Rainfall-Runoff Modeling and Hydrological Responses to the Projected Climate Change for Upper Baro Basin, Ethiopia

This paper presents the results of Rainfall-Runoff modeling and simulation of hydrological responses under changing climate using HEC-HMS model. The basin spatial data was processed by HEC-GeoHMS and imported to HEC-HMS. The calibration and validation of the HEC-HMS model was done using the observed hydrometeorological data (1989-2018) and HEC-GeoHMS output data. The goodness-of-fit of the model was measured using three performance indices: Nash and Sutcliffe coefficient (NSE) = 0.8, Coefficient of Determination (R2) = 0.8, and Percent Difference (D) = 0.03, with values showing very good performance of the model. Finally, the optimized HEC-HMS model has been applied to simulate the hydrological responses of Upper Baro Basin to the projected climate change for mid-term (2040s) and long-term (2090s) A1B emission scenarios. The simulation results have shown a mean annual percent decrease of 3.6 and an increase of 8.1 for Baro River flow in the 2040s and 2090s scenarios, respectively, compared to the baseline period (2000s). A pronounced flow variation is rather observed on a seasonal basis, reaching a reduction of 50% in spring and an increase of 50% in autumn for both mid-term and long-term scenarios with respect to the base period. Generally, the rainfall-runoff model is developed to solve, in a complementary way, the two main problems in water resources management: the lack of gauged sites and future hydrological response to climate change data of the basin and the region in general. The study results imply that seasonal and time variation in the hydrologic cycle would most likely cause hydrologic extremes. And hence, the developed model and output data are of paramount importance for adaptive strategies and sustainable water resources development in the basin.

Integrated Hydrological Modeling of the Godavari River Basin in Maharashtra Using the SWAT Model: Streamflow Simulation and Analysis

Hydrological modeling plays a crucial role in efficiently managing water resources and understanding the hydrologic behavior of watersheds. This study aims to simulate daily streamflow in the Godavari River Basin in Maharashtra using the Soil and Water Assessment Tool (SWAT). SWAT is a process-based hydrological model used to predict water balance components, sediment levels, and nutrient contamination. In this research, we used integrated remote sensing and GIS data, including Digital Elevation Models (DEM), land use and land cover (LULC) maps, soil maps, and observed precipitation and temperature data, as input for developing the SWAT model to assess surface runoff in this large river basin. The Godavari River Basin under study was divided into 25 sub-basins, comprising 151 hydrological response units categorized by unique land cover, soil, and slope characteristics using the SWAT model. The model was calibrated and validated against observed runoff data for two time periods: 2003-2006 and 2007-2010 respectively. Model performance was assessed using the Nash-Sutcliffe efficiency (NSE) and the coefficient of determination (R2). The results show the effectiveness of the SWAT2012 model, with R2 value of 0.84 during calibration and 0.86 during validation. NSE values also ranged from 0.84 during calibration to 0.85 during validation. These findings enhance our understanding of surface runoff dynamics in the Godavari River Basin under study and highlight the suit-ability of the SWAT model for this region.

Hydrological daily rainfall-runoff simulation with BTOPMC model and comparison with Xin'anjiang model

A grid-based distributed hydrological model, the Block-wise use of TOPMODEL （BTOPMC）, which was developed from the original TOPMODEL, was used for hydrological daily rainfall-runoff simulation. In the BTOPMC model, the runoff is explicitly calculated on a cell-by-cell basis, and the Muskingum-Cunge flow concentration method is used. In order to test the model＇s applicability, the BTOPMC model and the Xin＇anjiang model were applied to the simulation of a humid watershed and a semi-humid to semi-arid watershed in China. The model parameters were optimized with the Shuffle Complex Evolution （SCE-UA） method. Results show that both models can effectively simulate the daily hydrograph in humid watersheds, but that the BTOPMC model performs poorly in semi-humid to semi-arid watersheds. The excess-infiltration mechanism should be incorporated into the BTOPMC model to broaden the model＇s applicability.

