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Droughts can cause devastating impacts on water and land resources and therefore monitoring these events forms an integral part of planning. The most common approach for detecting drought events and assessing their intensity is use of the Standardized Precipitation Index (SPI), which requires abundant precipitation records at good spatial distribution. This may restrict SPI usage in many regions around the world, particularly in areas with limited numbers of ground meteorological stations. Therefore, the use of remotely sensed derived data of precipitation can contribute to drought monitoring. In this study, remotely sensed precipitation estimates from the POWER/Agroclimatology archive of NASA and their derived SPI for different time intervals were evaluated against gauged observations of precipitation from 13 different stations in arid and semiarid locations in Jordan. Results showed significant correlations between remotely sensed and ground data with relatively high R values (0.67–0.91), particularly where seasonal precipitation exceeded 50 mm/year. For evaluation of remotely sensed data in SPI calculation, several objective functions were used; the results showed that SPI based on satellite estimates (SAT-SPI) showed good performance in detecting extreme droughts and indicating wet/dry conditions. However, SAT-SPI showed high tendency to overestimate drought intensity. Based on these findings, remotely sensed precipitation from the POWER/Agroclimatology archive showed good potential for use in detecting extreme meteorological drought with the provision of careful interpretation of the data. These types of studies are essential for evaluating the applicability of new drought monitoring information and tools to support decision-making at relevant scales.

An application of the Soil Moisture Agricultural Drought Index (SMADI) for global agricultural drought monitoring is presented. The index integrates surface soil moisture from the Soil Moisture and Ocean Salinity (SMOS) mission with the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) and allows for global drought monitoring at medium spatial scales (0.05°). Biweekly maps of SMADI were obtained from year 2010 to 2015 over all agricultural areas on Earth. The SMADI time-series were compared with state-of-the-art drought indices over the Iberian Peninsula. Results show a good agreement between SMADI and the Crop Moisture Index (CMI) retrieved at five weather stations (with correlation coefficient, R from −0.64 to −0.79) and the Soil Water Deficit Index (SWDI) at the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) (R=𢈒0.83). Some preliminary tests were also made over the continental United States using the Vegetation Drought Response Index (VegDRI), with very encouraging results regarding the spatial occurrence of droughts during summer seasons. Additionally, SMADI allowed to identify distinctive patterns of regional drought over the Indian Peninsula in spring of 2012. Overall results support the use of SMADI for monitoring agricultural drought events world-wide.

Monitoring drought impacts in forest ecosystems is a complex process, because forest ecosystems are composed of different species with heterogeneous structural compositions. Even though forest drought status is a key control on the carbon cycle, very few indices exist to monitor and predict forest drought stress. The Forest Drought Indicator (ForDRI) is a new monitoring tool developed by the National Drought Mitigation Center (NDMC) to identify forest drought stress. ForDRI integrates 12 types of data, including satellite, climate, evaporative demand, ground water, and soil moisture, into a single hybrid index to estimate tree stress. The model uses Principal Component Analysis (PCA) to determine the contribution of each input variable based on its covariance in the historical records (2003–2017). A 15-year time series of 780 ForDRI maps at a weekly interval were produced. The ForDRI values at a 12.5km spatial resolution were compared with normalized weekly Bowen ratio data, a biophysically based indicator of stress, from nine AmeriFlux sites. There were strong and significant correlations between Bowen ratio data and ForDRI at sites that had experienced intense drought. In addition, tree ring annual increment data at eight sites in four eastern U.S. national parks were compared with ForDRI values at the corresponding sites. The correlation between ForDRI and tree ring increments at the selected eight sites during the summer season ranged between 0.46 and 0.75. Generally, the correlation between the ForDRI and normalized Bowen ratio or tree ring increment are reasonably good and indicate the usefulness of the ForDRI model for estimating drought stress and providing decision support on forest drought management.

