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 DEM‐based 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.     

Error Assessment for Height Above the Nearest Drainage Inundation Mapping

Real‐time flood inundation mapping is vital for emergency response to help protect life and property. Inundation mapping transforms rainfall forecasts into meaningful spatial information that can be utilized before, during, and after disasters. While inundation mapping has traditionally been conducted on a local scale, automated algorithms using topography data can be utilized to efficiently produce flood maps across the continental scale. The Height Above the Nearest Drainage method can be used in conjunction with synthetic rating curves (SRCs) to produce inundation maps, but the performance of these inundation maps needs to be assessed. Here we assess the accuracy of the SRCs and calculate statistics for comparing the SRCs to rating curves obtained from hydrodynamic models calibrated against observed stage heights. We find SRCs are accurate enough for large‐scale approximate inundation mapping while not as accurate when assessing individual reaches or cross sections. We investigate the effect of terrain and channel characteristics and observe reach length and slope predict divergence between the two types of rating curves, and SRCs perform poorly for short reaches with extreme slope values. We propose an approach to recalculate the slope in Manning’s equation as the weighted average over a minimum distance and assess accuracy for a range of moving window lengths.     

Estimating Floodwater Depths from Flood Inundation Maps and Topography

Information on flood inundation extent is important for understanding societal exposure, water storage volumes, flood wave attenuation, future flood hazard, and other variables. A number of organizations now provide flood inundation maps based on satellite remote sensing. These data products can efficiently and accurately provide the areal extent of a flood event, but do not provide floodwater depth, an important attribute for first responders and damage assessment. Here we present a new methodology and a GIS‐based tool, the Floodwater Depth Estimation Tool (FwDET), for estimating floodwater depth based solely on an inundation map and a digital elevation model (DEM). We compare the FwDET results against water depth maps derived from hydraulic simulation of two flood events, a large‐scale event for which we use medium resolution input layer (10 m) and a small‐scale event for which we use a high‐resolution (LiDAR; 1 m) input. Further testing is performed for two inundation maps with a number of challenging features that include a narrow valley, a large reservoir, and an urban setting. The results show FwDET can accurately calculate floodwater depth for diverse flooding scenarios but also leads to considerable bias in locations where the inundation extent does not align well with the DEM. In these locations, manual adjustment or higher spatial resolution input is required.     

A CyberGIS Integration and Computation Framework for High‐Resolution Continental‐Scale Flood Inundation Mapping

We present a Digital Elevation Model‐based hydrologic analysis methodology for continental flood inundation mapping (CFIM), implemented as a cyberGIS scientific workflow in which a 1/3rd arc‐second (10 m) height above nearest drainage (HAND) raster data for the conterminous United States (CONUS) was computed and employed for subsequent inundation mapping. A cyberGIS framework was developed to enable spatiotemporal integration and scalable computing of the entire inundation mapping process on a hybrid supercomputing architecture. The first 1/3rd arc‐second CONUS HAND raster dataset was computed in 1.5 days on the cyberGIS Resourcing Open Geospatial Education and Research supercomputer. The inundation mapping process developed in our exploratory study couples HAND with National Water Model forecast data to enable near real‐time inundation forecasts for CONUS. The computational performance of HAND and the inundation mapping process were profiled to gain insights into the computational characteristics in high‐performance parallel computing scenarios. The establishment of the CFIM computational framework has broad and significant research implications that may lead to further development and improvement of flood inundation mapping methodologies.     

Mapping Outlets of Iowa Flood Center and National Water Center River Networks for Hydrologic Model Comparison

River networks based on Digital Elevation Model (DEM) data differ depending on the DEM resolution, accuracy, and algorithms used for network extraction. As spatial scale increases, the differences diminish. This study explores methods that identify the scale where networks obtained by different methods agree within some margin of error. The problem is relevant for comparing hydrologic models built around the two networks. An example is the need to compare streamflow prediction from the Hillslope Link Model (HLM) operated by the Iowa Flood Center (IFC) and the National Water Model (NWM) operated by the National Water Center of the National Oceanic and Atmospheric Administration. The HLM uses landscape decomposition into hillslopes and channel links while the NWM uses the NHDPlus dataset as its basic spatial support. While the HLM resolves the scale of the NHDPlus, the outlets of the latter do not necessarily correspond to the nodes of the HLM model. The authors evaluated two methods to map the outlets of NHDPlus to outlets on the IFC network. The methods compare the upstream areas of the channels and their spatial location. Both methods displayed similar performance and identified matches for about 80% of the outlets with a tolerance of 10% in errors in the upstream area. As the aggregation scale increases, the number of matches also increases. At the scale of 100 km², 90% of the outlets have matches with tolerance of 5%. The authors recommend this scale for comparing the HLM and NWM streamflow predictions.     

