Rainfall-runoff Analysis Research Articles

Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using ensemble rainfall–runoff analysis (ERRA): proof of concept

Abstract. A classical approach to understanding hydrological behavior is the unit hydrograph and its many variants, but these often assume linearity (runoff response is proportional to effective precipitation), stationarity (runoff response to a given unit of rainfall is identical, regardless of when it falls), and spatial homogeneity (runoff response depends only on spatially averaged precipitation). In the real world, by contrast, runoff response is typically nonlinear, nonstationary, and spatially heterogeneous. Quantifying this nonlinearity, nonstationarity, and spatial heterogeneity is essential to unraveling the mechanisms and subsurface properties controlling hydrological behavior. Here, I present proof-of-concept demonstrations illustrating how nonlinear, nonstationary, and spatially heterogeneous rainfall–runoff behavior can be quantified, directly from data, using ensemble rainfall–runoff analysis (ERRA), a data-driven, model-independent method for quantifying rainfall–runoff relationships across a spectrum of time lags. I show how ERRA uses nonlinear deconvolution to quantify how catchments' runoff responses vary with precipitation intensity and to estimate their precipitation-weighted runoff response distributions. I further illustrate how ERRA combines nonlinear deconvolution with de-mixing techniques to reveal how runoff response depends jointly on precipitation intensity and nonstationary ambient conditions, including antecedent wetness and vapor pressure deficit. I demonstrate how ERRA's de-mixing techniques can be used to quantify spatially heterogeneous runoff responses in different parts of a catchment, even if those subcatchments are not separately gauged. I also illustrate how ERRA's broken-stick deconvolution capabilities can be used to quantify multiscale runoff responses that combine hydrograph peaks lasting for hours and recessions lasting for weeks, well beyond the average spacing between storms. ERRA can unscramble these multiple effects on runoff response even if they are overprinted on each other through time and even if they are corrupted by autoregressive moving average (ARMA) noise. Results from this approach may be informative for catchment characterization, process understanding, and model–data comparisons; they may also lead to a better understanding of storage dynamics and landscape-scale connectivity. An R script is provided to perform the necessary calculations, including uncertainty analysis.

An integrated design discharge calculation system for small to mid-sized ungauged catchments in Serbia

Abstract. This study deals with the application of freely available datasets on the global and local level coupled with open source GIS software in hydrological modelling, greatly improving cost and time effectiveness of hydrological analyses of ungauged catchments. The automated calculation system was developed as an extremely useful tool for hydrologists for the quick estimation of design discharges. The morphometric module of the calculation system showed excellent results, saving a great deal of time that would otherwise been spent on manual processing of geospatial data. Using the automated calculation system, the methodologies currently in use in hydrological practice in Serbia were evaluated. The distributed velocity method was introduced as a way of calculating hydrograph lag time. The comparison between design discharges calculated by rainfall-runoff analysis and by frequency analysis yielded very good resemblance. Examples of real-world application for over 2500 catchments are shown.

Urban impervious cover characterization for rainfall-runoff analysis: Impacts of data availability and resolution

Urban watershed hydrologic and water quality models, and stormwater control measure (SCM) designs, rely on accurate determination of stormwater runoff volume and impervious areas contributing to runoff. This research assesses the impacts of data availability and spatial resolution on two of the main indicators of urban impervious cover: total impervious area (TIA) and directly connected impervious area (DCIA). The research also explores the relationship between TIA, DCIA, and the effective impervious area (EIA), another important indicator of urban impervious cover. Using spatial programming, surface runoff was tracked, from originated to discharged locations, in nine urban catchments (ranging from 16 to 2035 ha) in Minnesota, USA, under various scenarios combining geospatial data types and resolutions. TIA fractions derived from low-resolution, nationally available imperviousness data were not statistically different than those from high-resolution land cover (LC) data modified by “unshading” to remove tree canopies overlaying impervious surfaces. Comparison of DCIA and EIA values indicated that modified LC is preferable for accurate DCIA delineation in ungauged catchments. EIA was 65 % of DCIA under the assumption that all rooftops were connected to sewer systems, while it exceeded DCIA by 34 % assuming all rooftops were unconnected, underscoring the importance of rooftop connectivity information in urban areas. The assumption of 0 % to 40 % rooftop connectivity resulted in comparable DCIA and EIA values in the study catchments. Using roads as the runoff collection system (instead of sewer inlets) did not significantly alter DCIA values at 5 % level, when unshaded LC was used. Given the absence of observed runoff data in most urban areas, DCIA serves as an appropriate imperviousness indicator for determining runoff in ungauged urban watersheds. However, achieving accurate DCIA delineation necessitates high-resolution LC data, emphasizing the need for relevant urban management organizations to develop such data.

