Parameter-elevation Regressions On Independent Slopes Model Research Articles

Using the Airborne Snow Observatory to Assess Remotely Sensed Snowfall Products in the California Sierra Nevada

AbstractThe Airborne Snow Observatory (ASO) performed two acquisitions over two mountainous basins in California on 29 January and 3 March 2017, encompassing two atmospheric river events that brought heavy snowfall to the area. These surveys produced high‐resolution (50 m) maps of snow depth and snow water equivalent (SWE) that were used to estimate monthly areal snowfall accumulation. Comparison of ASO snow accumulation with point measurements showed that the ASO estimates ranged from −10 to +16% relative bias across three sites, which is likely inflated by the disagreement in areal representation of the quantities from the actual errors in these products. The aggregated SWE accumulations from ASO are then used to evaluate a suite of in situ based and remote sensing precipitation products. During the study period, Parameter‐Elevation Regressions on Independent Slopes Model (PRISM) and Mountain Mapper estimates had relative bias <10% compared with ASO‐based estimates of snow accumulation, but satellite and radar products largely underestimate snowfall accumulation compared to ASO (up to 50%). Despite their underestimation, satellite and radar products show correlation coefficients >0.8 with ASO snow accumulation over the selected grids at the monthly scale. Finally, we leveraged the fine‐scale sampling of the spatially complete ASO products to show that by moving from 100 m to 2 km spatial scales, the perceived bias errors SWE at point locations increased by an order of magnitude, displaying a nonlinear relationship. The study demonstrates that ASO acquisitions in cold months can bring a new and effective approach to spatial evaluation of precipitation products.

Downscaling of ERA-Interim Temperature in the Contiguous United States and Its Implications for Rain–Snow Partitioning

Abstract Precipitation phase has an important influence on hydrological processes. The Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) uses temperature data from reanalysis products to implement rain–snow classification. However, the coarse resolution of reanalysis data may not reveal the spatiotemporal variabilities of temperature, necessitating appropriate downscaling methods. This study compares the performance of eight air temperature Ta downscaling methods in the contiguous United States and six mountain ranges using temperature from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) as the benchmark. ERA-Interim Ta is downscaled from the original 0.75° to 0.1°. The results suggest that the two purely statistical downscaling methods [nearest neighbor (NN) and bilinear interpolation (BI)] show similar performance with each other. The five downscaling methods based on the free-air temperature lapse rate (TLR), which is calculated using temperature and geopotential heights at different pressure levels, notably improves the accuracy of Ta. The improvement is particularly obvious in mountainous regions. We further calculated wet-bulb temperature Tw, for rain–snow classification, using Ta and dewpoint temperature from ERA-Interim and PRISM. TLR-based downscaling methods result in more accurate Tw compared to NN and BI in the western United States, whereas the improvement is limited in the eastern United States. Rain–snow partitioning is conducted using a critical threshold of Tw with Snow Data Assimilation System (SNODAS) snowfall data serving as the benchmark. ERA-Interim-based Tw using TLR downscaling methods is better than that using NN/BI and IMERG precipitation phase. In conclusion, TLR-based downscaling methods show promising prospects in acquiring high-quality Ta and Tw with high resolution and improving rain–snow partitioning, particularly in mountainous regions.

Hydrologic responses to climate change using downscaled GCM data on a watershed scale

AbstractThe changing climate has raised significant concerns for water resources, especially on a watershed scale. In this study, the downscaled global circulation model (GCM) products were further bias corrected and evaluated for the period of 1981–2099. Driven by the bias-corrected products, a calibrated Precipitation-Runoff Modeling System (PRMS) model was used to assess long-term hydrologic responses in the Lehman Creek watershed, eastern Nevada. The results of this study show: (1) the Parameter–elevation Regressions on Independent Slopes Model (PRISM) products offer a reliable replacement for limited observations for bias correction using quantile mapping (QM) technique; (2) average increases of 2.3 °C, 2.2 °C, and 35.1 mm in maximum temperature, minimum temperature, and precipitation by the end of century; (3) an annual streamflow increase of 7.6–11.6% with greatest increases in April and greatest decreases in June; (4) 20 days' earlier shift in annual peak flow – as indicated by the date of winter-spring center of volume – by the end of the century. For management of local water resources, this study provides a better understanding of variations in the streamflow rate and timing to a potential climate change in the study area as well as corresponding uncertainties in the estimation processes.

