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Tree-ring reconstructions of streamflow can provide long-term perspectives of flow variability that may be useful in management strategies for water resources. Relative to other portions of the United States, there is a dearth of tree-ring based streamflow reconstructions in the Midwest. The Wabash River is an important water resource in Indiana and Illinois where many sectors rely on it. Here, to better understand the historic hydroclimate within the Wabash River Basin, we reconstructed June–September streamflow for a tributary of the Wabash River using three statistical approaches: the traditional basin approach, and the recently-developed climate-footprint, and drought-reconstruction approaches. Our reconstructions explained 53–71% of the variance for the common period (1941–1984). We find that in our three reconstructions, there are extreme droughts and pluvials that exceed those during the instrumental period, including a sustained five-decade low-flow event in the early 19th century. Overall, we find that the traditional basin approach produced the most robust model but had the shortest reconstruction length of the three approaches, as it requires the predictor chronologies to be derived from within the basin. Our results indicate that there are merits and drawbacks to each approach with the general pattern of sacrificing variance explained for reconstruction length.

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.

In recent decades, South Asia has experienced declining air quality, with much of the attention being focused on extremely high levels of particulate matter. Here, we analyze tropospheric ozone (O3), formaldehyde (HCHO), and nitrogen dioxide (NO2) to assess other measures of air quality across South Asia from 2008 to 2018. The IASI-Forli retrieved tropospheric ozone data was validated with ozonesonde, reanalysis (ERA5), satellite (TES), and model simulation products (GEOS-Chem and TOMCAT/SLIMCAT). Space-based observations of these three trace gases were used to conduct a spatio temporal analysis over South Asia using trend analysis (Theil-Sen and linear regression), change-point detection (Pettitt's test), and hotspot identification (Getis-Ord Gi*). We used the formaldehyde-nitrogen dioxide ratio (FNR) to identify NOx limited, VOC limited, and transitional regimes in South Asia. Counter to previous studies, a statistically significant decrease of HCHO (-0.0041 DU yr-1) and O3 (-0.064 DU yr-1) was detected for South Asia; however, NO2 is increasing the 0.001 DU yr-1 over South Asia during 2008-18. The Indo-Gangetic Plains emerged as being critically affected by the three trace gases. Certain parts of southern and south-eastern India are gradually emerging as NO2 and HCHO hotpots. No significant O3 hotspots were discernible, though coldspots existed along the Himalaya belt of India, Nepal, and Bhutan and mountainous tracts of Pakistan. FNR indicates the reduction of NOx in NOx-limited regime of the Indo-Gangetic Plains reduced the formation of tropospheric O3 over South Asia.

In many cold climates, rain-on-snow (ROS) events are associated with severe snowmelt and flooding. ROS events also rapidly deplete snowpacks, reducing the potential for subsurface water storage later in the year. We incorporated a simple, easily transferable energy-balance ROS model into the Soil and Water Assessment Tool (SWAT) to assess how ROS melt affects winter floods and summer hydrological drought over a large area such as the North American Great Lakes Basin, where ROS is associated with severe snowmelt events. Using 504 random parameter sets, the modification consistently improved simulations and led to higher winter flows and lower summer flows compared with the unmodified SWAT. The SWAT ROS model also had improved performance for simulating daily streamflow in 501 (99.4%) of the 504 random parameter sets and improved performance for simulating daily snowpack in 504 (100%) of the sets, compared to the unmodified SWAT. We then used streamflow and snow water equivalent as the objectives in a multivariable, multi-site calibration to compare SWAT ROS with the unmodified SWAT. The SWAT ROS model simulated 51% larger peak (0.95 quantile) daily flows during winter and spring and 58% lower daily summer low (0.05 quantile) flows across the Great Lakes Basin, compared to the unmodified SWAT. We also used observed snowpack data to demonstrate how the unmodified SWAT can miss daily ROS melt events, delaying snowmelt to later in the season. Thus, the unmodified SWAT model appears to be misrepresenting the streamflow distribution in areas where ROS events occur, which is particularly important for modeling extreme events. When simulating conditions in cold climates at diverse scales up to large basins or continents, hydrological models such as SWAT must incorporate ROS, as many other models already do. This has implications for water resources management confronted with hydrological extremes in ROS-prone areas around the world, especially where climate change is altering ROS melt events.

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.

Municipal parks and recreation departments (PRDs) have a crucial responsibility for stewarding urban and peri-urban ecology given they manage the majority of urban green spaces. However, the extent of climate adaptation planning and management activities by such agencies at the national level is unknown. This study aimed to fill this gap by conducting a national survey of the directors of PRDs (n = 413) to understand the progress and drivers of climate change adaptation efforts for urban green spaces. Overall, we found that while measured impacts of climate change were positively correlated with PRDs' climate-change adaptation plans and actions, most PRDs perceived few impacts of climate change on urban green spaces. We also found that levels of adaptation varied across ecoregions, with direct experiences with climate change impacts being a primary contributor to action. These findings indicate that U.S. municipal PRDs are unprepared for the changing climate and are unlikely to initiate urban green space adaptation planning and action until substantial climate change impacts are experienced firsthand.

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.

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.

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 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.

