Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska

Katrina E Bennett,Scott Lindsey,Jessica E Cherry,Ben Balk

doi:10.5194/hess-23-2439-2019

Abstract

Abstract. Remotely sensed snow cover observations provide an opportunity to improve operational snowmelt and streamflow forecasting in remote regions. This is particularly true in Alaska, where remote basins and a spatially and temporally sparse gaging network plague efforts to understand and forecast the hydrology of subarctic boreal basins and where climate change is leading to rapid shifts in basin function. In this study, the operational framework employed by the United States (US) National Weather Service, including the Alaska Pacific River Forecast Center, is adapted to integrate Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed observations of fractional snow cover area (fSCA) to determine if these data improve streamflow forecasts in interior Alaska river basins. Two versions of MODIS fSCA are tested against a base case extent of snow cover derived by aerial depletion curves: the MODIS 10A1 (MOD10A1) and the MODIS Snow Cover Area and Grain size (MODSCAG) product over the period 2000–2010. Observed runoff is compared to simulated runoff to calibrate both iterations of the model. MODIS-forced simulations have improved snow depletion timing compared with snow telemetry sites in the basins, with discernable increases in skill for the streamflow simulations. The MODSCAG fSCA version provides moderate increases in skill but is similar to the MOD10A1 results. The basins with the largest improvement in streamflow simulations have the sparsest streamflow observations. Considering the numerous low-quality gages (discontinuous, short, or unreliable) and ungauged systems throughout the high-latitude regions of the globe, this result is valuable and indicates the utility of the MODIS fSCA data in these regions. Additionally, while improvements in predicted discharge values are subtle, the snow model better represents the physical conditions of the snowpack and therefore provides more robust simulations, which are consistent with the US National Weather Service's move toward a physically based National Water Model. Physically based models may also be more capable of adapting to changing climates than statistical models corrected to past regimes. This work provides direction for both the Alaska Pacific River Forecast Center and other forecast centers across the US to implement remote-sensing observations within their operational framework, to refine the representation of snow, and to improve streamflow forecasting skill in basins with few or poor-quality observations.

Highlights

Arctic climate change is rapidly transforming the north with a myriad of impacts on the hydrologic realm, which has important implications for the largest biome on Earth, the boreal forest
We investigated the response to altering model parameter SCTOL, which can be used by forecasters to combine the strength of the areal depletion curve (ADC) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data and is similar to partial rule-based direct insertion approach; the parameter can be altered without any additional changes to the Community Hydrologic Prediction System (CHPS) model framework
This study focuses on developing tools that can, with a minor amount of testing, be brought into the River Forecast Centers (RFCs)’s CHPS modeling framework and used to improve physical estimates of fractional snow cover area (fSCA) across basins of interest

Summary

Introduction

Arctic climate change is rapidly transforming the north with a myriad of impacts on the hydrologic realm, which has important implications for the largest biome on Earth, the boreal forest. Extreme events are changing; annual maximum streamflow trends indicate that Alaskan riverine systems are experiencing streamflow declines, while minimum flow trends are largely increasing (Bennett et al, 2015). All of these shifts are leading to increased streamflow variability (Stuefer et al, 2017), which has strong impacts on the infrastructure and economy of Alaska, and the Arctic as a whole (Instanes et al, 2016), leading to a substantial task in terms of observing, understanding, mitigating, and adapting to these effects. The Far North (Arctic and subarctic) is rapidly developing its hydroelectric water resources, unlike the contiguous US, and needs accurate decision support for managing this infrastructure (Cherry et al, 2017; Sturm et al, 2017)

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