IHACRES Model Research Articles

Assessing flash flood inundation from an extreme rainfall event: case study: Wadi Al Jizzi Oman

Abstract Flash floods present a significant risk to urbanized arid regions, and assessing their inundation patterns is crucial for effective disaster management. Extreme hydrologic events due to aridity and climate change are shaping human lives and major activities in numerous countries at an unprecedented pace. This study aims to assess flash floods from extreme storm events in an arid catchment using high-resolution data. The study applied two models on the event of a single storm, namely the IHACRES and AHP models. The observed flow was used for models' validation. The average flow output determined with the IHACRES model was approximately 0.47 m3/s while the flow output resulting from the AHP model was 0.45 m3/s. The efficiency showed that the IHACRES performed better in evaluating extreme events with an average of 0.88 while the AHP model showed an efficiency of 0.68. The quantitative simulation of both models is likely to have good applicability for simulating single storm events in arid catchments. The validated IHACRES and AHP models offer valuable tools for simulating flash flood. The study's outcomes have implications for flood management policy and infrastructure planning, ensuring a more resilient response to extreme flood events in arid regions globally.

Application of system dynamics model for reservoir performance under future climatic scenarios in Gelevard Dam, Iran

Abstract This study aimed to investigate the effects of climate change scenarios on five indicators: reliability, vulnerability, resilience, sustainability, and the deficiency of the Gelevard Dam (GD) in Iran. Downscaling was performed from 2020 to 2040 in the future using the Can Ems2-GCM based on different climate scenarios and employing the support vector machines. The IHACRES model was used to simulate the inflow of GD. The cultivation pattern optimization function was performed by utilizing the LINGO software. Similarly, the flow-storage model was created using Vensim software. The results demonstrated the reduction of inflow by 15, 36, and 37% during RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. The results showed that if the optimal cultivation pattern (OCP) were to be applied, during different climatic scenarios, water supply would not be difficult in the next 11, 5, and 4 years, respectively, yet after that, water shortage would gradually appear. The findings concluded that although the implementation of OCP would improve the five indicators in all water consumption sectors, the GD reservoir would not be able to answer the demands in the future. Therefore, it would be necessary to implement practices to increase water productivity in all sectors.

River runoff estimation with satellite rainfall in Morocco

ABSTRACT In African countries, the lack of observed rainfall data is a major obstacle for efficient water resources management. The objective of this study is to evaluate satellite rainfall products’ ability to estimate river runoff over 12 basins in Morocco using four hydrological models: IHACRES, MISDc, GR4J, and CREST. Satellite products available with a short latency are compared: EUMETSAT H SAF, SM2RAIN-ASCAT, and IMERG. The best results to reproduce river runoff were obtained with SM2RAIN-ASCAT in combination with the IHACRES model, with the highest Kling-Gupta efficiency criterion and probability of detection of extreme runoff. The hydrological model performances differed across catchments and satellite rainfall products, which highlights the need to carefully select hydrological models for a given application. Thus, it is advisable to evaluate satellite rainfall products with different types of hydrological models. This first evaluation over Moroccan basins suggests that SM2RAIN-ASCAT could be a reliable alternative to observed rainfall for hydrological modelling.

System dynamics modeling of lake water management under climate change

Lake Urmia, the twentieth largest lake in the world, is the most valuable aquatic ecosystem in Iran. The lake water level has decreased in recent years due to human activities and climate change. Several studies have highlighted the significant roles of climatic and anthropogenic factors on the shrinkage of the lake. Management policies for water resources harvesting must be adopted to adapt to climate change and avoid the consequent problems stemming from the drought affecting Lake Urmia, and rationing must be applied to the upstream water demands. This study analyzes strategies and evaluates their effectiveness in overcoming the Urmia Lake crisis. Specifically, system dynamics analysis was performed for simulating the water volume of Lake Urmia, and the Hadley Centre coupled model was applied to project surface temperature and precipitation for two future periods: 2021–2050 and 2051–2080. Six management scenarios were considered for decreasing the allocation of agricultural water demand corresponding to two options: (1) one-reservoir option (Bukan reservoir only), and (2) six-reservoir option. The net inflow of Urmia Lake was simulated for the two future periods with the IHACRES model and with artificial neural network models under the six management scenarios. The annual average volumes of Lake Urmia would be 30 × 109 and 12 × 109 m3 over the first and second future periods, respectively, without considering the management scenarios. The lake volumes would rise by about 50% and 75% for the first and second periods, respectively under the management scenarios that involve strict protective measures and elimination of the effect of all dams and their reservoirs. Implementing strict measures would increase the annual average lake volume to 21 × 109 m3 in the second period; yet, this volume would be less than the long-term average and strategic volume. The human water use would be completely eliminated under Scenario 6. Nevertheless, Lake Urmia would experience a considerable loss of storage because of drought.

