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In this paper, we address the critical task of 24-h streamflow forecasting using advanced deep-learning models, with a primary focus on the transformer architecture which has seen limited application in this specific task. We compare the performance of five different models, including persistence, long short-term memory (LSTM), Seq2Seq, GRU, and transformer, across four distinct regions. The evaluation is based on three performance metrics: Nash-Sutcliffe Efficiency (NSE), Pearson's r , and normalized root mean square error (NRMSE). Additionally, we investigate the impact of two data extension methods: zero-padding and persistence, on the model's predictive capabilities. Our findings highlight the transformer's superiority in capturing complex temporal dependencies and patterns in the streamflow data, outperforming all other models in terms of both accuracy and reliability. Specifically, the transformer model demonstrated a substantial improvement in NSE scores by up to 20% compared to other models. The study's insights emphasize the significance of leveraging advanced deep learning techniques, such as the transformer, in hydrological modeling and streamflow forecasting for effective water resource management and flood prediction., Competing Interests: The authors declare there is no conflict., (© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).)

Accurately modeling and predicting flood flows across multiple sites within a watershed presents significant challenges due to potential issues of insufficient accuracy and excessive computational demands in existing methodologies. In response to these challenges, this study introduces a novel approach centered around the use of vine copula models, termed RDV-Copula (reduced-dimension vine copula construction approach). The core of this methodology lies in its ability to integrate and extract complex data before constructing the copula function, thus preserving the intricate spatial–temporal connections among multiple sites while substantially reducing the vine copula's complexity. This study performs a synchronization frequency analysis using the devised copula models, offering valuable insights into flood encounter probabilities. Additionally, the innovative approach undergoes validation by comparison with three benchmark models which vary in dimensions and nature of variable interactions. Furthermore, the study conducts stochastic simulations, exploring both unconditional and conditional scenarios across different vine copula models. Applied in the Shifeng Creek watershed, China, the findings reveal that vine copula models are superior in capturing complex variable relationships, demonstrating significant spatial interconnectivity crucial for flood risk prediction in heavy-rainfall events. Interestingly, the study observes that expanding the model's dimensions does not inherently enhance simulation precision. The RDV-Copula method not only captures comprehensive information effectively but also simplifies the vine copula model by reducing its dimensionality and complexity. This study contributes to the field of hydrology by offering a refined method for analyzing and simulating multi-site flood flows. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI), including models like ChatGPT, is transforming water resources management by improving hydrological modeling, water quality assessment, and flood prediction. AI techniques such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) have enhanced streamflow predictions and groundwater management, particularly in data-scarce regions. AI-powered systems like Smart Microclimate Control Systems (SMCS) optimize agricultural practices, leading to better resource conservation and higher crop yields. However, the scalability and applicability of AI across diverse environments pose challenges, especially where data is limited. The success of AI models depends on data quality, requiring ongoing interdisciplinary research to refine these technologies for real-world use. Additionally, tools like ChatGPT, while valuable for knowledge dissemination and data analysis, raise concerns about accuracy in critical decision-making contexts. In conclusion, while AI offers significant potential for improving water resources management, addressing challenges related to data quality, model scalability, and interdisciplinary collaboration is essential for achieving sustainable and effective outcomes. [ABSTRACT FROM AUTHOR]

Hydraulic geometry parameters describing river hydrogeomorphic relationships are critical for determining a channel's capacity to convey water and sediment which is important for flood forecasting. Although well‐established, power‐law hydraulic geometry curves have been widely used to understand riverine systems and mapping flooding inundation worldwide for the past 70 years, we have become increasingly aware of their limitations. In the present study, we have moved beyond these traditional power‐law relationships, testing the ability of machine‐learning models to provide improved predictions of river width and depth. For this work, we have used an unprecedentedly large river measurement data set (HYDRoSWOT) as well as a suite of watershed predictor data to develop novel data‐driven approaches to better estimate river geometries over the contiguous United States (CONUS). Our Random Forest, XGBoost, and neural network models out‐performed the traditional, regionalized power law‐based hydraulic geometry equations for both width and depth, providing R‐squared values of as high as 0.75 for width and as high as 0.67 for depth, compared with R‐squared values of 0.45 for width and 0.18 for depth from the regional hydraulic geometry equations. Our results also show diverse performance outcomes across stream orders and geographical regions for the different machine‐learning models, demonstrating the value of using multi‐model approaches to maximize the predictability of river geometry. The developed models have been used to create the newly publicly available STREAM‐geo data set, which provides river width, depth, width/depth ratio, and river and stream surface area (%RSSA) for nearly 2.7 million NHDPlus stream reaches across the contiguous US. Plain Language Summary: Scientists and river managers use measurements of river geometry such as width and depth to forecast floods and understand river behavior. However, the methods used to estimate river geometry that have been used for decades are imprecise and thus lead to poor predictions of river discharge dynamics. Here, we've used new machine learning‐based modeling approaches to provide better predictions of river width and depth. We tested different machine‐learning models, which were developed based on the HYDRoSWOT set of measurements of rivers across the U.S. These new models all provide better estimates of river width and depth than the old methods. Our research can help us to provide better estimates of flood dynamics and improve our understanding of rivers across the U.S. Key Points: Machine Learning models outperform regional (physiographic) hydraulic geometry equations for predicting stream width and depthModel performance varies by stream orders and geographical regions, demonstrating the utility of multi‐model machine‐learning approachesThe STREAM‐geo data set provides predictions of river width, depth, width‐to‐depth ratio, and river area for the NHDPlus stream reaches [ABSTRACT FROM AUTHOR]

