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L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.

espanolLa educacion tiene por tarea preparar individuos para retos, necesidades y oportunidades de un mundo globalizado; ahora con tecnologia permeando las actividades del ser humano, teorias de aprendizaje tradicionales sin reconocer elementos de la alfabetizacion digital (conductismo, cognitivismo y constructivismo). Demandan cambios de pensamiento y mas aun con la presencia del COVID-19, pandemia que impulso aislamientos fisicos sociales; aumentando la necesidad de dominar escenarios digitales. Lo anterior resalta al pensamiento computacional, como estrategia efectiva aplicada con el modelo presentado por el MIT-Harvard, con marco teorico definido y aplicado desde el ano 2012. El objetivo de la investigacion es diagnosticar el nivel de formacion de competencias del pensamiento computacional para resolver problemas; y establecer desde un estudio cuantitativo con diseno no experimental transeccional la correlacional entre: las dimensiones conceptual, practica y perspectiva computacional, con el desarrollo de competencias en pensamiento computacional para resolver problemas, implementado en un colegio publico de Colombia con 133 estudiantes de educacion media. El instrumento de diagnostico, validado y categorizado por 32 problemas, dimensiona las habilidades para generar competencias, con similitudes al sistema educativo colombiano que promueve los aprendizajes con: el saber, saber hacer y saber ser; lo anterior se consolida como insumos para la generacion de un ecosistema virtual de aprendizaje. EnglishEducation has the task of preparing individuals for challenges, needs and opportunities in a globalized world; now with technology permeating human activities, traditional learning theories without recognizing elements of digital literacy (be-haviorism, cognitivism and constructivism). Education requires changes of thought and even more so with the presence of COVID-19, a pandemic that prompted social physical isolation; increasing the need to master digital scenarios in edu-cational communities. The previous situation consolidates computational thinking (CT), as an effective strategy applied in the model presented by MIT-Harvard, with a defined theoretical framework and applied since 2012. The objective of the research is to diagnose the level of training of competencies of the computational thinking to solve problems; and establish from a quantitative study with a non-experimental transectional design the correlation between: the conceptual, practical and computational perspective dimensions, with the development of skills to solve problems, showed a positive correla-tion greater than 0.462 and significance of 0.000 between the theoretical foundation, the practice to consolidate learning and the development of capacities to make optimal decisions in the search for solutions; implemented in a public school in Colombia with 133 high school students, under training characterized by isolation and social distancing, from totally virtual settings. The diagnostic instrument, validated and categorized by 32 problems, measures the skills to generate competen-cies, with similarities to the Colombian educational system that promotes learning with: knowing, knowing how to do and knowing how to be; The above is consolidated as inputs for the generation of a virtual learning ecosystem.

El artículo dilucida un estudio de caso, con enfoque cualitativo y nivel descriptivo, sobre experiencias que caracterizan la utilización investigativa del gestor bibliográfico digital colaborativo llamado en adelante (GBDC). Por ello, se construyó una escala que devela los distintos usos y niveles educativos de estudio del GBDC para cualificar la investigación y la producción académica; de ahí, que se busque analizar las tendencias de uso en las tesis de maestría. En dicho estudio, participaron 111 estudiantes de módulos adscritos al área de investigación aplicada. Los resultados dejan ver al GBDC como un medio de recopilación, organización y compartición de documentos para el trabajo colaborativo, además de que su uso en la investigación se ubica en un nivel alto. Se puede inferir que en el programa de maestría existe un diseño curricular abierto y flexible con inclusión en tecnologías de información y la solidificación de una red virtual de aprendizaje basada en la socialización de experiencias de valor.

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.

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.

Current estimations of sediment transport at the watershed scale are limited by the difficulty of accurately simulating the sediment transfer along the main stem. The typical approach to simulating...

La era digital y los continuos avances tecnológicos generan cambios en la forma de vivir, actuar y pensar, exigiendo nuevas habilidades para ser competitivo e innovador. Los avances originan nuevas habilidades; inventos como la rueda, la bombilla, el automóvil, la computadora y la Internet, fueron estableciendo varios tipos de analfabetismo. Inicialmente el término comprendía a la incapacidad de leer y escribir; después la necesidad de una segunda lengua, y finalmente, destrezas mínimas para atender los retos de la sociedad del conocimiento con nuevos recursos digitales. Para el siglo XXI se llama alfabetización digital, con cambios obligatorios en la forma de pensar, actuar y resolver problemas. Como respuesta surge el pensamiento computacional, visto como la capacidad para resolver problemas con apoyo de la tecnología e informática. En el abanico de estrategias para lograr su desarrollo, se presentan los resultados acumulados en 2 años, desde un semillero de investigación.

We investigate the validity of implicit assumptions in regional flood frequency analysis (RFFA) using Monte Carlo-style simulations of three distributed hydrological models forced with rainfall eve...

