scholarly journals Climatic water indices for viticulture

2018 ◽  
Vol 53 (6) ◽  
pp. 765-768
Author(s):  
Marco Antônio Fonseca Conceição ◽  
Jorge Tonietto ◽  
Reginaldo Teodoro de Souza
Keyword(s):  
Climate Data ◽  
Water Index ◽  
Historical Series ◽  
Weather Stations ◽  
Water Indices

Abstract: The objective of this work was to evaluate the performance of vineyard water indices in different grape-growing regions. The climate data used come from the historical series of weather stations located in 18 countries. The evaluated indices were the following: dryness, Zuluaga, humidity, aridity, moisture, and the grapevine water index. The grapevine water index and the indices of drought, moisture, and aridity exhibit similar performances, which makes them suitable to be used equivalently in climatological studies of grapevine regions.

scholarly journals Variabilidade Espacial da Erosividade das Chuvas para o Estado da Paraíba (Variability of Spatial Erosivity of Rainfall in the Paraíba State)

2014 ◽  
Vol 7 (4) ◽  
pp. 691
Author(s):  
Bernardo Starling Dorta do Amaral ◽  
João Filadelfo de Carvalho Neto ◽  
Richarde Marques da Silva ◽  
José Carlos Dantas
Keyword(s):  
Water Resources ◽  
Rainfall Variability ◽  
Climatic Conditions ◽  
Climate Data ◽  
Rainfall Characteristics ◽  
R Factor ◽  
Erosivity Index ◽  
The Mean ◽  
Weather Stations ◽  
Soil Loss Equation

As características específicas das chuvas variam entre regiões, e o conhecimento da sua potencialidade erosiva é necessário para o planejamento dos recursos hídricos. Este estudo determinou a erosividade, analisou a variabilidade espacial da precipitação e o coeficiente de chuva para o Estado da Paraíba mediante técnicas de Sistemas de Informação Geográfica. Para a realização deste estudo foram utilizados dados climatológicos de 98 estações climatológicas da Embrapa, com séries de 1911 a 1990. Em seguida as informações sobre a erosividade foram processadas cartograficamente. O valor médio anual da erosividade das chuvas com base no índice EI30 para o Estado da Paraíba foi de 5.032,03 MJ.mm/ha/h, valor que representa o Fator “R” da Equação Universal de Perdas de Solo (USLE). As equações de regressão entre erosividade e precipitação e coeficiente de chuva não foram significativas. As principais conclusões são que: (a) os índices de erosividade encontrados são maiores na zona litorânea do que nas demais porções do Estado, e (b) as erosividades encontradas variaram de acordo com os valores da precipitação.   A B S T R A C T Specific rainfall characteristics vary among regions and their erosion potential must be known for the planning of water resources. This study analyzed the erosivity and rainfall variability and precipitation coefficient for Paraíba State based on Geographic Information Systems techniques. In order In this paper 98 climatological stations of Embrapa were used, with rainfall data of 1911 to 1990. For this study we use d climate data from 98 weather stations of Embrapa, with series from 1911 to 1990. Additionally we processed the information of the erosivity index cartographically by year and microregions. The mean annual value of erosivity was 5,032.03 MJ.mm/ha/h, which is to be used as “R” Factor in the Universal Soil Loss Equation (USLE) for Paraíba State and surrounding regions with similar climatic conditions. The main conclusions are that: (a) erosivity indexes are higher in coastal areas than in inland areas, and (b) the erosivity range according to the precipitation.   Keywords: erosivity, rainfall, water resources   

scholarly journals Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Water ◽  
2017 ◽  
Vol 9 (4) ◽  
pp. 256 ◽  
Author(s):  
Yan Zhou ◽  
Jinwei Dong ◽  
Xiangming Xiao ◽  
Tong Xiao ◽  
Zhiqi Yang ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Landsat 8 ◽  
Open Surface ◽  
Body Mapping ◽  
Water Index ◽  
Landsat 7 ◽  
Surface Water Bodies ◽  
Water Indices ◽  
Sentinel 2

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

scholarly journals Local knowledge based perceptions on climate change and its impacts in the Rakaposhi valley of Gilgit-Baltistan, Pakistan

