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To better understand how farmers perceive and adapt to climate change, climate trends and a survey of farmer attitudes and behavior in the upper Brahmaputra valley zone (UBVZ) of India were analyzed. Rainfall and temperature trends were estimated in combination with the results from a detailed questionnaire of 384 farmers across 20 villages in rainfed areas of the UBVZ. From 1971 to 2007, the annual mean temperature in the UBVZ increased by 0.15 °C/decade while summer rainfall decreased markedly. Logistic regression was used for modeling the perceptions and adaptation behavior of farmers. Farmers perceptions of climate change tended to closely match those estimated from the climate data, but farmers with better access to water resources, credit, and those with higher family income, higher production, and larger farm sizes had more options to adapt and were more likely to adopt techniques to cope with climate change and variability. Factors such as age, education level, and family size of respondents were less likely to impact farmers’ decisions to adapt to climate change.

Tropical cyclones (TCs) are an important source of precipitation for much of the eastern United States. However, our understanding of the spatiotemporal variability of tropical cyclone precipitation (TCP) and the connections to large-scale atmospheric circulation is limited by irregularly distributed rain gauges and short records of satellite measurements. To address this, we developed a new gridded (0.25° × 0.25°) publicly available dataset of TCP (1948–2015; Tropical Cyclone Precipitation Dataset, or TCPDat) using TC tracks to identify TCP within an existing gridded precipitation dataset. TCPDat was used to characterize total June–November TCP and percentage contribution to total June–November precipitation. TCP totals and contributions had maxima on the Louisiana, North Carolina, and Texas coasts, substantially decreasing farther inland at rates of approximately 6.2–6.7 mm km−1. Few statistically significant trends were discovered in either TCP totals or percentage contribution. TCP is positively related to an index of the position and strength of the western flank of the North Atlantic subtropical high (NASH), with the strongest correlations concentrated in the southeastern United States. Weaker inverse correlations between TCP and El Niño–Southern Oscillation are seen throughout the study site. Ultimately, spatial variations of TCP are more closely linked to variations in the NASH flank position or strength than to the ENSO index. The TCP dataset developed in this study is an important step in understanding hurricane–climate interactions and the impacts of TCs on communities, water resources, and ecosystems in the eastern United States.

Using an ensemble of 10 statistically downscaled global climate model (GCM) simulations, we project future climate change impacts on the state of Indiana (IN) for two scenarios of greenhouse gas concentrations (a medium scenario—RCP4.5 and a high scenario—RCP 8.5) for three future time periods (2020s, 2050s, 2080s). Relative to a 1971–2000 baseline, the projections show substantial changes in temperature (T) for IN, with a change in the annual ensemble mean T for the 2080s RCP8.5 scenario of about 5.6 °C (10.1 °F). Such changes also indicate major changes in T extremes. For southern IN, the number of days with daily maximum T above 35 °C (95 °F) is projected to be about 100 days per year for the 2080s RCP8.5 scenario, as opposed to an average of 5 days for the historical baseline climate. Locations in northern IN could experience 50 days per year above 35 °C (95 °F) for the same conditions. Energy demand for cooling, as measured by cooling degree days (CDD), is projected to increase nearly fourfold in response to this extreme warming, but heating demand as measured by heating degree days (HDD) is projected to decline by 30%, which would result in a net reduction in annual heating/cooling energy demand for consumers. The length of the growing season is projected to increase by about 30 to 50 days by the 2080s for the RCP8.5 scenario, and USDA hardiness zones are projected to shift by about one full zone throughout IN. By the 2080s, all GCM simulations for the RCP8.5 scenario show higher annual precipitation (P) over the Midwest and IN. Projected seasonal changes in P include a 25–30% increase in winter and spring by the 2080s for the RCP8.5 scenarios and a 1–7% decline in summer and fall P (although there is a low model agreement in the latter two seasons). Rising T is projected to cause systematic decreases in the snow-to-rain ratio from Nov-Mar. Snow is projected to become uncommon in southern IN by the 2080s for the RCP8.5 scenario, and snowfall is substantially reduced in other areas of the state. The combined effects of these changes in T, P, and snowfall will likely result in increased surface runoff and flooding during winter and spring.