This study addresses the limitations of and the common challenges faced by one-dimensional river-routing methods in hydrological models, including the National Water Model (NWM), in accurately representing coastal regions. We developed a two-way coupling between the NWM and the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM). The approach demonstrated improvements in modeling coastal river dynamics, particularly during extreme events like Hurricane Matthew. The coupled model successfully captured tidal influences, storm surge effects, and complex river–river interactions that the standalone NWM missed. The approach revealed more accurate representations of peak discharge timing and magnitude as well as water storage and release in coastal floodplains. However, we also identified challenges in reconciling variable representations between hydrological and hydraulic models. This work not only enhances the understanding of coastal–riverine interactions but also provides valuable insights for the development of next-generation hydrological models. The improved modeling capabilities have implications for flood forecasting, coastal management, and climate change adaptation in vulnerable coastal areas. [ABSTRACT FROM AUTHOR]

Cities with sloping terrain are more susceptible to flooding during heavy rains. Traditional hydraulic models struggle to meet computational demands when addressing such emergencies. This study presented an integration of the one-dimensional Storm Water Management Model (SWMM) and the two-dimensional LISFLOOD-FP model, where the head difference at coupled manholes between the two models functioned as the connection. Based on its calculation results, this study extracted the characteristic parameters of the rainfall data, simplified the SVR calculation method and developed a high-efficiency solution for determining the maximum ponding depth. The cost time of this model was stable at approximately 1.0 min, 95% faster compared to the one from the mechanism model for 5 h simulation under the same working conditions. By conducting this case study in Jiujiang, China, the feasibility of this algorithm was well demonstrated. [ABSTRACT FROM AUTHOR]

Accurately estimating snow hydrology parameters, including snow coverage mapping and snow depth, plays a significant role in comprehending water resource dynamics, flood forecasting, and environmental management in regions influenced by snow cover. These parameters are critical for hydrological models that simulate snowmelt and runoff, which are essential for predicting water availability and managing water resources in snow-covered areas. Traditional methods for estimating these parameters often rely on manual measurements or simplistic models, which can be inadequate for capturing the complexity of snow-related processes. In recent years, there has been a growing interest in leveraging deep learning techniques for snow hydrology parameter estimation, offering the potential to overcome these limitations. This review paper comprehensively analyzes the current state, challenges, and future directions of image-based approaches in snow hydrology parameter estimation. By harnessing the power of automated methods, particularly deep learning, these approaches demonstrate the ability to capture intricate spatial and temporal relationships present in image data. A comparative analysis between traditional and image-based methods highlights the strengths of automated approaches, including scalability and accuracy. Integration of image sensors, such as satellite imagery and crowd-sourced data, is explored as a crucial component of snow hydrology parameter estimation. Various satellite image sources, including Sentinel 1-2, Landsat, and MODIS, are discussed in terms of their suitability for snow hydrological applications. Despite the promise of image-based approaches, challenges remain, including data availability, model interpretability, and transferability. The paper identifies future research directions, emphasizing the exploration of novel deep learning architectures and uncertainty quantification techniques to address these challenges. In conclusion, this review underscores the importance of image-based approaches for advancing snow hydrology parameter estimation. By addressing challenges and maximizing potential impact, these approaches have the potential to revolutionize snow hydrological modeling and environmental management. [ABSTRACT FROM AUTHOR]

