This study evaluates the applicability of numerical weather prediction output supplemented with remote sensing data for near real-time operational estimation of hourly evapotranspiration (ET). Rapid ...Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) systems were selected to provide forcing data for a Penman-Monteith model to calculate the Actual Evapotranspiration (AET) over Iowa. To investigate how the satellite-based remotely sensed net radiation ( R n ) estimates might potentially improve AET estimates, Geostationary Operational Environmental Satellite derived R n (GOES- R n ) data were incorporated into each dataset for comparison with the RAP and HRRR R n -based AET evaluations. The authors formulated a total of four AET models—RAP, HRRR, RAP-GOES, HRRR-GOES, and validated the respective ET estimates against two eddy covariance tower measurements from central Iowa. The implementation of HRRR-GOES for AET estimates showed the best results among the four models. The HRRR-GOES model improved statistical results, yielding a correlation coefficient of 0.8, a root mean square error (mm hr−1) of 0.08, and a mean bias (mm hr−1) of 0.02 while the HRRR only model results were 0.64, 0.09, and 0.04, respectively. Despite limited in situ observational data to fully test a proposed AET estimation, the HRRR-GOES model clearly showed potential utility as a tool to predict AET at a regional scale with high spatio-temporal resolution.
The Iowa Flood Center (IFC) developed a pilot infrastructure to explore rainfall metadata (descriptive statistics) and generate rainfall products over the Iowa domain based on the NEXRAD Level II ...data directly accessible through cloud storage (e.g., Amazon Web Services). Known as IFC-Cloud-NEXRAD, it resembles the Hydro-NEXRAD portal that provided researchers with ready access to NEXRAD radar data. Taking advantage of the cloud storage benefits (unlimited storage and instant access), IFC-Cloud-NEXRAD reduces the common challenges of most data exploration systems, which often lead to massive data acquisition/ingestion and rapid filling of limited system storage. Its map-based interface allows researchers to select a space-time domain of interest, retrieve and visualize pre-calculated rainfall metadata, and generate radar-derived rainfall products. Because the system provides generalized approaches to compute metadata and process data for rainfall estimation, the framework presented in this study would be readily transferrable to other geographic regions and larger scale applications.
•IFC-Cloud-NEXRAD demonstrates utility of the NEXRAD Level II data accessible through the cloud storage.•The system offers metadata search and visualization, as well as rainfall product generation.•The system reduces temporal and spatial restriction arising from the limited system storage.
► Analyses of annual maximum daily rainfall for 212 stations in the Midwest US. ► Abrupt changes are responsible for violations of the stationarity assumption. ► It is difficult to detect a climate ...change signal in these rainfall records. ► The results of this study suggest that these records exhibit a heavy tail behavior. ► Strong indication of clustering of heavy rainfall events.
Annual maximum daily rainfall time series from 221 rain gages in the Midwest United States with a record of at least 75
years are used to study extreme rainfall from a regional perspective. The main topics of this study are: (i) seasonality of extreme rainfall; (ii) temporal stationarity and long-term persistence of annual maximum daily rainfall; (iii) frequency analyses of annual maximum daily rainfall based on extreme value theory; and (iv) clustering of heavy rainfall events and impact of climate variables on the frequency of occurrence of heavy rainfall events.
Annual maximum daily rainfall in the Midwest US exhibits a marked seasonality, with the largest frequencies concentrated in the period May–August. Non-parametric tests are used to examine the validity of the stationarity assumption in terms of both abrupt and slowly varying temporal changes. About 10% of the stations show a change-point in mean and/or variance. Increasing monotonic patterns are detected at 19 stations. Quantile regression analyses suggest that the number of stations with a significant increasing trend tends to decrease for increasing quantiles. Temporal changes in the annual maximum daily rainfall time series are also examined in terms of long-term persistence. Conclusive statements about the presence of long-term persistence in these records are, however, not possible due to the large uncertainties associated with the estimation of the Hurst exponent from a limited sample. Modeling of annual maximum daily rainfall records with the Generalized Extreme Value (GEV) distribution shows well-defined spatial patterns for the location and scale parameters but not for the shape parameter. Examination of the upper tail properties of the annual maximum daily rainfall records points to a heavy tail behavior for most of the stations considered in this study. The largest values of the 100-year annual maximum daily rainfall are found in the area between eastern Kansas, Iowa, and Missouri. Finally, we use the Poisson regression as a framework for the examination of clustering of heavy rainfall. Our results point to a clustering behavior due to temporal fluctuations in the rate of occurrence of the heavy rainfall events, which is modulated by climatic factors representing the influence of both Atlantic and Pacific Oceans.