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

Pertinent characteristics of three dimensionless and comparable model-performance or model-efficiency measures are examined and compared. Our model-assessment recommendations apply to many types of environmental models. Representing measures based on sums-of-squared errors or "quadratic measures" (by far, the most widely used class) is Nash and Sutcliffe's (1970) well-known efficiency measure (E). Our assessments of E, by and large, also apply to similar-in-form benchmark measures (Seibert, 2001). Legates and McCabe's (1999) version of E (E1) and Willmott et?al.'s (2012) refined index of agreement (dr) represent the less-often-employed but more interpretable class of measures based on sums of error magnitudes. Conceptual and algebraic arguments are used in conjunction with assessments of many parameter sets of the Soil and Water Assessment Tool (SWAT) hydrologic model, over the period 1950-2005. Our findings suggest that, of the three measures, dr has the broadest utility, followed in order by E1 and E. Properties of a refined index of agreement (dr) are characterized and explained.Two other widely used indices (E1 and E) are compared with the refined index.Our findings indicate that, of the three indices, dr has the broadest utility.Parameterizations of a streamflow model are used for evaluation and comparison.

Many important environmental, geographic, and geophysical variables are observed at the global scale, so it is important to extend point-pattern methods to the sphere. Here, we develop the K function on the sphere and then evaluate several global data structures and networks. The assumption of complete spatial randomness (CSR) gives a K function that is proportional to the square of distance in Euclidean space, but we find that CSR on the sphere is proportional to 1 − cos ( angular distance ) . To evaluate the spherical K function, we analyze two global data structures: latitude-longitude and equidistant hexagonal grids. Due to over-representation of polar regions, latitude-longitude grids produce clustered point patterns. The hexagonal grid is equal-area on the sphere and produces a dispersed point pattern. We also analyze the structure of an observing system for global climate research that is intended to have relatively even spatial coverage. We find that, due to its coastal and continental biases, the climate-station network produces clustered point patterns over a range of distances. Our focus is on planetary-scale geographic point distributions, but we foresee applications of spherical K functions in disciplines such as astronomy, geodetics, anatomy, and neuroscience. Software for spherical point-pattern analysis is available from the authors.

Spatial processes having covariance functions that depend solely on the distance between locations are known as homogeneous. Many random processes on the sphere are not homogeneous, especially in the latitudinal dimension. As a result, we study a class of statistical processes that exhibit axial symmetry, whereby their covariance function depends on differences in longitude alone. We develop a new and simplified representation for a valid axially symmetric process, reducing computational complexity considerably. In addition, we explore longitudinally reversible processes and the construction of parametric models for axially symmetric processes.

The predominant fire regime associated with ponderosa pine (Pinus ponderosa) forests in the southwestern US has shifted from the historic norm of frequent, low-severity fires to less frequent mixed-severity and crown fires. This change in the severity of fire has altered ponderosa pine forests from the open stands typical of pre-settlement times to even-aged, high-density stands at increased risk of crown fire. As a result, restoration plans and post-fire management practices must consider the spatial and temporal variability of fire severity in both mixed-severity and crown fire events because fire-severity patterns strongly influence post-fire ecological conditions. This study examines the landscape pattern of fire severity in the Ponil Complex Fire and applies a moving-window approach to post-fire landscape pattern measurement. The moving-window approach allows examination of the quantitative and spatial variability of landscape pattern, producing a more nuanced description of forest pattern when compared to whole-landscape or patch-based metrics. The fire resulted in a complex mosaic of fire patches and forest-structure changes. In high-severity fire patches, mean and median values of many post-fire landscape metrics were markedly different from those in low and moderate-severity patches. Landscape pattern in high-severity patches also had the greatest variability of metric values, suggesting that high-severity fire patches require a spatially mediated management response to fire. Categorical fire-severity maps and traditional landscape-pattern assessment would not be able to identify these spatially variable post-fire conditions.

Commonly used sums-of-squares-based error or deviation statistics—like the standard deviation, the standard error, the coefficient of variation, and the root-mean-square error—often are misleading indicators of average error or variability. Sums-of-squares-based statistics are functions of at least two dissimilar patterns that occur within data. Both the mean of a set of error or deviation magnitudes (the average of their absolute values) and their variability influence the value of a sum-of-squares-based error measure, which confounds clear assessment of its meaning. Interpretation problems arise, according to Paul Mielke, because sums-of-squares-based statistics do not satisfy the triangle inequality. We illustrate the difficulties in interpreting and comparing these statistics using hypothetical data, and recommend the use of alternate statistics that are based on sums of error or deviation magnitudes.

Three statistical models that estimate daily maximum ozone (O3) concentrations in the lower Fraser Valley of British Columbia (BC) are specified using measured concentrations from two monitoring stations during the time period 1978–1985. The three models are (1) a univariate deterministic/stochastic model, (2) a univariate autoregressive integrated moving average (ARIMA) model, and (3) a bivariate temperature and persistence based regression model. The three models as well as a persistence forecast are tested by comparison with O3 concentrations observed during 1986; it is concluded that the bivariate model is superior to both unvariate models and persistence. The ARIMA model has nearly the same predictive capability as persistance while the mixed deterministic/stochastic model performs the worst. This suggests that the traditional time series technique of decomposing a series into a trend, a cycle and a stochastic component may not be appropriate for O3 air quality forecasting.

Probabilistic forecasts are often employed to estimate the potential for high pollutant concentrations. To develop a probabilistic forecast of ozone concentrations, we suggest that use be made of the inherent properties of seasonality and autocorrelation in O 3 time series. A non-stationary, autocorrelated stochastic process is used to simulate a conditional probability density function (p.d.f.) which quantifies the effects of seasonality and autocorrelation. To illustrate the utility of such a model, the simulated conditional p.d.f. is shown to be clearly superior to an ordinary p.d.f. developed from summer ozone data.