An Overview of Flood Management from a Mathematical Perspective

Floods are among the world’s most devastating natural disasters, resulting in loss of life, damage to property, and widespread infrastructure disruptions that impact millions. Flood modelling is a crucial tool for forecasting and effectively mitigating the consequences of such disasters. This study offers an overview of flood management strategies such as structural and non-structural strategies, and the current state of flood modelling, the challenges it faces, and potential future advancements. The scope of flood modelling encompasses various approaches, such as hydrological and hydraulic models, numerical simulations, rainfall-runoff analysis, remote sensing and geographic information systems (GIS), computational intelligence and robotics. The assessment delves into some of the merits and demerits of different models used to forecast the trajectory and consequences of flood events. This study explores the potential avenues for progress and innovation within the realm of flood modelling, including the integration of modern technology and multifaceted models. To enhance the control of flood hazards and reduce the societal influence of floods, the report underscores the imperative need for continuous research in the field of flood management through conventional and mathematical modelling.

Subgrid Model of Water Storage in Paddy Fields for a Grid-Based Distributed Rainfall–Runoff Model and Assessment of Paddy Field Dam Effects on Flood Control

Paddy field dams are basin-level flood control measures that promote rainwater storage; however, a general runoff model cannot adequately describe the water balance in paddy fields. This study develops a subgrid model for evaluating paddy water balance considering land use on a computational grid. Subgrid models can account for the storage effect of paddy field dams without disregarding the general grid-based distributed rainfall–runoff model framework. To investigate the effect of current paddy field storage and the introduction of paddy field dams on reducing peak flood discharge, rainfall–runoff analysis was conducted using the proposed model in the Kashima River basin, which flows into Lake Inba-numa in Chiba Prefecture, Japan. The computational results indicated that the rainwater storage effect of current paddy fields reduces the peak river discharge, suggesting that the drainage process of the paddy field should be incorporated into runoff models. Furthermore, the storage effect of paddy fields became more pronounced as the height of the drainage pipe in the paddy field dam increased. The calculated results quantitatively show the flood control effect of paddy field storage over the entire basin; thus, the proposed subgrid model may be a useful tool for promoting basin-level flood control measures.

Characteristics of strata movement and method for runoff disaster management for shallow multiseam mining in gully regions: A case study

Multiseam mining in gully regions has resulted in severe and complex disaster chains, including crack development, mountain landslides, river blockage, and intensified mine water inflow. To prevent and control water inrush disasters, it is imperative to investigate the characteristics and mechanizations of overburden failure under the combined effect of gully terrain and repeated mining in coal seams. This study takes multiseam mining within the gully region of the Xiqu Coal Mine as a case study. The comprehensive analytical method that integrates surface exploration, Interferometric Synthetic Aperture Radar (InSAR) measurement, numerical simulation, and rainfall-runoff analysis, is used to anatomize the entire process of overburden failure induced by downward multiseam mining and the accumulation process of runoff in the gully region. The results reveal that compared to single-seam mining activities, repeated mining and the occurrence of key stratum (KS) structure failure triggered the comprehensive subsidence of the interburden, producing a cutting-type fracture and stepped subsidence, while the overburden exhibited severe damage. Compared to gentle surface topography, gully slopes are prone to shear sliding under the effects of mining subsidence and gravity, resulting in the connection of the downward cracks of the slope and the upward cracks of the overburden. In addition, if the accumulation formed by gully landslides due to repeated mining blocks the river channel and forms a barrier lake during the flood season, it may cause a surge in underground water. High-precision terrain synthesized by UAV tilt photogrammetry is used to simulate the rainfall inundation range and time of different durations during the “100-year return period” rainstorm in the study area. This study proposes a zoning prevention and control method for surface runoff disasters based upon the characteristics of surface deformation and surface inundation time; this method involves filling surface cracks in runoff areas and submerged and crack development areas, implementing slope cutting and control in potential landslide areas, and strengthening the riverbed in submerged and maximum settlement areas.