Independent Evaluation of Frozen Precipitation from WRF and PRISM in the Olympic Mountains

Abstract Estimates of precipitation from the Weather Research and Forecasting (WRF) Model and the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) are widely used in complex terrain to obtain spatially distributed precipitation data. The authors evaluated both WRF (4/3 km) and PRISM’s (800-m annual climatology) ability to estimate frozen precipitation using the hydrologic model Structure for Unifying Multiple Modeling Alternatives (SUMMA) and a unique set of spatiotemporal snow depth and snow water equivalent (SWE) observations collected for the Olympic Mountain Experiment (OLYMPEX) ground validation campaign during water year 2016. When SUMMA was forced with WRF precipitation and used a calibrated, wet-bulb-temperature-based method for partitioning rain versus snow, its estimation of near-peak SWE was biased low by 21% on average. However, when SUMMA was allowed to partition WRF total precipitation into rain and snow based on output from WRF’s microphysical scheme (WRFMPP), simulations of snow depth and SWE were near equal to or better than simulations that used PRISM-derived precipitation with the calibrated partitioning method. Over all sites, WRFMPP and simulations that used PRISM-derived precipitation had relatively unbiased estimates of near-peak SWE, but both simulated absolute errors in near-peak SWE of 30%–60% at a few locations. Since, on average, WRFMPP had similar errors to PRISM, WRFMPP suggested a promising path forward in hydrology, as it was independent of gauge data and did not require SWE observations for calibration. Furthermore, in similar maritime environments, hydrologic modelers should pay close attention to decisions regarding rain-versus-snow partitioning, wind speed, and incoming longwave radiation.

Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS

Abstract. This study applied the exponential filter to produce an estimate of root-zone soil moisture (RZSM). Four types of microwave-based, surface satellite soil moisture were used. The core remotely sensed data for this study came from NASA's long-lasting AMSR-E mission. Additionally, three other products were obtained from the European Space Agency Climate Change Initiative (CCI). These datasets were blended based on all available satellite observations (CCI-active, CCI-passive, and CCI-combined). All of these products were 0.25° and taken daily. We applied the filter to produce a soil moisture index (SWI) that others have successfully used to estimate RZSM. The only unknown in this approach was the characteristic time of soil moisture variation (T). We examined five different eras (1997–2002; 2002–2005; 2005–2008; 2008–2011; 2011–2014) that represented periods with different satellite data sensors. SWI values were compared with in situ soil moisture data from the International Soil Moisture Network at a depth ranging from 20 to 25 cm. Selected networks included the US Department of Energy Atmospheric Radiation Measurement (ARM) program (25 cm), Soil Climate Analysis Network (SCAN; 20.32 cm), SNOwpack TELemetry (SNOTEL; 20.32 cm), and the US Climate Reference Network (USCRN; 20 cm). We selected in situ stations that had reasonable completeness. These datasets were used to filter out periods with freezing temperatures and rainfall using data from the Parameter elevation Regression on Independent Slopes Model (PRISM). Additionally, we only examined sites where surface and root-zone soil moisture had a reasonably high lagged r value (r > 0. 5). The unknown T value was constrained based on two approaches: optimization of root mean square error (RMSE) and calculation based on the normalized difference vegetation index (NDVI) value. Both approaches yielded comparable results; although, as to be expected, the optimization approach generally outperformed NDVI-based estimates. The best results were noted at stations that had an absolute bias within 10 %. SWI estimates were more impacted by the in situ network than the surface satellite product used to drive the exponential filter. The average Nash–Sutcliffe coefficients (NSs) for ARM ranged from −0. 1 to 0.3 and were similar to the results obtained from the USCRN network (0.2–0.3). NS values from the SCAN and SNOTEL networks were slightly higher (0.1–0.5). These results indicated that this approach had some skill in providing an estimate of RZSM. In terms of RMSE (in volumetric soil moisture), ARM values actually outperformed those from other networks (0.02–0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06 and SNOTEL average RMSE values were higher (0.05–0.07). These values were close to 0.04, which is the baseline value for accuracy designated for many satellite soil moisture missions.

