review of Bair et al. “Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan” (tc-2017-196)

2017 ◽  
Author(s):  
Steven Fassnacht
2017 ◽  
Author(s):  
Edward H. Bair ◽  
Andre Abreu Calfa ◽  
Karl Rittger ◽  
Jeff Dozier

Abstract. In many mountains, snowmelt provides most of the runoff. In Afghanistan, few ground-based measurements of the snow resource exist. Operational estimates use imagery from optical and passive microwave sensors, but with their limitations. An accurate approach reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance, but reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE early in the snowmelt season, we consider physiographic and remotely-sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques – bagged regression trees and feed-forward neural networks – produced similar mean results, with 0–14 % bias and 46–48 mm RMSE on average. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that the methods can accurately estimate SWE during the snow season in remote mountains.


2018 ◽  
Vol 12 (5) ◽  
pp. 1579-1594 ◽  
Author(s):  
Edward H. Bair ◽  
Andre Abreu Calfa ◽  
Karl Rittger ◽  
Jeff Dozier

Abstract. In the mountains, snowmelt often provides most of the runoff. Operational estimates use imagery from optical and passive microwave sensors, but each has its limitations. An accurate approach, which we validate in Afghanistan and the Sierra Nevada USA, reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance. However, reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE throughout the snowmelt season, we consider physiographic and remotely sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques – bagged regression trees and feed-forward neural networks – produced similar mean results, with 0–14 % bias and 46–48 mm RMSE on average. Nash–Sutcliffe efficiencies averaged 0.68 for all years. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that these methods can accurately estimate SWE during the snow season in remote mountains, and thereby provide an independent estimate to forecast runoff and validate other methods to assess the snow resource.


2020 ◽  
Vol 24 (10) ◽  
pp. 4887-4902
Author(s):  
Fraser King ◽  
Andre R. Erler ◽  
Steven K. Frey ◽  
Christopher G. Fletcher

Abstract. Snow is a critical contributor to Ontario's water-energy budget, with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n=383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50 % (16 mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction; however, only the RF method captures the nonlinearity in the bias and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5∘ N and east (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes.


1987 ◽  
Vol 9 ◽  
pp. 244-245
Author(s):  
W.J. Campbell ◽  
E.G. Josberger ◽  
P. Gloersen ◽  
A.T.C. Chang

During spring 1984, a joint agency research effort was made to explore the use of satellite passive microwave techniques to measure snow-water equivalents in the upper Colorado River basin. This study involved the near real-time acquisition of microwave radiances from the Scanning Multichannel Microwave Radiometer (SMMR) aboard the Nimbus-7 satellite, coupled with quasi-simultaneous surface measurements of snow-pack depth and profiles of temperature, density, and crystal size within the basin. A key idea in this study was to compare, for the same space and time-scales, the SMMR synoptic physics data taken in the basin. Such a snow-measurement program was logistically difficult, but two field teams took detailed snow-pit measurements at 18 sites in Colorado, Utah, and Wyoming during the last 2 weeks of March, when the snow-pack is normally at its maximum extent and depth. These observations were coupled with snow-water-equivalent measurements from Soil Conservation Service SNOTEL sites. Microwave- gradient ratio, Gr (Gr is the difference of the vertically polarized radiances at 8 mm and 17 mm divided by the sum), maps of the basin were derived in a near real-time mode every 6 days from SMMR observations. The sequential Gr maps showed anomalously low values in the Wyoming snow-pack when compared to the other states. This near real-time information then directed the field teams to Wyoming to carry out an extensive survey, which showed that these values were due to the presence of depth hoar; the average crystal sizes were more than twice as large as in the other areas. SMMR can be used to monitor the spatial distribution and temporal evolution of crystal size in snow-packs. Also, scatter diagrams of snow-water equivalents from the combined snow-pit and SNOTEL observations versus Gr from the Wyoming part, and the Colorado and Utah part, of the basin can be used to estimate snow-water equivalents for various parts of the basin.


2019 ◽  
Vol 55 (5) ◽  
pp. 3739-3757 ◽  
Author(s):  
Patrick D. Broxton ◽  
Willem J. D. Leeuwen ◽  
Joel A. Biederman

Author(s):  
Pedro J. Restrepo

The U.S. National Weather Service (NWS) is the agency responsible for flood forecasting. Operational flow forecasting at the NWS is carried out at the 13 river forecasting centers for main river flows. Flash floods, which occur in small localized areas, are forecast at the 122 weather forecast offices. Real-time flood forecasting is a complex process that requires the acquisition and quality control of remotely sensed and ground-based observations, weather and climate forecasts, and operation of reservoirs, water diversions, and returns. Currently used remote-sense observations for operational hydrologic forecasts include satellite observations of precipitation, temperature, snow cover, radar observations of precipitation, and airborne observations of snow water equivalent. Ground-based observations include point precipitation, temperature, snow water equivalent, soil moisture and temperature, river stages, and discharge. Observations are collected by a number of federal, state, municipal, tribal and private entities, and transmitted to the NWS on a daily basis. Once the observations have been checked for quality, a hydrologic forecaster uses the Community Hydrologic Prediction System (CHPS), which takes care of managing the sequence of models and their corresponding data needs along river reaches. Current operational forecasting requires an interaction between the forecaster and the models, in order to adjust differences between the model predictions and the observations, thus improving the forecasts. The final step in the forecast process is the publication of forecasts.


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