Precipitation Estimation in Mountainous Terrain Using Multivariate Geostatistics. Part I: Structural Analysis

Phenological Mapping of Invasive Insects: Decision Support for Surveillance and Management

Readily accessible and easily understood forecasts of the phenology of invasive insects have the potential to support and improve strategic and tactical decisions for insect surveillance and management. However, most phenological modeling tools developed to date are site-based, meaning that they use data from a weather station to produce forecasts for that single site. Spatial forecasts of phenology, or phenological maps, are more useful for decision-making at area-wide scales, such as counties, states, or entire nations. In this review, we provide a brief history on the development of phenological mapping technologies with a focus on degree-day models and their use as decision support tools for invasive insect species. We compare three different types of phenological maps and provide examples using outputs of web-based platforms that are presently available for real-time mapping of invasive insects for the contiguous United States. Next, we summarize sources of climate data available for real-time mapping, applications of phenological maps, strategies for balancing model complexity and simplicity, data sources and methods for validating spatial phenology models, and potential sources of model error and uncertainty. Lastly, we make suggestions for future research that may improve the quality and utility of phenological maps for invasive insects.

Mapping the spatial variability of rainfall from a physiographic-based multilinear regression: model development and application to the Southwestern Iberian Peninsula

A physiographic-based multilinear regression model supported by GIS was developed to estimate spatial rainfall variability in the Southwest Iberian Peninsula. The area study includes a wide diversity of landscape features and comprises four Portuguese regions and one Spanish province (totalizing 28,860 km²). The region suffers a very strong Mediterranean influence, with a major cleavage between winter and summer seasons. Thus, the analysis was carried out separately for the wet (October to March) and dry (April to September) semesters. From an initial set of 10 explanatory physiographic variables, five were selected to be used in the multilinear regression, as they allowed generating models by map algebra that fitted well with the last 40 years of monthly rainfall data records. These records were obtained from 163 weather stations, filtered from an initial set of 230 (142 stations in Portugal and 88 in Spain). The correlation between the physiographic-based multilinear regression model and a model obtained by interpolation from rainfall historical data showed to be good or very good in approximately 75% of the area under study. Results show that physiographic-based models can be effectively used to estimate rainfall where there is a lack of rain gauges, or to densify spatial resolution of rainfall between rain gauges.

Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction

Accurate pollutant prediction is essential in fields such as meteorology, meteorological disasters, and climate change studies. In this study, long short-term memory (LSTM) and deep neural network (DNN) models were applied to six pollutants and comprehensive air-quality index (CAI) predictions from 2015 to 2020 in Korea. In addition, we used the network method to find the best data sources that provide factors affecting comprehensive air-quality index behaviors. This study had two steps: (1) predicting the six pollutants, including fine dust (PM10), fine particulate matter (PM2.5), ozone (O3), sulfurous acid gas (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) using the LSTM model; (2) forecasting the CAI using the six predicted pollutants in the first step as predictors of DNNs. The predictive ability of each model for the six pollutants and CAI prediction was evaluated by comparing it with the observed air-quality data. This study showed that combining a DNN model with the network method provided a high predictive power, and this combination could be a remarkable strength in CAI prediction. As the need for disaster management increases, it is anticipated that the LSTM and DNN models with the network method have ample potential to track the dynamics of air pollution behaviors.

A Comparative Analysis of Infiltration Models for Groundwater Recharge from Ephemeral Stream Beds: A Case Study in Al Madinah Al Munawarah Province, Saudi Arabia

Groundwater recharge is strongly influenced by the infiltration process. In this research, the Philip, Horton, Kostiakov, and Green–Ampt infiltration models were tested for the ability to describe the infiltration process in the ephemeral stream beds located in Al Madinah Al Munawarah Province in Saudi Arabia. Infiltration data were obtained from double-ring infiltrometer tests in 14 locations distributed over the province. The method of least squares through an objective function optimization formalism is utilized to estimate the parameters of each model. The results show high variability in the parameters of each model over the tests. Individual tests showed that some models were better for representing specific tests than other models. On average, the Kostiakov empirical model was the best at describing the 14 infiltration tests with only 2 empirical parameters, since it had the minimum root mean square error (RMSE) for the cumulative infiltration depth F (1.13 cm), and it also had the same RMSE for the infiltration rates f (0.1 cm/min), similar to other models. Moreover, the Kostiakov model had an acceptable correlation coefficient R = 0.61 for f, and R = 0.99 for F. The results imply significant variability in the groundwater recharge rates from flash floods in the region.

