Ungauged Areas Research Articles

Global hydrological parameter estimates to local applications: Influence of forcing and catchment properties

AbstractData scarcity in many areas around the world represents a major problem for hydrological model calibrations. Global parameter estimates and global forcing can provide possibilities to access hydrological responses in ungauged regions. In this study, we applied HBV global parameter estimates considering uncertainty in the Upper Neckar and Upper Danube catchments, Germany, to answer what are the influencing factors and how good are their local applications. We tested simulations with precipitation in spatial resolutions from 0.05 to 0.2° and with local/global sources. Results show that the general performance is acceptable to good (Kling-Gupta efficiency, KGE: 0.51–0.79) in both catchments using local or global precipitation. The influence of spatial resolutions is insignificant while using local precipitation slightly increases performance in both catchments. Catchment properties such as complex topography and special karst subsurface may lead to a deterioration of performance by 0.2 of median KGE in the Upper Danube compared with the Upper Neckar catchment. The median correlation coefficient, runoff ratio and relative error suggest that using global parameter estimates can reproduce seasonality and long-term water balance in our studied region. Our study highlights the potential of using global parameter estimates and global forcing in ungauged areas.

Evaluation of GPM and TRMM and Their Capabilities for Capturing Solid and Light Precipitations in the Headwater Basin of the Heihe River

Obtaining accurate precipitation data in mountainous regions is important but challenging. In ungauged areas, remotely sensed precipitation products are useful supplements and alternatives to measured precipitation products. However, their ability to detect solid precipitation and light precipitation in mountain areas is still unclear. The primary objective of this study is to evaluate two satellite precipitation products, Global Precipitation Measurement (GPM) and Tropical Precipitation Measuring Mission (TRMM), in the headwaters of an inland river on the northeastern Tibetan Plateau (the Heihe river basin), with a specific focus on their performance regarding light precipitation and solid precipitation. The achieved results reveal that both GPM and TRMM perform poorly over the Heihe river basin, with low Correlation Coefficient value and Critical Success Index value, particularly in winter. Based on the coupled Time-Variant Gain Model-Degree Day Factor Model (TVGM-DDF) initiated in this paper, the GPM is more applicable in terms of running hydrological models. With the aim of detecting solid precipitation, the GPM is more capable of detecting solid precipitation but still unsatisfactory at two stations. In the case of light precipitation, both products underestimate light precipitation. In general, the performance of the two products in the Heihe river basin is not satisfactory and should be enhanced in upcoming explorations. This study provides a strong foundation for choosing alternate precipitation data for related research in the mountain basin.

Evaluation of Sentinel-3A altimetry over Songhua river Basin

Satellite radar altimetry is an important tool for monitoring water levels of inland water bodies, particularly in poorly gauged or ungauged areas. This study reveals for the first time the performance of water levels derived from Sentinel-3A altimetry in the Songhua River Basin in northeast China. This study compares water levels derived via different retrackers (SAMOSA+, OCOG, Ocean, MWaPP, and NPPR) from altimetry in virtual stations (VSs) to water levels measured by gauges. To improve the influence of sandbars or islands and surrounding land cover, a modified retracker called AMPD-PF (Automatic Multiscale-based Peak Detection retracker using Physically-based model Fitting) is implemented here, by combing the physical modeling of SAR altimeter waveform with a shortest-path algorithm to find the best possible sub-waveform representing the signal of water bodies. The accuracy assessment is based on comparing water level anomalies from 26 VSs and those from the nearest adjacent gauges with an average distance of 8.98 km in the Songhua River Basin from 2016 to 2019. The median of the root-mean-square error (RMSE) differences at the virtual stations ranges from 0.46 to 0.82 m, and the median of the Nash-Sutcliffe efficiency (NSE) among different retracker varies from 0.49 to 0.85 among different retracker. The performance of Sentinel-3A altimetry in the Songhua River Basin is not poor. Results also show a highest accuracy of water levels from AMPD-PF, especially for the non-flood season (January to June and October to December). In addition, we find that, especially for the virtual stations unaffected by the sand bars and islands, AMPD-PF give the best performance with a median RMSE value of 0.33 m and a median NSE value of 0.86. Factor analysis indicates that the variations of these metrics can not be directly related to the river width but might be related to the cultivated land types and the presence of sand bars and islands in the vicinity of rivers over the Songhua River Basin. Moreover, it is confirmed that a near-parallel orientation of the river with respect to the satellite ground track often leads to poorer performance at virtual stations. This suggests that if one is aiming at calibrating a hydrodynamic or hydrological model by combining altimetry, the VSs with the near-parallel orientation are not necessary to be considered when the percentage of the non-parallel crossings of all crossings is big enough.

