Streamflow timing errors (in the units of time) are rarely explicitly evaluated but are useful for model evaluation and development. Wavelet-based approaches have been shown to reliably quantify ...timing errors in streamflow simulations but have not been applied in a systematic way that is suitable for model evaluation. This paper provides a step-by-step methodology that objectively identifies events, and then estimates timing errors for those events, in a way that can be applied to large-sample, high-resolution predictions. Step 1 applies the wavelet transform to the observations and uses statistical significance to identify observed events. Step 2 utilizes the cross-wavelet transform to calculate the timing errors for the events identified in step 1; this includes the diagnostic of model event hits, and timing errors are only assessed for hits. The methodology is illustrated using real and simulated stream discharge data from several locations to highlight key method features. The method groups event timing errors by dominant timescales, which can be used to identify the potential processes contributing to the timing errors and the associated model development needs. For instance, timing errors that are associated with the diurnal melt cycle are identified. The method is also useful for documenting and evaluating model performance in terms of defined standards. This is illustrated by showing the version-over-version performance of the National Water Model (NWM) in terms of timing errors.
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.
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.
Geodetic‐quality GPS systems can be used to measure average snow depth in the ∼1000 m2 area around the GPS antenna, a sensing footprint size intermediate between in situ and satellite observations. ...SWE can be calculated from density estimates modeled on the GPS‐based snow depth time series. We assess the accuracy of GPS‐based snow depth, density, and SWE data at 18 GPS sites via comparison to manual observations. The manual validation survey was completed around the time of peak accumulation at each site. Daily snow depth derived from GPS reflection data is very similar to the mean snow depth measured manually in the ∼1000 m2 scale area around each antenna. This comparison spans site‐averaged depths from 0 to 150 cm. The GPS depth data exhibit a small negative bias (−6 cm) across this range of snow depths. Errors tend to be smaller at sites with more usable GPS ground tracks. Snow bulk density is modeled using the GPS snow depth time series and model parameters are estimated from nearby SNOTEL sites. Modeled density is within 0.02 g cm−3 of the density measured in a single snow pit at the validation sites, for 12 of 18 comparisons. GPS‐based depth and modeled density are multiplied to estimate SWE. SWE estimates are very accurate over the range observed at the validation sites, from 0 to 60 cm (R2 = 0.97 and bias = −2 cm). These results show that the near real‐time GPS snow products have errors small enough for monitoring water resources in snow‐dominated basins.
Key Points
GPS‐based snow depth measurements validated at 18 sites near‐peak snow accumulation
SWE is calculated from snow density modeled on GPS‐based depth observations
Near real‐time GPS‐based snow depth and SWE are accurate enough for most applications
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The National Weather Service (NWS) Office of Water Prediction (OWP), in conjunction with the National Center for Atmospheric Research and the NWS National Centers for Environmental Prediction (NCEP) ...implemented version 2.1 of the National Water Model (NWM) into operations in April of 2021. As with the initial version implemented in 2016, NWM v2.1 is an hourly cycling analysis and forecast system that provides streamflow guidance for millions of river reaches and other hydrologic information on high‐resolution grids. The NWM provides complementary hydrologic guidance at current NWS river forecast locations and significantly expands guidance coverage and water budget information in underserved locations. It produces a full range of hydrologic fields, which can be leveraged by a broad cross section of stakeholders ranging from the emergency responder and water resource communities, to transportation, energy, recreation and agriculture interests, to other water‐oriented applications in the government, academic and private sectors. Version 2.1 of the NWM represents the fifth major version upgrade and more than doubles simulation skill with respect to hourly streamflow correlation, Nash Sutcliffe Efficiency, and bias reduction, over its original inception in 2016. This paper will discuss the driving factors underpinning the creation of the NWM, provide a brief overview of the model configuration and performance, and discuss future efforts to improve NWM components and services.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
We propose a reduced-order deep-learning surrogate model for dynamic systems described by time-dependent partial differential equations. This method employs space–time Karhunen–Loève expansions ...(KLEs) of the state variables and space-dependent KLEs of space-varying parameters to identify the reduced (latent) dimensions. Subsequently, a deep neural network (DNN) is used to map the parameter latent space to the state variable latent space.
An approximate Bayesian method is developed for uncertainty quantification (UQ) in the proposed KL-DNN surrogate model. The KL-DNN method is tested for the linear advection–diffusion and nonlinear diffusion equations, and the Bayesian approach for UQ is compared with the deep ensembling (DE) approach, commonly used for quantifying uncertainty in DNN models. It was found that the approximate Bayesian method provides a more informative distribution of the PDE solutions in terms of the coverage of the reference PDE solutions (the percentage of nodes where the reference solution is within the confidence interval predicted by the UQ methods) and log predictive probability. The DE method is found to underestimate uncertainty and introduce bias.