Short-term Prediction of Urban Inundation based on Radar-predicted Rainfall through Real-time SWMM Parameter Calibration

In recent times, the rapid pace of climate change has been causing urban inundation in areas where drainage of inland water is difficult. In response to this concern, studies that use SWMM are being actively conducted to predict urban inundation. However, the parameters applied to SWMM use historical values, resulting in suboptimal accuracy in predictions. Therefore, this study employed the genetic algorithm, among various optimization techniques, to bolster the accuracy of rainfall-runoff analysis of the SWMM model. This was achieved by linking ASOS data and real-time ground-observation rainfall to real-time water gauge data. Consequently, the water level could now be predicted by applying radar predicted rainfall at 10-minute intervals to the completed SWMM module. The required time for prediction of urban inundation was about 2 minutes. This timeframe proved highly practical when the radar rainfall data was entered into the SWMM module at 10-minute intervals. The water level predictions, when applied to the four rainfall scenarios, also aligned within a reasonable range.

Assessment of surface water resource and allocation optimization for diverse demands in Ethiopia's upper Bilate Watershed

Water planning and management is very crucial for all computing sectors for water uses to maximize the scarce and allocated water uses based on their demands sustainably in Ethiopia's upper Bilate watershed. The shortage of surface water, especially during dry months, has become a major point of contention between upstream and downstream water users in the upper Bilate River. Therefore, the key objectives of the study are to evaluate the surface water resources and determine the best distribution for a range of requirements in the watershed. The historical climatic and stream flow daily data for the period of 2010–2019 have been used for the analysis. Hydrologic Engineering Center Hydrologic Modeling System-Geospatial Hydrologic Modeling Extension with the Hydrologic Engineering Center Hydrologic Modeling System was used for rainfall-runoff analysis. The model output further represents that the yearly overall surface water of the watershed is 502 MC M. Estimated annual environmental requirement is 75.32MCM which is 15% of the average annual available flow in watershed. Current annual irrigation, livestock, and domestic water demand were estimated to be 19.34 MC M, 12.39 MC M, and 79.4 MC M, respectively. The net amount of water delivered was 19.25 MC M, 79.28 MC M, and 12.36 MC M for irrigation, domestic, and Livestock demands, respectively, in the reference scenario. In the currently available (reference) scenario, 99.8% of the water supply need had been fulfilled, yet only 0.2% of the requirements for water were unmet. Average annual water demand of 111.13 MC M in the current scenario growth to 176.08 MC M when the future growth scenario. In contrast, for the future irrigation development and population projections scenario, 69.8% of the supply-demand became acquainted, and 30.2% of the demand for water remained unfulfilled in 2035. Therefore, to realize good water availability for productive use and allocate water optimal manner constructing a hydraulic structure (dam) upstream of the watershed was recommended.