Research on Parameter Regionalization of Distributed Hydrological Model Based on Machine Learning

In the past decade, more than 300 people have died per year on average due to mountain torrents in China. Mountain torrents mostly occur in ungauged small and medium-sized catchments, so it is difficult to maintain high accuracy of flood prediction. In order to solve the problem of the low accuracy of flood simulation in the ungauged areas, this paper studies the influence of different methods on the parameter regionalization of distributed hydrological model parameters in hilly areas of Hunan Province. According to the terrain, landform, soil and land use characteristics of each catchment, we use Shortest Distance, Attribute Similarity, Support Vector Regression, Generative Adversarial Networks, Classification and Regression Tree and Random Forest methods to create parameter regionalization schemes. In total, 426 floods of 25 catchments are selected to calibrate the model parameters, and 136 floods of 8 catchments are used for verification. The results showed that the average values of the Nash–Sutcliffe coefficients of each scheme were 0.58, 0.64, 0.60, 0.66, 0.61 and 0.68, and the worst values were 0.27, 0.31, 0.25, 0.43, 0.35 and 0.59. The random forest model is the most stable solution and significantly outperforms other methods. Using the random forest model to regionalize parameters can improve the accuracy of flood simulation in ungauged areas, which is of great significance for flash flood forecasting and early warning.

Detection of the Land Surface Temperature Changes in Ma’an Governorate using Remote Sensing Data during the Period (1990-2018)

This paper investigates multi-temporal land surface temperature (LST) for large ungauged areas of Ma’an Governorate, Jordan, based on changes in normalized difference vegetation index (NDVI) using remotely sensed data. Five mosaic images were taken for both the 1990 and 2018 years by Landsat 5 (TM) and Landsat 8 (OLI) (path/row, 174/36- 174/37). These were used as the basic data source, where most of Ma’an Governorate has no meteorological stations. The five-mosaic images for both Landsat 5 and Landsat 8-OLI were taken in September. LST and NDVI maps have been generated to determine the changes in LTS during the monitoring period. The results showed that the minimum value of LST increased by 4°C, and the mean surface temperature increased nearly by 2°C between 1990 and 2018. The average LST has been rising at a rate of 0.071°C/y.

A stochastic approach to simulate realistic continuous snow depth time series

We propose a stochastic approach for simulation of realistic continuous snow depth time series using a snow depth estimation model and a stochastic weather generation model. The snow depth estimation model consists of three steps: defining the precipitation type, estimating the snow ratio, and estimating the decreased snow depth. First, air temperature and relative humidity are used as indices to determine the type of precipitation when precipitation occurs. When the type is identified as snow, the snow ratio is estimated, converting the precipitation depth into depth of fresh snow. Here, the snow ratio is estimated through sigmoidal relationship with the air temperature. Lastly, the amount of decreased snow depth was estimated using a novel temperature index snowmelt equation that can consider depth-dependent melting of snowpack. The model was applied to four meteorological stations of Korea and yielded high Nash Sutcliffe efficiency values which ranged between 0.745 and 0.875 for calibration, and ranged between 0.432 and 0.753 for validation. This calibrated snow depth estimation model was then applied to synthetic weather data (precipitation, temperature, and relative humidity) that was generated by stochastic weather generation model to simulate continuous snow depth time series. The simulated snow depth data accurately reproduced standard and extreme value statistics of the observed data, the latter of which were consistent with the current Korean Building Code. This model can be widely used not only in the field of snow related risk analysis, but also in data simulation for ungauged areas and future trend study using the climate projection.