Hydrologic Modeling of an Extreme Flood in the Guadalupe River in Texas1

Sharif, Hatim O., Almoutaz A. Hassan, Sazzad Bin-Shafique, Hongjie Xie, and Jon Zeitler, 2010. Hydrologic Modeling of an Extreme Flood in the Guadalupe River in Texas. Journal of the American Water Resources Association (JAWRA) 1-11. DOI: 10.1111/j.1752-1688.2010.00459.x Abstract: Many of the storms creating the greatest rainfall depths in Texas, measured over durations ranging from one minute to 48 hours, have occurred in the Texas Hill Country area. The upstream portion of the Guadalupe River Basin, located in the Texas Hill Country, is susceptible to flooding and rapid runoff due to thin soils, exposed bedrock, and sparse vegetation, in addition to the Balcones Escarpment uplift contributing to precipitation enhancement. In November 2004, a moist air mass from the Gulf of Mexico combined with moist air from the Pacific Ocean resulted in the wettest November in Texas since 1895. Although the peak discharges were not the highest on record, the U.S. Geological Survey (USGS) stream gauge on the Guadalupe River at Gonzales, Texas reported a daily mean discharge of 2,304 m³/s on November 23, 2004 (average discharge is 53 m³/s). In this paper, we examine the meteorological conditions that led to this event and apply a two-dimensional, physically based, distributed-parameter hydrologic model to simulate the response of a portion of the basin during this event. The study results clearly demonstrate the ability of physically based, distributed-parameter simulations, driven by operational radar rainfall products, to adequately model the cumulative effect of two rainfall events and route inflows from three upstream watersheds without the need for significant calibration.     

Conceptual Framework for the National Flood Interoperability Experiment

The National Flood Interoperability Experiment is a research collaboration among academia, National Oceanic and Atmospheric Administration National Weather Service, and government and commercial partners to advance the application of the National Water Model for flood forecasting. In preparation for a Summer Institute at the National Water Center in June‐July 2015, a demonstration version of a near real‐time, high spatial resolution flood forecasting model was developed for the continental United States. The river and stream network was divided into 2.7 million reaches using the National Hydrography Dataset Plus geospatial dataset and it was demonstrated that the runoff into these stream reaches and the discharge within them could be computed in 10 min at the Texas Advanced Computing Center. This study presents a conceptual framework to connect information from high‐resolution flood forecasting with real‐time observations and flood inundation mapping and planning for local flood emergency response.     

A Probabilistic,Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems

This study contributes a bathtub‐style inundation prediction model with abstractions of coastal processes (i.e., storm surge and wave runup) for flood forecasting at medium‐range (weekly to monthly) timescales along the coastline of large lakes. Uncertainty from multiple data sources are propagated through the model to establish probabilistic bounds of inundation, providing a conservative measure of risk. The model is developed in a case study of the New York Lake Ontario shoreline, which has experienced two record‐setting floods over the course of three years (2017–2019). Predictions are developed at a parcel‐level and are validated using inundation accounts from an online survey and flyover imagery taken during the recent flood events. Model predictions are compared against a baseline, deterministic model that accounts for the same processes but does not propagate forward data uncertainties. Results suggest that a probabilistic approach helps capture observed instances of inundation that would otherwise be missed by a deterministic inundation model. However, downward biases are still present in probabilistic predictions, especially for parcels impacted by wave runup. The goal of the tool is to provide community planners and property owners with a conservative, parcel‐level assessment of flood risk to help inform short‐term emergency response and better prepare for future flood events.     

