Floods are one of the most devastating natural hazards across the world, with India being one of the worst affected countries in terms of fatalities and economic damage. In-depth research is required ...in order to understand the complex hydrometeorological and geomorphic factors at play and design solutions to minimize the impact of floods. But the existence of a historical inventory of floods is imperative to promote such research endeavors. Though, a few global inventories exist, they lack the spatio-temporal fidelity necessary to make them useful for computational research due to reasons such as concentrating exclusively on large floods, limited temporal scope, non-standard data formats. Therefore, there is an urgent need for developing a new database that combines data from global and hitherto-underutilized local datasets using an extensible and common schema. This paper describes the ongoing effort of building the India Flood Inventory (IFI), which is the first freely available, analysis-ready geospatial dataset over the region with detailed qualitative and quantitative information regarding floods, including spatial extents. The paper outlines the methodology that has been adopted as well as some preliminary findings using the data contained in this inventory. This dataset is expected to advance the understanding of flood processes in the worst affected region of the world.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
•Sensitivity of streamflow simulation to hydrologic modeling resolution is assessed.•Two radar-based precipitation input of different resolution are used for comparison.•Streamflow prediction ...improved with higher resolution up to about 500m and 15min.•There are trade-offs among resolution and timeliness and accuracy of prediction.
Urban flash flooding is a serious problem in large, highly populated areas such as the Dallas–Fort Worth Metroplex (DFW). Being able to monitor and predict flash flooding at a high spatiotemporal resolution is critical to providing location-specific early warnings and cost-effective emergency management in such areas. Under the idealized conditions of perfect models and precipitation input, one may expect that spatiotemporal specificity and accuracy of the model output improve as the resolution of the models and precipitation input increases. In reality, however, due to the errors in the precipitation input, and in the structures, parameters and states of the models, there are practical limits to the model resolution. In this work, we assess the sensitivity of streamflow simulation in urban catchments to the spatiotemporal resolution of precipitation input and hydrologic modeling to identify the resolution at which the simulation errors may be at minimum given the quality of the precipitation input and hydrologic models used, and the response time of the catchment. The hydrologic modeling system used in this work is the National Weather Service (NWS) Hydrology Laboratory’s Research Distributed Hydrologic Model (HLRDHM) applied at spatiotemporal resolutions ranging from 250m to 2km and from 1min to 1h applied over the Cities of Fort Worth, Arlington and Grand Prairie in DFW. The high-resolution precipitation input is from the DFW Demonstration Network of the Collaborative Adaptive Sensing of the Atmosphere (CASA) radars. For comparison, the NWS Multisensor Precipitation Estimator (MPE) product, which is available at a 4-km 1-h resolution, was also used. The streamflow simulation results are evaluated for 5 urban catchments ranging in size from 3.4 to 54.6km2 and from about 45min to 3h in time-to-peak in the Cities of Fort Worth, Arlington and Grand Prairie. The streamflow observations used in evaluation were obtained from water level measurements via rating curves derived from 1-D steady-state non-uniform hydraulic modeling. The results indicate that a spatiotemporal resolution of 500m and 15min or higher is a good choice for streamflow prediction using HLRDHM and CASA QPE in the study area, but that, due to the nonlinear accretion of random errors in QPE and imperfect model dynamics, there are trade-offs to consider among resolution, timeliness of prediction and prediction accuracy.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Predicting major floods during extreme rainfall events remains an important challenge. Rapid changes in flows over short timescales, combined with multiple sources of model error, makes it difficult ...to accurately simulate intense floods. This study presents a general data assimilation framework that aims to improve flood predictions in channel routing models. Hurricane Florence, which caused catastrophic flooding and damages in the Carolinas in September 2018, is used as a case study. The National Water Model (NWM) configuration of the WRF-Hydro modeling framework is interfaced with the Data Assimilation Research Testbed (DART) to produce ensemble streamflow forecasts and analyses. Instantaneous streamflow observations from 107 United States Geological Survey (USGS) gauges are assimilated for a period of 1 month.
