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Rain-on-snow (ROS) melt events reduce the amount of water stored in the snowpack while also exacerbating flooding. The hydrologic implications of changing ROS events in a warming climate, however, are still uncertain. This research used a calibrated and validated Soil and Water Assessment Tool (SWAT) hydrologic model, modified with energy budget equations to simulate ROS melt and forced with a climate model ensemble representing moderate greenhouse gas concentrations, to simulate changes to ROS melt in the North American Great Lakes Basin from 1960–2069. The changes to ROS events between the historic period (1960–1999) and mid-century (2040–2069) represent an approximately 30 % reduction in melt in warmer, southern subbasins but less than 5 % reduction in melt in colder, northern subbasins. Additionally, proportionally more rainfall reduces the formation of snowpacks, with area-weighted combined winter and spring rain-to-snow ratios rising from approximately 1.5 historically to 1.9 by the mid-21st century. Areas with historic mean combined winter and spring air temperatures lower than −2 ∘C have ROS regimes that are resilient to mid-21st century warming projections, but ROS occurrence in areas that have mean combined winter and spring temperatures near the freezing point are sensitive to changing air temperatures. Also, relationships between changes in the timing of ROS melt and water yield endure throughout spring but become weak by summer. As the influence of ROS melt events on hydrological systems is being altered in a changing climate, these conclusions are important to inform adaptive management of freshwater ecosystems and human uses in regions of the globe that are sensitive to changes in ROS events.

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.

Families living in poverty in the United States rely on earnings, federal government programmes, the emergency food system (EFS) and a variety of informal networks to get adequate nutrition. The EFS is a large network of emergency food operations (EFOs) that serve as a supplement both for people receiving Supplemental Nutrition Assistance Program benefits and for those requiring food aid who do not qualify for government assistance. The static view of the distribution of EFOs across 11 counties in south-central Indiana typically employed by policymakers and non-governmental organizations shows this largely rural area to be well covered. However, when examined by the time of day and day of month that EFOs in the region operate, coverage proves illusory. Here, we map and analyse the spatiotemporal availability of services offered by these EFOs and develop measures of availability of EFOs, dependent on EFO open hours, service area limitations and numbers of food-insecure individuals. This allows policymakers and non-governmental organizations to better gauge the adequacy of the EFS within a given region. Food pantries are part of the emergency food network in the United States, but they are an understudied component of food access. The Pantry Accessibility Index and Individual Accessibility Index developed here allow measurement of the spatiotemporal availability of these services to the food insecure of south-central Indiana.

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.

Vegetation in semi-arid regions is often a spatially heterogeneous mix of bare soil, herbaceous vegetation, and woody plants. Detecting the fractional cover of woody plants versus herbaceous vegetation, in addition to non-photosynthetically active cover types such as biological soil crusts, dry grasses, and bare ground, is crucial for understanding spatial patterns and temporal changes in semi-arid savanna ecosystems. With and without the inclusion of LiDAR-derived canopy height information, we tested different configurations of unsupervised linear unmixing classification of hyperspectral data to estimate fractional cover of tall woody plants, herbaceous vegetation, and other non-photosynthetically active cover types. To perform this analysis, we used 1 m resolution hyperspectral and LiDAR data collected by the NEON airborne observation platform at the Santa Rita Experimental Range in Arizona. Our results show that both hyperspectral and canopy height information are needed in the linear unmixing algorithm to correctly identify tall woody plants versus herbaceous vegetation. However, including height data is not always sufficient to be able to separate the two vegetation types: selecting the optimum number of endmembers is also crucial for separating these two types of photosynthetically active vegetation. In comparison to our reference dataset, fractional cover accuracy was highest for the woody plant class based on two accuracy assessment methods, although the fractional cover of dense woody plant is likely underestimated because linear unmixing also detects sub-canopy elements. The accuracy of the herbaceous vegetation class was lower than the other classes, likely because of the presence of both active and senesced grasses. Our study presents the first use of both hyperspectral and canopy height information to separately detect fractional cover of woody plants versus herbaceous vegetation in addition to other bare soil cover types that should be useful for fractional cover classification in other semi-arid savanna ecosystems where hyperspectral and LiDAR data are available. Unsupervised linear unmixing using high spatial resolution hyperspectral data alone could not identify tall woody plants versus herbaceous vegetation in a semiarid savanna ecosystem; instead, canopy height information was needed to separate the two vegetation types.Even when including canopy height information, accurate separation of the two vegetation types was dependent on the number of endmembers included in the unmixing algorithm due to the presence of additional dominant spectral signatures that did not correspond to photosynthetically active vegetation.Fractional cover estimates for the woody plant class were the most accurate, although dense woody plant patches are likely underestimated. Unsupervised linear unmixing using high spatial resolution hyperspectral data alone could not identify tall woody plants versus herbaceous vegetation in a semiarid savanna ecosystem; instead, canopy height information was needed to separate the two vegetation types. Even when including canopy height information, accurate separation of the two vegetation types was dependent on the number of endmembers included in the unmixing algorithm due to the presence of additional dominant spectral signatures that did not correspond to photosynthetically active vegetation. Fractional cover estimates for the woody plant class were the most accurate, although dense woody plant patches are likely underestimated.

