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• Integrated modeling reduces dependence on calibration using Manning's n. • Integrated model simulates flooding for different antecedent conditions accurately. • Performance across two watersheds shows the broad applicability of integrated models. Modeling and mapping of floods using one (1D) and two-dimensional (2D) models has resulted in significant progress in our understanding of river hydraulics and hydrology. The existing modeling approach can be improved further through a better understanding of the interactions between river hydrodynamics and subsurface processes. Saturated conditions in subsurface and floodplains can lead to longer and deeper inundation from low intensity but continuous flood events. To understand the combined effect of surface-subsurface hydrology and hydrodynamics on streamflow generation and subsequent inundation during floods, this study uses an integrated surface water-groundwater (SW-GW) modeling approach for simulating flood conditions. Simulation of a high flooding event for the Upper Wabash River basin in Indiana, USA, shows that the river-floodplain hydrodynamics is simulated more accurately without the need for extensive calibration by using a two-dimensional integrated SW-GW model. Additionally, an integrated model provides a more realistic simulation of flood hydrodynamics for different antecedent soil conditions. Specifically, results show that the streamflow, flood stages and inundation area obtained for the dry scenario are significantly lower when compared to the saturated scenario. These findings are further validated by applying the integrated modeling approach for an entirely different watershed (Croton River) in the state of New York. Overall, the findings from this study suggest that the current practice of simulating floods which assumes an impervious surface may not be providing realistic estimates of flood inundation, and that an integrated approach incorporating all the hydrologic and hydraulic processes in the river system must be adopted. [ABSTRACT FROM AUTHOR]

Abstract: This study assesses a large‐scale hydrologic modeling framework (WRF‐Hydro‐RAPID) in terms of its high‐resolution simulation of evapotranspiration (ET) and streamflow over Texas (drainage area: 464,135 km2). The reference observations used include eight‐day ET data from MODIS and FLUXNET, and daily river discharge data from 271 U.S. Geological Survey gauges located across a climate gradient. A recursive digital filter is applied to decompose the river discharge into surface runoff and base flow for comparison with the model counterparts. While the routing component of the model is pre‐calibrated, the land component is uncalibrated. Results show the model performance for ET and runoff is aridity‐dependent. ET is better predicted in a wet year than in a dry year. Streamflow is better predicted in wet regions with the highest efficiency ~0.7. In comparison, streamflow is most poorly predicted in dry regions with a large positive bias. Modeled ET bias is more strongly correlated with the base flow bias than surface runoff bias. These results complement previous evaluations by incorporating more spatial details. They also help identify potential processes for future model improvements. Indeed, improving the dry region streamflow simulation would require synergistic enhancements of ET, soil moisture and groundwater parameterizations in the current model configuration. Our assessments are important preliminary steps towards accurate large‐scale hydrologic forecasts. [ABSTRACT FROM AUTHOR]