Using SWAT Model and Field Data to Determine Potential of NASA-POWER Data for Modelling Rainfall-Runoff in Incalaue River Basin

Incalaue is a tributary of Lugenda River in NSR (Niassa Special Reserve) in North-Eastern Mozambique. NSR is a data-poor remote area and there is a need for rainfall-runoff data to inform decisions on water resources management, and scientific methods are needed for this wide expanse of land. This study assessed the potential of a combination of NASA-POWER (National Aeronautics and Space Administration and Prediction of Worldwide Energy Resources) remotely sensed rainfall data and FAO (Food and Agriculture Organization of the United Nations) soil and land use/cover data for modelling rainfall-runoff in Incalaue river basin. DEM (Digital Elevation Model) of 1:250,000 scale and a grid resolution of 30 m × 30 m downloaded from USGS (the United States Geological Survey) website;clipped river basin FAO digital soil and land use/cover maps;and field-collected data were used. SWAT (Soil and Water Assessment Tool) model was used to assess rainfall -runoff data generated using the NASA-POWER dataset and gauged rainfall and river flow data collected during fieldwork. FAO soil and land use/cover datasets which are globally available and widely used in the region were used for comparison with soil data collected during fieldwork. Field collected data showed that soil in the area is predominantly sandy loam and only sand content and bulk density were uniformly distributed across the soil samples. SWAT model showed a good rainfall-runoff relationship using NASA-POWER data for the area (R2 = 0.7749) for the studied period (2019-2021). There was an equally strong rainfall-runoff relationship for gauged data (R2 = 0.8131). There were uniform trends for the rainfall, temperature, and relative humidity in NASA-POWER meteorological data. Timing of peaks and lows in rainfall and river flow observed in the field and modelled were confirmed by residents as the trend in the area. This approach was used because there was no historical rainfall and river flow data since the river basin is ungauged for hydrologic data. The study showed that NASA-POWER data has the potential for use for modelling the rainfall-runoff in the basin. The difference in rainfall-runoff relationship with field-collected data could be because of landscape characteristics or topsoil layer not catered for in the FAO soil data.

Rainfall-runoff simulation and flood forecasting for Huaihe Basin

The main purpose of this study was to forecast the inflow to Hongze Lake using the Xin＇anjiang rainfall-runoff model. The upper area of Hongze Lake in the Huaihe Basin was divided into 23 sub-basins, including the surface of Hongze Lake. The influence of reservoirs and gates on flood forecasting was considered in a practical and simple way. With a one-day time step, the linear and non-linear Muskingum method was used for channel flood routing, and the least-square regression model was used for real-time correction in flood forecasting. Representative historical data were collected for the model calibration. The hydrological model parameters for each sub-basin were calibrated individually, so the parameters of the Xin＇anjiang model were different for different sub-basins. This flood forecasting system was used in the real-time simulation of the large flood in 2005 and the results are satisfactory when compared with measured data from the flood.

Rainfall-runoff modeling for storm events in a coastal forest catchmen t using neural networks

The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both tempor al and spatial variabilities. In this article, a rainfall-runoff model using th e artificial neural networks (ANN) is proposed for simula ting the runoff in storm events. The study uses the data from a coa stal forest catchment located in Seto Inland Sea, Japan. This article studies the accuracy of the short-term rainfall forecast obta ined by ANN time-series analysis techniques and using antecedent rainfa ll depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of ANN rai nfall-runoff model presented in this paper shows a reasonable agreement in rainfall-runoff modeling with high accuracy.

The effects of rainfall regimes and rainfall characteristics on peak discharge in a small debris flow-prone catchment