Reason for less water supply shortages under climate change condition: evaluation of future rainfall data

Abstract This study evaluates the appropriateness of general circulation model-based future rainfall data used in Korea. The evaluation is done through the analysis of long-term occurrence characteristics of dry years, as well as the analysis of the water supply system including the daily based rainfall-runoff analysis and reservoir operation. This study considers the Boryeong Dam basin in Korea as a study basin. Summarizing the results is as follows. First, the future rainfall data show that the occurrence frequency of dry years is similar to the observed, but the occurrence frequency of consecutive multi-year dry years is far smaller than the observed. Second, the future rainfall data result in no or far less water supply shortages. This is mainly due to the fact that the Boryeong Dam has the ability to overcome the one-year drought and the future rainfall data contain far fewer multi-year droughts. However, these results clearly indicate the problems of the future rainfall data, especially in the long-term persistence of rainfall. It is thus disappointing that these future climate rainfall data may not be used to evaluate the water supply system in the future, at least in the Boryeong Dam basin.

Development of a Simple Design Method for an Infiltration Facility using Curve Number

As climate change accelerates, the scale and intensity of natural disasters are increasing, and the frequency of localized heavy rains is increasing rapidly. Urban watersheds have a high ratio of impervious areas. So there is a high possibility of flooding in the event of torrential rain, and large-scale damage can occur because the population and material resources are highly concentrated. River improvements, sewerage maintenance, and rainwater storage installations are frequently used to reduce flood damage. More recently, low-impact development (LID) techniques have been proposed; however, LID facilities are installed on a small scale and in various locations. In addition, they require many parameters for a rainfall-runoff analysis; consequently, the design of these facilities require too much manpower and time. In this study, we analyze the rainfall-runoff reduction effect based on the size of a facility to present a simpler, more efficient design method. Next, the curve number (CN) corresponding to the capacity of each facility is estimated and the correlation between the facility’s area ratio and the CN is analyzed. As a result, a relational expression that can calculate the new CN with the same rainfall-runoff reduction effect as the installed LID facility was derived by using the CN before the installation of the LID facility and the facility area ratio. Thus, we present a simple design method using the recalculated CN without directly entering LID facilities into a storm water management model (SWMM).

A comparison of the performance of SWAT and artificial intelligence models for monthly rainfall–runoff analysis in the Peddavagu River Basin, India

Abstract Rainfall–runoff (R–R) analysis is essential for sustainable water resource management. In the present study focusing on the Peddavagu River Basin, various modelling approaches were explored, including the widely used Soil and Water Assessment Tool (SWAT) model, as well as seven artificial intelligence (AI) models. The AI models consisted of seven data-driven models, namely support vector regression, artificial neural network, multiple linear regression, Extreme Gradient Boosting (XGBoost) regression, k-nearest neighbour regression, and random forest regression, along with one deep learning model called long short-term memory (LSTM). To evaluate the performance of these models, a calibration period from 1990 to 2005 and a validation period from 2006 to 2010 were considered. The evaluation metrics used were R2 (coefficient of determination) and NSE (Nash–Sutcliffe Efficiency). The study's findings revealed that all eight models yielded generally acceptable results for modelling the R–R process in the Peddavagu River Basin. Specifically, the LSTM demonstrated very good performance in simulating R–R during both the calibration period (R2 is 0.88 and NSE is 0.88) and the validation period (R2 is 0.88 and NSE is 0.85). In conclusion, the study highlighted the growing trend of adopting AI techniques, particularly the LSTM model, for R–R analysis.

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

This study investigates the application of the Gaussian Radial Basis Function Neural Network (GRNN), Gaussian Process Regression (GPR), and Multilayer Perceptron Optimized by Particle Swarm Optimization (MLP-PSO) models in analyzing the relationship between rainfall and runoff and in predicting runoff discharge. These models utilize autoregressive input vectors based on daily-observed TRMM rainfall and TMR inflow data. The performance evaluation of each model is conducted using statistical measures to compare their effectiveness in capturing the complex relationships between input and output variables. The results consistently demonstrate that the MLP-PSO model outperforms the GRNN and GPR models, achieving the lowest root mean square error (RMSE) across multiple input combinations. Furthermore, the study explores the application of the Empirical Mode Decomposition-Hilbert-Huang Transform (EMD-HHT) in conjunction with the GPR and MLP-PSO models. This combination yields promising results in streamflow prediction, with the MLP-PSO-EMD model exhibiting superior accuracy compared to the GPR-EMD model. The incorporation of different components into the MLP-PSO-EMD model significantly improves its accuracy. Among the presented scenarios, Model M4, which incorporates the simplest components, emerges as the most favorable choice due to its lowest RMSE values. Comparisons with other models reported in the literature further underscore the effectiveness of the MLP-PSO-EMD model in streamflow prediction. This study offers valuable insights into the selection and performance of different models for rainfall-runoff analysis and prediction.

