Drought Classes Research Articles

Satellite-Based Meteorological and Agricultural Drought Monitoring for Agricultural Sustainability in Sri Lanka

For Sri Lanka, as an agricultural country, a methodical drought monitoring mechanism, including spatial and temporal variations, may significantly contribute to its agricultural sustainability. Investigating long-term meteorological and agricultural drought occurrences in Sri Lanka and assessing drought hazard at the district level are the main objectives of the study. Standardized Precipitation Index (SPI), Rainfall Anomaly Index (RAI), and Vegetation Health Index (VHI) were used as drought indicators to investigate the spatial and temporal distribution of agriculture and meteorological droughts. Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data from 1989 to 2019 was used to calculate SPI and RAI. MOD13A1 and MOD11A2 data from Moderate Resolution Imaging Spectroradiometer (MODIS) from 2001 to 2019, were used to generate the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). Agricultural drought monitoring was done using VHI and generated using the spatial integration of VCI and TCI. Thus, various spatial data analysis techniques were extensively employed for vector and raster data integration and analysis. A methodology has been developed for the drought declaration of the country using the VHI-derived drought area percentage. Accordingly, for a particular year, if the country-wide annual extreme and severe drought area percentage based on VHI drought classes is ≥30%, it can be declared as a drought year. Moreover, administrative districts of Sri Lanka were classified into four hazard classes, No drought, Low drought, Moderate drought, and High drought, using the natural-beak classification scheme for both agricultural and meteorological droughts. The findings of this study can be used effectively by the relevant decision-makers for drought risk management (DRM), resilience, sustainable agriculture, and policymaking.

Analyzing the extent of drought in the Rajasthan state of India using vegetation condition index and standardized precipitation index

Drought is the most complex but least understood of all natural hazards that impacts around 55 million people globally every year. A hydrological drought (drought here after) is when the lack of rainfall goes on long enough to empty rivers and lower water tables and causes severe water shortage. In recent years, Geographic Information System (GIS) and remote sensing (RS) have played a crucial role in studying different hazards either natural or man-made. This study stresses upon the use of RS technique for drought risk assessment in Rajasthan state of India for the year 2002 and 2019. In this study, an effort has been made to analyze the extent of drought using two drought indices, namely vegetation-based vegetation condition index (VCI) and meteorology-based standardized precipitation index (SPI). For VCI calculation, which indicates impact of soil moisture on vegetation, we used moderate resolution imaging spectroradiometer (MODIS), Normalized difference vegetation index (NDVI) data from year 2000 to 2019. Monthly SPI was calculated using rainfall data obtained from the Rajasthan water resource department. SPI values were interpolated to get the spatial pattern of meteorological drought. Whole area categorised into five drought classes including extreme drought, severe drought, moderate drought, mild drought and no drought, following the recommended value breaks. The results revealed that about 83.87% area was under drought in the year 2002 thus considered as drought year. It has also been calculated that only 15.62% area was showing drought condition in year 2019 especially in the western part of Rajasthan. Correlation analysis performed between VCI and SPI resulted in a coefficient of correlation of 0.92 and 0.79 in year 2002 and 2019, respectively. The high correlation endorses the reliability of RS data for drought-related analysis.

Impact of meteorological drought on vegetation in non-irrigated lands

Drought is a natural phenomenon that causes a lot of damages annually in various sectors, including agriculture and natural resources. The aim of this study is to evaluate the influence of meteorological drought index on vegetation index. For this purpose, the standard precipitation index (SPI) as a meteorological drought index is calculated using the precipitation data of 28 meteorological stations located on the area of Lorestane province, Iran, during the years 1987–2017. Then, the vegetation condition index (VCI) is computed using normalized difference vegetation index (NDVI) images that obtained from MODIS images of Terra satellite during 2000–2017. Dry, normal, and wet years were obtained based on the SPI results for 2008, 2013, and 2016, respectively. SPI and VCI were correlated using Pearson's correlation method. The results of the relationship between VCI and SPI showed that the highest Pearson correlation coefficient related to 9-month SPI in November was equal to 0.64. Multivariate linear regression was also performed between SPI and VCI, and the results showed that SPI was significantly correlated with VCI at 5% level over a period of 9 and 12 months. Finally, a confusion matrix was used to evaluate the compliance of the SPI and VCI drought classes. Results showed that the VCI had the highest compliance in the moderate drought class with SPI.