Bias Correction of Long-Term Satellite Monthly Precipitation Product (TRMM 3B43) over the Conterminous United States

Abstract The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has provided a valuable precipitation dataset for hydrometeorological studies (1998–2015). However, TMPA shows some differences when compared to the ground-based estimates. In this study, a correction model is developed to improve the accuracy of the TRMM precipitation monthly product by reducing the bias compared to the ground-based estimates. The TRMM 3B43 precipitation product is compared with the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) and with gridded precipitation estimates acquired from the CPC Unified Precipitation Project, two ground-based precipitation estimates, in the conterminous United States. The bias between the satellite and ground-based estimates is compared with mean surface temperature and elevation, respectively. A weak linear relationship is observed between the bias and temperature but a moderate inverse linear relationship is observed between the bias and elevation. Based on these observations, a linear model is developed for the TRMM 3B43–PRISM bias and elevation. The developed model is calibrated and validated using Monte Carlo cross validation with 25% of the available data as a calibration set and the remaining 75% of the data as a validation set. The estimated model parameters are then used in a correction formula for the TRMM 3B43 dataset for elevations above 1500 m above mean sea level. The corrected TRMM 3B43 product is verified for the high-elevation regions over the entire United States as well as in two high-elevation local regions in the western United States. The results show a significant improvement in the accuracy of the monthly satellite product in the high elevations of the United States.

Testing the daily PRISM air temperature model on semiarid mountain slopes

AbstractStudies in mountainous terrain related to ecology and hydrology often use interpolated climate products because of a lack of local observations. One data set frequently used to develop plot‐to‐watershed‐scale climatologies is PRISM (Parameter‐elevation Regression on Independent Slopes Model) temperature. Benefits of this approach include geographically weighted station observations and topographic positioning modifiers, which become important factors for predicting temperature in complex topography. Because of the paucity of long‐term climate records in mountain environments, validation of PRISM algorithms across diverse regions remains challenging, with end users instead relying on atmospheric relationships derived in sometimes distant geographic settings. Presented here are results from testing observations of daily temperature maximum (TMAX) and minimum (TMIN) on 16 sites in the Walker Basin, California‐Nevada, located on open woodland slopes ranging from 1967 to 3111 m in elevation. Individual site mean absolute error varied from 1.1 to 3.7°C with better performance observed during summertime as opposed to winter. We observed a consistent cool bias in TMIN for all seasons across all sites, with cool bias in TMAX varying with season. Model error for TMIN was associated with elevation, whereas model error for TMAX was associated with topographic radiative indices (solar exposure and heat loading). These results demonstrate that temperature conditions across mountain woodland slopes are more heterogeneous than interpolated models (such as PRISM) predict, that drivers of these differences are complex and localized in nature, and that scientific application of atmospheric/climate models in mountains requires additional attention to model assumptions and source data.

High-Resolution Statistical Downscaling in Southwestern British Columbia

AbstractKnowledge from high-resolution daily climatological parameters is frequently sought after for increasingly local climate change assessments. This research investigates whether applying a simple postprocessing methodology to existing statistically downscaled temperature and precipitation fields can result in improved downscaled simulations useful at the local scale. Initial downscaled daily simulations of temperature and precipitation at 10-km resolution are produced using bias correction constructed analogs with quantile mapping (BCCAQ). Higher-resolution (800 m) values are then generated using the simpler climate imprint technique in conjunction with temperature and precipitation climatologies from the Parameter-Elevation Regression on Independent Slopes Model (PRISM). The potential benefit of additional downscaling to 800 m is evaluated using the “Climdex” set of 27 indices of extremes established by the Expert Team on Climate Change Detection and Indices (ETCCDI). These indices are also calculated from weather station observations recorded at 22 locations within southwestern British Columbia, Canada, to evaluate the performance of both the 10-km and 800-m datasets in replicating the observed quantities. In a 30-yr historical evaluation period, Climdex indices computed from 800-m simulated values display reduced error relative to local station observations than those from the 10-km dataset, with the greatest reduction in error occurring at high-elevation sites for precipitation-based indices.

