Assessment of rainfall-runoff process using Arc-GIS and HEC-HMS in Gelana Watershed, Rift Valley basin Ethiopia.

doi:10.21203/rs.3.rs-3555518/v1

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Assessment of rainfall-runoff process using Arc-GIS and HEC-HMS in Gelana Watershed, Rift Valley basin Ethiopia.

https://doi.org/10.21203/rs.3.rs-3555518/v1

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Estimating runoff from rainfall is necessary to know the amount of water available in a river basin and to better understand the occurrence of flooding. In different basins, hydrological models have been used to determine hydrological processes and available water resources. This study investigates the use of distributed hydrological models to estimate rainfall-runoff processes in the Gelana River basin in West Guji Region,Ethiopia. The hydrological model HEC-HMS (Hydrologic Engineering Center, HydrologicalModeling System) is used in conjunction with the geospatial hydrological model extension, HEC-GeoHMS in the ArcGIS environment. DEMs (Digital Elevation Models)of the study areas were processed using HEC-GeoHMS, an ArcView GIS extension for watershed delineation, terrain preprocessing, and watershed processing was used to estimate the HEC-HMS continuous rainfall runoff simulation. The meteorological models were developed within the HEC-HMS from the recorded daily rainfall data for the hydrological years 2000 to 2010. In HEC-HMS, the basin model includesthree important processes; loss, conversion and basic flow. The control specifications have been defined for the specified time period and time step within a day. Soil conservation service curvenumber (SCS-CN), SCS unit hydrology method, and Muskingum methodwere used to calculate losses, conversion,and routing, respectively. For model calibration the coefficient of determination, Nash-Sutcliffe coefficient, and PBIAS resulted in 0.75, 0.72 and 3.4 respectively for calibration and 0.79, 0.77 and -7 respectively for validation. The results indicate that HEC-HMS can be used for the hydrological modeling of the watershed which will be necessary for soil and water conservation of the area.

HEC-HMS

hydrological modeling

HEC-GeoHMS

Gelana watershed

Rainfall-runoff

SCS-CN

The two most important natural resources needed for agricultural development are land and water. The effective use and management of these resources will impact crop productivity. Simulation and prediction of rainfall and associated river flows are important in water resources planning and management (Lampurlanés et al., 2016). Any hydrological investigation, such as rainfall and streamflow estimates, is the first step in designing water-related research, requiring the measurement and collection of hydrological and meteorological data. However, like other developing countries, Ethiopia lacks stations to measure the quality and quantity of hydrometeorological data (Yitbarek et al., 2022), and according to information from experts, some of the stations are not functioning properly because there aren't enough skilled technicians to operate and maintain the current instruments (Halwatura & Najim, 2013). Therefore, an alternative hydrological model is needed. To construct soil and water conservation structures, an understanding of hydrological phenomena is required, such as flow fluctuations with changes in climatic, geographical or physical parameters(J.C. Refsgaard, 1990). When constructing and planning soil and water conservation structures, it is preferable to estimate the stream flows with regard to their frequency and amplitude (Sentis, 2010). Runoff can result from excessive rainfall when the amount of water released exceeds the capacity of a river, lake or man-made reservoir.Water levels in rivers and lakes under various boundary conditions are often modeled or predicted using rainfall-runoff models (J.C. Refsgaard, 1990).

In water science, hydrological models can be clustered, semidistributed, distributed, steady, unsteady, deterministic, or stochastic. The priority of the models is determined by the characteristics of the river basin, the research purpose and the hydrological forecasting purpose. Hydrological models are used to determine the hydrological response of a river basin to rainfall. In general, hydrological models are simplified simulations of complex hydrological systems (Diriba, 2021; Melesse & Abtew, 2015) and can classify in different ways, but the major classification is as a Physical model which represents the scaled-down representation of the real-world system in the laboratory for example simulation of open channel flow. HEC-HMS (Hydrologic Engineering Center - Hydrologic Modeling System, U.S. Army Corps of Engineers) is one of the hydrologic models used to simulate rainfall-runoff processes and routing (HEC HMS, 2000). The HEC model represents a watershed with integrated hydrologic and hydraulic components to simulate the response of watershed surface runoff to precipitation. Many studies demonstrate the ability of the HEC-HMS model to simulate and predict flow rates and its relevance in different geographical contexts (Wang et al., 2016). Loss, variability, and baseflow are the three main processes included in the HEC-HMS basin model.Within a portion of a river basin or catchment called a sub-basin, each component of the model performs a specific function of the rainfall flow process (Scharffenberg, 2013).