Assessment of drought hazard, vulnerability and risk in Iran using GIS techniques

The drought has enormous adverse effects on agriculture, water resources and environment, and causes damages around the world. Drought risk assessment and prioritization of drought management can help decision makers and planners to manage the adverse effects of drought. This paper aims to determine the risk of drought in Iran. At the first stage, standardized precipitation index (SPI) was calculated for the period 1981–2016. Then the probability map of different drought classes or drought hazard probability map were prepared. After that the indicator-based vulnerability assessment method was used to determine the drought vulnerability index. Five indices including climate, topography, waterway density, land use and groundwater resources were chosen as the most critical factors of drought in Iran and followed by the analytical hierarchy process questionnaire, the weights of each index were obtained based on expert opinions. Fuzzy membership maps of each index and sub-index were prepared using ArcGIS software. The drought vulnerability map of Iran was plotted using these weights and maps of each indicator. Finally, the drought risk map of Iran was provided by multiplying drought hazard and vulnerability maps. According to the 43-completed questionnaires by experts, climate index has the highest vulnerability to drought. Climate does not have an important role in drought hazard index, but it is the most crucial factor to classified drought vulnerability index. The results showed that central, northeast, southeast and west parts of Iran are at high risks of drought. There are regions with different risks in Iran due to unusual weather and climatic conditions. We realized that the climate and the groundwater situation is almost the same in the central, east and south parts of Iran, because the land use plays a crucial role in the drought vulnerability and risk in these areas. The drought risk decreases from the center of Iran to the southwest and northwest.

Drought hazard depending on elevation and precipitation in Lorestan, Iran

Drought is a type of destructive hydro-climatic disaster that has a significant impact on agriculture and natural resources. Meteorological drought features, including severity, duration, and peak, exhibit various patterns in different climatic regions and geographical conditions. Several indices have been developed to assess the drought potential of different areas across the world, among which the drought hazard index (DHI) is one of the most commonly employed for semi-arid and the flat regions. This study investigates the effects of the elevation and precipitation on DHI, which has received little attention in previous studies. For this purpose, DHIs were computed for 15 stations located at different elevations of the study area over 31 years by considering the occurrence of various drought classes with different weights. The results showed that the elevation and mean precipitation both have inverse effects on the DHI in the study area, which means that the DHI increases when the elevation and mean precipitation are low. The results of the estimation of the DHI for three different periods (i.e., 6, 9, and 12 months) revealed that a 9-month period is optimum for DHI estimations in terms of the reliability of the results.

Application of the Hidden Markov Bayesian Classifier and Propagation Concept for Probabilistic Assessment of Meteorological and Hydrological Droughts in South Korea

Drought is one of the most destructive natural hazards and results in negative effects on the environment, agriculture, economics, and society. A meteorological drought originates from atmospheric components, while a hydrological drought is influenced by properties of the hydrological cycle and generally induced by a continuous meteorological drought. Several studies have attempted to explain the cross dependencies between meteorological and hydrological droughts. However, these previous studies did not consider the propagation of drought classes. Therefore, in this study, to consider the drought propagation concept and to probabilistically assess the meteorological and hydrological drought classes, characterized by the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI), respectively, we employed the Markov Bayesian Classifier (MBC) model that combines the procedure of iteration of feature extraction, classification, and application for assessment of drought classes for both SPI and SRI. The classification results were compared using the observed SPI and SRI, as well as with previous findings, which demonstrated that the MBC was able to reasonably determine drought classes. The accuracy of the MBC model in predicting all the classes of meteorological drought varies from 36 to 76% and in predicting all the classes of hydrological drought varies from 33 to 70%. The advantage of the MBC-based classification is that it considers drought propagation, which is very useful for planning, monitoring, and mitigation of hydrological drought in areas having problems related to hydrological data availability.

