Integrated Flood Analysis System Research Articles

River discharge estimation in the Punatshangchu River Basin, Bhutan using an integrated flood analysis system

River discharge estimation in the Punatshangchu River Basin, Bhutan using an integrated flood analysis system

Evaluation of Satellite Precipitation Products for Estimation of Floods in Data-Scarce Environment

Utilization of satellite precipitation products (SPPs) for reliable flood modeling has become a necessity due to the scarcity of conventional gauging systems. Three high-resolution SPPs, i.e., Integrated Multi-satellite Retrieval for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), data were assessed statistically and hydrologically in the sparsely gauged Chenab River basin of Pakistan. The consistency of rain gauge data was assessed by the double mass curve (DMC). The statistical metrics applied were probability of detection (POD), critical success index (CSI), false alarm ratio (FAR), correlation coefficient (CC), root mean square error (RMSE), and bias (B). The hydrologic evaluation was conducted with calibration and validation scenarios for the monsoon flooding season using the Integrated Flood Analysis System (IFAS) and flow duration curve (FDC). Sensitivity analysis was conducted using ±20% calibrating parameters. The rain gauge data have been found to be consistent with the higher coefficient of determination (R2). The mean skill scores of GSMaP were superior to those of CHIRPS and IMERG. More bias was observed during the monsoon than during western disturbances. The most sensitive parameter was the base flow coefficient (AGD), with a high mean absolute sensitivity index value. During model calibration, good values of performance indicators, i.e., R2, Nash−Sutcliffe efficiency (NSE), and percentage bias (PBIAS), were found for the used SPPs. For validation, GSMaP performed better with comparatively higher values of R2 and NSE and a lower value of PBIAS. The FDC exhibited SPPs’ excellent performance during 20% to 40% exceedance time.

Flood Predictability of One-Way and Two-Way WRF Nesting Coupled Hydrometeorological Flow Simulations in a Transboundary Chenab River Basin, Pakistan

Remote sensing precipitation or precipitation from numerical weather prediction (NWP) is considered to be the best substitute for in situ ground observations for flood simulations in transboundary, data-scarce catchments. This research was aimed to evaluate the possibility of using a combination of a satellite precipitation product and NWP precipitation for better flood forecasting in the transboundary Chenab River Basin (CRB) in Pakistan. The gauge-calibrated satellite precipitation product, i.e., Global Satellite Mapping of Precipitation (GSMaP_Gauge), was selected to calibrate the Integrated Flood Analysis System (IFAS) model for the 2016 flood event in the Chenab River at the Marala Barrage gauging site in Pakistan. Precipitation from the Global Forecast System (GFS) NWP, with nine different lead times up to 4 days, was used in the calibrated IFAS model to predict the flood hydrograph in the Chenab River. The hydrologic simulations, with global GFS forecasts, were unable to predict the flood peak for all lead times. Then, the Weather Research and Forecasting (WRF) model was used to downscale the precipitation forecasts with one-way and two-way nesting approaches. In the WRF model, the CRB was centered in two domains of 25 km and 5 km resolutions. The downscaled precipitation forecasts were subsequently supplied to the IFAS model, and the predicted simulations were compared to obtain the optimal flood peak simulation in the Chenab River. It was found in this study that the simulated hydrographs, at different lead times, from the precipitation of two-way WRF nesting exhibited superior performance with the highest R2 and Nash–Sutcliffe efficiency (NSE) and the lowest percent bias (PBIAS) compared with one-way nesting. Moreover, it was concluded that the combination of GFS forecast and two-way WRF nesting can provide high-quality precipitation prediction to simulate flood hydrographs with a remarkable lead time of 96 h when applying coupled hydrometeorological flow simulation.

Simulating future flood risks under climate change in the source region of the Indus River

AbstractPakistan experiences extreme flood events almost every year during the monsoon season. Recently, flood events have become more disastrous as their frequency and magnitude have increased due to climate change. This situation is further worsened due to the limited capacity of existing water reservoirs and their ability to absorb and mitigate peak floods. Thus, the simulation of stream flows using projected data from climate models is essential to assess flood events and proper water resource management in the country. This study investigates the future floods (in near future and far future periods) using the integrated flood analysis system (IFAS) model under the RCP2.6, RCP4.5, and RCP8.5 climate change scenarios. Downscaled and bias corrected climatic data of six general circulation models and their ensemble were used in this study. The IFAS model simulated the stream flow efficiently (R2 = 0.86–0.93 and Nash–Sutcliffe efficiency = 0.72–0.92) in the Jhelum River basin (JRB), Kabul River basin (KRB), and upper Indus River basin (UIRB) during the calibration and validation periods. The simulation results of the model showed significant impact of projected climate change on stream flows that will cause the mean monthly stream flow in the JRB to be lower, while that of the KRB and UIRB to be higher than that of the historical period. The highest flow months are expected to shift from May–June (Jhelum basin) and June–July (Kabul basin) to April–May with no changes in the UIRB. Higher frequencies of low to medium floods are projected in the KRB and UIRB, while the JRB expects fewer flood events. Based on the results from the IFAS model, it is concluded that stream flow in the study area will increase with several flood events.