Recent observation-based studies have shown that satellite-based antecedent soil moisture can provide useful information on runoff production. The patterns uncovered can be used to benchmark the degree of coupling between antecedent soil moisture, rainfall totals and runoff production, and to determine if hydrologic models can reproduce these patterns for a particular model parameterization of their rainfall-runoff processes. The goal of our study is twofold; First, it derives the relationships between runoff ratio and its major controls, including rainfall total, antecedent soil moisture, and vegetation using remotely sensed data products. Second, it aims to determine if the model is capable to reproduce these relationships and use them to validate model parameters and streamflow predictions. For this purpose, SMAP (Soil Moisture Active Passive) satellite-based soil moisture, S-band radar rainfall, MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation index, and USGS (United States Geological Survey) daily streamflow observations are used. The study domain consists of thirty-eight basins less than 1000 km2 located in an agricultural region in the United States Midwest. For each basin, daily streamflow predictions, before and after adjustments to the hydrologic model are compared with observations. The comparisons are done for four years (2015-2018) using multiple performance metrics. This study could serve as a data-driven approach for parameterization of rainfall-runoff partitioning in hydrologic models using remotely sensed observations.

We use three hydrological models and the stochastic storm transposition (SST) framework to investigate the validity of implicit assumptions in the empirical methodology of regionalization of flood frequencies (RFF) for prediction in ungauged basins. In particular, we investigate the long-standing hypothesis that for a set of catchments physical homogeneity of meteorological and infiltration processes implies statistical homogeneity of flood peak distributions. Our modeling (theoretical) results do not support this hypothesis. We also show that power-law regressions (i.e. log-log linearity) do not seem to be an appropriate model to connect distributions across scales (either quantiles or distribution parameters). Finally, even though our results support the most fundamental hypothesis in RFF that the underlying distribution of peak flows is invariant under translation in the river network, our results do not support the simple-scaling or multi-scaling frameworks. First, we show that some moments of the distribution cannot be inferred from area alone, violating the definition put forward by Gupta et al. (1994). Second, the resulting scale invariant distributions that we identified are different from LP-III and GEV and cannot be rejected by data as valid distributions. Our framework provides a new avenue to test methods for flood data analysis and it opens the door towards a unified physics-informed framework for prediction of flood frequencies in ungauged basins embedded in gauged regions.

We use numerical solutions of the Richard’s Equations for 3D porous media to investigate the influence of agricultural subsurface drainage as a hydrologic process and its effect on the hydrologic r...

Subsurface drainage systems are common in agricultural landscapes around the world, and yet there is insufficient understanding on how they modify the hydrologic flow regime, from the field to the watershed scale. Here, we study the effect of agricultural subsurface drainage (tiling) on the hydrologic response as a function of the watershed scale by using the field scale model DRAINMOD in conjunction with a linearized routing equation for two watersheds in eastern Iowa. Note that we primarily used the stream network and climate forcings in these watersheds and focused on a synthetic analysis to uncover the effect of tiling, while keeping other factors constant. Tile drainage was observed to increase low flows, decrease intermediate flows, and have minimal effect on the largest floods. The reduction in peakflows was observed to be dependent on the event size, antecedent conditions, and spatial scale, such that the greatest flow reductions were apparent at the intermediate scale (100 to 1,000 km2), with lower reduction at the largest scale (>10,000 km2). The scale dependence can be attributed to the fact that peakflows at larger scales are typically not caused by a single rainfall event, but by the accumulation of flows over a longer temporal window. We further demonstrate that peakflows can vary for a basin with the same percentage of tiling but a different spatial organization of the tiled hillslopes. We connect our results to the Width Function, a well-known geomorphological descriptor of river network topology, and demonstrate how tile placement in relation to the width function controls watershed response. Although we focus primarily on peakflows, our results indicate that the impact of tiling on the flow distribution is also scale dependent, and alterations to this distribution will, in turn, control sediment and nutrient transport in a basin.

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 12 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions. We introduce RainyDay, a software tool for assessment of rainfall-driven hazards.RainyDay couples rainfall remote sensing with stochastic storm transposition (SST).SST explicitly considers multi-scale hazard impacts of rainfall space-time structure.SST offers advantages potential advantages for nonstationary flood hazard modeling.Limitations exist in complex terrain and due to biases in remote sensing data.

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.

