Milk production as an indicator of drought vulnerability of cities located in the brazilian semiarid region

Several spectral indices have been used to estimate droughts, however, these indicators only give evidence of a dry spell leaving out its impacts on significant economic activities performed in a given region. In this context, livestock breeding in one of the most important activities to analyze in the Brazilian semiarid region. The aim of this study was to evaluate the relationship between the drought indices, obtained through remote sensory devices, and annual milk production (2004 – 2014), identifying the most affected cities by the drought and were considered the most vulnerable and in need of special attention during dry periods. In order to analyze the data, the hierarchical grouping technique and correlation analyses between milk production and VCI - Vegetation Condition Index, TCI - Temperature Condition Index, VHI - Vegetation Health Index, PCI - Precipitation Condition Index and SDCI - Scaled Drought Condition indices were used. The intense correlation between milk production and the drought indices may be related to the dependency of the cities’ economies on natural resources. On the other hand, the diversification of the cities’ economic activities may enable access to various resources and drought vulnerability reductioninfo:eu-repo/semantics/publishedVersio

Monitoring Spatio-temporal pattern of drought using multi-satellite data during the period 2000 - 2018 (Case study: Iran)

Due to declining rainfall in the last two decades, drought has become a major problem in the world, especially in arid and semi-arid regions such as Iran, so monitoring and managing it is important. Remote sensing and geographic information system (GIS) and remote sensing (RS) provide the ability to study various indicators to evaluate the types of droughts. So, in the present study, the drought of Iran using multi remote sensing indicators including precipitation condition index (PCI), temperature condition index (TCI), Vegetation Conditions Index (VCI), and the integrated under the heading the scaled drought condition Index (SDCI) during the statistical period 2000 to 2018 were evaluated. To evaluate the accuracy of the obtained results, these results were compared with the standardized precipitation-evapotranspiration index (SPEI). The results of this study showed that the three indices of PCI, VCI, and TCI are well matched. The results of the SDCI index indicated that severe droughts occurred in 2000, 2008, and 2017, which are consistent with SPEI index. It should be noted that minor differences between the two indicators (SDCI and SPEI) can be justified by the fact that the SPEI index is a climatic index that considers two parameters of temperature and precipitation for annual drought assessment, while the SDCI index in addition assessment to temperature and precipitation factors (‎meteorological drought), it also considers ‎agriculture drought and more comprehensively evaluates drought. Finally, it can be mentioned that based on the calculations performed, the SDCI has been more effective in assessing drought than other indicators used

Spatial-Temporal Patterns of Agricultural Drought in Upper Progo Watershed Based on Remote Sensing and Land Physical Characteristics

Agricultural drought is alarmed by meteorological drought characterized by lower year-to-year rainfall. Under long period and continuous water deficits, plants may demonstrate stress symptoms and wilt or die. Furthermore, agricultural drought leads to crop failures and threaten the food security of an area. Progo Hulu sub-watershed is a major agricultural area in Temanggung Regency. Spatial-temporal pattern-based information about agricultural drought can be a basis for decision making in drought mitigation. This study aims to analyze spatial and temporal distribution patterns of drought, analyze the physical characteristics of land and their influence on drought pattern, and establish a prediction model of drought distribution patterns based on four physical characteristics of the land. Landsat 8 imagery is used to determine the spatial and temporal patterns of agricultural drought in Upper Progo watershed using an improved Temperature vegetation Dryness Index (iTVDI). Slope, land use, landform, and soil texture are the physical characteristics of land as the variables to determine the most influential factor of drought pattern. They are analyzed using multiple regression analysis techniques. Pixel samples are obtained through purposive sampling method based on land units. The results reveal that the spatial-temporal distribution of agricultural drought occurs rapidly on the slopes and foothills of Sumbing and Sindoro. These areas have the highest average value of the iTVDI index. Agricultural drought extends gradually in line with the number of days without rainfall. Landform is a physical characteristic that most influences the distribution of agricultural drought. The established model by utilizing four variables of physical characteristics generates an average value which almost similar to the iTVDI value produced by remote sensing data. The model can be useful to estimate drought distribution based on the number of days without rainfall