Linking the complementary evaporation relationship with the Budyko framework for ungauged areas in Australia

Abstract. While the calibration-free complementary relationship (CR) has performed excellently in predicting terrestrial evapotranspiration (ETa), how to determine the Priestley–Taylor coefficient (αe) is a remaining question. In this work, we evaluated this highly utilizable method, which only requires atmospheric data, with in situ flux observations and basin-scale water-balance estimates (ETwb) in Australia, proposing how to constrain it with a traditional Budyko equation for ungauged locations. We found that the CR method with a constant αe transferred from fractional wet areas performed poorly in reproducing the mean annual ETwb in unregulated river basins, and it underperformed advanced physical, machine-learning, and land surface models in closing grid-scale water balance. This problem was remedied by linking the CR method with a traditional Budyko equation that allowed for an upscaling of the optimal αe from gauged basins to ungauged locations. The combined CR–Budyko framework enabled us to reflect climate conditions in αe, leading to more plausible ETa estimates in ungauged areas. The spatially varying αe conditioned by local climates enabled the CR method to outperform the three ETa models in reproducing the grid-scale ETwb across the Australian continent. We argued here that the polynomial CR with a constant αe could result in biased ETa, and it can be constrained by a traditional Budyko equation for improvement.

Pattern recognition describing spatio-temporal drivers of catchment classification for water quality

Classification using spatial data is foundational for hydrological modelling, particularly for ungauged areas. However, models developed from classified land use drivers deliver inconsistent water quality results for the same land uses and hinder decision-making guided by those models. This paper explores whether the temporal variation of water quality drivers, such as season and flow, influence inconsistency in the classification, and whether variability is captured in spatial datasets that include original vegetation to represent the variability of biotic responses in areas mapped with the same land use. An Artificial Neural Network Pattern Recognition (ANN-PR) method is used to match catchments by Dissolved Inorganic Nitrogen (DIN) patterns in water quality datasets partitioned into Wet vs Dry Seasons and Increasing vs Retreating flows. Explainable artificial intelligence approaches are then used to classify catchments via spatial feature datasets for each catchment. Catchments matched for sharing patterns in both spatial data and DIN datasets were corroborated and the benefit of partitioning the observed DIN dataset evaluated using Kruskal Wallis method. The highest corroboration rates for spatial data classification with DIN classification were achieved with seasonal partitioning of water quality datasets and significant independence (p < 0.001 to 0.026) from non-partitioned datasets was achieved. This study demonstrated that DIN patterns fall into three categories suited to classification under differing temporal scales with corresponding vegetation types as the indicators. Categories 1 and 3 included dominance of woodlands in their datasets and catchments suited to classify together change depending on temporal scale of the data. Category 2 catchments were dominated by vineforest and classified catchments did not change under different temporal scales. This demonstrates that including original vegetation as a proxy for differences in DIN patterns will help guide future classification where only spatially mapped data is available for ungauged catchments and will better inform data needs for water modelling.

Intensity–Duration–Frequency Curves in a Data-Rich Era: A Review

Intensity–duration–frequency (IDF) curves are widely used in the hydrological design of hydraulic structures. This paper presents a wide review of methodologies for constructing IDF curves with a specific focus on the choice of the dataset type, highlighting the main characteristics, possible uncertainties, and benefits that can be derived from their application. A number of studies based on updating IDFs in relation to climate change are analyzed. The research was based on a comprehensive analysis of more than 100 scientific papers and reports, of which 80 were found to be suitable for the aim of this study. To classify the articles, the key was mainly intensity–duration–frequency curves in relation to the types of datasets most used for their construction, specific attention was paid to the case study area. The paper aims to answer the following research questions. (i) What is the contribution of a data-rich era? (ii) Are remotely sensed data reliable to build IDFs in ungauged or partially gauged watersheds? (ii) How is uncertainty dealt with when developing IDFs? Remotely sensed data appear to be an alternative to rain-gauge data in scarcely gauged or ungauged areas; however, rain-gauge data are still a preferred dataset in the development of IDFs. The main aim of the present work is to provide an overview of the state of the art on the use of different types of data to build IDFs. The paper is intended to support the inclusion of different data types in hydrological applications.