Sensitivity analysis and optimization of land use/cover and aquifer parameters for improved calibration of hydrological model

Integrated Flood Analysis System (IFAS) model, based on Tank model philosophy, is a widely used flood forecasting model that has the capability to simulate the catchment processes of any river system provided the surface and aquifer parameters of each sub-model are accurately calibrated. In this study, sensitivity analysis and optimization of hydrogeological parameters of Tank model have been performed to identify the key hydrogeological parameters and their significance in simulating the stream flows in the basins of two important rivers of Pakistan – Jhelum River and Chenab Rivers – respectively. IFAS includes a set of four sub-models namely: surface tank model, sub-surface tank model, aquifer tank model and river course model. Each of the sub-models simulates its own flow processes using surface/aquifer parameters. In this study, sensitivity analysis is performed to identify the parameters that significantly affect the model performance to simulate the flows in the river. Linear stochastic metamodels of Jhelum River and Chenab River Basins developed in this study played the role of metamodels or surrogate functions to determine the ranges of parameter values in different flow periods. The outcome demonstrates when the aquifer tank parameters values obtained from metamodels are applied, the simulation results in a nearly accurate calibration, which clearly indicates the efficiency of present methodology and the important role of hydrogeological parameters. Further, the analysis of the variability in the effectiveness of these parameters in different flow periods as well as for different catchments areas depicts spatial-temporal heterogeneous characteristics. This confirms that the analysis should be directed independently for each study basin because the results of sensitivity analysis are not transferable among catchments.

River discharge estimation in the Punatshangchu River Basin, Bhutan using an integrated flood analysis system

River discharge estimation in the Punatshangchu River Basin, Bhutan using an integrated flood analysis system

Application study of IFAS and LSTM models on runoff simulation and flood prediction in the Tokachi River basin

Abstract Floods are often caused by short-term heavy rainfall. An Integrated Flood Analysis System (IFAS) model is good at runoff simulation and a Long Short-Term Memory (LSTM) model is good at learning massive data and realizing rainfall forecast. In this paper, the applicability of the IFAS model to runoff simulation in the Tokachi River basin and the LSTM model to forecast hourly rainfall was studied, and the accuracy of flood prediction was also studied by inputting the optimal rainfall data forecasted by the LSTM model into the IFAS model. The research results show that the IFAS model can accurately simulate the runoff process in the Tokachi River basin. In the calibration period and the verification period, the Nash–Sutcliffe Efficiency coefficient (NSE) of all simulation results are above 0.75; the LSTM model can achieve forecast hourly rainfall with high precision, the NSE of best forecast results is 0.86; the IFAS model can achieve flood prediction with high precision by using the optimal rainfall data forecasted by the LSTM model, the NSE of simulation result is 0.81. The above conclusions show that it is of great significance to combine the hourly rainfall forecasted by the LSTM model with the IFAS model for flood prediction.

PREDIKSI DEBIT SUNGAI BENGAWAN SOLO MENGGUNAKAN NUMERICAL WEATHER MODEL GLOBAL FORECAST SYSTEM DAN INTEGRATED FLOOD ANALYSIS SYSTEM (Prediction of discharge in Bengawan Solo River using Numerical Weather Model Global Forecast System and Integrated Flood Analysis System)

Flood is one of the most common hydro-meteorological disasters. Bengawan Solo is one of the watersheds in Indonesia that also hit by this disaster. This study discusses the flood disaster in the Bengawan Solo area in early March 2019. The purpose of this study is to conduct a discharge simulation using numerical weather model Global Forecast System (GFS) data through Integrated Flood Analysis System (IFAS) so it is possible to predict discharge in the future. There are three types of numerical weather model GFS data that have been downscale using weather research and forecasting model which differentiated based on spin-up time. The numerical weather model product is then used as rainfall data input for IFAS simulation. Based on the analysis, the flood discharge simulation using an 84-hour spin-up time has a satisfactory performance in describing the change in discharge with respect to time. This happens because numerical weather models will be better at quantifying processes that occur on a meso scale with spatial scale of 10 to 1000 km. The result of this research shows that it is possible to predict river discharge up to 84 hours before the disaster so this is can support the mitigation process for hydrometeorological disasters.

