0.2	1.0	1.000	0.493	1.562	2.062	0.25	0.25
11	1911.22	0.22	1.1	1.013	0.499	1.825	2.325	0.28	0.26
12	1911.24	0.24	1.2	1.025	0.505	2.105	2.605	0.30	0.28
13	1911.26	0.26	1.3	1.037	0.511	2.4	2.900	0.35	0.32
14	1911.28	0.28	1.4	1.048	0.517	2.712	3.212	0.38	0.35
15	1911.3	0.30	1.5	1.059	0.522	3.039	3.539	0.38	0.36
16	1911.32	0.32	1.6	1.07	0.528	3.383	3.883	0.40	0.37
April 25, 2017 (10.6 mm rainfall)
0	1949.00	0.00	0.0	0.78	0.385	0.00	0.250	0.03	0.03
1	1949.03	0.03	0.1	0.811	0.4	0.028	0.278	0.03	0.03
2	1949.06	0.06	0.2	0.842	0.415	0.081	0.331	0.05	0.03
3	1949.09	0.09	0.3	0.869	0.428	0.154	0.404	0.08	0.10
4	1949.12	0.12	0.4	0.895	0.441	0.244	0.494	0.10	0.11
5	1949.15	0.15	0.5	0.915	0.451	0.348	0.598	0.14	0.11
6	1949.18	0.18	0.6	0.935	0.461	0.468	0.718	0.15	0.14
7	1949.21	0.21	0.7	0.951	0.469	0.599	0.849	0.20	0.15
8	1949.24	0.24	0.8	0.967	0.477	0.745	0.995	0.25	0.20
9	1949.27	0.27	0.9	0.984	0.485	0.904	1.154	0.28	0.32
10	1949.30	0.30	1.0	1.000	0.493	1.076	1.326	0.32	0.33
11	1949.33	0.33	1.1	1.013	0.499	1.257	1.507	0.35	0.37
12	1949.36	0.36	1.2	1.025	0.505	1.45	1.700	0.38	0.37
13	1949.39	0.39	1.3	1.037	0.511	1.653	1.903	0.41	0.45
14	1949.42	0.42	1.4	1.048	0.517	1.868	2.118	0.45	0.47
15	1949.45	0.45	1.5	1.059	0.522	2.094	2.344	0.49	0.55
16	1949.48	0.48	1.6	1.070	0.528	2.331	2.581	0.54	0.63

3.4. Model Calibration and Validation

The observed runoff obtained from Table 3 of the structure located at the beginning of the studied waterway (Kh6) was imported into the HEC-RAS software as the input data in the boundary conditions. After simulating the runoff, the water depth was measured in the RS850 section at the end of the waterway. The roughness/Manning coefficient variable (n) was changed so that the two depths become compatible. For this purpose, both series of data were combined and randomly divided into two series. The 70% (24 data) and 30% (10 data) of the data were considered for calibration and validation, respectively. The results of the fitting line and the correlation coefficient of the data in both stages are shown in Fig. 5.

Table 3

Results of HEC-RAS model evaluation
Indices	R²	MSE	MAD	RMSE	MAPE
Amount	0.96	0.001	0.023	0.029	10.29

Figure 5 showed that the HEC-RAS model in the calibration process was well matched to the observed data. In addition, the calibrated model was able to estimate the observed data with an accurate estimation, which indicates the proper performance of the model. Further evaluation of the model accuracy was performed with statistical criteria. Table 3 shows the results of statistical indices for the comparison between the observed and simulated water levels in the waterway.

The coefficient of determination, R², is a value between zero and one, which indicates the direction of change of the independent relative to the dependent variable. The R² = 1 indicates the complete conformity of the data. The value R² in Table 3 shows that there is a high correlation between the recorded and calculated values of water levels in the waterway. The other criteria shown in Table 3 were used to evaluate the model accuracy in terms of the amount of error. The MAD, and MSE also showed low error and model efficiency. The MAPE index is expressed as a percentage and the closer the error criteria are to zero, the better the model prediction and the lower the error. Therefore, it can be said that the HEC-RAS model has a high efficiency in runoff simulation. The efficiency of HEC-RAS model in flood simulation has been reported by (Juan and Luis 2019; Amini et al. 2021).

