Identification of changes in the rainfall regime in Chihuahua's state (México)

The impacts of Climate Change are not homogeneous globally or for a country or region as a whole. Consequently, it is essential to carry out studies to identify its effects in particular areas. Due to its geographical and topographic characteristics, Chihuahua's state is vulnerable to the adverse effects of Climate Change. The scarce availability of water resources leads to problems of social pressure and economic impact. This paper analyzes the alteration of the rainfall regime in Chihuahua's state and its association with Climate Change. For this, historical characterization is used; trend analysis using the Mann Kendall test; and calculation of 10 indices of climatic extremes proposed by the Group of Experts for Detection and Climate Change Indices for the precipitation variable. The results showed that the precipitation patterns in the south and southeast of Chihuahua's state have been gradually modifying, with a downward trend in annual accumulated and reduction of wet days. Still, in counterpart, there is a slight intensification of extreme rainfall. This fact added to the growing demand for water resources in the entity, requests for public policies for sustainable management and responsible use by users. Otherwise, there is a risk of experiencing negative effects associated with the over-exploitation of water, not only for the resource users but also for the environment.

Future Projection of Precipitation Changes in the Júcar and Segura River Basins (Iberian Peninsula) by CMIP5 GCMs Local Downscaling

The basins of the Júcar and Segura rivers, on the Mediterranean coast of the Iberian Peninsula, present a special water problem and are of particular interest regarding climate change. These basins are very vulnerable to a possible scenario of decreasing water resources. Recent studies on historic rainfall since 1955 have indicated an ongoing loss of precipitation in their headwaters, especially in the case of the Júcar river. The aim of the present study is to perform climate projections for the precipitation variable for several future periods (2021–2040, 2051–2070, 2081–2100) and emission scenarios (RCPs 4.5, 8.5) within the Júcar and Segura River Basin authorities. For this purpose, a set of CMIP5 global models have been used, as well as the CDRD-HR-EIP-1955-2016 database, as a source of local observed information. This database comprises nearly 900 precipitation series in both basins and has been used in recent studies to determine historic trends of change in these basins. A statistical downscaling of the global models for all available observed series has been applied using the LARS-WG method. The results, although variable according to the CMIP5 model used, show the continuation of the patterns of precipitation change in the future, as already observed in the historical series. The results also predict a clear reduction in precipitation in the long term. However, torrential rainfall tends to increase in the coastal areas in relation to that observed in the short-term predictions. These results, due to their high spatial resolution, are of great interest for their use in small-scale hydrological and spatial planning (regional and local), which is one of the current challenges of climate modeling.

GPM-Based Multitemporal Weighted Precipitation Analysis Using GPM_IMERGDF Product and ASTER DEM in EDBF Algorithm

To obtain the high-resolution multitemporal precipitation using spatial downscaling technique on a precipitation dataset may provide a better representation of the spatial variability of precipitation to be used for different purposes. In this research, a new downscaling methodology such as the global precipitation mission (GPM)-based multitemporal weighted precipitation analysis (GMWPA) at 0.05° resolution is developed and applied in the humid region of Mainland China by employing the GPM dataset at 0.1° and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m DEM-based geospatial predictors, i.e., elevation, longitude, and latitude in empirical distribution-based framework (EDBF) algorithm. The proposed methodology is a two-stepped process in which a scale-dependent regression analysis between each individual precipitation variable and the EDBF-based weighted precipitation with geospatial predictor(s), and to downscale the predicted multitemporal weighted precipitation at a refined scale is developed for the downscaling of GMWPA. While comparing results, it shows that the weighted precipitation outperformed all precipitation variables in terms of the coefficient of determination (R2) value, whereas they outperformed the annual precipitation variables and underperformed as compared to the seasonal and the monthly variables in terms of the calculated root mean square error (RMSE) value. Based on the achieved results, the weighted precipitation at the low-resolution (e.g., at 0.75° resolution) along-with the original resolution (e.g., at 0.1° resolution) is employed in the downscaling process to predict the average multitemporal precipitation, the annual total precipitation for the year 2001 and 2004, and the average annual precipitation (2001–2015) at 0.05° resolution, respectively. The downscaling approach resulting through proposed methodology captured the spatial patterns with greater accuracy at higher spatial resolution. This work showed that it is feasible to increase the spatial resolution of a precipitation variable(s) with greater accuracy on an annual basis or as an average from the multitemporal precipitation dataset using a geospatial predictor as the proxy of precipitation through the weighted precipitation in EDBF environment.

Aedes aegypti – Space distribution and temporal variation breeding sites in the West of Santa Catarina State

This article is a retrospective ecological study, of descriptive analysis, that evaluates the spatial distribution of Aedes aegypti breeding sites and its correlation with environmental and climate characteristics of the western region of Santa Catarina. Its objective was to analyze the spatial distribution of breeding sites by mapping and correlating with climatic and environmental variables in the period between 2009 and 2017, by mapping breeding sites with Ae. Aegypti larvae by the use of Google Maps, correlating the temperature and precipitation averages. It was found a positive relation between temperature increase and the number of outbreaks, and it also established that the high rainfall averages in this region may influence the spread of the mosquito, but this information could not be confirmed in the present study. It was concluded that the temperature was a determining factor in the dissemination of the vector, being higher than the precipitation variable in the analyzed region.

