Parameter-elevation Regressions On Independent Slopes Model Research Articles

Characteristics of US drought and pluvials from a high‐resolution spatial dataset

AbstractAnomalous precipitation can be a devastating natural disaster having significant impacts on economies, society and the environment. This study examines the spatial and temporal characteristics of anomalous precipitation throughout the United States to obtain a greater understanding of drought/pluvial regimes, including intensity, duration and spatial coverage based on a high‐resolution precipitation dataset. With the help of the parameter‐elevation regression on independent slopes model (PRISM), approximately 4 km data for the United States is used to construct the Palmer drought severity index (PDSI) and the standardized precipitation index (SPI) for the period 1895–2003. Increased resolution from the PRISM dataset shows that drought/pluvial areas are not as homogeneous as seen in previous studies based on climate divisions and larger scales. While size, duration and locations of drought/pluvial events are influenced by the index used, the largest annual drought/pluvial events occur more frequently in the central United States, with a higher percentage of extreme values occurring in the western portion of the country. Four large pluvial events have occurred in the United States during the study period, with three of these having occurred during the past 30 years. Copyright © 2007 Royal Meteorological Society

Development of scale‐free climate data for Western Canada for use in resource management

AbstractApplying climate data in resource management requires matching the spatial scale of the climate and resource databases. Interpolating climate data in mountainous regions is difficult. In this study, we present methodology to generate scale‐free climate data through the combination of interpolation techniques and elevation adjustments. We apply it to monthly temperature and precipitation normals for 1961–90 produced by the Parameter‐elevation Regressions on Independent Slopes Model (PRISM) for British Columbia, Yukon Territories, the Alaska Panhandle, and parts of Alberta and the United States. Equations were developed to calculate biologically relevant climate variables including various degree‐days, number of frost‐free days, frost‐free period, and snowfall from monthly temperature and precipitation data. Estimates of climate variables were validated using an independent dataset from weather stations that were not included in the development of the model. Weather station records generally agreed well with estimated climate variables and showed significant improvements over original PRISM climate data. A stand‐alone MS Windows application was developed to perform all calculations and to integrate future climate predictions from various global circulation models. We demonstrate the use of this application by showing how climate change may affect lodgepole pine seed planning zones for reforestation in British Columbia. Copyright © 2006 Royal Meteorological Society.

Climatologically Aided Mapping of Daily Precipitation and Temperature

AbstractAccurately mapped meteorological data are an essential component for hydrologic and ecological research conducted at broad scales. A simple yet effective method for mapping daily weather conditions across heterogeneous landscapes is described and assessed. Daily weather data recorded at point locations are integrated with long-term-average climate maps to reconstruct spatially explicit estimates of daily precipitation and temperature extrema. The method uses ordinary kriging to interpolate base station data spatially into fields of approximately 2-km grain size. The fields are subsequently adjusted by 30-yr-average climate maps [Parameter-Elevation Regression on Independent Slopes Model (PRISM)], which incorporate adiabatic lapse rates, orographic effects, coastal proximity, and other environmental factors. The accuracy assessment evaluated an interpolation-only approach and the new method by comparing predicted and observed values from an independent validation dataset. The results of the accuracy assessment are compared for a 24-yr period for California. For all three weather variables, mean absolute errors (MAE) of the climate-imprint method were considerably smaller than those of the interpolation-only approach. MAE for predicted daily precipitation was ±2.5 mm, with a bias of +0.01. MAE for predicted daily minimum and maximum temperatures were ±1.7° and ±2.0°C, respectively, with corresponding biases of −0.41° and −0.38°C. MAE differed seasonally for all three weather variables, but the method was stable despite variation in the number of base stations available for each day.

Atmospheric deposition maps for the Rocky Mountains

Variability in atmospheric deposition across the Rocky Mountains is influenced by elevation, slope, aspect, and precipitation amount and by regional and local sources of air pollution. To improve estimates of deposition in mountainous regions, maps of average annual atmospheric deposition loadings of nitrate, sulfate, and acidity were developed for the Rocky Mountains by using spatial statistics. A parameter-elevation regressions on independent slopes model (PRISM) was incorporated to account for variations in precipitation amount over mountainous regions. Chemical data were obtained from the National Atmospheric Deposition Program/National Trends Network and from annual snowpack surveys conducted by the US Geological Survey and National Park Service, in cooperation with other Federal, State and local agencies. Surface concentration maps were created by ordinary kriging in a geographic information system, using a local trend and mathematical model to estimate the spatial variance. Atmospheric-deposition maps were constructed at 1-km resolution by multiplying surface concentrations from the kriged grid and estimates of precipitation amount from the PRISM model. Maps indicate an increasing spatial trend in concentration and deposition of the modeled constituents, particularly nitrate and sulfate, from north to south throughout the Rocky Mountains and identify hot-spots of atmospheric deposition that result from combined local and regional sources of air pollution. Highest nitrate (2.5–3.0 kg/ha N) and sulfate (10.0–12.0 kg/ha SO4) deposition is found in northern Colorado.