Drought Monitoring and Prediction using K-Nearest Neighbor Algorithm

Drought is a climate phenomenon which might occur in any climate condition and all regions on the earth. Effective drought management depends on the application of appropriate drought indices. Drought indices are variables which are used to detect and characterize drought conditions. In this study, it was tried to predict drought occurrence, based on the standard precipitation index (SPI), using k-nearest neighbor modeling. The model was tested by using precipitation data of Kerman, Iran. Results showed that the model gives reasonable predictions of drought situation in the region. Finally, the efficiency and precision of the model was quantified by some statistical coefficients. Appropriate values of the correlation coefficient (r=0.874), mean absolute error (MAE=0.106), root mean square error (RMSE=0.119) and coefficient of residual mass (CRM=0.0011) indicated that the present model is suitable and efficien

Research frame work at LACCOST, UFPE, Brazil

After finishing PhD sandwich (Rodrigo) under co-supervision of Professor Bernhard Heck in 2010 at GIK (Geodetic Institute of Geodesy) KIT, new ideas came true to start a laboratory of research dedicated to coastal studies (LACCOST) at Federal University of Pernambuco, Brazil. Also the contact made at GIK with Professor Joseph Awange spreading his ideas about “Environmental Geodesy” add latter an international cooperation with Curtin University, Australia, improving this team and including beside coastal related studies researches with spatial geodesy as background to support questions about the environment, using Brazil and South America as study case. The objectives of this paper is firstly to thank Professor Heck for keeping always looking for international cooperation with naturally become an example and model to follow up and his incredible skills to support researches all over the world. Secondly propagate what has been the topic of master’s students showing researches under development at this laboratory

A new station-enabled multi-sensor integrated index for drought monitoring

Remote sensing data are frequently incorporated into drought indices used widely by research and management communities to assess and diagnose current and historic drought events. The integrated drought indices combine multiple indicators and reflect drought conditions from a range of perspectives (i.e., hydrological, agricultural, meteorological). However, the success of most remote sensing based drought indices is constrained by geographic regions since their performance strongly depends on environmental factors such as land cover type, temperature, and soil moisture. To address this limitation, we propose a framework for a new integrated drought index that performs well across diverse climate regions. Our framework uses a geographically weighted regression model and principal component analysis to composite a range of vegetation and meteorological indices derived from multiple remote sensing platforms and in-situ drought indices developed from meteorological station data. Our new index, which we call the station-enabled Geographically Independent Integrated Drought Index (GIIDI_station), compared favorably with other common drought indices such as Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI). Using Pearson correlation analyses between remote sensing and in-situ drought indices during the growing season (April to October) from 2002 to 2011, we show that GIIDI_station had the best correlations with in-situ drought indices. Across the entire study region of the continental United States, the performance of GIIDI_station was not affected by common environmental factors such as precipitation, temperature, land cover and soil conditions. Taken together, our results suggest that GIIDI_station has considerable potential to improve our ability of monitoring drought at regional scales, provided local meteorological station data are available

Drought severity trend analysis based on the Landsat time-series dataset of 1998-2017 in the Iraqi Kurdistan Region

Drought is a natural hazard that significantly impacts economic, agricultural, environmental, and social aspects and is characteristic of Iraq's climate, particularly the Iraqi Kurdistan Region (IKR). For studying the spatiotemporal characteristics of drought severity in the IKR, a time-series of 120 Landsat images (TM, 7 ETM+, and OLI sensors) over twenty years (1998-2017) was assembled. Twenty separate mosaics of six Landsat scenes were used to derive the Vegetation Condition Index (VCI). The VCI index was employed to capture the drought severity in the study area. Results revealed that 1999, 2000, and 2008 were the most severe drought years. The results also indicated that severe droughts increased by 29.1%, 25.0%, and 26.9 through 1999, 2000, and 2008, respectively. Furthermore, a drop in precipitation averages occurred in the two years and significantly reduced the VCI values. Statistical analysis exhibited significant correlations between the VCI and each precipitation, and crop yield was 0.81 and 0.478, respectively. It can be concluded that the IKR experienced severe to extremely severe agricultural droughts, which caused significant reductions in crop yields, particularly in 2000 and 2008