Application of precipitation products for flood modeling of transboundary river basin: a case study of Jhelum Basin

The main objective of this study is the flood modeling of the River Jhelum Basin in the Jammu and Kashmir using the Integrated Flood Analysis System (IFAS) hydrological model. The region having an area of 33,000 km2 has a limited rain gauge coverage. The data inaccessibility is accentuated due to the mountainous topography, glaciers, and remote areas. This research makes innovative use of the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) dataset to overcome the data scarcity for the basin. For flood modeling, a regionalized approach was adopted, whereby the four main tributaries of the River Jhelum Basin were separately modeled. The parameters, thus, determined, were later used in the modeling of the River Jhelum, the main river of the basin. The model was calibrated on the medium-high flood of 2010 and validated on the very high flood of 2014, obtaining good results as indicated by Nash–Sutcliffe Model Efficiency Coefficient (NSE) of 0.77. Later, the model was executed using the Global Satellite Mapping of Precipitation (GSMaP). It performed adequately for the high flood event of 2014, while for the flood of 2010, it gave poor results. Besides, this study also demonstrates the efficacy of a new generation of computer models that work by accessing the free global datasets available on the web for hydrological modeling.

Optimal Multi-Reservoir Operation for Hydropower Production in the Nam Ngum River Basin

This research aims to investigate optimal hydropower production of multi-reservoirs in Lao PDR and develop optimal reservoir rule curves. The Nam Ngum 1 and 2 (NN1 and NN2, respectively) reservoirs in the Nam Ngum River basin (NNRB), which is located in the middle of Laos, are selected as study areas. Mixed integer nonlinear programming (MINLP) is developed as an optimization model to maximize the hydropower production of joint reservoir operation of NN1 and NN2. The optimal operation rule curves are established by using the storage level estimated by the optimization model. Given the limited sideflow data, an integrated flood analysis system (IFAS) and water balance equation are used to simulate the sideflow into NN1 reservoir. A good fit is observed between the monthly streamflow simulated by IFAS and that calculated by the water balance equation. Compared with the observed data, the MINLP model can increase the annual and monthly hydropower production by 20.22% (6.01% and 14.21% for NN1 and NN2, respectively). The water storage level estimated by the MINLP model is used to build the operation rule curves. Results show that the MINLP model of multi-reservoir is a useful and effective approach for multi-reservoir operations and is expected to hold high application value for similar reservoirs in NNRB.

Flood and Inundation Forecasting in the Sparsely Gauged Transboundary Chenab River Basin Using Satellite Rain and Coupling Meteorological and Hydrological Models

Abstract Flood forecasting in a transboundary river basin is challenging due to insufficient data sharing between countries in the upper and lower reaches of a basin. A solution is the use of satellite-observed rainfall and numerical weather prediction (NWP) for hydrological forecasting. We applied this method to the transboundary sparsely gauged Chenab River basin in Pakistan and India to reproduce the exceptionally high flood in 2014. We employed global NWPs by three weather centers to consider forecast uncertainty and downscaled them using the Weather Research and Forecasting (WRF) Model to prepare precipitation inputs. For hydrological simulations, we used a kinematic wave model, the Integrated Flood Analysis System (IFAS), for the upper-reach basin with high mountains and steep slopes, and we used a diffusive-wave rainfall–runoff–inundation (RRI) model for low altitudes and mild slopes. In our forecasting experiment, the precipitation by the global NWP was not able to predict flood peaks consistently. However, the downscaled rainfall by regional NWP showed good performance in predicting flood waves quantitatively, and a multimodel approach provided added value in issuing reliable warning as early as 6 days in advance. A confident streamflow forecasting near the border of the countries also led to reliable inundation forecasting by the RRI model in the lower-reach basin.

