|Title & Presenters
March 28, 2017
12:00 PM – 1:30 PM ET
|Unified Water Modeling: Droughts, Floods, Fish
|Speakers and Topics
|Christa Peters-Lidard (NASA Goddard Spaceflight Center)
Similarity Assessment of NLDAS model outputs for drought estimation
Edward Clark (NOAA National Water Center)
Gabriele Villarini (University of Iowa)
Brian Wells (NOAA Southwest Fisheries Science Center)
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Christa Peters-Lidard – Multi-model ensembles are often used to produce ensemble mean drought estimates that tend to have increased simulation skill over any individual model output. If multi-model outputs are too similar, an individual LSM would add little additional information to the multi-model ensemble, whereas if the models are too dissimilar, it may be indicative of systematic errors in their formulations or configurations. We present a formal similarity assessment of the North American Land Data Assimilation System (NLDAS) multi-model ensemble outputs to assess their utility to the ensemble, using a confirmatory factor analysis. Outputs from four NLDAS Phase 2 models currently running in operations at NOAA/NCEP and four new/upgraded models that are under consideration for the next Phase of NLDAS are employed in this study. The results show that the runoff estimates from the LSMs were most dissimilar whereas the models showed greater similarity for root zone soil moisture and snow water equivalent. Generally, the NLDAS operational models showed weaker association with the common factor of the ensemble and the newer versions of the LSMs showed stronger association with the common factor, with the model similarity increasing at longer timescales. Tradeoffs between the similarity metrics and accuracy measures indicated that the NLDAS operational models demonstrate a larger span in the similarity-accuracy space compared to the new LSMs. The results indicate that simultaneous consideration of model similarity and accuracy at the relevant timescales are necessary in the development of a multi-model ensemble for drought monitoring.
Edward Clark – The National Weather Service (NWS) Office of Water Prediction (OWP), in conjunction with the National Center for Atmospheric Research (NCAR) and the NWS National Centers for Environmental Prediction (NCEP) recently implemented version 1.0 of the National Water Model (NWM) into operations. This model is an hourly cycling uncoupled analysis and forecast system that provides streamflow for 2.7 million river reaches and other hydrologic information on 1km and 250m grids. It will provide complementary hydrologic guidance at current NWS river forecast locations and significantly expand guidance coverage and type in underserved locations.
The core of this system is the NCAR-supported community Weather Research and Forecasting (WRF)-Hydro hydrologic model. It ingests forcing from a variety of sources including Multi-Sensor Multi-Radar (MRMS) radar-gauge observed precipitation data and High Resolution Rapid Refresh (HRRR), Rapid Refresh (RAP), Global Forecast System (GFS) and Climate Forecast System (CFS) forecast data. WRF-Hydro is configured to use the Noah-Multi Parameterization (Noah-MP) Land Surface Model (LSM) to simulate land surface processes. Separate water routing modules perform diffusive wave surface routing and saturated subsurface flow routing on a 250m grid, and Muskingum-Cunge channel routing down National Hydrogaphy Dataset Plus V2 (NHDPlusV2) stream reaches. River analyses and forecasts are provided across a domain encompassing the Continental United States (CONUS) and hydrologically contributing areas, while land surface output is available on a larger domain that extends beyond the CONUS into Canada and Mexico (roughly from latitude 19N to 58N). The system includes an analysis and assimilation configuration along with three forecast configurations. These include a short-range 15 hour deterministic forecast, a medium-Range 10 day deterministic forecast and a long-range 30 day 16-member ensemble forecast. United Sates Geologic Survey (USGS) streamflow observations are assimilated into the analysis and assimilation configuration, and all four configurations benefit from the inclusion of 1,260 reservoirs.
Version 1.0 of the NWM provides a foundation that supports out-year growth in operational hydrologic forecasting capability. Goals for Version 1.0 NWM include: Providing forecast streamflow guidance for underserved locations; Producing spatially continuous national estimates of hydrologic states (soil moisture, snow pack, etc.); Seamlessly interfacing real-time hydrologic products into an advanced geospatial intelligence framework; Providing a modeling architecture that permits rapid infusion of new data, science and technology. Version 1.1 of the NWM is scheduled for implementation in May 2017, with subsequent versions of the NWM planned to be implemented on an annual basis beginning in Fall 2017.
An overview of the National Water Model will be given during this talk, including details on how the model fits into the current set of NWS modeling tools and operational activities. The role of hydrology in unified modeling will also be discussed.
Gabriele Vilarini – This presentation examines the forecasting skill of eight Global Climate Models (GCMs) from the North-American Multi-Model Ensemble (NMME) project (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) over seven major regions of the continental United States and four major regions of Europe. The skill of the monthly forecasts is quantified using the mean square error skill score. This score is decomposed to assess the accuracy of the forecast in the absence of biases (potential skill) and in the presence of conditional (slope reliability) and unconditional (standardized mean error) biases. We summarize the forecasting skill of each model according to the initialization month of the forecast and lead time, and test the models’ ability to predict extended periods of extreme conditions conducive to historical flood and drought events. We also assess the forecasting skill associated with different multi-model averaging techniques.
Brian Wells – I overview our work using output from numerical ocean and biological models to assess influences of environmental conditions on variability in population and community dynamics along coastal California Current System (CCS). Evaluation of models demonstrates that physical and biological outputs are coherent with empirical data at appropriate spatial and temporal scales and are suitable for quantifying ecosystem dynamics on California shelf waters. I address a variety of ecological hypotheses by confronting model output with biophysical observations. I elucidate mechanisms connecting spatial and temporal upwelling dynamics to observed krill and forage fish abundances. I use model output to estimate interannual variability of biophysical habitat of juvenile Chinook salmon collected from shipboard surveys. I then use results to elucidate mechanisms influencing region-specific survival of Chinook salmon populations along CCS.