For the nonlinear diffusion equation, we compare the KL-DNN method with the Fourier Neural Operator (FNO) method and find that KL-DNN is 10% more accurate and needs less training time than the FNO method.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Research focuses on observing and predicting spatial distribution of snow depth at the kilometer scale. Observation of spatial snow depth distribution is considered by its estimation from random, ...sparse observations and important factors affecting this estimation. Predicting spatial distribution of both snow depth and melt rates begins from simple hypothesis wherein the spatial distribution of snow depth is structured by the spatial distribution of controlling variables. Predictions made by this structured view are evaluated in spatial modeling of peak-accumulation snow depth and applied to spatial distribution of a point-scale, temperature-index model of snowmelt runoff using minimal parameter complexity. High-resolution light detection and ranging (LiDAR) measurements provide a rich backdrop for understanding estimation from sparse observations and developing our structured view of snow distribution. The data are used to illuminate the effects of sample size on estimation skill, the uncertainty in estimation due to random sampling, the effect of model resolution on estimation skill, and the difference between cross-validated skill and skill based on the entire distribution. None of these topics have previously been explored in the literature. The effect of predictor quality is also investigated. LiDAR derived predictors are compared to readily available predictors downloaded from the internet. Hierarchical cluster analysis is used to decompose spatial non-stationarity of snow depth and results match qualitative understanding of the spatial distribution of physical controls. The same methodology is then used to decompose spatial non-stationarity of physical controls and infer patterns of snow depth distribution independent of observations. Even when using readily-available predictors, predicted patterns require at least 100–200 observations to be matched by standard estimation methods. Predicted patterns are then applied to formulate a parameterized spatial distribution of a 1-dimensional, temperature-index model to account for heterogeneity of both snow accumulation and melt. Our new method introduces fewer or comparable parameters as the current subgrid distribution, the areal depletion curve. Given highly uncertain parameter selection in practical application, we demonstrate that our more physically intuitive method virtually always results in significant improvement in simulated streamflow timing when compared to the depletion curve method.
In situ gauge networks are often used in hydrologic model calibration, but these networks are limited or nonexistent in many regions. The upcoming Surface Water Ocean Topography (SWOT) mission ...promises to fill this observation gap by providing discharge estimates for rivers wider than 100 meters. SWOT observation utility for model parameter selection in regions devoid of in situ gauges is assessed using proxy SWOT discharge estimates derived from an observing system simulation experiment and Monte Carlo methods. The sensitivity of the parameter selection to measurement error and observation frequency is also evaluated. Single- and multi-point parameter selection are performed for ten sub-basins within the Susitna and upper Tanana river basins in Alaska. SWOT is expected to observe Alaskan river points 4-7 times per 21-day repeat cycle with 120-km swath coverage. For an expected SWOT measurement error of 35%, parameter estimation is successful for 50% (90%) of sub-basins using single- (multi-) point parameter selection. Decreasing observation frequency to simulate lower latitudes resulted in success for only 10% of midlatitude and tropical sub-basins for single-point selection, whereas multi-point selection was successful in 80% (60%) of midlatitude (tropical) sub-basins. Single-point parameter selection is more sensitive to measurement error than multi-point parameter selection. The results strongly support the use of multi-point over single-point parameter selection, yielding robust results nearly independent of observation frequency. Most importantly, this study suggests SWOT can be used to successfully select hydrologic model parameters in basins without an in situ gauge network.
Melon (
L.) is a phenotypically diverse eudicot diploid (2
= 2
= 24) has climacteric and non-climacteric morphotypes and show wide variation for fruit firmness, an important trait for transportation ...and shelf life. We generated 13,789 SNP markers using genotyping-by-sequencing (GBS) and anchored them to chromosomes to understand genome-wide fixation indices (
) between various melon morphotypes and genomewide linkage disequilibrium (LD) decay. The
between accessions of
and
was 0.23. The
between
and various
accessions was in a range of 0.19-0.53 and between
and
accessions was in a range of 0.21-0.59 indicating sporadic to wide ranging introgression. The EM (Expectation Maximization) algorithm was used for estimation of 1436 haplotypes. Average genome-wide LD decay for the melon genome was noted to be 9.27 Kb. In the current research, we focused on the genome-wide divergence underlying diverse melon horticultural groups. A high-resolution genetic map with 7153 loci was constructed. Genome-wide segregation distortion and recombination rate across various chromosomes were characterized. Melon has climacteric and non-climacteric morphotypes and wide variation for fruit firmness, a very important trait for transportation and shelf life. Various levels of QTLs were identified with high to moderate stringency and linked to fruit firmness using both genome-wide association study (GWAS) and biparental mapping. Gene annotation revealed some of the SNPs are located in β-D-xylosidase, glyoxysomal malate synthase, chloroplastic anthranilate phosphoribosyltransferase, and histidine kinase, the genes that were previously characterized for fruit ripening and softening in other crops.
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