Streamflow,Sediment Transport,and Geomorphic Change during the 2011 Flood on the Missouri River Near Bismarck–Mandan,ND

Geomorphic change from extreme events in large managed rivers has implications for river management. A steady‐state, quasi‐three‐dimensional hydrodynamic model was applied to a 29‐km reach of the Missouri River using 2011 flood data. Model results for an extreme flow (500‐year recurrence interval [RI]) and an elevated managed flow (75‐year RI) were used to assess sediment mobility through examination of the spatial distribution of boundary or bed shear stress (τ_b) and longitudinal patterns of average τ_b, velocity, and kurtosis of τ_b. Kurtosis of τ_b was used as an indicator of planform channel complexity and can be applied to other river systems. From differences in longitudinal patterns of sediment mobility for the two flows we can infer: (1) under extreme flow, the channel behaves as a single‐thread channel controlled primarily by flow, which enhances the meander pattern; (2) under elevated managed flows, the channel behaves as multithread channel controlled by the interaction of flow with bed and channel topography, resulting in a more complex channel; and (3) for both flows, the model reach lacks a consistent pattern of deposition or erosion, which indicates migration of areas of erosion and deposition within the reach. Despite caveats and limitations, the analysis provides useful information about geomorphic change under extreme flow and potential implications for river management. Although a 500‐year RI is rare, extreme hydrologic events such as this are predicted to increase in frequency.     

Impact of Changing Climate and Land Cover on Flood Magnitudes in the Delaware River Basin,USA

Changing climate and land cover are expected to impact flood hydrology in the Delaware River Basin over the 21st Century. HEC‐HMS models (U.S. Army Corps of Engineers Hydrologic Engineering Center‐Hydrologic Modeling System) were developed for five case study watersheds selected to represent a range of scale, soil types, climate, and land cover. Model results indicate that climate change alone could affect peak flood discharges by ?6% to +58% a wide range that reflects regional variation in projected rainfall and snowmelt and local watershed conditions. Land cover changes could increase peak flood discharges up to 10% in four of the five watersheds. In those watersheds, the combination of climate and land cover change increase modeled peak flood discharges by up to 66% and runoff volumes by up to 44%. Precipitation projections are a key source of uncertainty, but there is a high likelihood of greater precipitation falling on a more urbanized landscape that produces larger floods. The influence of climate and land cover changes on flood hydrology for the modeled watersheds varies according to future time period, climate scenario, watershed land cover and soil conditions, and flood frequency. The impacts of climate change alone are typically greater than land cover change but there is substantial geographic variation, with urbanization the greater influence on some small, developing watersheds.     

Potential Impact of Climate Change on Hurricane Flooding Inundation,Population Affected and Property Damages in Corpus Christi1

Frey, Ashley E., Francisco Olivera, Jennifer L. Irish, Lauren M. Dunkin, James M. Kaihatu, Celso M. Ferreira, and Billy L. Edge, 2010. Potential Impact of Climate Change on Hurricane Flooding Inundation, Population Affected and Property Damages in Corpus Christi. Journal of the American Water Resources Association (JAWRA) 1–11. DOI: 10.1111/j.1752-1688.2010.00475.x Abstract: The effect of climate change on storm-surge flooding and the implications for population and structural damages on the city of Corpus Christi, Texas, was investigated. The study considered the influence of sea level rise and hurricane intensification, both influenced by climate change. Combinations of future carbon dioxide equivalent emission rates and carbon dioxide doubling sensitivities, based on findings of the Intergovernmental Panel on Climate Change, were considered to define future climate scenarios. A suite of physically based numerical models for hurricane winds and the resulting waves, surge, and morphological change at the coast were used to determine flooded areas, population affected, and property damages for Hurricanes Bret, Beulah, and a version of Carla shifted south from its original track, under present and predicted future climate conditions. A comparison of the economic damages for current climate conditions and for the 2080s climate scenario shows that, for Carla (shifted), there will be an increase in the range of $270-1,100 million; for Beulah, of $100-390 million; and, for Bret, of $30-280 million. A similar analysis was also conducted for 2030s predicted climate scenarios. Overall, the comparison of the results for the different climate conditions indicates what the destructive consequences of climate change could be, even within the somewhat short time frame of 80 years considered here.     