Impacts of alternative climate information on hydrologic processes with SWAT: A comparison of NCDC, PRISM and NEXRAD datasets

Precipitation and temperature are two primary drivers that significantly affect hydrologic processes in a watershed. A network of land-based National Climatic Data Center (NCDC) weather stations has been typically used as a primary source of climate input for agro-ecosystem models. However, the network may lack the density to adequately capture spatial climate variability throughout large watersheds. High-resolution weather datasets based on 4km×4km grid, such as Next Generation Weather Radar (NEXRAD) and Parameter–Elevation Regressions on Independent Slopes Model (PRISM), have become increasingly available as alternatives to conventional land-based networks. The goal of this study was to evaluate impacts of the three weather datasets, NCDC, NEXRAD, and PRISM, on hydrologic processes in an agricultural catchment in Kansas. A method of collecting and processing three sets of weather input datasets was developed and applied to a calibrated Soil and Water Assessment Tool (SWAT) model for the Smoky Hill River watershed (SHRW) in west-central Kansas, which is sparsely covered by NCDC weather stations with fair to poor range of NEXRAD coverage. SHRW is a typical agricultural catchment in the Central Great Plains; research findings here may be applicable to large areas of the US with similar topography and climate conditions. The SWAT model based on PRISM dataset was able to capture daily streamflow alterations with a greater accuracy compared to NCDC and NEXRAD based SWAT models. With three different weather inputs, SWAT with NCDC consistently overestimated monthly stream discharges, while the SWAT models based on NEXRAD and PRISM datasets tended to underestimate monthly high flows of over 8m3s−1 and overestimate monthly low flows of below 1m3s−1. In general, all models overestimated streamflow in dry years and underestimated streamflow in wet years, however, the PRISM-based model generated smaller bias than the models utilizing NEXRAD or NCDC. The use of PRISM resulted in better statistical performance metrics for streamflow. The conducted study suggests that gridded weather datasets can significantly improve simulated streamflow at daily, monthly and yearly scales as compared to traditional land-based networks.

GEFS Precipitation Forecasts and the Implications of Statistical Downscaling over the Western United States

Abstract Contemporary operational medium-range ensemble modeling systems produce quantitative precipitation forecasts (QPFs) that provide guidance for weather forecasters, yet lack sufficient resolution to adequately resolve orographic influences on precipitation. In this study, cool-season (October–March) Global Ensemble Forecast System (GEFS) QPFs are verified using daily (24 h) Snow Telemetry (SNOTEL) observations over the western United States, which tend to be located at upper elevations where the orographic enhancement of precipitation is pronounced. Results indicate widespread dry biases, which reflect the infrequent production of larger 24-h precipitation events (≳22.9 mm in Pacific ranges and ≳10.2 mm in the interior ranges) compared with observed. Performance metrics, such as equitable threat score (ETS), hit rate, and false alarm ratio, generally worsen from the coast toward the interior. Probabilistic QPFs exhibit low reliability, and the ensemble spread captures only ~30% of upper-quartile events at day 5. In an effort to improve QPFs without exacerbating computing demands, statistical downscaling is explored based on high-resolution climatological precipitation analyses from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM), an approach frequently used by operational forecasters. Such downscaling improves model biases, ETSs, and hit rates. However, 47% of downscaled QPFs for upper-quartile events are false alarms at day 1, and the ensemble spread captures only 56% of the upper-quartile events at day 5. These results should help forecasters and hydrologists understand the capabilities and limitations of GEFS forecasts and statistical downscaling over the western United States and other regions of complex terrain.