Flood Simulations in Mid-Latitude Agricultural Land Using Regional Current and Future Extreme Weathers

Recent extreme weather events like the August 2016 flood disaster have significantly affected farmland in mid-latitude regions like the Tokachi River (TR) watershed, the most productive farmland in Japan. The August 2016 flood disaster was caused by multiple typhoons that occurred in the span of two weeks and dealt catastrophic damage to agricultural land. This disaster was the focus of our flood model simulations. For the hydrological model input, the rainfall data with 0.04° grid space and an hourly interval were provided by a regional climate model (RCM) during the period of multiple typhoon occurrences. The high-resolution data can take account of the geographic effects, hardly reproduced by ordinary RCMs. The rainfall data drove a conceptual, distributed rainfall–runoff model, embedded in the integrated flood analysis system. The rainfall–runoff model provided discharges along rivers over the TR watershed. The RCM also provided future rainfall data with pseudo-global warming climate, assuming that the August 2016 disaster could reoccur again in the late 21st century. The future rainfall data were used to conduct a future flood simulation. With bias corrections, current and future flood simulations showed the potential inundated areas along riverbanks based on flood risk levels. The crop field-based agricultural losses in both simulations were estimated. The future cost may be two to three times higher as indicated by slightly higher simulated future discharge peaks in tributaries.

Assessing Satellite-Based Precipitation Products to Create Flood Forecasting in the Da River Basin, Vietnam

The Da River Basin is an international basin where available access to hydrological data is limited; it has a total basin area of 52,900 km2, about 50% of the area in which it is located, Vietnam. The Da River is the primary source of water for agriculture in 25 provinces and cities, and the primary source of drinking water for more than 30 million people in both urban and rural areas. It has huge economic and historical value. However, flood forecasting for the Da River basin has not been adequately addressed yet because of the challenge of the inconsistency, scarcity, poor spatial representation, as well as difficult access and incompleteness of the availability of ground observed rainfall data. In this research, the IFAS model has been utilized to assess the benefits of using satellite-based precipitation products to create flood forecasting for the whole research area. The results showed that the Integrated Flood Analysis System (IFAS) model was able to integrate the satellite-based precipitation products for simulating the flood event in the Da River basin. Also, the 3B42RT algorithm showed a definite improvement in reproducing the flood peak and low flow very well in the research area. These results could be used to enhance the effectiveness of flood management strategy in the basin.

Development of a flood forecasting system using IFAS: a case study of scarcely gauged Jhelum and Chenab river basins

Development of a well-calibrated, distributed hydrological model for flood forecasting based on rainfall and snowmelt is quite challenging, especially when in situ data is limited or unavailable. This paper presents the study carried out to parameterise the Integrated Flood Analysis System (IFAS) model for the trans-boundary, scarcely gauged catchments of Jhelum and Chenab rivers in Pakistan. Rainfall-runoff analysis was performed with a two-layered tank configuration, integrating snowmelt and dam and barrage operation from the very upstream in India to Trimmu Barrage in Pakistan. A grid size of 5 × 5 km was considered. Global map topography, land cover and soil data was utilised. The model was tested considering different magnitudes of floods of the years 2014, 2015 and 2017. The results showed that the satellite rainfall product, i.e. Global Satellite Mapping of Precipitation (GSMaP-NRT), underestimated the rainfall volume, compared to the ground-gauged rainfall. The GSMaP-IF correction method showed poor performance owing to the lack of ground observatory rainfall data for correcting the trans-boundary part of the basin. The GSMaP-Type1 correction method showed good results, except for the confluence point where complex flow conditions were not properly reproduced by the model. In addition, the incorporation of dam and barrages in the model improved the simulated flow results. It is concluded that the satellite rainfall estimates must be corrected to improve the results. Snowmelt module estimated the snowmelt contribution as 3 to 7% and 4 to 23% of the average daily discharge during the monsoon season at Mangla Dam and Marala Barrage, respectively, during 2014 and 2015. This study assessed various correction methods and concluded that the model and methodology used in the study functioned well with suitable precipitation.

Flood Hazard Assessment in Agricultural Areas: The Case of the District of Pélébina in the Municipality of Djougou, Bénin

Flood is a natural disaster and causes loss of life and property destruction. Its impacts are large on agriculture especially in West African countries where smallholder farmers account for 80% of all the farms. The objective of this study was to assess flood risks in the inland valley of Dosir located in the district of Pélébina, northern Benin. Rainfall, discharge and water level in the riverbed were monitored using rain gauge, acoustic current meter and pressure sensors, respectively. The hydrological functioning of the inland valley was simulated using the Integrated Flood Analysis System (IFAS) model. The calibration was done based on the land use map (GlobalMap) and the soil water holding capacity map (UNEP). Our study demonstrated the existence of a high flood hazard in the inland valley of Dosir which reacts very quickly to rainfall solicitations. The IFAS model has shown a good performance in simulating the runoff in the riverbed of the inland valley with a coefficient of determination of 0.65. The IFAS model can be used to design a flood management system in the district of Pelebina. Further studies are needed to assess the exposure and vulnerability of farmers to flood risk.