2019 ◽  
Vol 12 (2) ◽  
pp. 222-237 ◽  
Author(s):  
Laxmi Dutt Bhatta ◽  
Erica Udas ◽  
Babar Khan ◽  
Anila Ajmal ◽  
Roheela Amir ◽  
...  
Keyword(s):  
Climate Change ◽  
Human Health ◽  
Local Knowledge ◽  
Plant Distribution ◽  
Precipitation Pattern ◽  
Climate Data ◽  
Local Perceptions ◽  
Content Type ◽  
Knowledge Based ◽  
Weather Stations

Purpose The purpose of this paper is to understand local perceptions on climate change and its impacts on biodiversity, rangeland, agriculture and human health. Design/methodology/approach A household survey with 300 interviewees and focus group discussions with key stakeholders were conducted and validated at two steps, using the climate data from the nearest weather stations and reviewing literatures, to correlate the local perceptions on climate change and its impacts. Findings Majority of the respondents reported an increase in temperature and change in the precipitation pattern with increased hazardous incidences such as floods, avalanches and landslides. Climate change directly impacted plant distribution, species composition, disease and pest infestation, forage availability, agricultural productivity and human health risks related to infectious vector-borne diseases. Research limitations/implications Because of the remoteness and difficult terrain, there are insufficient local weather stations in the mountains providing inadequate scientific data, thus requiring extrapolation from nearest stations for long-term climate data monitoring. Practical implications The research findings recommend taking immediate actions to develop local climate change adaptation strategies through a participatory approach that would enable local communities to strengthen their adaptive capacity and resilience. Social implications Local knowledge-based perceptions on climate change and its impacts on social, ecological and economic sectors could help scientists, practitioners and policymakers to understand the ground reality and respond accordingly through effective planning and implementing adaptive measures including policy formulation. Originality/value This research focuses on combining local knowledge-based perceptions and climate science to elaborate the impacts of climate change in a localised context in Rakaposhi Valley in Karakoram Mountains of Pakistan.

scholarly journals A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

2020 ◽  
Vol 12 (10) ◽  
pp. 1611
Author(s):  
Feifei Pan ◽  
Xiaohuan Xi ◽  
Cheng Wang
Keyword(s):  
Image Classification ◽  
Landsat Imagery ◽  
Google Earth ◽  
Water Bodies ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Blue Band ◽  
Ground Truth Data ◽  
Water Index ◽  
Water Indices

A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this research. Two unsupervised (the zero-water index threshold H0 method and Otsu’s automatic threshold selection method) and one supervised (the k-nearest neighbor (KNN) method) image classification algorithms were employed for conducting the image classification. Through comparing a total of 2880 Landsat image classification results with the alternate ground truth data, this study showed that (1) it is not necessary to use some supervised image classification methods for extracting water bodies from Landsat imagery given the high computational cost associated with the supervised image classification algorithms; (2) the unsupervised classification algorithms such as the H0 and Otsu methods could achieve comparable accuracy as the KNN method, although the H0 method produced more large error outliers than the Otsu method, thus the Otsu method is better than the H0 method; and (3) the ultra-blue band-based AWEInsuB is the best water index for the H0 method, and the ultra-blue band-based MNDWI2uB is the best water index for both the Otsu and KNN methods.

scholarly journals Climatic records and linkage along an altitudinal gradient in the southern slope of Nepal Himalaya

2017 ◽  
Vol 53 ◽  
pp. 47-56 ◽  
Author(s):  
Binod Dawadi
Keyword(s):  
Regression Models ◽  
Similar Rate ◽  
High Mountain ◽  
Climate Data ◽  
Southern Slope ◽  
Nepal Himalaya ◽  
Lower Elevation ◽  
Similar Range ◽  
Weather Stations ◽  
Highly Correlated

To validate the climatic linkages under different topographic conditions, observational climate data at four automated weather stations (AWS) in different elevations, ranging from 130 m asl. to 5050 m asl., on the southern slope of the Nepal Himalayas was examined. the variation of means and distribution of daily, 5-days, 10-days, and monthly average/sum of temperature/ precipitation between the stations in the different elevation was observed. Despite these differences, the temperatures records are consistent in different altitudes, and highly correlated to each other while the precipitation data shows comparatively weaker correlation. The slopes (0.79-1.18) with (R2 >0.64) in the regression models for high Mountain to high Himalaya except in November and 0.56-1.14 (R2 >0.50) for mid-hill and high Mountain except January, December, June indicate the similar rate of fluctuation of temperature between the stations in the respective region. These strong linkages and the similar range of fluctuation of temperature in the different elevation indicate the possibilities of their use of lower elevation temperature data to represent the higher elevation sites for paleoclimatic calibration. However, the associations of precipitation between the stations at the different elevation are not as strong as the temperature due to heterogeneous topographical features and steep altitudinal contrast.