Land cover has been increasingly recognized as an important factor affecting hydrologic processes at the basin and regional level. Therefore, improved understanding of how land cover change affects hydrologic systems is needed for better management of water resources. The objective of this study is to investigate the effects of land cover change on the duration and severity of high and low flows by using the Soil Water Assessment Tool model, Bayesian model averaging and copulas. Two basins dominated by different land cover in the Ohio River basin are used as study area in this study. Two historic land covers from the 1950s and 1990s are considered as input to the Soil Water Assessment Tool model, thereby investigating the hydrologic high and low flow response of different land cover conditions of these two basins. The relationships between the duration and severity of both low and high flow are defined by applying the copula method; changes in the frequency of the duration and severity are investigated. The results show that land cover changes affect both the duration and severity of both high and low flows. An increase in forest area leads to a decrease in the duration and severity during both high and low flows, but its impact is highest during extreme flows. The results also show that the land cover changes have had significant influences on changes in the joint return periods of duration and severity of low and high flows. While this study sheds light on the role of land cover change on severity and duration of high and low flow conditions, more studies using various land cover conditions and climate types are required in order to draw more reliable conclusions in the future. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Hydrologic models for a particular watershed or a region are created for addressing a specific research or management problem, and most of the models do not get reused after the project is completed. Similarly, multiple models may exist for a particular geographic location from different researchers or organizations. To avoid the duplication of efforts, and enable model reuse and enhancement through collaborative efforts, a prototype cyberinfrastructure, called SWATShare, is developed for sharing, execution and visualization of Soil and Water Assessment Tool (SWAT). The objective of this paper is to present the software architecture, functional capabilities and implementation of SWATShare as a collaborative environment for hydrology research and education using the models published and shared in the system. Besides the capability of publishing, sharing, discovery and downloading of SWAT models, some of the functions in SWATShare such as model calibration are supported by providing access to high performance computing resources including the XSEDE and cloud. Additionally, SWATShare can create dynamic spatial and temporal plots of model outputs at different scales. SWATShare can also be used as an educational tool within a classroom setting for comparing the hydrologic processes under different geographic and climatic settings. The utility of SWATShare for collaborative research and education is demonstrated by using three case studies. Even though this paper focuses on the SWAT model, the system’s architecture can be replicated for other models as well. [ABSTRACT FROM AUTHOR]

The article discusses a featured series in this journal since the February 2017 issue on applications of Soil and Water Assessment Tool (SWAT) for new hydrologic and water quality challenges. The series included reports on topics such as water balance components in SWAT, aquifer-dominated systems, and bioenergy cropping.

Drought is a natural disaster that affects millions of people across the globe. Lack of rainfall reduces crop yields and livestock productivity and, in turn, food availability and income. In developing countries, these effects are even more detrimental. As droughts become more frequent, adaptation is a fundamental concern for countries and their policymakers. To support a development of drought adaptation policies, a combined hydrologic and system dynamics model was developed for a region in East Africa, focused on the Horn of Africa (i.e., a region bordering Kenya, Somalia, and Ethiopia), an area that has endured multiple droughts in the last few decades. The model simulates the interdependencies between water availability, land degradation, food availability, and socioeconomic welfare. The impacts of new adaptation policies on the region were evaluated over a 10-year simulation period using historical weather data. It was found that a combination of increased hydraulic infrastructure and innovative agricultural practice policy can reduce domestic water deficits by 54-100 % while increasing the income per capita up to 285 % over the 10 years. Innovatively combining hydrologic and systems dynamic modeling produces a realistic simulation of water scarcity and the effects on natural systems. Implementation of policies within the model aids the selection process by evaluating multiple options, quantifying the effectiveness the policies have on individual stakeholder livelihood (i.e., pastoralist, agro-pastoralists, and farmers), and analyzing the overall outcome to ensure equitable costs and benefits among the stakeholders. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of rainfall estimates from next generation radar (NEXRAD) uncertainties, data assimilation technique is performed by considering NEXRAD and available rain gauges which can be used to assimilate spatially uniform Multisensor Precipitation Estimator (MPE) scheme and non-uniform (based on rainfall interpolation and bias interpolation) NEXRAD bias estimations during a storm event by Kalman filtering. Even though NEXRAD provides a better spatial representation of rainfall variability than rain gauge information, it suffers from uncertainties and biases due to Z-R (reflectivity-rainfall) conversion method and limitation of available rain gauge information for NEXRAD bias correction in a particular river basin. Analysis of correcting NEXRAD bias rainfall with three different bias correction schemes is described in this study. The prediction accuracy of the STORE DHM (Storage Released based Distributed Hydrologic Model) simulations is also evaluated by using three different NEXRAD bias corrected rainfall inputs. The Upper Wabash River (17,907 km²) and the Upper Cumberland River (38,160 km²) basins are selected as the study areas to evaluate rainfall input sensitivity on different spatial characteristics. Use of spatially non-uniform NEXRAD bias correction schemes results has better rainfall and prediction accuracy compared to that of spatially uniform NEXRAD bias correction rainfall. [ABSTRACT FROM AUTHOR]