Earthquake‐triggered landslides are a significant hazard in seismically active regions, but our ability to assess the hazard they pose in near real‐time is limited. In this study, we present a new globally applicable model for seismically induced landslides based on the most comprehensive global dataset available; we use 23 landslide inventories that span a range of earthquake magnitudes and climatic and tectonic settings. We use logistic regression to relate the presence and distribution of earthquake‐triggered landslides with spatially distributed estimates of ground shaking, topographic slope, lithology, land cover type, and a topographic index designed to estimate variability in soil wetness to provide an empirical model of landslide distribution. We tested over 100 combinations of independent predictor variables to find the best‐fitting model, using a diverse set of statistical tests. Blind validation tests show the model accurately estimates the distribution of available landslide inventories. The results indicate that the model is reliable and stable, with high “balanced accuracy” (correctly vs. incorrectly classified pixels) for the majority of test events. A cross validation analysis shows high balanced accuracy for a majority of events as well. By combining near‐real time estimates of ground shaking with globally available landslide susceptibility data, this model provides a tool to estimate the distribution of coseismic landslide hazard within minutes of the occurrence of any earthquake worldwide for which a U.S. Geological Survey ShakeMap is available.

Mangroves have been massively degraded and deforested throughout the world, despite their known ecological and economic values. This study demonstrates the use of ALOS Global Digital Surface Model World (AW3D30) DSM dataset acquired between 2006 and 2011 to map canopy height of different succession stages of mangroves. We employed our previously developed canopy height model for assessing mangrove canopy height in three sites that vary in latitudinal position and mangrove degradation levels, ranging from relatively pristine mangrove ecosystems in Mimika Papua, Indonesia and Sundarbans, Bangladesh to severely degraded mangrove ecosystems in the Mahakam Delta, Indonesia. The study demonstrates good agreement between modeled and measured canopy height from these three sites as indicated by small mean absolute errors of 3.38 m at Mimika, 2.54 m at Sundarbans, and 2.89 m in the Mahakam Delta site. These findings further showed that, in the absence of synthetic aperture radar (SAR) based elevation data (such as SRTM and TanDEM-X), the ALOS PRISM data can be used to map mangrove heights (or in combination with SAR-based elevation data). Our results also pointed out that the ALOS data can differentiate the variability of canopy height estimation between an intact mangrove and severely degraded mangrove ecosystem.

The objective of this study was to evaluate the spatiotemporal dynamics of large area mangrove deforestation, aquaculture pond building, and the subsequent abandonment of ponds in a large delta in Indonesia, namely the Mahakam Delta. So, we developed and applied a novel methodology for exploring the lifespan of aquaculture ponds. Using historical multispectral and radar data, the lifespans of aquaculture ponds across the delta were estimated via a chronological analysis of the landscape into four different states: primary mangroves → deforested mangroves → ponds → abandoned/inactive ponds. Specifically, a combination of sequential classification and rule-based techniques were used to: 1) produce a time series of land cover maps from 1994 to 2015 and 2) quantify lifespans of aquaculture ponds in the delta. Results show that of the 110,000 ha of primary mangrove forests in the delta in 1994, 62% had been deforested by 2015, with a 4.5% annual rate of loss on average. The lifespan of aquaculture ponds in the delta varied between 1 and 22+ years, with most of the ponds having productive lifespans of 10 to 13 years. Ponds with relatively longer lifespans were located near the existing settlements in the delta. This study showed that the productive lifespan of most aquaculture ponds in deforested mangrove lands of Mahakam delta is relatively short, information that should be useful for developing appropriate management plans for the delta or similar coastal mangrove ecosystems. The abandoned ponds can potentially be rehabilitated for shrimp and fish production after applying appropriate restorative treatments or be targeted for mangrove restoration projects.

To classify trees along a spectrum of isohydric to anisohydric behavior is a promising new framework for identifying tree species' sensitivities to drought stress, directly related to the vulnerability of carbon uptake of terrestrial ecosystems with increased hydroclimate variability. Trees with isohydric strategies regulate stomatal conductance to maintain stationary leaf water potential, while trees with anisohydric strategies allow leaf water potential to fall, which in the absence of significant hydraulic cavitation will facilitate greater rates of carbon uptake. Despite the recognition of the gas exchange consequences of isohydric and anisohydric strategies for individual tree species, there have been few studies regarding whether isohydric trees produces distinct spectral signatures under drought stress that can be remotely sensed. Here, we examined the capability of four vegetation indices (PRI, NDVI, NDVI705, and EVI) to capture the differences in spectral responses between isohydric and anisohydric trees within a deciduous forest in central Indiana, USA. Both leaf-level spectral measurements and canopy-scale satellite observations were used to compare peak growing-season spectral signatures between a drought and a non-drought year. At the leaf scale, two vegetation indices (NDVI and NDVI705) failed to capture the drought signal or the divergent isohydric/anisohydric behavior. EVI successfully captured the drought signal at both leaf and canopy scales, but failed to capture the divergent behavior between isohydric and anisohydric tree species during the drought. PRI captured both drought signals and divergent isohydric/anisohydric behavior at both leaf and canopy scales once normalized between sunlit (backward direction images) and shaded (forward direction images) portions of canopy, which indicates drought stress and subsequent photosynthetic downregulation are greater in the sunlit portion of canopy. This study presents a significant step forward in our ability to directly mapping emergent isohydricity at different scales based on divergent spectral signatures between sunlit and shaded canopies.

