Multi-datasets to monitor and assess meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region.

This study analyzed the meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region from 1994 to 2016. In recent decades, this region has faced prolonged and severe droughts, leading to marked reductions in agricultural productivity and significant challenges to food security and water availability. The datasets employed included a digital elevation model, land use and cover data, soil characteristics, climatic data (temperature, wind speed, solar radiation, humidity, and precipitation), runoff data, images from the MODIS/TERRA and AQUA sensors (MOD09A1 and MODY09A1 products), and soil water content. A variety of methods and products were used to study these droughts: the meteorological drought was analyzed using the Standardized Precipitation Index (SPI) derived from observed precipitation data, while the hydrological drought was assessed using the Standardized Soil Index (SSI), the Nonparametric Multivariate Standardized Drought Index (NMSDI), and the Parametric Multivariate Standardized Drought Index (PMSDI). These indices were determined using water balance components, including streamflow and soil water content, from the Soil Water Assessment Tool (SWAT) model, and evapotranspiration data from the Surface Energy Balance Algorithm for Land (SEBAL). The findings indicate that the methodology effectively identified variations in water dynamics and drought periods in a headwater basin within Brazil's semiarid region, suggesting potential applicability in other semiarid areas. This study provides essential insights for water resource management and resilience building in the face of adverse climatic events, offering a valuable guide for decision-making processes.

Geospatial cluster analysis of the state, duration and severity of drought over Paraíba State, northeastern Brazil.

Droughts threaten water resources, agriculture, socio-economic activities and the population at the global and regional level, so identifying areas with homogeneous drought behaviors is an important consideration in improving the management of water resources. The objective of this study is to identify homogenous zones over Paraíba State in relation to the state, duration and severity of droughts that have occurred over the last 20 years (1998-2017) using hierarchical cluster analysis based on both gauge-measured and Tropical Rainfall Measuring Mission (TRMM) estimated rainfall data (TMPA 3B42). The drought series were calculated using the Standardized Precipitation Index (SPI) based on eight time scales and were grouped according to drought state, duration and severity time series. The integrated results of state, duration and severity of droughts indicate that there is a basis for dividing Paraíba State into two major regions (a) Zone I, formed by Mata Paraibana and Agreste Paraibano, and (b) Zone II, composed by Borborema and Sertão Paraibano. This division is evident when assessing short-term droughts, but in the case of long-term droughts, Paraíba State has a high similarity in terms of drought state, duration, and severity. Factors such as proximity to the ocean, active climatic systems, and the local relief configuration were identified as influencing the drought regime. Finally, it is concluded that TMPA rainfall estimates represent a valuable source of data to regionalize and identify drought patterns over this part of Brazil and that other studies of this type should be carried out to monitor these phenomena based on other satellite-based rainfall data, including the Global Precipitation Mission (GPM).

Evaluation of the TRMM product for monitoring drought over Paraíba State, northeastern Brazil: a trend analysis.

Droughts are complex natural phenomena that influence society's development in different aspects; therefore, monitoring their behavior and future trends is a useful task to assist the management of natural resources. In addition, the use of satellite-estimated rainfall data emerges as a promising tool to monitor these phenomena in large spatial domains. The Tropical Rainfall Measuring Mission (TRMM) products have been validated in several studies and stand out among the available products. Therefore, this work seeks to evaluate TRMM-estimated rainfall data's performance for monitoring the behavior and spatiotemporal trends of meteorological droughts over Paraíba State, based on the standardized precipitation index (SPI) from 1998 to 2017. Then, 78 rain gauge-measured and 187 TRMM-estimated rainfall time series were used, and trends of drought behavior, duration, and severity at eight time scales were evaluated using the Mann-Kendall and Sen tests. The results show that the TRMM-estimated rainfall data accurately captured the pattern of recent extreme rainfall events that occurred over Paraíba State. Drought events tend to be drier, longer-lasting, and more severe in most of the state. The greatest inconsistencies between the results obtained from rain gauge-measured and TRMM-estimated rainfall data are concentrated in the area closest to the coast. Furthermore, long-term drought trends are more pronounced than short-term drought, and the TRMM-estimated rainfall data correctly identified this pattern. Thus, TRMM-estimated rainfall data are a valuable source of data for identifying drought behavior and trends over much of the region.