Implementation and evaluation of a monthly water balance model over the US on an 800 m grid

AbstractWe simulate the 1950–2010 water balance for the conterminous U.S. (CONUS) with a monthly water balance model (MWBM) using the 800 m Parameter‐elevation Regression on Independent Slopes Model (PRISM) data set as model input. We employed observed snow and streamflow data sets to guide modification of the snow and potential evapotranspiration components in the default model and to evaluate model performance. Based on various metrics and sensitivity tests, the modified model yields reasonably good simulations of seasonal snowpack in the West (range of bias of ±50 mm at 68% of 713 SNOTEL sites), the gradients and magnitudes of actual evapotranspiration, and runoff (median correlation of 0.83 and median Nash‐Sutcliff efficiency of 0.6 between simulated and observed annual time series at 1427 USGS gage sites). The model generally performs well along the Pacific Coast, the high elevations of the Basin and Range and over the Midwest and East, but not as well over the dry areas of the Southwest and upper Plains regions due, in part, to the apportioning of direct versus delayed runoff. Sensitivity testing and application of the MWBM to simulate the future water balance at four National Parks when driven by 30 climate models from the Climate Model Intercomparison Program Phase 5 (CMIP5) demonstrate that the model is useful for evaluating first‐order, climate driven hydrologic change on monthly and annual time scales.

Early detection of drought onset using near surface temperature and humidity observed from space

ABSTRACTDrought is associated with severe societal impacts ranging from shortages of water for human consumption to agricultural failure and famine. An important aspect of drought forecast is determining the onset, which is critical for early warning efforts and water resources and agriculture planning. Indices of precipitation shortage have been widely used to detect the onset of drought because precipitation deficits often lead to shortages in other hydrologic variables such as soil moisture and runoff. The present work demonstrates that atmospheric temperature and humidity observations from the Atmospheric Infrared Sounder (AIRS) contain information that can be used to detect drought onset earlier than that obtained from precipitation deficit. By calculating the standardized indices for precipitation, near-surface temperature, vapour pressure deficit, and relative humidity, we show that in many regions of the world signals of drought onset can be detected from near-surface temperature and humidity data a few months earlier than those obtained from precipitation deficit. In particular, vapour pressure deficit showed higher effectiveness than relative humidity or temperature only. The outcome was generally consistent for the three- and six-month accumulations studied here. Further analysis using 65 years (1960–2014) of monthly temperature and humidity data derived from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) data set over the continental United States suggests that there is a good agreement between drought early detection signals obtained from AIRS and that from ground stations during the overlapped (2003–2014) period. Analysis using longer record suggests that the frequency of successful early detection of drought onset using temperature and humidity data shows regional shift towards eastern United States in the recent years.

Testing the generalized complementary relationship of evaporation with continental-scale long-term water-balance data

The original and revised versions of the generalized complementary relationship (GCR) of evaporation (ET) were tested with six-digit Hydrologic Unit Code (HUC6) level long-term (1981–2010) water-balance data (sample size of 334). The two versions of the GCR were calibrated with Parameter-Elevation Regressions on Independent Slopes Model (PRISM) mean annual precipitation (P) data and validated against water-balance ET (ETwb) as the difference of mean annual HUC6-averaged P and United States Geological Survey HUC6 runoff (Q) rates. The original GCR overestimates P in about 18% of the PRISM grid points covering the contiguous United States in contrast with 12% of the revised version. With HUC6-averaged data the original version has a bias of −25mmyr−1 vs the revised version’s −17mmyr−1, and it tends to more significantly underestimate ETwb at high values than the revised one (slope of the best fit line is 0.78 vs 0.91). At the same time it slightly outperforms the revised version in terms of the linear correlation coefficient (0.94 vs 0.93) and the root-mean-square error (90 vs 92mmyr−1).