Above-ground forest productivity can be reliably estimated from tree-ring width measurements. In doing so, annual growth is linked to the tree’s basal area increment (BAI), which is the change in cross-sectional area associated with each annual ring. When BAI is estimated from ring-width series, a value for the diameter of the tree is required. This diameter is ideally measured in the field, but can also be estimated as the sum of the annual ring widths. Tree biomass can also be estimated directly from the diameter estimates derived from tree-rings. Summing the ring widths, however, typically underestimates the tree’s true diameter. To evaluate this potential bias in diameter, we compared field-measured diameter and diameter estimated from the sum of the ring widths using tree-ring chronologies for seven common species in the eastern United States. We then evaluated the impacts of using the biased diameter estimates on derived BAI and biomass values. To simulate field-sampling error (i.e., failure to reach the pith when obtaining a core sample), we re-calculated BAI and biomass after removing a portion of the innermost rings from each tree. Comparisons of these various methods quantify the substantial and consistent underestimations in forest productivity estimates. To reduce the bias in diameter when using ring widths, we developed a regression model to adjust the diameter using core samples. This model is predicated on having some field-measured diameter values available at a site to calibrate and validate the model, but it can then be used to produce estimates at similar sites with similar species where no field-measured diameter values are available. Values of BAI and biomass derived from model-estimated diameter were more accurate at representing absolute growth than values produced by using the sum of the ring widths. Assessing the interannual variations in tree-growth is dependent on having metrics that accurately reflect the area and mass of wood produced. Our results suggest that published estimates of BAI and biomass using the sum of the ring widths to estimate diameter have substantially underestimated these productivity metrics. Our new procedure allows for more reliable estimates of productivity metrics that use diameter-at-breast height derived from tree rings.

Monitoring land-cover change and understanding the dynamics of forest cover is critically important for the management of forest ecosystems. This study assesses land cover change (1990–2015) in the...

There is ongoing interest to develop remote sensing methods for mapping and monitoring the spatial distribution and biomass of mangroves. In this study, we develop a suite of methods to evaluate the combination of Landsat-8, ALOS PALSAR, and SRTM data for mapping spatial distribution of mangrove composition, canopy height, and aboveground biomass in the wide intertidal zones and coastal plains of Mimika district, Papua, Indonesia. Image segmentation followed by visual interpretation of composite PALSAR images was used to delineate mangrove areas, whereas a flexible statistical rule based classification of spectral signatures from Landsat-8 images was used to classify mangrove associations. The overall accuracy of land cover classification was 94.38% with a kappa coefficient of 0.94 when validated with field inventory data and Google Earth images. Mangrove height and aboveground biomass were mapped using the SRTM DEM, which were calibrated with field-measured data via quantile regression models. There was a strong correlation between the SRTM DEM and the 0.98 quantile of field canopy heights (H .98 ), which was used to represent the tallest trees in each of 196 10 m radius subplots (r = 0.84 and R 2 = 0.804). Model performance was evaluated through 10,000 bootstrapped simulations, producing a mean absolute error (MAE) of 3.0 m for canopy height estimation over 30 m pixels of SRTM data. Quantile regression revealed a relatively strong non-linear relationship between the SRTM derived canopy height model and aboveground biomass measured in 0.5 ha mangrove inventory plots (n = 33, R 2 = 0.46). The model results produced estimates of mean standing biomass of 237.52 ± 98.2 Mg/ha in short canopy ( Avicennia / Sonneratia ) stands to 353.52 ± 98.43 Mg/ha in mature tall canopy ( Rhizophora ) dominated forest. The model estimates of mangrove biomass were within 90% confidence intervals of area-weighted biomass derived from field measurements. When validated at the landscape scale, the difference between modeled and measured aboveground mangrove biomass was 3.48% with MAE of 105.75 Mg/ha. These results indicate that the approaches developed here are reliable for mapping and monitoring mangrove composition, height, and biomass over large areas of Indonesia.

