Sagy Cohen

WBMsed, a distributed global-scale riverine sediment flux model: Model description and validation

Quantifying continental sediment flux is a fundamental goal of earth-system science. Ongoing measurements of riverine-suspended sediment fluxes to the oceans are limited (<10% of rivers) and intrabasin measurements are even scarcer. Numerical models provide a useful bridge to this measurement gap and offer insight to past and future trends in response to human and environmental changes. BQART is a global empirical model that calculates long-term suspended sediment loads. The Psi statistical model accounts for intra- and interannual variability in these BQART sediment flux predictions. Here BQART and Psi are compiled as a new module of the WBMplus global daily water balance/transport model, a central component in the FrAMES hydrological-biogeochemical modeling scheme. The resulting model (WBMsed) simulates spatially and temporally explicit (pixel scale and daily) sediment fluxes over continental Earth. We test WBMsed predictions with (1) observed sediment loads at 95 river mouths and to the original BQART predictions for these rivers, and (2) 11 years of daily sediment flux observations of 11 USGS stations. The results show that WBMsed captures the multiyear average, interannual and intraannual trends but considerably over- and underpredict daily fluxes for extreme discharge periods. These over- and underpredictions are mainly driven by respective mispredictions of water discharge fluxes. Future improvements to WBMsed to address these limitations are provided.

The effects of sediment transport, weathering, and aeolian mechanisms on soil evolution

Aeolian-derived soils are found throughout the world. Soil evolution processes in aeolian-dominated landscapes differ from processes in bedrock-weathering landscapes by a number of key aspects including the lack of (1) soil production depth dependency, (2) surface armoring, and (3) grain size self-organization in the soil profile. We use here a soil evolution model (mARM5D) to study the differences between aeolian and bedrock-weathering-dominated landscapes by analyzing soil evolution on a hillslope under various aeolian and bedrock-soil supply settings subject to fluvial and diffusive sediment transport. The model simulates spatial and temporal variation in soil particle size distribution (PSD) and profile depth for each grid cell on the landscape, as a function of physical weathering, aeolian deposition, and diffusive and fluvial sediment transport. Our results indicate that surface armoring plays a major role in soil evolution. Under bedrock-weathering-dominated conditions, armoring reduces soil erosion and in conjunction with depth-dependent soil production, leads to steady state soil grading and depth and a relatively uniform soil distribution. In contrast, aeolian-dominated landscapes tend to have considerable spatial variability in soil depth and PSD. Our results also indicate that in contrast with diffusive transport, which is assumed to be PSD independent, fluvial sediment transport is strongly influenced by the soil production mechanism (aeolian or bedrock weathering). Based on the results presented here, we propose that aeolian-dominated landscapes are more responsive to environmental changes (e.g., climatic and anthropogenic) compared with bedrock-weathering landscapes. We further propose that this sensitivity may help explain the patchy soil distribution that is often observed in aeolian-dominated regions.

Estimating floodwater depths from flood inundation maps and topography

Remote sensing analysis is routinely used to map flood inundation during flooding events or retrospectively for planning and research activities. Quantification of the depth of floodwater is important for emergency response, relief operations, damage assessment etc. The Floodwater Depth Estimation Tool (FwDET) calculates water depth based on topographic analysis using standard GIS tools within a Python script. FwDET’s low input requirements (DEM and inundation polygon) and high computational efficiency lend it as a useful tool for emergency response and large-scale applications. Operational use of FwDET is described herein as part of emergency response activation of the Global Flood Partnership (GFP) during the 2017 USA Hurricane Season and May 2018 flooding in Sri Lanka. Use of FwDET during Hurricanes Harvey (Texas and Louisiana), Irma (Florida) and Maria (Puerto Rico) demonstrated its utility by producing large-scale water depth products at near-real-time at relatively high spatial resolution. Despite FwDET’s success, limitations of the tool stemmed from bureaucratic disallowance of non-governmental remote sensing products by U.S. federal emergency response agencies, misclassified remotely sensed floodwaters and challenges obtaining global high resolution DEMs specifically for the aforementioned Sri Lankian flooding. While global-scale DEM products at 30m resolution are freely available, these datasets are of integer precision and thus have limited vertical resolution. This limitation is significant primarily in flat (e.g. coastal) locations and flooded domains comprised of relatively small patches of water.