Flood simulation in sub-humid regions is one of the difficult issues in hydrology. Liulin experimental watershed, a typical sub-humid region in northern China, was selected for flood simulation. 20 rainfall–runoff events from 1995 to 2021 were selected to calibrate and validate the sub-distributed HEC-HMS model. The applicability of the model to flood simulation in the Liulin experimental watershed was explored. The influences of different runoff generation methods (SCS-CN method and initial constant method) and simulation time steps (1 h and 30 min) on flood simulation were compared. The applicability of the model to different antecedent moisture conditions and different flood characteristics was also analyzed. The results showed that all the schemes of rainfall–runoff models with different runoff generation methods and time steps have satisfactory performance in simulating floods. When the time step is 1 h, the initial constant runoff generation method was more suitable for runoff simulation, however, when the time step is 30 min, the SCS-CN runoff generation method was more robust. As the simulation time step decreased, the model performance was improved, but the improvement amplitude was greater when the SCS-CN method was used. In addition, the model performed better when antecedent moisture was higher, and the flood was single-peak. When the measured peak discharge was lower than 100 m3/s, the model could simulate the peak discharge and peak time better, and conversely, the model could simulate the flood volume and flood hydrograph better. This study is valuable for flood forecasting in sub-humid areas. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Water Resources / Recursos Hídricos is the property of Associacao Portuguesa dos Recursos Hidricos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Land use and land cover (LULC) change is one of the primary contributors to hydrological change in urban watersheds and can potentially influence stream flow and flood volume. Understanding the impacts of LULC change on urban hydrological processes is critical to effective urban water management and minimizing flood risks. In this context, this study aims to determine the impacts of LULC change on hydrological response in a fast transitioning watershed for the predicted years of 2050 and 2080. This research employs the hybrid land use classification technique, Cellular Automata–Markov (CA–Markov) model to predict land use changes, utilizing land use data from 2001, 2013, and 2021. Additionally, it incorporates a calibrated, event-specific hydrologic model known as the Personal Computer Storm Water Management Model (PCSWMM) to assess alterations in hydrological responses for storm events of various magnitudes. The findings indicate a transition of the watershed into an urbanized landscape, replacing the previous dominance of agriculture and forested areas. The initial urban area, constituting 11.6% of the total area in 2021, expands to cover 34.1% and 44.2% of the total area by 2050 and 2080, respectively. Due to the LULC changes, there are increases in peak discharge of 5% and 6.8% and in runoff volume of 8% and 13.3% for the years 2050 and 2080 for a 100-year return period storm event. Yet, the extent of these changes intensifies notably during storm events with lower return periods. This heightened impact is directly attributed to the swift urbanization of the watershed. These results underscore the pressing necessity to regulate LULC change to preserve the hydrological equilibrium. [ABSTRACT FROM AUTHOR]

Runoff from heavily glacierised catchments, its seasonality and the contribution from different storage units are highly relevant for assessing the seasonal and long-term water supply and flood prediction. Modelling studies on runoff from such basins often use simulated meteorological input (e.g. downscaling products) based on remote observations. We investigate the contribution of snow, firn and ice to runoff in the Vernagtferner basin (Ötztal Alps) from 2020 to 2022, using a physical modelling chain driven by a local observation network. We use the SNOWPACK model to simulate snow/ice development at observation sites and the Alpine3D model to calculate accumulation and melt at the catchment scale. Basin discharge is estimated using a gridded version of the HBV-ETH model. This approach largely reproduces observed glacier mass balances, while modelled and measured basin discharge are in good agreement. Snowmelt dominates discharge in the early melt season, while ice melt becomes increasingly important during summer. This is in strong contrast to the near-equilibrium mass balances in the 1980s, when ice melt played a minor role for annual discharge. The strong reduction of the accumulation area leads to a fundamental change from a snowmelt regime to an ice melt regime, which is especially pronounced in 2022. [ABSTRACT FROM AUTHOR]

This paper presents an efficient flood early warning system developed for the city of Mandra, Greece which experienced a devastating flood event in November 2017 resulting in significant loss of life. The location is of particular interest due to both its small-sized water basin (20 km 2 upstream of the studied cross-section), necessitating a rapid response time for effective flood warning calculations, and the lack of hydrometric data. To address the first issue, a database of pre-simulated flooding events with a 2D hydrodynamic model corresponding to synthetic precipitations with different return periods was established. To address the latter issue, the hydrological model was calibrated using qualitative information collected after the catastrophic event, compensating for the lack of hydrometric data. The case study demonstrates the establishment of a hybrid (online–offline) flood early warning system in data-scarce environments. By utilizing pre-simulated events and qualitative information, the system provides valuable insights for flood forecasting and aids in decision-making processes. This approach can be applied to other similar locations with limited data availability, contributing to improved flood management strategies and enhanced community resilience. [ABSTRACT FROM AUTHOR]

Ephemeral wetlands are globally important systems that are regulated by regular cycles of wetting and drying, which are primarily controlled by responses to relatively short-term weather events (e.g., precipitation and evapotranspiration). Climate change is predicted to have significant effects on many ephemeral wetland systems and the organisms that depend on them through altered filling or drying dates that impact hydroperiod. To examine the potential effects of climate change on pine flatwoods wetlands in the southeastern United States, we created statistical models describing wetland hydrologic regime using an approximately 8-year history of water level monitoring and a variety of climate data inputs. We then assessed how hydrology may change in the future by projecting models forward (2025≥2100) under six future climate scenarios (three climate models each with two emission scenarios). We used the model results to assess future breeding conditions for the imperiled Reticulated Flatwoods Salamander (Ambystoma bishopi), which breeds in many of the study wetlands. We found that models generally fit the data well and had good predictability across both training and testing data. Across all models and climate scenarios, there was substantial variation in the predicted suitability for flatwoods salamander reproduction. However, wetlands with longer hydroperiods tended to have fewer model iterations that predicted at least five consecutive years of reproductive failure (an important metric for population persistence). Understanding potential future risk to flatwoods salamander populations can be used to guide conservation and management actions for this imperiled species. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Water Resources Journal / Revue Canadienne des Ressources Hydriques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

