Parameter-elevation Relationships On Independent Slopes Model Research Articles

Relative humidity, temperature, and hypertensive disorders of pregnancy: Findings from the Project Viva cohort

BackgroundPreeclampsia is a multi-system hypertensive disorder of pregnancy that is a leading cause of maternal and fetal morbidity and mortality. Prior studies disagree on the cause and even the presence of seasonal patterns in its incidence. Using unsuitable time windows for seasonal exposures can bias model results, potentially explaining these inconsistencies. ObjectivesWe aimed to investigate humidity and temperature as possible causes for seasonal trends in preeclampsia in Project Viva, a prebirth cohort in Boston, Massachusetts, considering only exposure windows that precede disease onset. MethodsUsing the Parameter-elevation Relationships on Independent Slopes Model (PRISM) Climate Dataset, we estimated daily residential temperature and relative humidity (RH) exposures during pregnancy. Our primary multinomial regression adjusted for person-level covariates and season. Secondary analyses included distributed lag models (DLMs) and adjusted for ambient air pollutants including fine particulates (PM2.5). We used Generalized Additive Mixed Models (GAMMs) for systolic blood pressure (SBP) trajectories across hypertensive disorder statuses to confirm exposure timing. ResultsWhile preeclampsia is typically diagnosed late in pregnancy, GAMM-fitted SBP trajectories for preeclamptic and non-preeclamptic women began to diverge at around 20 weeks’ gestation, confirming the need to only consider early exposures. In the primary analysis with 1776 women, RH in the early second trimester, weeks 14–20, was associated with significantly higher odds of preeclampsia (OR per IQR increase: 1.81, 95% CI: 1.10, 2.97). The DLM corroborated this window, finding a positive association from weeks 12–20. There were no other significant associations between RH or temperature and preeclampsia or gestational hypertension in any other time period. DiscussionThe association between preeclampsia and RH in the early second trimester was robust to model choice, suggesting that RH may contribute to seasonal trends in preeclampsia incidence. Differences between these results and those of prior studies could be attributable to exposure timing differences.

Regionalization of hydroclimate variables in the contiguous United States

We apply a hierarchical clustering algorithm to the Parameter-elevation Relationships on Independent Slopes Model (PRISM) database. The method employs linkage clustering while forcing spatial contiguity. We apply it to the lower-48 United States, deriving regions that are based on temperature and precipitation averages and anomalies, as well as statistical parameters underlying several drought and intense precipitation measures. Resulting regions make intuitive sense from the perspective of driving influences on temperature and precipitation averages and anomalies, and are compatible with results from another empirically derived clustering scheme. Regions selected for individual variables show high similarity across different time frames. There is slightly less similarity when comparing regions created for different monthly or daily hydroclimate variables, and relatively low similarity between monthly vs. daily measures. It is unlikely that any one regionalization solution could summarize hydroclimate extremes given the wide range of variables used to describe them, but geographically sensitive datasets like PRISM and flexible algorithms provide useful methods for regionalization that can aid in drought monitoring and forecasting, and with impacts and planning associated with heavy precipitation.

Agreement in extreme precipitation exposure assessment is modified by race and social vulnerability.

Epidemiologic investigations of extreme precipitation events (EPEs) often rely on observations from the nearest weather station to represent individuals' exposures, and due to structural factors that determine the siting of weather stations, levels of measurement error and misclassification bias may differ by race, class, and other measures of social vulnerability. Gridded climate datasets provide higher spatial resolution that may improve measurement error and misclassification bias. However, similarities in the ability to identify EPEs among these types of datasets have not been explored. In this study, we characterize the overall and temporal patterns of agreement among three commonly used meteorological data sources in their identification of EPEs in all census tracts and counties in the conterminous United States over the 1991-2020 U.S. Climate Normals period and evaluate the association between sociodemographic characteristics with agreement in EPE identification. Daily precipitation measurements from weather stations in the Global Historical Climatology Network (GHCN) and gridded precipitation estimates from the Parameter-elevation Relationships on Independent Slopes Model (PRISM) and the North American Land Data Assimilation System (NLDAS) were compared in their ability to identify EPEs defined as the top 1% of precipitation events or daily precipitation >1 inch. Agreement among these datasets is fair to moderate from 1991 to 2020. There are spatial and temporal differences in the levels of agreement between ground stations and gridded climate datasets in their detection of EPEs in the United States from 1991 to 2020. Spatial variation in agreement is most strongly related to a location's proximity to the nearest ground station, with areas furthest from a ground station demonstrating the lowest levels of agreement. These areas have lower socioeconomic status, a higher proportion of Native American population, and higher social vulnerability index scores. The addition of ground stations in these areas may increase agreement, and future studies intending to use these or similar data sources should be aware of the limitations, biases, and potential for differential misclassification of exposure to EPEs. Most importantly, vulnerable populations should be engaged to determine their priorities for enhanced surveillance of climate-based threats so that community-identified needs are met by any future improvements in data quality.

