Development and Application of an Urban Flood Forecasting and Warning Process to Reduce Urban Flood Damage: A Case Study of Dorim River Basin, Seoul

Early and accurate flood forecasting and warning for urban flood risk areas is an essential factor to reduce flood damage. This paper presents the urban flood forecasting and warning process to reduce damage in the main flood risk area of South Korea. This process is developed based on the rainfall-runoff model and deep learning model. A model-driven method was devised to construct the accurate physical model with combined inland-river and flood control facilities, such as pump stations and underground storages. To calibrate the rainfall-runoff model, data of gauging stations and pump stations of an urban stream in August 2020 were used, and the model result was presented as an R2 value of 0.63~0.79. Accurate flood warning criteria of the urban stream were analyzed according to the various rainfall scenarios from the model-driven method. As flood forecasting and warning in the urban stream, deep learning models, vanilla ANN, Long Short-Term Memory (LSTM), Stack-LSTM, and Bidirectional LSTM were constructed. Deep learning models using 10-min hydrological time-series data from gauging stations were trained to warn of expected flood risks based on the water level in the urban stream. A forecasting and warning method that applied the bidirectional LSTM showed an R2 value of 0.9 for the water level forecast with 30 min lead time, indicating the possibility of effective flood forecasting and warning. This case study aims to contribute to the reduction of casualties and flood damage in urban streams and accurate flood warnings in typical urban flood risk areas of South Korea. The developed urban flood forecasting and warning process can be applied effectively as a non-structural measure to mitigate urban flood damage and can be extended considering watershed characteristics.

Floods and their problems: Land uses and soil types perspectives

The phenomenon of flooding that occurs in almost all regions of the earth causes loss of property and damage to public facilities and causes the loss of many human lives. There are many reports related to the causes of flooding with various solutions offered to overcome the flood problem. However, it seems that these efforts have not been able to eliminate the flood problem. Hydrologists have widely reported various factors that are the cause of flooding with an extensive scope. Therefore, this paper is limited to discussing flooding and its problems, specifically the river flood, from the perspective of land use and soil types. Changes in land use in a watershed can cause an increase in the runoff coefficient. Likewise, different types of soil have different abilities in passing water into the ground. Open land (without land cover) tends to be prone to erosion, reducing the soil’s infiltration capacity and increased surface runoff. Increasing the runoff coefficient will increase the peak discharge in a watershed. The decrease in the river capacity due to sediment can cause a river flood. To support this argument, a rainfall-runoff model, particularly the tank model, is also discussed, taking into account the various uses and types of soil in a watershed. Efforts to anticipate the river flood are also considered for formulating flood disaster control policies in a watershed.

Using NDII patterns to constrain semi-distributed rainfall-runoff models in tropical nested catchments

A parsimonious semi-distributed rainfall-runoff model has been developed for flow prediction. In distribution, attention is paid to both timing of runoff and heterogeneity of moisture storage capacities within sub-catchments. This model is based on the lumped FLEXL model structure, which has proven its value in a wide range of catchments. To test the value of distribution, the gauged Upper Ping catchment in Thailand has been divided into 32 sub-catchments, which can be grouped into 5 gauged sub-catchments where internal performance is evaluated. To test the effect of timing, firstly excess rainfall was calculated for each sub-catchment, using the model structure of FLEXL. The excess rainfall was then routed to its outlet using the lag time from storm to peak flow (TlagF) and the lag time of recharge from the root zone to the groundwater (TlagS), as a function of catchment size. Subsequently, the Muskingum equation was used to route sub-catchment runoff to the downstream sub-catchment, with the delay time parameter of the Muskingum equation being a function of channel length. Other model parameters of this semi-distributed FLEX-SD model were kept the same as in the calibrated FLEXL model of the entire Upper Ping basin, controlled by station P.1 located at the centre of Chiang Mai Province. The outcome of FLEX-SD was compared to: 1) observations at the internal stations; 2) the calibrated FLEXL model; and 3) the semi-distributed URBS model - another established semi-distributed rainfall-runoff model. FLEX-SD showed better or similar performance both during calibration and especially in validation. Subsequently, we tried to distribute the moisture storage capacity by constraining FLEX-SD on patterns of the NDII (normalized difference infrared index). The readily available NDII appears to be a good proxy for moisture stress in the root zone during dry periods. The maximum moisture holding capacity in the root zone is assumed to be a function of the maximum seasonal range of NDII values, and the annual average NDII values to construct 2 alternative models: FLEX-SD-NDIIMax-Min and FLEX-SD-NDIIAvg, respectively. The additional constraint on the moisture holding capacity by the NDII improved both model performance and the realism of the distribution. Distribution of Sumax using annual average NDII values was found to be well correlated with the percentage of evergreen forest in 31 sub-catchments. Spatial average NDII values were proved to be highly corresponded with the root zone soil moisture of the river basin, not only in the dry season but also in the water limited ecosystem. To check how well the model represents root zone soil moisture, the performance of the FLEX-SD-NDII model was compared to time series of the soil wetness index (SWI). The correlation between the root zone storage and the daily SWI appeared to be very good, even better than the correlation with the NDII, because NDII does not provide good estimates during wet periods. The SWI, which is partly model-based, was not used for calibration, but appeared to be an appropriate index for validation.