Peak discharge plays an important role in triggering channelized debris flows.The rainfall regimes and rainfall characteristics have been demonstrated to have important influences on peak discharge.In order to explore the relationship between rainfall regimes and peak discharge,a measuring system was placed at the outlet of a small,debris flow-prone catchment.The facility consisted of an approximately rectangular stilling basin,ending with a sharp-crested weir.Six runoff events were recorded which provided a unique opportunity for characterizing the hydrological response of the debris flow-prone catchment.Then,a rainfall–runoff model was tested against the flow discharge measurements to have a deep understanding of hydrological response.Based on the calibrated rainfall-runoff model,twelve different artificially set rainfall patterns were regarded as the input parameters to investigate the effect of rainfall regimes on peak discharge.The results show that the rainfall patterns have a significant effect on peak discharge.The rainfall regimes which have higher peak rainfall intensity and peak rainfall point occur at the later part of rainfall process are easy to generate larger peak discharge in the condition of the same cumulative rainfall and duration.Then,in order to explore the relationship between rainfall characteristics and peak discharge under different cumulative precipitation and different duration,167 measured rainfall events were also collected.On the basis of rainfall depth,rainfall duration,and maximum hourly intensity,all the rainfall events were classified into four categories by using K-mean clustering.Rainfall regime 1 was composed of rainfall events with a moderate mean P(precipitation),a moderate D(duration),and a moderate I60(maximum hourly intensity).Rainfall regime 2 was the group of rainfall events with a high mean P,long D.Rainfall regime 3,however,had a low P and a long D.The characteristic of Rainfall regime 4 was high I60 and short duration with large P.The results show that the rainfall regime 2 and 4 are easier to generate peak discharge as the rainfall intensity plays an important role in generating peak discharge.The results in this study have implications for improving peak discharge prediction accuracy in debris flow gully.

Runoff Simulation Using Radar and Rain Gauge Data

The conceptual rainfall-runoff model TOPMODEL is used to simulate runoffs of the Meishan and Nianyushan catchments during the summers of 1998 and 1999 in the GAME/HUBEX (GEWEX Asia Monsoon Experiment /HUAIHE River Basin Experiment) project. The rainfall distributions are estimated by weather radar and rain gauge networks according to different methods. Observed and simulated runoffs are compared and analyzed for both catchments. Results show that (1) the runoff of the catchment is best simulated by radar data combined with rain gauge network data from inside the catchment, and (2) the rainfall estimated by radar adjusted by a few rain gauges outside the catchment can be used to simulate runoff equally as well as using the dense rain gauge network alone.

Water Quality Model Establishment for Middle and Lower Reaches of Hanshui River,China

With the development of industry and agriculture,nitrogen,phosphorus and other nutrients in the Hanshui River greatly increase and eutrophication has become an important threat to the water quality of the Hanshui River,especially in the middle and lower reaches.The primary objective of this study was to establish the water quality model for the middle and lower reaches of the Hanshui River based on the model of MIKE 11.The main pollutants migration and transformation process could be simulated using the water quality model.The rainfall-runoff model,hy-drodynamic model and water quality model were established using MIKE 11.The pollutants,such as chemical oxygen demand(COD),biochemical oxygen demand(BOD),ammonia nitrogen,nitrate nitrogen,phosphorus,dissolved oxy-gen(DO),were simulated and predicted using the above three models.A set of methods computing non-point source pollution load of the Hanshui River Basin was proposed in this study.The simulated and observed values of COD,BOD5,ammonia,nitrate,DO,and total phosphorus were compared after the parameter calibration of the water quality model.The simulated and observed results match better,thus the model can be used to predict water quality in the fu-ture for the Hanshui River.The pollution trend could be predicted using the water quality model according pollution load generation.It is helpful for government to take effective measures to prevent the water bloom and protect water quality in the river.

Improvement of LCM model and determination of model parameters at watershed scale for flood events in Hongde Basin of China

Considering the fact that the original two-parameter LCM model can only be used to investigate rainfall losses during the runoff period because the initial abstraction is not included, the LCM model was redefined as a three-parameter model, including the initial abstraction coefficient l, the initial abstraction Ia, and the rainfall loss coefficient R. The improved LCM model is superior to the original two-parameter model, which only includes r and R, where r is the initial rainfall loss index and can be calculated with l using the Soil Conservation Service curve number （SCS-CN） method, with r = 1/（1 ＋ λ）. The trial method was used to determine the parameter values of the improved LCM model at the watershed scale for 15 flood events in the Hongde Basin in China. The results show that larger r values are associated with smaller R values, and the parameter R ranges widely from 0.5 to 2.0. In order to improve the practicability of the LCM model, r = 0.833 with λ = 0.2 is reasonable for simplifying calculation. When the LCM model is applied to arid and semi-arid regions, rainfall without yielding runoff should be deducted from the total rainfall for more accurate estimation of rainfall-runoff.