Tree rings are widely considered to be a reliable proxy record of variations in climate and soil moisture. Here, using data from the Midwest United States (US), we provide documentation of a deteriorating relationship between radial tree growth and drought that is consistent across multiple species and locations. We find that traditional methods for drought reconstructions produce models that have rapidly declining validation statistics in recent decades. Split-sample calibration-verification that uses the first and second halves of the record can be problematic, as those two samples may not represent a sufficiently wide range of soil moisture conditions. To investigate this problem, we develop a randomized validation procedure that generates an empirical distribution of calibration and validation statistics. We place validation statistics derived from traditional methods in the generated distribution and compare them to a stratified approach that ensures each calibration model is composed of a sample that includes both dry and wet years. We find that the deteriorating relationship between tree growth and soil moisture is an artifact of the absence of drought over an extended period of time. A model that forces each calibration period to contain extreme drought years is statistically validated.. Nonetheless, if the current pluvial continues in the Midwest US, the linear relationship between tree rings and soil moisture will likely continue to deteriorate to the point where tree rings in the region will have a reduced ability to estimate past drought conditions.

Characterizing the impacts of climatic change on hydrologic processes is critical for managing freshwater systems. Specifically, there is a need to evaluate how the two major components of streamflow, baseflow and stormflow, have responded to recent trends in climate. We derive baseflow and stormflow for 674 sites throughout the United States from 1980 to 2010 to examine their associations with precipitation, potential evapotranspiration, and maximum/minimum temperature. The northeastern (NE) and southwestern (SW) United States display consistent trends in baseflow and stormflow: increasing during fall and winter in the NE and decreasing during all seasons in the SW. Trends elsewhere and at other times of the year are more variable but still associated with changes in climate. Counter to expectations, baseflow and stormflow trends throughout the United States tend to change concurrently. These trends are primarily associated with precipitation trends, but increases in PET are influential and likely to become important in the future.

Remote-sensing data can be useful for investigating the bio-optical properties of the ocean. Among these bio-optical properties, chlorophyll-a content is of great importance. The standard NASA empirical ocean-colour OC algorithms are used widely to estimate global chlorophyll-a content. Despite their simplicity and effectiveness, these regression-based models have two shortcomings that we investigate here: 1 the general form of the models is a fourth-order polynomial that results in multicollinearity, and 2 the models have the same parameters for all ocean regions i.e. they use global approaches. To resolve the first issue, we use partial least squares PLS, which allows for an orthogonal transformation such that the covariance between the transformed independent variables and the dependent variable is maximized. To investigate the second issue, we use geographically weighted regression GWR to reveal the spatial variation of estimated parameters, demonstrating how the global model underperforms in some locations. GWR results show that model coefficients vary substantially between eastern and western portions of the same ocean basin. By including sea-surface temperature SST as an additional independent variable in the PLS model, we also develop a new approach that provides additional explanatory power and makes the global estimation of chlorophyll-a content more valid.

Regional and local climate extremes, and their impacts, result from the multifaceted interplay between large-scale climate forcing, local environmental factors (physiography), and societal vulnerability. In this paper, we review historical and projected changes in temperature and precipitation extremes in the United States, with a focus on strengths and weaknesses of (1) commonly used definitions for extremes such as thresholds and percentiles, (2) statistical approaches to quantifying changes in extremes, such as extreme value theory, and (3) methods for post-processing (downscaling) global climate models (GCMs) to investigate regional and local climate. We additionally derive regional and local estimates of changes in temperature extremes by applying a quantile mapping approach to high-resolution gridded daily temperature data for 6 U.S. sub-regions. Consistent with the background warming in the parent GCMs, we project decreases in regional and local cold extremes and increases in regional and local warm extremes throughout the domain, but the downscaling approach removes bias and produces substantial spatial variability within the relatively small sub-regions. We finish with recommendations for future research on regional climate extremes, suggesting that focus be placed on improving understanding of extremes in the context of large-scale circulation and evaluating the corresponding cascade of scale interactions within GCMs.