The model includes several components to address precipitation, streamflow, and layout. For example, the HEC-HMS model has been widely used in many hydrological studies due to its simplicity and usability in conventional methods (Halwatura & Najim, 2013). Many scientists have conducted important hydrological studies using the HEC-HMS model, which has demonstrated its ability to simulate and predict streamflow. It is applicable in many different geographical areas such as arid environments, tropical river basins, semi-arid regions; river basins in small areas, etc. to solve the biggest possible problems, used HEC-HMS for rainfall-runoff simulation(Gebre, 2015; Laouacheria & Mansouri, 2015). HEC-HMS model has been also used to simulate rainfall-runoff process with geo-informatics and atmospheric models for flood forecasting and early warnings in different regions of the world(Halwatura & Najim, 2013; Khaddor et al., 2015).

The HEC-GeoHMS (Geospatial Hydrologic Model Extension) is a public domain software package for use with GIS (geographic information system), GeoHMS ArcView, and Spatial Analyst to develop multiple model inputs hydrographic pattern. After analyzing digital elevation model (DEM) information, HEC-GeoHMS converts drainage routes and basin boundaries into hydrological data structures that represent the basin's response to rainfall (Hoogestraat, 2011). An important feature of the HEC-GeoHMS model is the assignment of the curve numbers (CN), which are related to the land use/land cover and soil type of the Gelana catchment. This study's major goal is to create the HEC-HMS hydrological model employing hydrological, meteorological, and remotely sensed data. The Gelana watershed's rainfall-runoff processes were simulated using the hydrological model in conjunction with HEC-GeoHMS and GIS to determine the flow. The HEC-HMS model's input parameters were computed. Study area

The Gelana catchment is situated in the rift valley lakes' Abaya-Chamo sub-basin. The catchment is located in southern Ethiopia. Geographically, the watershed lies between latitudes 5⁰ 25° and 6°18′ N and longitudes 37⁰ 50° and 380°E. The existing stream is the Gelana River, which flows south of the Yirga Chefe (upper valley) and passes through a small gorge before dropping into Lake Abaya. Due to poor soil, uneven terrain, and extensive forested areas around the Gelana and Jelo rivers, the Gelana basin was not included (Final feasibility study 2007 Gelana Irrigation project, 2007). The traditional classification of the country's climate based on altitude and temperature suggests the presence of five climatic zones, including Wurch (cold climate at altitudes above 3000 m above sea level), Dega (temperate climate like highlands with altitude 2500–3000 m), Woina Dega (hot 1,500-2,500 m above sea level), Kola (hot and arid type, below 1,500 m above sea level) and Bereha (hot and super dry type) (NMSA, 2001). As a result, the Gelana's categorization in the watershed spans from semi-arid in the lowland to humid in the highland. The rain surrounding Gelana Catchment exhibits a bimodal pattern, with maximum precipitation above 2649.1 mm/year in the highlands area during the months of April, May, and October, while precipitation is frequently below 515.6 mm/year at the lowest height in the low land. In the lowland region of the Gelana watershed, the average monthly maximum temperature ranges from 20.9⁰C to 28.09⁰C, and in the catchment's high land, it ranges from 8.25⁰C to 13.54⁰C.

Data Availability

The digital elevation model (DEM) (12.5 *12.5 m resolution) were downloaded from the Alaska satellite facility (https://asf.alaska.edu/) and used to obtain spatial information, use in the estimation of initial model parameters, stream networks definition and to determine stream length, to evaluate the river bed slope to delineate the watershed, sub-basins, and to calculate its parameters. The DEM of the study area is shown in the Fig. 2. Meteorological data of four stations within the project area and nearby such as rainfall, maximum and minimum temperature, wind speed, relative humidity, and sunshine hour are collected from the Ethiopian Meteorological Agency (EMA). The meterelogical stations in the Gelana watershed are shown in Table 1. The daily stream flow data of the Gelana river at the Tore stream flow recording station recorded period from 2000 to 2010 was obtained from the Ministry of Water, Irrigation, and Energy in order to compare the model output with the observed flow. Rainfall is a crucial component of the meterological data for the HEC HMS model. According to the rainfall data recorded for the four rainfall stations obtained from NMSA used in the study area the average annual rainfall of 1445.68 mm at the Dilla Station, 1397.5mm at Fisehagenet Station, 1371.5mm at Yirgachefe Station and relatively the lowest average annual recorded rainfall (988.5mm) existed at Hageremariam station. The annual average rainfall data for the watershed is shown in Fig. 3

