Recent Posters
An online tool for Operational Probabilistic Drought Forecasting System (OPDFS): a Statistical-Dynamical Framework
AGU Fall Meeting 2017
Mahkameh Zarekarizi, Hamid Moradkhani, and Hongxiang Yan
The Operational Probabilistic Drought Forecasting System (OPDFS) is an online tool recently developed at Portland State University for operational agricultural drought forecasting.  This is an integrated statistical-dynamical framework issuing probabilistic drought forecasts monthly for the lead times of 1, 2, and 3 months. The statistical drought forecasting method utilizes copula functions in order to condition the future soil moisture values on the antecedent states. Due to stochastic nature of land surface properties, the antecedent soil moisture states are uncertain; therefore, data assimilation system based on Particle Filtering (PF) is employed to quantify the uncertainties associated with the initial condition of the land state, i.e. soil moisture. PF assimilates the satellite soil moisture data to Variable Infiltration Capacity (VIC) land surface model and ultimately updates the simulated soil moisture. The OPDFS builds on the NOAA’s seasonal drought outlook by offering drought probabilities instead of qualitative ordinal categories and provides the user with the probability maps associated with a particular drought category. A retrospective assessment of the OPDFS showed that the forecasting of the 2012 Great Plains and 2014 California droughts were possible at least one month in advance.  The OPDFS offers a timely assistance to water managers, stakeholders and decision-makers to develop resilience against uncertain upcoming droughts.
AGU Fall Meeting 2017
Behzad Ahmadi, Mahkameh Zarekarizi, and Hamid Moradkhani
Comprehensive Analysis of Drought Persistence, Hazard, and Recovery across the CONUS
Drought is a creeping intertwined natural hazard affecting society more than any other natural disaster and causing enormous damages on economy and ecosystems. Better understanding of potential drought hazard can help water managers and stakeholders devising mitigation plans to minimize the adverse effects of droughts. In this study, soil moisture, simulated by the Variable Infiltration Capacity (VIC) land surface model, is used to analyze the probability of agricultural drought with different severities across the CONUS. Due to the persistence of soil moisture, a drought episode at a particular time is affected by its earlier status; therefore, this study has utilized a Copula function to model the selected hydrologic variable over the time. The probability of drought intensity for each unit is presented spatially. If the unit remains in the drought condition at the same or lower intensity, drought persists and if it improves above a pre-defined threshold, the unit recovers. Results show that the west of US is more vulnerable to drought persistence in summer and spring while the Midwest and Northeast of US are experiencing drought persistence in fall and winter. In addition, the analysis reveals that as the intensity of drought in a given season decreases the following season has higher chance of recovery.
Precipitation extremes and their relation to climatic indices in the Pacific Northwest, USA
EGU 2016
Mahkameh Zarekarizi, Arun Rana and Hamid Moradkhani
Recently research has focused on the influence of climate indices on precipitation extremes. In the current study, we present the analysis of the precipitation-based extremes in Columbia River Basin (CRB) in the Pacific North-West USA. We first analyzed the precipitation-based extreme indices using statistically downscaled past and future climate projections from ten GCMs. Seven different precipitation-based indices that help inform about the flood duration/intensity are used in the study. These indices would give firsthand information on spatial and temporal scales for different service sectors including energy, agriculture, forestry etc. in the area. Temporally, historical and future projections are analyzed over the whole CRB using ten CMIP5 models. For each scenario, we have mapped out these indices over the area to see the spatial variation of past and future extremes. The analysis shows that high values of extreme indices are clustered in either western or southern parts of the basin while northern part of the basin is experiencing high increase in the indices in future scenarios. Here we focus our attention on evaluating the relation of these extreme and climate indices in historical period to understand which climate indices have more impact on extremes over CRB. Various climate indices are evaluated for their relationship using Principal Component Analysis (PCA) and Singular Value Decomposition (SVD). Results indicated that, out of fifteen climate indices used in the study, CRB is being most affected negatively by East Pacific (EP), Western Pacific Index (WP), Eastern Asia (EA) and North Atlantic Oscillation (NAO).