Effects of inter-annual climate variability on grape harvest timing in rainfed hilly vineyards of Piedmont (NW Italy)

2020 ◽  
Author(s):  
Danilo Rabino ◽  
Marcella Biddoccu ◽  
Giorgia Bagagiolo ◽  
Guido Nigrelli ◽  
Luca Mercalli ◽  
...  
Keyword(s):  
Climate Variability ◽  
Weather Data ◽  
Maximum Temperature ◽  
Data Series ◽  
Positive Trend ◽  
Climate Variables ◽  
Climate Data ◽  
Historical Series ◽  
Nw Italy ◽  
Level Of Significance

<p>Historical weather data represent an extremely precious resource for agro-meteorology for studying evolutionary dynamics and for predictive purposes, to address agronomical and management choices, that have economic, social and environmental effect. The study of climatic variability and its consequences starts from the observation of variations over time and the identification of the causes, on the basis of historical series of meteorological observations. The availability of long-lasting, complete and accurate datasets is a fundamental requirement to predict and react to climate variability. Inter-annual climate changes deeply affect grapevine productive cycle determining direct impact on the onset and duration of phenological stages and, ultimately, on the grape harvest and yield. Indeed, climate variables, such as air temperature and precipitation, affect evapotranspiration rates, plant water requirements, and also the vine physiology. In this respect, the observed increase in the number of warm days poses a threat to grape quality as it creates a situation of imbalance at maturity, with respect to sugar content, acidity and phenolic and aromatic ripeness.</p><p>A study was conducted to investigate the relationships between climate variables and harvest onset dates to assess the responses of grapevine under a global warming scenario. The study was carried out in the “Monferrato” area, a rainfed hillslope vine-growing area of NW Italy. In particular, the onset dates of harvest of different local wine grape varieties grown in the Vezzolano Experimental Farm (CNR-IMAMOTER) and in surrounding vineyards (affiliated to the Terre dei Santi Cellars) were recorded from 1962 to 2019 and then related to historical series of climate data by means of regression analysis. The linear regression was performed based on the averages of maximum and minimum daily temperatures and sum of precipitation (1962–2019) calculated for growing and ripening season, together with a bioclimatic heat index for vineyards, the Huglin index. The climate data were obtained from two data series collected in the Experimental farm by a mechanical weather station (1962-2002) and a second series recorded (2002-2019) by an electro-mechanical station included in Piedmont Regional Agro-meteorological Network. Finally, a third long-term continuous series covering the period from 1962 to 2019, provided by Italian Meteorological Society was considered in the analysis.</p><p>The results of the study highlighted that inter-annual climate variability, with a general positive trend of temperature, significantly affects the ripening of grapes with a progressive anticipation of the harvest onset dates. In particular, all the considered variables excepted precipitation, resulted negatively correlated with the harvest onset date reaching a high level of significance (up to P< 0.001). Best results have been obtained for maximum temperature and Huglin index, especially by using the most complete dataset. The change ratios obtained using datasets including last 15 years were greater (in absolute terms) than results limited to the period 1962-2002, and also correlations have greater level of significance. The results indicated clearly the relationships between the temperature trend and the gradual anticipation of harvest and the importance of having long and continuous historical weather data series available.</p>

scholarly journals An evaluation of ASCAT surface soil moisture products with in-situ observations in Southwestern France

2009 ◽  
Vol 13 (2) ◽  
pp. 115-124 ◽  
Author(s):  
C. Albergel ◽  
C. Rüdiger ◽  
D. Carrer ◽  
J.-C. Calvet ◽  
N. Fritz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Mountainous Area ◽  
Surface Soil Moisture ◽  
Water Index ◽  
In Situ Observations ◽  
Weather Stations ◽  
Term Data