River ice-jams can create severe flooding along many rivers in cold regions. While ice-jams often form during the spring breakup, the mid-winter breakup can cause ice-jamming and flooding. Although many studies have already been focused on forecasting spring ice-jam flooding, studies related to forecasting mid-winter breakup jamming and flooding severity are sparse. The main purpose of this research is to develop a stochastic framework to forecast the severity of mid-winter ice-jam flooding along the transborder (New Brunswick/Maine) Saint John River of North America. A combination of hydrological (MESH) and hydraulic model (RIVICE) simulations was applied to develop the stochastic framework. A mid-winter breakup along the river that occurred in 2018 has been hindcasted as a case study. The result shows that the modelling framework can capture the real-time ice-jam severity. The results of this research will help to improve the capacity of ice-jam flood management in cold regions. HIGHLIGHTS First attempt to forecast freeze-up/winter breakup flood severity.; Stochastic modelling approach for winter breakup.; Hydraulic modelling for winter ice-jam formation.; Estimating real-time ice-jam flood severity.; Estimating freeze-up ice volume using modelling results.;

In the field of hydrology, event-based models are commonly used for flood-flow prediction in catchments, for use in flood forecasting, flood risk assessment, and infrastructure design. The models are simplistic, as they do not consider longer-term catchment processes such as evaporation and transpiration. This paper examines the relative performance of two widely used models, the American HEC-HMS model, the Australian RORB model, and a newer model, the RRR model. The evaluation is conducted on four case study catchments in Australia. The first two models, HEC-HMS and RORB, do not include baseflow, necessitating the estimation of baseflow through alternate means. By contrast, the RRR model includes baseflow, by extracting a separate loss from the rainfall, and then routing the resultant flow through the catchment, much like quickflow, but with a longer delay time. The models are calibrated and then verified with weighted mean parameter values on an independent set of events in each case study catchment. This gives an indication of the ability of the models to correctly predict flow, which is important when the models are used with design rainfalls to predict design flows. The results demonstrate that all models perform adequately on the four examined catchments, but the RRR model exhibits superior calibration, and, to a lesser extent, better validation compared to the other two models. [ABSTRACT FROM AUTHOR]

River ice-jams can create severe flooding along many rivers in cold regions. While ice-jams often form during the spring breakup, the midwinter breakup can cause ice-jamming and flooding. Although many studies have already been focused on forecasting spring ice-jam flooding, studies related to forecasting mid-winter breakup jamming and flooding severity are sparse. The main purpose of this research is to develop a stochastic framework to forecast the severity of mid-winter ice-jam flooding along the transborder (New Brunswick/Maine) Saint John River of North America. A combination of hydrological (MESH) and hydraulic model (RIVICE) simulations was applied to develop the stochastic framework. A mid-winter breakup along the river that occurred in 2018 has been hindcasted as a case study. The result shows that the modelling framework can capture the real-time ice-jam severity. The results of this research will help to improve the capacity of ice-jam flood management in cold regions. [ABSTRACT FROM AUTHOR]

Accurate representation of river channel geometry is important for hydrologic and hydraulic modeling of fluvial systems. Often, channel geometry is estimated using simple rating curves that can be applied across various spatial scales. However, such methods are limited to power law relations that do not employ many potentially relevant catchment and river attributes. This paper introduce a new dataset, IFMHA (Inventory of Field Measurement of Hydraulic Attributes), to enable research studies on channel geometry and streamflow characteristics. IFMHA is derived from the National Water Information System (NWIS) site inventory for surface water field measurements and stream attributes from the National Hydrography Dataset (NHD). IFMHA includes 2,802,532 records from 10,050 sites (NWIS streamgaging stations). The dataset utility is demonstrated here by presenting a series of conceptual models for estimating channel geometry parameters (i.e., channel mean depth, channel maximum depth, wetted perimeter, and roughness) based on the available field attributes within IFMHA. Such a dataset and attributed channel geometry parameters can enhance the performance of operational flood forecasting frameworks (e.g. National Water Model) by providing more accurate initial conditions used in hydrologic and hydraulic routing models. • A comprehensive dataset with more observations aids data-intensive models. • IFMHA offers stream traits: mean/max depth, wetted perimeter, roughness for shapes. • Channel geometry variations minimally affect Manning's roughness coefficient values. [ABSTRACT FROM AUTHOR]