Table 1

Selected Meteorological Stations Information
No	Station Name	Latitude	Longitude	Elevation	Recorded rainfall data
No	Station Name	Latitude	Longitude	Elevation	start	end
1	Yirgachefe	6.1507	38.2020	1856	1986		2016
2	Fishagenet	6.0667	38.1833	2240	1986		2016
3	Hageremariam	5.6532	38.2333	1861	1986		2017
4	Dilla	6.3667	38.3	1579	1986		2017

HEC-HMS Hydrological Modelling

HEC-HMS is hydrologic modeling software developed by the US Army Corps of Engineers Hydrologic Engineering Center (Feldman A. D., 2000). It aims to reproduce rainfall and runoff processes in a variety of geographical contexts, including floods, water supply, large river basins, small urban areas and natural watershed flows. The Hydrological Engineering Center's "HEC-HMS" hydrological modeling system is a program for simulating rainfall flows in dendritic watersheds

(USACE, 2008). HEC-HMS version 4.3, which combines Arc-GIS and HEC-GeoHMS for river basin development, was used in this study. The HEC-HMS model was created to simulate rainfall-runoff processes in geographical locations ranging from large watersheds to small flows from natural and urban watersheds. It is physically based and theoretically semi-distributed. Therefore, large-scale hydrological research activities, including losses, flow conversion, open channel layout, meteorological data processing, rainfall-runoff simulation and parameter estimation, are due to That's simplified.

(USACE, 2008). Additionally, HEC-HMS uses separate models to calculate runoff, a direct runoff model and a baseflow model. It contains nine different loss algorithms, some of which are for continuous simulation and others for event simulation. Additionally, it features several conversion processes, including the Clark Unit Hydrograph.

(Banitt, 2010) In this study, the Soil Conservation Service Curve Number loss method was selected to estimate direct runoff from a specific or design rainfall (Khaddor et al., 2015). The volume of water intercepted, infiltrated, stored, evaporated, or released is calculated using this method and subtracting precipitation to determine the runoff volume. It was chosen because it is a simple method for estimating direct runoff from a rainstorm, which is based solely on the number of curves, which is a function of soil type and land use/cover are the main factors influencing the generation of flows within the country river basin, is widely applied in a variety of environments and provides superior results compared to the initial and constant loss rate method (Sardoii et al., 2012).

The SCS-CN method is based on the fact that the accumulated rainfall-excess depends on the cumulative precipitation, soil type, land use and the previous moisture conditions as estimated in the following relationship (Mishra S.K., 2003):

Pe = (P – Ia)² / (P-Ia + S)

Where Pe is the accumulated precipitation excess at time t (mm); P is the accumulated rainfall depth at time t (mm); Ia is the initial abstraction (mm) = 0.2S; and S is the potential maximum retention (mm). The maximum retention, S, and watershed characteristics are related through an intermediate dimensionless parameter, the curve number (CN) as:

$$S=25.4\left[\frac{1000}{CN}-10\right]$$

Where CN is the SCS curve number used to represent the combined effects of the primary characteristics of the catchment area. Regarding the transform method, the Soil Conservation Service Unit Hydrograph model was chosen to transform excess precipitation into runoff. The lag time (T_lag) is the only input for this method. It is the time from the center of mass of excess rainfall to the hydrograph peak and is calculated based on the time of concentration Tc, as

𝑇_𝑙𝑎𝑔 = 0.6𝑇_𝑐

Where T_lag and T_c are in minute.

The routing method selected is the Muskingum method, which was developed by (McCarthy, 1938). It allows calculating the output hydrograph at the downstream end of the channel from the input hydrograph at the upstream end. Two parameters are required; Travel time (K) of the flood wave through the transport section and the dimensionless weight (X) corresponds to the attenuation of the flood wave as it moves through the reach. Routing parameters in models are typically derived by calibration using measured flow plots (Birkhead & James, 2002).