Rainfall-runoff modelling using the machine learning and conceptual hydrological models

This study compares the capability of simple data-driven and process-driven models to simulate daily discharge in a snow dominated semi-arid watershed in relation to its hydro-meteorological characteristics. M5 model tree was considered for daily discharge simulation of Taleghan watershed in north of Iran, and the results were compared with those of IHACRES and HSPF models. Results showed that with the same meteorological input data, the HSPF model performed best in predicting the daily runoff followed by the IHACRES model. M5 model overestimated the daily runoff in low flow season (May-December) as the water required to fill the watershed storage capacity was not considered by the model. Using the stream discharge of the prior day (Qt-1) as additional input to the M5 model resulted in much improved simulation of daily discharge (RMSE = 3.55, NSE = 0.94, KGE = 0.96).

Streamflow evaluation using IHACRES model in Kelantan river basin, Malaysia

Kelantan is a flood-prone area where in the past years flood had occurred quite frequently. Determining a hydrological model that can represent Kelantan River basin by giving plausible simulated runoff according to the observed runoff is essential as this will allow appropriate prediction of future flood by using forecasted rainfall and other data. In this study, the IHACRES model was used to simulate runoff and the calibrated simulated runoff by daily scale and seasonal flood events were compared with observed runoff. In general, the IHACRES model performed better in seasonal scale as compared with annual scale in terms of calibration. However, performance of IHACRES degraded during validation stage, whereby the model tends to underestimate the high peak flows but estimate rather more accurate when no peak flows were present. In terms of annual scale, the best model was obtained by calibrating the streamflow in 2012 – 2013 (2 years), the validation results were not satisfactory with NSE = 0.473 and PBIAS = 27.7%. On the other hand, for seasonal analysis, the best model was obtained by calibrating the data of NEM 6 (November 2017 – March 2018). 3 out of 5 of the validation periods show unsatisfactory results (NSE ≤ 0.50). NEM 1 (November 2012 – March 2013) show the best validation results with NSE = 0.853. Further calibration is required in order to enhance the accuracy of the model.

Surface runoff prediction and comparison using IHACRES and GR4J lumped models in the Mono catchment, West Africa

Abstract. This study aims to assess simulated surface runoff before and after dam construction in the Mono catchment (West Africa) using two lumped models: GR4J (Rural Engineering with 4 Daily Parameters) and IHACRES (Identification of unit Hydrographs and Component flows from Rainfall, Evapotranspiration and Stream data) over two different periods (1964–1986 and 1988–2010). Daily rainfall, mean temperature, evapotranspiration and discharge in situ data were collected for the period 1964–2010. After the model's initialization, calibration and validation; performances analysis have been carried out using multi-objectives functions developed in R software (version 3.5.3). The results indicate that statistical metrics such as the coefficient of determination (R2), the Kling–Gupta Efficiency (KGE), the Nash–Sutcliffe coefficient (NSE) and the Percent of Bias (PBIAS) provide satisfactory insights over the first period of simulation (1964–1986) and low performances over the second period of simulation (1988–2010). In particular, IHACRES model underestimates extreme high runoff of Mono catchment between 1964 and 1986. Conversely, GR4J model overestimates extreme high runoff and has been found to be better for runoff prediction of the river only between 1964 and 1986. Moreover, the study deduced that the robustness of runoff simulation between 1964 and 1986 is better than between 1988 and 2010. Therefore, the weakness of simulated runoff between 1988 and 2010 was certainly due to dam management in the catchment. The study suggests that land cover changes impacts, soil proprieties and climate may also affect surface runoff in the catchment.