Mapping the climate of Puerto Rico, Vieques and Culebra

AbstractSpatially explicit climate data contribute to watershed resource management, mapping vegetation type with satellite imagery, mapping present and hypothetical future ecological zones, and predicting species distributions. The regression‐based parameter–elevation regressions on independent slopes model (PRISM) uses spatial data sets, a knowledge base and expert interaction to generate grids of climate variables that are compatible with geographical information systems. This study applied PRISM to generate maps of mean monthly and annual precipitation and minimum and maximum temperature for the Caribbean islands of Puerto Rico, Vieques and Culebra over the 1963–95 averaging period. PRISM was run under alternative parameterizations that simulated simpler interpolation methods as well as the full PRISM model. For temperature, the standard PRISM parameterization was compared with a hypsometric method (HYPS), in which the temperature–elevation slope was assumed to be −6.5 °C/km. For precipitation, the standard PRISM parameterization was compared with an inverse‐distance weighting interpolation (IDW). Spatial temperature patterns were linked closely to elevation, topographic position, and coastal proximity. Both PRISM and HYPS performed well for July maximum temperature, but HYPS performed relatively poorly for January minimum temperature, due primarily to lack of a spatially varying temperature–elevation slope, vertical atmospheric layer definition, and coastal proximity guidance. Mean monthly precipitation varied significantly throughout the year, reflecting seasonally differing moisture trajectories. Spatial precipitation patterns were associated most strongly with elevation, upslope exposure to predominant moisture‐bearing winds, and proximity to the ocean. IDW performed poorly compared with PRISM, due largely to the lack of elevation and moisture‐availability information. Overall, the full PRISM approach resulted in greatly improved performance over simpler methods for precipitation and January minimum temperature, but only a small improvement for July maximum temperature. Comparisons of PRISM mean annual temperature and precipitation maps with previously published hand‐drawn maps showed similar overall patterns and magnitudes, but the PRISM maps provided much more spatial detail. Copyright © 2003 Royal Meteorological Society

ESTIMATION OF PARAMETERS CHARACTERIZING FREQUENCY DISTRIBUTIONS OF TIMES BETWEEN STORMS

Long records of precipitation data of the order of minutes are often needed for hydrology studies, but few such records exist over widespread areas. Storm simulation is an alternative approach to meet this need. One element of storm simulation is modeling the durations of dry periods between storms (storm–occurrence modeling). However, simple methods of characterizing storm occurrences and estimating simulation parameters are needed for practical storm simulation. One method for characterizing times between storms (TBS) is by using the “exponential method.” This method assumes that TBS greater than a minimum TBS follows an exponential distribution. Two parameters are necessary in the exponential method, the minimum time between independent storms (called critical time between storms, or CTBS), and the average time between independent storms (ATBS). Methods for estimating these parameters were explored using 34 recording rain gauges over a 225,000 km2 area encompassing the Plains area of eastern Colorado and its periphery. Monthly CTBS values ranged from 0.2 d to 2.5 d with a median of 0.8 d. Monthly ATBS ranged from 2.0 d to 13.0 d with a median of 4.5 d. Monthly mapping of CTBS and ATBS allows estimation of these two parameters and incorporates observed spatial and temporal variation. All regressions of CTBS vs. average monthly precipitation (Pmo), ATBS vs. Pmo, and CTBS vs. ATBS using collapsed monthly data yielded poor correlations. However, analysis of monthly data at individual stations yielded good log–linear correlations between ATBS and Pmo, and fair linear correlations between CTBS and ATBS. Intercept and slope parameters for each of the two regressions were correlated across the study area. The results suggest that further categorization of raw precipitation data into “wet,” “dry,” and “normal” categories may minimize errors in computing CTBS and ATBS. Subsequent mapping of regression parameters may improve the CTBS– and ATBS–estimating methods discussed in this article. A fixed CTBS (e.g., 6 hr often used in precipitation analyses) did not capture the month–to–month variability observed in measured data, and was much shorter than required for statistical independence between storms (6 hr is only 22% to 36% of values of CTBS found in this study). PRISM (parameter–elevation regressions on independent slopes model) maps of Pmo across the U.S. are a source of readily available Pmo data for use in the regressions investigated. The methods developed for estimating CTBS and ATBS are a promising simple parameterization framework for quantifying the temporal and spatial characteristics of independent storms and frequency distributions of TBS for stochastic–storm generation and other hydrological investigations.

