Abstract
Computational hydrological models and simulations are fundamental pieces of the workflow of contemporary hydroscience research, education, and professional engineering activities. In support ...of hydrological modelling efforts, web-enabled tools for data processing, storage, computation, and visualization have proliferated. Most of these efforts rely on server resources for computation and data tasks and client-side resources for visualization. However, continued advancements of in-browser, client-side compute performance present an opportunity to further leverage client-side resources. Towards this end, we present an operational rainfall-runoff model and simulation engine running entirely on the client side using the JavaScript programming language. To demonstrate potential uses, we also present an easy-to-use in-browser interface designed for hydroscience education. Although the use case presented here is self-contained, the core technologies can extend to leverage multi-core processing on single machines and parallelization capabilities of multiple clients or JavaScript-enabled servers. These possibilities suggest that client-side hydrological simulation can play a central role in a dynamic, interconnected ecosystem of web-ready hydrological tools.
Regional Distributed Hydrological models are being adopted around the world for prediction of streamflow fluctuations and floods. However, the details of the hydraulic geometry of the channels in the ...river network (cross sectional geometry, slope, drag coefficients, etc.) are not always known, which imposes the need for simplifications based on scaling laws and their prescription. We use a distributed hydrological model forced with radar-derived rainfall fields to test the effect of spatial variations in the scaling parameters of Hydraulic Geometric (HG) relationships used to simplify routing equations. For our experimental setup, we create a virtual watershed that obeys local self-similarity laws for HG and attempt to predict the resulting hydrographs using a global self-similar HG parameterization. We find that the errors in the peak flow value and timing are consistent with the errors that are observed when trying to replicate actual observation of streamflow. Our results provide evidence that local self-similarity can be a more appropriate simplification of HG scaling laws than global self-similarity.
•Proposition of simple benchmarks for real-time streamflow forecasting.•Use of basic hydrologic insights for the development of benchmarks.•Proposed benchmarks demonstrate good performance according ...to several metrics.•Benchmarks useful for developers of physics-based and data-based hydrologic models.
In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improvements in the performance metrics from the more complicated approaches. The benchmarks include temporal and spatial persistence, persistence corrected for baseflow and streamflow, as well as river distance weighted runoff obtained from space-time distributed rainfall. In the development of the benchmarks, we use basic hydrologic insights such as flow aggregation by the river network, scale-dependence in basin response, streamflow partitioning into quick flow and baseflow, water travel time, and rainfall averaging by the basin width function. The study uses 140 streamflow gauges in Iowa that cover a range of basin scales between 7 and 37,000 km2. The data cover 17 years. This work demonstrates that the proposed benchmarks can provide good performance according to several commonly used metrics. For example, streamflow forecasting at half of the test locations across years achieves a Kling-Gupta Efficiency (KGE) score of 0.6 or higher at one-day ahead lead time, and 20% of cases reach the KGE of 0.8 or higher. The proposed benchmarks are easy to implement and should prove useful for developers of data-based as well as physics-based hydrologic models and real-time data assimilation techniques.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The authors examine the impact of assimilating satellite-based soil moisture estimates on real-time streamflow predictions made by the distributed hydrologic model HLM. They use SMAP (Soil Moisture ...Active Passive) and SMOS (Soil Moisture Ocean Salinity) data in an agricultural region of the state of Iowa in the central U.S. They explore three different strategies for updating model soil moisture states using satellite-based soil moisture observations. The first is a “hard update” method equivalent to replacing the model soil moisture with satellite observed soil moisture. The second is Ensemble Kalman Filter (EnKF) to update the model soil moisture, accounting for modeling and observational errors. The third strategy introduces a time-dependent error variance model of satellite-based soil moisture observations for perturbation of EnKF. The study compares streamflow predictions with 131 USGS gauge observations for four years (2015–2018). The results indicate that assimilating satellite-based soil moisture using EnKF reduces predicted peak error compared to that from the open-loop and hard update data assimilation. Furthermore, the inclusion of the time-dependent error variance model in EnKF improves overall streamflow prediction performance. Implications of the study are useful for the application of satellite soil moisture for operational real-time streamflow forecasting.
