Evaluating river cross section geometry for a hydraulic river routing model : Guadalupe and San Antonio river basins

textA new methodology is presented to construct reliable river channel cross section approximations. These approximations are based on the idea of downstream hydraulic geometry as well as supported by the information collected by the USGS streamflow measurement stations across the study area. A hydraulic river routing model (SPRNT) is run with the newly constructed cross section approximations. Initial conditions for the simulation are estimated based on the steady state solution for the model. Boundary conditions or lateral inflows for the river network are estimated based on the outputs of a Land Surface model: Noah, which provides surface and sub-surface runoff for every catchment area in the San Antonio and Guadalupe river basins. Simulations are compared with observed measurements from the USGS stations.Civil, Architectural, and Environmental Engineerin

River network routing on the NHDPlus dataset

International audienceThe mapped rivers and streams of the contiguous United States are available in a geographic information system (GIS) dataset called National Hydrography Dataset Plus (NHDPlus). This hydrographic dataset has about 3 million river and water body reaches along with information on how they are connected into net- works. The U.S. Geological Survey (USGS) National Water Information System (NWIS) provides stream- flow observations at about 20 thousand gauges located on theNHDPlus river network.Ariver networkmodel called Routing Application for Parallel Computation of Discharge (RAPID) is developed for the NHDPlus river network whose lateral inflow to the river network is calculated by a land surface model. A matrix-based version of the Muskingum method is developed herein, which RAPID uses to calculate flow and volume of water in all reaches of a river network with many thousands of reaches, including at ungauged locations. Gauges situated across river basins (not only at basin outlets) are used to automatically optimize the Muskingum parameters and to assess river flow computations, hence allowing the diagnosis of runoff com- putations provided by land surfacemodels.RAPIDis applied to theGuadalupe and SanAntonioRiver basins in Texas, where flow wave celerities are estimated at multiple locations using 15-min data and can be reproduced reasonably with RAPID. This river model can be adapted for parallel computing and although the matrix method initially adds a large overhead, river flow results can be obtained faster than with the traditionalMuskingummethod when using a few processing cores, as demonstrated in a synthetic study using the upper Mississippi River basin

Improving Flood Inundation and Streamflow Forecasts in Snowmelt Dominated Regions

Much effort has been dedicated to expanding hydrological forecasting capabilities and improving understanding of the continental-scale hydrological modeling used to predict future hydrologic conditions and quantify consequences of climate change. In 2016, the National Oceanic and Atmospheric Administration’s (NOAA) Office of Water Prediction implemented the National Water Model (NWM) to provide nationally consistent, operational hydrologic forecasting capability across the continental U.S. The primary goal of this research was to develop hydrological tools that include modeling of flood inundation mapping and snowmelt contributions to river flow in snowmelt-dominated regions across the Western U.S. This dissertation first presents terrain analysis enhancements developed to reduce the overestimation of flooded areas, observed where barriers such as roads cross rivers, from the continental-scale flood inundation mapping method that uses NWM streamflow forecasts. Then, it reports on a systematic evaluation of the NWM snow outputs against observed snow water equivalent (SWE) and snow-covered area fraction (SCAF) at point locations across the Western U.S. This evaluation identified the potential causes responsible for discrepancies in the model snow outputs and suggests opportunities for future research directed towards model improvements. Then, it presents improvements to SWE modeling by quantifying the improvements when using better model inputs and implementing humidity information in separating precipitation into rain and snow. These results inform understanding of continental-scale hydrologic processes and how they should be modeled

Assessment Of Streamflow Predictions Generated Using Multimodel And Multiprecipitation Product Forcing

This study assesses streamflow predictions generated by two distributed hydrologic models, the Hillslope Link Model (HLM) and the National Water Model (NWM), driven by three radar-based precipitation forcing datasets. These forcing data include the Multi-Radar Multi-Sensor (MRMS), and the Iowa Flood Center\u27s single-polarization-based (IFC-SP) and dual-polarization-based (IFC-DP) products. To examine forcing-and model-dependent aspects of the representation of hydrologic processes, we mixed and matched all forcing data and models, and simulated streamflow for 2016–18 based on six forcing–model combinations. The forcing product evaluation using independent ground reference data showed that the IFC-DP radar-only product\u27s accuracy is comparable to MRMS, which is rain gauge corrected. Streamflow evaluation at 140 U.S. Geological Survey (USGS) stations in Iowa demonstrated that the HLM tended to perform slightly better than the NWM, generating streamflow with smaller volume errors and higher predictive power as measured by Kling–Gupta efficiency (KGE). The authors also inspected the effect of estimation errors in the forcing products on streamflow generation and found that MRMS\u27s slight underestimation bias led to streamflow underestimation for all simulation years, particularly with the NWM. The less biased product (IFC-DP), which has higher error variability, resulted in increased runoff volumes with larger dispersion of errors compared to the ones derived from MRMS. Despite its tendency to underestimate, MRMS showed consistent performance with lower error variability as reflected by the KGE. The dispersion observed from the evaluation metrics (e.g., volume error and KGE) seems to decrease as scale becomes larger, implying that random errors in forcing are likely to average out at larger-scale basins. The evaluation of simulated peaks revealed that an accurate estimation of peak (e.g., time and magnitude) remains challenging, as demonstrated by the highly scattered distribution of peak errors for both hydrologic models