Projecting live fuel moisture content via deep learning

Background Live fuel moisture content (LFMC) is a key environmental indicator used to monitor for high wildfire risk conditions. Many statistical models have been proposed to predict LFMC from remotely sensed data; however, almost all these estimate current LFMC (nowcasting models). Accurate modelling of LFMC in advance (projection models) would provide wildfire managers with more timely information for assessing and preparing for wildfire risk. Aims The aim of this study was to investigate the potential for deep learning models to predict LFMC across the continental United States 3 months in advance. Method Temporal convolutional networks were trained and evaluated using a large database of field measured samples, as well as year-long time series of MODerate resolution Imaging Spectroradiometer (MODIS) reflectance data and Parameter-elevation Relationships on Independent Slopes Model (PRISM) meteorological data. Key results The proposed 3-month projection model achieved an accuracy (root mean squared error (RMSE) 27.52%; R2 0.47) close to that of the nowcasting model (RMSE 26.52%; R2 0.51). Conclusions The study is the first to predict LFMC with a 3-month lead-time, demonstrating the potential for deep learning models to make reliable LFMC projections. Implications These findings are beneficial for wildfire management and risk assessment, showing proof-of-concept for providing advance information useful to help mitigate the effect of catastrophic wildfires.

Potential Impacts of Different Occupational Outdoor Heat Exposure Thresholds among Washington State Crop and Construction Workers and Implications for Other Jurisdictions.

Occupational heat exposure is associated with substantial morbidity and mortality among outdoor workers. We sought to descriptively evaluate spatiotemporal variability in heat threshold exceedances and describe potential impacts of these exposures for crop and construction workers. We also present general considerations for approaching heat policy-relevant analyses. We analyzed county-level 2011–2020 monthly employment (Bureau of Labor Statistics Quarterly Census of Employment and Wages) and environmental exposure (Parameter-elevation Relationships on Independent Slopes Model (PRISM)) data for Washington State (WA), USA, crop (North American Industry Classification System (NAICS) 111 and 1151) and construction (NAICS 23) sectors. Days exceeding maximum daily temperature thresholds, averaged per county, were linked with employment estimates to generate employment days of exceedances. We found spatiotemporal variability in WA temperature threshold exceedances and crop and construction employment. Maximum temperature exceedances peaked in July and August and were most numerous in Central WA counties. Counties with high employment and/or high numbers of threshold exceedance days, led by Yakima and King Counties, experienced the greatest total employment days of exceedances. Crop employment contributed to the largest proportion of total state-wide employment days of exceedances with Central WA counties experiencing the greatest potential workforce burden of exposure. Considerations from this analysis can help inform decision-making regarding thresholds, timing of provisions for heat rules, and tailoring of best practices in different industries and areas.