Drought severity classification based on threshold level method and drought effects on NPP

Threshold level method is well-known for drought identification with the advantage of its simplicity. However, there is no criterion for classification of drought classes in this method. Therefore, a K index based on threshold level method was proposed and verified to perform well in drought assessment in the Luanhe River basin, China. Meteorological data and remote sensing data were used to calculate the net primary productivity (NPP) of the basin by the CASA model. Results showed that the model had a good performance in comparison with the downloaded yearly remote sensing NPP and monthly PsnNet. The NPP in the basin tended to increase slowly during the year of 2000–2010, and the NPP in the downstream was generally larger than upstream. In three selected representative drought years, drought reduced NPP by about 15–25%, and during the growth season, drought may reduce NPP by about 4.6%, 2.7%, and 1.9% in July, August, and September, respectively, due to reduced precipitation. NPP on grasslands and agricultural land were more susceptible to drought than forests. The result of gray incidence analysis showed that the effects of drought on NPP had a certain delay with about 5 months.

Comparison of parametric and nonparametric standardized precipitation index for detecting meteorological drought over the Indian region

In this study, Standardized Precipitation Index (SPI) derived from parametric and nonparametric methods using 0.25∘ gridded rainfall data from 1901 to 2013 (113 years) generated by India Meteorological Department (IMD) was compared for understanding drought conditions over the Indian region. The parametric SPI was computed using a three-parameter Gamma distribution function, whereas nonparametric SPI was computed using Gringorten, Weibull, and Hazen plotting positions, on a 4-month cumulative rainfall data of June–September (SPI-4) representing the southwest monsoon season. Nonnormality is a major concern if equal-sized intervals are drawn for interpretation, and SPI being a normalized index wherein classes are standard deviations from normal, its impact on drought assessment needs to be understood. Accordingly, in our study, normality tests were performed using the Shapiro-Wilk method on SPI derived from both parametric and nonparametric methods. The SPI showed 100% of grid cells conforming to normality in the case of nonparametric methods, whereas in the case of parametric approach it was only 80%. The remaining 20% of nonnormality in parametric SPI is spread over montane, tropical wet, and semi-arid regions of India. Furthermore, differences in the estimation of dryness are observed in the range of 1.0 to 2.5% between nonparametric and parametric SPI for the drought years considered this study. The quantile analysis on all grid cells for the drought year 2002 showed an important fact that at 0.025 quantile only 2.6% of grid cells are in the extremely dry condition as per parametric SPI, whereas in the case of nonparametric SPI it is 6.9%. For the drought year 1939 in grid cells where normality is not followed in parametric SPI, Cohen’s kappa (κ = 0.15) under extreme dryness category indicates large disagreements between parametric and nonparametric SPI. The temporal analysis of Cohen’s kappa computed for each grid cell over drought years shows that in 22.5% of cases the drought category between nonparametric and parametric SPI is not in perfect agreement. Hence, the nonparametric SPI can better categorize the drought classes, representing well the extent of dryness and normality conditions, it is highly recommended for drought assessment over India.

Short-Term Spatio-Temporal Drought Forecasting Using Random Forests Model at New South Wales, Australia