The term “flash drought” is frequently invoked to describe droughts that develop rapidly over a relatively short timescale. Despite extensive and growing research on flash drought processes, predictability, and trends, there is still no standard quantitative definition that encompasses all flash drought characteristics and pathways. Instead, diverse definitions have been proposed, supporting wide-ranging studies of flash drought but creating the potential for confusion as to what the term means and how to characterize it. Use of different definitions might also lead to different conclusions regarding flash drought frequency, predictability, and trends under climate change. In this study, we compared five previously published definitions, a newly proposed definition, and an operational satellite-based drought monitoring product to clarify conceptual differences and to investigate the sensitivity of flash drought inventories and trends to the choice of definition. Our analyses indicate that the newly introduced Soil Moisture Volatility Index definition effectively captures flash drought onset in both humid and semi-arid regions. Analyses also showed that estimates of flash drought frequency, spatial distribution, and seasonality vary across the contiguous United States depending upon which definition is used. Definitions differ in their representation of some of the largest and most widely studied flash droughts of recent years. Trend analysis indicates that definitions that include air temperature show significant increases in flash droughts over the past 40 years, but few trends are evident for definitions based on other surface conditions or fluxes. These results indicate that “flash drought” is a composite term that includes several types of events and that clarity in definition is critical when monitoring, forecasting, or projecting the drought phenomenon.

Monitoring soil moisture and its association with rainfall variability is important to comprehend the hydrological processes and to set proper agricultural water use management to maximize crop growth and productivity. In this study, the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture product was applied to assess the dynamics of residual soil moisture in autumn (September to November) and its response to the long-term variability of rainfall in the Upper Blue Nile Basin (UBNB) of Ethiopia from 1992 to 2017. The basin was found to have autumn soil moisture (ASM) ranging from 0.09–0.38 m3/m3, with an average of 0.26 m3/m3. The ASM time series resulted in the coefficient of variation (CV) ranging from 2.8%–28% and classified as low-to-medium variability. In general, the monotonic trend analysis for ASM revealed that the UBNB had experienced a wetting trend for the past 26 years (1992–2017) at a rate of 0.00024 m3/m3 per year. A significant wetting trend ranging from 0.001 to 0.006 m3/m3 per year for the autumn season was found. This trend was mainly showed across the northwest region of the basin and covers about 18% of the total basin area. The spatial patterns and variability of rainfall and ASM were also found to be similar, which implies the strong relationship between rainfall and soil moisture in autumn. The spring and autumn season rainfall explained a considerable portion of ASM in the basin. The analyses also signified that the rainfall amount and distribution impacted by the topography and land cover classes of the basin showed a significant influence on the characteristics of the ASM. Further, the result verified that the behavior of ASM could be controlled by the loss of soil moisture through evapotranspiration and the gain from rainfall, although changes in rainfall were found to be the primary driver of ASM variability over the UBNB.

The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and the contribution of soil roughness parameters to estimate volumetric residual soil moisture (RSM) in the Upper Blue Nile (UBN) basin, Ethiopia. The backscatter contribution of crop residue water content was estimated using Landsat sensor product and the water cloud model (WCM). The surface roughness parameters were estimated from the Oh and Baghdadi models. A feed-forward artificial neural network (ANN) method was tested for its potential to translate SAR backscattering and surface roughness input variables to RSM values. The model was trained for three inversion configurations: i) SAR backscattering from vertical transmit and vertical receive (SAR VV) polarization only, ii) using SAR VV and the standard deviation of surface heights (hrms), and iii) SAR VV, hrms, and optimal surface correlation length (leff). Field-measured volumetric RSM data were used to train and validate the method. The results showed that the ANN soil moisture estimation model performed reasonably well for the estimation of RSM using the single input variable of SAR VV data only. The ANN prediction accuracy was slightly improved when SAR VV and the surface roughness parameters (hrmsand leff) were incorporated into the prediction model. Consequently, the ANN&rsquo, s prediction accuracy with root mean square error (RMSE) = 0.035 cm3/cm3, mean absolute error (MAE)= 0.026 cm3/cm3, and r= 0.73 was achieved using the third inversion configuration. The result implies the potential of Sentinel-1 SAR data to accurately retrieve RSM content over an agricultural site covered by stubbles. The soil roughness parameters are also potentially an important variable to soil moisture estimation using SAR data although their contribution to the accuracy of RSM prediction is slight in this study. In addition, the result highlights the importance of combining Sentinel-1 SAR and Landsat images based on an ANN approach for improving RSM content estimations over crop residue areas.