3.5. Impact of Check Dams

To compare the changes in the flow pattern due to the construction of mechanical structures on a waterway, considering the floods with different return periods, the calibrated HEC-RAS model for the conditions before and after construction was implemented and the results were examined.

3.5.1. Water Profile

In floods with different amounts of discharge, flow depth is the main variable of flow pattern. Changes in water profiles due to the construction of mechanical structures are a function of variables related to flow, waterway bed, and dimensions of the structures. In this study, by considering the flood with different return periods, the flow depth before and after the construction of mechanical structures in the Khalehbazeh waterway was investigated. The simulation results obtained by the HEC-RAS model are shown in Fig. 6 (a & b) in the output format of the software.

From the comparison of graphs in, Fig. 6 (a & b) shows that the water level has a lower slope after the construction of mechanical structures. At the location of the structures, the slope decreases and the flow to the location of the next structure follows the natural slope of the waterway. The water profiles shown in Fig. 7b indicated that after the construction, in floods with lower return periods, the water heights are closer to each other than before the construction. This indicates more effects of structures on floods with a lower return period, 2 to 25 years, compared to floods with periods of 50 and 100 years. These results are consistent with the findings by (Amini et al. 2016). Figure 7b shows the corresponding water level of the floods with different return periods on a structure in the studied waterway simulated by calibrated HEC-RAS model.

Field studies showed that in a number of structures, the dimensions are impropriated with the flood and sediment potential of the basin. Figure 7 shows that the structure has been submerged in floods with return periods of more than 10 years. In addition, in the structures upstream of the waterway, most of the structures were filled with sediment after one year of construction.

3.5.2. Flow velocity

Apart from flow depth, the flow velocity is the main component of the flow pattern and a determining factor in waterway erosion. Increasing the flow velocity from the threshold velocity for sediment entrainment causes waterway erosion (Bose and Dey 2013; Amini and Solaimani 2018). Reducing flow velocity downstream of hydraulic structures is one of the important design factors to protect the structure and prevent erosion of the waterway. Scouring in the vicinity of a structure depends on geometric factors, flow conditions including the discharge and velocity from the overflow, and the type of waterway streambed material. In this study, in order to determine the flow velocity tendency during the flood, the flow velocity in the waterway was simulated by the HEC-RAS model. A sample of flow velocity distribution results is shown in Fig. 8.

Figure 8 shows that, before re-reaching the natural stream bed, the flow has a high velocity after the mechanical structure. So that in the range of overflow, the rate of this flow velocity reaches 3 m/s in a 100-yr flood which is consistence with the findings from (Aslam and Lasminto 2020). The maximum velocity is lower in a 25-yr flood. However, these velocities cause erosion along the waterway when they cross the basin and reach the waterway. The flow velocities reach a normal velocity corresponding to the slope of the initial waterway stream bed after some long distances.

Field observations indicated the lack of proper design of stilling basins in a number of structures constructed in the studied waterway. In other words, these stilling basins were sometimes not able to control the flow energy and consequently reduce the flow rate over the overflow. These results are consistent with the results from (Amini 2022) who stated that in some cases, due to improper choice of the construction site, the number, type, and dimensions of remedial structures do not help much to remove the initial flood energy.

3.5.3 Flood duration

Estimation of flood travel time is one of the most important topics in physiographic and hydrological studies of watersheds and is the critical component of the dimensionless unit hydrograph (Yavari et al. 2020). Construction of mechanical structures in watersheds, especially in waterways leading to residential areas is one of the executive solutions to change the concentration-time and reduce the slope of waterways. In this study, the flood duration of the studied waterway with different return periods of 2, 5, 10, 25, 50, and 100 years in two cases without and with the structure by the HEC-RAS model was simulated. The results of 2- and 100-yr floods are shown in Fig. 9. In addition, simulation results of all the studied floods and their changes are presented in Table 4.