Reducing uncertainty and improving robustness and spatio-temporal extrapolation capabilities remain key challenges in hydrological modeling especially for flood forecasting over large areas. Parsimonious model structures and effective optimization strategies are crucially needed to tackle the difficult issue of distributed hydrological model calibration from sparse integrative discharge data, that is in general high dimensional inverse problems. This contribution presents the first evaluation of Variational Data Assimilation (VDA), very well suited to this context but still rarely employed in hydrology because of high technicality, and successfully applied here to the spatially distributed calibration of a newly taylored grid-based parsimonious model structure and corresponding adjoint, over a large sample. It is based on the Variational Data Assimilation (VDA) framework of SMASH (Spatially distributed Modelling and ASsimilation for Hydrology) platform, underlying the French national flash flood forecasting system Vigicrues Flash. It proposes an upgraded distributed hourly rainfall-runoff model structure employing GR-based operators, including a non-conservative flux, and its adjoint obtained by automatic differentiation for VDA. The performances of the approach are assessed over annual, seasonal and floods timescales via standard performance metrics and in spatio-temporal validation. The gain of using the proposed non-conservative 6-parameters model structure is highlighted in terms of performance and robustness, compared to a simpler 3-parameters structure. Spatially distributed calibrations lead to a significant gain in terms of reaching high performances in calibration and temporal validation on the catchments sample, with median efficiencies respectively of NSE = 0.88 (resp. 0.85) and NSE = 0.8 (resp. 0.79) over the total time window on period p 2 (resp. p 1). Simulated signatures in temporal validation over 1443 (resp. 1522) flood events on period p 2 (resp. p 1) are quite good with median flood (NSE ; KGE) of (0.63; 0.59) (resp. (0.55; 0.53)). Spatio-temporal validations, i.e. on pseudo ungauged cases, lead to encouraging performances also. Moreover, the influence of certain catchment characteristics on model performance and parametric sensitivity is analyzed. Best performances are obtained for Oceanic and Mediterranean basins whereas it performs less well over Uniform basins with significant influence of multi-frequency hydrogeological processes. Interestingly, regional sensitivity analysis revealed that the non conservative water exchange parameter and the production parameter, impacting the simulated runoff amount, are the most sensitive parameters along with the routing parameter especially for faster responding catchments. This study is a first step in the construction of a flexibe and versatile multi-model and optimization framework with hydbrid methods for regional hydrological modeling with multi-source data assimilation. [ABSTRACT FROM AUTHOR]

In the higher latitudes of the northern hemisphere, ice jam related flooding can result in millions of dollars of property damages, loss of human life and adverse impacts on ecology. Since ice-jam formation mechanism is stochastic and depends on numerous unpredictable hydraulic and river ice factors, ice-jam associated flood forecasting is a very challenging task. A stochastic modelling framework was developed to forecast real-time ice jam flood severity along the transborder (New Brunswick/Maine) Saint John River of North America during the spring breakup 2021. Modélisation environnementale communautaire—surface hydrology (MESH), a semi-distributed physically-based land-surface hydrological modelling system was used to acquire a 10-day flow forecast. A Monte-Carlo analysis (MOCA) framework was applied to simulate hundreds of possible ice-jam scenarios for the model domain from Fort Kent to Grand Falls using a hydrodynamic river ice model, RIVICE. First, a 10-day outlook was simulated to provide insight on the severity of ice jam flooding during spring breakup. Then, 3-day forecasts were modelled to provide longitudinal profiles of exceedance probabilities of ice jam flood staging along the river during the ice-cover breakup. Overall, results show that the stochastic approach performed well to estimate maximum probable ice-jam backwater level elevations for the spring 2021 breakup season. [ABSTRACT FROM AUTHOR]

In the hydrological sciences, the outstanding challenge of regional modeling requires to capture common and event-specific hydrologic behaviors driven by rainfall spatial variability and catchment physiography during floods. The overall objective of this study is to develop robust understanding and predictive capability of how rainfall spatial variability influences flood peak discharge relative to basin physiography. A machine-learning approach is used on a high-resolution dataset of rainfall and flooding events spanning 10 years, with rainfall events and basins of widely varying characteristics selected across the continental United States. It overcomes major limitations in prior studies that were based on limited observations or hydrological model simulations. This study explores first-order dependencies in the relationships between peak discharge, rainfall variability, and basin physiography, and it sheds light on these complex interactions using a multidimensional statistical modeling approach. Among different machine-learning techniques, XGBoost is used to determine the significant physiographical and rainfall characteristics that influence peak discharge through variable importance analysis. A parsimonious model with low bias and variance is created that can be deployed in the future for flash flood forecasting. The results confirm that, although the spatial organization of rainfall within a basin has a major influence on basin response, basin physiography is the primary driver of peak discharge. These findings have unprecedented spatial and temporal representativeness in terms of flood characterization across basins. An improved understanding of subbasin scale rainfall spatial variability will aid in robust flash flood characterization as well as with identifying basins that could most benefit from distributed hydrologic modeling. Significance Statement: To improve understanding of the effect of precipitation on floods, a machine-learning workflow is designed to scrutinize hydrological processes and is applied on a database of flood events over the United States. The model accurately reproduces observed maximum streamflow. It reflects physical hydrologic behavior that is consistent across basins, thereby addressing the challenge of regional modeling and improving upon traditional hydrological models. Rainfall spatial variability has a major influence on flood peak discharge, although basin geomorphology is the primary driver. This grants an improved understanding of the conditions under which floods are generated, allowing for better predictions and warning. [ABSTRACT FROM AUTHOR]