Assessment of cropland inundation due to the operation of the Reelfoot Lake spillway in West Tennessee

Running Reelfoot Bayou (RRB) is the outlet canal of Reelfoot Lake, the largest natural lake in Tennessee. RRB is not able to contain discharge from Reelfoot Lake greater than the bankfull discharge of 28 m³/s (1000 ft³/s), which typically occurs at the beginning of the growing season (April–June). Historically, the planting of crops has been delayed until flooding subsides and cropland has drained. The objective of this study is a preliminary quantification of cropland inundation to determine its spatial distribution in the RRB floodplain. Inundated croplands in the RRB floodplain were delineated over a range of spillway discharges from 2 to 57 m³/s (70–2000 ft³/s), using one-dimensional–two-dimensional hydrodynamic modeling and multispectral satellite images (Landsat 8 and Sentinel-2). The composite maps made by combining the simulated and image-derived flood maps were overlaid on the United States Department of Agriculture CropScape layer to determine the inundation of individual summer crops during the growing season. About 25% of the inundated croplands are flooded at discharges of RRB less than 28 m³/s, implying wetland hydrology. The results of this analysis can be used to inform operational management of the Reelfoot Lake spillway.     

Spectrally Driven Classification of High Spatial Resolution,Hyperspectral Imagery: A Tool for Mapping In-Stream Habitat

Streams represent an essential component of functional ecosystems and serve as sensitive indicators of disturbance. Accurate mapping and monitoring of these features is therefore critical, and this study explored the potential to characterize aquatic habitat with remotely sensed data. High spatial resolution, hyperspectral imagery of the Lamar River, Wyoming, USA, was used to examine the relationship between spectrally defined classes and field-mapped habitats. Advantages of this approach included enhanced depiction of fine-scale heterogeneity and improved portrayal of gradational zones between adjacent features. Certain habitat types delineated in the field were strongly associated with specific image classes, but most included areas of diverse spectral character; spatially buffering the field map polygons strengthened this association. Canonical discriminant analysis (CDA) indicated that the ratio of the variability among groups to that within a group was an order of magnitude greater for spectrally defined image classes (20.84) than for field-mapped habitat types (1.82), suggesting that unsupervised image classification might more effectively categorize the fluvial environment. CDA results also suggested that shortwave-infrared wavelengths were valuable for distinguishing various in-stream habitats. Although hyperspectral stream classification seemed capable of identifying more features than previously recognized, the technique also suggested that the intrinsic complexity of the Lamar River would preclude its subdivision into a discrete number of classes. Establishing physically based linkages between observed spectral patterns and ecologically relevant channel characteristics will require additional research, but hyperspectral stream classification could provide novel insight into fluvial systems while emerging as a potentially powerful tool for resource management.     

Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data1

Nishat, Bushra and S.M. Mahbubur Rahman, 2009. Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data. Journal of the American Water Resources Association (JAWRA) 45(6):1313‐1327. Abstract: Large‐scale water resources modeling can provide useful insights on future water availability scenarios for downstream nations in anticipation of proposed upstream water resources projects in large international river basins (IRBs). However, model set up can be challenging due to the large amounts of data requirement on both static states (soils, vegetation, topography, drainage network, etc.) and dynamic variables (rainfall, streamflow, soil moisture, evapotranspiration, etc.) over the basin from multiple nations and data collection agencies. Under such circumstances, satellite remote sensing provides a more pragmatic and convenient alternative because of the vantage of space and easy availability from a single data platform. In this paper, we demonstrate a modeling effort to set up a water resources management model, MIKE BASIN, over the Ganges, Brahmaputra, and Meghna (GBM) river basins. The model is set up with the objective of providing Bangladesh, the lowermost riparian nation in the GBM basins, a framework for assessing proposed water diversion scenarios in the upstream transboundary regions of India and deriving quantitative impacts on water availability. Using an array of satellite remote sensing data on topography, vegetation, and rainfall from the transboundary regions, we demonstrate that it is possible to calibrate MIKE BASIN to a satisfactory level and predict streamflow in the Ganges and Brahmaputra rivers at the entry points of Bangladesh at relevant scales of water resources management. Simulated runoff for the Ganges and Brahmaputra rivers follow the trends in the rated discharge for the calibration period. However, monthly flow volume differs from the actual rated flow by (?) 8% to (+) 20% in the Ganges basin, by (?) 15 to (+) 12% in the Brahmaputra basin, and by (?) 15 to (+) 19% in the Meghna basin. Our large‐scale modeling initiative is generic enough for other downstream nations in IRBs to adopt for their own modeling needs.     

