Fine Spatiotemporal Resolution Research Articles

Early Holocene plant macrofossils indicate cool refugia for subalpine plant taxa in Acadia National Park, Maine

Identifying refugia— specifically places where species can persist during periods of regionally unsuitable climate— is increasingly important for conservation practitioners and land managers charged with protecting biodiversity in a rapidly warming world. Currently, many researchers assist in this process by building models to predict areas of refugia using climate data projected into the future under different climate scenarios; however, the coarse spatial scale of future climate data can be orders of magnitude larger than the scale of refugia on the landscape. Conservation paleobiology is an emerging field that can contribute to the identification of climate refugia by looking at the macrofossil records contained in sediments to better understand the response of species to past climate change within a small area, and allows us to ground-truth hypotheses about specific areas functioning as climate refugia. Here, we present a conservation paleobiology case study to update vulnerability assessments for subalpine plant species in Acadia National Park and locate potential future refugia on the landscape. We analyzed plant macrofossils in a sediment core from Sargent Mountain Pond in Acadia National Park (Maine, United States) at a fine spatiotemporal resolution to test the hypothesis that the area served as a past climate refugium for the subalpine species it currently hosts. We found that, when compared to a pollen record from a forest hollow core collected on Mount Desert Island, the macrofossils reflect a more stable presence of subalpine taxa throughout the Holocene Climatic Optimum (8,000–5,000 BP) than was observed at lower elevations. Our results indicate the importance of a complementary approach that combines plant macrofossils and pollen in addition to modeling to identify refugia and better understand the vulnerability of species and communities to climate change.

A framework for estimating all-weather fine resolution soil moisture from the integration of physics-based and machine learning-based algorithms

Due to the effects of radio frequency interference and the limitations of algorithms under specific conditions, most of the currently available microwave-based soil moisture (SM) products are spatially discontinuous and have coarse spatial resolution, whereas optical observations also reveal various data gaps due to cloud contamination. Hence, the prediction of SM over invalid pixels and disaggregation from coarse to high scales are two main processes for obtaining SM at fine spatiotemporal resolution (e.g., daily/1-km). In the present study, two methods with respect to disaggregation-first or prediction-first were investigated from the synergetic use of the widely recognized European Space Agency-Climate Change Initiative (ESA-CCI) SM product and Moderate Resolution Imaging Spectroradiometer (MODIS) images over the Tibetan Plateau (TP) region. Specifically, the Disaggregation based on Physical And Theoretical scale Change (DisPATCh) algorithm and the generalized regression neural network (GRNN) were implemented in the disaggregation and prediction, respectively. In DisPATCh, spatially complete land surface temperature (LST), normalized difference vegetation index (NDVI) and digital elevation model (DEM) were provided as essential inputs to downscale the microwave-based ESA-CCI to a spatial resolution of 1 km, whereas MODIS-derived LST, NDVI, land surface albedo and DEM were considered in the GRNN prediction. Following the two methods, the daily/1-km SM dataset over a period of three years was finally estimated. Assessments with ground in-situ SM measurements over the TP region reveal an acceptable accuracy with unbiased root mean square errors of ∼ 0.06 m3/m3, indicating the potential to obtain operational daily/1-km spatially continuous SM products in future developments.

A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic

The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks.We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation.We evaluate the model’s predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks).The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance.

Non-parametric severity-duration-frequency analysis of drought based on satellite-based product and model fusion techniques.

Climate change has increased the severity and frequency of droughts over the last decades. To alleviate the adverse impacts of droughts, an effective planning and management framework requires high-resolution spatiotemporal data. TRMM multi-satellite precipitation analysis (TMPA) dataset provides sufficient accuracy with fine spatio-temporal resolution. However, it only covers a short temporal span, which limits its applicability for drought studies. This paper presents a methodology for efficient and accurate temporal extension of TMPA using four artificial intelligence (AI)-based models. To improve AI-based model precipitation estimations, fusion techniques including Orness, Orlike, and genetic algorithm (GA)-based weighting methods were employed. Results show that fusion approaches provide more accurate estimates of precipitation. Different timescales of n-SPI time series and drought spatial maps were prepared to visually evaluate the performance of long-term TMPA (LT-TMPA) alongside statistical error indices. The results confirm that this dataset is effective for meteorological drought monitoring over southern Iran. Finally, drought risk assessment was carried out to determine the spatiotemporal characteristics of droughts through severity-duration-frequency (SDF) contour maps. In contrast to the traditional SDF curves, SDF contour maps provide a superior understanding of drought for policymakers since they preserve spatial information.

Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada

Arctic amplification has many impacts on sea-ice extent, thickness, and flux. It becomes critical to monitor sea-ice conditions at finer spatio-temporal resolution. We used a simple convolutional neural network (CNN) on the RADARSAT-2 dual-polarized ScanSAR wide archive available over Coronation Gulf, Canada, to assess which SAR parameter improves model performances to classify sea ice from water on a large volume of data covering 11 years of ice and surface water conditions. An overall accuracy of 90.1% was achieved on 989 scenes of 100% ice cover or ice-free conditions. An accuracy of 86.3% was achieved on the last year of data (134 scenes) which was kept out of the training process to test the model on an independent dataset. A better accuracy is obtained at lower incidence angles and the HH polarization provides the most information to classify ice from water. To achieve the best accuracy, the incidence angle and the noise equivalent sigma-nought had to be included as input to the model. A comparison done with the ASI passive microwave product shows similar errors in total sea ice concentration when using the Canadian Ice Service regional charts as reference. Nonetheless, errors from both datasets differ and the CNN outputs show greater potential to reduce masked areas, given the better spatial resolution, enabling data classification closer to land and identify features not captured by the ASI dataset.

Resolution changes relationships: Optimizing sampling design using small scale zooplankton data

Marine research surveys are an integral tool in understanding the marine environment. Recent technological advances have allowed the development of automated or semi-automated methods for the collection of marine data. These devices are often easily implemented on existing surveys and can collect data at finer spatiotemporal resolutions than traditional devices. We used two automated instruments: the Plankton Imager and FerryBox, to collect information on zooplankton, temperature, salinity and chlorophyll in the Celtic Sea. The resulting data were spatiotemporally aligned and merged to decreasing spatial resolutions to explore how distribution patterns and the relationship between variables change across different spatial resolutions. Relative standard deviation was used to describe variability of merged data within grid cells. All variables displayed large, area-wide spatial patterns excluding copepod size which remained consistent across the study area. Copepod biomass and abundance displayed high variations across small spatial scales. Decreasing the sampling resolution changed the description of the data where small spatial changes (those that occur over scales < 3 km) were lost and area wide patterns were emphasized. Furthermore, we found that the choice of resolution can affect both the statistical strength and significance of relationships with high variability at lower resolutions due to the mismatch between the scales of ecological processes and sampling. Determining the optimum sampling resolution to answer a specific question will be dependent upon several factors, mainly the variable measured, season, location and scale of process, which all drive variation. These considerations should be a key element of survey design, helping move towards an integrated approach for an improved understanding of ecosystem processes and gaining a more holistic description of the marine environment.

Satellite prediction of coastal hypoxia in the northern Gulf of Mexico

The growing number, size, and frequency of coastal hypoxia increasingly threaten marine ecosystem health and essential ecosystem services for human well-being. It is therefore urgent to use continuous and consistent observation and develop advanced tools to characterize and track the spatial and temporal change of coastal hypoxia. Satellite imagery with fine spatiotemporal resolution and global coverage has shown great potential for monitoring environmental changes, yet has rarely been applied to hypoxia mapping. To advance the understanding, we synthesized satellite-derived ocean color variables and dissolved oxygen measurements collected during 2014, and used random forest regression, lagged linear regression, and functional data analysis to estimate the spatiotemporal change of the hypoxia zone in the Gulf of Mexico. The three models achieved similar predictive accuracy (±1.2–1.4 mg/L dissolved oxygen), but the random forest regression performed the best in estimating the bottom dissolved oxygen from satellite-derived variables. Our models also revealed time lags of roughly 0–5 and 16–19 days between the surface water process (e.g., algae bloom and ocean warming) and bottom water hypoxia, which was rarely considered in previous hypoxia studies using satellite data. Finally, our models showed that the area of Gulf hypoxia increased gradually from May and reached a peak during mid-July and mid-August in 2014, and the hypoxia zone occurred in the estuary of the Mississippi River and Suwannee River during roughly 25% of summer days. In addition to predicting the size of hypoxic zones, our study provides additional information on where, when, and how long hypoxic zones persist with greater spatial details and enables modeling hypoxic zones at near-real-time (e.g., days) temporal scales. More importantly, we demonstrate the great potential of applying satellite remote sensing for spatially explicit hypoxia mapping, which could promote more cost-effective coastal hypoxia monitoring and assessment practices.

