Characterization and Propagation of Historical and Projected Droughts in the Umatilla River Basin, Oregon, USA

Climate change is expected to have long-term impacts on drought and wildfire risks in Oregon as summers continue to become warmer and drier. This paper investigates the projected changes in drought characteristics and drought propagation in the Umatilla River Basin in northeastern Oregon for mid-century(2030–2059) and late-century(2070–2099) climate scenarios. Drought characteristics for projected climates were determined using downscaled CMIP5 climate datasets from ten climate models and Soil and Water Assessment Tool to simulate effects on hydrologic processes. Short-term(three months) drought characteristics(frequency, duration, and severity) were analyzed using four drought indices, including the Standardized Precipitation Index(SPI-3), Standardized Precipitation-Evapotranspiration Index(SPEI-3), Standardized Streamflow Index(SSI-3), and the Standardized Soil Moisture Index(SSMI-3). Results indicate that short-term meteorological droughts are projected to become more prevalent, with up to a 20% increase in the frequency of SPI-3drought events. Short-term hydrological droughts are projected to become more frequent(average increase of 11% in frequency of SSI-3 drought events), more severe, and longer in duration(average increase of 8% for short-term droughts).Similarly, short-term agricultural droughts are projected to become more frequent(average increase of 28% in frequency of SSMI-3 drought events) but slightly shorter in duration(average decrease of 4%) in the future. Historically, drought propagation time from meteorological to hydrological drought is shorter than from meteorological to agricultural drought in most sub-basins. For the projected climate scenarios, the decrease in drought propagation time will likely stress the timing and capacity of water supply in the basin for irrigation and other uses.

On the Use of Landsat-5 TM Satellite for Assimilating Water Temperature Observations in 3D Hydrodynamic Model of Small Inland Reservoir in Midwestern US

Accuracy of hydrodynamic and water quality numerical models developed for a specific site is dependent on multiple model parameters and variables whose values are attained via calibration processes and/or expert knowledge. Real time variations in the actual aquatic system at a site necessitate continuous monitoring of the system so that model parameters and variables are regularly updated to reflect accurate conditions. Multiple sources of observations can help adjust the model better by providing benefits of individual monitoring technology within the model updating process. For example, remote sensing data provide a spatially dense dataset of model variables at the surface of a water body, while in-situ monitoring technologies can provide data at multiple depths and at more frequent time intervals than remote sensing technologies. This research aims to present an overview of an integrated modeling and data assimilation framework that combines three-dimensional numerical model with multiple sources of observations to simulate water column temperature in a eutrophic reservoir in central Indiana. A variational data assimilation approach is investigated for incorporating spatially continuous remote sensing temperature observations and spatially discrete in-situ observations to change initial conditions of the numerical model. The results demonstrate the challenges in improving the model performance by incorporating water temperature from multi-spectral remote sensing analysis versus in-situ measurements. For example, at a eutrophic reservoir in Central Indiana where four images of multi-spectral remote sensing data were assimilated in the numerical model, the overall error for the four images reduced from 20.9% (before assimilation) to 15.9% (best alternative after the assimilation). Additionally, best improvements in errors were observed on days closer to the starting time of model’s assimilation time window. However, when the original and updated model results for the water column temperature were compared to the in-situ measurements during the data assimilation period, the error was found to have actually increased from 1.8℃ (before assimilation) to 2.7℃ (after assimilation). Sampling depth differences between remote sensing observations and in-situ measurements, and spatial and temporal sampling of remote sensing observations are considered as possible reasons for this contrary behavior in model performance. The authors recommend that additional research is needed to further examine this behavior.