HEC-GeoHMS Development

Figure 4 illustrates terrain pre-processing and model creation using HEC-GeoHMS. DEM wells were filled, flow direction and flow accumulation were calculated, and watersheds were divided during field preprocessing. Draw and store basin boundaries as different shape files. The longest flow path and basin center are determined after these basins are combined. The generated model has been processed and is ready to be exported to the HEC-HMS program. HEC-GeoHMS was used for further processing, including the estimation of hydrological parameters such as curve numbers.

Basin model

HEC HMS software is used to perform hydrological modeling. The U.S. Army Corps of Engineers created the HEC-HMS Digital Surface Water Model (computer software), which models the behavior of watersheds, canals, and water control structures to predict water flows, water level and time (USACE, 2008). A basin model stores data regarding the characteristics and connections of the schematic's sub-basin, reach, junction, source, sink, and reservoir objects in any combination. This model was created using the HEC-GeoHMS Arc-GIS extension. The basin model for the study area is shown in Fig. 5.

Loss model

The loss models in HEC-HMS normally calculate the runoff volume by computing the volume of water that is intercepted, infiltrated, stored, evaporated, or transpired and subtracting it from the precipitation( Al-Mukhtar M & Al-Yaseen F, 2019). In this instance, the loss method used is the soil conservation service curve number loss approach. The United States Department of Agriculture's Soil Conservation Service (SCS) developed the SCS-CN method in 1954 by the National Engineering Handbook (NEH-4) Section of Hydrology (Khaddor et al., 2015). The curve number (CN) is an index developed by the Soil Conservation Service (SCS) (now called the Natural Resources Conservation Service (NRCS)) that was developed to estimate the amount of precipitation that penetrates into the ground and amount of surface runoff. It was first developed for agricultural watersheds and later used in urban areas (Shukur, 2017). The SCS-CN technique was one of the first and simplest rainfall-runoff modeling methods, providing an empirical relationship to estimate abstraction and initial runoff by considering four Key characteristics of the watershed that generate runoff, soil type, land use, surface conditions and previous moisture status. The model calculates direct runoff for a given rainfall event and estimates peak surface runoff amounts and rates in agricultural, forestry, and urban watersheds (Khaddor et al., 2015). This approach is based on the original abstraction and the real retention. As a result, these data were taken from the study area's LULC and soil data. A watershed's Curve Number (CN) can be calculated as a function of the soil type, antecedent watershed moisture, and land use. The composite or weighted CN for the watershed, which is made up of various soil types and land uses, was computed. Generally, for this study area, the initial parameters for the SCS CN method are shown in Table 2.

Table 2

SCS CN Method Initial Parameters
Sub basin	Area (km² )	Weighted CN	Potential Retention S (mm/h r.)	Initial Abstraction Ia (mm)	Lag time
W830	197.1498866	82	81	16.1	216
W790	0.88059592	81.5	85	17	105
W760	80.6781262	78	92	17.5	177
W720	70.577173	78.4	99	18.21	189
W690	0.46619784	77.5	111	21.34	128
W680	90.96037856	77.2	117	23.5	185
W620	114.8400679	76.8	121	23.98	132
W980	63.66190504	76.4	127	24.01	234
W970	44.08159576	76.2	136	24.35	221
W960	34.67993932	75.19	145	27.12	162
W950	1.19139448	75.1	156	28.12	112
W550	88.29269092	74.8	162	33.01	232
W540	103.2369217	74.6	164	34.98	201

Transform model

In HEC-HMS, different models are used to simulate the development of runoff directly from excess rainfall over the river basin, as a change from excess rainfall to point flow. Therefore, in this study, the East Sea unit hydrological method was chosen for analysis. In the SCS CN rainfall runoff method, excess rainfall is converted to point runoff using the SCS unit hydrology method developed by the Soil Conservation Service based on the average HU obtained from collected rainfall and runoff measured from a large number of small agricultural watersheds across the USA (Feldman A. D., 2000). In the HEC-HMS SCS UH method, the peak discharge of the basin is determined based on the lag time of the drainage area shown in Table 2.