The increasing drought severities and consequent devastating impacts on society over the Indian semi-arid regions demand better drought monitoring and early warning systems. Operational agricultural drought assessment methods in India mainly depend on a single input parameter such as precipitation and are based on a sparsely located in-situ measurements, which limits monitoring precision. The overarching objective of this study is to address this need through the development of an integrated agro-climatological drought monitoring approach, i.e., combined drought indicator for Marathwada (CDI_M), situated in the central part of Maharashtra, India. In this study, satellite and model-based input parameters (i.e., standardized precipitation index (SPI-3), land surface temperature (LST), soil moisture (SM), and normalized difference vegetation index (NDVI)) were analyzed at a monthly scale from 2001 to 2018. Two quantitative methods were tested to combine the input parameters for developing the CDI_M. These methods included an expert judgment-based weight of each parameter (Method-I) and principle component analysis (PCA)-based weighting approach (Method-II). Secondary data for major types of crop yields in Marathwada were utilized to assess the CDI_M results for the study period. CDI_M maps depict moderate to extreme drought cases in the historic drought years of 2002, 2009, and 2015–2016. This study found a significant increase in drought intensities (p ≤ 0.05) and drought frequency over the years 2001–2018, especially in the Latur, Jalna, and Parbhani districts. In comparison to Method-I (r ≥ 0.4), PCA-based (Method-II) CDI_M showed a higher correlation (r ≥ 0.60) with crop yields in both harvesting seasons (Kharif and Rabi). In particular, crop yields during the drier years showed a greater association (r > 6.5) with CDI_M over Marathwada. Hence, the present study illustrated the effectiveness of CDI_M to monitor agricultural drought in India and provide improved information to support agricultural drought management practices.

Developing a robust drought monitoring tool is vital to mitigate the adverse impacts of drought. A drought monitoring system that integrates multiple agrometeorological variables into a single drou...

Global food security is negatively affected by drought. Climate projections show that drought frequency and intensity may increase in different parts of the globe. These increases are particularly hazardous for developing countries. Early season forecasts on drought occurrence and severity could help to better mitigate the negative consequences of drought. The objective of this study was to assess if interannual variability in agricultural productivity in Chile can be accurately predicted from freely-available, near real-time data sources. As the response variable, we used the standard score of seasonal cumulative NDVI (zcNDVI), based on 2000–2017 data from Moderate Resolution Imaging Spectroradiometer (MODIS), as a proxy for anomalies of seasonal primary productivity. The predictions were performed with forecast lead times from one- to six-month before the end of the growing season, which varied between census units in Chile. Predictor variables included the zcNDVI obtained by cumulating NDVI from season start up to prediction time; standardised precipitation indices derived from satellite rainfall estimates, for time-scales of 1, 3, 6, 12 and 24 months; the Pacific Decadal Oscillation and the Multivariate ENSO oscillation indices; the length of the growing season, and latitude and longitude. For each of the 758 census units considered, the time series of the response and the predictor variables were averaged for agricultural areas resulting in a 17-season time series per unit for each variable. We used two prediction approaches: (i) optimal linear regression (OLR) whereby for each census unit the single predictor was selected that best explained the interannual zcNDVI variability, and (ii) a multi-layer feedforward neural network architecture, often called deep learning (DL), where all predictors for all units were combined in a single spatio-temporal model. Both approaches were evaluated with a leave-one-year-out cross-validation procedure. Both methods showed good prediction accuracies for small lead times and similar values for all lead times. The mean Rcv2 values for OLR were 0.95, 0.83, 0.68, 0.56, 0.46 and 0.37, against 0.96, 0.84, 0.65, 0.54, 0.46 and 0.38 for DL, for one, two, three, four, five, and six months lead time, respectively. Given the wide range of climates and vegetation types covered within the study area, we expect that the presented models can contribute to an improved early warning system for agricultural drought in different geographical settings around the globe.