Hydrological evaluation of gridded climate datasets in a texas urban watershed using soil and water assessment tool and artificial neural network

Precipitation is a vital component of the hydrologic cycle, and successful hydrological modeling largely depends on the quality of precipitation input. Gridded precipitation datasets are gaining popularity as a convenient alternative for hydrological modeling. However, many of the gridded precipitation data have not been adequately assessed across a range of conditions. This study compared three gridded precipitation datasets, Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), and Parameter-elevation Relationships on Independent Slopes Model (PRISM). This study used the conventional gauge observation as reference data and evaluated the suitability of the three sources of gridded rainfall data to drive rainfall-runoff simulations. The Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) were used to create daily streamflow simulations in the Leon Creek Watershed (LCW) in San Antonio, Texas, with the TRMM, CFSR, PRISM, and gauge rainfall data used as inputs. A direct comparison of the gridded data sources showed that the TRMM data underestimates the volume of rainfall, while PRISM data most closely matches the volume of rainfall when compared to the gauge rainfall observations. The hydrological simulation results showed that the PRISM and TRMM rainfall data driven models had preferable results to the CFSR and gauge driven models, in terms of both graphical comparison and goodness-of-fit indicator values. Additionally, no significant discrepancy was found between SWAT and ANN simulation results when the same precipitation data source was used, while SWAT and ANN simulation results varied in an identical pattern when different precipitation data sources were applied.

Dynamical downscaling of global reanalysis data for high-resolution spatial modeling of snow accumulation/melting at the central/southern Sierra Nevada watersheds

Data scarcity is one of the major constraints for hydrological modeling at a mountainous region with complex terrains. In this study, WRF (Weather Research and Forecasting model) model was configured to produce high-resolution atmospheric data for spatial modeling of snow at seven watersheds in central/southern Sierra Nevada.The configured WRF model downscaled a global reanalysis dataset into hourly time intervals and 3-km spatial grid resolution. Performance of the configured WRF model was validated by comparing basin averages of daily precipitation and daily mean temperature between the WRF simulation and data from Parameter-elevation Relationships on Independent Slopes Model (PRISM). The WRF model demonstrated satisfactory performance in simulating peaks and variations of daily precipitation and daily mean temperature. The atmospheric data which were obtained by the dynamical downscaling of Climate Forecast System Reanalysis dataset (CFSR) were then used for calibration and validation of the snow model for the seven watersheds. The dynamical downscaling and the subsequent simulation of snow were performed through the application of The Watershed Environmental Hydrology Hydro-Climate Model (WEHY-HCM) into the central/southern Sierra Nevada. The daily scale validation of the calibrated snow model showed that the simulated snow water equivalent (SWE) was mostly in agreement with observed SWE, however, tended to somewhat underestimate SWE. The snow model simulation with the precipitation input from PRISM indicated that the results using the dynamical downscaling had comparable performance with the results using PRISM precipitation, while some observation points showed improvements of the simulated SWE by using PRISM precipitation. Spatial comparison of the snow cover between the snow model simulations and Moderate Resolution Imaging Spectroradiometer (MODIS) observations showed that the snow model was capable of detecting the snow cover at high elevation bands while the snow cover detection at low elevation bands, which correspond to the boundary between snow cover and no snow cover area, was less accurate.

Actual precipitation, predicted precipitation, and large herbivore condition in arid and semi-arid southern New Mexico

Precipitation affects large herbivore performance, but data is often available from only a limited number of weather stations that may not correspond with home ranges. Remotely-sensed estimates could potentially address this by providing estimates of precipitation at any point on the landscape. We tested whether 4-km resolution precipitation estimates derived from the Parameter-elevation Relationships on Independent Slopes Model (PRISM) were related to nutritional condition of 3 species of large herbivores and accurately predicted precipitation as measured at weather stations in arid New Mexico. Canonical correlation analyses indicated that condition of elk, mule deer, and pronghorn was related to both PRISM estimates and actual precipitation, but lactation status always had a stronger effect on condition with the exception of pronghorn. Both PRISM and actual precipitation showed comparable influence of precipitation, and the same general ranking of importance of seasonal precipitation, on condition. PRISM estimates were generally positively related to actual precipitation, but deviance was often high and PRISM tended to underestimate actual precipitation. For our study populations, PRISM provided no advantage over the use of dispersed weather stations for assessing nutritional condition. Performance of PRISM may have been hindered because of lack of spatial autocorrelation in precipitation within our sites.