Comparative Rainfall-Runoff analysis of the Upper Murat River Basin in Turkey in context of Hydro-Meteorological variables

This study was carried out to examine rainfall-runoff relationship in the Upper Murat River Basin in Turkey. Murat River Basin, which forms the study area, includes Tunceli, Muş regions and Upper Murat within the Upper Euphrates in Eastern Anatolia, forms the southern branch of the upper basin of the Euphrates River and corresponds to the Middle Euphrates section. Previous studies were carried out for entire Murat River Basin. The novelty of this study is that it focuses on Upper Murat Basin, and gives a different perspective to rainfall-runoff relationship by considering precipitation elasticity in addition to the existing studies. To figure out the basin characteristics, geographic information system based on digital elevation model was used. Annual mean flow records of four flow observation stations (1969–2019), annual total precipitation records and mean temperature records of seventeen meteorological observation stations (1964–2019) were used in the analyses. Rainfall-runoff and temperature-runoff relationship are shown on the graph. Trend analyses were performed by linear regression and Mann-Kendall trend test. Flow parameters were determined for all sub-basins and entire Upper Murat River Basin. The rational method and Thiessen polygons were used to determine flow coefficients. Besides, precipitation elasticities were determined by using elasticity equation to determine the sensitivity of streamflow to climate changes. Determining trends in precipitation, streamflow and temperature values, determination of flow coefficients and precipitation elasticities enabled a comparative rainfall-runoff analysis of the basin. Although there is no exact correlation between the results compared, a new perspective on the basin is presented. Runoff coefficients of sub-basins change between 0.42 and 0.72. Runoff coefficient for entire upper basin is 0.49. In precipitation elasticities, the values calculated for relevant sub-basins for last forty years change between 0.24 and 0.56. Precipitation elasticity for entire upper basin is 0.66. According to the results of the trend analysis, an upward trend was detected in precipitation at 2 stations, a downward trend at 3 stations, an upward trend was detected at 9 stations in temperatures, and a downward trend at 1 station in flows.

Evaluating Dry and Wet Season Precipitation from Remotely Sensed Data Using Artificial Neural Networks for Floodplain Mapping in an Ungauged Watershed

This research creates a framework for modelling the rainfall-runoff process using satellite precipitation data and a floodplain map in ungauged urban watersheds. The combined effects of urbanisation and climate change over the past few decades have increased the number of flooding incidents. Accurate prediction of the flood-prone zone is crucial for policymakers and system managers to build the watershed's resilience during catastrophic flooding events. Precipitation and runoff data are crucial for hydraulic and hydrologic analysis and for identifying flood-prone areas. However, it is difficult to obtain precipitation and discharge data for hydrologic analysis in data-scarce regions. In this context, this research employs satellite precipitation products for rainfall-runoff analysis, which is subsequently utilised in a hydraulic model to delineate a flood-prone zone in an ungauged watershed. The Hydrologic Engineering Centre-River Analysis System (HEC-RAS) and Hydrologic Modelling System (HEC-HMS) models were utilised in the study region to simulate and analyse interactions between rainfall, runoff, and the extent of the flood zone. Setting up and calibrating the HEC-HMS model using a satellite precipitation product is required for the dry and wet seasons. For the wet and dry seasons, HEC-HMS gets validated with an R-square value of 0.72 and 0.85, respectively. Three types of simulations were conducted in the calibrated HEC-HMS model to create the hydrograph with 25-, 50-, and 100-year of rainfall return periods. Finally, the one dimensional HEC-RAS model generates a flood inundation map for the pertinent flooding occurrences from the acquired peak hydrograph. By comparing the values of the specified return periods, the produced flood map depicts the affected area during various return periods of flooding events and provides a quantifiable display of inundation extent percentage (IE%).