Local Responses to Inundation and De-Farming in the Reservoir Region of the Three Gorges Project (China)

Large-scale infrastructural developments in rural areas often impose significant direct and indirect impacts on environment and people. The Three Gorges Project to dam the Yangtze River in China will create a huge reservoir, inundate farmlands and villages, and incur large-scale resettlement. The concurrent de-farming program to reforest marginal farmlands on steep slopes imposes additional stresses on local people. This study evaluates the ecological and economic adjustments in rural areas affected by both projects, and explores villagers’ knowledge, attitudes, perceptions, and expectations vis-à-vis the drastic changes. Eleven villages in Yunyang County in Sichuan Province, stratified into three zones based on topography and agriculture, were assessed by field studies, questionnaire surveys, maps, satellite imagery, and census and government reports. Multiple regressions identified predictors for 17 dependent variables. Spatial variations in the difficult terrain imposed zone-differentiated agricultural constraints, ecological impacts, and human responses. The dominant farming population—mainly young adults working as migrant laborers in cities—has adopted some nonagricultural work to supplement incomes. Expected per-capita standardized farmland (SF) exceeded threshold SF, which surpasses existing SF. Motivations to reclaim more farmlands, de-farm marginal lands, and become migrant laborers were explained by different multiple-regression predictors. Reduction in farmland stock by inundation and de-farming, aggravated by unwillingness towards nonlocal resettlement, would impose ecological pressures and stimulate demands for nonfarming incomes. Common anticipation of better future income and occupation has been subdued by unfavorable feedbacks from early relocatees. Future environmental and landscape changes are hinged upon changing human responses. Government policies could be informed by research findings to match economic, ecological, and social realities.     

Nutrient Bioassays of Growth Parameters for Algae in the North Bosque River of Central Texas1

Abstract: Nutrient dose‐response bioassays were conducted using water from three sites along the North Bosque River. These bioassays provided support data for refinement of the Soil and Water Assessment Tool (SWAT) model used in the development of two phosphorus TMDLs for the North Bosque River. Test organisms were native phytoplanktonic algae and stock cultured Pseudokirchneriella subcapitata (Korshikov) Hindak. Growth was measured daily by in vivo fluorescence. Algal growth parameters for maximum growth (μ_max) and half‐saturation constants for nitrogen (K_N) or phosphorus (K_P) were determined by fitting maximum growth rates associated with each dose level to a Monod growth rate function. Growth parameters of native algae were compared between locations and to growth parameters of P. subcapitata and literature values. No significant differences in half‐saturation constants were indicated within nutrient treatment for site or algal type. Geometric mean K_N was 32 μg/l and for K_P 7 μg/l. A significant difference was detected in maximum growth rates between algae types but not between sites or nutrient treatments. Mean μ_max was 1.5/day for native algae and 1.2/day for stock algae. These results indicate that watershed‐specific maximum growth rates may need to be considered when modeling algal growth dynamics with regard to nutrients.     

Impacts of Human Behavioral Heterogeneity on the Benefits of Probabilistic Flood Warnings: An Agent‐Based Modeling Framework

Flood forecasts and warnings are intended to reduce flood‐related property damages and loss of human life. Considerable research has improved flood forecasting accuracy (e.g., more accurate prediction of the occurrence of flood events) and lead time. However, the delivery of improved forecast information alone is not necessarily sufficient to reduce flood damage and loss of life, as people have varying responses and reactions to flood warnings. This study develops an agent‐based modeling framework that evaluates the impacts of heterogeneity in human behaviors (i.e., variation in behaviors in response to flood warnings), as well as residential density, on the benefits of flood warnings. The framework is coupled with a traffic model to simulate evacuation processes within a road network under various flood warning scenarios. The results show the marginal benefit associated with providing better flood warnings is significantly constrained if people behave in a more risk‐tolerant manner, especially in high‐density residential areas. The results also show significant impacts of human behavioral heterogeneity on the benefits of flood warnings, and thus stress the importance of considering human behavioral heterogeneity in simulating flood warning‐response systems. Further study is suggested to more accurately model human responses and behavioral heterogeneity, as well as to include more attributes of residential areas to estimate and improve the benefits of flood warnings.     