Long-term climate patterns in Alaskan surface temperature and precipitation and their biological consequences

Mean monthly climate maps of Alaskan surface temperature and precipitation produced by the parameter-elevation regression on independent slopes model (PRISM) were analyzed. Alaska is divided into interior and coastal zones with consistent but different climatic variability separated by a transition region; it has maximum interannual variability but low long-term mean variability. Pacific decadal oscillation (PDO)- and El Nino Southern Oscillation (ENSO)-type events influence Alaska surface temperatures weakly (1-2/spl deg/C) statewide. PDO has a stronger influence than ENSO on precipitation but its influence is largely localized to coastal central Alaska. The strongest influence of Arctic oscillation (AO) occurs in northern and interior Alaskan precipitation. Four major ecosystems are defined. A major eco-transition zone occurs between the interior boreal forest and the coastal rainforest. Variability in insolation, surface temperature, precipitation, continentality, and seasonal changes in storm track direction explain the mapped ecosystems. Lack of westward expansion of the interior boreal forest into the western shrub tundra is influenced by the coastal marine boundary layer (enhanced cloud cover, reduced insolation, cooler surface and soil temperatures).

A knowledge-based approach to the statistical mapping of climate

The demand for spatial climate data in digital form has risen dramatically in recent years. In response to this need, a variety of statistical techniques have been used to facilitate the pro- duction of GIS-compatible climate maps. However, observational data are often too sparse and unrepresentative to directly support the creation of high-quality climate maps and data sets that truly represent the current state of knowledge. An effective approach is to use the wealth of expert knowl- edge on the spatial patterns of climate and their relationships with geographic features, termed 'geospatial climatology', to help enhance, control, and parameterize a statistical technique. Described here is a dynamic knowledge-based framework that allows for the effective accumulation, application, and refinement of climatic knowledge, as expressed in a statistical regression model known as PRISM (parameter-elevation regressions on independent slopes model). The ultimate goal is to develop an expert system capable of reproducing the process a knowledgeable climatologist would use to create high-quality climate maps, with the added benefits of consistency and repeata- bility. However, knowledge must first be accumulated and evaluated through an ongoing process of model application; development of knowledge prototypes, parameters and parameter settings; test- ing; evaluation; and modification. This paper describes the current state of a knowledge-based framework for climate mapping and presents specific algorithms from PRISM to demonstrate how this framework is applied and refined to accommodate difficult climate mapping situations. A weighted climate-elevation regression function acknowledges the dominant influence of elevation on climate. Climate stations are assigned weights that account for other climatically important factors besides elevation. Aspect and topographic exposure, which affect climate at a variety of scales, from hill slope to windward and leeward sides of mountain ranges, are simulated by dividing the terrain into topographic facets. A coastal proximity measure is used to account for sharp climatic gradients near coastlines. A 2-layer model structure divides the atmosphere into a lower boundary layer and an upper free atmosphere layer, allowing the simulation of temperature inversions, as well as mid-slope precipitation maxima. The effectiveness of various terrain configurations at producing orographic precipitation enhancement is also estimated. Climate mapping examples are presented.

EFFECTS OF IMPROVED PRECIPITATION ESTIMATES ON AUTOMATED RUNOFF MAPPING: EASTERN UNITED STATES1

ABSTRACT: We evaluated maps of runoff created by means of two automated procedures. We implemented each procedure using precipitation estimates of both 5‐km and 10‐km resolution from PRISM (Parameter‐elevation Regressions on Independent Slopes Model). Our goal was to determine if using the 5‐km PRISM estimates would improve map accuracy. Visual inspection showed good general agreement among our runoff maps, as well as between our maps and one produced using a manual method. A quantitative uncertainty analysis comparing runoff interpolated from our maps with gage data that had been withheld showed slightly smaller actual and percentage interpolation errors for the 5‐km PRISM‐based maps. Our analyses suggest a modest region‐wide improvement in runoff map accuracy with the use of PRISM‐based precipitation estimates of 5‐km (compared to 10‐km) resolution.