Evaluation of Inundation Probability and Inundation Depth through Rainfall–Runoff Analysis

Because of their enclosed nature, underground spaces are more vulnerable to flooding than above-ground structures. As a result, flooding disasters have the potential to cause widespread casualties. Major domestic plan manuals such as ‘Waterproof Standards Plan Manual for Underground Space Flood Prevention’ suggest that the expected flood height is determined and measures are established to ensure safety for underground facilities located in areas where flooding is expected, but no clear methods and standards are presented. In this study, an inundation prediction chart was prepared by performing a one-dimensional (EPA-SWMM) model analysis using the probability rainfall, respectively. Then, data on the depth of inundation where the underground facility is located were extracted using a two-dimensional (FLO2D) model analysis. Using inundation depth and weather data, the probability of inundation for underground facilities and the expected inundation height were quantitatively evaluated through @RISK. Based on the quantitative assessment, it was determined that there was a 7.7% possibility of inundation in an underground space in the case of a 2 h time frame (average inundation depth: 0.1557 m), and a 13.3% possibility in the case of a 3 h time frame (average inundation depth: 0.1655 m). It would be possible to contribute to the development of countermeasures and manuals for underground space flooding using the quantitative assessment described above.

LENS-GRM Applicability Analysis and Evaluation

Recently, there have been many abnormal natural phenomena caused by climate change. Anthropogenic factors associated with insufficient water resource management can be another cause. Among natural causes, rainfall intensity and volume often induce flooding. Therefore, accurate rainfall estimation and prediction can prevent and mitigate damage caused by these hazards. Sadly, uncertainties often hinder accurate rainfall forecasting. This study investigates the uncertainty of the Korean rainfall ensemble prediction data and runoff analysis model in order to enhance reliability and improve prediction. The objectives of this study include: (i) evaluating the spatial characteristics and applicability of limited area ensemble prediction system (LENS) data; (ii) understanding uncertainty using parameter correction and generalized likelihood uncertainty estimation (GLUE) and grid-based rainfall-runoff model (GRM); (iii) evaluating models before and after LENS-GRM correction. In this study, data from the Wicheon Basin was used. The informal likelihood (R2, NSE, PBIAS) and formal likelihood (log-normal) were used to evaluate model applicability. The results confirmed that uncertainty of the behavioral model exists using the likelihood threshold when applying the runoff model to rainfall forecasting data. Accordingly, this method is expected to enable more reliable flood prediction by reducing the uncertainties of the rainfall ensemble data and the runoff model when selecting the behavioral model for the user’s uncertainty analysis. It also provides a basis for flood prediction studies that apply rainfall and geographical characteristics for rainfall-runoff uncertainty analysis.

Evaluation of cloud seeding on the securement of additional water resources in the Boryeong Dam Basin, Korea

This study evaluates the effect of cloud seeding on the increase of runoff or securing of additional water resources. Unlike previous studies, this study concentrates more on the change of various components of the hydrologic processes. Both the event-based analysis and the annual analysis are conducted to evaluate the change of these components. The Boryeong Dam Basin in Korea is considered as a study basin. The WRF (Weather Research and Forecasting) model is used in the cloud seeding simulation, while the PRMS (Precipitation Runoff Modeling System) model is used in the rainfall–runoff analysis. The target years of this study are 2018 and 2019. As results, the event-based analysis shows that the infiltration, effective rainfall, interflow, and direct runoff are increased relatively more among the eight rainfall-runoff components considered in this study. In particular, the increase of interflow is noticeable. More than 60 % of the increased rainfall is found to contribute to the runoff increase. In the annual analysis, this ratio is found to become even higher. The ratio is estimated to be more than 90 %, which is mostly due to the contribution of baseflow. As important factors controlling the effectiveness of cloud seeding, the effects of antecedent soil moisture and threshold daily rainfall depth on the increase of total runoff are also evaluated. The results show that wet soil condition is more favorable to the successful cloud seeding experiment to secure additional water resources. Also, it is confirmed that the cloud seeding experiment can be more effective when the daily rainfall is more than 20 mm. Finally, the results of this study imply the efficiency of cloud seeding and its economic values are potentially high.