Short-term exposure to air pollution and mental disorders: a case-crossover study in New York City

There is growing evidence suggesting that exposure to air pollutants is associated with mental disorders. We investigated the impact of short-term exposures to both fine particulate matter (PM2.5) and ozone (O3) assessed at fine spatiotemporal resolutions on emergency room (ER) visits related to mental disorders using 475 755 records from New York City between 2010 and 2016. We assessed the short-term impact of daily air pollution exposure on ER visits using a case-crossover design with conditional logistic regression. We further tested whether the impact of PM2.5 and O3 varied by individuals’ age, sex, and race/ethnicity, and if associations were modified by the degree of visibility of greenspace at individuals’ residences. Results suggested that the relative risk of visiting an ER for mental-disorders increased by 2.78% (95% CI: 1.82%–3.76%) with a 10 increase in ambient PM2.5 concentration over a 4 day (d) moving average (lag 0–3 d) and increased by 0.71% (95% CI: 0.28%–1.15%) with a 10 ppb increase in O3 concentration on a single day lag (lag 1 d), and that these effects were modified by age and race/ethnicity, but not by sex or surrounding greenery. Specifically, we found that age group 19–35 years old and non-Whites were more susceptible to the effects of ambient air pollution exposure. In terms of specific disorders, we found that both PM2.5 and O3 have an effect on ER visits for psychotic disorders, but not dementia. Our findings suggest that short-term exposure to ambient air pollution is associated with increased ER visits for mental disorders. Further research is needed to determine the underlying mechanisms by which exposure to PM2.5 and O3 is linked to these ER visits.

A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau

Amplifying landslide hazards in the backdrop of warming climate and intensifying human activities calls for an integrated framework for accurately evaluating landslide susceptibility at fine spatiotemporal resolutions, which is critical for the mitigation of increasingly high landslide disaster risks. Yet, dynamic landslide susceptibility mapping is still lacking. Using high-quality data, from 14,435 landslides and non-landslides, we developed an efficient holistic framework for evaluating landslide susceptibility, considering landslide-relevant internal and external factors based on cloud computing platform and algorithmic models, which enables dynamic updating of a landslide susceptibility map at the regional scale, particularly in regions with highly complicated topographical features such as the Hengduan Mountains, as considered in this study. We compared Classification and Regression Trees (CART), Support Vector Machines (SVM), and Random Forest (RF) classifiers to screen out the best portfolio model for landslide susceptibility mapping on the Google Earth Engine (GEE) platform. We found that the Random Forest (RF) classifier integrated with synergy mode had the best modeling performance, with 90.48% and 89.24% accuracy and precision, respectively. We also found that forests and grasslands had the controlling effect on the occurrence of landslides, while human activities had a notable inducing effect on the occurrence of landslides within the Hengduan Mountains. This study highlights the performance of the holistic landslide susceptibility evaluation framework proposed in this study and provides a viable technique for landslide susceptibility evaluation in other regions of the globe.

A spatiotemporal transcriptomic network dynamically modulates stalk development in maize.

Maize (Zea mays) is an important cereal crop with suitable stalk formation which is beneficial for acquiring an ideal agronomic trait to resist lodging and higher planting density. The elongation pattern of stalks arises from the variable growth of individual internodes driven by cell division and cell expansion comprising the maize stalk. However, the spatiotemporal dynamics and regulatory network of the maize stalk development and differentiation process remain unclear. Here, we report spatiotemporally resolved transcriptomes using all internodes of the whole stalks from developing maize at the elongation and maturation stages. We identified four distinct groups corresponding to four developmental zones and nine specific clusters with diverse spatiotemporal expression patterns among individual internodes of the stalk. Through weighted gene coexpression network analysis, we constructed transcriptional regulatory networks at a fine spatiotemporal resolution and uncovered key modules and candidate genes involved in internode maintenance, elongation, and division that determine stalk length and thickness in maize. Further CRISPR/Cas9-mediated knockout validated the function of a cytochrome P450 gene, ZmD1, in the regulation of stalk length and thickness as predicted by the WGCN. Collectively, these results provide insights into the high genetic complexity of stalk development and the potentially valuable resources with ideal stalk lengths and widths for genetic improvements in maize.

