[1] This paper presents a tool for calculating the Palmer Drought Severity Index (PDSI) and associated drought indices. The PDSI is a widely used drought index, yet the complexity and lack of transparency associated with the calculation of the PDSI makes it difficult for a researcher to independently calculate the index. Researchers are often forced to use PDSI values supplied by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) or some other third party. The MATLAB tool presented here is easy to use, thoroughly documented, and transparent. The tool was developed by checking an independently developed code against NCDC's FORTRAN code. Discrepancies between the MATLAB tool and the NCDC code are documented. Researchers using the tool will be able to easily calculate the Palmer drought indices for data inputs of any length and at any spatial scale.
Realistic environmental models used for decision making typically require a highly parameterized approach. Calibration of such models is computationally intensive because widely used parameter estimation approaches require individual forward runs for each parameter adjusted. These runs construct a parameter-to-observation sensitivity, or Jacobian, matrix used to develop candidate parameter upgrades. Parameter estimation algorithms are also commonly adversely affected by numerical noise in the calculated sensitivities within the Jacobian matrix, which can result in unnecessary parameter estimation iterations and less model-to-measurement fit. Ideally, approaches to reduce the computational burden of parameter estimation will also increase the signal-to-noise ratio related to observations influential to the parameter estimation even as the number of forward runs decrease. In this work a simultaneous increments, an iterative ensemble smoother (IES), and a randomized Jacobian approach were compared to a traditional approach that uses a full Jacobian matrix. All approaches were applied to the same model developed for decision making in the Mississippi Alluvial Plain, USA. Both the IES and randomized Jacobian approach achieved a desirable fit and similar parameter fields in many fewer forward runs than the traditional approach; in both cases the fit was obtained in fewer runs than the number of adjustable parameters. The simultaneous increments approach did not perform as well as the other methods due to inability to overcome suboptimal dropping of parameter sensitivities. This work indicates that use of highly efficient algorithms can greatly speed parameter estimation, which in turn increases calibration vetting and utility of realistic models used for decision making.
Water scarcity is intensified by drought, a phenomenon that impacts many sectors of society and affects virtually all climate zones. The Palmer drought indices are widely used by scientists and policy makers to understand drought and model its components. Despite the spatial heterogeneity and variability in variables required by the Palmer model, regional index values are most commonly used for real-time drought assessment. Local stakeholders charged with developing flexible and tailored water management policies have articulated the need for drought indices calculated at finer spatial resolutions than a regional scale. We use the Pacific Northwest United States (U.S.) as a study area to demonstrate the differences between drought indices calculated for U.S. climate divisions with those calculated at a 0.5° by 0.5° latitude/longitude resolution. Our results indicate that regional values of the two cumulative Palmer drought indices do not represent finer-resolution values well. For half of the study area, the pictures of drought (as determined by regional and finer-resolution values) are drastically different more than 30% of the time. Thus, quite often water managers do not have a clear understanding of the relative severity of drought in their area, which can have serious implications for drought mitigation and adaptation.
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