The authors investigated the relation between the width function and the regional variability of peak flows. The authors explored 34 width function descriptors (WFDs), in addition to drainage area, ...as potential candidates for explaining the regional peak flow variability. First, using hydrologic simulations of uniform rainfall events with variable rainfall duration and constant rainfall intensity for 147 watersheds across the state of Iowa, they demonstrated that WFDs are capable of explaining spatial variability of peak flows for individual rainfall‐runoff events under idealized physical conditions. This theoretical exercise indicates that the inclusion of WFDs should drastically improve regional peak flow estimates with a reduction of the root mean square error by more than half in comparison with a regression model based on drainage area only. The authors followed the simulation with an analysis of estimated peak flow quantiles from 94 stream gauges in Iowa to determine if the WFDs have a similar explanatory power. The correlations between WFDs and peak flow quantiles are not as high as those found for simulated events, which indicates that results from event scale simulations do not translate directly to peak flow quantiles. The spatial variability of peak flow quantiles is influenced by other physical and statistical processes that are also variable in space. These results are consistent with recent work on event‐based scaling of peak flows that shows that the spatiotemporal variability of flood mechanisms is larger than the one expected from geomorphology alone.
Key Points
A total of 34 width function descriptors (WFDs), in addition to A, are examined as potential candidates for explaining the regional peak flow variability
The WFDs are capable of explaining spatial variability of peak flows for individual rainfall‐runoff events under restricted physical conditions
The WFDs did not sufficiently explain the regional variability of the peak flow quantiles
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
We present evidence that the El Niño phenomenon intensifies the annual cycle of malaria cases for Plasmodium vivax and Plasmodium falciparum in endemic areas of Colombia as a consequence of ...concomitant anomalies in the normal annual cycle of temperature and precipitation. We used simultaneous analyses of both variables at both timescales, as well as correlation and power spectral analyses of detailed spatial (municipal) and temporal (monthly) records. During "normal years," endemic malaria in rural Colombia exhibits a clear-cut "normal" annual cycle, which is tightly associated with prevalent climatic conditions, mainly mean temperature, precipitation, dew point, and river discharges. During historical El Niño events (interannual time scale), the timing of malaria outbreaks does not change from the annual cycle, but the number of cases intensifies. Such anomalies are associated with a consistent pattern of hydrological and climatic anomalies: increase in mean temperature, decrease in precipitation, increase in dew point, and decrease in river discharges, all of which favor malaria transmission. Such coupling explains why the effect appears stronger and more persistent during the second half of El Niño's year (0), and during the first half of the year (+1). We illustrate this finding with data for diverse localities in Buenaventura (on the Pacific coast) and Caucasia (along the Cauca river floodplain), but conclusions have been found valid for multiple localities throughout endemic regions of Colombia. The identified coupling between annual and interannual timescales in the climate-malaria system shed new light toward understanding the exact linkages between environmental, entomological, and epidemiological factors conductive to malaria outbreaks, and also imposes the coupling of those timescales in public health intervention programs.
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BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
AbstractThe authors presents a set of automated geographic information system (GIS)-based geoprocessing tools for the implementation of one-dimensional hydrodynamic (1D-HD) models that simulate an ...unsteady open channel flow through a channel network. The authors’ aim is to determine the degree of automation that is granted in the process of preparing river bathymetry and model parameters for 1D-HD model simulations. The authors present two case implementations to illustrate the use of the geoprocessing tools and to demonstrate how they can simplify some of the preprocessing tasks required for preparing model parameters. The authors present a statistical analysis of the automated algorithms performance for finding bank stations and show that it performs adequately in the authors’ study basins.
Although prior studies have evaluated the role of sampling errors associated with local and regional methods to estimate peak flow quantiles, the investigation of epistemic errors is more difficult ...because the underlying properties of the random variable have been prescribed using ad‐hoc characterizations of the regional distributions of peak flows. This study addresses this challenge using representations of regional peak flow distributions derived from a combined framework of stochastic storm transposition, radar rainfall observations, and distributed hydrologic modeling. The authors evaluated four commonly used peak flow quantile estimation methods using synthetic peak flows at 5,000 sites in the Turkey River watershed in Iowa, USA. They first used at‐site flood frequency analysis using the Pearson Type III distribution with L‐moments. The authors then pooled regional information using (1) the index flood method, (2) the quantile regression technique, and (3) the parameter regression. This approach allowed quantification of error components stemming from epistemic assumptions, parameter estimation method, sample size, and, in the regional approaches, the number of pooled sites. The results demonstrate that the inability to capture the spatial variability of the skewness of the peak flows dominates epistemic error for regional methods. We concluded that, in the study basin, this variability could be partially explained by river network structure and the predominant orientation of the watershed. The general approach used in this study is promising in that it brings new tools and sources of data to the study of the old hydrologic problem of flood frequency analysis.