Flood Modeling in Coastal Cities and Flow through Vegetated BMPs: Conceptual Design

Undeniably, climate-change-induced sea-level rise has served as a powerful driving force for the occurrence of coastal storms more severe than ever before. Low-lying metropolitan coastal areas, in particular, have become increasingly vulnerable to coastal flooding as a result of the rapid expansion of urbanization along flood-prone areas. Safeguarding coastal communities from the repercussions of flooding through the utilization of best management practices (BMPs) is one of the most effective ways of alleviating the situation. However, solely relying on inshore BMPs might not be the best possible approach when the storm event’s intensity can be reduced before the storm reaches the shoreline through the installment of offshore BMPs. Moreover, many recent studies have confirmed the cost-effectiveness and usefulness of natural elements, especially when combined with engineering solutions. This paper assesses the overall suitability and effectiveness of various combinations of nature-based inshore and offshore BMPs for coastal flood mitigation of storms with different intensities with applications to the Southern Brooklyn area in New York City. Moreover, inevitable conditions stemming from climate variability and change have made the assumption of stationary recurrence of extreme events unreliable. Furthermore, historical events of remarkable impacts have not been considered differently in the attainment of the flood design values. Thus, as one of the main objectives of this study, and in order to consider both, a nonstationary partial frequency analysis is utilized, including the major historical storms jointly with the precipitation data in the study area to obtain 100-year flood design values. To achieve the goals of the study, first, the offshore simulation was carried out using the Delft3D hydrodynamic model, developed by Deltares, in order to find the water level hydrograph for the ungauged areas of interest, with and without offshore BMPs followed by the simulation of the flood events on the inland coastal area using gridded surface subsurface hydrologic analysis (GSSHA), a module of the watershed modeling system (WMS) developed by the USACE, to assess the effectiveness of inshore BMPs. The proposed scenarios are tested against Superstorm Sandy, Hurricane Irene, and the 100-year flood hydrographs. The results showed a remarkable coastal flood wave height and water level reduction when integrating offshore and inshore green BMPs.

Hybrid approach for flood susceptibility assessment in a flood-prone mountainous catchment in China

• We developed a hybrid approach integrating physical-based flood models with random forest for flood susceptibility assessment. • We identified the relatively significant flood-inducing factors. • The geographical and hydromorphological factors had greater effects on riverine flooding. • This flood susceptibility assessment is suitable in ungauged areas. Flooding is one of the most destructive natural hazards that has caused catastrophic effects worldwide. Recently, machine learning methods have become widespread in flood susceptibility assessments due to their efficiency and robustness. However, flood inventory data acquired from historical flooding records and field surveys in existing studies are sparse and not enough to develop accurate machine learning models. Thus, we developed a hybrid approach that integrates hydrodynamic model (HEC-HMS/RAS), rapid flood model (height above the nearest drainage, HAND) and machine learning model (random forest, RF) for flood susceptibility assessment. The proposed approach was then implemented in the Xinluo watershed, a flood-prone mountainous catchment in China. The results showed that the spatially continuous flood inventory map generated from HEC-HMS/RAS model satisfies the big data requirements of the RF model, and the proposed hybrid approach demonstrates the effectiveness and efficiency in flood susceptibility assessment with overall accuracy and area under the ROC curve values of 0.915 and 0.912, respectively. The geographical and hydromorphological factors had greater effects on riverine flooding with average contribution of 27.8% and 27.6%. In comparison, the average contribution of land cover and meteorological factors were 24.3% and 20.3%, respectively. The method proposed in this study is effective in assessing and forecasting flood susceptibility and can facilitate resilience planning as well as integrated flood management.

Monitoring drought in ungauged areas using satellite altimetry: The Standardized River Stage Index

The lack of in-situ hydrological observations, especially in remote and developing areas, is one of the main challenges of drought monitoring. This paper proposes a novel drought index, the Standardized River Stage Index (SRSI), based on data retrieved from satellite altimetry missions. The novelties of the SRSI are that it can quantitatively monitor drought of river water deficits at multiple timescales and that it does not require in-situ observations. The Mekong River is used as the case study to demonstrate and validate the SRSI calculated from the multi-mission satellite altimetry data (Envisat and Jason-2/3). The log-logistic distribution is best fitted for SRSI calculation for the highest goodness-of-fit for river stages for the Mekong River. The comparison with the Standardized Streamflow Index (SSI) from the in-situ discharge observations shows that the SRSI can reasonably capture the drought of river water deficit. It is effective in identifying the timing and intensity of severe drought events, such as the 2019 Thailand drought. The SRSI is also compared with the Standardized Precipitation Evapotranspiration Index and the GRACE Drought Index in revealing drought features such that the river water deficit of Mekong River has time lags of 2 to 3 months for precipitation deficits and water storage deficits. Hence, the SRSI is more suitable for monitoring drought of river water deficit, because it directly links to the streamflow via physical stage-discharge relationship. Overall, the SRSI provides an effective indicator of drought of river water deficit when in-situ observations are unavailable. It can serve in the real-time drought early warning in remote and developing areas.