Numerical Experiments Applying Simple Kriging to Intermittent and Log-Normal Data

This study evaluates the effect of considering data intermittency and log-normality in applications of simple Kriging. Several sets of synthetic data, both intermittent and log-normal, were prepared for this purpose, and then four different Kriging applications were repeated with these synthetic data under different assumptions of data intermittency and log-normality. The effects of these assumptions on the simple Kriging applications were evaluated and compared with each other. As a result, it was found that the derived correlation length of a variogram becomes longer when considering both data intermittency and log-normality, and the sill height becomes smaller when data intermittency is high. The data field generated by simple Kriging was also closer to the original data when considering both data intermittency and log-normality. In the application to rain rate data, the effect of considering data intermittency was confirmed. However, the effect of considering data log-normality was found to be vague. The general assumption of log-normality in relation to the rain rate data seems not to be so valid, at least not for the rain rate data considered in this study.

Improving Mean Annual Precipitation Prediction Incorporating Elevation and Taking into Account Support Size

Accounting for secondary exhaustive variables (such as elevation) in modelling the spatial distribution of precipitation can improve their estimate accuracy. However, elevation and precipitation data are associated with different support sizes and it is necessary to define methods to combine such different spatial data. The paper was aimed to compare block ordinary cokriging and block kriging with an external drift in estimating the annual precipitation using elevation as covariate. Block ordinary kriging was used as reference of a univariate geostatistical approach. In addition, the different support sizes associated with precipitation and elevation data were also taken into account. The study area was the Calabria region (southern Italy), which has a spatially variable Mediterranean climate because of its high orographic variability. Block kriging with elevation as external drift, compared to block ordinary kriging and block ordinary cokriging, was the most accurate approach for modelling the spatial distribution of annual mean precipitation. The three measures of accuracy (MAE, mean absolute error; RMSEP, root-mean-squared error of prediction; MRE, mean relative error) have the lowest values (MAE = 112.80 mm; RMSEP = 144.89 mm, and MRE = 0.11), whereas the goodness of prediction (G) has the highest value (75.67). The results clearly indicated that the use of an exhaustive secondary variable always improves the precipitation estimate, but in the case of areas with elevations below 120 m, block cokriging makes better use of secondary information in precipitation estimation than block kriging with external drift. At higher elevations, the opposite is always true: block kriging with external drift performs better than block cokriging. This approach takes into account the support size associated with precipitation and elevation data. Accounting for elevation allowed to obtain more detailed maps than using block ordinary kriging. However, block kriging with external drift produced a map with more local details than that of block ordinary cokriging because of the local re-evaluation of the linear regression of precipitation on block estimates.

Water Balance Backward: Estimation of Annual Watershed Precipitation and Its Long-Term Trend with the Help of the Calibration-Free Generalized Complementary Relationship of Evaporation

Watershed-scale annual evapotranspiration (ET) is routinely estimated by a simplified water balance as the difference in catchment precipitation (P) and stream discharge (Q). With recent developments in ET estimation by the calibration-free generalized complementary relationship, the water balance equation is employed to estimate watershed/basin P at an annual scale as ET + Q on the United States (US) Geological Survey’s Hydrologic Unit Code (HUC) 2- and 6-level watersheds over the 1979–2015 period. On the HUC2 level, mean annual PRISM P was estimated with a correlation coefficient (R) of 0.99, relative bias (RB) of zero, root-mean-squared-error (RMSE) of 54 mm yr−1, ratio of standard deviations (RS) of 1.08, and Nash–Sutcliffe efficiency (NSE) of 0.98. On the HUC6 level, R, RS, and NSE hardly changed, RB remained zero, while RMSE increased to 90 mm yr−1. Even the long-term linear trend values were found to be fairly consistent between observed and estimated values with R = 0.97 (0.81), RMSE = 0.63 (1.63) mm yr−1, RS = 0.99 (1.05), NSE = 0.92 (0.59) on the HUC2 and HUC6 (in parentheses) levels. This calibration-free water-balance method demonstrates that annual watershed precipitation can be estimated with an acceptable accuracy from standard atmospheric/radiation and stream discharge data.