Data-driven mapping of hourly wind speed and its potential energy resources: A sensitivity analysis

Renewable energies play a significant role to mitigate the impacts of climate change. In countries like Spain, there is a significant potential of wind energy production which might be a key resource. In this research, we obtain wind power at 80 m height and wind turbine energy (assuming a specific turbine). To achieve this objective we produce an optimal mapping of the hourly “instantaneous surface wind speed” (height 10 m), based on the available data. An extensive region (Granada Province, south Spain) is studied with a spatial resolution of 300 m, during a long period (1996–2016). It allows us to assess the intra- and inter-daily variability of wind energy resources. Several interpolation approaches are tested and a cross validation experiment is applied to identify the optimal approach. The obtained maps were compared with the results obtained in the stations with two common frequency distributions (Rayleigh and Weibull). This is the first time that this sensitivity integrated analysis is performed over an extensive region (12600 km2) for a long time period (20 years) at fine spatiotemporal resolution (300 m, hourly scale). The results can be very valuable for a preliminary analysis of potential optimal location of wind energies facilities.

Monitoring drought impacts on crop productivity of the U.S. Midwest with solar-induced fluorescence: GOSIF outperforms GOME-2 SIF and MODIS NDVI, EVI, and NIRv

• SIF with finer resolution improves the monitoring of drought impacts on crop. • Finer resolution SIF can more accurate assessment of crop anomalies and yield. • SIF is more sensitive to water and heat stress conmpare with VIs and NIRv. • SIF enables more accurate assessment of crop seasonal cycles. • C4 plants are better adapted to drought stress than C3 plants. The frequency and severity of drought are increasing in the context of global warming. Elucidating the responses of crop productivity to drought is essential for informing agricultural management and ensuring food security. Here we used satellite-derived solar-induced chlorophyll fluorescence (SIF) data and vegetation indices to evaluate the impacts of the 2012 drought on crop productivity in the U.S. Midwest. We used SIF from the global, OCO-2 based SIF product (GOSIF; 0.05°, 8-day), GOME-2 SIF product (0.5°, monthly), and three MODIS-derived vegetation indices (NDVI, EVI, and NIRv). We compared the seasonal cycles and anomalies of SIF and VIs from 2008 to 2018. We also examined to what extent these proxies could capture the variations of gross primary production (GPP) for eddy covariance flux sites. SIF and VIs were able to capture the seasonal cycle in drought and normal years. SIF better captured the photosynthesis changes due to water and heat stresses than vegetation indices. In particular, GOSIF data with the finer spatio-temporal resolution was a good monitor of crop response to drought. Crop yield decreased by 25% in the 2012 drought relative to the multi-year mean, while GOSIF, NDVI, EVI, and NIRv reduced by 22%, 4%, 10%, and 8%, respectively. GOSIF had the strongest relationship with crop yield (R 2 = 0.91), followed by NIRv (R 2 = 0.89), EVI (R 2 = 0.68) and NDVI (R 2 = 0.48). Compared to the crop yield data, the mean difference of the yield estimates based on GOSIF, EVI, and NIRv were 379.32, 328.43, and 503.67 kg/ha, respectively. For both corn and soybeans, yield anomalies were better correlated with GOSIF anomalies than with NIRv and EVI anomalies. Our study demonstrated that SIF with finer spatio-temporal resolution has great potential for monitoring the responses of crop productivity to drought.