Towards actionable climate and flood prediction : understanding and advancing land surface modeling with enriched geospatial information

Land surface models (LSMs) are central to our understanding and prediction of the terrestrial hydrological cycle. This dissertation focuses on using enriched geospatial information from remote sensing (RS) and geographic information system (GIS) to advance the snow and river routing component of state-of-the-art LSMs, and assessing their roles in predicting temperature, precipitation, and streamflow. In Chapters 2 and 3, the first systematic studies are conducted to quantify the role of land snow data assimilation (DA) in seasonal climate forecast. Using 7-yr DA products that assimilated the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) and the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS), I find a local improvement of 5%–25% in the temperature forecast, where the delayed improvement at higher latitudes is explained by incoming solar radiation that is key to the snow–atmosphere coupling. Focusing on the Asia monsoon, I detect an improvement in the precipitation forecast, which is more robust over central north India with sensor-dependent behaviors in different seasons. The results clarify that to successfully translate DA to useful atmospheric prediction skill, the regional snow–atmosphere coupling, the DA uncertainties, and the monsoon sensitivity to thermal forcing over land need to be jointly considered. In Chapters 4 and 5, I introduce a vector-based river routing model to be coupled with traditional grid-based LSMs. By conducting comprehensive model evaluations in the Texas “Flash Flood Alley” in high-impact historical floods, I identify the model strengths and weaknesses in simulating flood discharges. The best modeling results are then used to reveal the hydrometeorological factors responsible for a record-breaking local flood, which includes the rainfall location and basin physiographic features, the initial wetness in the deeper soil layer, and the flow velocity in the river network. The assessed modeling advancements have actionable societal implications because they apply to the Community Land Model 4 (CLM4) and the Noah model with multi-parameterizations (Noah-MP), both LSMs are adopted by major operational forecasting centers. They may also inform future LSM developments that aim to unify the “top-down” atmospheric modeling and the “bottom-up” hydrological modeling approaches in a generic framework

Flow and transport modeling in large river networks

textThe work presented in this dissertation discusses large scale flow and transport in river networks and investigates advantages and disadvantages of grid-based and vector-based river networks. This research uses the Mississippi River basin as a continental-case study and the Guadalupe and San Antonio rivers and Seine basin in France as regional-case studies. The first component of this research presents an extension of regional river flow modeling to the continental scale by using high resolution river data from NHDPlus dataset. This research discovers obstacles of flow computations for river a network with hundreds of thousands river segments in continental scales. An upscaling process is developed based on the vector-based river network to decrease the computational effort, and to reduce input file size. This research identifies drainage area as a key factor in the flow simulation, especially in a wet climate. The second component of this research presents an enhanced GIS framework for a steady-state riverine nitrogen transport modeling in the San Antonio and Guadalupe river network. Results show that the GIS framework can be applied to represent a spatial distribution of flow and total nitrogen in a large river network with thousands of connected river segment. However, time features of the GIS environment limit its applicability to large scale time-varied modeling. The third component shows a modeling regional flow and transport with consideration of stream-aquifer interactions at a regional scale at high resolution. The STICS- Eau-Dyssée combined system is implemented for entire seine basin to compute daily nitrate flux in the Seine grid river network. Results show that river-aquifer exchange has a significant impact on river flow and transport modeling in larger river networks.Civil, Architectural, and Environmental Engineerin

Enhancing the Structure of the WRF-Hydro Hydrologic Model for Semiarid Environments

