A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation

Terrestrial evaporation (E) is a key climatic variable that depends on a plethora of environmental factors. The constraints that modulate the evaporation from plant leaves (or transpiration, Et) are particularly complex, yet often assumed to interact linearly in global models due to our limited knowledge based on local experimental studies. Here, we combine in situ and satellite observations with deep learning to model transpiration stress (St), i.e. the reduction of Et from its theoretical maximum. Then, we embed the new St formulation within a process-based model of E to yield a global hybrid E model. In this hybrid, the St formulation is bidirectionally coupled to the the host model at daily timescales. Comparisons against in situ data and satellite-based proxies demonstrate an enhanced ability to estimate St and E globally. Therefore, the proposed approach provides a framework to improve the estimation of E in Earth System Models and our understanding of this crucial climatic variable.

The Vegetation–Climate System Complexity through Recurrence Analysis

Multiple studies revealed that pasture grasslands are a time-varying complex ecological system. Climate variables regulate vegetation growing, being precipitation and temperature the most critical driver factors. This work aims to assess the response of two different Vegetation Indices (VIs) to the temporal dynamics of temperature and precipitation in a semiarid area. Two Mediterranean grasslands zones situated in the center of Spain were selected to accomplish this goal. Correlations and cross-correlations between VI and each climatic variable were computed. Different lagged responses of each VIs series were detected, varying in zones, the year’s season, and the climatic variable. Recurrence Plots (RPs) and Cross Recurrence Plots (CRPs) analyses were applied to characterise and quantify the system’s complexity showed in the cross-correlation analysis. RPs pointed out that short-term predictability and high dimensionality of VIs series, as well as precipitation, characterised this dynamic. Meanwhile, temperature showed a more regular pattern and lower dimensionality. CRPs revealed that precipitation was a critical variable to distinguish between zones due to their complex pattern and influence on the soil’s water balance that the VI reflects. Overall, we prove RP and CRP’s potential as adequate tools for analysing vegetation dynamics characterised by complexity.

Effect of Climate Variables on Yield of Major Crop in Samastipur District of Bihar: A time Series Analysis

Climate change influences crop yield vis-a-vis crop production to a greater extent in Bihar. Climate change and its impacts are well recognizing today and it will affect both physical and biological system. Therefore, this study has been planned to assess the effect of climate variables on yield of major crops, adaptation measures undertaken in Samastipur district of Bihar. Secondary data on yield of maize and wheat crops were collected for the period from 1999-2019 to describe the effects of climate variable namely rainfall, maximum and minimum temperature on yield of maize and wheat. Analysis of time series data on climate variables indicated that annual rainfall was positively related to yields while maximum and minimum temperature had a negative but significant impact on maize and wheat yields. It actually revealed that other factors, such as; type of soil, soil fertility and method of farming may also be responsible for crop yield. Trend in cost as well as income of farmers indicated that income and cost of cultivation has no significant relationship with climate variable. On the basis of above observation it may be concluded that level of income of farmers changed due to change in the other factors rather than change in climatic variable over the period under study as cost of cultivation increases with increased in the price of input over the period but not due to change in climatic variable.

Estimating extreme dry spell risk in Ichkeul Lake Basin (Northern Tunisia): a comparative analysis of annual maxima series with a Gumbel distribution

This paper analyses a 42 year time series of daily precipitation in Ichkeul Lake Basin (northern Tunisia) in order to predict extreme dry-spell risk. Dry events are considered as a sequence of dry days separated by rainfall events from each other. Thus the rainy season is defined as a series of rainfall and subsequent dry events. Rainfall events are defined as the uninterrupted sequence of rainy days, when at last on one day more than a threshold amount of rainfall has been observed. A comparison of observed and estimated maximum dry events (42 year return period) showed that Gumbel distribution fitted to annual maximum series gives better results than the exponential (E) distribution combined with partial duration series (PDS). Indeed, the classical Gumbel approach slightly underestimated the empirical duration of dry events. The AMS–G approach was successfully applied in the study of extreme hydro-climatic variable values. The results reported here could be applied in estimating climatic drought risks in other geographical areas.

Towards a dynamic landslide susceptibility assessment: evaluation of a novel climate-related variable