Impacts of rainfall and catchment characteristics on bioretention cell performance

Although many studies have evaluated the impacts of bioretention cell (BRC) design elements on hydrologic performance,few have investigated the roles played by site characteristics and rainfall patterns.The objectives of this study were to assess the impacts of rainfall and catchments with different characteristics on the hydrologic performance of BRCs and identify important factors in sizing bioretention when hydrologic performance was oriented for the design using a modeling approach.A 10-year record of rainfall data was used to identify the frequency and magnitude of rainfall events.The results showed that although the small and medium rainfall events were dominant they contributed less to the total rainfall depth than the large rainfall events.The ratio of runoff coefficient to imperviousness can be used as an indicator to explain why BRCs perform differently with the same design strategy under the same rainfall events.Rainfall patterns had significant impacts on the hydrologic performance of BRCs by influencing the overflow and underdrain flow.BRCs performed better for rainfall events with a longer duration and lower rainfall intensity because they generated smoother runoff processes into the BRCs.On the basis of these results,the runoff coefficient is suggested for BRC surface design.

Two-dimensional hydrodynamic robust numerical model of soil erosion based on slopes and river basins

Erosion is an important issue in soil science and is related to many environmental problems,such as soil erosion and sediment transport.Establishing a simulation model suitable for soil erosion prediction is of great significance not only to accurately predict the process of soil separation by runoff,but also improve the physical model of soil erosion.In this study,we develop a graphic processing unit(GPU)-based numerical model that combines two-dimensional(2D)hydrodynamic and Green-Ampt(G-A)infiltration modelling to simulate soil erosion.A Godunov-type scheme on a uniform and structured square grid is then generated to solve the relevant shallow water equations(SWEs).The highlight of this study is the use of GPU-based acceleration technology to enable numerical models to simulate slope and watershed erosion in an efficient and high-resolution manner.The results show that the hydrodynamic model performs well in simulating soil erosion process.Soil erosion is studied by conducting calculation verification at the slope and basin scales.The first case involves simulating soil erosion process of a slope surface under indoor artificial rainfall conditions from 0 to 1000 s,and there is a good agreement between the simulated values and the measured values for the runoff velocity.The second case is a river basin experiment(Coquet River Basin)that involves watershed erosion.Simulations of the erosion depth change and erosion cumulative amount of the basin during a period of 1-40 h show an elevation difference of erosion at 0.5-3.0 m,especially during the period of 20-30 h.Nine cross sections in the basin are selected for simulation and the results reveal that the depth of erosion change value ranges from-0.86 to-2.79 m and the depth of deposition change value varies from 0.38 to 1.02 m.The findings indicate that the developed GPU-based hydrogeomorphological model can reproduce soil erosion processes.These results are valuable for rainfall runoff and soil erosion predictions on rilled hillslopes and river basins.

Particle Swarm Optimization for Identifying Rainfall-Runoff Relationships

Rainfall-runoff processes can be considered a single input-output system where the observed rainfall and runoff are inputs and outputs, respectively. Conventional models of these processes cannot simultaneously identify unknown structures of the system and estimate unknown parameters. This study applied a combinational optimization and Particle Swarm Optimization (PSO) for simultaneous identification of system structure and parameters of the rainfall-runoff relationship. Subsystems in proposed model are modeled using combinations of classic models. Classic models are used to transform the system structure identification problem into a combinational optimization and can be selected from those typically used in the hydrological field. A PSO is then applied to select the optimized subsystem model with the best data fit. The parameters are estimated simultaneously. The proposed model is tested in a case study of daily rainfall-runoff for the upstream Kee-Lung River. Comparison of the proposed method with simple linear model (SLM) shows that, in both calibration and validation, the PSO simulates the time of peak arrival more accurately compared to the SLM. Analytical results also confirm that the PSO accurately identifies the system structure and parameters of the rainfall-runoff relationship, which are a useful reference for water resource planning and application.