This study focused on the utility of coarse surface soil moisture observations for applications that require high resolution surface soil moisture information. This was accomplished by quantifying the information content of average soil moisture for three different spatial scales of 81 km2, 790 km2, and 4400 km2. In situ point observations of soil moisture from 31 stations in Iowa were used to develop a spatial stochastic model that assumes hillslope-scale model parameters are independent. Soil moisture dry-downs and wetting regimes were analyzed using rain gauge and soil moisture sensor data. The statistical nature of dry-downs were parameterized using a power-law decay, and soil moisture increases due to rainfall were parameterized using a non-dimensional logistic curve that is a function of soil moisture deficit. The resulting stochastic model is used to quantify the magnitude of the standard deviation, σ ( θ ) , and skewness G ( θ ) as a function of the areal average. We show that the greatest information content (small spatial standard deviation) of average observation corresponded to values near the minimum or the maximum soil moisture with σ ( θ ) 5 % , while average observations for intermediate soil moisture values had the lowest information content with σ ( θ ) > 20 % . The differences in information content as a function of the areal average were consistent with the statistical nature of soil moisture that can be interpreted as small range bounded variable. However, this study provides quantitative estimates for the magnitude of the sub-grid and basin scale variability, documenting the utility for applications that require high resolution information. These results form the basis for the investigation of spatial runoff production in response to rainfall and to inform plot scale agriculture applications.

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.

This paper presents a procedure to derive historical-annual and historical-monthly flow duration curves (FDC) that are monotonic and continuous for agricultural, unregulated, ungauged sites...

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 developed a non-proprietary web-browser based open-source software that allows users to visualize and evaluate hydrologic space-time data in an interactive environment. Hydrovise is client-side browser-based software that interprets a configuration file to construct control elements in the Graphical User Interface for visualizations of space-time data and model simulation evaluations. It leverages the concept of three-dimensional data cubes that facilitate query in space, time, and variable dimension(s) without the requirement for a database system. Using a configuration file, users can define data sources as local file system resources and or external data sources (e.g., online data services). This capability makes Hydrovise a flexible and portable solution where users can share their hydrologic data in an interactive web environment. This paper provides the software description with four distinct example use cases including, but not limited to, time-series data visualization and evaluation, grid-based and river network-based data visualizations.

Rainfall maps that are derived from satellite observations provide hydrologists with an unprecedented opportunity to forecast floods globally. However, the limitations of using these precipitation estimates with respect to producing reliable flood forecasts at multiple scales are not well understood. To address the scientific and practical question of applicability of space-based rainfall products for global flood forecasting, a data evaluation framework is developed that allows tracking the rainfall effects in space and time across scales in the river network. This provides insights on the effects of rainfall product resolution and uncertainty. Obtaining such insights is not possible when the hydrologic evaluation is based on discharge observations from single gauges. The proposed framework also explores the ability of hydrologic model structure to answer questions pertaining to the utility of space-based rainfall observations for flood forecasting. To illustrate the framework, hydrometeorological data collected during the Iowa Flood Studies (IFloodS) campaign in Iowa are used to perform a hydrologic simulation using two different rainfall–runoff model structures and three rainfall products, two of which are radar based [stage IV and Iowa Flood Center (IFC)] and one satellite based [TMPA–Research Version (RV)]. This allows for exploring the differences in rainfall estimates at several spatial and temporal scales and provides improved understanding of how these differences affect flood predictions at multiple basin scales. The framework allows for exploring the differences in peak flow estimation due to nonlinearities in the hydrologic model structure and determining how these differences behave with an increase in the upstream area through the drainage network. The framework provides an alternative evaluation of precipitation estimates, based on the diagnostics of hydrological model results.

Installation of subsurface drainage systems is one of the most common modifications of the agricultural landscape, and while it is well accepted that these systems alter the hydrologic regime, the nature and magnitude of such alterations remains poorly understood. We explore the impact of drainage systems using the field-scale model DRAINMOD and rainfall and soils data for Iowa. Our objective is to understand how climate, landscape and anthropogenic controls modify the hydrological response at the field scale. We show that drainage systems do not significantly alter the annual peak flows (Q P ). This is because Q P is typically generated by the largest storms of the year for which the additional soil storage created by the drains does not significantly alter the total quick-flow volume of water entering the streams, and thus the hydrograph peaks. We identify a threshold storm size (∼6 cm/day for Iowa) beyond which tiles have minimal impact on the peak flow. Effects are apparent, however, for peak flows generated by other storms in which the percent of peak flow reduction is a function of the storm size and the antecedent moisture conditions. The effect of the drains on runoff production is further investigated using the daily Flashiness Index (FI). For soils with high hydraulic conductivity (K), tile drains increase the FI due to faster flow routing through subsurface drains, while for soils with low K, drainage decreases flashiness due to availability of increased soil storage that reduces surface runoff. We conclude that tile drains homogenize spatial patterns in hydrologic response by minimizing response differences between soil types. Furthermore, we investigate the effects of tile spacing and show that the FI decreases with an increase in drain spacing up to an optimal spacing ( S M ), beyond which FI increases with greater spacing. The FI-S M relationship was found to be a function of soil type and rainfall intensity, with the U-shaped behavior more apparent for low K soils and high rainfall intensity.