The authors explore the uncertainty implied in the estimation of changes in flood frequency due to climate change at the basins of the Cedar River and Skunk River in Iowa, United States. The study ...focuses on the influence of climate change on the 100-year flood, used broadly as a reference flow for civil engineering design. Downscaled rainfall projections between 1960–2099 were used as forcing into a hydrological model for producing discharge projections at locations intersecting vulnerable transportation infrastructure. The annual maxima of the discharge projections were used to conduct flood frequency analyses over the periods 1960–2009 and 1960–2099. The analysis of the period 1960–2009 is a good predictor of the observed flood values for return periods between 2 and 200 years in the studied basins. The findings show that projected flood values could increase significantly in both basins. Between 2009 and 2099, 100-year flood could increase between 47% and 52% in Cedar River, and between 25% and 34% in South Skunk River. The study supports a recommendation for assessing vulnerability of infrastructure to climate change, and implementation of better resiliency and hydraulic design practices. It is recommended that engineers update existing design standards to account for climate change by using the upper-limit confidence interval of the flood frequency analyses that are currently in place.
Iowa and the Nishnabotna watershed (Iowa), Midwest U.S.
Historically, Iowa and the Midwest have faced floods during the summer season. Some historical floods on record are the 2008 and 2013 floods. ...In March 2019, a meteorological bomb cyclone set the conditions for an unexpected major snow-related flood. This study (1) presents a comprehensive analysis of the March 2019 flood and asses the early-spring peak flows trends, (2) explores the use of a parsimonious hydrological model with a snow component, and (3) validates the model performance for the last 20 years.
The March 2019 event was an extreme flood event that set records on at least 10% of the USGS gauges in Iowa. Moreover, the early spring peak flow analysis showed a significant increasing trend between February and April. In this period, the trend is positive for most gauges, with more than a 30% increase at an annual rate of 4% of the mean yearly peak flow. These findings showed the relevance of snow-detonated floods and their regional understanding. Considering the results' significance, we provided evidence that HLM and a conceptual snow component can represent, forecast, and provide insights regarding snow-detonated events.
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•Snow-detonated peak flows have an increasing trend in Iowa.•The March 2019 flood affected Iowa's West region, highlighting the relevance of snow-detonated events.•The implemented hydrological model represents snow accumulation, melting, and associated streamflow fluctuations.•The model is adequate to analyze snow-related processes and impacts on the region.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The central hypothesis of a nonlinear geophysical flood theory postulates that, given space‐time rainfall intensity for a rainfall‐runoff event, solutions of coupled mass and momentum conservation ...differential equations governing runoff generation and transport in a self‐similar river network produce spatial scaling, or a power law, relation between peak discharge and drainage area in the limit of large area. The excellent fit of a power law for the destructive flood event of June 2008 in the 32,400‐km2 Iowa River basin over four orders of magnitude variation in drainage areas supports the central hypothesis. The challenge of predicting observed scaling exponent and intercept from physical processes is explained. We show scaling in mean annual peak discharges, and briefly discuss that it is physically connected with scaling in multiple rainfall‐runoff events. Scaling in peak discharges would hold in a non‐stationary climate due to global warming but its slope and intercept would change.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
We present a novel approach to determine spatially distributed routing parameters for the distributed hydrological Hillslope Link Model (HLM), an ordinary differential equations‐based streamflow ...forecasting model implemented and tested in Iowa. We being by developing a technique to determine two model parameters that control the channel routing equation in gauged catchments draining less than 1,300 km2. Then, we implement a parameter regionalization methodology using machine learning classification techniques and a bootstrap procedure, in which we trained 400 Random Forests (RFs) using physical and geomorphological features for classification. We made a regional interpolation using an ensemble of selected RF realizations that exhibited the best performance. We used as benchmark of our results a more straightforward interpolation technique based on USGS Hydrological Units Codes. We performed simulations of the HLM over the entire state of Iowa between 2012 and 2018 using the two regionalization methods, comparing them to the operational model used by the Iowa Flood Center, which applies a single set of parameter values to the entire domain. After evaluating the results at 148 USGS stations, the Random‐Forest approach captures the value of observed peak flows more precisely without losing performance in terms of the Kling Gupta Efficiency index. The improvements obtained using our proposed strategy that uses data, hydrological modeling, and a machine learning technique to identify and regionalize routing parameters are modest, indicating that the parameters that control the rainfall‐runoff transformation dominate uncertainty in our flood forecast model.
