scholarly journals Challenges in Forecasting the 2011 Runoff Season in the Colorado Basin

2013 ◽  
Vol 14 (4) ◽  
pp. 1364-1371 ◽  
Author(s):  
Kevin Werner ◽  
Kristen Yeager
Keyword(s):  
Great Basin ◽  
Colorado River ◽  
Mitigation Measures ◽  
Snow Covered Area ◽  
Covered Area ◽  
Weather And Climate ◽  
Forecasting System ◽  
Colorado Basin ◽  
Seasonal Weather ◽  
Severe Flooding

Abstract Historically large snowpack across the upper Colorado basin and the Great Basin in 2011 presented the potential for widespread and severe flooding. While widespread flooding did occur, its impacts were largely moderated through a combination of sustained cool weather during the melt season and mitigation measures based on forecasts. The potential for more severe flooding persisted from April through the first part of July as record-high snowpacks slowly melted. NOAA's Colorado Basin River Forecast Center (CBRFC) is the primary office responsible for generating river forecasts in support of emergency and water management within the Colorado River basin. This paper describes the 2011 runoff season in the basin and examines the skill of CBRFC forecasts for that season. The primary goal of this paper is to raise awareness of the research and development areas that could, if successfully integrated into the CBRFC river forecasting system, improve forecasts in similar situations in the future. The authors identify three areas of potential forecast improvement: 1) improving week two to seasonal weather and climate predictions, 2) incorporation of remotely sensed snow-covered area, and 3) improving coordination between reservoir operations and forecasts.

Snow Cover of the Upper Colorado River Basin from Satellite Passive Microwave and Visual Imagery

1989 ◽  
Vol 20 (2) ◽  
pp. 73-84 ◽  
Author(s):  
Edward G. Josberger ◽  
Edouard Beauvillain
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Colorado River ◽  
Microwave Radiometer ◽  
Passive Microwave ◽  
Colorado River Basin ◽  
Upper Colorado River Basin ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Cover Extent

A comparison of passive microwave images from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and visual images from the Defense Meteorological Satellite Program (DMSP) of the Upper Colorado River Basin shows that passive microwave satellite imagery can be used to determine the extent of the snow cover. Eight cloud-free DMSP images throughout the winter of 1985-1986 show the extent of the snowpack, which, when compared to the corresponding SMMR images, determine the threshold microwave characteristics for snow-covered pixels. With these characteristics, the 27 sequential SMMR images give a unique view of the temporal history of the snow cover extent through the first half of the water year. Beginning mid-November, the snow-covered area rapidly increases from near zero to 80 percent by the middle of January. During late February the snow-covered area decreases as a result of basin-wide warming. The microwave determinations initially overestimate the decrease in snow cover, as a result of liquid water in the snowpack, but the return of cooler temperatures restores the veracity of the passive microwave determinations.

scholarly journals Evaluation of a Microwave Emissivity Module for Snow Covered Area with CMEM in the ECMWF Integrated Forecasting System

2020 ◽  
Vol 12 (18) ◽  
pp. 2946
Author(s):  
Yoichi Hirahara ◽  
Patricia de Rosnay ◽  
Gabriele Arduini
Keyword(s):  
Radiative Transfer ◽  
Weather Prediction ◽  
Low Frequency ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Emission Model ◽  
Snow Covered Area ◽  
Covered Area ◽  
Forecasting System

The Community Microwave Emission Modelling platform (CMEM) has been developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) as the forward operator radiative transfer model for low frequency passive microwave brightness temperatures (TB). It is used at ECMWF for L-band TB monitoring over snow free areas. In this paper, upgrades to CMEM are presented in order to explore forward modelling in snow-covered areas for coupled land-atmosphere numerical weather prediction systems. The upgrades enable to use CMEM on an extended range of frequencies and the Helsinki University of Technology multi-layer snow emission model is implemented. Offline CMEM experiments are evaluated against AMSR2 (Advanced Microwave Scanning Radiometer 2) observations showing that simulated TB is improved when using a multi-layer snow scheme, compared to a single-layer scheme. The improvements mainly result from a better representation of snow characteristics in the multi-layer snowpack model. CMEM is also evaluated in the Integrated Forecasting System and coupled to RTTOV (Radiative Transfer for TOVS). The numerical results show improved simulated TB at low frequency V polarization over snow-covered area compared to a configuration using emissivity atlas. Degradations at frequencies higher than 20 GHz indicate that further improvements are required in the emissivity and snowpack properties modelling.

Improving Runoff Simulations using Satellite-observed Time-series of Snow Covered Area

2003 ◽  
Vol 34 (4) ◽  
pp. 281-294 ◽  
Author(s):  
R.V. Engeset ◽  
H-C. Udnæs ◽  
T. Guneriussen ◽  
H. Koren ◽  
E. Malnes ◽  
...  
Keyword(s):  
Time Series ◽  
Radar Sensors ◽  
Noaa Avhrr ◽  
Runoff Forecasting ◽  
Snow Covered Area ◽  
Covered Area ◽  
Satellite Sensors ◽  
Major Floods ◽  
Using Data ◽  
Southern Norway