The Intermediate Complexity Atmospheric Research Model (ICAR)

Abstract With limited computational resources, there is a need for computationally frugal models. This is particularly the case for atmospheric sciences, which have long relied on either simplistic analytical solutions or computationally expensive numerical models. The simpler solutions are inadequate for many problems, while the cost of numerical models makes their use impossible for many problems, most notably high-resolution climate downscaling applications spanning large areas, long time periods, and many global climate projections. Here the Intermediate Complexity Atmospheric Research model (ICAR) is presented to provide a new step along the modeling complexity continuum. ICAR leverages an analytical solution for high-resolution perturbations to wind velocities, in conjunction with numerical physics schemes, that is, advection and cloud microphysics, to simulate the atmosphere. The focus of the initial development of ICAR is for predictions of precipitation, and eventually temperature, humidity, and radiation at the land surface. Comparisons between ICAR and the Weather Research and Forecasting (WRF) Model for simulations over an idealized mountain are presented, as well as among ICAR, WRF, and the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) observation-based product for a year-long simulation over the Colorado Rockies. In the ideal simulations, ICAR matches WRF precipitation predictions across a range of environmental conditions with a coefficient of determination r2 of 0.92. In the Colorado Rockies, ICAR, WRF, and PRISM show very good agreement, with differences between ICAR and WRF comparable to the differences between WRF and PRISM in the cool season. For these simulations, WRF required 140–800 times more computational resources than ICAR.

Complementary‐relationship‐based 30 year normals (1981–2010) of monthly latent heat fluxes across the contiguous United States

AbstractThirty year normal (1981–2010) monthly latent heat fluxes (ET) over the conterminous United States were estimated by a modified Advection‐Aridity model from North American Regional Reanalysis (NARR) radiation and wind as well as Parameter‐Elevation Regressions on Independent Slopes Model (PRISM) air and dew‐point temperature data. Mean annual ET values were calibrated with PRISM precipitation (P) and validated against United States Geological Survey runoff (Q) data. At the six‐digit Hydrologic Unit Code level (sample size of 334) the estimated 30 year normal runoff (P – ET) had a bias of 18 mm yr−1, a root‐mean‐square error of 96 mm yr−1, and a linear correlation coefficient value of 0.95, making the estimates on par with the latest Land Surface Model results but without the need for soil and vegetation information or any soil moisture budgeting.

High-Elevation Precipitation Patterns: Using Snow Measurements to Assess Daily Gridded Datasets across the Sierra Nevada, California*

AbstractGridded spatiotemporal maps of precipitation are essential for hydrometeorological and ecological analyses. In the United States, most of these datasets are developed using the Cooperative Observer (COOP) network of ground-based precipitation measurements, interpolation, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) to map these measurements to places where data are not available. Here, we evaluate two daily datasets gridded at ° resolution against independent daily observations from over 100 snow pillows in California’s Sierra Nevada from 1990 to 2010. Over the entire period, the gridded datasets performed reasonably well, with median total water-year errors generally falling within ±10%. However, errors in individual storm events sometimes exceeded 50% for the median difference across all stations, and in many cases, the same underpredicted storms appear in both datasets. Synoptic analysis reveals that these underpredicted storms coincide with 700-hPa winds from the west or northwest, which are associated with post-cold-frontal flow and disproportionately small precipitation rates in low-elevation valley locations, where the COOP stations are primarily located. This atmospheric circulation leads to a stronger than normal valley-to-mountain precipitation gradient and underestimation of actual mountain precipitation. Because of the small average number of storms (<10) reaching California each year, these individual storm misses can lead to large biases (~20%) in total water-year precipitation and thereby significantly affect estimates of statewide water resources.

Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002–2012)

Abstract. We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over the contiguous United States (CONUS) for the period 2002–2012. This comparison effort includes satellite multi-sensor data sets (bias-adjusted TMPA 3B42, near-real-time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation data sets are compared with surface observations from the Global Historical Climatology Network-Daily (GHCN-D) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (±6%). However, differences at the RFC are more important in particular for near-real-time 3B42RT precipitation estimates (−33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near-real-time counterpart 3B42RT. However, large biases remained for 3B42 over the western USA for higher average accumulation (≥ 5 mm day−1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in. day−1) over the Pacific Northwest. Furthermore, the conditional analysis and a contingency analysis conducted illustrated the challenge in retrieving extreme precipitation from remote sensing estimates.