Identifying Dominant Processes in Time and Space: Time‐Varying Spatial Sensitivity Analysis for a Grid‐Based Nitrate Model

AbstractDistributed models have been increasingly applied at finer spatiotemporal resolution. However, most diagnostic analyses aggregate performance measures in space or time, which might bias subsequent inferences. Accordingly, this study explores an approach for quantifying the parameter sensitivity in a spatiotemporally explicit way. We applied the Morris method to screen key parameters within four different sampling spaces in a grid‐based model (mHM‐Nitrate) for NO3‐N simulation in a mixed landuse catchment using a 1‐year moving window for each grid. The results showed that an overly wide range of aquatic denitrification rates could mask the sensitivity of the other parameters, leading to their spatial patterns only related to the proximity to outlet. With adjusted parameter space, spatial sensitivity patterns were determined by NO3‐N inputs and hydrological transport capacity, while temporal dynamics were regulated by annual wetness conditions. The relative proportion of parameter sensitivity further indicated the shifts in dominant hydrological/NO3‐N processes between wet and dry years. By identifying not only which parameter(s) is(are) influential, but where and when such influences occur, spatial sensitivity analysis can help evaluate current model parameterization. Given the marked sensitivity in agricultural areas, we suggest that the current NO3‐N parameterization scheme (land use‐dependent) could be further disentangled in these regions (e.g., into croplands with different rotation strategies) but aggregated in non‐agricultural areas; while hydrological parameterization could be resolved into a finer level (from spatially constant to land use‐dependent especially in nutrient‐rich regions). The spatiotemporal sensitivity pattern also highlights NO3‐N transport within soil layers as a focus for future model development.

A remote sensing assessment index for urban ecological livability and its application

ABSTRACT Remote sensing provides us with an approach for the rapid identification and monitoring of spatiotemporal changes in the urban ecological environment at different scales. This study aimed to construct a remote sensing assessment index for urban ecological livability with continuous fine spatiotemporal resolution data from Landsat and MODIS to overcome the dilemma of single image-based, single-factor analysis, due to the limitations of atmospheric conditions or the revisit period of satellite platforms. The proposed Ecological Livability Index (ELI) covers five primary ecological indicators – greenness, temperature, dryness, water-wetness, and atmospheric turbidity – which are geometrically aggregated by non-equal weights based on an entropy method. Considering multisource time-series data of each indicator, the ELI can quickly and comprehensively reflect the characteristics of the Ecological Livability Quality (ELQ) and is also comparable at different time scales. Based on the proposed ELI, the urban ecological livability in the central urban area of Wuhan, China, from 2002 to 2017, in the different seasons was analyzed every 5 years. The ELQ of Wuhan was found to be generally at the medium level (ELI ≈0.6) and showed an initial trend of degradation but then improved. Moreover, the ecological livability in spring and autumn and near rivers and lakes was found to be better, whereas urban expansion has led to the outward ecological degradation of Wuhan, but urban afforestation has enhanced the environment. In general, this paper demonstrates that the ELI has an exemplary embodiment in urban ecological research, which will support urban ecological protection planning and construction.

An Operational Downscaling Method of Solar-Induced Chlorophyll Fluorescence (SIF) for Regional Drought Monitoring

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and a promising indicator of drought monitoring, but the ability of high-resolution satellite-derived SIF for drought monitoring has not been widely investigated due to a lack of data. The lack of high spatiotemporal resolution satellite SIF hinders the resolution enhancement of SIF derived by downscaling or reconstruction algorithms. The TROPOspheric Monitoring Instrument (TROPOMI) SIF provides an alternative with finer spatiotemporal resolution. We present an operational downscaling method to generate 500 m 16-day SIF (TSIF) using Neural Networks over a local spatiotemporal window. The results showed that our method is very robust against overfitting, and TSIF has a strong spatiotemporal consistency with TROPOMI SIF (TROPOSIF) with R2=0.956 and RMSE=0.054 mWm−2sr−1nm−1. Comparison with another SIF product (CASIF) showed a spatiotemporal consistency with TSIF. Comparison with tower gross primary productivity (GPP) from AmeriFlux in California showed a strong correlation with R2 for multiple ecosystems ranging from 0.58 to 0.88. We explored the capacity of TSIF for monitoring a drought event in Henan, China, showing that TSIF is more sensitive to drought and precipitation compared to the Enhanced Vegetation Index. Our TSIF is a very promising indicator for regional drought monitoring.