Floods are one of the most devastating natural disasters that can cause large economic damage and endanger human lives. Flood forecasting is one of the flood risk mitigation measures serving to protect human lives and social estate. The Danube River Basin (DRB) is the world's most international river basin, flowing through the territory of 19 countries, covering more than 800,000 km2. The frequency of floods in the DRB increased in the last decades, urging the need for a more effective and harmonized regional and cross‐border cooperation in the field of flood forecasting. Reliable and comprehensive hydrologic data are the basis of flood forecasting. This paper provides an overview of the national flood forecasting systems in the DRB. Detailed information about meteorological and hydrological measurements, flood modelling, forecasting, and flood warnings is provided for 12 countries that cover almost 95% of the total DRB area. Notably, significant differences exist among the countries in terms of the measuring network density, the models used as well as forecasting and warnings methodology. These differences can be attributed to the geographical and climatological setting, political situation, historical forecasting development, etc. It can be seen that there is still much room left for improvements of measurement networks (e.g., density, measured parameters) and models used that could be improved to enhance the flood forecasting in the DRB. [ABSTRACT FROM AUTHOR]

Flood forecasting is essential to minimize the impacts and costs of floods, especially in urbanized watersheds. Radar rainfall estimates are becoming increasingly popular in flood forecasting because they provide the much-needed realtime spatially distributed precipitation information. The current study evaluates the use of radar quantitative precipitation estimates (QPEs) in hydrological model calibration for streamflow simulation and flood mapping in an urban setting. First, S-band and C-band radar QPEs were integrated into event-based hydrological models to improve the calibration of model parameters. Second, rain gauge and radar precipitation estimates' performances were compared for hydrological modeling in an urban watershed to assess radar QPE's effects on streamflow simulation accuracy. Third, flood extent maps were produced using coupled hydrological--hydraulic models integrated within the Hydrologic Engineering Center Real-Time Simulation (HEC-RTS) framework. It is shown that the bias correction of radar QPEs can enhance the hydrological model calibration. The radar--gauge merging obtained a Kling--Gupta efficiency, modified peak flow criterion, Nash--Sutcliffe efficiency, and volume error improvement of about +0.42, +0.12, +0.78, and -0.23, respectively, for S-band and +0.64, +0.36, +1.12, and -0.34, respectively, for C-band radar QPEs. Merged radar QPEs are also helpful in running hydrological models calibrated using gauge data. The HEC-RTS framework can be used to produce flood forecast maps using the bias-corrected radar QPEs. Therefore, radar rainfall estimates could be efficiently used to forecast floods in urbanized areas for effective flood management and mitigation. Canadian flood forecasting systems could be efficiently updated by integrating bias-corrected radar QPEs to simulate streamflow and produce flood inundation maps. [ABSTRACT FROM AUTHOR]

Soil moisture anomalies underpin a number of critical hydrological phenomena with socioeconomic consequences, yet systematic studies of soil moisture predictability are limited. Here, we use a data-adaptive technique, Linear Inverse Modeling, which has proved useful as an indication of predictability in other fields, to investigate the predictability of soil moisture in northern California. This approach yields a model of soil moisture at 10 stations in the region, with results that indicate the possibility of skillful forecasts at each for lead times of 1–2 weeks. An important advantage of this model is the a priori identification of forecasts of opportunity—conditions under which the model’s forecasts may be expected to have particularly high skill. Given that forecast errors (and inversely, their skill) can be estimated in advance, these findings have the potential to greatly increase the utility of soil moisture forecasts for practical applications including drought and flood forecasting. [ABSTRACT FROM AUTHOR]

Microtopography on a building roof will direct rainfall from roofs to the ground through downspouts and transform the rainfall spatial distribution from plane to points. However, the issues on whether and how the building-induced rainfall redistribution (BIRR) influences hydrologic responses are still not well understood despite the numerous downspouts in the urban area. Hence, this study brings the roof layer into a grid-based urban hydrologic model (gUHM) to quantitatively evaluate the impacts of BIRR, aiming to enhance the understanding of building effects in urban hydrology and subsequently to identify the necessity of incorporating BIRR into flood forecasting. Nine land development strategies and 27 rainfall conditions are considered herein to characterize the changing circumstance. Results indicate that the impacts of BIRR depend on multiple circumstance factors and are nonnegligible in urban hydrology. The BIRR causes not only bidirectional impacts on the hydrologic characteristic values (e.g., peak flow and runoff volume) but also an obvious alteration of the hydrograph. Overall, the BIRR tends to increase the peak flow, and more importantly, the impact will be aggravated by the increase of rainfall intensity with the maximum relative error of peak flow approaching 10%. This study contributes to a better understanding of building effects on urban hydrology and a step forward to reduce the uncertainty in urban flood warnings. [ABSTRACT FROM AUTHOR]