Elevation‐dependent precipitation response to El Niño‐Southern oscillation revealed in headwater basins of the US central Rocky Mountains

AbstractWe used nonparametric analyses of gridded precipitation data from the Parameter‐Elevation Relationships on Independent Slopes Model (PRISM) to examine teleconnections between the El Niño‐Southern Oscillation (ENSO) and elevation‐related precipitation variability from 1950 to 2016 over the Snake‐Salt, Wind‐Bighorn, Green, and Platte basins, which span the North American continental divide. Our analyses parse data into three‐month‐long meteorological seasons and use the Japan Meteorological Agency (JMA) index to examine seasonal precipitation during El Niño and La Niña years. The results reveal statistically significant elevation‐related variability in seasonal precipitation response: ENSO‐related differences in precipitation were most widespread during winter, with below‐normal precipitation during El Niño and above‐normal precipitation during La Niña. These connections were particularly clear, with p‐values <.01, in analyses focused on high elevations, where most precipitation falls. Hence, the most apparent ENSO‐related impacts on precipitation were observed during months and at elevations that are crucial to snowpack growth. In addition, our analyses of low elevations of the Platte Basin reveal above‐normal spring and fall precipitation during El Niño and below‐normal fall precipitation during La Niña. Comparison of PRISM and North American Regional Reanalysis (NARR) datasets for the period of overlap (1979–2016) suggest that the elevational response is robust. Together, our findings expand the scope of documented ENSO teleconnections in western North America and underscore the importance of parsing precipitation response over temporal and spatial scales that are relevant to the timing and location of dominant precipitation inputs in the topographically diverse western United States.

Seasonal Analysis of the 2011–2017 North American Monsoon near its Northwest Boundary

The seasonal extent of the North American Monsoon (NAM) is highly variable and potentially sensitive to future climate change. Our objective was to determine how regional monsoonal patterns influence mountain precipitation near the NAM northwest boundary. Among the data we analyzed, a unique opportunity was provided by hourly observations collected on the Sheep Range (2300 m asl), in the Mojave Desert of southern Nevada, during 2011–2017. Long-term 800-m Parameter-elevation Relationships on Independent Slopes Model (PRISM) precipitation time series showed that the site is representative of mountain areas in the NAM northwest region. Based on in situ observations, we divided the water year into three seasons: cool (1 October through 31 March), early warm (1 April through last day with dewpoint <9.4 °C), and late warm (first day with dewpoint ≥9.4 °C through 30 September). Dewpoint temperature differed by about 8 °C between early warm season (mean of −6.3 °C) and late warm season (mean of 2.3 °C). According to ANCOVA model results, increasing hourly dewpoint associated with afternoon thunderstorms in the late warm season had the greatest relationship with hourly precipitation (F-value = 237.8, p-value < 0.01). Except for 2016, more precipitation fell at our study site during the late than the early warm season. Late warm season precipitation contributed the most (43–56%) to total water-year precipitation during the 2012–2015 extended drought. Southwestern USA regional composites of vertically integrated water vapor transport (IVT) suggested that water vapor in the cool and early warm season originated from the Pacific Ocean to the west, while a transition to a NAM-like pattern of northward IVT coincided with the late warm season.

Defining hydroclimatic regions using daily rainfall characteristics in the southern Appalachian Mountains

ABSTRACT This paper addresses warm season hydroclimatic variability in the southern Appalachian region of the southeastern U.S., where precipitation can vary as much as 127 mm or more, with maximum seasonal totals exceeding 736 mm in extreme cases. Despite the occurrence of droughts, floods, and their socioecological impacts, hydroclimate variability is still poorly understood. This study characterizes the regional scale variations in the hydroclimate by examining the daily distribution of precipitation patterns in different topographic environments. Parameter-elevation relationships on independent slopes model (PRISM) gridded precipitation estimates are used to identify the location and frequency of different types of rainfall events. Several types of clustering algorithms are used as a regionalization approach to define areas where the precipitation regime exhibits similarities in its frequency of occurrence. The results are compared with internal validation statistics and a visualization is used to assess how well the resulting hydroclimatic regions align with different topographic environments. This study reveals the intricate spatial footprint of dry and wet regimes and demonstrates how clustering applications can be used with gridded climate data to determine where extremes are most likely to develop across mountain catchments.