Despite efforts to detect and mitigate wind farm clutter in weather radar observations of rainfall, these signatures propagate to quantitative precipitation estimates. In this study, the authors ...investigate the hydrologic impact of wind farm clutter in the Multi‐Radar Multi‐Sensor rainfall products. The study uses the probability of detection method to identify wind farm clutter in data from Iowa for the years 2016 and 2017. Using the physically based distributed hydrologic model called the Hillslope‐Link Model, the authors show that streamflow (flood) prediction errors are generally significant at smaller basin communities where wind farms occupy a large portion of the upstream basins. These errors due to wind farm clutter show systematic decrease with the basin scales. The results from this study have implications for real‐time streamflow forecasts provided automatically by the National Water Model at the National Oceanic and Atmospheric Administration, particularly at small riverine communities.
Plain Language Summary
Wind farms corrupt radar‐rainfall estimates used for flood forecasting. Erroneous streamflow forecasts affect small communities where wind farms occupy a large portion of the upstream basins. The effect is difficult to detect at larger scales typically monitored by the USGS streamflow gauging network as the wind farms occupy a small fraction of the basins at those scales.
Key Points
Impact of wind farm clutter on streamflow predictions is higher at communities where wind farms cover a large portion of the upstream basins
Detection of wind farm effect is difficult at the USGS‐monitored basin scales as they occupy a small fraction of the drainage area
Wind farm effect on streamflow predictions shows systematic decrease with increasing basin scales
Using data collected from collocated hillslopes in central Iowa, the United States, the authors (1) explored the spatial variability of runoff coefficient at the event scale by examining the ...relationships between the standard deviation and coefficient of variation of runoff coefficient and the mean and (2) analyzed the temporal persistence of spatial pattern of runoff coefficient using Spearman rank and Pearson correlation coefficient. This study considered 12 cropland hillslopes with 0–20% native prairie vegetation coverage distributed at different hillslope locations. Seventy runoff events over the period 2008–2011 were investigated, of which 51 occurred during crop active growing season, when the hydrologic responses of crops and prairie vegetation are similar. For these events, the spatial coefficient of variation had a median value of 0.80, which indicate high variation of event‐scale runoff coefficients across neighboring hillslopes. This spatial variation largely cannot be consistently explained by the individual hillslope structural properties investigated. The standard deviation and mean of runoff coefficient showed a convex upward relationship across the range of runoff coefficients, with the maximum standard deviation value at the mean runoff coefficient of about 0.48. The coefficient of variation exponentially decreased with increasing runoff coefficient. For 71% of the cases, the results of both correlation analyses were statistically significant (p ≤ 0.05), which indicate stable spatial pattern of runoff coefficient across events. This temporal persistence could be disrupted under extremely dry and wet conditions. The spatial variation‐mean empirical relation and the temporal persistence of spatial pattern provide insight for parameterizing spatial variability of runoff coefficient in distributed hydrologic models.
Key Points
Event‐scale runoff coefficient was highly variable across neighboring hillslopes with spatial separation ranging from tens of meters to 3000 m
Spatial variability of runoff coefficient followed predictable patterns with respect to spatial mean of runoff coefficient
Temporal persistence of the spatial pattern of runoff coefficient was observed and it could be disrupted under extremely dry and wet conditions
•We simulate rainfall and other catchment physical variables to study their effect on the scaling of peak-discharges.•The effect of hillslope velocity decreases with increasing drainage area and ...rainfall duration.•The effect of channel velocity increases with increasing drainage area.•Increasing antecedent soil moisture and hillslope velocity will decrease the scaling exponent.•Increasing rainfall intensity, antecedent soil moisture, and hillslope velocity will increase the intercept.
We have conducted extensive hydrologic simulation experiments in order to investigate how the flood scaling parameters in the power-law relationship Q(A)=αAθ, between peak-discharges resulting from a single rainfall–runoff event Q(A) and upstream area A, change as a function of rainfall, runoff coefficient (Cr) that we use as a proxy for catchment antecedent moisture state, hillslope overland flow velocity (vh), and channel flow velocity (vc), all of which are variable in space. We use a physically-based distributed numerical framework that is based on an accurate representation of the drainage network and apply it to the Cedar River basin (A=16,861km2), which is located in Eastern Iowa, USA. Our work is motivated by seminal empirical studies that show that the flood scaling parameters α and θ change from event to event. Uncovering the underlying physical mechanism behind the event-to-event variability of α and θ in terms of catchment physical processes and rainfall properties would significantly improve our ability to predict peak-discharge in ungauged basins (PUB). The simulation results demonstrate how both α and θ are systematically controlled by the interplay among rainfall duration T, spatially averaged rainfall intensity EI, as well as ECr, Evh, and vc. Specifically, we found that the value of θ generally decreases with increasing values of EI, ECr, and Evh, whereas its value generally increases with increasing T. Moreover, while α is primarily controlled by EI, it increases with increasing ECr and Evh. These results highlight the fact that the flood scaling parameters are able to be estimated from the aforementioned catchment rainfall and physical variables, which can be measured either directly or indirectly.