Climate Change Impacts on Inflows into Lake Eppalock Reservoir from Upper Campaspe Catchment

Climate change has significant effects on societies and ecosystems. Due to the strong link between climate and the hydrological cycle, water resources is one of the most affected fields by climate change. It is of great importance to investigate climate change effects on streamflows by producing future streamflow projections under different scenarios to create adaptation measures and mitigate potential impacts of climate change. The Upper Campaspe Catchment (UCC), located at North Central Victoria in Australia, is a significant catchment as it provides a large portion of total inflow to the Lake Eppalock Reservoir, which supplies irrigation to the Campaspe Irrigation district and urban water to Bendigo, Heathcote, and Ballarat cities. In this study, climate change effects on monthly streamflows in the UCC was investigated using high resolution future climate data from CSIRO and MIROC climate models in calibrated IHACRES hydrological model. The IHACRES model was found to be very successful to simulate monthly streamflow in UCC. Remarkable streamflow reductions were projected based on the climate input from both models (CSIRO and MIROC). According to the most optimistic scenario (with the highest projected streamflows) by the MIROC-RCP4.5 model in near future (2035–2064), the Upper Campaspe River will completely dry out from January to May. The worst scenario (with the lowest streamflow projection) by the CSIRO-RCP8.5 model in the far future (2075–2104) showed that streamflows will be produced only for three months (July, August, and September) throughout the year. Findings from this study indicated that climate change will have significant adverse impacts on reservoir inflow, operation, water supply, and allocation in the study area.

Uncertainties in agricultural water supply under climate change: Aidoghmoush basin, Iran

The planning of future supplies of agricultural water is beset by uncertainties stemming from inaccuracies in general circulation models, assumed greenhouse gases emissions scenarios (GHGESs), imperfect models employed for estimating reservoir inflows and approximate methods for estimating agricultural water demand. The uncertainty of providing agricultural water under climate change was assessed, relying on simulations involving baseline (1971–2000) and future periods (2040–2069 and 2070–2099). Climatic variables were simulated using six atmosphere–ocean general circulation models (AOGCMs) driven by GHGESs A2 and B2 in the Aidoghmoush basin, Iran. Projection of reservoir inflow was performed using the IHACRES model and artificial neural network (ANNs). Agricultural water demand was calculated using the FAO–Penman–Monteith and Hargreaves-Samani (HS) methods. Eight modelling scenarios were considered based on combinations of AOGCMs, GHGESs, reservoir inflow and agricultural water demand projections. Reservoir operation rules were calculated with a particle swarm optimisation algorithm. The results show that agricultural water demand will increase in future periods compared with the baseline period. The operation rule derived from the combination of the HS and ANN models (under GHGES A2) showed the best performance in 2040–2069 by achieving the highest reliability (93%) of water supply. The operation rule derived from the combination of HS and ANN models (under GHGES B2) achieved the highest reliability (95%) of water supply in 2070–2099. The results provide adjusted reservoir operation rules under uncertainty caused by climate change and related impacts on water resources management.

Optimal water allocation at different levels of climate change to minimize water shortage in arid regions (Case Study: Zayandeh-Rud River Basin, Iran)