The authors have analyzed twentieth-century temperature and precipitation trends and long-term persistence from 19 climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This study is focused on continental areas (60°S-60°N) during 1930-2004 to ensure higher reliability in the observations. A nonparametric trend detection method is employed, and long-term persistence is quantified using the Hurst coefficient, taken from the hydrology literature. The authors found that the multimodel ensemble-mean global land-average temperature trend (0.07°C decade-1) captures the corresponding observed trend well (0.08°C decade-1). Globally, precipitation trends are distributed (spatially) at about zero in both the models and in the observations. There are large uncertainties in the simulation of regional-/local-scale temperature and precipitation trends. The models' relative performances are different for temperature and precipitation trends. The models capture the long-term persistence in temperature reasonably well. The areal coverage of observed long-term persistence in precipitation is 60% less (32% of land area) than that of temperature (78%). The models have limited capability to capture the long-term persistence in precipitation. Most climate models underestimate the spatial variability in temperature trends. The multimodel ensemble-average trend generally provides a conservative estimate of local/regional trends. The results of this study are generally not biased by the choice of observation datasets used, including Climatic Research Unit Time Series 3.1; temperature data from Hadley Centre/Climatic Research Unit, version 4; and precipitation data from Global Historical Climatology Network, version 2. [ABSTRACT FROM AUTHOR]

Summary: Streamflow trends in Indiana are evaluated for 31 USGS gauging stations that have 50years or more continuous unregulated streamflow records. Trends are computed for selected streamflow statistics to include annual and seasonal low, medium, and high flow conditions by using four variations of the Mann–Kendall test. These variations include: (i) Mann–Kendall without autocorrelation, (ii) Mann–Kendall with lag-1 autocorrelation and trend-free pre-whitening, (iii) Mann–Kendall with complete autocorrelation structure, and (iv) Mann–Kendall with long term persistence. Mann–Kendall test is also applied to the precipitation data using the above four approaches to explore the relationship between precipitation and stream flow trends. Overall, there is an increasing trend in low and medium flow conditions across Indiana. However, these trend results are affected by the selection of flow statistic, annual/seasonal statistics, data length and consideration of autocorrelation structure. Specifically, the number of stations that show trends in low and medium flow statistics decreases significantly (up to 70%) when autocorrelation structure is considered in the analysis. The precipitation trends, however, are not affected by the autocorrelation structure, suggesting that factors other than precipitation trends are related to streamflow trends in Indiana. In addition, high flows are found to be less correlated to precipitation compared to low flows in heavily drained northern Indiana. This suggests that the subsurface drainage is playing a role in the observed streamflow trends in Indiana, but this issue needs further investigation. Overall, this study provides an elaborate view of past streamflow trends in Indiana which should be useful for further research including understanding the impact of future biofuel activities on Indiana hydrology and climate. [Copyright &y& Elsevier]

Summary: Numerical modeling of flow dynamics in river channels may require the spatial interpolation of scattered measurements of bathymetry elevation to obtain elevations at computational mesh nodes. This paper examines the various spatial interpolation methods used in interpolating river channel bathymetry. Commonly used interpolation methods, namely inverse distance weighting, spline (tension and regularized), natural neighbor, TopoGrid, and ordinary kriging (isotropic and anisotropic) are evaluated within the ArcGIS environment by using root mean square error (RMSE) criteria. To study the anisotropic effects, the interpolation methods are evaluated in two different coordinate systems: a Cartesian (x, y) coordinate system and a flow-oriented (s, n) coordinate system. Using the data from the Brazos River in Texas, it is shown that TopoGrid, natural neighbor and ordinary kriging, which do not account of anisotropy in the data, performed better than anisotropic kriging in the Cartesian coordinate system. In the flow-oriented coordinate system, the performance of anisotropic spatial interpolation methods is significantly better (40% reduction in RMSE) compared to the isotropic interpolation methods. A modified version of inverse distance weighting method, called elliptical inverse distance weighting (EIDW), is developed to account for river anisotropy. The RMSE results from application of EIDW are close to that from anisotropic kriging, thus providing a simple and computationally faster alternative to complex kriging methods. [Copyright &y& Elsevier]