Estimating Hourly Surface Solar Irradiance from GK2A/AMI Data Using Machine Learning Approach around Korea

Surface solar irradiance (SSI) is a crucial component in climatological and agricultural applications. Because the use of renewable energy is crucial, the importance of SSI has increased. In situ measurements are often used to investigate SSI; however, their availability is limited in spatial coverage. To precisely estimate the distribution of SSI with fine spatiotemporal resolutions, we used the GEOstationary Korea Multi-Purpose SATellite 2A (GEO-KOMPSAT 2A, GK2A) equipped with the Advanced Meteorological Imager (AMI). To obtain an optimal model for estimating hourly SSI around Korea using GK2A/AMI, the convolutional neural network (CNN) model as a machine learning (ML) technique was applied. Through statistical verification, CNN showed a high accuracy, with a root mean square error (RMSE) of 0.180 MJ m−2, a bias of −0.007 MJ m−2, and a Pearson’s R of 0.982. The SSI obtained through a ML approach showed an accuracy higher than the GK2A/AMI operational SSI product. The CNN SSI was evaluated by comparing it with the in situ SSI from the Ieodo Ocean Research Station and from flux towers over land; these in situ SSI values were not used for training the model. We investigated the error characteristics of the CNN SSI regarding environmental conditions including local time, solar zenith angle, in situ visibility, and in situ cloud amount. Furthermore, monthly and annual mean daily SSI were calculated for the period from 1 January 2020 to 31 January 2022, and regional characteristics of SSI around Korea were analyzed. This study addressed the availability of satellite-derived SSI to resolve the limitations of in situ measurements. This could play a principal role in climatological and renewable energy applications.

Coupling of satellite-derived precipitation products with Bartlett-Lewis model to estimate intensity-frequency-duration curves for remote areas

Intensity-frequency-duration (IFD) curves (also known as intensity-duration-frequency (IDF) curves in some regions) are widely used to take into consideration the probability of occurrence of extreme precipitation for the design of water related infrastructure. Derivation of IFD curves is based on statistical analysis of observed extreme precipitation events and thus the reliability of the curves mostly depends on the record length of observations. At sub-hourly timescales however, ground-based precipitation observations are often absent or very limited in terms of spatial coverage and record length, particularly in remote areas around the world. Given the availability of satellite-derived precipitation products (SPPs) with finer spatiotemporal resolution and near-global coverage, there is great potential to use these products in the planning and design of infrastructure. This paper proposes a novel framework, SIFD (Satellite-derived IFD curves) to combine long-term daily gauge data and sub-hourly SPP to estimate IFD curves for time intervals from 0.5 to 24 h. A recent variant of the Random Parameter Bartlett-Lewis Rectangular Pulse model (RPBLRPX) was used to disaggregate daily gauge data to derive IFD curves. In the absence of sub-daily rain gauge data, the statistics required for the RPBLRPX model were obtained using the Integrated Multi-satellitE Retrievals for GPM (IMERG; 30-min, 0.1° resolution) product derived from the Global Precipitation Measurement (GPM) mission. Performance of the SIFD framework was tested against gauge data from seven Australian sites across a wide range of climatologies. The disaggregation scheme coupled with the RPBLRPX model was capable of reproducing the wet-period mean intensity, wet-period standard deviation, and wet-period fraction with reasonable accuracy across the sub-daily timescales. The coefficient of determination (r2) values were 0.39 (mean intensity), 0.38 (standard deviation), and 0.87 (wet-period fraction) at 0.5 h timescale. Furthermore, despite some differences in methodology and record length, the IFD estimates computed using the SIFD framework were fairly close to those of the Bureau of Meteorology (BoM). The skill metrics for Sydney were the best with a bias ratio (BR) of 1.01, root mean square error (RMSE) of 13% of the mean of the BoM estimates, and a Nash-Sutcliffe coefficient of efficiency (NSE) of 0.97. The worst case was found at Hobart (BR = 1.43, RMSE = 66%, and NSE = 0.26). Overall, the results indicate that the proposed SIFD framework could provide a viable alternative to estimate unbiased IFD curves at the sub-hourly timescale in the absence of adequate sub-hourly ground-based observations.