Climate change is one of the most important factors influencing the future of the world's environment. The most important impacts of climate change are changes in water supply and demand in different regions of the world. In this study, different climate change patterns in two RCP4.5 and RCP8.5 emission scenarios (RCP: Representative Concentration Pathway), were adopted for the Zayandeh-Rud River Basin, Iran, through weighting of GCMs (General Circulation Models). These climate change patterns are including ideal, medium, and critical patterns. Using the LARS-WG model (Long Ashton Research Station Weather Generator), the outputs of the GCMs were downscaled statistically and the daily temperature and precipitation time series were generated from 2020 to 2044. Then, based on this information, the inflow volume into the Zayandeh-Rud Reservoir was predicted by the IHACRES model (Identification of unit Hydrograph and Component flows from Rainfall, Evaporation and Streamflow) and the agricultural water demand was also estimated based on future evapotranspiration. Finally, using GAMS (General Algebraic Modeling System) software, water resources in this basin were allocated based on the basic management scenario (B) and the water demand management scenario (D). The results showed that the average monthly temperature will increase by 0.6 to 1.3 °C under different climate change patterns. On the other hand, on the annual basis, precipitation will decrease by 6.5 to 31% and inflow volume to the Zayandeh-Rud Reservoir will decrease by 21 to 38%. The results also showed that the water shortages based on the baseline management scenario (B) will be between 334 and 805 MCM (Million Cubic Meters). These range of values varies between 252 and 787 MCM in the water demand management scenario (D). In general, the water shortage can be reduced in the Zayandeh-Rud River Basin with water demand control, but complete resolution of this problem in this region requires more integrated strategies based on a sustainable development, such as a fundamental change in the cropping pattern, prevention of population growth and industrial development.

ANALISIS MODEL KONSEPTUAL DEBIT AIR PADA DAS INDRAGIRI HILIR MENGGUNAKAN MODEL IHACRES

The modelling of Indragiri Hilir drainage basin is very necessary, considered by Indragiri Hilir area which sometimes overflows into residential areas and disturbs residents' activities. Conceptual analysis of water discharge through the Ihacres software could help to analyze the flow of Indragiri Hilir drainage basin. Rainfall-runoff modeling is used to predict runoff values, one of which is the IHACRES model. The IHACRES model produces nonlinear loss module parameters and linear hydrograph module units. AWLR that will be used is Kuantan Rengat station, Rain Data that will be used are from Tembilahan station and climatology from Air Molek station. Determination of the success of the model used equation R2 and R to calculate the deviation that occurs. The calibration, verification and simulation phase starts in 2010-2015. The result of conceptual analysis of water discharge of Indragiri Hilir drainage basin, In the calibration stage of the IHACRES Model, the best scheme is scheme 2 with R2 value 0.861 and R value 0.864. While the verification phase is carried out with the following year the best scheme is scheme 3 with the highest R2 value with R2 -2,550 and R-value 1,603 and the simulation scheme is the best scheme 5 with R2-1,904 and R-1,341.

Assessment of climate change impact on surface runoff, statistical downscaling and hydrological modeling

Over the last few decades, the climate change has been increased due to the increased industrial activities, greenhouse gas emissions, and CO2 level. This change has affected the water resource management so that the amount of water entered from upstream of watersheds has been transformed every year, and the water resource management has become difficult for the surface runoff, entered water, flood and drought. The problem becomes more serious when the study area (Kan watershed) is located upstream of such urban watershed as Tehran, where the climate change studies on the water resources are very important. In this study, using the Statistical downscaling model (SDSM), the data of CanESM2 Canadian general circulation model (GCM) was downscaled under the Representative Concentration Pathway (RCP) 2.6, RCP4.5, and RCP8.5. In order to study the climate change, the artificial neural network (ANN) and IHACRES models were used over the period of 2010–2040. The study results showed that the temperature is increased in the upcoming period of 2006–2100 (0.8–5.6 C°), and the highest temperature changes are related to winter and summer. The precipitation in the upcoming period shows an increasing trend on the annual average, but, in general, it can be said that the 4–55% precipitation shows an increasing trend. The runoff in the upcoming period of 2010–2040 under RCP2.6, RCP4.5 and RCP8.5 is −4, 26 and −2 percent in the ANN model and 26, 28 and 33 percent in the IHACRES model, respectively.

Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model

Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall–runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A1B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam.