The partitioning of rain and snow during atmospheric river (AR) storms is a critical factor in flood forecasting, water resources planning, and reservoir operations. Forecasts of atmospheric rain–snow levels from December 2016 to March 2017, a period of active AR landfalls, are evaluated using 19 profiling radars in California. Three forecast model products are assessed: a global forecast model downscaled to 3-km grid spacing, 4-km river forecast center operational forecasts, and 50-km global ensemble reforecasts. Model forecasts of the rain–snow level are compared with observations of rain–snow melting-level brightband heights. Models produce mean bias magnitudes of less than 200 m across a range of forecast lead times. Error magnitudes increase with lead time and are similar between models, averaging 342 m for lead times of 24 h or less and growing to 700–800 m for lead times of greater than 144 h. Observed extremes in the rain–snow level are underestimated, particularly for warmer events, and the magnitude of errors increases with rain–snow level. Storms with high rain–snow levels are correlated with larger observed precipitation rates in Sierra Nevada watersheds. Flood risk increases with rain–snow levels, not only because a greater fraction of the watershed receives rain, but also because warmer storms carry greater water vapor and thus can produce heavier precipitation. The uncertainty of flood forecasts grows nonlinearly with the rain–snow level for these reasons as well. High rain–snow level ARs are a major flood hazard in California and are projected to be more prevalent with climate warming. [ABSTRACT FROM AUTHOR]

The goal of the National Oceanic and Atmospheric Administration's (NOAA) Warn-on-Forecast (WoF) program is to provide frequently updating, probabilistic model guidance that will enable National Weather Service (NWS) forecasters to produce more continuous communication of hazardous weather threats (e.g., heavy rainfall, flash floods, damaging wind, large hail, and tornadoes) between the watch and warning temporal and spatial scales. To evaluate the application of this WoF concept for probabilistic short-term flash flood prediction, the 0-3-h rainfall forecasts from NOAA National Severe Storms Laboratory's (NSSL) experimental WoF System (WoFS) were integrated as the forcing to the NWS operational hydrologic modeling core within the Flooded Locations and Simulated Hydrographs (FLASH) system. Initial assessment of the potential impacts of probabilistic short-term flash flood forecasts from this coupled atmosphere-hydrology (WoFS-FLASH) modeling system were evaluated in the 2018 Hydrometeorology Testbed Multi-Radar Multi-Sensor Hydrology experiment held in Norman, Oklahoma. During the 3-week experiment period, a total of nine NWS forecasters analyzed three retrospective flash flood events in archive mode. This study will describe specifically what information participants extracted from the WoFS-FLASH products during these three archived events, and how this type of information is expected to impact operational decision-making processes. Overall feedback from the testbed participants' evaluations show promise for the coupled NSSL WoFS-FLASH system probabilistic flash flood model guidance to enable earlier assessment and detection of flash flood threats and to advance the current warning lead time for these events. [ABSTRACT FROM AUTHOR]

The Surface Water and Ocean Topography (SWOT) mission will measure water surface elevations and inundation extents of rivers of the world but with limited temporal sampling. By comparing flood location and duration of 4,664 past flood events recorded by the Dartmouth Flood Observatory to SWOT's orbit ephemeris, we estimate that SWOT would have seen 55% of these, with higher probabilities associated with more extreme events and with those that displaced more than 10,000 people. However, SWOT measurements will exhibit uneven temporal sampling and may require a combination of data obtained at different times to accurately characterize large events. This is illustrated using recent flooding in the United States, in eastern Iowa and in Houston and surrounding areas from Hurricane Harvey. SWOT data have significant potential to improve flood forecasting models by offering data needed to enhance flow routing modeling, provided that users can overcome the potential hurdles associated with its temporal and spatial sampling characteristics. Plain Language Summary: The Surface Water and Ocean Topography (SWOT) satellite mission will simultaneously measure water surface elevations and inundated areas for the Earth's land surface. Such information can be valuable for improving flood models and their calibration; however, SWOT temporal sampling will be limited, with most locations in the world being seen once every 7 to 10 days, which may cause it to miss floods. Using a record of global flood information, including duration and location, compiled by the Dartmouth Flood Observatory and the expected satellite orbit, we estimated that, if already operational, SWOT would have collected at least one measurement over 55% of these events. We illustrate SWOT data coverage using flood inundation maps generated for flooding in eastern Iowa (2008) and in Houston and surrounding areas, Texas, caused by Hurricane Harvey (2017). Due to the novelty of this kind of hydrological information, particularly in the way SWOT samples rivers in time and space, early engagement of potential users may be instrumental to maximize the utility of this open source of worldwide observations of rivers, lakes, and inundated land. Key Points: SWOT temporal and spatial observation capabilities and limitations are evaluated using recent floods in eastern Iowa and from Hurricane HarveyDuring its life cycle, SWOT may observe hundreds of flood events, including over data‐scarce regionsDartmouth Flood Observatory data suggest that more destructive floods tend to last longer and are more likely to be observed by SWOT [ABSTRACT FROM AUTHOR]