Estimation of Global Irrigation Water Use by the Integration of Multiple Satellite Observations

AbstractQuantification of the global irrigation water use (IWU) is crucial to understanding the anthropogenic disturbance of the natural hydrological cycle and optimal agricultural water management. However, it is challenging to obtain time series data with the conventional survey‐based approach, while the current satellite‐based IWU estimations are subject to data gaps and the model structure. In this paper, we propose a comprehensive framework to couple the different processes associated with irrigation and integrate multiple satellite observations to estimate the global IWU. The ensemble IWU estimate demonstrates an improved performance when compared to the IWU obtained from individual satellite observations. The results show reasonable correlation with the survey‐based irrigation water withdrawal in states of the US (bias = −0.42 km3), provinces of China (bias = −3.10 km3), and country statistics from the Food and Agriculture Organization (bias = −10.84 km3). Large amounts of IWU are apparent in India, China, the US, Europe, and Pakistan, making up &gt;70% of the global IWU. A general underestimation of IWU is found both in this work and previous studies, due to the coarse resolution and asynchronism of the various satellite products, the changes in irrigated areas, and the deficiency in detecting irrigation events under the case of saturated soil moisture. Nevertheless, we demonstrate advantages in integrating multiple satellite observations to reduce the uncertainty in estimating global IWU. However, additional efforts are needed to produce high‐quality and finer spatiotemporal resolution satellite‐based products, to further improve the accuracy of the global IWU estimation.

PyCLKDE: A big data‐enabled high‐performance computational framework for species habitat suitability modeling and mapping

AbstractSpecies habitat suitability modeling and mapping (HSM) at large spatial scales (e.g., continental, global) and at fine spatiotemporal resolutions helps understand spatiotemporal dynamics of species distributions (e.g., migratory birds). Such HSM endeavors often involve “big” environmental and species datasets, which traditional software tools are often incapable of handling. To overcome the computational challenges facing conducting big data‐involved HSM tasks, this study develops a big data‐enabled high‐performance computational framework to conduct HSM efficiently on large numbers of species records and massive volumes of environmental covariates. As a demonstration of its usability, PyCLKDE was implemented based on the computational framework for flexibly integrating multi‐source species data for HSM. The computing performance of PyCLKDE was thoroughly evaluated through experiments modeling and mapping Empidonax virescens habitat suitability in the continental Americas using high‐resolution environmental covariates and species observations obtained from citizen science projects. Results show that PyCLKDE can effectively exploit computing devices with varied computing capabilities (CPUs and GPUs on high‐end workstations or commodity laptops) for parallel computing to accelerate HSM computations. PyCLKDE thus enables conducting big data‐involved HSM using commonly available computing resources. Using PyCLKDE as an example, efforts are called for to develop similar geocomputation tools based on the proposed framework to realize the potential of effectively performing geospatial big data analytics utilizing heterogenous computing resources on “personal‐grade” computing resources.

A Taylor Expansion Algorithm for Spatial Downscaling of MODIS Land Surface Temperature

Land surface temperature (LST) with fine spatiotemporal resolution is a much-needed parameter in the earth surface system. The LST downscaling is an efficient way to improve the spatiotemporal resolution of LST and has been developed rapidly in recent years. Due to the simple operations and discernable effects of statistical regression and its extension algorithms, these algorithms have been widely researched. However, most statistical regression models assume scale invariance, which makes the downscaled LST inaccurate. This study analyzed the scale effect in the process of LST upscaling/downscaling, then proposed a new algorithm based on Taylor expansion for Moderate Resolution Imaging Spectroradiometer (MODIS) LST downscaling. The Taylor expansion algorithm estimates regression coefficients between LST and auxiliary parameters in the consistent scale. It is tested in three typical areas of different landscape with different auxiliary parameters, and the results are significantly improved compared to the traditional algorithm. However, the new algorithm may introduce the temporal discrepancy between MODIS LST and empirical concavity factor (S), which is estimated with Landsat 8 data, into the downscaling procedure in some circumstances. To discuss the influence of temporal discrepancy, we designed three schemes for pairing MODIS and S and analyzed the downscaled results. The results show that the proposed algorithm got the best downscaled results when the MODIS LST acquired time is consistent with the time of S. When the time is inconsistent, the pairing scheme of a similar season gives better results than that of different seasons. The algorithm performs generally well so long as the spatial distribution of auxiliary parameters in the date of Landsat 8 acquisition is similar to the date of MODIS acquisition.