Recent unprecedented events have highlighted that the existing approach to managing flood risk is inadequate for complex urban systems because of its overreliance on simplistic methods at coarse‐resolution large scales, lack of model physicality using loose hydrologic‐hydraulic coupling, and absence of urban water infrastructure at large scales. Distributed models are a potential alternative as they can capture the complex nature of these events through simultaneous tracking of hydrologic and hydrodynamic processes. However, their application to large‐scale flood mapping and forecasting remains challenging without compromising on spatiotemporal resolution, spatial scale, model accuracy, and local‐scale hydrodynamics. Therefore, it is essential to develop techniques that can address these issues in urban systems while maximizing computational efficiency and maintaining accuracy at large scales. This study presents a physically based but computationally efficient approach for large‐scale (area > 103 km2) flood modeling of extreme events using a distributed model called Interconnected Channel and Pond Routing. The performance of the proposed approach is compared with a hyperresolution‐fixed‐mesh model at 60‐m resolution. Application of the proposed approach reduces the number of computational elements by 80% and the simulation time for Hurricane Harvey by approximately 4.5 times when compared to the fixed‐resolution model. The results show that the proposed approach can simulate the flood stages and depths across multiple gages with a high accuracy (R2 > 0.8). Comparison with Federal Emergency Management Agency building damage assessment data shows a correlation greater than 95% in predicting spatially distributed flooded locations. Finally, the proposed approach can estimate flood stages directly from rainfall for ungaged streams. Plain Language Summary: Climate change and land development or urbanization is expected to exacerbate both the intensity and frequency of extreme flooding worldwide. As the flood severity rises, there is a growing need to develop flood prediction and alert systems that provide fast and reliable forecasts. Currently, it is extremely difficult to identify how much, when, and where the flooding will occur, which can create uncertainty in evacuation planning and preparation. This study proposes a method to improve the urban flood prediction by incorporating more physicality into the numerical flood models, which enables a better estimation of the depth, location, and arrival time of flooding. The graphical elements used to construct the models result in a better representation of the real‐world physical features, thereby improving the accuracy of flood simulation. Moreover, the approach presented here also decreases the computation time required to simulate flooding, which is vital for providing timely forecasts. The proposed methods are tested across a large and complex urban system using the rainfall from Hurricane Harvey (2017) and validated using three additional flood events in Texas, United States. Key Points: Fully distributed hydraulics‐hydrology, flexible surface mesh, and spatiotemporally adaptive hydraulic time stepping are incorporatedUnstructured mesh development is controlled by incorporating graphical features designed to maximize efficiency while maintaining accuracyProposed approach is 80% more efficient in simulating the flood hydrodynamics of Hurricane Harvey compared to a fixed‐resolution model [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including the creation of a digital catchment for Clear Creek in Iowa, dynamic digital catchment for publishing data and modelling tools to be shared on the Internet, and cyberinfrastructure-based flood mitigation system for Taiwan.