<p>The Mont Cervin and Mont Emilius Mountain Communities (Aosta Valley, North-West Italy) are particularly predisposed to shallow landslide phenomena due to their morphological and geological characteristics. In addition, short intense rainfalls, which are considered one of the main landslides triggering factors, are expected to increase over the Alpine region due to climate changes. This study was carried out to provide a potentially dynamic landslide susceptibility map, adaptable to these changes, for the two Communities (total area 670 km<sup>2</sup>). To achieve this goal, the susceptibility analysis was set up on a statistical basis, using the Logistic Regression method. The objectives of this study were:</p><ol><li>to verify the completeness of the database of dated shallow landslides, and define an optimal training set with the addition of non-landslide points;</li> <li>to find a potentially dynamic variable, statistically and physically significant, which summarizes the landslide-climate relationships;</li> <li>to derive a parsimonious model for the definition of landslide susceptibility that includes this variable.</li> </ol><p>For the period 1990-2018, 293 dated records of shallow landslides were extracted from the Landslide Regional Database. For non-landslide points, two sampling algorithms (Random and Stratified Sampling) and different sample sizes (from a minimum of one to a maximum of three times the number of landslide points) were evaluated. For the same period, the precipitation and temperature data were obtained from the time series available in Regional archives. The relationships between the triggering of landslides and the characteristics of the preceding precipitation (e.g., amount and intensity for durations ranging from 0.5 hours to 30 days) were studied using graphs and correlation indices, to determine the climatic variable to be used in the statistical analysis. Other geological-environmental data (e.g. elevation, land use, lithology) were downloaded from the Regional geoportal and then processed in a GIS environment to obtain traditional predictive variables. Logistic Regression analysis was implemented in SPSS. The models were evaluated through the confusion matrix, optimized keeping only the statistically significant variables, and validated through a 70% (training) - 30% (test) subdivision of the input data and the calculation of the Area Under the Curve (AUC values). The climatic variable was expressed in terms of the average annual number of exceedances of a rainfall intensity-duration landslide-triggering threshold, validated for the study area. The optimal sample of non-landslide points was obtained through Random Sampling and is equal to 1.15 times the number of landslide points. Statistically significant predictors were altitude, land use, slope and exceedances of the threshold. Applying the optimized model (discriminating probability 0.5), the true positives reached the 89.6% and 88.9% on training and test points, respectively. The resulting AUC values ​​for the training and test curves are 83.1% and 82.1%, respectively. Both indicators show that the model is robust and has good predictive power. The susceptibility map obtained from the developed model was reclassified through the geometrical interval method and 93% of the landslides fell into the high and very high susceptibility classes.</p>

EVALUATION AND IMPACT OF CLIMATIC VARIABILITY ON GUINEA CORN (SORGHUM BICOLOR) IN SELECTED STATE IN NIGERIA

Climate variability is one of the serious environmental challenges that have received a lot of public outcry in most parts of the world due to its consequence on agricultural production and other sectors of the national economy and general wellbeing. This study, therefore, sought to examine the effects of climate variability on crops production in the Bakori Local Government Area of Kastina State, Nigeria. Rainfall, temperature and selected crops (Sorghum) data from the farmers living in Bkori and cultivate Guinea corn every year. The data were analyzed using correlation and regression analysis in SPSS and the trend the function of Microsoft Excel.). The study identified positive crop yield while comparing temperature trend sorghum temperature characteristics, the most important climatic variable that influences the yields of Sorghum in Bakori is temperature and rainfall. This has beeachieved by monitoring 100 farmers at different locations in the study area and the use of farm inputs and monitoring of crop-climate relationships to achieve improved crop yield.

INFLUENCE OF FOREST COVERAGE IN THE SURFACE ALBEDO

The surface albedo controls the energy balance between the surface and the atmosphere, being a primordial variable to identify climatic variations. The objective of this study was to evaluate the changes of the surface albedo in different Land Use and Land Cover in the Atlantic Forest biome from images TM/Landsat 5 and OLI/Landsat 8, verifying its variation in 30 years. The images used were path-row 221-080, which covered the Floresta Nacional de São Francisco de Paula on the dates of 1987 and 2017. The albedo was obtained by the method of the Surface Energy Balance Algorithm for Land, while the mapping of Land Use and Land Cover was performed by the Bhattacharyya algorithm, identifying four thematic classes. Finally, the albedo was crossed with the thematic classes, evidencing their variation in function of the changes in the land cover. The surface albedo ranged from 6 to 22%, but the year 1987 concentrated albedo values higher than in 2017. The native forest presented superior albedo to the Forest Plantations in both dates due to the structure of the canopy of this class. The spatial analysis of the albedo exposes the relation of this climatic variable to the cover of the terrestrial surface. Thus changes in the vegetation cover cause alterations in the albedo, influencing changes in the radiation and atmospheric fluxes.

Dengue outbreaks pattern in southern Guatemala

This study analyses time series of dengue occurrence in the southern region of Guatemala. Temporal patterns of epidemic outbreaks in the department of Escuintla were investigated using the official reports from 2001 to 2013. In order to identify underlying associations with climate behavior, the epidemiological data were compared with historical reports available for temperature, rainfall and humidity. Preliminary results reveal that waves of dengue outbreaks exhibit a periodic pattern modulated by climatic conditions. A hierarchical cluster analysis allowed to indirectly estimate the degree of association of each climatic variable with dengue occurrences, showing the dominance of rainfall in dengue outbreaks patterns in three different localities. A further prospective analysis was performed to check whether epidemic trends driven by rainfall are hold in the subsequent years. Results presented here give support to predictive models for dengue incidence driven by climate.

Hubungan Iklim Terhadap Produksi Serta Pendapatan Petani Karet di Kabupaten Padang Lawas Utara

This study examines the relationship of Climate Variables with Rubber Yield And Farmer Income In Three Subdistricts of Padang Lawas Utara. This study aims to (1) to determine the effect of climate variable to rubber yield and, (2) To know the difference of farmer's income in rainy season and dry season. This research was conducted in March until May 2017. The result of research is climatic variable especially rainfall and rainy day very significant for influential rubber yields in Three Subdistricts in Padang Lawas Utara. This may indicate that increasing rainfall amounts with higher rainy days cause a decrease in tapping days resulting in reduction of rubber productivity. There results also showed that farmers' income in rainy season difference in dry season whereas farmer income in dry season is higher than rainy season