The use of quantitative precipitation forecast (QPF) in hydrologic forecasting is commonplace, but QPF is subject to considerable error. When QPF is included as a model forcing in the hydrological forecast process, significant error propagates through the hydrologic predictions. Two questions arise: 1) are the resulting observed hydrologic forecast errors sufficiently large to suggest the use of zero QPF in the forecast process, and 2) if the use of nonzero QPF is indicated, how many periods (hours) of QPF (1, 6, 12, ..., 72 h) should be used? Also, do forecast conditions exist under which the use of QPF should be different? This study presents results from two real-time hydrologic forecast experiments, focused on the NOAA/NWS Ohio River Forecast Center (OHRFC). The experiments rely on forecasts from subbasins at 39 forecast point locations, ranging in drainage area, geographic location within the Ohio River Valley, and watershed response time. Results from an experiment, spanning all flow ranges, for the 10 August 2007–31 August 2009 period, show that nonzero QPF produces smaller hydrologic forecast error than zero QPF. A second experiment, 23 January 2009–15 September 2010, suggests that QPF should be limited to 6–12-h duration for flood forecasts. Beyond 12 h, hydrologic forecast error increases substantially across all forecast ranges, but errors are much larger for flood forecasts. Increased durations of QPF produce smaller forecast error than shorter QPF durations only for nonflood forecasts. Experimental results are shown to be consistent with NWS April 2001–October 2016 forecast verification statistics for the OHRFC. [ABSTRACT FROM AUTHOR]

This study presents findings from a real-time forecast experiment that compares legacy deterministic hydrologic stage forecasts to ensemble mean and median stage forecasts from the NOAA/NWS Meteorological Model-Based Ensemble Forecast System (MMEFS). The NOAA/NWS Ohio River Forecast Center (OHRFC) area of responsibility defines the experimental region. Real-time forecasts from subbasins at 54 forecast point locations, ranging in drainage area, geographic location within the Ohio River valley, and watershed response time serve as the basis for analyses. In the experiment, operational hydrologic forecasts, with a 24-h quantitative precipitation forecast (QPF) and forecast temperatures, are compared to MMEFS hydrologic ensemble mean and median forecasts, with model forcings from the NOAA/NWS National Centers for Environmental Prediction (NCEP) North American Ensemble Forecast System (NAEFS), over the period from 30 November 2010 through 24 May 2012. Experiments indicate that MMEFS ensemble mean and median forecasts exhibit lower errors beginning at about lead time 90 h when forecasts at all locations are aggregated. With fast response basins that peak at ≤24 h, ensemble mean and median forecasts exhibit lower errors much earlier, beginning at about lead time 36 h, which suggests the viability of using MMEFS ensemble forecasts as an alternative to OHRFC legacy forecasts. Analyses show that ensemble median forecasts generally exhibit smaller errors than ensemble mean forecasts for all stage ranges. Verification results suggest that OHRFC MMEFS NAEFS ensemble forecasts are reasonable, but needed improvements are identified. [ABSTRACT FROM AUTHOR]

This paper describes the conceptual framework and software components of the automated hydrological monitoring system (AHMS), developed as a part of the project aimed at recovery, modernization, and development of the hydrometeorological network and hydrological forecasting system in the Amur basin in Russia. AHMS information technology platform provides sustainable functioning of the observation network, data exchange (within regional hydrometeorological state agencies), and interaction with external information systems. [ABSTRACT FROM AUTHOR]

An accurate, high-resolution precipitation estimation based on rain gauge and radar observations is essential in various meteorological applications. Although numerous studies have demonstrated the effectiveness of merging two information sources rather than using separate sources, approaches that simultaneously consider the local radar reflectivity, the neighborhood rain gauge observations, and the temporal information are much less common. In this paper, we present a new framework for real-time quantitative precipitation estimation (QPE). By formulating the QPE as a continuous conditional random field (CCRF) learning problem, the spatiotemporal correlations of precipitation can be explored more thoroughly. Based on the CCRF, we further improve the accuracy of the precipitation estimation by introducing geographical and temporal attention. Specifically, we first present a data-driven weighting scheme to merge the first law of geography into the proposed framework, and hence, the neighborhood sample closer to the estimated grid can receive more attention. Second, the temporal attention penalizes the similarity between two adjacent timestamps via the discrepancy of two-view estimates, which can model the local temporal consistency and tolerate some drastic changes. A sufficient evaluation is conducted on 11 rainfall processes that occurred in 2015, and the results confirm the advantage of our proposal for real-time precipitation estimation. [ABSTRACT FROM AUTHOR]