[Spatial distribution of anthracnose (Colletotrichum gloeosporioides Penz) in avocado in the State of Mexico, Mexico]

Persea americana is a species of great nutritional and economic importance for Mexico, however, like any other agricultural crop, it is affected by pests and diseases that limit its worldwide commercialization. The phytopathogenic fungus Colletotrichum gloeosporioides is the causative agent of anthracnose in avocado and manifests itself in the early stages of fruit development as well as in post-harvest and storage, under conditions of high relative humidity (80%) and at temperatures from 20°C, causing losses economic up to 20% of production. Applying geostatistical methods the present study aims to define the spatial distribution of anthracnose in Hass avocado fruits in four municipalities of the State of Mexico during the period from January to June 2017. The results show that the distribution of anthracnose was adjusted to gaussian and exponential models in most, the infestation maps made through the kriging show more than one center of aggregation of the disease, based on it the infested surface was estimated, finding an infestation of more than 50% in the first samples and up to 98% in the samplings belonging to the month of June in all the areas studied.

Space-Time Geostatistical Assessment of Hypoxia in the Northern Gulf of Mexico

Nearly every summer, a large hypoxic zone forms in the northern Gulf of Mexico. Research on the causes and consequences of hypoxia requires reliable estimates of hypoxic extent, which can vary at submonthly time scales due to hydro-meteorological variability. Here, we use an innovative space-time geostatistical model and data collected by multiple research organizations to estimate bottom-water dissolved oxygen (BWDO) concentrations and hypoxic area across summers from 1985 to 2016. We find that 27% of variability in BWDO is explained by deterministic trends with location, depth, and date, while correlated stochasticity accounts for 62% of observational variance within a range of 185 km and 28 days. Space-time modeling reduces uncertainty in estimated hypoxic area by 30% when compared to a spatial-only model, and results provide new insights into the temporal variability of hypoxia. For years with shelf-wide cruises in multiple months, hypoxia is most severe in July in 59% of years, 29% in August, and 12% in June. Also, midsummer cruise estimates of hypoxic area are only modestly correlated with summer-wide (June-August) average estimates ( r² = 0.5), suggesting midsummer cruises are not necessarily reflective of seasonal hypoxic severity. Furthermore, summer-wide estimates are more strongly correlated with nutrient loading than midsummer estimates.

Variation of Hydrothermal Conditions under Climate Change in Naqu Prefecture, Tibet Plateau, China

Analysis of the suitability of hydrothermal conditions for vegetation growth would benefit the ecological barrier construction, water resources protection and climate change adaptation. The suitability of hydrothermal conditions in Naqu Prefecture was studied based on the spatial displacement of 500 mm precipitation and 2000 °C accumulated temperature contours. Results showed that the 500 mm precipitation contour had a shifting trend toward the southwest, with a 3.3-year and 7.1-year period, respectively, in the longitudinal and latitudinal direction, and the longitude changed suddenly around 1996. The 2000 °C accumulated temperature contour had a shifting trend toward the northwest, with a 1.8-year period and a 7-year sub-period in the longitudinal direction; the longitude had a catastrophe point between 1966 and 1967, while the latitude had a catastrophe point between 2005 and 2006. When located in the same vegetation zone, the annual precipitation in Naqu Prefecture was higher than the national average, while the accumulated temperature was lower than the national average, indicating that areas with suitable hydrothermal conditions suitable for vegetation growth showed a northwestward shift tendency. This research would help to support some recommendations for plants' ecological system protection in alpine areas, and also provide guidelines for climate change adaptation.

Domestic and International Climate Migration from Rural Mexico

Evidence is increasing that climate change and variability may influence human migration patterns. However, there is less agreement regarding the type of migration streams most strongly impacted. This study tests whether climate change more strongly impacted international compared to domestic migration from rural Mexico during 1986-99. We employ eight temperature and precipitation-based climate change indices linked to detailed migration histories obtained from the Mexican Migration Project. Results from multilevel discrete-time event-history models challenge the assumption that climate-related migration will be predominantly short distance and domestic, but instead show that climate change more strongly impacted international moves from rural Mexico. The stronger climate impact on international migration may be explained by the self-insurance function of international migration, the presence of strong migrant networks, and climate-related changes in wage difference. While a warming in temperature increased international outmigration, higher levels of precipitation declined the odds of an international move.