HEC-HMS calibration and validation

The SCS unit hydrological method and the Muskingum method are used to calculate baseflow and convert excess rainfall to direct runoff. These techniques are chosen after considering all types of methods to find the best solution. The HEC-HMS modeling technique of each individual method requires input of parameters and values to produce a simulated streamflow hydrograph.

By systematic search of the best fit of the observed and simulated stream flow hydrograph, the calibrated values were determined for each sub basin of Gelana watershed. In order to get the optimum parameter values after manually calibrating the model, an automatic trial and error method applied (Feldman A. D., 2000). The Nelder and Mead method uses descending simplex to evaluate all parameters simultaneously and which parameters need adjustment.This automatic calibration process is used to minimize a specific objective function, such as total absolute error, total square error, peak percent error, and weighted mean square error peak number (HEC, 2006). In our study the sum of squared error objective function used because it gives large weight to large error and less weight to small error (USAC, 2001a). Therefore, automated calibration in conjunction with manual calibration was used to determine a practical range of the parameter values preserving the hydrograph shape and minimum error in volume. The calibration from (2000–2006) and validation from (2007–2010) period used. Validation is the key criteria to test hydrological model performance with independent data serious (Vaze J et al., 2011). During validation period, the calibrated model without changing the parameters, the goodness fit statics also computed.

Model performance criteria

After the model was run for the different scenarios, the result checked for their effectiveness using model evaluation criteria. The model performance was evaluated for both calibration and validation in different ways including coefficient of determination (R²) and Nash-Sutcliffe efficiency(Neitsch, 2011) By visually inspecting and comparing the calculated and observed hydrograph.

Coefficient of correlation (R²)

$${R}^{2}=\frac{\sum _{i=1}^{n}\left[[{\left(Qm-Qam\right)\left(Qsi-Qasi\right)]}^{2}\right]}{\sqrt{\sum _{i=1}^{n}{\left(Qm-Qam\right)}^{2}{\sum }_{i=1}^{n}{\left(Qsi-Qasi\right)}^{2}}};0\le {R}^{2}\le 1$$

Where:

Qobs = observed discharge

Qsim = simulated discharge

obs = mean of observed discharge

sim = mean of simulated discharge

R² is indicates how the simulated data correlates to the observed values of data. The range of R² is extends from 0 (Unacceptable) to 1(best).

Nash-Sutcliffe efficiencies

$$NSE=1-\frac{{\left[\left(Qm-Qasi\right)\right]}^{2}}{\sqrt{\sum _{i=1}^{n}{\left[\left(Qm-Qam\right)\right]}^{2}}};-\infty \le NSE\le 1$$

Where:

Qobs = observed discharge

Qsim = simulated discharge

obs = mean of observed discharge

sim = mean of simulated discharge

Nash-Sutcliffe efficiencies can range from -∞ to 1.

An efficiency of ENS = 1 corresponds to a perfect match of modelled discharge to the observed data. An efficiency of ENS = 0 indicates that the model predictions are as accurate as the mean of the observed data, whereas an efficiency less than zero (-∞<ENS < 0) occurs when the observed mean is a better predictor than the model. The closer the model efficiency is to 1, the more accurate the model is (Neitsch, 2011).

Percent bias (PBIAS)

It measures the average tendency of the simulated data to be larger or smaller than the observed values. PBIAS is expressed in percentage; the lower the absolute value of the PBIAS is the better will be the model performance (Gupta, 1998).

$$PBIAS=\left[\frac{\sum _{i=1 }^{n}{Q}_{sim}- \sum _{i=1}^{n}{Q}_{obs}}{\sum _{i=1}^{n}{Q}_{Obs}}\right]*100$$

Where:

Qobs = observed discharge

Qsim = simulated discharge

obs = mean of observed discharge

sim = mean of simulated discharge

The general methodology followed by this study is shown in Fig. 6.

HEC-HMS hydrological modelling of the Gelana watershed

The HEC HMS hydrological model has been calibrated manually and automatically to optimize to obtain the best possible option fit. The calibration and validation performance of the HEC-HMS 4.2 is carried out by comparing of the daily simulated runoff with the observed stream flow at the out let of the catchments. Runoff from the sub-basins was estimated by using the SCS-CN, SCS Unit Hydrograph and Muskingum methods for loss, transformation, and routing calculations, respectively. Some hydrological parameters were obtained by the calibration process that performed by comparing the simulated flow with the observed flow data measured by using a discharge gauge, which is located near to the outlet of the catchment. To assess the performance of the model predictability of representing the hydrological simulation of the reality of the basin. Three basic statistical hydrological model performance check used. The R² (Relation coefficient), ENS (Nash Sutcliffe efficiency) and PBIAS are used for the evaluation of HEC HMS model.