In August 2016, the National Weather Service Office of Water Prediction (NWS/OWP) of the National Oceanic and Atmospheric Administration (NOAA) implemented the operational National Water Model (NWM) to simulate and forecast streamflow, soil moisture, and other model states throughout the contiguous United States. Based on the architecture of the WRF-Hydro hydrologic model, the NWM does not currently resolve channel infiltration, an important component of the water balance of the semiarid western United States. Here, we demonstrate the benefit of implementing a conceptual channel infiltration function (from the KINEROS2 semidistributed hydrologic model) into the WRF-Hydro model architecture, configured as NWM v1.1. After calibration, the updated WRF-Hydro model exhibits reduced streamflow errors for the Walnut Gulch Experimental Watershed (WGEW) and the Babocomari River in southeast Arizona. Model calibration was performed using NLDAS-2 atmospheric forcing, available from the NOAA National Centers for Environmental Prediction (NCEP), paired with precipitation forcing from NLDAS-2, NCEP Stage IV, or local gauge precipitation. Including channel infiltration within WRF-Hydro results in a physically realistic hydrologic response in the WGEW, when the model is forced with high-resolution, gauge-based precipitation in lieu of a national product. The value of accounting for channel loss is also demonstrated in the Babocomari basin, where the drainage area is greater and the cumulative effect of channel infiltration is more important. Accounting for channel infiltration loss thus improves the streamflow behavior simulated by the calibrated model and reduces evapotranspiration bias when gauge precipitation is used as forcing. However, calibration also results in increased high soil moisture bias, which is likely due to underlying limitations of the NWM structure and calibration methodology.University Corporation for Atmospheric Science (UCAR) COMET Cooperative Project; NOAA Joint Technology Transfer Initiative (JTTI) Federal Grant [NA17OAR4590183]6 month embargo; published online 22 April 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Assessing terrestrial nitrogen dynamics in land surface models : impacts on water and carbon balances and implications for environmental modeling

Land Surface Models (LSMs) are crucial for understanding the diverse impacts of climate variability, encompassing floods, droughts, and human-induced factors like urbanization and agriculture on water, carbon, and nitrogen dynamics. This dissertation aims to evaluate the Noah-MP LSM's performance with explicit Carbon and Nitrogen components (Noah-MP-CN). It assesses the consequences of integrating terrestrial nitrogen dynamics into carbon and water simulations, evaluates nitrogen leaching processes, and explores factors influencing land nitrogen memory. Chapter 1 emphasizes the importance of incorporating terrestrial nitrogen dynamics into LSMs. Chapter 2 focuses on the Texas Gulf region, revealing enhanced representations of key variables in Noah-MP-CN. Chapters 3 and 4 delve into basin-scale spatiotemporal variabilities for the San Antonio and Guadalupe River Basins, identifying strengths and weaknesses in simulating nitrogen leaching and exploring land nitrogen memory effects. Chapter 5 summarizes core findings, acknowledges limitations, and outlines future research paths. The research's implications are substantial, especially with Noah-MP's adoption by major forecasting centers, influencing future model developments in the broader land-river-ocean environmental context.Earth and Planetary Science

Use of WRF-Hydro over the Northeast of the US to Estimate Water Budget Tendencies in Small Watersheds

In the Northeast of the US, climate change will bring a series of impacts on the terrestrial hydrology. Observations indicate that temperature has steadily increased during the last century, including changes in precipitation. This study implements the Weather Research and Forecasting (WRF)-Hydro framework with the Noah-Multiparameterization (Noah-MP) model that is currently used in the National Water Model to estimate the tendencies of the different variables that compounded the water budget in the Northeast of the US from 1980 to 2016. We use North American Land Data Assimilation System-2 (NLDAS-2) climate data as forcing, and we calibrated the model using 192 US Geological Survey (USGS) Geospatial Attributes of Gages for Evaluating Streamflow II (Gages II) reference stations. We study the tendencies determining the Kendall-Theil slope of streamflow using the maximum three-day average, seven-day minimum flow, and the monotonic five-day mean times series. For the water budget, we determine the Kendall-Theil slope for changes in monthly values of precipitation, surface and subsurface runoff, evapotranspiration, transpiration, soil moisture, and snow accumulation. The results indicate that the changes in precipitation are not being distributed evenly in the components of the water budget. Precipitation is decreasing during winter and increasing during the summer, with the direct impacts being a decrease in snow accumulation and an increase in evapotranspiration. The soil tends to be drier, which does not translate to a rise in infiltration since the surface runoff aggregated tendencies are positive, and the underground runoff aggregated tendencies are negative. The effects of climate change on streamflows are buffered by larger areas, indicating that more attention needs to be given to small catchments to adapt to climate change

Real-time Flood Forecasting And Information System For The State Of Iowa

Iowa Flood Center\u27s automated real-time flood forecasting and information system serves as a complement to the National Water Center\u27s proposed national system