A Structural Overview through GR(s) Models Characteristics for Better Yearly Runoff Simulation

In rainfall-runoff modelling, a monthly timescale and an annual one are sufficient for the management of deductions. However, to simulate the flow at a large time-step (annual), we generally precede the use of a model working for a finer time-step (daily) while aggregating the desired outputs. The finest time-steps are considered, apriori, as the most performant. By passing from one time-step to another, and in order to work in the desired time-step (annual) and calculate the potential gains or loss, this article proposed a comparative study between the aggregation method of outputs of a modal working at a finer time step, and a method in which we use a conceived model from the beginning. To ensure this comparative and empirical approach, the choice has been focused on (GRs) models to a daily time-step (GR4J), monthly time step (GR2M) and annual time step (GR1A). The modelling platform used is the same for all three models taking into account the specificities of each one: the same data sample, the same optimization method, and the same function criterion are used during the construction of these models. Due to the moving between these time steps, results show that the best way to simulate the annual flow is to use an appropriate and designed modal initially conceived to this time step. Indeed, this simulation seems to be less effective when using a model at a finer time-step (daily).

Calibration and Validation of the GR2M Hydrologic Model in the Kouilou-Niari Basin in Southwestern Congo-Brazzaville

The hydrologic simulation of a catchment area, described as the transformation of rainfall into runoff, generally uses hydrologic model. This work opts for the global conceptual hydrologic model GR2M, a monthly time step model, to study the Kouilou-Niari basin, the second most important ones of the Republic of Congo. This includes two parameters to model the hydrologic behavior of a catchment area. The choice of the conceptual model GR2M is justified by the reduced number of parameters and the monthly time scale. The objective of this study is to determine the characteristic parameters of the GR2M model, by a calibrating and a validating procedure. The use of these parameters enables to follow the evolution of the water resources from the climatic variables. It has been first carried out a characterization of some physical, geological and climatic factors governing the flow, by dealing with the main climatic variables which constitute the inputs of the hydrologic model. Then, a hydrologic rainfall-runoff modeling allows to calibrate and validate the model at monthly time scale. Taking into account the number of parameters involved in hydrologic processes and the complexity of the cathment area, this model gives acceptable results throughout the Kouilou-Niari basin. The values of the Nash-Sutcliffe criterion and those of the correlation coefficient obtained are greater than 80% in validation, which explains the performance and robustness of the GR2M model on this basin.

Recommending the IHACRES model for water resources assessment and resolving water conflicts in Africa

The International Association of Hydrological Sciences （IAHS） recognized the lack of hydro- logical data as a world-wide problem in 2002 and adopted the Prediction of Ungauged Basins （PUB） as a decadal research agenda during the period of 2003 to 2012. One of the objectives is to further develop methodologies for prediction in ungauged basins and to reduce uncertainties in model prediction. Estimation of stream flows is required for flood control, water quality control, valley habitat assessment and water budget of a country. However, the majority of water catchments, streams and valleys are ungauged in most developing countries. The main objective of this paper is to introduce the IHACRES （Identification of Hy- drographs and Components from Rainfall, Evaporation and Stream） model into African hydrological plan- ning as a methodology for water resources assessment, which in turn can be used to resolve water conflicts between communities and countries and to study the climate change issues. This is because the IHACRES model is applied for the estimation of flows in ungauged catchments whose physical catchments descriptors （PCDs） can be determined by driving variables （i.e. rainfall and temperature）; and also in gauged streams but whose gauging stations are no longer operational but historical data are available for model calibration. The model provides a valuable insight into the hydrologic behaviour of the upper water sources for valleys as well as provides a useful methodology for water resources assessment in situations of scarce financial resources in developing countries. In addition, it requires relatively few parameters in its calibration and has been successful applied in previous regionalization studies. It will also make possible the equitable distri- bution of water resources in international basins and rivers＇ catchments. This paper does not apply the model anywhere, but recommends it as a methodology for water resources assessment in order to cure water conflicts on the African continent.

