The objective of this study is to develop a novel methodology integrating remote sensing, geographic information system technology and local spatial autocorrelation geo-computation for quick drought assessment. One group of drought indices, based on the condition of the vegetation, includes the Normalized Difference Vegetation Index (NDVI), Anomaly of Normalized Difference Vegetation Index (NDV...

We present here the first experiments for an integrated system that is under development for drought monitoring and water resources assessment in Tuscany Region in central Italy. The system is based on the cross-evaluation of the Standardized Precipitation Index (SPI), Vegetation Indices from remote sensing (from MODIS and SEVIRIMSG), and outputs from the distributed hydrological model MOBIDIC,...

Long-term streamflow data are vital for analysis of hydrological droughts. Using an artificial neural network (ANN) model and nine tree-ring indices, this study reconstructed the annual streamflow of the Sacramento River for the period from 1560 to 1871. Using the reconstructed streamflow data, the copula method was used for bivariate drought analysis, deriving a hydrological drought return per...

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were ...

Recently, the number and intensity of hydrological droughts have been increasing; thus, it is necessary to identify respond them quickly. Since primary data in Lithuania are water levels, converting these into discharge takes additional time, there a need develop methodology or adapt analyze detect droughts. This paper examines concept standardized level index (SWLI) calculation, which based on...