Snow influences land–atmosphere interactions in snow-dominated areas, and snow melt contributes to basin streamflows. However, estimating snowpack properties such as the snow depth (SD) and snow ...water equivalent (SWE) from land surface model simulations remains a challenge. This is, in part, due to uncertainties in the atmospheric forcing variables, which propagate into hydrological model predictions. This study implements the Weather Research and Forecasting (WRF)-Hydro framework with the Noah-Multiparameterization (Noah-MP) land surface model in the NOAA’s National Water Model (NWM) version 2.0 configuration to estimate snow in a single column and subsequently the streamflow across the Aroostook River’s sub-basins in Maine for water years (WY) 2014–2016. This study evaluates how differences between two atmospheric forcing datasets, the North American Land Data Assimilation version 2 (NLDAS-2) and in situ (Station), translate into differences in the simulation of snow. NLDAS-2 was used as the meteorological forcing in the retrospective NWM 2.0 simulations. The results from the single-column study showed that differences in the simulated SWE and SD were linked to differences in the 2 m air temperature (T2m), which influenced the precipitation partitioning of rain and snow, as parameterized in Noah-MP. The negative mean bias of −0.7 K (during the accumulation period) in T2m for NLDAS-2, compared to the Station forcing, was a major factor that contributed to the positive mean bias of +52 mm on average in the peak SWE in the NLDAS-2-forced Noah-MP simulation during the study period. The higher T2m values at the Station led to higher sensible heat fluxes towards the snowpack, which led to a higher amount of net energy at the snow’s surface and melt events during the accumulation season in Station-forced Noah-MP simulations. The results from the retrospective NWM version 2.0′s simulation in the basin showed that the streamflow estimates were closer to the United States Geological Survey gage observations at the two larger sub-basins (NSE = 0.9), which were mostly forested, compared to the two smaller sub-basins (NSE ≥ 0.4), which had more agricultural land-use. This study also showed that the spring snowmelt timing was captured quite well by the timing of the decline in the simulated SWE and SD, providing an early indication of melt in most sub-basins. The simulated fractional snow cover area (fSCA) however provided less information about the changes in snow or onset of snowmelt as it was mostly binary (full snow cover in winter), which differed from the more realistic fSCA values shown by the Moderate Resolution Imaging Spectroradiometer.
A soil erosion and sediment transport model (WRF-Hydro-Sed) is introduced to WRF-Hydro. As a process-based, fully distributed soil erosion model, WRF-Hydro-Sed accounts for both overland and channel ...processes. Model performance is evaluated using observed rain gauge, streamflow, and sediment concentration data during rainfall events in the Goodwin Creek Experimental Watershed in Mississippi, USA. Both streamflow and sediment yield can be calibrated and validated successfully at a watershed scale during rainfall events. Further discussion reveals the model’s uncertainty and the applicability of calibrated hydro- and sediment parameters to different events. While an intensive calibration over multiple events can improve the model’s performance to a certain degree compared with single event-based calibration, it might not be an optimal strategy to carry out considering the tremendous computational resources needed.
Snowmelt provides a reliable water resource for meeting domestic, agricultural, industrial and hydropower demands. Consequently, estimating the available snow water equivalent is essential for water ...resource management of snowy regions. Due to the spatiotemporal variability of the snowfall pattern in mountainous areas and difficult access to high altitudes areas, snow measurement is one of the most challenging hydro-meteorological data collection efforts. Development of an optimum snow measurement network is a complex task that requires integration of meteorological, hydrological, physiographical and economic studies. In this study, site selection of snow measurement stations is carried out through an integrated process using observed snow course data and analysis of historical snow cover images from National Oceanic Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) at both regional and local scales. Several important meteorological and hydrological factors, such as monthly and annual rainfall distribution, spatial distribution of average frequency of snow observation (FSO) for two periods of snow falling and melting season, as well as priority contribution of sub-basins to annual snowmelt runoff are considered for selecting optimum station network. The FSO maps representing accumulation of snowfall during falling months and snowpack persistence during melting months are prepared in the GIS based on NOAA-AVHRR historical snow cover images. Basins are partitioned into 250 m elevation intervals such that within each interval, establishment of new stations or relocation/removing of the existing stations were proposed. The decision is made on the basis of the combination of meteorological, hydrological and satellite information. Economic aspects and road access constraints are also considered in determining the station type. Eventually, for the study area encompassing a number of large basins in southwest of Iran, several new stations and relocation of some existing stations are proposed.
