Effect of Spatial and Temporal Scales on Habitat Suitability Modeling:A Case Study of Ommastrephes bartramii in the Northwest Pacific Ocean

Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.

Surface Humidity Changes in Different Temporal Scales

As the key driven factor of hydrological cycles and global energy transfer processes, water vapour in the atmosphere is important for observing and understanding climatic system changes. In this study, we utilized the multi-dimensional Kolmogorov-Zurbenko filter (KZ filter) to assimilate a near-global high-resolution (monthly 1°?× 1°?grid) humidity climate observation database that provided consistent humidity estimates from 1973 onwards;then we examined the global humidity movements based on different temporal scales that separated out according to the average spectral features of specific humidity data. Humidity climate components were restored with KZ filters to represent the long-term trends and El Nino-like interannual movements. Movies of thermal maps based on these two climate components were used to visualize the water vapour fluctuation patterns over the Earth. Current results suggest that increases in water vapour are found over a large part of the oceans and the land of Eurasia, and the most confirmed increasing pattern is over the south part of North Atlantic and around the India subcontinent;meanwhile, the surface moisture levels over lands of south hemisphere are becoming less.

Dependence of the Accuracy of Precipitation and Cloud Simulation on Temporal and Spatial Scales

Precipitation and associated cloud hydrometeors have large temporal and spatial variability, which makes accurate quantitative precipitation forecasting difficult. Thus, dependence of accurate precipitation and associated cloud simulation on temporal and spatial scales becomes an important issue. We report a cloud- resolving modeling analysis on this issue by comparing the control experiment with experiments perturbed by initial temperature, water vapor, and cloud conditions. The simulation is considered to be accurate only if the root-mean-squared difference between the perturbation experiments and the control experiment is smaller than the standard deviation. The analysis may suggest that accurate precipitation and cloud simulations cannot be obtained on both fine temporal and spatial scales simultaneously, which limits quanti- tative precipitation forecasting. The accurate simulation of water vapor convergence could lead to accurate precipitation and cloud simulations on daily time scales, but it may not be beneficial to precipitation and cloud simulations on hourly time scales due to the dominance of cloud processes.

Assessing the quality of chlorophyll-a concentration products under multiple spatial and temporal scales

Abstract The chlorophyll-a concentration data obtained through remote sensing are important for a wide range of scientific concerns.However,cloud cover and limitations of inversion algorithms of chlorophyll-a concentration lead to data loss,which critically limits studying the mechanism of spatial-temporal patterns of chlorophyll-a concentration in response to marine environment changes.If the commonly used operational chlorophyll-a concentration products can offer the best data coverage frequency,highest accuracy,best applicability,and greatest robustness at different scales remains debatable to date.Therefore,in the present study,four commonly used operational multi-sensor multi-algorithm fusion products were compared and subjected to validation based on statistical analysis using the available data measured at multiple spatial and temporal scales.The experimental results revealed that in terms of spatial distribution,the chlorophyll-a concentration products generated by averaging method(Chl1-AV/AVW)and GSM model(Chl1-GSM)presented a relatively high data coverage frequency in Case Ⅰ water regions and extremely low or no data coverage frequency in the estuarine coastal zone regions and inland water regions,the chlorophyll-a concentration products generated by the Neural Network algorithm(Chl2)presented high data coverage frequency in the estuarine coastal zone Case 2 water regions.The chlorophyll-a concentration products generated by the OC5 algorithm(ChlOC5)presented high data coverage frequency in Case I water regions and the turbid Case Ⅱ water regions.In terms of absolute precision,the Chl1-AV/AVW and Chl1-GSM chlorophyll-a concentration products performed better in Class I water regions,and the Chl2 product performed well only in Case Ⅱ estuarine coastal zones,while presenting large errors in absolute precision in the Case Ⅰ water regions.The ChlOC5 product presented a higher precision in Case Ⅰ and Case Ⅱ water regions,with a better and more stable performance in both regions compared to the other products.

Trends and Scales of Observed Soil Moisture Variations in China

A new soil moisture dataset from direct gravimetric measurements within the top 50-cm soil layers at 178 soil moisture stations in China covering the period 1981 1998 are used to study the long-term and seasonal trends of soil moisture variations, as well as estimate the temporal and spatial scales of soil moisture for different soil layers. Additional datasets of precipitation and temperature difference between land surface and air （TDSA） are analyzed to gain further insight into the changes of soil moisture. There are increasing trends for the top 10 cm, but decreasing trends for the top 50 cm of soil layers in most regions. Trends in precipitation appear to dominantly influence trends in soil moisture in both cases. Seasonal variation of soil moisture is mainly controlled by precipitation and evaporation, and in some regions can be affected by snow cover in winter. Timescales of soil moisture variation are roughly 1-3 months and increase with soil depth. Further influences of TDSA and precipitation on soil moisture in surface layers, rather than in deeper layers, cause this phenomenon. Seasonal variations of temporal scales for soil moisture are region-dependent and consistent in both layer depths. Spatial scales of soil moisture range from 200-600 km, with topography also having an affect on these. Spatial scales of soil moisture in plains are larger than in mountainous areas. In the former, the spatial scale of soil moisture follows the spatial patterns of precipitation and evaporation, whereas in the latter, the spatial scale is controlled by topography.

Temporal and Spatial Scale Dependence of Precipitation Analysis over the Tropical Deep Convective Regime

Data from Goddard cumulus ensemble model experiment are used to study temporal and spatial scale dependence of tropical rainfall separation analysis based on cloud budget during Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment （TOGA COARE）. The analysis shows that the calculations of model domain mean or time-mean grid-scale mean simulation data overestimate the rain rates of the two rainfall types associated with net condensation but they severely underestimate the rain rate of the rainfall type associated with net evaporation and hydrometeor convergence.