Assessment and Hydrological Validation of Merged Near-Real-Time Satellite Precipitation Estimates Based on the Gauge-Free Triple Collocation Approach

Obtaining accurate near-real-time precipitation data and merging multiple precipitation estimates require sufficient in-situ rain gauge networks. The triple collocation (TC) approach is a novel error assessment method that does not require rain gauge data and provides reasonable precipitation estimates by merging data; this study assesses the TC approach for producing reliable near-real-time satellite-based precipitation estimate (SPE) products and the utility of the merged SPEs for hydrological modeling of ungauged areas. Three widely used near-real-time SPEs, including the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) early/late run (E/L) series, and the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Dynamic Infrared Rain Rate (PDIR) products, are used in the Beijiang basin in south China. The results show that the TC-based merged SPEs generally outperform all original SPEs, with higher consistency with the in-situ observations, and show superiority over the simple equal-weighted merged SPEs used for comparison; these findings indicate the superiority of the TC approach for utilizing the error characteristics of input SPEs for multi-SPE merging for ungauged areas. The validation of the hydrological modeling utility based on the Génie Rural à 4 paramètres Journalier (GR4J) model shows that the streamflow modeled by the TC-based merged SPEs has the best performance among all SPEs, especially for modeling low streamflow because the integration with the PDIR outperforms the IMERG products in low streamflow modeling. The TC merging approach performs satisfactorily for producing reliable near-real-time SPEs without gauge data, showing great potential for near-real-time applications, such as modeling rainstorms and monitoring floods and flash droughts in ungauged areas.

Near real-time estimation of high spatiotemporal resolution rainfall from cloud top properties of the MSG satellite and commercial microwave link rainfall intensities

High spatiotemporal resolution rainfall is needed in predicting flash floods, local climate impact studies and agriculture management. Rainfall estimation techniques like satellites and the commercial microwave links (MWL) rainfall estimation have independently made significant advancements in high spatiotemporal resolution rainfall estimation. However, their combination for rainfall estimation has received little attention, while it could benefit many applications in ungauged areas. This study investigated the usability of the random forest (RF) algorithm trained with MWL rainfall and Meteosat Second Generation (MSG) based cloud top properties for estimating high spatiotemporal resolution rainfall in the sparsely gauged Kenyan Rift Valley. Our approach retrieved cloud top properties for use as predictor variables from rain areas estimated from the MSG data and estimated path average rainfall intensities from the MWL to serve as the target variable. We trained and validated the RF algorithm using parameters derived through optimal parameter tuning. The RF rainfall intensity estimates were compared with gauge, MWL, Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Multisensor Precipitation Estimate (MPE) to evaluate its rainfall intensities from point and spatial perspectives. The results can be described as good, considering they were achieved in near real-time, pointing towards a promising rainfall estimation alternative based on the RF algorithm applied to MWL and MSG data. The applicative benefits of this technique could be huge, considering that many ungauged areas have a growing MWL network and MSG and, in the future, Meteosat Third Generation (MTG) coverage.