Snowmelt can be a significant contributor to major floods, and hence updated snow information is very important to flood forecasting services. This study assesses whether operational runoff simulations could be improved by applying satellite-derived snow covered area (SCA) from both optical and radar sensors. Currently the HBV model is used for runoff forecasting in Norway, and satellite-observed SCA is used qualitatively but not directly in the model. Three catchments in southern Norway are studied using data from 1995 to 2002. The results show that satellite-observed SCA can be used to detect when the models do not simulate the snow reservoir correctly. Detecting errors early in the snowmelt season will help the forecasting services to update and correct the models before possible damaging floods. The method requires model calibration against SCA as well as runoff. Time-series from the satellite sensors NOAA AVHRR and ERS SAR are used. Of these, AVHRR shows good correlation with the simulated SCA, and SAR less so. Comparison of simultaneous data from AVHRR, SAR and Landsat ETM+ for May 2000 shows good inter-correlation. Of a total satellite-observed area of 1,088 km2, AVHRR observed a SCA of 823 km2 and SAR 720 km2, as compared to 889 km2 using ETM+.

scholarly journals Assessment of MERRA-2 and ERA5 to Model the Snow Water Equivalent in the High Atlas (1981–2019)

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 890
Author(s):  
Mohamed Wassim Baba ◽  
Abdelghani Boudhar ◽  
Simon Gascoin ◽  
Lahoucine Hanich ◽  
Ahmed Marchane ◽  
...  
Keyword(s):  
River Discharge ◽  
Snow Water Equivalent ◽  
High Elevation ◽  
Water Runoff ◽  
High Atlas ◽  
Seasonal Snow ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Melt water runoff from seasonal snow in the High Atlas range is an essential water resource in Morocco. However, there are only few meteorological stations in the high elevation areas and therefore it is challenging to estimate the distribution of snow water equivalent (SWE) based only on in situ measurements. In this work we assessed the performance of ERA5 and MERRA-2 climate reanalysis to compute the spatial distribution of SWE in the High Atlas. We forced a distributed snowpack evolution model (SnowModel) with downscaled ERA5 and MERRA-2 data at 200 m spatial resolution. The model was run over the period 1981 to 2019 (37 water years). Model outputs were assessed using observations of river discharge, snow height and MODIS snow-covered area. The results show a good performance for both MERRA-2 and ERA5 in terms of reproducing the snowpack state for the majority of water years, with a lower bias using ERA5 forcing.

Improved linear interpolation method for the estimation of snow-covered area from optical data

2002 ◽  
Vol 82 (1) ◽  
pp. 64-78 ◽  
Author(s):  
Sari Metsämäki ◽  
Jenni Vepsäläinen ◽  
Jouni Pulliainen ◽  
Yrjö Sucksdorff
Keyword(s):  
Interpolation Method ◽  
Linear Interpolation ◽  
Optical Data ◽  
Snow Covered Area ◽  
Covered Area ◽  
Linear Interpolation Method

Impacts on ecosystems, corrective restoration practices, and prospects for recovery: nine case studies in the continental United States

2017 ◽  
Vol 39 (6) ◽  
pp. 431 ◽  
Author(s):  
T. A. Jones
Keyword(s):  
United States ◽  
Ecosystem Services ◽  
Chesapeake Bay ◽  
Case Studies ◽  
Great Basin ◽  
Mojave Desert ◽  
Flood Control ◽  
Colorado River ◽  
Longleaf Pine ◽  
Elwha River

Ecological restoration in the United States is growing in terms of the number, size, and diversity of projects. Such efforts are intended to ameliorate past environmental damage and to restore functioning ecosystems that deliver desired levels of ecosystem services. In nine current restoration case studies from across the continental United States, this paper details (1) the impacts of the original disturbance and compounding secondary issues that compel restoration, (2) the corrective practices applied to advance restoration goals, and (3) the prospects for recovery of ecosystem services, including those involving associated animal populations. Ecosystem-altering impacts include flood control (Kissimmee River), flood control and navigation (Atchafalaya Basin), damming for irrigation-water storage (Colorado River) and hydroelectric power (Elwha River), logging and fire suppression (longleaf pine forest), plant invasions that decrease fire-return intervals (Great Basin shrublands, Mojave Desert), nutrient and sediment loading of watersheds (Chesapeake Bay, Mississippi River delta), and conversion of natural lands to agriculture (tallgrass prairie). Animal species targeted for recovery include the greater sage-grouse (Great Basin shrublands), the red-cockaded woodpecker (longleaf pine forest), the south-western willow flycatcher (Colorado River and its tributaries), the desert tortoise (Mojave Desert), eight salmonid fish (Elwha River), and the blue crab and eastern oyster (Chesapeake Bay).

Snow-Covered Area Utilization in Runoff Forecasts

1979 ◽  
Vol 105 (1) ◽  
pp. 53-66
Author(s):  
Albert Rango ◽  
A. Jean Brown ◽  
Michael Rosenzweig ◽  
Jack F. Hannaford ◽  
Roderick L. Hall
Keyword(s):  
Snow Covered Area ◽  
Covered Area

scholarly journals Nimbus-7 SMMR Derived Global Snow Cover Parameters

1987 ◽  
Vol 9 ◽  
pp. 39-44 ◽  
Author(s):  
A.T.C. Chang ◽  
J.L. Foster ◽  
D.K. Hall
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Theoretical Calculations ◽  
Winter Season ◽  
Western China ◽  
High Plains ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

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