Analysis of the Effect of Uncertainty in Rainfall-Runoff Models on Simulation Results Using a Simple Uncertainty-Screening Method

Various uncertainty analysis methods have been used in various studies to analyze the uncertainty of rainfall-runoff models; however, these methods are difficult to apply immediately as they require a long learning time. In this study, we propose a simple uncertainty-screening method that allows modelers to investigate relatively easily the uncertainty of rainfall-runoff models. The 100 best parameter values of three rainfall-runoff models were extracted using the efficient sampler DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, and the distribution of the parameter values was investigated. Additionally, the ranges of the values of a model performance evaluation statistic and indicators of hydrologic alteration corresponding to the 100 parameter values for the calibration and validation periods was analyzed. The results showed that the Sacramento model, which has the largest number of parameters, had uncertainties in parameters, and the uncertainty of one parameter influenced all other parameters. Furthermore, the uncertainty in the prediction results of the Sacramento model was larger than those of other models. The IHACRES model had uncertainty in one parameter related to the slow flow simulation. On the other hand, the GR4J model had the lowest uncertainty compared to the other two models. The uncertainty-screening method presented in this study can be easily used when the modelers select rainfall-runoff models with lower uncertainty.

Applicability Assessment and Uncertainty Analysis of Multi-Precipitation Datasets for the Simulation of Hydrologic Models

Hydrologic models are essential tools for understanding hydrologic processes, such as precipitation, which is a fundamental component of the water cycle. For an improved understanding and the evaluation of different precipitation datasets, especially their applicability for hydrologic modelling, three kinds of precipitation products, CMADS, TMPA-3B42V7 and gauge-interpolated datasets, are compared. Two hydrologic models (IHACRES and Sacramento) are applied to study the accuracy of the three types of precipitation products on the daily streamflow of the Lijiang River, which is located in southern China. The models are calibrated separately with different precipitation products, with the results showing that the CMADS product performs best based on the Nash–Sutcliffe efficiency, including a much better accuracy and better skill in capturing the streamflow peaks than the other precipitation products. The TMPA-3B42V7 product shows a small improvement on the gauge-interpolated product. Compared to TMPA-3B42V7, CMADS shows better agreement with the ground-observation data through a pixel-to-point comparison. The comparison of the two hydrologic models shows that both the IHACRES and Sacramento models perform well. The IHACRES model however displays less uncertainty and a higher applicability than the Sacramento model in the Lijiang River basin.

Reservoir Water-Quality Projections under Climate-Change Conditions

Reservoirs are key components of water infrastructure that serve many functions (water supply, hydropower generation, flood control, recreation, ecosystem services, etc.). Climate change affects the hydrology of the tributary areas to reservoirs, which may profoundly impact their operation and possibly the reservoirs’ water quality, among which the temperature gradient and the total dissolved solids (TDS) are key qualitative characteristics of reservoirs, especially those with irrigation function. This study examines water-quality changes in the Aidoghmoush reservoir (East Azerbaijan-Iran) under climate-change conditions in the period 2026–2039. The temperature and precipitation climate variables are calculated by the HadCM3 model driven by emission scenario A2 in the baseline period (1987–2000), and these variables are then projected over the future period (2026–2039). The average annual runoff under climate-change conditions is simulated by the IHACRES model. The results show the future average annual runoff would decrease by about 1% compared to the baseline conditions. The CE-QUAL-W2 model is employed to simulate the reservoir water quality. It is predicted the surface air temperature would increase by 1.3 °C under the climate-change scenario compared to the baseline condition, and the temperatures of the reservoir’s surface- and bottom-waters would increase by 1.19 and 1.24 °C, respectively. The average TDS near the reservoir surface would increase by 44.5 g/m3 (4.3%) relative to baseline TDS. The TDS near the reservoir surface are projected to be highest in autumn and winter for baseline and future conditions. This research shows that changes due to climate change are potentially severe, and presents a methodology that could assist managers and planners to find optimal strategies of reservoir operation to cope with changes in thermal stratification and TDS. This paper results identify the reservoir levels from which to withdraw water with the best water-quality characteristics.