Spatio-Temporal Variation in Rainfall Erosivity over Jordan Using Annual and Seasonal Precipitation

The objective of this research is to estimate the annual and seasonal rainfall erosivity over Jordan based on three different regression models. Readily available annual and seasonal precipitation data with long records (40 - 53 years) pertaining to 40 weather stations were utilized to estimate rainfall erosivity. The spatial distribution of rainfall erosivity over Jordan is controlled largely by morphological (relief) and climatic factors. The lowest R-values (28 MJ mm.ha-1.h-1.yr-1) are found in the arid zone, where the average annual rainfall is below 100 mm, whereas the highest R-values are found in the northern highlands (505 MJ mm.ha-1.h-1.yr-1) where the average annual rainfall approaches 650 mm. The correlation between annual and seasonal precipitation (mm) and annual erosivity exhibits a very strong relationship (R varies from 0.964 to 1.0, and all correlations are significant at 0.01 level [2-tailed test]). Moderate positive correlations were achieved between latitude (N) and the mean annual/seasonal precipitation (R ranges from 0.407 to 0.642, and all correlations are significant at 0.01 level [2-tailed test]). Spatial differences observed in erosivity, afforded a substantial source of information and maps for predicting erosion in Jordan. According to the present analysis, two parameters proved to be useful to predict rainfall erosivity on a national level. These parameters are the average annual precipitation, and latitude.

Impacts of Rainfall Intensity on Soil Solute Loss in a Small Basin

[Objective] The study aimed to analyze the impacts of rainfall intensity on soil solute loss. [ Method] Hydrus-1D model was used to sim- ulate surface runoff process and soil solute loss process under different rainfall intensities in Meilin basin, Yixing City, and its reliability was verified. Afterwards, the model was used to simulate the movement of total nitrogen （TN） in the soil under various rainfall intensities. [Result] Hydrus-1D model had a good effect in the simulation of soil moisture and TN content in surface soil. During the rainfall, TN loss from surface soil （0 -20 cm） accounted for above 95% of total loss from the whole soil （0 -100 cm）. In addition, TN loss increased with the enhancement of rainfall intensities. However, TN loss tended to be stable when rainfall intensity exceeded 0.030 0 cm/min. [Conclusion] The research could provide theoretical refer- ences for the control of agricultural nonpoint source pollution in future.

Verification of the MIKE11-NAM Model for Simulating Streamflow

Modeling watershed hydrological processes are important for water resources planning, development, and management. In this study, the MIKE 11-NAM (Nedbor-Afstromings Model model) was evaluated for simulation of streamflow from the Bina basin located in the Madhya Pradesh State of India. The model was calibrated and validated on a daily basis using five years (1994-1998) observed hydrological data. In addition, a model sensitivity analysis was performed on nine MIKE 11-NAM parameters to identify sensitive model parameters. Statistical and graphical approaches were used to assess the performance of the model in simulating the streamflow of the basin. Results show that during daily model calibration, the model performed very well with a coefficient of determination (R2) and the percentage of water balance error (WBL) values 0.87% and -8.63%, respectively. In addition, the model performed good during the validation period with R2 and WBL values of 0.68% and -6.72%, respectively. Model sensitivity analysis results showed that Overland flow runoff coefficient (CQOF), Time constant for routing overland flow (CK1,2) and Maximum water content in root zone storage (Lmax) were found as the most influential and sensitive model parameters for simulating streamflow. Overall, the model’s performance was satisfactory based on R2 and EI metrics.

ENHANCING RAINFALL-RUNOFF POLLUTION MODELING BY INCORPORATION OF NEGLECTED PHYSICAL PROCESSES

The growing need to mitigate rainfall-runoff pollution,especially first flush,calls for accurate quantification of pollution load and the refined understanding of its spatial-temporal variation.The wash-off model has advantages in modeling rainfall-runoff pollution due to the inclusion of two key physical processes,build-up and wash-off.However,this disregards pollution load from wet precipitation and the relationship between rainfall and runoff,leading to uncertainties in model outputs.This study integrated the Soil Conservation Service curve number(SCS-CN)into the wash-off model and added pollutant load from wet precipitation to enhance the rainfall-runoff pollution modeling.The enhanced wash-off model was validated in a typical rural-residential area.The results showed that the model performed better than the established wash-off model and the commonly-used event mean concentrations method,and identified two different modes of pollution characteristics dominated by land pollution and rainfall pollution,respectively.In addition,the model simulated more accurate pollutant concentrations at high-temporal-resolution.From this,it was found that 12%of the total runoff contained 80%to 95%of the total load for chemical oxygen demand,total N,and total P,whereas it contained only 15%of the total load for NH4+-N.The enhanced model can provide deeper insights into non-point pollution mitigation.