Drought monitoring in el Salvador through remotely sensed variables using the Google Earth Engine platform

La sequía es un fenómeno que genera grandes pérdidas económicas para la sociedad y se están observando más frecuentemente debido al cambio climático. En Centroamérica este fenómeno se relaciona con la distribución anómala de la precipitación (P) en un período corto, dentro de la estación lluviosa. Específicamente, en El Salvador, el fenómeno denominado “canícula”, está asociado a una disminución importante de la P cuya duración es de pocos días, por lo que es difícil de monitorearlo sólo con la P, como se hace actualmente. En el presente se han desarrollado muchos indicadores para caracterizar las sequías; en particular, se destacan la precipitación estandarizada y los índices de condición propuestos por Kogan (1995), que admiten diversas fuentes de información. En este trabajo se aplicaron cinco indicadores de déficit hídrico - la P estandarizada, la evapotranspiración (ET), el índice de condición de la humedad del suelo (HSCI), el índice de condición de la vegetación (VCI) y estrés hídrico (EH) - para evaluar las sequías en El Salvador. Para ello se utilizó información satelital, bases de datos climáticas y la interface de programación disponible en la plataforma Google Earth Engine. Se analizó el comportamiento de los indicadores en el periodo 2015-2019 y en particular, el año extremadamente seco 2015, para determinar la capacidad de monitoreo de los indicadores utilizados. Los resultados obtenidos sugieren que el conjunto de índices propuesto permite monitorear la sequía, identificando el inicio, el impacto y la extensión territorial en El Salvador.Drought is a phenomenon that causes great economic losses in the society and is being observed more frequently due to climate change. In Central America this event is related to the anomalous distribution of precipitation (P) in a short period, within the rainy season. Specifically, in El Salvador, the phenomenon socalled “canícula” is associated to a significant decrease in P that lasts few days, making difficult to monitor it with P alone, as it is currently done. At present, many indicators have been developed to characterize droughts. In particular, the standardized precipitation and the condition indices proposed by Kogan (1995) that use various sources of information, stand out. In this work, five indicators of water deficit were applied - the standardized P, evapotranspiration (ET), the soil moisture condition index (HSCI), the vegetation condition index (VCI) and water stress (EH)- to assess droughts in El Salvador. For this, satellite information, climate database and the application programming interface available on the Google Earth Engine platform were used. The behaviour of the indexes in the period 2015-2019 was analysed, particularly the extremely dry year 2015, to determine the monitoring capacity of the indicators used. The results obtained suggest that the proposed set of indicators allows monitoring the drought, by identifying the onset, impact and territorial extension of it in El Salvador.Fil: Córdova, O.. Ministerio de Medio Ambiente y Recursos Naturales. Dirección General del Observatorio Ambiental; El SalvadorFil: Venturini, Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; ArgentinaFil: Walker, Elisabet. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentin

Temporal analysis of drought coverage in a watershed area using remote sensing spectral indexes

The development of several time series analysis programs using satellite images has provided many applications based on resources from geostatistics field. Currently, the use of statistical tests applied to vegetation indexes has enabled the analysis of different natural phenomena, such as drought events in watershed areas. The objective of this article is to provide a comparative analysis between NDVI and EVI vegetation index data made available by MOD13Q1 project of MODIS sensor for drought mapping using vegetation condition index (VCI) in the Serra Azul stream sub-basin, MG. The methodology adopted the Cox-Stuart statistical test for seasonality analysis and Pearson's linear correlation to verify the influence of different indexes on delimitation of drought in a watershed. The results indicated the NDVI vegetation index as more efficient than EVI in spatial characterization of studied watershed region, mainly in identification of seasonality. The VCI proved to be highly feasible for monitoring drought in study period between 2013 and 2018, allowing the effective delimitation of drought conditions in the Serra Azul stream sub-basin. In addition, the effectiveness of MODIS sensor data in characterizing drought events that affected the study area was proven