Abstract. A long term data acquisition effort of profile soil moisture is currently underway at 13 automatic weather stations located in Southwestern France. In this study, the soil moisture measured in-situ at 5 cm is used to evaluate the normalised surface soil moisture (SSM) estimates derived from coarse-resolution (25 km) active microwave data of the ASCAT scatterometer instrument (onboard METOP), issued by EUMETSAT for a period of 6 months (April–September) in 2007. The seasonal trend is removed from the satellite and in-situ time series by considering scaled anomalies. One station (Mouthoumet) of the ground network, located in a mountainous area, is removed from the analysis as very few ASCAT SSM estimates are available. No correlation is found for the station of Narbonne, which is close to the Mediterranean sea. On the other hand, nine stations present significant correlation levels. For two stations, a significant correlation is obtained when considering only part of the ASCAT data. The soil moisture measured in-situ at those stations, at 30 cm, is used to estimate the characteristic time length (T) of an exponential filter applied to the ASCAT product. The best correlation between a soil water index derived from ASCAT and the in-situ soil moisture observations at 30 cm is obtained with a T-value of 14 days.

scholarly journals The SASSCAL contribution to climate observation, climate data management and data rescue in Southern Africa

2015 ◽  
Vol 12 (1) ◽  
pp. 171-177 ◽  
Author(s):  
F. Kaspar ◽  
J. Helmschrot ◽  
A. Mhanda ◽  
M. Butale ◽  
W. de Clercq ◽  
...  
Keyword(s):  
Data Management ◽  
Southern Africa ◽  
National Level ◽  
Climate Data ◽  
Time Data ◽  
Observation Network ◽  
Management Concepts ◽  
Weather Stations ◽  
Building Activities ◽  
Science Service

Abstract. A major task of the newly established "Southern African Science Service Centre for Climate Change and Adaptive Land Management" (SASSCAL; www.sasscal.org) and its partners is to provide science-based environmental information and knowledge which includes the provision of consistent and reliable climate data for Southern Africa. Hence, SASSCAL, in close cooperation with the national weather authorities of Angola, Botswana, Germany and Zambia as well as partner institutions in Namibia and South Africa, supports the extension of the regional meteorological observation network and the improvement of the climate archives at national level. With the ongoing rehabilitation of existing weather stations and the new installation of fully automated weather stations (AWS), altogether 105 AWS currently provide a set of climate variables at 15, 30 and 60 min intervals respectively. These records are made available through the SASSCAL WeatherNet, an online platform providing near-real time data as well as various statistics and graphics, all in open access. This effort is complemented by the harmonization and improvement of climate data management concepts at the national weather authorities, capacity building activities and an extension of the data bases with historical climate data which are still available from different sources. These activities are performed through cooperation between regional and German institutions and will provide important information for climate service related activities.

scholarly journals Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary

2015 ◽  
Vol 8 (3-4) ◽  
pp. 11-20 ◽  
Author(s):  
András Gulácsi ◽  
Ferenc Kovács
Keyword(s):  
Drought Index ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Short Wave ◽  
Drought Indices ◽  
High Temporal Resolution ◽  
Spectral Indices ◽  
Water Index ◽  
Water Indices

Abstract In this study a new remote sensing drought index called Difference Drought Index (DDI) was introduced. DDI was calculated from the Terra satellite’s MODIS sensor surface reflectance data using visible red, near-infrared and short-wave-infrared spectral bands. To characterize the biophysical state of vegetation, vegetation and water indices were used from which drought indices can be derived. The following spectral indices were examined: Difference Vegetation Index (DVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Difference Water Index (DWI), Normalized Difference Water Index (NDWI), Difference Drought Index (DDI) and Normalized Difference Drought Index (NDDI). Regression analysis with the Pálfai Drought Index (PaDi) and average annual yield of different crops has proven that the Difference Drought Index is applicable in quantifying drought intensity. However, after comparison with reference data NDWI performed better than the other indices examined in this study. It was also confirmed that the water indices are more sensitive to changes in drought conditions than the vegetation ones. In the future we are planning to monitor drought during growing season using high temporal resolution MODIS data products.

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