Droughts can cause significant damage to agriculture and water resources, leading to severe economic losses and loss of life. One of the most important aspect is to develop effective tools to forecast drought events that could be helpful in mitigation strategies. The understanding of droughts has become more challenging because of the effect of climate change, urbanization and water management; therefore, the present study aims to forecast droughts by determining an appropriate index and analyzing its changes, using climate variables. The work was conducted in three different phases, first being the determination of Standard Precipitation Evaporation Index (SPEI), using global climatic dataset of Climate Research Unit (CRU) from 1901–2018. The indices are calculated at different monthly intervals which could depict short-term or long-term changes, and the index value represents different drought classes, ranging from extremely dry to extremely wet. However, the present study was focused only on forecasting at short-term scales for New South Wales (NSW) region of Australia and was conducted at two different time scales, one month and three months. The second phase involved dividing the data into three sample sizes, training (1901–2010), testing (2011–2015) and validation (2016–2018). Finally, a machine learning approach, Random Forest (RF), was used to train and test the data, using various climatic variables, e.g., rainfall, potential evapotranspiration, cloud cover, vapor pressure and temperature (maximum, minimum and mean). The final phase was to analyze the performance of the model based on statistical metrics and drought classes. Regarding this, the performance of the testing period was conducted by using statistical metrics, Coefficient of Determination (R2) and Root-Mean-Square-Error (RMSE) method. The performance of the model showed a considerably higher value of R2 for both the time scales. However, statistical metrics analyzes the variation between the predicted and observed index values, and it does not consider the drought classes. Therefore, the variation in predicted and observed SPEI values were analyzed based on different drought classes, which were validated by using the Receiver Operating Characteristic (ROC)-based Area under the Curve (AUC) approach. The results reveal that the classification of drought classes during the validation period had an AUC of 0.82 for SPEI 1 case and 0.84 for SPEI 3 case. The study depicts that the Random Forest model can perform both regression and classification analysis for drought studies in NSW. The work also suggests that the performance of any model for drought forecasting should not be limited only through statistical metrics, but also by examining the variation in terms of drought characteristics.

On the use of Markov chain models for drought class transition analysis while considering spatial effects

Prediction of drought class transitions has been received increasing interest in the field of water resource management. Markov chain models are effective prediction tools that are widely used to analyse drought class transitions by describing the temporal dependency of drought events. However, geophysical events or phenomena (such as drought events) can exhibit spatial effects resulting from spatial heterogeneity and/or dependency. This means that on the one hand the drought processes may vary over space, and on the other hand the state change of a drought event may not only depend on its previous state but also on the previous states of its neighbours, and it is thus unreasonable to directly apply Markov chain models without considering spatial effects. Therefore, this paper proposes a framework that considers spatial effects when employing drought class transition analysis. Three types of Markov chain models are introduced (traditional, local and spatial). To test for the existence of spatial effects, spatial clustering technology is selected to identify spatial heterogeneity, and a Q statistic is used to determine the existence of spatial dependency. Based on the results of these tests, a corresponding type of Markov chain models is then selected to analyse drought class transitions. Monthly rainfall time series data for Southwest China from 1951 to 2010 are employed in a case study, and the results show that spatial heterogeneity exists for both the 3- and 9-month SPI time series; however, the existence of spatial dependency is not confirmed. Forward and backward estimation rules are also obtained for drought class transitions using local Markov chain models.

Regional meteorological drought assessment in Iraq

Regional assessment of drought characteristics provides essential information for water resources management. The study aimed to analyze the regional meteorological drought in Iraq for the period (1970–2013) using monthly rainfall data collected from 22 meteorological stations scattered all over the country. We have used the Standard Precipitation Index (SPI), theory of runs, GIS, and drought severity-area-frequency (SAF) curves to analyze the accumulated deficits, drought duration, severity, and the frequency of each of the drought classes. Results showed that study area suffered from recurrent drought periods, and the severest drought periods were from 1997 to 2001 and from 2007 to 2010. In addition, the majority of the droughts were of the class (mild dry) according to the SPI classification with an occurrence percentage of 33.44%. The highest drought rates characterized the northeastern zone of Iraq compared with other parts of study area in terms of the accumulated deficit of rainfall and drought duration. Results showed that dividing the study area into three homogeneous zones improved the formulation of SAF curves and made it more realistic. The return periods of the dry years within the study period ranged from 2 to 100 years and for the three zones of the study area.