The objective of this study is to incorporate a time-dependent Soil Moisture Accounting (SMA) based Curve Number method (SMA_CN) in Soil and Water Assessment Tool (SWAT) and compare its performance with the existing CN method in SWAT by simulating the hydrology of two agricultural watersheds in Indiana, USA. Results show that fusion of the SMA_CN method causes decrease in runoff volume and increase in profile soil moisture content, associated with larger groundwater contribution to the streamflow. In addition, the higher amount of moisture in the soil profile slightly elevates the actual evapotranspiration. The SMA-based SWAT configuration consistently produces improved goodness-of-fit scores and less uncertain outputs with respect to streamflow during both calibration and validation. The SMA_CN method exhibits a better match with the observed data for all flow regimes, thereby addressing issues related to peak and low flow predictions by SWAT in many past studies. Comparison of the calibrated model outputs with field-scale soil moisture observations reveals that the SMA overhauling enables SWAT to represent soil moisture condition more accurately, with better response to the incident rainfall dynamics. While the results from the modification of the CN method in SWAT are promising, more studies including watersheds with various physical and climatic settings are needed to validate the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Looking only at climate change impacts provides partial information about a changing hydrologic regime. Understanding the spatio-temporal nature of change in hydrologic processes, and the explicit contributions from both climate and land use drivers, holds more practical value for water resources management and policy intervention. This study presents a comprehensive assessment on the spatio-temporal trend of Blue Water (BW) and Green Water (GW) in a 490,000 km 2 temperate mid-latitude basin (Ohio River Basin) over the past 80 years (1935–2014), and from thereon, quantifies the combined as well as relative contributions of climate and land use changes. The Soil and Water Assessment Tool (SWAT) is adopted to simulate hydrologic fluxes. Mann-Kendall and Theil-Sen statistical tests are performed on the modeled outputs to detect respectively the trend and magnitude of changes at three different spatial scales – the entire basin, regional level, and sub-basin level. Despite the overall volumetric increase of both BW and GW in the entire basin, changes in their annual average values during the period of simulation reveal a distinctive spatial pattern. GW has increased significantly in the upper and lower parts of the basin, which can be related to the prominent land use change in those areas. BW has increased significantly only in the lower part, likely being associated with the notable precipitation change there. Furthermore, the simulation under a time-varying climate but constant land use scenario identifies climate change in the Ohio River Basin to be influential on BW, while the impact is relatively nominal on GW; whereas, land use change increases GW remarkably, but is counterproductive on BW. The approach to quantify combined/relative effects of climate and land use change as shown in this study can be replicated to understand BW-GW dynamics in similar large basins around the globe. [ABSTRACT FROM AUTHOR]

Automatic calibration (autocalibration) of models is a standard practice in hydrologic sciences. However, hydrologic modelers, while performing autocalibrations, spend considerable amount of time in data pre-processing, coding, and running simulations rather than focusing on science questions. Such inefficiency, as this paper outlines, stems from: (i) platform dependence, (ii) limited computational resource, (iii) limited programming literacy, (iv) limited model structure and source code literacy, and (v) lack of data-model interoperability in the so-called autocalibration process. By expanding and enhancing an existing web-based modeling platform SWATShare, developed for the Soil and Water Assessment Tool (SWAT) hydrologic model, this paper demonstrates a generalizable pathway to making autocalibration efficient via cyberinfrastructure (CI) solutions. SWATShare is a collaborative platform for sharing and visualization of SWAT models, model results, and metadata online. This paper describes the front and back end architectures of SWATShare for enabling efficient SWAT model autocalibration on the web. In addition, this paper also demonstrates three implementation case studies to validate the autocalibration workflow and results. Results from these implementations show that SWATShare autocalibration can produce streamflow hydrograph and parameters that are comparable with commonly used offline SWATCUP calibration outputs. In some instances, the parameter values from SWATShare calibration are more physically relevant than those from SWATCUP. Although the discussion in this paper is in the context of SWAT and SWATShare, the conceptual and technical design presented here can be used as an Open Science blueprint for similar CI-enabled developments in other hydrologic models, and more importantly, in other domains of Earth system sciences. • Outlines five root causes of inefficiency in hydrologic model autocalibration. • Describes a pathway to efficient autocalibration using a cyberinfrastructure (CI). • Uses the SWAT model sharing platform SWATShare as an example application. • Presents three implementation case studies to validate the new CI capabilities. [ABSTRACT FROM AUTHOR]