In the face of escalating instances of inland and flash flooding spurred by intense rainfall and hurricanes, the accurate prediction of rapid streamflow variations has become imperative. Traditional ...data assimilation methods face challenges during extreme rainfall events due to numerous sources of error, including structural and parametric model uncertainties, forcing biases, and noisy observations. This study introduces a cutting-edge hybrid ensemble and optimal interpolation data assimilation scheme tailored to precisely and efficiently estimate streamflow during such critical events. Our hybrid scheme uses an ensemble-based framework, integrating the flow-dependent background streamflow covariance with a climatological error covariance derived from historical model simulations. The dynamic interplay (weight) between the static background covariance and the evolving ensemble is adaptively computed both spatially and temporally. By coupling the National Water Model (NWM) configuration of the WRF-Hydro modeling system with the Data Assimilation Research Testbed (DART), we evaluate the performance of our hybrid prediction system using two impactful case studies: (1) West Virginia's flash flooding event in June 2016 and (2) Florida's inland flooding during Hurricane Ian in September 2022. Our findings reveal that the hybrid scheme substantially outperforms its ensemble counterpart, delivering enhanced streamflow estimates for both low and high flow scenarios, with an improvement of up to 50 %. This heightened accuracy is attributed to the climatological background covariance, mitigating bias and augmenting ensemble variability. The adaptive nature of the hybrid algorithm ensures reliability, even with a very small time-varying ensemble. Moreover, this innovative hybrid data assimilation system propels streamflow forecasts up to 18 h in advance of flood peaks, marking a substantial advancement in flood prediction capabilities.
We adapted the WRF‐Hydro modelling system to Hurricane Florence (2018) and performed a series of diagnostic experiments to assess the influence of initial soil moisture and precipitation magnitude on ...flood simulation over the Cape Fear River basin in the United States. Model results suggest that: (1) The modulation effect of initial soil moisture on the flood peak is non‐linear and weakens as precipitation magnitude increases. There is a threshold value of the soil saturation, below and above which the sensitivity of flood peak to the soil moisture differentiates substantially; (2) For model spin‐up, streamflow needs longer time to reach the ‘practical’ equilibrium (10%) than the soil moisture and latent heat flux. The model uncertainty from spin‐up can propagate through the hydrometeorological modelling chain and get amplified into the flood peak; (3) For ensemble flood modelling with a hydrometeorological system, modelling uncertainty is dominated by the precipitation forecast. Spin‐up induced uncertainty can be minimized once the model reaches the ‘practical’ equilibrium.
Bias from initial soil moisture propagate into model simulation and get amplified into simulated flood peak.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
•MLEF outperform EnKF consistently over varying conditions.•MLEF is not very sensitive to modeling of observational errors.•Heteroscedastic modeling of observation errors does not improve over ...homoscedastic.•The soil moisture ensemble results are quite different between the two.