Key Points
Synthetic peak flows are used to investigate the epistemic and sampling errors associated with local and regional methods to estimate PFQs
Error components stemming from epistemic assumptions, parameter estimation method, sample size, and the number of pooled sites are evaluated
The spatial variability of the skewness of the peak flows is partially explained by river network structure and orientation of the watershed
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The scaling of peak flows associated with a probability of exceedance (Qp) or a specific rainfall‐runoff event (QR) with respect to drainage area (A) is known as flood scaling and it has been widely ...used in peak flow regionalization. The attenuation and aggregation processes within the hillslopes and river network in a rainfall‐runoff event, provide a framework to test the scaling of QR. Although scaling of Qp has been reported in empirical studies, its physical interpretation is compromised, since Qp at each site could come from different rainfall‐runoff events. To address this problem, the authors explored the effect of actual variabilities of rainfall and soil moisture fields, and the effect of the river network structure, in the scaling of peak flows of 85 rainfall‐runoff events and peak flow quantiles that were observed in the Iowa River Basin at 43 streamflow gauges. The authors established empirical evidence that addresses two questions: (1) What does control the performance of the scaling of observed QR? (2) What is the interplay between sampling errors and the selection of explanatory variables in the construction of regional regression models for QR and Qp? For the first question, the authors found that the slope magnitude in the scaling of the rainfall intensity fields with respect to A controls the scaling' performance of QR. Regarding the second question, the authors demonstrate that the inclusion of river network descriptors should improve the regional equations to estimate peak flow quantiles unless stream gauging sampling errors affect the analysis.
Key Points
The performance of the scaling of peak flows from rainfall‐runoff events is explained by the scaling structure of rainfall intensity fields
The river network structure represented by width function descriptors can improve the regional equations to estimate peak flow quantiles
The selection of width function descriptors in regional equations is hampered by sampling errors and limited number of streamflow gauges
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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.
•We developed a client-side web-browser based software to lower code barrier for hydrologic data visualization and evaluations.•Hydrovise uses a configuration file to generate a flexible and customizable graphical user interface dynamically.•The data model leverages space-time-variable data cubes to relax the requirement for a database system.•Example use cases include real-time streamflow data browser, and hydrologic model performance evaluations and visualizations.•Target audience are graduate students, research scientists, and decision-makers with limited expertise in web development.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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 1–2 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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Accurate and timely flood prediction can reduce the risk of flooding, bolster preparedness, and help build resilience. In this study, we have developed a flood forecasting system prototype and ...checked its potential for carrying out operational flood forecasting in the state of Nebraska. This system builds upon some of the core components of the Iowa Flood Information System (IFIS), which is a state-of-the-art platform widely recognized around the world. We implemented our platform on a pilot basin in Nebraska (Elkhorn River basin) by installing eight stream sensors and setting up the hydrologic model component of IFIS, i.e., the Hillslope Link Model (HLM). Due to their importance in the Midwest, we particularly emphasized the snow processes and developed an improved HLM model that can account for different aspects of snow (rain-snow-partitioning, snowmelt, and snow accumulation) through simple parameterizations. Results show that the more thorough treatment of snow processes in the hydrologic model, as proposed herein, leads to better flood peak simulations. In this paper, we discuss different steps involved in developing the flood forecasting system prototype, along with the associated challenges and opportunities.
•We developed a flood forecasting system prototype for a pilot basin in Nebraska.•We developed an improved version of the popular HLM model to include snow processes.•We identified the vulnerability of eight bridges to flooding.•We designed a simple web interface showing the streamflow in a distributed manner in the basin.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•We studied field-scale hydrologic impacts of tile drains using the model DRAINMOD.•Climate, landscape and anthropogenic controls on hydrologic response were evaluated.•Tiles did not impact mean ...annual peakflows, or flow events >6cm/day for Iowa soils.•Tiles homogenize hydrologic response by minimizing response differences among soils.•Flashiness is a function of tile spacing with an optimal spacing that minimizes it.
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 (QP). This is because QP 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 (∼6cm/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 (SM), beyond which FI increases with greater spacing. The FI-SM 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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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