The effect of introducing radar data assimilation into the WRF Model to improve high-resolution rainfall forecasts that are used for flash flood forecasting is analyzed. The authors selected 12 heavy rainfall events and performed two WRF 24-h simulations that produced quantitative precipitation forecasts (QPFs) for each, one using the standard configuration in forecast mode (QPF-Cold) and one using radar data assimilated at initialization (QPF-Hot). Simulation outputs are compared with NWS stage IV QPEs for storm placement, area over threshold coverage, and equitable threat scores. The two QPF products and stage IV data are used to force the distributed hydrological model CUENCAS for the same 800 km × 800 km domain centered over Iowa (and to calculate peak flows across the river network). The hydrological model responses to the three products are compared in terms of spatial location and flood intensity. In general, QPF-Hot outperformed QPF-Cold in replicating stage IV QPE statistics. However, QPF-Hot was too wet in the first 2 h of the event, and storms created by the radar-assimilation techniques dissipated quickly, with rainfall forecasts resembling QPF-Cold after 12 h. Flash flooding predicted by CUENCAS using QPF-Hot was more consistent with stage IV in terms of placement and intensity; however, results were not consistent for all events evaluated. The most encouraging result is that expected flash flooding was indeed predicted in all 12 cases using QPF-Hot and not QPF-Cold even though placement and intensity were not a perfect match. The initial results of this study indicate that radar assimilation improves WRF’s ability to capture the character of storms, promising more accurate guidance for flash flood warnings.

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.

This study evaluates the performance of Iowa Flood Center’s real-time distributed hydrologic model, Hillslope-Link Model (HLM). The HLM provides information about current and future streamflow conditions for over 1000 locations in Iowa, including small communities and stream gauge locations. The HLM uses a calibration-free approach in which a system of ordinary nonlinear differential equations represents the processes of rainfall to runoff translation as well as the transport of water in the river network. The authors developed a comprehensive evaluation framework to improve the model performance through modification of its structure, and not by parameter calibration. The authors explored modifications to the runoff generation modeling, and to the approach for routing water in channels. Model performance was assessed using different metrics: Nash Sutcliffe efficiency (NSE); Kling Gupta efficiency (KGE); mean absolute error of river stages (MAE); percent bias (PB); annual peak difference; ratio of hits, misses and false alarms, and timing difference. The inclusion of an interflow component alleviated problems of volume underestimation. Timing of model simulations was found to be more accurate when the routing component considered the channel stream order. In the study, effect of rainfall input on the model performance was verified with Stage IV, which includes bias adjustment using gauge information, and a radar rainfall product created in real-time by IFC without bias correction. Open-loop simulations obtained with Stage IV and IFC rainfall show similar performance. Out of all the simulations obtained with Stage IV, 88% have NSE > 0; 95% have KGE > 0; 65% have MAE 40%; average peak estimation errors are below 5% at large basins and below 20% at small basins; and the average timing difference is smaller than three hours. The characteristics of landforms and the impact of agricultural practices in the region are related to the spatial distribution of model performance. The study provides the basis for future improvements of the model.

Following results by Crow et al. (2017) [Geophys. Res. Lett. 44, 5495–5503] on the impact of antecedent soil moisture on runoff production, we investigate total runoff production during individual rainfall-runoff events in agricultural landscapes as a function of antecedent soil moisture, total rainfall, and vegetation cover for catchments with drainage areas ranging from 80 to 1000 km 2 in the state of Iowa, USA. For our study, we use Enhanced SMAP soil moisture estimates, the MODIS enhanced vegetation index (EVI), gauge-corrected Stage IV radar rainfall, and USGS streamflow data. We analyze the event runoff ratio as a function of event-scale rainfall, antecedent SMAP soil moisture and soil-moisture-deficit-normalized rainfall for the events in a period from March 31, 2015 to October 31, 2018. Our goal is to confirm the relationships identified by Crow et al. (2017) in heavily managed agricultural landscapes and to refine some of their methodological steps to quantify the role of additional variables controlling runoff production. To this end, we define three different strategies to identify rainfall-runoff events and add a baseflow separation step to better insulate event scale stormflow runoff. We test the effects of antecedent soil moisture, rainfall, and vegetation on the event-scale runoff ratio. The antecedent SMAP soil moisture and event-scale rainfall are found to have significant predictive power in estimating event runoff ratio. Soil moisture deficit-normalized rainfall, introduced as the ratio of event-scale rainfall to available space in top soil before initiation of the event, exhibited a more distinct relationship with runoff ratio. The long-term analysis of runoff ratio, rainfall, and MODIS EVI indicated that, in an agricultural region, vegetation plays a significant role in determining event-scale runoff ratios. The methodology and outcome of our study have direct implications on real-time flood forecasting and long-term hydrologic assessments.

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.