When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE). In terms of quantifying average error, however, absolute-value-based measures such as the mean absolute error (MAE) are more interpretable than MSE or RMSE. Part of that historical preference for sums-of-squares measures is that they are mathematically amenable to decomposition and one can then form ratios, such as those based on separating MSE into its systematic and unsystematic components. Here, we develop and illustrate a decomposition of MAE into three useful submeasures: (1) bias error, (2) proportionality error, and (3) unsystematic error. This three-part decomposition of MAE is preferable to comparable decompositions of MSE because it provides more straightforward information on the nature of the model-error distribution. We illustrate the properties of our new three-part decomposition using a long-term reconstruction of streamflow for the Upper Colorado River.

The impacts of inland flooding caused by tropical cyclones (TCs), including loss of life, infrastructure disruption, and alteration of natural landscapes, have increased over recent decades. While these impacts are well documented, changes in TC precipitation extremes—the proximate cause of such inland flooding—have been more difficult to detect. Here, we present a latewood tree-ring–based record of seasonal (June 1 through October 15) TC precipitation sums (ΣTCP) from the region in North America that receives the most ΣTCP: coastal North and South Carolina. Our 319-y-long ΣTCP reconstruction reveals that ΣTCP extremes (≥0.95 quantile) have increased by 2 to 4 mm/decade since 1700 CE, with most of the increase occurring in the last 60 y. Consistent with the hypothesis that TCs are moving slower under anthropogenic climate change, we show that seasonal ΣTCP along the US East Coast are positively related to seasonal average TC duration and TC translation speed.

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.

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 (O

The selection of calibration and validation time periods in hydrologic modelling is often done arbitrarily. Nonstationarity can lead to an optimal parameter set for one period which may not accurately simulate another. However, there is still much to be learned about the responses of hydrologic models to nonstationary conditions. We investigated how the selection of calibration and validation periods can influence water balance simulations. We calibrated Soil and Water Assessment Tool hydrologic models with observed streamflow for three United States watersheds (St. Joseph River of Indiana/Michigan, Escambia River of Florida/Alabama, and Cottonwood Creek of California), using time period splits for calibration/validation. We found that the choice of calibration period (with different patterns of observed streamflow, precipitation, and air temperature) influenced the parameter sets, leading to dissimilar simulations of water balance components. In the Cottonwood Creek watershed, simulations of 50‐year mean January streamflow varied by 32%, because of lower winter precipitation and air temperature in earlier calibration periods on calibrated parameters, which impaired the ability for models calibrated to earlier periods to simulate later periods. Peaks of actual evapotranspiration for this watershed also shifted from April to May due to different parameter values depending on the calibration period's winter air temperatures. In the St. Joseph and Escambia River watersheds, adjustments of the runoff curve number parameter could vary by 10.7% and 20.8%, respectively, while 50‐year mean monthly surface runoff simulations could vary by 23%–37% and 169%–209%, depending on the observed streamflow and precipitation of the chosen calibration period. It is imperative that calibration and validation time periods are chosen selectively instead of arbitrarily, for instance using change point detection methods, and that the calibration periods are appropriate for the goals of the study, considering possible broad effects of nonstationary time series on water balance simulations. It is also crucial that the hydrologic modelling community improves existing calibration and validation practices to better include nonstationary processes.

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.

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.

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.

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 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

Spatially weighted averages of Palmer Drought Severity Index (PDSI) over central and southern California show that the 1 year 2014 drought was not as severe as previously reported, but it still is the most severe in the 1895–2014 instrumental record. Using the typical adjustment procedure that matches the mean and standard deviation of tree ring PDSI values to those of instrumental data shows over 10 droughts from 800 to 2006 that were more severe than the 1 year 2014 drought, with the 2014 drought having a return period of 140–180 years. Quantile mapping allows for a closer correspondence between instrumental and tree ring PDSI probability distributions and produces return periods of 700–900 years for the 1 year 2014 drought. Associated cumulative 3 and 4 year droughts, however, are estimated to be much more severe. The 2012–2014 drought is nearly a 10,000 year event, while the 2012–2015 drought has an almost incalculable return period and is completely without precedent.

Optical vegetation indices VIs have been used to retrieve and assess biophysical variables from satellite reflectance data. These indices, however, also are sensitive to a number of confounding factors, such as canopy geometry, soil optical properties, and solar position. This suggests that VIs should be used cautiously for biophysical parameter estimation. Among biophysical variables, chlorophyll content is of particular importance as an indicator of photosynthetic activity. The goal of this study is to investigate the performance of multispectral optical VIs for chlorophyll content estimation in the world’s largest mangrove forest, the Sundarbans, and to compare these with machine-learning algorithms MLAs. To this end, we have investigated the performance of 15 multispectral VIs and six state-of-the-art MLAs that are widely used for adaptive data fitting. The MLAs are Artificial Neural Networks ANNs, Genetic Algorithm GA, Gaussian Processes for Machine Learning GPML, Kernel Ridge Regression KRR, Locally Weighted Polynomials LWP, and Multivariate Adaptive Regression Splines MARS. We use an in situ data set of reflectance and chlorophyll measurements to develop and validate our models. Each MLA was evaluated 500 times with random partitions of training and validation data. Results showed that the weight optimization and term selection used within GA produce the most reliable chlorophyll content estimation. However, green normalized difference VI GNDVI is a simple and computationally efficient VI that produces results that are nearly as accurate as GA in terms of model fit and performance. Results also show that all methods except ANNs and MARS produce a quasi-linear relationship between spectral reflectance and chlorophyll content. Statistical transformations of GNDVI and chlorophyll content have the capability of further reducing model error.

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