Hydrological modelling of catchments

A semi-distributed hydrological modelling technique applied for Gelana catchments in order to increase the performance of the model. The catchments are classified into sub basins and each sub basin parameters manually adjusted by trial and error method and automatically optimized to get the best fit. In Fig. 7, the daily hydrograph of the simulated runoff caught the observed flow during calibration period (1/1/2000-1/12/2006), it is well simulated, but the peak flow is under predicted in the model. Based on the calibrated parameters and values the model is validated from (1//1/2007-31/12/2010), and the performance a little bit improved. As it is shown in Fig. 9, the daily hydrograph well simulated with observed stream flow, however as like calibration period, there is also under prediction in the peak flow. The model performance was checked using ENS, R² and PBIAS, the result obtained are satisfactory and acceptable to simulate the basin runoff for future projection as it indicated in (Table 3).

Table 3

The model calibration and validation value
Model performance Evaluation Criteria	Calibration	Validation
Coefficient of determination (R²)	0.75	0.79
Nash–Sutcliffe efficiency (NSE)	0.74	0.77
Percent of bias (PBIAS)	3.4	-7

In the present study, hydrological modelling of Gelana watershed is carried out using HEC-HMS. The SCS-CN is used to represent loss method, the transform method is Soil Conservation Service Unit Hydrograph, and Muskingum-Cunge as channel routing method. The model is evaluated using coefficient of determination, Nash-Sutcliffe efficiency and PBIAS. Daily timescale calibration and validation results over the study area show good performance with R², NSE and PBIAS (%) 0.75, 0.72 and 3.4 respectively for calibration and 0.79, 0.77 and − 7 respectively for validation. When compared to the gauged flow model well simulated the daily stream flow at the out let of the catchment, however there is a slight under prediction of the high flows; this is the draw backs of hydrological models. The calibrated and validated HEC HMS model is useful for better planning and management of water resources in the watershed for the basin having similar hydrologic characteristics

Author Contributions

Material and data collection, data preparation, analysis of the result and discussions are done by the author, The first draft of the manuscript checked and submitted by the author. Author read and approved the final manuscript.

Funding

The author declare that no funds, grants, or other support were received during the research and preparation of this manuscript.

Ethics declarations

Ethical Approval

This paper has neither been published nor been under review for publication elsewhere.

Consent to Participate

The author prepare and submit this paper for a publication in this journal.

Consent to Publish

The author would like to publish the paper in this journal.

Competing Interests

The author have no relevant financial or non-financial interest to disclose.

Availability of data and materials

The data and materials presented in this study are available on request from the corresponding author.

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First submitted to journal
02 Nov, 2023

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Assessment of rainfall-runoff process using Arc-GIS and HEC-HMS in Gelana Watershed, Rift Valley basin Ethiopia.

Status:

Version 1

Abstract

Figures

Introduction

Material and methods

Data Availability

HEC-HMS Hydrological Modelling

HEC-GeoHMS Development

Basin model

Loss model

Transform model

HEC-HMS calibration and validation

Model performance criteria

Coefficient of correlation (R²)

Nash-Sutcliffe efficiencies

Result and Discussion

HEC-HMS hydrological modelling of the Gelana watershed

Hydrological modelling of catchments

Conclusion

Declarations

References

Status:

Version 1

Assessment of rainfall-runoff process using Arc-GIS and HEC-HMS in Gelana Watershed, Rift Valley basin Ethiopia.

Status:

Version 1

Abstract

Figures

Introduction

Material and methods

Data Availability

HEC-HMS Hydrological Modelling

HEC-GeoHMS Development

Basin model

Loss model

Transform model

HEC-HMS calibration and validation

Model performance criteria

Coefficient of correlation (R2)

Nash-Sutcliffe efficiencies

Result and Discussion

HEC-HMS hydrological modelling of the Gelana watershed

Hydrological modelling of catchments

Conclusion

Declarations

References

Status:

Version 1

Coefficient of correlation (R²)