The City of Cedar Rapids was significantly affected by the June 2008 flood. However, little is known about the role anthropogenic warming during this event, not only in terms of hydrologic response ...but also of impacts. Here we use a continuous distributed hydrologic model forced with precipitation with and without external forcing and show that the impacts of this flood were likely magnified because of increased anthropogenic warming; compared to the baseline scenario with the external forcing removed, this event was ∼1.28-fold larger in flood extent, an approximate 3.4-time larger in the number of affected buildings, and an estimated 5.8- and 7.1-time larger in structural and content damage, respectively. While much of the effort up to this point has focused on the attribution of the physical hazard, our results highlight the cascading increase of the contribution of the external forcing (mainly from anthropogenic forcing) moving from hazard to human impacts.
•The late-time recession processes in nested basins are linear and homogeneous.•This may arise from the hierarchical recession processes at the hillslope scale.•Diagnostic simulations indicate an ...organized-random representation of watersheds.•This finding sheds light on the spatial aggregation of hydrologic processes.
Recession analysis across scales can provide insight into the spatial aggregation of hydrologic processes. Accordingly, we analyzed individual late-time recession curves from 25 nested USGS stream gauges over a period of ∼150days with negligible precipitation during the 2012–2013 North American drought. These gauges are located in the Iowa and Cedar River basins and drain areas ranging from ∼70 to 17,000km2. Our data analyses show that these late-time recession processes can be represented by a linear reservoir model with a constant recession time scale of about 34days, indicating linear and homogeneous recession behaviors at the large scales investigated. However, others have shown that the early-time recession process becomes more nonlinear as spatial scale and, thus, spatial variability increases. We developed a distributed drainage model as a diagnostic tool to understand these seemingly contradictory recession characteristics at multiple spatial scales and different stages. With a hierarchical description of the recession variability at the hillslope scale, our model can simultaneously produce the increasing nonlinear early-time and the linear and homogenous late-time recession behaviors at larger scales. The hierarchical representation classifies hillslopes according to the Strahler orders of the stream links into which they drain. We postulate that a larger difference in recession behaviors will occur between hillslopes from different orders than between those from the same order. Overall, this study shows how the spatial randomness and nonrandomness of small-scale process variability control the hydrologic responses at larger scales and suggests a combined (nonrandom–random) representation of watersheds for aggregating hydrologic processes.
The Iowa Flood Center (IFC), established following the 2008 record floods, has developed a real-time flood forecasting and information dissemination system for use by all Iowans. The system ...complements the operational forecasting issued by the National Weather Service, is based on sound scientific principles of flood genesis and spatial organization, and includes many technological advances. At its core is a continuous rainfall–runoff model based on landscape decomposition into hillslopes and channel links. Rainfall conversion to runoff is modeled through soil moisture accounting at hillslopes. Channel routing is based on a nonlinear representation of water velocity that considers the discharge amount as well as the upstream drainage area. Mathematically, the model represents a large system of ordinary differential equations organized to follow river network topology. The IFC also developed an efficient numerical solver suitable for high-performance computing architecture. The solver allows the IFC to update forecasts every 15 min for over 1,000 Iowa communities. The input to the system comes from a radar-rainfall algorithm, developed in-house, that maps rainfall every 5 min with high spatial resolution. The algorithm uses Level II radar reflectivity and other polarimetric data from the Weather Surveillance Radar-1988 Dual-Polarimetric (WSR-88DP) radar network. A large library of flood inundation maps and real-time river stage data from over 200 IFC “stream-stage sensors” complement the IFC information system. The system communicates all this information to the general public through a comprehensive browser-based and interactive platform. Streamflow forecasts and observations from Iowa can provide support for a similar system being developed at the National Water Center through model intercomparisons, diagnostic analyses, and product evaluations.
Key theoretical and empirical results from the past two decades have established that peak discharges resulting from a single rainfall‐runoff event in a nested watershed exhibit a power law, or ...scaling, relation to drainage area and that the parameters of the power law relation, henceforth referred to as the flood scaling exponent and intercept, change from event to event. To date, only two studies have been conducted using empirical data, both using data from the 21 km2 Goodwin Creek Experimental Watershed that is located in Mississippi, in an effort to uncover the physical processes that control the event‐to‐event variability of the flood scaling parameters. Our study expands the analysis to the mesoscale Iowa River basin (A = 32,400 km2), which is located in eastern Iowa, and provides additional insights into the physical processes that control the flood scaling parameters. Using 51 rainfall‐runoff events that we identified over the 12 year period since 2002, we show how the duration and depth of excess rainfall, which is the portion of rainfall that contributes to direct runoff, control the flood scaling exponent and intercept. Moreover, using a diagnostic simulation study that is guided by evidence found in empirical data, we show that the temporal structure of excess rainfall has a significant effect on the scaling structure of peak discharges. These insights will contribute toward ongoing efforts to provide a framework for flood prediction in ungauged basins.
Key Points:
Fifty‐one rainfall‐runoff events obtained from the Iowa River basin are analyzed
Scaling invariance of peak discharges frequently occurs in a mesoscale basin
Excess rainfall depth and duration control the scaling of peak discharges
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