PREDICTIVE MODELING OF Mimosa tenuiflora (WILLD) POIRET: HOW CAN CLIMATE CHANGE AFFECT ITS POTENTIAL DISTRIBUTION RANGE?

Studies that characterize the effects of climatic factors on the geographic distribution of arboreal individuals are of fundamental importance, especially for widely exploited species of wood potential, such as Mimosa tenuiflora (Willd) Poiret (jurema-preta). In this sense, the objective of this work was to predict the climatically adequate areas for the occurrence of Mimosa tenuiflora, present (1960-1990) and future (2070). We used the Maxent algorithm to relate the occurrence records of the species to the climatic variables. For the year 2070, we test two scenarios and three general atmospheric circulation models, HadGEM2-ES, GISS-E2-R and MIROC-ESM. Modeling for the present presented an AUC index (area under the curve) of 0.94 (± 0.02), indicating a good fit of the model used. For the future scenario, the AUC value ranged from 0.88 to 0.89 and 0.87 to 0.88 for the optimistic and pessimistic scenarios, respectively. The highest percentage of contribution was to the annual precipitation variable. The areas of adequacy occupied the states of Ceará and Rio Grande do Norte in higher intensity and almost all of them. When compared to the present, the geographic territory with high suitability for the future presented a reduction from 28.7% to 53.7% in the optimistic scenario and 30.9% to 59.4% in the pessimistic scenario. The information obtained can be used as a subsidy for the establishment of commercial plantations, the definition of management and conservation strategies, and the creation of an in situ conservation bank for Mimosa tenuiflora species.

Alternative methodology to gap filling for generation of monthly rainfall series with GIS approach

ABSTRACT As an alternative to Gap filling in monthly average rainfall series, we attempted to present a methodology for the generation of series only with the observed data available in the rainfall stations present in the study area and its surroundings. For this, a computational tool was developed with a GIS approach, using scripts in the Python language, to automate the study steps. Two calculation alternatives for the mean precipitation, variable Thiessen polygons or variable inverse distance weights (IDW), were considered. Random gaps were imposed from a series of data without gaps allowing us to evaluate the presented methodology. The results of the series calculated according to this methodology were compared to two methods of Gap filling. The behavior of the series was evaluated through the analysis of position and dispersion measurements as well as the temporal behavior by the evaluation of the correlograms and periodograms. The results are found to be satisfactory, which demonstrates the equivalence of the proposal with results found with the gap filling methods under the tested conditions. The differences found between the series were small, which was reflected in the Nash-Sutcliffe Indexes. There were no significant differences between the calculation alternatives by Thiessen polygons or IDW weights.

How Drought Affects Agricultural Insurance Policies: The Case of Italy

Despite their growing intensity and the enormous costs, adverse meteorological events are still perceived as “exceptional”. Among the adverse weather events, the management of drought risk plays a key role due to the more pressing problem of the scarcity of water resources. In this context, agricultural insurance can represent a financial and risk mitigation tool for farmers. In this perspective, the aims of this study are: (1) to analyze, through a systematic review, the main findings of the scientific literature focused on the empirical and theoretical approach to the relation between adverse weather events in agriculture, risk and insurance; (2) to collect agroclimatic and insurance data for each Italian province for the period 2004-2011, (3) to measure the influence of climatic agroclimatic variables on insurance variables, i.e. Total Premiums, Insured Value and Certificates.The results of the analysis show the significance of the precipitation variable and its negative effect with each insurance dependent variable. The same result can be observed focusing on the effect of minimum temperature on two insurance variables, i.e. Total Premiums and Certificates. Models tested explain a range between 44% and 51% of the variation in our insurance dependent variables.

Statistical Properties of Global Precipitation in the NCEP GFS Model and TMPA Observations for Data Assimilation

Assimilation of satellite precipitation data into numerical models presents several difficulties, with two of the most important being the non-Gaussian error distributions associated with precipitation, and large model and observation errors. As a result, improving the model forecast beyond a few hours by assimilating precipitation has been found to be difficult. To identify the challenges and propose practical solutions to assimilation of precipitation, statistics are calculated for global precipitation in a low-resolution NCEP Global Forecast System (GFS) model and the TRMM Multisatellite Precipitation Analysis (TMPA). The samples are constructed using the same model with the same forecast period, observation variables, and resolution as in the follow-on GFS/TMPA precipitation assimilation experiments presented in the companion paper. The statistical results indicate that the T62 and T126 GFS models generally have positive bias in precipitation compared to the TMPA observations, and that the simulation of the marine stratocumulus precipitation is not realistic in the T62 GFS model. It is necessary to apply to precipitation either the commonly used logarithm transformation or the newly proposed Gaussian transformation to obtain a better relationship between the model and observational precipitation. When the Gaussian transformations are separately applied to the model and observational precipitation, they serve as a bias correction that corrects the amplitude-dependent biases. In addition, using a spatially and/or temporally averaged precipitation variable, such as the 6-h accumulated precipitation, should be advantageous for precipitation assimilation.