Projected climate change impacts upon dew yield in the Mediterranean basin

Water scarcity is increasingly raising the need for non-conventional water resources, particularly in arid and semi-arid regions. In this context, atmospheric moisture can potentially be harvested in the form of dew, which is commonly disregarded from the water budget, although its impact may be significant when compared to rainfall during the dry season. In this study, a dew atlas for the Mediterranean region is presented illustrating dew yields using the yield data collected for the 2013 dry season. The results indicate that cumulative monthly dew yield in the region can exceed 2.8mm at the end of the dry season and 1.5mm during the driest months, compared to <1mm of rainfall during the same period in some areas. Dew yields were compared with potential evapotranspiration (PET) and actual evapotranspiration (ET) during summer months thus highlighting the role of dew to many native plants in the region. Furthermore, forecasted trends in temperature and relative humidity were used to estimate dew yields under future climatic scenarios. The results showed a 27% decline in dew yield during the critical summer months at the end of the century (2080).

Climate Shocks and the Timing of Migration from Mexico

Although evidence is increasing that climate shocks influence human migration, it is unclear exactly when people migrate after a climate shock. A climate shock might be followed by an immediate migration response. Alternatively, migration, as an adaptive strategy of last resort, might be delayed and employed only after available in-situ (in-place) adaptive strategies are exhausted. In this paper, we explore the temporally lagged association between a climate shock and future migration. Using multilevel event-history models, we analyze the risk of Mexico-U.S. migration over a seven-year period after a climate shock. Consistent with a delayed response pattern, we find that the risk of migration is low immediately after a climate shock and increases as households pursue and cycle through in-situ adaptive strategies available to them. However, about three years after the climate shock, the risk of migration decreases, suggesting that households are eventually successful in adapting in-situ.

Amplification or suppression: Social networks and the climate change-migration association in rural Mexico

Increasing rates of climate migration may be of economic and national concern to sending and destination countries. It has been argued that social networks - the ties connecting an origin and destination - may operate as "migration corridors" with the potential to strongly facilitate climate change-related migration. This study investigates whether social networks at the household and community levels amplify or suppress the impact of climate change on international migration from rural Mexico. A novel set of 15 climate change indices was generated based on daily temperature and precipitation data for 214 weather stations across Mexico. Employing geostatistical interpolation techniques, the climate change values were linked to 68 rural municipalities for which sociodemographic data and detailed migration histories were available from the Mexican Migration Project. Multi-level discrete-time event-history models were used to investigate the effect of climate change on international migration between 1986 and 1999. At the household level, the effect of social networks was approximated by comparing the first to the last move, assuming that through the first move a household establishes internal social capital. At the community level, the impact of social capital was explored through interactions with a measure of the proportion of adults with migration experience. The results show that rather than amplifying, social capital may suppress the sensitivity of migration to climate triggers, suggesting that social networks could facilitate climate change adaptation in place.

Validating spatiotemporal predictions of an important pest of small grains

BACKGROUND

Arthropod pests are typically managed using tactics applied uniformly to the whole field. Precision pest management applies tactics under the assumption that within-field pest pressure differences exist. This approach allows for more precise and judicious use of scouting resources and management tactics. For example, a portion of a field delineated as attractive to pests may be selected to receive extra monitoring attention. Likely because of the high variability in pest dynamics, little attention has been given to developing precision pest prediction models. Here, multimodel synthesis was used to develop a spatiotemporal model predicting the density of a key pest of wheat, the Russian wheat aphid, Diuraphis noxia (Kurdjumov).

RESULTS

Spatially implicit and spatially explicit models were synthesized to generate spatiotemporal pest pressure predictions. Cross-validation and field validation were used to confirm model efficacy. A strong within-field signal depicting aphid density was confirmed with low prediction errors.

CONCLUSION

Results show that the within-field model predictions will provide higher-quality information than would be provided by traditional field scouting. With improvements to the broad-scale model component, the model synthesis approach and resulting tool could improve pest management strategy and provide a template for the development of spatially explicit pest pressure models.