Knowledge extraction from trained ANN drought classification model

Artificial neural networks (ANNs) are one of the most widely used techniques for solving a variety of problems including classification and function approximation type of problems. However, the use of ANNs in hydrology is limited to academics and research; and hydrologists/policymakers are skeptical to use the ANNs in operational hydrology, especially where a premium is placed on the comprehensibility of the systems. This is probably because of the perceived ‘black-box’ nature of the ANNs as they do not provide an insight into their learning process or elucidation of obtaining a particular result. Since a long time, researchers have been trying to improve the comprehensibility of trained ANN models by knowledge extraction for improving their reliability and acceptability among the decision makers for operation purposes. There are few such attempts in hydrology particularly for ANN models developed for function approximation problems such as flow forecasting and evaporation estimation. In this paper, a novel approach is proposed that is capable of extracting knowledge from trained ANN models for classification type of problems in hydrology. This is achieved by extracting knowledge in terms of rules by inducing a Decision Tree using input–output relation of a trained ANN model for forecasting drought classes with standardized precipitation index for Indian Meteorological sub-divisions. The reported researches on knowledge extraction in hydrology is primarily been for the function approximation type of problems, however, the present approach is fundamentally different from them and useful for knowledge extraction exclusively for classification problems in hydrology. The findings of this research indicate that definite rules are learned by ANN models during their training to forecast a specific drought class. These extracted rules are simple, easily implementable, and can be used as the drought class forecasting tool in a drought management activity. The results suggest that due to the nature of the drought index used, the rules extracted from one region may be suitable for drought monitoring of other regions.

Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index

Determining the quantitative occurrence of droughts, discovering the spatial correlation of droughts, and predicting the occurrence of undesirable classes of drinking water quality and aquifer farming is of high importance. In this research, the Standardized Precipitation Index (SPI) was calculated and analyzed with a monthly survey of 26,027 wells in 609 study areas over a period of 20 years. After analyzing the missing data, the annual rainfall was forecasted in 362 synoptic stations of the country based on an artificial intelligence model. In addition, statistical relationships were extracted in order to achieve a comprehensive and historical map of the state of shortages and surpluses of water resources, as well as verification of artificial intelligence relationships in predicting base data cultivars. The results indicated that the “mild drought” indicator was steeper than the “near-normal drought” indicator. Eventually, the southern and eastern regions and certain parts of the northeast of the country in the period from 2005 to 2015 were placed in the 7th and 8th classes, which indicates severe drought. The analysis of the period 1994–2014 showed that the plains of the Sistan and Baluchestan Province in the south-east region of the country have been significantly more affected by the droughts. With the exception of the central parts of Khorasan, the general eastern, southeast, and southern regions of the country can be considered as an absolute drought class for the long term.

IMPROVING THE LAND SURFACE GENERAL DROUGHT INDEX MODEL

Abstract. Drought affects all human activities and ecosystems. Nearly 40 percent of the world’s population inhabit Drylands, and they depend on agriculture for their food, security and livelihoods. Among the remote sensing indices developed, the Land Surface General Drought Index (LSGDI) was recently proposed. This paper proposes an improved model of LSGDI to face the issue of drought in semi-arid and arid regions. The experiment was conducted for the Maga’s floodplain, in North-Cameroon. The method uses satellite images of Landsat in 1987, 2003 and 2018, for January and March or April, corresponding to the middle and the end of the dry season. A Vegetation Moisture Index (VMI) and a Normalized Difference Soil Drought Index (NDSoDI) are both developed. On an orthogonal plan, their projections give a drought line that expresses the improved LSGDI (LSGDI2) as the root sum square of the NDSoDI and the VMI. The LSGDI2 results are ranged in [0.09 – 0.14] interval, which is used to define the threshold and ease the qualifiers for drought classes. The visual patterns easily match the sandy areas of the original Landsat images with the highest values, while the vegetation and water areas match the lowest values. Compared with the LSGDI and Second Modified Perpendicular drought Index (MPDI1), the new index reflectance values are higher. Finally, although LSGDI2 curve’s evolution follows the NDSoDI one at 94%, the new spectral index values depends on the both components, helping to map highest values of drought and moisture in Maga’s floodplain, for a sustainable rice culture expansion.