Mapping Locust Habitats in the Amudarya River Delta,Uzbekistan with Multi-Temporal MODIS Imagery

Reed beds of Phragmites australis in the River Amudarya delta near the Aral Sea constitute permanent breeding areas of the Asian Migratory locust, Locusta migratoria migratoria. Every year, thousands of hectares are treated with broad-spectrum insecticides to prevent locust swarms from damaging crops in adjacent areas. To devise efficient locust monitoring and management plans, accurate and updated information about the spatial distribution of reeds is necessary. Given the vast geographic extent of the delta, traditional, ground survey methods are inadequate. Remotely sensed data collected by the MODIS sensor aboard the TERRA satellite provide a useful tool to characterize the spatial distribution of reeds. Multi-temporal MODIS data, collected at different times of the growing season, were used to generate spectral-temporal signatures for reeds and other land cover classes. These spectral-temporal signatures were matched with reed phenology. MODIS information was digitally classified to generate a land cover map with an overall accuracy of 74%. MODIS data captured 87% of the ground-verified reed locations. Estimates derived from MODIS data indicate that 18% of the study area was covered by reeds. However, high commission error resulted from misclassification of reeds mixed with shrubs class and shrubs class as reeds. This could have resulted in overprediction of the area covered by reeds. Additional research is needed to minimize the overlap between reeds and other vegetation classes (shrubs, and reed and shrub mix). Nevertheless, despite its relatively low spatial resolution (250 m), multi-temporal MODIS data were able to adequately capture the distribution of reeds. Instead of blanketing the fragile wetland ecosystem of the Amudarya delta with chemical anti-locust treatments, plant protection specialists can use this information to devise ecologically sound pest management plans aimed at reducing the adverse environmental impact in the zone of the Aral Sea ecological catastrophe. MODIS methodology to identify reed stands can be applicable to the Migratory locust habitats in other geographic areas.     

Evaluation of Satellite‐Derived Rainfall Data for Multiple Physio‐Climatic Regions in the Santiago River Basin,Mexico

Assessment of water resources requires reliable rainfall data, and rain gauge networks may not provide adequate spatial representation due to limited point measurements. The Tropical Rainfall Measuring Mission (TRMM) provides rainfall data at global scale, and has been used with good results. However, TRMM data are an indirect measurement of rainfall, and therefore must be validated for its proper use. In this work, a validation scheme was designed and implemented to compare the TRMM Version 7 (V7) monthly rainfall product at different time frames with data measured in two hydrologic subregions of the Santiago River Basin (SRB) in Mexico: Río Alto Santiago and Río Bajo Santiago (RBS). Additionally, three physio‐climatic regions provide an assessment of the interplay of topography, distance from coastal regions, and seasonal weather patterns on the correspondence between both datasets. The TRMM V7 rainfall product exhibited good agreement with the rain gauge data particularly for the RBS and for the whole SRB during wettest summer and autumn seasons. However, strong regional dependence was observed due to differences in climate and topography. Overall, in spite of some noted underestimations, the monthly TRMM V7 rainfall product was found to provide useful information that can be used to complement limited monitoring as is the case of RBS. An improved combined rainfall product could be generated and thus gaining the most benefits from both data sources.     

A Decade of Geomorphic and Hydraulic Response to the La Valle Dam Project,Baraboo River,Wisconsin

We investigate stream response to the La Valle Dam removal and channel reconstruction by estimating channel hydraulic parameter values and changes in sedimentation within the reservoir. The designed channel reconstruction after the dam removal included placement of a riffle structure at the former dam site. Stream surveys undertaken in 1984 by Federal Emergency Management Agency and in 2001 by Doyle et al. were supplemented with surveys in 2009 and 2011 to study the effects of the instream structure. We created a model in HEC‐RAS IV and surface maps in Surfer© using the 1984, 2009, and 2011 surveys. The HEC‐RAS IV model for 2009 channel conditions indicates that the riffle structure decreases upstream channel shear stress and velocity, causing renewed deposition of sediment within the former reservoir. We estimate by 2009, 61% of former reservoir sediments were removed during dam removal and channel reconstruction. Between 2009 and 2011 renewed sedimentation within the former reservoir represented approximately 7.85% of the original reservoir volume. The HEC‐RAS IV models show the largest impacts of the dam and riffle structure occur at flood magnitudes at or below bankfull. Thus, the riffle and the dam similarly alter channel hydraulics and sediment transport. As such, our models indicate that the La Valle Dam project was a dam replacement rather than a removal. Our results confirm that channel reconstruction method can alter channel hydraulics, geomorphology, and sediment mobility.