Short-term climate trends and the Swiss needle cast epidemic in Oregon’s public and private coastal forestlands

Swiss needle cast (SNC) is a fungal disease of Douglas-fir (Pseudotsuga menziesii) that is having important consequences on tree growth in the Pacific Northwest (PNW) region of the USA. Once considered innocuous in PNW forests, SNC symptom expression has increased rapidly in extent and intensity in recent decades. Previous research has linked the disease epidemiology of SNC to climate, and observations indicate a link with forestry practices of the 20th century as well, particularly with the conversion of old growth and mature mixed-conifer forests to young monocultures of Douglas-fir on private forestlands. Given the sensitivity of SNC intensification to forestry practices and changing climatic conditions, it seems plausible that disease behavior response to short-term directional climate changes could differ between contrasting forest management regimes. We compared the relationship between trends in canopy energy and water flux parameters detected during the spring and summer months (May–August) along the Pacific Coast of Oregon from 2003 to 2012, and the distribution of SNC symptoms in 2012 on private and public lands. Canopy energy and water exchange parameters were calculated with MODIS Land Surface Temperature (LST), and evapotranspiration (ET) data, and with Parameter-elevation Relationships on Independent Slopes Model (PRISM) precipitation data. We found that a higher level of deviance in SNC presence/absence could be explained on private land compared with public land. Proximity to coast explained 9.3% of the deviance on private land and 6.7% on public land. Trends in LST during May and August emerged as important and explained 7% of the deviance in SNC symptom distribution on private land compared with 2% on public land. When combined with proximity to coast, May and August LST explained 14% of the deviance in SNC symptom expression on private land and 8.7% on public land. We found a significant difference between public and private ownership for the proportion of SNC (p = 0.0006), and a significant interaction between ownership and distance to coast (p = 0.0019), such that across public and private ownership, distance to coast has a different effect. LST may provide valuable information on leaf wetness, or thermal properties of the canopy, possibly capturing both early season and late season dynamics important to SNC epidemiology. We find evidence that recent short-term directional climate changes may have contributed to differences in symptom development in Douglas-fir forests on private and public land, with symptoms more prevalent on private land.

Flood Simulation in South Carolina Watersheds Using Different Precipitation Inputs

Flooding induced by extreme rainfall events causes tremendous loss of life and property and infrastructure failure. Accurate representation of precipitation, which has high variation in space and time, is critical to hydrologic model simulations and flood analyses. In this study, we examined responses of differently sized United States Geological Survey (USGS) hydrologic units to heavy precipitation using three different data sets. The first consists of rainfall observed at individual meteorological gauges. The second uses the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC) 4 km gridded radar-estimated precipitation (GRIB) Stage IV data. The third one derives from the method we developed that blends gauge data with the spatial coverage of the Parameter-elevation Relationships on Independent Slopes Model (PRISM) data. We examined how two watersheds in South Carolina respond to the three different representations of heavy rainfall, using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) developed by the U.S. Army Corps of Engineers. We found that the latter two precipitation inputs that consider spatial representation of rainfall yielded similar performance and improved simulated streamflow as compared to simulation using rainfall observed at individual meteorological gauges. The method we developed overcomes the spatial sparsity of rain gauges required for interpolation and extends availability of precipitation surfaces. Our study advances the understanding of advantages and limitations of different precipitation products for flood simulation.

Watershed- to continental-scale influences on winter stormflow in the Southern Blue Ridge Mountains

Spatial and temporal influences on the winter (December-March) stormflow characteristics of fifteen United States Geological Survey (USGS)-gaged watersheds in the Southern Blue Ridge Mountains are identified: (1) watershed-scale differences in geomorphology; (2) continental-scale teleconnections during periods of wetness/dryness (based on the relative amount of winter precipitation over a consistent 20 year dataset); and (3) land cover in the context of soil parent material (e.g., development on alluvium/colluvium). Multiple regression was used to determine how much variance could be explained in five hydrologic variables describing the flashiness of peak flow (three original metrics), total seasonal flashiness (Richards Baker flashiness index), and the ratio of total winter stormflow to total discharge (the stormflow index). Models were constrained to three uncorrelated (|0.65|) variables to prevent overfitting to the dataset. Average-, dry-, and wet-years were subset using the z-scores for winter precipitation derived from the 4 km monthly PRISM (Parameter-elevation Relationships on Independent Slopes Model) dataset, for the period of 1986–2006. Relief, slope, and landscape connectivity explain the majority of explained variance in all five of the hydrologic variables during all time periods. During dry-, average-, and wet-years, atmospheric circulation patterns (i.e., North Atlantic Oscillation and Pacific/North American Pattern) explain more variance than total seasonal precipitation (PRISM), which is not true in the majority of the all-years models. Land cover explains only a small portion of the variance in regional stormflow and only when sub-divided based on soil parent material. Results provide a framework for connecting watershed-scale characteristics to regional- and continental-scale processes.