Flood forecasting methods for a semi‐arid and semi‐humid area in Northern China

AbstractThe Double‐Excess (DE) model is a flood forecasting model which was developed to reflect the characteristics of runoff generation in semi‐arid and semi‐humid areas. However, the empirical unit hydrograph used in the subbasin confluence module often has low precision because of the difficulty associated with parameter quantification. This study improves the subbasin confluence module of the DE model by coupling the geomorphologic instantaneous unit hydrograph (GIUH) to establish the improved DE model (DE‐GIUH) to calculate the subbasin confluence based on topographic physical characteristics. The improved model is applied to the Wangjiahui Hydrological Station Basin in Northern China. Compared with the conventional DE model and the widely used hydrologic modelling system (HEC‐HMS) model, the results show that DE‐GIUH improved flood forecasting, including the component peak discharge, flood peak appearance time and flood progress. The qualified rate (QR) of all three models reached grade A (QR ≥ 85.0%). However, the proportion of the deterministic coefficient (DC) reaching grade B or above (DC ≥ 0.70) improved from 50% (HEC‐HMS) and 30% (DE) to 90% (DE‐GIUH) in the calibration period, and from 62.5% and 25% to 75% in the validation period. In particular, for large and medium floods, the proportion of DC reaching grade B and above is 100% for DE‐GIUH, which is much higher than that of the other two models. The proposed improved model provides an alternative method for flood prediction in ungauged areas.

An evidence for enhancing the design hydrograph estimation for small and ungauged basins in Ethiopia

Study regionEthiopia and Upper Blue Nile Basin. Study focusEstimating design hydrograph characteristics (e.g. flood peak discharge, volume and duration) is a crucial topic in hydrology and this is particularly challenging in data scarce regions. In recent years, significant innovations in hydrological modeling were introduced, allowing for more advanced analyses also in ungauged areas, in doing so limiting the application of empirical approaches (e.g. the rational formula). In the present work we summarize the current practice in Ethiopia and explore the possibility to apply a simplified continuous modeling for estimating design hydrographs. New hydrological insights for the regionThis study supports the conclusion that COSMO4SUB (Continuous Simulation Model for Small and Ungauged Basins) framework, a simplified continuous rainfall-runoff model specifically tailored for small and ungauged basins, can be applied in Ethiopia. Indeed, the minimal information available in the country are sufficient for applying such modeling approach, that can provide useful outputs for a variety of practical hydrological studies.

Adequacy of satellite derived data for streamflow simulation in three Hexi inland river basins, Northwest China

The limited gauge measurements usually constrain the accurate hydrological modeling of runoff, especially in the ungauged or gauge-sparsely areas with complex terrain conditions. Satellite derived data provide alternative forcing data for hydrological modeling in ungauged areas, while quantitative evaluation on their performance is still needed. This study explores the adequacy and accuracy of satellite derived data based on the gauge observations over three inland river basins in the arid Hexi Corridor, Northwest China, where the satellite-based products are adopted to drive the HBV-Light model, including six satellite precipitation datasets (CHIRPS, CMORPH, GPM, GSMaP, MSWEP, PERSIANN) and the widely used evaporation product (GLEAM) combing with MODIS land surface temperature product (MOD11A1). Seven statistical metrics (CC, ME, RMSE, BIAS, POD, FAR, CSI) are adopted to assess satellite-based precipitation estimates, and three indices (NSE, R2, PBIAS) are used to evaluate the hydrological performance. Results indicate that: (1) For precipitation evaluation at point-to-pixel scale, all products have a relatively poor agreement with gauged observations with the CC ranging from 0.16 to 0.56. Both MSWEP and GPM outperformed other products at daily scale with higher CC values, and showed the capability of detecting the precipitation events with higher POD and CSI. PERSIANN, GSMaP, GPM, and CMORPH tend to underestimate the precipitation amount in three river basins. (2) For the hydrological evaluation, the statistical assessment showed that the GPM presents the best results at daily scale, while underestimates streamflow amount in three watersheds. All the grided precipitation datasets show higher NSE and R2 in the relatively humid HHRB and XYRB than SLRB. (3) The combination of MOD11A1, GLEAM and satellite-based precipitation as forcing data of the HBV-Light can achieve good performance in the three inland river basins. This study aims to provide an additional reference for the hydrological application of satellite derived data in gauge-sparsely areas located in high altitude and arid regions.