Using a landform evolution model to study ephemeral gullying in agricultural fields: the effects of rainfall patterns on ephemeral gully dynamics

Water driven soil erosion is a major cause of land degradation worldwide. Ephemeral Gullies (EGs) are considered key contributors to agricultural catchment soil loss. Despite their importance, the parameters and drivers controlling EG dynamics have not been adequately quantified. Here we investigate the effects of rainfall characteristics on EGs, using the physically based Landform Evolution Model (LEM) CAESAR-Lisflood. An initial goal of this study was to test the feasibility of using a LEM to estimate EG dynamics based on easily obtainable and moderate spatial resolution (2x2 m) Digital Elevation Model (DEM). EG evolution was simulated for two rainfall seasons in a 0.37 km2 agricultural plot situated in a semiarid catchment in central Israel. The 2014 rainfall season was used to calibrate the model and the 2015 season was used for validation. The model overall well predicted the EG network structure and average depth but tended to underestimate the EG length. Next, the effects of rainfall characteristics on EG dynamics were investigated by comparing simulations employing seven rainfall scenarios. Four of these scenarios differ in their overall rainfall volume relative to observed precipitation (+20%, +10%, -10%, -20%). The remaining three scenarios vary in the temporal distribution of rainfall during each storm, allowing us to isolate the effect of rainfall intensity on EG evolution. The results show that: (1) EG dynamics strongly correlated to changes in rainfall volume; (2) small scale morphological behavior varies between rainfall scenarios, resulting in different meandering and connectivity variability; (3) EG evolution is divided into two main stages: an initial rapid development occurring after the first two weeks of the rainy season, followed by a stable development period; (4) a 12 mm hour-1 intensity threshold was observed to initiate and, later, modify EGs; and (5) inner storm rainfall variability can have a considerable effect on EG evolution. This article is protected by copyright. All rights reserved.

Intercomparison of Satellite Remote Sensing‐Based Flood Inundation Mapping Techniques

The objective of this study was to determine the accuracy of five different digital image processing techniques to map flood inundation extent with Landsat 8–Operational Land Imager satellite imagery. The May 2016 flooding event in the Hempstead region of the Brazos River, Texas is used as a case study for this first comprehensive comparison of classification techniques of its kind. Five flood water classification techniques (i.e., supervised classification, unsupervised classification, delta-cue change detection, Normalized Difference Water Index [NDWI], modified NDWI [MNDWI]) were implemented to characterize flooded regions. To identify flood water obscured by cloud cover, a digital elevation model (DEM)–based approach was employed. Classified floods were compared using an Advanced Fitness Index to a “reference flood map” created based on manual digitization, as well as other data sources, using the same satellite image. Supervised classification yielded the highest accuracy of 86.4%, while unsupervised, MNDWI, and NDWI closely followed at 79.6%, 77.3%, and 77.1%, respectively. Delta-cue change detection yielded the lowest accuracy with 70.1%. Thus, supervised classification is recommended for flood water classification and inundation map generation under these settings. The DEMbased approach used to identify cloud-obscured flood water pixels was found reliable and easy to apply. It is therefore recommended for regions with relatively flat topography. (KEY TERMS: flooding; remote sensing; inundation mapping; geospatial analysis; image classification.) Munasinghe, Dinuke, Sagy Cohen, Yu-Fen Huang, Yin-Phan Tsang, Jiaqi Zhang, and Zheng Fang, 2018. Intercomparison of Satellite Remote Sensing-Based Flood Inundation Mapping Techniques. Journal of the American Water Resources Association (JAWRA) 54 (4): 834–846. https://doi.org/10.1111/1752-1688.12626

Featured Collection Introduction: National Water Model

The National Water Center (NWC), operated by the National Oceanic and Atmospheric Administration’s (NOAA) Office of Weather Prediction and located on the campus of the University of Alabama, is the hub for the new National Water Model (NWM) of the United States (U.S.). In 2015, the NWC Innovators’ Program and the Consortium of Universities for the Advancement of Hydrologic Science, Inc. launched the inaugural NWC Summer Institute to engage the academic community in developing applications of the NWM. Held annually since then, the Summer Institute brings a group of graduate students to the NWC and the University of Alabama to work with faculty advisors and NWC staff on group projects, with the goal of rapidly prototyping new ideas. During the first Summer Institute (June–July 2015), the overarching goal was to demonstrate a prototype NWM, exploring whether streamflow on 2.7 million stream reaches of the U.S. could be simulated and forecast in real time using NOAA weather products and the National Center for Atmospheric Research Weather Research and Forecasting (WRF)Hydro model. These results were summarized in a featured collection in the Journal of the American Water Resources Association in 2017 (Volume 53, Issue 2; see Nelson 2017). The second Summer Institute (June 6–July 20, 2016) included 34 graduate students from 21 U.S. universities, supported by two Student Coordinators (Adnan Rajib and Peirong Lin), five Research Theme Leaders (Sagy Cohen, Ibrahim Demir, Alfonso Mejia, Sarah Praskievicz, and Albert Van Dijk), and the students’ academic advisors at their home institutions. The 2016 Summer Institute was led by David Maidment and Ed Clark. By June 2016, a first version of the NWM was in the process of being made operational on NOAA computational facilities, and so the focus of the 2016 Summer Institute was on translating the NWM forecasts of discharge into flood-inundation mapping and flood emergency response (Maidment et al. 2016), broadly grouped into four research themes: Inundation Mapping, Flood Modeling, Forecast Errors, and Emergency Response. This featured collection includes papers representative of these research themes, both by student participants in the 2016 Summer Institute and by researchers who facilitated it.