PRIDE 모델 버전 2.0의 검증

In this study we evaluated the performance of the PRIDE (PRISM based Dynamic downscaling Error correction) model version 2.0 to provide user groups with model performance for daily temperature and daily precipitation using 54 observation station data and Regional Climate Model (RCM) HadGEM3- RA data for 30 years from 1981 to 2010. As a result of the verification of the independent period, the PRIDE model was found to effectively correct the systematic error of the RCM. This was because the PRIDE model basically calculated seasonal cycle of daily time scale using observational grid data produced based on PRISM (Parameter-elevation Relationships on Independent Slopes Model) and used PRISM to replace seasonal cycle of the RCM. As a result of the verification by quantitative measures, the temperature simulated by the PRIDE model showed a 56% to 80% reduction in the systematic error of the RCM on average. The precipitation simulated by the PRIDE model showed a reduction of the systematic error of the RCM by about 41%. The PRIDE model reproduced the bi-modal peak of summer precipitation, but RCM did not simulate well. As a result of analyzing the histogram, the PRIDE model was found to improve the temperature characteristics of the RCM, which simulated less frequently than the observations, at a high temperature interval. The rainfall simulated by the PRIDE model reduced the error of the number of days of precipitation simulated by the RCM by about 43% and also reduced the error by about 46% in light precipitation days.

A warm-season comparison of WRF coupled to the CLM4.0, Noah-MP, and Bucket hydrology land surface schemes over the central USA

In climate modeling studies, there is a need to choose a suitable land surface model (LSM) while adhering to available resources. In this study, the viability of three LSM options (Community Land Model version 4.0 [CLM4.0], Noah-MP, and the five-layer thermal diffusion [Bucket] scheme) in the Weather Research and Forecasting model version 3.6 (WRF3.6) was examined for the warm season in a domain centered on the central USA. Model output was compared to Parameter-elevation Relationships on Independent Slopes Model (PRISM) data, a gridded observational dataset including mean monthly temperature and total monthly precipitation. Model output temperature, precipitation, latent heat (LH) flux, sensible heat (SH) flux, and soil water content (SWC) were compared to observations from sites in the Central and Southern Great Plains region. An overall warm bias was found in CLM4.0 and Noah-MP, with a cool bias of larger magnitude in the Bucket model. These three LSMs produced similar patterns of wet and dry biases. Model output of SWC and LH/SH fluxes were compared to observations, and did not show a consistent bias. Both sophisticated LSMs appear to be viable options for simulating the effects of land use change in the central USA.

High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset

AbstractA 69‐station, densely spaced rain gauge network was maintained over the period 1951–1958 in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA. This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10‐m elevation grid filtered to an approximately 7‐km effective wavelength explained the most variance in precipitation (R2 = 0.82–0.95). A ‘dump zone’ of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation–elevation relationship.These data and maps provided a rare ‘ground‐truth’ for estimating uncertainty in the national‐scale Parameter‐elevation Relationships on Independent Slopes Model (PRISM) precipitation grids for this location and time period. Differences between PRISM and ground‐truth were compared with uncertainty estimates produced by the PRISM model and cross‐validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations.The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12–20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high‐density data exist to gain a more comprehensive picture of the uncertainties in national‐level datasets, and can be used in network optimization exercises.