Derivation of flood hydrographs using SCS synthetic unit hydrograph technique for Housha catchment area

Abstract SCS (Soil Conservation Service) synthetic unit hydrograph technique was used to estimate the storm runoff overflows entering the Housha tunnel. The flood hydrographs were derived under different storm event durations for the ungauged Husha Catchment area. Geomorphological and hydrological parameters of the watershed were extracted using GIS measurement tools. The data identified various parameters (time to peak, time of base, and peak flow) of the synthetic unit hydrograph. The peak discharge (Qp in m3 s−1 cm−1) of rainfall excess for different time durations (5, 10, 20, 30, 60, 120, 180, 360, and 720 minutes) was estimated. The results revealed that the peak discharge (Qp) decreased with the increase in time of rainfall excess. The maximum peak discharge (27.5 in m3 s−1 cm−1) was reached 84 minutes after the beginning of the rainfall storm for 5 minutes of rainfall excess whereas the minimum peak discharge (5.2 in m3 s−1 cm−1) was reached after 7 hours and 22 minutes for 12 hours of rainfall excess. Geographic information system (GIS) data-based SCS synthetic unit hydrograph model was verified by comparing the simulated runoff with the estimated runoff from measured rainfall data of the watershed.

Advancing flood warning procedures in ungauged basins with machine learning

Flood prediction across scales and more specifically in ungauged areas remains a great challenge that limits the efficiency of flood risk mitigation strategies and disaster preparedness. Building upon the recent success of Machine Learning (ML) models on streamflow prediction, this work presents a prototype ML-based framework for flood warning and flood peak prediction. The fundamental elements of the proposed system consist of a) a Long-Short Term Memory (LSTM) model for classifying storm events to Flood/no-Flood given a threshold based on the 90th flow percentile and b) the flood peak prediction models. The selected ML-models for flood peak prediction are the Histogram based Gradient Boosting Regressor and the Random Forest. One of the strengths, and reason for selection, of these decision-tree models is their degree of interpretability. This is exploited in the study to help us spatially disentangle the role of both the static and dynamic drivers of flood peak response. Our analysis is presented for 18 distinct hydroclimatic regions across the contiguous US. Results reveal a significant regional dependence on both predictive performance and dominant flood predictors, which emphasize the variability in the complexity of a catchment’s hydrologic behavior as well as its impact on forecasting flood response. Evaluation of the drivers of flood peaks noted distinct dependencies among the dynamic and static predictors considered in our models for flood peaks of different severity. Specifically, low-moderate flood events showed a clear preponderance for the static catchment attributes over dynamic predictors like precipitation whereas precipitation was the dominant driver for the highest flood peaks. The proposed flood peak prediction models were compared against a state-of-the-art LSTM model and were shown to outperform in ungauged basins for the majority of basins. Overall, the proposed system classified storms correctly for >80% in all cases and exhibited a percent relative difference in flood peak estimates of <30% in most cases.

Estimation of Threshold Rainfall in Ungauged Areas Using Machine Learning

In recent years, Korea has seen abnormal changes in precipitation and temperature driven by climate change. These changes highlight the increased risks of climate disasters and rainfall damage. Even with weather forecasts providing quantitative rainfall estimates, it is still difficult to estimate the damage caused by rainfall. Damaged by rainfalls differently for inch watershed, but there is a limit to the analysis coherent to the characteristic factors of the inch watershed. It is time-consuming to analyze rainfall and runoff using hydrological models every time it rains. Therefore, in fact, many analyses rely on simple rainfall data, and in coastal basins, hydrological analysis and physical model analysis are often difficult. To address the issue in this study, watershed characteristic factors such as drainage area (A), mean drainage elevation (H), mean drainage slope (S), drainage density (D), runoff curve number (CN), watershed parameter (Lp), and form factor (Rs) etc. and hydrologic factors were collected and calculated as independent variables, and the threshold rainfall calculated by the Ministry of Land, Infrastructure and Transport (MOLIT) was calculated as a dependent variable and used in the machine learning technique. As for machine learning techniques, this study uses the support vector machine method (SVM), the random forest method, and eXtreme Gradient Boosting (XGBoost). As a result, XGBoost showed good results in performance evaluation with RMSE 20, MAE 14, and RMSLE 0.28, and the threshold rainfall of the ungauged watersheds was calculated using the XGBoost technique and verified through past rainfall events and damage cases. As a result of the verification, it was confirmed that there were cases of damage in the basin where the threshold rainfall was low. If the application results of this study are used, it is judged that it is possible to accurately predict flooding-induced rainfall by calculating the threshold rainfall in the ungauged watersheds where rainfall-outflow analysis is difficult, and through this result, it is possible to prepare for areas vulnerable to flooding.