Plain Language Summary
We present a novel strategy to estimate the routing parameters for a regional hydrological model that forecasts floods in Iowa. In the strategy, we use a model setup that dynamically controls runoff and a machine learning technique. We applied the runoff controlled modeling setup to estimate the routing parameters over 48 gauged small watersheds in Iowa. Then, we trained multiple random forests using hydrological features and the estimated routing parameters of each small gauged watershed. After the training, we regionalize the routing parameters using the results from the random forests with the best performance. We also interpolate the routing parameters using the USGS Hydrological Unit Codes (HUCs). We ran Hillslope Link Model between 2012 and 2018 using the random forest ensemble and HUCs interpolations and a set of fixed parameters. Using the random forest ensemble, we found a set of parameters that follow the Iowa landforms and improves estimation of the peak flow magnitude and timing.
Key Points
The distribution of routing‐related parameters in the Hillslope Link Model (HLM) has a significant impact on estimating peak flow magnitude and timing
We developed a runoff controller that separates hillslope and routing processes allowing the identification of routing‐related parameters
Our machine learning procedure obtained routing parameters that match the Iowa landforms and improved HLM peak flow estimation
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DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
This study evaluates the potential for a newly proposed non-linear subsurface flux equation to improve the performance of the hydrological Hillslope Link Model (HLM). The equation contains parameters ...that are functionally related to the hillslope steepness and the presence of tile drainage. As a result, the equation provides better representation of hydrograph recession curves, hydrograph timing, and total runoff volume. The authors explore the new parameterization’s potential by comparing a set of diagnostic and prognostic setups in HLM. In the diagnostic approach, they configure 12 different scenarios with spatially uniform parameters over the state of Iowa. In the prognostic case, they use information from topographical maps and known locations of tile drainage to distribute parameter values. To assess performance improvements, they compare simulation results to streamflow observations during a 17-year period (2002–2018) at 140 U.S. Geological Survey (USGS) gauging stations. The operational setup of the HLM model used at the Iowa Flood Center (IFC) serves as a benchmark to quantify the overall improvement of the model. In particular, the new equation provides better representation of recession curves and the total streamflow volumes. However, when comparing the diagnostic and prognostic setups, the authors found discrepancies in the spatial distribution of hillslope scale parameters. The results suggest that more work is required when using maps of physical attributes to parameterize hydrological models. The findings also demonstrate that the diagnostic approach is a useful strategy to evaluate models and assess changes in their formulations.
The concept of doing hydrology backwards, introduced in the literature in the last decade, relies on the possibility to invert the equations relating streamflow fluctuations at the catchment outlet ...to estimated hydrological forcings throughout the basin. In this work, we use a recently developed set of equations connecting streamflow oscillations at the catchment outlet to baseflow oscillations at the hillslope scale. The hillslope-scale oscillations are then used to infer the pattern of evaporation needed for streamflow oscillations to occur. The inversion is illustrated using two conceptual models of movement of water in the subsurface with different levels of complexity, but still simple enough to demonstrate our approach. Our work is limited to environments where diel oscillations in streamflow are a strong signal in streamflow data. We demonstrate our methodology by applying it to data collected in the Dry Creek Experimental Watershed in Idaho and show that the hydrology backwards principles yield results that are well within the order of magnitude of daily evapotranspiration fluctuations. Our analytic results are generic and they encourage the development of experimental campaigns to validate integrated hydrological models and test implicit parameterization assumptions.
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