Satellite Remote Sensing of Space-Time Plankton Variability in the Bay of Bengal: Connections to Cholera Outbreaks

Cholera bacteria exhibit strong association with coastal plankton. Characterization of space-time variability of chlorophyll, a surrogate for plankton abundance, in Northern Bay of Bengal is an essential first step to develop any methodology for predicting cholera outbreaks in the Bengal Delta region using remote sensing. This study quantifies the space-time distribution of chlorophyll, using data from SeaWiFS, in the Bay of Bengal region using ten years of satellite data. Variability of chlorophyll at daily scale, irrespective of spatial averaging, resembles white noise. At a monthly scale, chlorophyll shows distinct seasonality and chlorophyll values are significantly higher close to the coast than in the offshore regions. At pixel level (9 km) on monthly scale, on the other hand, chlorophyll does not exhibit much persistence in time. With increased spatial averaging, temporal persistence of chlorophyll increases and lag one autocorrelation stabilizes around 0.60 for 1296 km(2) or larger areal averages. In contrast to the offshore regions, spatial analyses of chlorophyll suggest that only coastal region has a stable correlation length of 100 km. Presence (absence) of correlation length in the coastal (offshore) regions, indicate that the two regions may have two separate processes controlling the production a phytoplankton This study puts a lower limit on space-time averaging of satellite measured plankton at 1296 km(2)-monthly scale to establish relationships with cholera incidence in Bengal Delta.

Climatologically Aided Mapping of Daily Precipitation and Temperature

Accurately mapped meteorological data are an essential component for hydrologic and ecological research conducted at broad scales. A simple yet effective method for mapping daily weather conditions across heterogeneous landscapes is described and assessed. Daily weather data recorded at point locations are integrated with long-term-average climate maps to reconstruct spatially explicit estimates of daily precipitation and temperature extrema. The method uses ordinary kriging to interpolate base station data spatially into fields of approximately 2-km grain size. The fields are subsequently adjusted by 30-yr-average climate maps [Parameter-Elevation Regression on Independent Slopes Model (PRISM)], which incorporate adiabatic lapse rates, orographic effects, coastal proximity, and other environmental factors. The accuracy assessment evaluated an interpolation-only approach and the new method by comparing predicted and observed values from an independent validation dataset. The results of the accuracy assessment are compared for a 24-yr period for California. For all three weather variables, mean absolute errors (MAE) of the climate-imprint method were considerably smaller than those of the interpolation-only approach. MAE for predicted daily precipitation was ±2.5 mm, with a bias of +0.01. MAE for predicted daily minimum and maximum temperatures were ±1.7° and ±2.0°C, respectively, with corresponding biases of −0.41° and −0.38°C. MAE differed seasonally for all three weather variables, but the method was stable despite variation in the number of base stations available for each day.

A Simplified Diagnostic Model of Orographic Rainfall for Enhancing Satellite-Based Rainfall Estimates in Data-Poor Regions

An extension of Sinclair's diagnostic model of orographic precipitation (“VDEL”) is developed for use in data-poor regions to enhance rainfall estimates. This extension (VDELB) combines a 2D linearized internal gravity wave calculation with the dot product of the terrain gradient and surface wind to approximate terrain-induced vertical velocity profiles. Slope, wind speed, and stability determine the velocity profile, with either sinusoidal or vertically decaying (evanescent) solutions possible. These velocity profiles replace the parameterized functions in the original VDEL, creating VDELB, a diagnostic accounting for buoyancy effects. A further extension (VDELB*) uses an on/off constraint derived from reanalysis precipitation fields. A validation study over 365 days in the Pacific Northwest suggests that VDELB* can best capture seasonal and geographic variations. A new statistical data-fusion technique is presented and is used to combine VDELB*, reanalysis, and satellite rainfall estimates in southern Africa. The technique, matched filter regression (MFR), sets the variance of the predictors equal to their squared correlation with observed gauge data and predicts rainfall based on the first principal component of the combined data. In the test presented here, mean absolute errors from the MFR technique were 35% lower than the satellite estimates alone. VDELB assumes a linear solution to the wave equations and a Boussinesq atmosphere, and it may give unrealistic responses under extreme conditions. Nonetheless, the results presented here suggest that diagnostic models, driven by reanalysis data, can be used to improve satellite rainfall estimates in data-sparse regions.