The Mississippi Delta in coastal Louisiana has some of the highest rates of land loss in the world. This land-loss crisis might become a global problem because most major deltas are expected to be vulnerable to land loss during the remainder of the 21st century and beyond. Despite this predicament, we do not understand how land loss in a deltaic environment proceeds in time and space. Here, we evaluate the spatial and temporal trends of land loss in the Lower Mississippi River Delta (LMRD) region using spatial statistics and landscape metrics. We used nearly 4800 Landsat images to construct a series of three-year cloud-free composites from 1983 to 2016. From these data, we created a stability index (SI), which is a dimensionless measure of the number of land-to-water transitions that a land pixel makes before being considered lost. Our results indicate that on the LMRD, 75% of land loss is a single transition from land to water, while about 25% of land pixels have two or more transitions before being considered lost. Using a local indicator of spatial association, we show that pixels with similar SI tend to cluster together. Single transition clusters (low SI) form elongated shapes, they are densely packed, and they are predominantly, but not always, found on marsh edges. On the other hand, multi-transition clusters (high SI) form square-like shapes, they are more fragmented, and they are usually found in marsh interiors. Our analysis further shows that the land-loss area within the spatial patches with both high and low stability is strongly related to the density of land patches and their shape (R2 of 0.717 and 0.545 respectively). Our analysis suggests that land-loss pixels on the marsh edges undergo different temporal patterns of land loss compared to those in the marsh interior. We hypothesize that this arises because wave edge-erosion drives land loss on marsh edges, whereas subsidence-related flooding drives land loss in marsh interiors. The stability index provides a useful way to characterize the processes causing land loss.

The southern Appalachian forests have been threatened by several large-scale disturbances, such as wildfire and infestation, which alter the forest ecosystem structures and functions. Hemlock Woolly Adelgid (Adelges tsugae Annand, HWA) is a non-native pest that causes widespread foliar damage and eventual mortality, resulting in irreversible tree decline in eastern (Tsuga canadensis) and Carolina (T. caroliniana) hemlocks throughout the eastern United States. It is important to monitor the extent and severity of these disturbances over space and time to better understand their implications in the biogeochemical cycles of forest landscapes. Using all available Landsat images, we investigate and compare the performance of Tasseled Cap Transformation (TCT)-based indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Disturbance Index (DI) in capturing the spectral-temporal trajectory of both abrupt and gradual forest disturbances (e.g., fire and hemlock decline). For each Landsat pixel, the temporal trajectories of these indices were fitted into a time series model, separating the inter-annual disturbance patterns (low frequency) and seasonal phenology (high frequency) signals. We estimated the temporal dynamics of disturbances based on the residuals between the observed and predicted values of the model, investigated the performance of all the indices in capturing the hemlock decline intensity, and further validated the results with the number of individual dead hemlocks identified from high-resolution aerial images. Our results suggested that the overall performance of NDVI, followed by TCT wetness, was most accurate in detecting both the disturbance timing and hemlock decline intensity, explaining over 90% of the variability in the number of dead hemlocks. Despite the overall good performance of TCT wetness in characterizing the disturbance regime, our analysis showed that this index has some limitations in characterizing disturbances due to its recovery patterns following infestation.

The Thrifty Food Plan (TFP) is the basis of benefit allocations within the USDA's Supplemental Nutrition Assistance Program (SNAP), which administers nearly $70 billion in benefits to over 42 million people annually. To produce the allocation of food within the TFP, the USDA uses a mathematical optimization model that solves for the daily apportionment across various food groups. The model is constrained by nutritional and consumption requirements to produce an "optimal" allocation. Despite the importance of the TFP, the computational solution developed by the USDA has received insufficient attention, with only a handful of articles written on the TFP optimization model. Here, we run three alternative objective functions that are simpler than the one used by USDA. Our first alternative objective function minimizes the sum of squared errors between the consumed market basket of goods and an allocated market basket of goods, the second alternative objective function minimizes the sum of the absolute value of the difference between the consumed market basket of goods and an allocated market basket of goods, and the third alternative objective function minimizes the weighted absolute deviation of allocations and actual consumption expressed as a proportion of observed consumption. A clear theoretical advantage of either of our methods is that they eliminate the need to arbitrarily set allocated consumption to nonzero values, as is the case for the logarithmic objective function used by USDA. In an operational sense, we find that our model formulations produce an allocation that fits actual consumption better than the objective function employed by the USDA.