Measuring and restructuring the risk in forecasting drought classes: an application of weighted Markov chain based model for standardised precipitation evapotranspiration index (SPEI) at one-month time scale

Drought monitoring and forecasting play a vital role in making drought mitigation policies. In previous research, several drought monitoring tools based on the probabilistic models have been developed for precise and accurate inferences of drought severity and its effects. However, the risk of inaccurate determination of drought classes always exists in probabilistic models. The aim of this paper is to reconnaissance the advantage of the weighted Markov chain (WMC) model to accommodate the erroneous drought classes in the monthly classifications of drought. It was assumed that to increase the precision in drought prediction, the role of standardised self-correlation coefficients as weight may incorporate to establish and restructure the accurate probabilities of risk for incoming expected drought classes in the WMC framework. Consequently, the current research is based on the experimental findings of seventeen meteorological stations located in the Northern Areas of Pakistan. In this study, the standardised precipitation evapotranspiration index (SPEI) at a 1-month time scale based drought monitoring approach is applied to quantify the historical classification of drought conditions. The exploratory analysis shows that the proportion of each drought class varies from zone to zone. However, a high proportion of near-normal drought classes has been observed in all the stations. For the prediction of future drought classes, transition probability matrices have been computed using R statistical software. Our findings show that the probability of occurrences of near-normal is very high. Overall, the results associated with this study show that the WMC method for drought forecasting is sufficiently flexible to incorporate the change of drought conditions; it may change both the transition probability matrix and the autocorrelation structure.

Propagation of the Multi-Scalar Aggregative Standardized Precipitation Temperature Index and its Application

Nowadays, drought monitoring with various probabilistic indices has become common. However, the interpretation and applicability issues of multi-scalar drought indices are the main problems in establishing accurate drought mitigation policies. In addition, the spatial structure of environmental variables such as rainfall, and the spatial distribution of meteorological stations have a vital role in the precise and accurate analysis. In this paper, a comprehensive drought index “the Multi-Scalar Aggregative Standardized Precipitation Temperature Index (MASPTI)” is proposed. In MASPTI procedure, temporal vectors of various time scales of SPTI index are accumulated by giving long term transient weights. These weights are determined from the steady state probabilities of drought classification states in each time scale. Application of the proposed index is based on spatio-temporal data of SPTI index at its various time scales. However, before proceeding to evaluate MASPTI, we first observed the spatial relevancy of important time scale of SPTI index using the machine learning wrapper Boruta algorithm. The preliminary evaluation of MASPTI is based on four meteorological stations located in different homogeneous climatic clusters in Pakistan. The comparative analysis includes the ordinal association, where historical qualitative series of drought classes attained from MASPTI are compared with existing SPTI time scales. Outcomes show that MASPTI has the ability to capture joint characterization of drought by incorporating long term probabilities as a transient weight.

Temporal Variation of Drought Indices: A Case Study in Egirdir Lake Basin

From the past to the present, many climate changes have occurred and have affected the world in a very large way. Especially towards the end of the 20th century, climatic changes and temperature increases. As a result, it brought various disasters with it. Drought has been one of the slowest and most dangerous disasters. Various calculation methods have been found for long-term drought. As one of the drought types, hydrological drought lasts for a long time; it poses more danger than other drought types. Since the importance of hydrological drought starts when the plant is planted in the soil and covers its life during the harvest, water is an effective parameter here. Therefore, hydrological drought is discussed in this study. One of the frequently used indexes in calculating hydrological drought is the Standard Precipitation Index (SPI) method developed by McKee et al. In a drought assessment made taking into account the Standardized Precipitation Index values, the period when the index is constantly negative is defined as the dry period. The first month in which the index falls below zero is considered the beginning of the drought, while the month when the index rises to a positive value is considered the end of the drought. The study area was obtained from Isparta Regional Meteorology General Directorate and rainfall data between 1929 and 2019 were taken. A total of 90 years of data were used. 12-month data sets were prepared. These data sets were organized and studied according to the type of calculation. According to the total drought classes of the average 90-year data of Isparta central district and the average annual drought of all months, drought was experienced for a period of 20 years.