Towards an improved ensemble precipitation forecast: A probabilistic post-processing approach

Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble precipitation forecasts based on the statistical relationship between observations and forecasts. More specifically, the approach relies on a transfer function that is developed based on a bivariate joint distribution between the observations and the simulations in the historical period. The transfer function is used to post-process the forecast. In this study, we propose a Bayesian EPP approach based on copula functions (COP-EPP) to improve the reliability of the precipitation ensemble forecast. Evaluation of the copula-based method is carried out by comparing the performance of the generated ensemble precipitation with the outputs from an existing procedure, i.e. mixed type meta-Gaussian distribution. Monthly precipitation from Climate Forecast System Reanalysis (CFS) and gridded observation from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) have been employed to generate the post-processed ensemble precipitation. Deterministic and probabilistic verification frameworks are utilized in order to evaluate the outputs from the proposed technique. Distribution of seasonal precipitation for the generated ensemble from the copula-based technique is compared to the observation and raw forecasts for three sub-basins located in the Western United States. Results show that both techniques are successful in producing reliable and unbiased ensemble forecast, however, the COP-EPP demonstrates considerable improvement in the ensemble forecast in both deterministic and probabilistic verification, in particular in characterizing the extreme events in wet seasons.

Statistical downscaling for daily precipitation in Korea using combined PRISM, RCM, and quantile mapping: Part 1, methodology and evaluation in historical simulation

In this study, we present the Parameter-elevation Relationships on Independent Slopes Model (PRISM)-based Dynamic downscaling Error correction (PRIDE) model, which is suitable for complex topographies, such as the Korean peninsula. The PRIDE model is constructed by combining the PRISM module, the Regional Climate Model (RCM) anomaly, and quantile mapping (QM) to produce high-resolution (1 km) grid data at a daily time scale. The results show that the systematic bias of the RCM was significantly reduced by simply substituting the climatological observational seasonal cycle at a daily timescale for each grid point obtained from the PRISM. QM was then applied to correct additional systematic bias by constructing the transfer functions under the cumulative density function framework between the model and observation using six types of transfer functions. K-fold cross-validation of the PRIDE model shows that the number of modeled precipitation days is approximately 90~121% of the number of observed precipitation days for the five daily precipitation classes, indicating that the PRIDE model reasonably estimates the observational frequency of daily precipitation under a quantile framework. The relative Mean Absolute Error (MAE) is also discussed in the framework of the intensity of daily precipitation.

A forest vulnerability index based on drought and high temperatures

ABSTRACT Increasing forest stress and tree mortality has been directly linked to combinations of drought and high temperatures. The climatic changes expected during the next decades – large increases in mean temperature, increased heat waves, and significant long-term regional drying in the western USA – will likely increase chronic forest stress and mortality. The aim of this research is to develop and apply a new forest vulnerability index (FVI) associated with drought and high temperatures across the Pacific Northwest region (PNW; Oregon and Washington) of the USA during the MODIS Aqua era (since 2003). Our technique incorporates the alterations to canopy water and energy exchange processes caused by drought and high temperatures with spatially continuous MODIS land surface temperature (LST) and evapotranspiration (ET), and with Parameter-elevation Relationships on Independent Slopes Model (PRISM) precipitation (P) data. With P and ET, we calculate a monthly water balance variable for each individual pixel normalized by forest type group (FTG), and then difference the water balance with the corresponding normalized monthly mean LST to calculate a monthly forest stress index (FSI). We then extract the pixel-specific (800-m resolution) statistically significant temporal trends of the FSI from 2003 to 2012 by month (April to October). The FVI is the slope of the monthly FSI across years, such that there is a FVI for each month. Statistically significant positive slopes indicate interannual increases in stress leading to expected forest vulnerability (positive FVI) for a given month. Positive FVI values were concentrated in the months of August and September, with peak vulnerability occurring at different times for different FTGs. Overall, increased vulnerability rates were the highest in drier FTGs such as Ponderosa Pine, Juniper, and Lodgepole Pine. Western Larch and Fir/Spruce/Mountain Hemlock groups occupy moister sites but also had relatively high proportion of positive FVI values. The Douglas-fir group had the second largest total area of increased vulnerability due to its large areal extent in the study area. Based on an analysis using imagery viewed in Google Earth, we confirm that areas with increased vulnerability are associated with greater amounts of stress and mortality. The FVI is a new way to conceptualize and monitor forest vulnerability based on first-order principles and has the potential to be generalized to other geographical areas.