The cubic smoothing spline has been a popular method for detrending tree-ring data since the 1980s. The common implementation of this procedure (e.g., ARSTAN, dplR) uses a unique method for determining the smoothing parameter that is widely known as the %n criterion. However, this smoothing parameter selection method carries the assumption that end point effects are ignorable. In this paper, we complete the mathematical derivation and show how the original method differs from the complete version, both in the interpretation of the smoothing parameter and in the spline fit. Frequency response curves (FRC) demonstrate how the smoothing parameter is affected by the original assumption. For example, the FRC results indicate that a tree core of 250-year length has a 14% difference in the cut-off frequency when looking at the 67%n criterion. The FRC analysis shows that the existing approach produces a more flexible fit than anticipated, i.e., it is removing more variance than previously thought. For example, a 67%n spline under the existing approach corresponds to a 53%n spline fit. By using both simulated tree-core sequences and a dataset from a Midwest forest, we discuss which conditions result in greater differences between the spline fits and which conditions will have small differences. Tree-core sequences that have more curvature, such as a large-amplitude growth release, will lead to greater differences. Finally, we provide approximations to the end-point effect procedure. For example, using an 83%n criterion under the original approach produces a spline fit approximating the 67%n fit under the complete approach. These approximations could be easily implemented within existing programs like ARSTAN.

Significance Climate change is expected to alter streamflow volume and variability in watersheds throughout the world, which will have impacts on agricultural, aquatic, and urban environments. Human modifications such as the building of reservoirs and the extraction of water for agricultural and urban uses, however, are thought to mask climate-induced changes in streamflow. Using observed streamflow data from over 3,000 sites from 1981 to 2015, this work identifies negative trends in streamflow throughout the southern and western portions of the United States and positive trends across the eastern portion of the United States and Canada. Within these regions, streamflow trends from human-modified sites are similar to unimpacted sites, indicating that the signal of climate change is apparent in all streamflow systems., Changes in climate are driving an intensification of the hydrologic cycle and leading to alterations of natural streamflow regimes. Human disturbances such as dams, land-cover change, and water diversions are thought to obscure climate signals in hydrologic systems. As a result, most studies of changing hydroclimatic conditions are limited to areas with natural streamflow. Here, we compare trends in observed streamflow from natural and human-modified watersheds in the United States and Canada for the 1981–2015 water years to evaluate whether comparable responses to climate change are present in both systems. We find that patterns and magnitudes of trends in median daily streamflow, daily streamflow variability, and daily extremes in human-modified watersheds are similar to those from nearby natural watersheds. Streamflow in both systems show negative trends throughout the southern and western United States and positive trends throughout the northeastern United States, the northern Great Plains, and southern prairies of Canada. The trends in both natural and human-modified watersheds are linked to local trends in precipitation and reference evapotranspiration, demonstrating that water management and land-cover change have not substantially altered the effects of climate change on human-modified watersheds compared with nearby natural watersheds.

Global climate models (GCMs) have biases when simulating historical climate conditions, which in turn have implications for estimating the hydrological impacts of climate change. This study examines the differences in projected changes of aridity [defined as the ratio of precipitation (P) over potential evapotranspiration (PET), or P/PET] and the Palmer drought severity index (PDSI) between raw and bias-corrected GCM output for the continental United States (CONUS). For historical simulations (1950–79) the raw GCM ensemble median has a positive precipitation bias (+24%) and negative PET bias (−7%) compared to the bias-corrected output when averaged over CONUS with the most acute biases over the interior western United States. While both raw and bias-corrected GCM ensembles project more aridity (lower P/PET) for CONUS in the late twenty-first century (2070–99), relative enhancements in aridity were found for bias-corrected data compared to the raw GCM ensemble owing to positive precipitation and negative PET biases in the raw GCM ensemble. However, the bias-corrected GCM ensemble projects less acute decreases in summer PDSI for the southwestern United States compared to the raw GCM ensemble (from 1 to 2 PDSI units higher), stemming from biases in precipitation amount and seasonality in the raw GCM ensemble. Compared to the raw GCM ensemble, bias-corrected GCM inputs not only correct for systematic errors but also can produce high-resolution projections that are useful for impact analyses. Therefore, changes in hydroclimate metrics often appear considerably different in bias-corrected output compared to raw GCM output.

Intrinsic random functions (IRF) provide a versatile approach when the assumption of second-order stationarity is not met. Here, we develop the IRF theory on the circle with its universal kriging application. Unlike IRF in Euclidean spaces, where differential operations are used to achieve stationarity, our result shows that low-frequency truncation of the Fourier series representation of the IRF is required for such processes on the circle. All of these features and developments are presented through the theory of reproducing kernel Hilbert space. In addition, the connection between kriging and splines is also established, demonstrating their equivalence on the circle., Comment: 14 pages, initial version

Replacing a commonly used statistical measure of average error with an alternative measure would give a more meaningful assessment of model performance.