Various data assimilation (DA) methods have been used and are being explored for use in operational streamflow forecasting. For ensemble forecasting, ensemble Kalman filter (EnKF) is an appealing candidate for familiarity and relative simplicity. EnKF, however, is optimal in the second-order sense, only if the observation equation is linear. As such, without an iterative approach, EnKF may not be appropriate for assimilating streamflow data for updating soil moisture states due to the strong nonlinear relationships between the two. Maximum likelihood ensemble filter (MLEF), on the other hand, is not subject to the above limitation. Being an ensemble extension of variational assimilation (VAR), MLEF also offers a strong connection with the traditional single-valued forecast process through the control, or the maximum likelihood, solution. In this work, we apply MLEF and EnKF as a fixed lag smoother to the Sacramento (SAC) soil moisture accounting model and unit hydrograph (UH) for assimilation of streamflow, mean areal precipitation (MAP) and potential evaporation (MAPE) data for updating soil moisture states. For comparative evaluation, three experiments were carried out. Comparison between homoscedastic vs. heteroscedastic modeling of selected statistical parameters for DA indicates that heteroscedastic modeling does not improve over homoscedastic modeling, and that homoscedastic error modeling with sensitivity analysis may suffice for application of MLEF for soil moisture updating using streamflow data. Comparative evaluation with respect to the model errors associated with soil moisture dynamics, the ensemble size and the number of streamflow observations assimilated per cycle showed that, in general, MLEF outperformed EnKF under varying conditions of observation and model errors, and ensemble size, and that MLEF performed well with an ensemble size as small as 5 while EnKF required a much larger ensemble size to perform closely to MLEF. Also, MLEF was not very sensitive to the uncertainty parameters and performed reasonably well over relatively wide ranges of parameter settings, an attribute desirable for operational applications where accurate estimation of such parameters is often difficult.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•Multiple radar-based quantitative precipitation estimates (QPE) are fused for improved accuracy.•Multiple procedures of varying complexity and parameter requirements are evaluated.•Only Simple ...Estimation (SE) improves over ingredient QPEs at all spatiotemporal scales.•SE is an algorithmically simple and computationally inexpensive post processor to QPE operations.
For monitoring and prediction of water-related hazards in urban areas such as flash flooding, high-resolution hydrologic and hydraulic modeling is necessary. Because of large sensitivity and scale dependence of rainfall–runoff models to errors in quantitative precipitation estimates (QPE), it is very important that the accuracy of QPE be improved in high-resolution hydrologic modeling to the greatest extent possible. With the availability of multiple radar-based precipitation products in many areas, one may now consider fusing them to produce more accurate high-resolution QPE for a wide spectrum of applications. In this work, we formulate and comparatively evaluate four relatively simple procedures for such fusion based on Fisher estimation and its conditional bias-penalized variant: Direct Estimation (DE), Bias Correction (BC), Reduced-Dimension Bias Correction (RBC) and Simple Estimation (SE). They are applied to fuse the Multisensor Precipitation Estimator (MPE) and radar-only Next Generation QPE (Q2) products at the 15-min 1-km resolution (Experiment 1), and the MPE and Collaborative Adaptive Sensing of the Atmosphere (CASA) QPE products at the 15-min 500-m resolution (Experiment 2). The resulting fused estimates are evaluated using the 15-min rain gauge observations from the City of Grand Prairie in the Dallas–Fort Worth Metroplex (DFW) in north Texas. The main criterion used for evaluation is that the fused QPE improves over the ingredient QPEs at their native spatial resolutions, and that, at the higher resolution, the fused QPE improves not only over the ingredient higher-resolution QPE but also over the ingredient lower-resolution QPE trivially disaggregated using the ingredient high-resolution QPE. All four procedures assume that the ingredient QPEs are unbiased, which is not likely to hold true in reality even if real-time bias correction is in operation. To test robustness under more realistic conditions, the fusion procedures were evaluated with and without post hoc bias correction of the ingredient QPEs.
The results show that only SE passes the evaluation criterion consistently. The performance of DE and BC are generally comparable; while DE is more attractive for computational economy, BC is more attractive for reducing occurrences of negative estimates. The performance of RBC is poor as it does not account for magnitude-dependent biases in the QPE products. SE assumes that the higher-resolution QPE product is skillful in capturing spatiotemporal variability of precipitation at its native resolution, and that the lower-resolution QPE product provides skill at its native resolution. While the above assumptions may not always be met, the simplicity and robustness observed in this work make SE an extremely attractive choice as a simple post-processor to the QPE process. Also, unlike the other procedures considered in this work, it is extremely easy to update the statistical parameters of SE in real time, similarly to the real-time bias correction currently used in MPE, for improved performance via self-learning.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
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