Intercomparison of a Lumped Model and a Distributed Model for Streamflow Simulation in the Naoli River Watershed, Northeast China

Water supply availability has significant impacts on the biggest base for commodity grain production: The Sanjiang Plain in northeast China. The SWAT (soil and water assessment tool) model and IHACRES (identification of unit hydrographs and component flows from rainfall, evapotranspiration and streamflow data) model were used for modelling streamflow variability in the upper Naoli River watershed to determine the applicability of hydrological models to the marsh rivers. Both the SWAT and IHACRES models were suitable for streamflow simulation, having R2 (coefficient of determination) and NS (Nash–Sutcliffe) values greater than 0.7, and PBIAS (percent bias) smaller than 25%. The IHACRES model was easy to use, with less data-preparation, and was found to be a better choice for runoff simulation in a watershed less affected by human activity. The simulation result was better in primeval times, i.e., 1956–1966, than the period 1967–2005, when its performance was found to be unfavorable. In contrast, the complex, processes-based SWAT model was found to be more appropriate for simultaneously simulating streamflow variability. In addition, the effects of land use change and human activities in the watershed—where agricultural activities are intensive—were evaluated. The study found that the SWAT model was potentially suitable for water resource planning and management.

Assessing the Impact of Climate Changes on Hydrological Drought Based on Reservoir Performance Indices (Case Study: ZayandehRud River Basin, Iran)

We investigated the potential impacts of climate changes on hydrological drought of the Zayandehrud basin in Iran. The meteorological data was simulated using the LARS-WG model by output downscaling of HADCM3, INCM3 and NCCCSM from 2011 to 2040 under A1B, A2 and B1 scenarios. In order to estimate the runoff of the basin, the IHACRES model was calibrated and validated by data obtained from the Koohrang station during 1987–2007. The runoff was calculated for 2011–2040 period based on the mean of downscaling values in the IHACRES model. Then, the impact of climate changes on hydrological drought was studied by a probabilistic approach and MSUI index. The results revealed a drastic reduction in normal levels and also a significant increase in moderate, severe, and very severe drought levels in the future compared to the base period. The MSUI index represents a 42.32% reduction in the normal level based on the HADCM3 model under A1B scenario as well as an 86.8%increasein the severe drought. Furthermore, the HADCM3 model using the probabilistic approach showed a 73% reduction under the A2 scenario for normal condition and an increase of 20% for severe drought.

Hydrological system analysis and modelling of the Kara River basin (West Africa) using a lumped metric conceptual model

ABSTRACTThis paper discusses the analysis and modelling of the hydrological system of the basin of the Kara River, a transboundary river in Togo and Benin, as a necessary step towards sustainable water resources management. The methodological approach integrates the use of discharge parameters, flow duration curves and the lumped conceptual model IHACRES. A Sobol sensitivity analysis is performed and the model is calibrated by applying the shuffled complex evolution algorithm. Results show that discharge generation in three nested catchments of the basin is affected by landscape physical characteristics. The IHACRES model adequately simulates the rainfall–runoff dynamics in the basin with a mean modified Nash-Sutcliffe efficiency measure of 0.6. Modelling results indicate that parameters controlling rainfall transformation to effective rainfall are more sensitive than those routing the streamflow. This study provides insights into understanding the catchment’s hydrological system. Nevertheless, further investigations are required to better understand detailed runoff generation processes.EDITOR M.C. Acreman; ASSOCIATE EDITOR N Verhoest

Development of Adaptive Strategies for Irrigation Water Demand Management under Climate Change

AbstractThe HadCM3 climate model is applied with greenhouse gas emission scenario A2 to predict climatic variables in the Aidoghmoush Basin (East Azerbaijan, Iran) over the period 2026–2039. The impacts of the predicted climate variables on reservoir inflow and irrigation water use are calculated with the IHACRES model and the Food and Agricultural Organization’s (FAO) evapotranspiration method, respectively. The presented results show that reservoir inflow and irrigation water use in the prediction period relative to the baseline period (1987–2000) would be decreased by 0.7% and increased by 6%, respectively. Therefore, the Aidoghmoush Basin is likely to face increasing water shortage under a changing climate. As a result, irrigation water use should be reduced by applying the following three adaptive strategies to climate change: delaying the planting date, applying irrigation deficit stress, and increasing the irrigation efficiency. Simulations are performed to evaluate the yield of the agricultural cr...