Geospatial drought severity analysis based on PERSIANN-CDR-estimated rainfall data for Odisha state in India (1983-2018).

Studying the behavior of drought and its short-, medium- and long-term features throughout a region is very important for the creation of adequate public policies and actions aimed at the economic and social development of the region. Furthermore, the frequency and intensity of weather-related natural hazards (rainfall, heatwaves and droughts) are increasing every year, and these extreme weather-related events are potent threats worldwide, particularly in developing countries, such as India. Thus, this paper aims to evaluate the drought behavior in the Odisha region of India (1983-2018) by using the standardized precipitation index (SPI) and the new drought severity classification (DS). PERSIANN-CDR-estimated rainfall data were used to provide 271 time series, which were equally spaced at intervals of 0.25°, over Odisha state. The accuracy of these time series was evaluated with rain gauge-measured data at multiple time scales, and it was observed that the PERSIANN-CDR-estimated rainfall data effectively captured the pattern of rainfall over Odisha state. It was noted that almost half of the mean annual rainfall was concentrated in July and August. On addition, northeastern Odisha and areas near the coast were the rainiest regions. Furthermore, the drought pattern was evaluated based on nine distinct four-year periods (SPI-48), and the results indicated that there was high spatiotemporal variability in drought occurrence among those periods; e.g., in the last four years, extreme drought events occurred throughout the state. For the DS severity index analysis, it was noted that the values tended to be more significant with the increase in the drought time scale. For short-term droughts, DS values were less significant throughout the region, whereas for the medium-term droughts, there was an increase in the DS values in all regions of Odisha, especially in the north-central region. For long-term droughts, the values were more significant throughout the region, especially in the areas with the highest rainfall levels. Finally, the PERSIANN-CDR data should also be analyzed in other regions of India, and the obtained results are useful for the identification of droughts throughout the region and for the management of water resources and can be replicated in any part of the world.

Integrated spatiotemporal trends using TRMM 3B42 data for the Upper São Francisco River basin, Brazil.

Trend analysis is an important issue for the decision-making processes. Thus, trends of rainfall, consecutive dry days (CDD), and consecutive wet days (CWD) in the Upper São Francisco River basin, Brazil, using daily rainfall data from the Tropical Rainfall Measuring Mission (TRMM) for recent 18 years, were analyzed. Instead of analyzing the trend of one average time series for one specific confidence level, a spatiotemporal analysis over the entire area with 169 continuous time series is done by applying the nonparametric Mann-Kendall and Sen tests for simultaneously 13 confidence levels and a new integrated confidence classification is proposed. The results show that the rainfall has increased during the less rainy periods (from June to October) and has decreased in the rainy periods (from November to May), with the highest and lowest confidence levels, respectively. An analysis of CDD and CWD shows that the number of CDD has decreased, while the number of CWD has increased, which revealed that the dry periods are more frequently interrupted for the period studied.

Drought assessment using a TRMM-derived standardized precipitation index for the upper São Francisco River basin, Brazil.

In this work, the use of Tropical Rainfall Measuring Mission (TRMM) rainfall data and the Standardized Precipitation Index (SPI) for monitoring spatial and temporal drought variabilities in the Upper São Francisco River basin is investigated. Thus, the spatiotemporal behavior of droughts and cluster regions with similar behaviors is identified. As a result, the joint analysis of clusters, dendrograms, and the spatial distribution of SPI values proved to be a powerful tool in identifying homogeneous regions. The results showed that the northeast region of the basin has the lowest rainfall indices and the southwest region has the highest rainfall depths, and that the region has well-defined dry and rainy seasons from June to August and November to January, respectively. An analysis of the drought and rain conditions showed that the studied region was homogeneous and well-distributed; however, the quantity of extreme and severe drought events in short-, medium- and long-term analysis was higher than that expected in regions with high rainfall depths, particularly in the south/southwest and southeast areas. Thus, an alternative classification is proposed to characterize the drought, which spatially categorizes the drought type (short-, medium-, and long-term) according to the analyzed drought event type (extreme, severe, moderate, and mild).