Simulation-optimization framework in urban flood management for historic and climate change scenarios

Abstract A simulation-optimization framework is established by integrating Hydrologic Engineering Center Hydraulic Modeling System (HEC-HMS) for computation of runoff, siting tool EPA System for Urban Storm-water Treatment and Analysis INtegration (EPA-SUSTAIN) for placement of Best Management Practices (BMPs), and Binary Linear Integer Programming (BLIP) for runoff reduction. The framework is applied to an urban catchment, namely Greater Hyderabad Municipal Corporation (GHMC). The rainfall-runoff analysis was conducted for extreme rainfalls for historic (2016) and future events in 2050 and 2064 under Representative Concentration Pathways (RCPs) 6.0 and 8.5. The simulation-optimization approach in the historic scenario yielded 495,607 BMPs occupying 76.99 km2 resulting in runoff reduction of 21.54 mm (198.76–177.22 mm). Achieved runoff reduction is 38.72 (428.35–389.63 mm) and 55.03 (602.65–547.62 mm), respectively, for RCPs 6.0 and 8.5, which could meet the water demands of GHMC for 10.33 and 11.53 days. Impacts of 10 different BMP configurations of varying costs (10–70%) and pollutant load reductions (0–3%) on runoff reduction are accomplished as part of sensitivity analysis.

Comparison of the performance of a hydrologic model and a deep learning technique for rainfall- runoff analysis

Rainfall-runoff analysis is the most important and basic analysis in water resources management and planning. Conventional rainfall-runoff analysis methods generally have used hydrologic models. Rainfall-runoff analysis should consider complex interactions in the water cycle process, including precipitation and evapotranspiration. In this study, rainfall-runoff analysis was performed using a deep learning technique that can capture the relationship between a hydrological model used in the existing methodology and the data itself. The study was conducted in the Yeongsan River basin, which forms a large-scale agricultural area even after industrialization, as the study area. As the hydrology model, SWAT (Soil and Water Assessment Tool) was used, and for the deep learning method, a Long Short-Term Memory (LSTM) network was used among RNNs (Recurrent Neural Networks) mainly used in time series analysis. As a result of the analysis, the correlation coefficient and NSE (Nash-Sutcliffe Efficiency), which are performance indicators of the hydrological model, showed higher performance in the LSTM network. In general, the LSTM network performs better with a longer calibration period. In other words, it is worth considering that a data-based model such as an LSTM network will be more useful than a hydrological model that requires a variety of topographical and meteorological data in a watershed with sufficient historical hydrological data.

Development and Application of an Interactive Coupling Rainfall-Runoff Model According to Soil Texture

In this study, we developed a model that simulates surface and subsurface flows for rainfall-runoff analyses using an interactive coupling method. To characterize the interaction between the surface and subsurface flows, we studied the coupling analysis model. The proposed model was designed following comparisons with existing models. For the analysis of the surface and subsurface flows, a governance equation was constructed. The goodness of fit of the model was also tested. To examine the sensitivity of the input parameters, simulations were performed while changing the major parameters of the model according to the soil texture. The developed model showed high applicability to actual watersheds after adjustment of the parameters. This model can be applied to the extension module for channel analysis; therefore, it can be used efficiently in urbanized watersheds wherein the upstream and downstream parts are pervious and impervious watersheds, respectively.