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Fengge Su, Yang Hong, and Dennis P. Lettenmaier

; McCollum et al. 2002 ; Gottschalck et al. 2005 ; Brown 2006 ; Ebert et al. 2007 ). Comparatively little work has been done to evaluate the suitability of existing satellite precipitation products as input for hydrologic models. Yilmaz et al. (2005) investigated the use of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite precipitation algorithm ( Sorooshian et al. 2000 ) in streamflow forecasting with a lumped hydrologic model over

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Shahrbanou Madadgar and Hamid Moradkhani

Carbone (2009) because of limiting the forecasts into the deterministic estimate of the mean drought status. Recently, Özger et al. (2012) developed a wavelet and fuzzy logic combination model for long-lead drought forecasting. The technique was found to outperform fuzzy logic, ANN, or coupled wavelet and fuzzy logic models, yet prior to an application it needs a significant work to find the appropriate independent predictors, which strongly affect the forecast. Without using any frequency

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Lizhi Tao, Xinguang He, and Rui Wang

( Peng et al. 2014 ). This makes monthly and seasonal precipitation predictions particularly difficult. Consequently, it has been challenging to obtain accurate and reliable precipitation predictions ( Wang et al. 2014 ). In the past decades, various methods have been developed for forecasting precipitation. These approaches roughly fall into two categories: empirical and dynamical ( He et al. 2015 ). The dynamical models based on the laws of physics such as general circulation models (GCMs) have

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Witold F. Krajewski, Ganesh R. Ghimire, and Felipe Quintero

1. Introduction In this paper we demonstrate, in a systematic way, that persistence is a hard-to-beat streamflow forecasting method ( Palash et al. 2018 ), a fact well known to operational forecasters. We limit our considerations to real-time forecasting in space and time in a river network. River networks aggregate water flow that originates from the transformation of rainfall and/or snowmelt to runoff. Additional recognition of the importance of river networks stems from the fact that many

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Lu Li, Wei Shangguan, Yi Deng, Jiafu Mao, JinJing Pan, Nan Wei, Hua Yuan, Shupeng Zhang, Yonggen Zhang, and Yongjiu Dai

. We used a comprehensive causal inference framework to analyze the SM– P feedback, which includes predictive modeling by RF, time series decomposition techniques, the ADF test, lag window selection, a hybrid feature selection method, and the nonlinear GC analysis. Our framework significantly improved the linear framework of Tuttle and Salvucci (2016) , and we highlighted the importance of considering the nonlinear atmospheric responses in the process of P formation and emphasized the nonlinear

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Timothy H. Raupach and Alexis Berne

and Zawadzki 2005 ; Chapon et al. 2008 ). Jameson (2015) provided a technique for upscaling single disdrometer measurements, which produces gridded simulations of nonparametric point DSDs that honor the statistical properties of the observations. The resulting gridded fields are useful for statistical characterization of the rainfall field, but are not appropriate for direct comparison to fields of observations. Lee et al. (2007) derived the spatial and temporal distributions of the DSD

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Benjamin J. Hatchett, Susan Burak, Jonathan J. Rutz, Nina S. Oakley, Edward H. Bair, and Michael L. Kaplan

avalanche and result when a failure initiates and propagates outward in a weak layer underlying a cohesive slab of snow, causing the slab to become unsupported ( Schweizer et al. 2003 ). Fatal slab avalanches are commonly triggered by human actions but also occur because of natural release mechanisms resulting from loading by newly fallen or wind-deposited snow. Forecasting slab avalanche occurrence is a major challenge because of the complex interactions among terrain, snowpack, and meteorology

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Xinxuan Zhang, Emmanouil N. Anagnostou, Maria Frediani, Stavros Solomos, and George Kallos

1. Introduction Heavy precipitation events (HPE) occurring over mountainous regions have a tendency to trigger devastating flash floods with consequential hazards such as landslides or debris flows (e.g., Malguzzi et al. 2006 ; Petrucci and Polemio 2009 ). These effects substantially impact society, which is in need of better forecasting tools to support early warning (e.g., Ruin et al. 2008 ; Schelfaut et al. 2011 ). Flash flood forecasting has been a very important topic in hydrologic

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Nicholas E. Wayand, Alan F. Hamlet, Mimi Hughes, Shara I. Feld, and Jessica D. Lundquist

observations of these forcing variables are typically available ( Weingartner and Pearson 2001 ; Lundquist et al. 2003 ). Table 1. Typical availability, estimation, and distribution techniques of hydrological modeling variables. In this paper we examine various combinations of 1) in situ observations, 2) empirical models (see section 3 ), and 3) physically based simulations from the Weather and Research Forecasting Model (WRF, described in section 3 ) ( Skamarock and Klemp 2008 ) in a well

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Marc Schleiss

-based quantitative precipitation forecast techniques: Eulerian persistence, Lagrangian persistence, and a neural network. They defined predictability as the lead time for which the cross correlation between observations and forecasts falls below a certain threshold. By successively smoothing the radar images, they were able to show that large-scale features are characterized by longer Lagrangian persistence, that is, that predictability increases with decreasing resolution. Using a large number of radar

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