Table 4

Time of flood travel along the waterway in different return periods
Waterway Length (m)	Flood duration (minutes)
	Without structures						With structures
	Return period (year)						Return period (year)
	2	5	10	25	50	100	2	5	10	25	50	100
250	48.0	40.2	36	33.6	31.2	29.4	130.8	90.0	73.2	61.2	52.8	47.4
450	43.2	36.6	32.4	30	27.6	26.4	120.0	82.2	66.6	55.8	48.0	43.2
551	39.6	33.0	29.4	27.0	25.2	24.0	115.8	79.2	64.2	53.4	45.6	40.8
701	38.4	32.4	28.2	26.4	24.6	23.4	113.4	77.4	62.4	51.6	44.4	39.6
904	36.6	30.6	27	25.2	23.4	22.2	105.0	70.8	57.6	47.4	40.8	36.6
1047	33.6	28.2	24.6	22.8	21.0	19.8	97.2	66.0	53.4	43.2	37.2	33.6
1197	31.8	26.4	23.4	21.6	20.4	18.6	95.4	64.2	51.6	42.0	36.0	32.4
1248	30.0	25.2	22.2	20.4	18.6	17.4	93.6	62.4	49.8	40.2	34.8	31.2
1366	29.4	24.6	21.6	19.8	18.6	17.4	91.8	61.2	49.2	39.6	34.2	30.0
1466	29.4	24.6	21.0	19.2	18.0	16.8	91.8	61.2	48.6	39.0	33.6	29.7
1688	28.2	23.4	20.4	18.6	17.4	16.2	90.6	60.0	48.0	38.4	33.0	29.4
1940	26.4	21.0	18.6	16.8	15.0	14.4	88.2	58.2	46.2	36.0	31.2	27.6
1948	23.4	18.6	16.8	14.4	13.2	12.6	81.0	52.8	42.0	33.0	28.2	24.6
2050	22.8	18.6	16.8	14.4	13.2	12.6	81.0	52.8	42.0	32.4	28.2	24.6
2151	22.8	18.6	16.2	14.4	13.2	12.0	79.8	52.2	40.8	31.8	27.6	24.0
2301	21.6	17.4	15.6	13.8	12.6	11.4	78.6	51.0	39.6	31.2	26.4	23.4
2503	20.4	16.8	14.4	12.6	12.0	10.8	69.0	45.0	35.4	27.6	23.4	20.4
2723	17.4	14.4	12.6	10.8	10.2	9.0	50.4	33.0	25.8	20.4	17.4	15.6
2943	9.60	7.80	7.2	6.0	5.4	5.4	28.8	19.2	15.0	12.0	10.8	9.6
3064	4.20	3.60	3.0	2.4	2.4	2.4	13.2	9.0	7.2	6.0	5.4	4.8
3074	1.80	1.80	1.2	1.2	1.2	1.2	04.2	3.0	2.4	2.4	1.8	1.8
3085	1.80	1.80	1.2	1.2	1.2	1.2	03.6	2.4	2.4	1.8	1.8	1.8
3096	1.80	1.20	1.2	1.2	1.2	0.6	03.0	2.4	1.8	1.8	1.2	1.2
3101	1.80	1.20	1.2	1.2	0.6	0.6	02.4	1.8	1.8	1.2	1.2	1.2
3123	1.80	1.20	1.2	1.2	0.6	0.6	01.8	1.8	1.2	1.2	1.2	1.2
3203	1.80	1.20	1.2	1.2	0.6	0.6	01.8	1.2	1.2	1.2	1.2	0.6
3253	0.60	0.60	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6	0.6
3498	0.00	0.00	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

A comparison of the graphs in Fig. 9 shows that a 2-yr flood has a longer flood duration, while a 100-yr flood has a shorter passage time and reaches the concentration point faster. The distance between the two graphs in Fig. 9a indicates the more influence of structures on the flood travel time of low flow in the waterway compared to high flow rates.