Multiagent System and Rainfall-Runoff Model in Hydrological Problems: A Systematic Literature Review

Typically, hydrological problems require approaches capable of describing and simulating part of the hydrological system, or the environmental consequences of natural or anthropic actions. Tools such as Multiagent System (MAS) and Rainfall-Runoff Model (RRM) have been used to help researchers to develop and better understand water systems. Thus, this study presents a Systematic Literature Review (SLR) on the joint use of MAS and RRM tools, in the context of hydrological problems. SLR was performed based on a protocol defined from the research question. Initially, 79 papers were found among six bibliographic databases. This total was reduced over four stages of selection, according to exclusion criteria. In the end, three papers were considered satisfactory within the scope of the research, where they were summarized, analyzed, and compared. While the MAS and RRM tools can interact with their results in a coupled model, SLR showed that there are still major challenges to be explored concerning the dynamics between them, as the steps of scales and validation. However, the coupling of MAS and RRM can provide an interesting alternative tool to analyse decision-making about water resources management systems.

Understanding the effect of changing land use on floods and soil erosion in the Cagayan de Oro catchment

<p>The destructive capability of typhoons affects lives and infrastructure around the world. Spatial analysis of historical typhoon records reveal an area of intense storm activity within the Southeast Asian (SEA) region. Within SEA is the Philippines, an archipelagic tropical country regularly struck by storms that often cause severe landslides, erosion and floods. Annually, ˜20 cyclones enter the Philippine Area of Responsibility, with about nine making landfall, causing high winds and intense rainfall. Thus, significant research in the Philippines has focused on increasing the resilience of ecosystems and communities through real-time disaster forecasting, structural protections, and programmes for sustainable watershed management (e.g. rehabilitation and conservation agriculture). This dissertation focused on the third aspect through computer modelling and scenario analysis. The study area is the Cagayan de Oro (CDO) catchment (˜1400km²) located in the Southern Philippines. The catchment experienced heavy flooding in 2012 from Typhoon Bopha and has major erosion problems due to mountainous slopes and heavy rainfall. Communities derive ecosystem services (ES) including agricultural production, water supply, recreation, mining resources, flood mitigation, etc. Since changes to the supply or distribution of these ES affects livelihoods and the hydrological response of the catchment to typhoon events, this research used the Land Utilisation and Capability Indicator (LUCI) model to understand the baseline ES and potential changes associated with basin management plans. This was the first detailed tropical application of LUCI, including parameterising it for Philippine soil and land cover datasets in CDO and extending its capability to be applied in future tropical areas. Aside from applying LUCI in a new geoclimatic region, this research contributed to the general development of LUCI through testing and improving its sediment delivery and inundation modelling. The sediment delivery was enhanced using the Revised Universal Soil Loss Equation (RUSLE) model that allows LUCI for the first time to account for impacts of specific land management such as agroforestry and contour cropping on erosion and sediment delivery. Progress was made in updating a flatwater inundation model for use with LUCI, including converting it to Python but this requires further development and testing before it is suitable for application in the Philippines. The development and rehabilitation scenarios showed improved flood mitigation, lower surficial soil erosion rates, and lower loads of nutrients compared to the baseline scenario. Additionally, ES mapping under different land cover scenarios has not been previously accomplished in CDO, and this research provides useful information to guide local decision-making and management planning. The rainfall-runoff model of LUCI was tested against the Hydrologic Engineering Center’s Hydrological Modelling System (HEC-HMS), showing good agreement with observed flow. Since the rainfall-runoff model of LUCI has been minimally utilised in past applications, this CDO application elucidated directions for future work around further testing under extreme rainfall events and climate change. Overall, this novel application of LUCI creates a framework to assist decision-making around land cover changes in the CDO, provides guidance around data requirements and parameterisation procedures to guide future international applications, and has significantly contributed to development and improvement of the LUCI framework to extend its modelling capabilities in the future.</p>