Hydrologic modelling using TRMM-based rainfall products for flood analysis

The low resolution and the limitation of data are the main problems for flood analysis, so that the flood prediction often becomes inaccurate. This led to the decision policy for flood disaster mitigation often becomes ineffective. Currently the available of satellite-estimated rainfall data is gradually becoming a cost-effective source of input for flood prediction under a variety of circumstances. This study developed a hydrologic modelling using Tropical Rainfall Measuring Mission (TRMM) satellites data and IFAS (Integrated Flood Analysis System) tool for flood analysis. Upper Rokan watershed with the AWLR (Automatic Water Level Recorder) station of Lubuk Bendahara and Pasir Pangaraian in Riau Province were picked up as study area of this research. The study areas are located in Rokan Hulu regency which is the most vulnerable regency against flood disaster in Riau Province. The results show that the hydrologic modelling using satellite data can be used as an alternative for flood analysis in the area with limited ground data. The flood discharge prediction shows an acceptable error with the ground measurement data. The wave shape error, the volume error, and the peak discharge error for Lubuk Bendahara station are 9.58%, 17.28%, and 10.81%, and for Pasir Pangaraian station are 33.62%, 8.60% and 5.88% respectively.

Review of development and applications of Integrated Flood Analysis System (IFAS) for flood forecasting in insufficiently-gauged catchments

Review of development and applications of Integrated Flood Analysis System (IFAS) for flood forecasting in insufficiently-gauged catchments

Database assessment of CMIP5 and hydrological models to determine flood risk areas

Solutions for water-related disasters may not be solved with a single scientific method. Based on this premise, we involved logic conceptions, associate sequential result amongst models, and database applications attempting to analyse historical and future scenarios in the context of flooding. The three main models used in this study are (1) the fifth phase of the Coupled Model Intercomparison Project (CMIP5) to derive precipitation; (2) the Integrated Flood Analysis System (IFAS) to extract amount of discharge; and (3) the Hydrologic Engineering Center (HEC) model to generate inundated areas. This research notably focused on integrating data regardless of system-design complexity, and database approaches are significantly flexible, manageable, and well-supported for system data transfer, which makes them suitable for monitoring a flood. The outcome of flood map together with real-time stream data can help local communities identify areas at-risk of flooding in advance.

Development of a flood forecasting system on the upper Indus catchment using IFAS

Flow forecast in the Upper Indus catchment in Pakistan is based on average peak flow travel time between key dams and barrages. There was also no flow forecasting system for the Kabul river sub‐basin where most of the 2010 floods victims were reported. A 5‐km spatially distributed tank model using Integrated Flood Analysis System (IFAS) was developed from the Indus upstream reach until the Taunsa barrage. A preliminary model parameterisation, which relied on global data, was updated with newly surveyed soil hydraulic data. The model calibration was performed with Pakistan Meteorological Department (PMD) rain gauge data. Nash‐Sutcliffe efficiencies ENS were calculated for simulated discharges at key gauges. ENS were very low or negative for past flood simulations. One of the reasons for these low efficiencies was the scarcity of local hydro‐meteorological data, including rainfall. Therefore, corrected JAXA satellite‐based rainfall estimates GSMaP‐NRT were considered input data. GSMaP‐NRT self‐correction method coefficients were calibrated for Upper Indus, but the results only improved slightly. However, upstream discharges as boundary conditions resulted in ENS reaching satisfactory averages over 0.7. Simulated hydrographs were then acceptable in terms of peak timing and/or height. Therefore, the conclusion was to recommend relying on upstream discharges as boundary conditions for operational use of the model.

Application of Integrated Flood Analysis System (IFAS) for Dungun River Basin

The Northeast monsoon happening during the months of October until January is the major rainy season found in the eastern part of Peninsular Malaysia. The Dungun river basin (1,858 km2) is exposed to this season thus experiencing characteristically regular flooding due to the prolong rainfall events. The annual rainfall over the river basins are 2,880 mm with great proportion falling in the months of December (19.4%). This study is to apply the Integrated Flood Analysis System (IFAS) model which Dungun river basin has been chosen for this study as the catchments have range of flood and relevant data that can be used to develop the model. The satellite data used in this study is provided by JAXA Global Rainfall Watch. The main feature of this real-time flood analysis model is the satellite-based rainfall data input employed during the model creation phase. The performance of the model for the river basins from satellite and ground-based rainfall data are compared using three error analysis methods.