Table 4 shows the values of flood times in the waterway in both cases without and with structures. Results show that at all return periods, the structures constructed on the waterway extended the flow time compared to the unstructured state. These structures act as an obstacle in the flow path and slow down its passage. According to Table 4, the maximum flood travel time is related to a 2-yr flood. Conversely, the shortest travel time is related to the highest return period. The data in Table 4 shows that a 2-yr flood, with flood duration of 48 and 130.8 minutes before and after the construction, respectively, had an average longer flood time and more changes as 82.8 minutes. These changes are 31.2 and 52.8 minutes for a 50-yr flood, and 29.4 and 47.4 minutes for a 100-yr flood. Yazdi et al. (2018) and Yavari et al. (2020) also reported a similar trend in flood passage time for different return periods.

In order to reduce the slope of waterways, flow velocity, erosion, and create suitable conditions for sedimentation and stabilization of the streambed, mechanical structures are constructed in the form of watershed management plans. There is currently a lack of attention for sedimentation and flow condition changes in the design and construction of many hydraulic structures, with reservoirs designed. In this study, the performance of check dams on change the flow pattern was investigated. The following conclusions can be drawn from the present study.

The evaluation of HEC-RAS model showed a high correlation between observed and calculated values of flow and water level in the waterway (R² = 0.96, RMSE = 0.029, MAPE = 10.39 and MAD = 0.023).
The flow velocity increases as it passes over the structure. In structures with ogee spillway it reaches up to 4.6 m/s and at the location of the re-joining of the structure to the natural riverbed reaches 3 m/s. In some check dams, the design of the stilling basin was not proportional to the flow rate and velocity, and contrary to the initial goals, the structures increased the velocity.
The study of water height changes in different return periods showed that the structures, in addition to the local change of flow height, reduce the flow height along the entire waterway path. This change was greater in foods with low return period.
Investigation of the time of flood showed that the check dams have caused a longer flood time. The 2-yr flood with discharge times of 48 and 130.8 minutes in the presence and absence of structures, respectively, was discharged with a delay of 82.8 minutes. These times were 31.2 and 52.8 minutes for a 50-yr flood, respectively, and 29.4 and 47.4 minutes for 100 year flood.

Funding

The Department of Rangeland and Watershed of Kurdistan (DRWK) sponsored this study under the contract NO. 94/78/26895, and AREEO managed and supported the research technically through research project No. 95120-29-53-2.

Conflicts of interest

The authors report there are no competing interests to declare.

Author Contributions

All authors contributed to the study conception and design. Material preparation, modeling, field and spatial data collection and analysis were performed by Ata Amini. The first draft of the manuscript was written by Ata Amini, Kaywan Othman and Yahya Parvizi commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability

All data generated or analyzed during this study are included in this published article

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The Effects of Check Dams on the Amount and Pattern of Flood using Hydrological Modeling

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Abstract

Figures

1. Introduction

2. Methodology

2.1. Hydrological Characteristics of the Study Sub-Basin

2.2. Research Methods

2.2.1 Field Surveys

2.2.2. Discharge with Different Return Periods

2.3. HEC-RAS model

2.3.1. Geometric Data

2.3.2. Flow data

2.3.3. Waterway Manning Coefficient

2.3.4. Preliminarily and Boundary Conditions

2.3.5. Model Calibration and Validation

2.3.6. Model Accuracy

3. Results And Discussion

3.1. Peak Flood Discharge

3.2. Manning Coefficient

3.3. Flood Discharge Measurement

3.4. Model Calibration and Validation

3.5. Impact of Check Dams

3.5.1. Water Profile

3.5.2. Flow velocity

3.5.3 Flood duration

4. Conclusion

Declarations

Data Availability

References

Additional Declarations

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