Understanding the effect of changing land use on floods and soil erosion in the Cagayan de Oro catchment

<p>The destructive capability of typhoons affects lives and infrastructure around the world. Spatial analysis of historical typhoon records reveal an area of intense storm activity within the Southeast Asian (SEA) region. Within SEA is the Philippines, an archipelagic tropical country regularly struck by storms that often cause severe landslides, erosion and floods. Annually, ˜20 cyclones enter the Philippine Area of Responsibility, with about nine making landfall, causing high winds and intense rainfall. Thus, significant research in the Philippines has focused on increasing the resilience of ecosystems and communities through real-time disaster forecasting, structural protections, and programmes for sustainable watershed management (e.g. rehabilitation and conservation agriculture). This dissertation focused on the third aspect through computer modelling and scenario analysis. The study area is the Cagayan de Oro (CDO) catchment (˜1400km²) located in the Southern Philippines. The catchment experienced heavy flooding in 2012 from Typhoon Bopha and has major erosion problems due to mountainous slopes and heavy rainfall. Communities derive ecosystem services (ES) including agricultural production, water supply, recreation, mining resources, flood mitigation, etc. Since changes to the supply or distribution of these ES affects livelihoods and the hydrological response of the catchment to typhoon events, this research used the Land Utilisation and Capability Indicator (LUCI) model to understand the baseline ES and potential changes associated with basin management plans. This was the first detailed tropical application of LUCI, including parameterising it for Philippine soil and land cover datasets in CDO and extending its capability to be applied in future tropical areas. Aside from applying LUCI in a new geoclimatic region, this research contributed to the general development of LUCI through testing and improving its sediment delivery and inundation modelling. The sediment delivery was enhanced using the Revised Universal Soil Loss Equation (RUSLE) model that allows LUCI for the first time to account for impacts of specific land management such as agroforestry and contour cropping on erosion and sediment delivery. Progress was made in updating a flatwater inundation model for use with LUCI, including converting it to Python but this requires further development and testing before it is suitable for application in the Philippines. The development and rehabilitation scenarios showed improved flood mitigation, lower surficial soil erosion rates, and lower loads of nutrients compared to the baseline scenario. Additionally, ES mapping under different land cover scenarios has not been previously accomplished in CDO, and this research provides useful information to guide local decision-making and management planning. The rainfall-runoff model of LUCI was tested against the Hydrologic Engineering Center’s Hydrological Modelling System (HEC-HMS), showing good agreement with observed flow. Since the rainfall-runoff model of LUCI has been minimally utilised in past applications, this CDO application elucidated directions for future work around further testing under extreme rainfall events and climate change. Overall, this novel application of LUCI creates a framework to assist decision-making around land cover changes in the CDO, provides guidance around data requirements and parameterisation procedures to guide future international applications, and has significantly contributed to development and improvement of the LUCI framework to extend its modelling capabilities in the future.</p>

Comparisons of distributed and lumped rainfall-runoff model for soil moisture estimation