On the Variability of Charleston South Carolina Winds, Atmospheric Temperatures, Water Levels, Waves and Precipitation

Atmospheric winds, air temperatures, water levels, precipitation and oceanic waves in the Charleston South Carolina (SC) coastal zone are evaluated for their intrinsic, internal variability over temporal scales ranging from hours to multi-decades. The purpose of this study was to bring together a plethora of atmospheric and coastal ocean state variable data in a specific locale, to assess temporal variabilities and possible relationships between variables. The questions addressed relate to the concepts of weather and climate. Data comprise the basis of this study. The overall distributions of atmospheric and coastal oceanic state variable variability, including wind speed, direction and kinematic distributions and state variable amplitudes over a variety of time scales are assessed. Annual variability is shown to be highly variable from year to year, making arithmetic means mathematically tractable but physically meaningless. Employing empirical and statistical methodologies, data analyses indicate the same number of intrinsic, internal modes of temporal variability in atmospheric temperatures, coastal wind and coastal water level time series, ranging from hours to days to weeks to seasons, sub-seasons, annual, multi-year, decades, and centennial time scales. This finding demonstrates that the atmosphere and coastal ocean in a southeastern U.S. coastal city are characterized by a set of similar frequency and amplitude modulated phenomena. Kinematic hodograph descriptors of atmospheric winds reveal coherent rotating and rectilinear particle motions. A mathematical statistics-based wind to wave-to-wave algorithm is developed and applied to offshore marine buoy data to create an hour-by-hour forecast capability from 1 to 24 hours;with confidence levels put forward. This affects a different approach to the conventional deterministic model forecasting of waves.

Influence of Recent Trial History on Interval Timing

Interval timing is involved in a variety of cognitive behaviors such as associative learning and decision-making.While it has been shown that time estimation is adaptive to the temporal context,it remains unclear how interval timing behavior is influenced by recent trial history.Here we found that,in mice trained to perform a licking-based interval timing task,a decrease of inter-reinforcement interval in the previous trial rapidly shifted the time of anticipatory licking earlier.Optogenetic inactivation of the anterior lateral motor cortex(ALM),but not the medial prefrontal cortex,for a short time before reward delivery caused a decrease in the peak time of anticipatory licking in the next trial.Electrophysiological recordings from the ALM showed that the response profiles preceded by short and long inter-reinforcement intervals exhibited task-engagement-dependent temporal scaling.Thus,interval timing is adaptive to recent experience of the temporal interval,and ALM activity during time estimation reflects recent experience of interval.

Streamflow prediction in ungauged catchments by using the Grunsky method

Establish a reliable rainfall-runoff relation capable of predicting runoff in ungauged basins is a matter of interest across the world for a long time and has been taking importance during the past decades.Regionalization approaches,hydrological models and machine learning techniques have been used to estimate runoff.However,returning some simplicity to the predictions might be necessary for practical uses.In this paper,we re-introduce C.E.Grunsky approach,developed in the early 1900s to predict runoff from values of precipitation on a two-equations system.Here,we analyze the Grunsky generalized method applied for 716 Brazilian catchments,on an interannual and monthly scales.First,we established the best method to find the rainfall-runoff relation coefficient for each catchment.Then,we evaluate the performance of the method on a local scale,i.e.,catchment by catchment.Lastly,we analyze the method of regionalization,by grouping the catchments into six hydrologically similar classes.For local scale,the Kling-Gupta Efficiency(KGE)values range from 0.87 to 0.93 on the interannual scale and is greater than 0.50 on the monthly scale.For the regionalized approach,KGE varies from 0.60 to 0.84 on an interannual scale.We also found suitable KGE values on a monthly scale,with more than 22%of catchments with KGE greater than 0.50,being the best performances in the Non-seasonal and Extremely-wet groups,and the worst performance in the Dry group.Our findings indicate that Grunsky approach is suitable to predict streamflow for Brazilian catchments on interannual and monthly scales.This simple and easy-to-use equation presents a reliable alternative to more complex methods to compute runoff by only using rainfall data.

Wavelet methods reveal big cat activity patterns and synchrony of activity with preys

Appropriate temporal and spatial scales are important prerequisites for obtaining reliable results in studies of wildlife activity patterns and interspecific interactions.The spread of camera-trap technology has increased interest in and feasibility of studying the activity patterns and interspecific interactions of wildlife.However,such studies are often conducted at arbitrary spatial and temporal scales,and the methods used impose scale on the study rather than determining how activity and species interactions change with spatial scale.In this study,we used a waveletbased approach to determine the temporal and spatial scales for activity patterns and interspecific interactions on Amur leopard and their ungulate prey species that were recorded using camera traps in the main Amur leopard occurrence region in northeast China.Wavelets identified that Amur leopards were more active in spring and fall than summer,and fluctuated with periodicities of 9 and 17 days,respectively.Synchronous relationships between leopards and their prey commonly occurred in spring and fall,with a periodicity of about 20 days,indicating the appropriate seasons and temporal scales for interspecific interaction research.The influence of human activities on the activity patterns of Amur leopard or prey species often occurred over longer time periods(60–64 days).Twodimensional wavelet analyses showed that interactions between leopard and prey were more significant at spatial scales of 1 km2.Overall,our study provides a feasible approach to studying the temporal and spatial scales for wildlife activity patterns and interspecific interaction research using camera trap data.