Evaluation of stochastic and artificial intelligence models in modeling and predicting of river daily flow time series

Modeling and forecasting the flow of rivers, especially in flood-prone areas using warning systems, enables officials to take the required measures for cutting the damage. On the other hand, they can adopt specific measures flood control and prevention. In the present study, two stochastic and three artificial intelligence (AI) models were compared, in modeling and predicting the daily flow of the Zilakirud river in northern Iran. The daily data belongs to the period of 2001–2015 (14 hydrological years from 23/Sep/2001 to 22/Sep/2015). First, the data was reviewed in terms of hydrological drought at the annual scale, using Streamflow Drought Index (SDI). The inputs for the models included the time lags of river daily flow. After choosing the input scenario, two approaches were tested for choosing the percentage of calibration and validation: (1) The last single year for validation; (2) The last 4 years for validation (about 30% of the data, which is a common method). A comparison between the models showed that the accuracy of AI models was higher than stochastic ones. Among the AI models, Group Method of Data Handling (GMDH) and Multilayer Perceptron (MLP) showed the best validation performance in both approaches. The findings showed that among the two approaches, approach (1) can show a better predicting accuracy with RMSE of 1.50 and 1.40 CMS for GMDH and MLP, respectively while in the second approach, the RMSE was 5.15 and 5.29 CMS for GMDH and MLP, respectively. Also, from the perspective of drought classes, the weakest result belonged to the moderately wet hydrological year (the hydrological year of 2011–2012) and the best performances was observed in the mild drought hydrological year (the hydrological year of 2014–2015).

Drought area of agricultural land using Tasseled Cap Transformation (TCT) method in Ciampel Subdistrict Karawang Regency

Drought is water availability that is far below the water needs for life, agriculture, economic activities, and the environment. The impact of severe drought in Indonesia occurred in 2015 due to the strong El Nino phenomenon and positive IOD. The Regional Disaster Management Agency (BPBD) of Karawang Regency noted that drought in 14 villages spread across three subdistricts in Karawang Regency expanded in 2019. One of them is the Ciampel subdistrict. The purpose of this research is to analyze the drought of agricultural land based on green vegetation, soil organic content, and soil moisture using Tasseled Cap Transformation (TCT) method in Ciampel Subdistrict, Karawang Regency in 2015 and 2019. This research uses Landsat 8 OLI imagery in August 2015, September 2015, July 2019, and September 2019. Agricultural land drought includes three indices, namely the brightness index, wetness index and greenness index. Overlay and scoring three drought parameters to making the map drought of agriculture land with four classes such as normal, moderate, high, and very high drought classes. The results show that the drought occurred in 2015 and 2019, but the dry area is more expansive in 2015 than 2019.

Past and future assessment of vegetation activity for the state of Amazonas-Brazil

Abstract: Tropical forest activities have been sensitive to climate change, making monitoring of these forests increasingly necessary. In the context of climate change, the Legal Amazon is a region of great global importance. In this study, the variables air temperature, rainfall, soil moisture, fire foci, land surface temperature, Normalized Difference Vegetation Index (NDVI) and the Vegetation Health Index (VHI) from 2001 to 2018 on a monthly scale were used to describe the past condition and future health of vegetation in the state of Amazonas-Brazil. The Autoregressive Integrated Moving Average (ARIMA) model was applied to the VHI series and its capacity was analyzed in the forecast of the observed (2001–2018) and future (2019–2030) time series. The results showed a decrease in VHI values for the period considered dry in the Amazon. Of each year, 2015 registered the lowest average of 30.73%, placing it in the mild drought class. However, in some months, such as September, October and November, which presented VHI values of 8.42%, 6.08% and 9.47% respectively, indicated extreme droughts. For the Mann-Kendall analysis, the variables soil moisture and air temperature showed a negative and positive trend with significant values for most months. The Heatmap clustering based on the Euclidean distance revealed that the most influential variables for the region were VHI and air temperature. The generated and validated ARIMA modeling well simulated VHI, presenting an average of the willmott coefficient (d) of 1 for the study period. The future 12-year projection (2019–2030) of VHI for the state of Amazonas showed that the model able to represent the seasonality of the series.