Soil moisture is an important parameter in landslides because of increased pore pressure and decreased shear strength. This research aims to derive soil moisture indicators from two hydrological models: the physically-based distributed hydrological model and the lumped model. Rainfall-Runoff-Inundation (RRI) Model is used to simulate the hydrological response of catchments to the rainfall-induced landslide in a distributed manner. Tank Model as a lumped hydrological model is also used in this study to simulate the dynamic of soil moisture. The study area is the upper Brantas River Basin, prone to landslides due to heavy rainfall and steep slope. Calibration of the model is conducted by tuning the model according to the river discharge data. The simulation indicates that acceptable performance is confirmed. Tank Model can provide the dynamic of the soil moisture. However, by using this approach, the spatial variation of the soil moisture cannot be presented. Regarding the quantitative amount of soil water content, RRI Model could make a reasonable simulation though the temporal variation is not adequately reproduced. Validation of this method with satellite soil moisture as well as ground measurement is also presented. The challenges of using these approaches to develop landslide hazard assessment are discussed.

Investigation of satellite rainfall-driven rainfall–runoff model using deep learning approaches in two different catchments in India

Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and &gt;50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's Eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in NSC values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments.

A Rainfall - Runoff Model for the Highly Regulated Lake Taupo Catchment, Using a Constrained Ensemble Kalman Filter to Improve the Accuracy and Reliability of Model Output

<p>Lake Taupo is the effective source of the Waikato River. The Waikato Power Scheme relies on the outflow from the lake for moderated flows throughout the year. As the lake is maintained between a 1.4m operating range, it is the inflows to the lake that determine the amount of water available to the scheme for electricity generation. These inflows have not been modelled in any detail prior to this dissertation. This dissertation aims to develop a predictive rainfall-runoff model that can provide accurate and reliable inflow and lake level forecasts for the Lake Taupo catchment. Model formulation is guided by a fundamental understanding of catchment hydrologic principles and an in-depth assessment of catchment hydrologic behaviour. The model is a semi-distributed physically-consistent conceptual model which aims to provide a parsimonious representation of different storages and flow pathways through a catchment. It has three linear sub-surface stores. Drainage to these stores is related to the size of the saturation zone, utilising the concept of a variable source area. This model is used to simulate inflows from gauged unregulated sub-catchments. It is also used to estimate the inflow from ungauged areas through regionalisation. For regulated sub-catchments, the model is modified to incorporate available data and information relating to the relevant scheme‟s operation, resource consent conditions and other physical and legislative constraints. The output from such models is subject to considerable uncertainty due to simplifications in the model structure, estimated parameter values and imperfect driving data. For robust decision making, it is important this uncertainty is reduced to within acceptable levels. In this study, a constrained Ensemble Kalman Filter (EnKF) is applied to the four unregulated gauged catchments to deal with model structure and data uncertainties. Used in conjunction with Monte Carlo simulations, all three sources of uncertainty are addressed. Simple mass and flux constraints are applied to the four (soil storage, baseflow, interflow and fastflow) model states. Without these constraints states can be adjusted beyond what is physically possible, compromising the integrity of model output. It is demonstrated that the application of a constrained EnKF improves the accuracy and reliability of model output.Due to the complexity of the Tongariro Power Scheme (TPS) and the limited data available to model it, the conceptual model is not suitable. Rather, a statistical probability analysis is used to estimate the discharge from this scheme given the month of the year, day of the week and hour of the day. Model output is combined and converted into a corresponding change in lake level. The model is evaluated over a wide range of hydrological and meteorological conditions. An in-depth critical evaluation is undertaken on eight events chosen a priori as representation of both extreme and „usual‟ conditions. The model provides reasonable predictions of lake level given the uncertainty with the TPS, complexity of the catchment and data/information constraints. The model performs particularly well in „normal‟ and dry conditions but also does a good job during rainfall events in light of errors associated with driving data. However, for real-time operational use the integration of the model with meteorological forecasts is required. Model recalibration would be required due to the issue of moving from point estimation to areal rainfall data. Once this is achieved, this operational model would allow robust decision-making and efficient management of the water resource for the Waikato Power Scheme. Although there is room for improvement, there is considerable scope for extending the application of the constrained EnKF and techniques for incorporating regulation to other catchments both in New Zealand and internationally.</p>