Estimation of Corn and Soybeans Yields of the US Midwest using Satellite Imagery and Climate Dataset

This paper described the estimation of corn and soybeans yields of four states in the US Midwest using time-series satellite imagery and climate dataset between 2001 and 2012. We first constructed a database for (1) satellite imagery acquired from Terra MODIS (Moderate Resolution Imaging Spectroradiometer) including NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), LAI (Leaf Area Index), FPAR (Fraction of Photosynthetically Active Radiation), and GPP (Gross Primary Productivity), (2) climate dataset created by PRISM (Parameter-Elevation Regressions on Independent Slopes Model) such as precipitation and mean temperature, and (3) US yield statistics of corn and soybeans. Then we built OLS (Ordinary Least Squares) regression models for corn and soybeans yields between 2001 and 2010 after correlation analyses and multicollinearity tests. These regression models were used in estimating the yields of 2011 and 2012. Comparisons with the US yield statistics showed the RMSEs (Root Mean Squared Errors) of 0.892 ton/ha and 1.095 ton/ha for corn yields in 2011 and 2012 respectively, and those of 0.320 ton/ha and 0.391 ton/ha for soybeans yields. This result can be thought of as a good agreement with the in-situ statistics because the RMSEs were approximately 10% of the usual yields: 9 ton/ha for corn and 3 ton/ha for soybeans. Our approach presented a possibility for extending to more advanced statistical modeling of crop yields using satellite imagery and climate dataset.

Filling in the gaps: Inferring spatially distributed precipitation from gauge observations over complex terrain

AbstractIn recent decades, computational hydrology has trended toward higher‐resolution distributed models of the land surface. The accuracy of these models is limited, by uncertainty in distributed precipitation forcings. In this research, different precipitation distribution schemes were compared through inter‐station transfer experiments, as well as within a distributed hydrologic model applied at ≤150 m resolution over four study catchments in complex terrain. Distributed precipitation estimates were derived using multiplicative spatial scaling (MSS) and map‐based precipitation (MBP) techniques including both climatological and time‐varying spatial information from a range of native spatial resolutions (500 m–4 km). The primary interest was to evaluate a novel application of satellite‐based snow water equivalent (SWE) reconstruction (RSWE) as a surrogate for cold season precipitation against a common source of spatial precipitation information: the Parameter‐elevation Regressions on Independent Slopes Model (PRISM). An elevation‐based orographic precipitation gradient and simple inverse‐distance interpolation were also included as a baseline. For the case of RSWE, MSS was very sensitive to differences between observed SWE and reconstructed SWE, producing positive biases in the catchment water balance. Over 12 year simulations, daily streamflow correlations from the uncalibrated model were highest for RSWE when adjusted for accumulation‐season sublimation, and for monthly PRISM, both achieving R=0.8, where the former performed better during anomalous years for both MSS and MBP. Annual water balance ratios were much more sensitive to the choice of distribution scheme, with large overestimates, > 30%, for RSWE products using the MSS techniques versus MBP (< 15%). Overall, PRISM performed best using MSS, while RSWE performed best using MBP.

Modeling Subalpine and Upper Montane Forest-Climate Interactions in Colorado

The correlation of the distribution of five subalpine and montane tree species with precipitation and temperature were modeled using GIS. The results were compared with data presented by Thompson et al. (2000). Distributions of subalpine fir (Abies concolor), Engelmann spruce (Picea engelmannii), lodgepole pine (Pinus contorta), limber pine (Pinus flexilis) and bristlecone pine (Pinus aristata) were compared to estimated precipitation and temperature fields that had been constructed from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), National Climatic Data Center (NCDC) station data, and Snowpack Telemetry (SNOTEL) system data. Plant distribution maps from Little (1971) and CoGAP (2001) were used to determine the temperature and precipitation associated with the selected tree species. The estimates from this study were compared to those of Thompson, Anderson & Bartlein (2000). In many cases precipitation and temperatures values were higher than those of Thompson, Anderson & Bartlein (2000). Suggestions are made to improve the predictive power of GIS analysis for mapping climate and plant variability.