Crop simulation models offer possibilities to evaluate and target agricultural information for sustainable intensification in countries like Ethiopia with inadequate resources for field research. The objectives of this research were to calibrate and evaluate the CERES-Maize, CROPGRO-Dry bean and CROPGRO-Soybean models for practices associated with conservation agriculture and fertilizer N, and to evaluate five generated weather datasets for Ethiopia. Data from multi-year field experiments and additional data obtained from previously conducted national variety trials were used for model evaluation. Generated weather datasets for six agroecologies were evaluated by comparison with observed data and by use of data in the models. Genetic coefficients used in the models for maize, dry bean and soybean were determined by model parametrization and calibration of phenology and yield. The models acceptably simulated the effects of N rate, maize-legume rotation, and crop residue retention plus tillage with average normalized deviation closer to zero, RMSE less or similar to standard deviation of observed data, and with normalized RMSE (nRMSE)

Long and temporally consistent rainfall time series are essential in climate analyses and applications. Rainfall data from station observations are inadequate over many parts of the world due to sparse or non-existent observation networks, or limited reporting of gauge observations. As a result, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. However, many satellite-based rainfall products with long time series suffer from coarse spatial and temporal resolutions and inhomogeneities caused by variations in satellite inputs. There are some satellite rainfall products with reasonably consistent time series, but they are often limited to specific geographic areas. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite-based rainfall products with relatively high spatial and temporal resolutions and quasi-global coverage. In this study, CHIRP and CHIRPS were evaluated over East Africa at daily, dekadal (10-day) and monthly time scales. The evaluation was done by comparing the satellite products with rain gauge data from about 1200 stations. The CHIRP and CHIRPS products were also compared with two similar operation satellite rainfall products: the African Rainfall Climatology version 2 (ARC2) and the Tropical Applications of Meteorology using Satellite data (TAMSAT). The results show that both CHIRP and CHIRPS products are significantly better than ARC2 with higher skill and low or no bias. These products were also found to be slightly better than the latest version of the TAMSAT product at dekadal and monthly time scales, while TAMSAT performed better at daily time scale. The performance of the different satellite products exhibits high spatial variability with weak performances over coastal and mountainous regions.

Land degradation (LD) is one of the most catastrophic outcomes of long-lasting drought events and anthropogenic activities. Assessing climate and human-induced impacts on land can provide information for decision makers to mitigate the effects of these phenomena. The Northeastern region of Brazil (NEB) is the most populous dryland on the planet, making it a highly vulnerable ecosystem especially when considering the lingering drought that started in 2012. The present work consisted of detecting trends in biomass [leaf area index (LAI)] anomalies as indicators of LD in NEB. We also assessed how the loss of vegetation impacts the LD cycle, by measuring trends in albedo and evapotranspiration (ET). LAI, albedo and ET data were derived from MODIS sensors at 8-day temporal and 500 m spatial resolutions. For precipitation anomalies, we relied on CHIRPS-v2 10-day temporal at 5 km spatial resolution data. For detecting trends, we applied the Theil-Sen slope analysis on time series of MODIS LAI, albedo and ET images. Trend analysis was performed for the periods ranging from 2002–2012 (no severe droughts) to 2002–2016 (including the last drought). LAI trends were more pronounced and had a stronger signal than ET and albedo, therefore, LAI was our choice for mapping LD. The first analysis highlighted the human-induced LD prone areas whereas the last detected drought-induced LD prone areas. Considering only the trending areas, which was about 23.4% of the total, 4.5% of this area has undergone human-induced degradation whereas drought was responsible for 73%, although, not mutually exclusive. As reported in the literature and official data, grazing intensification might be a factor driving human-induced degradation. We noticed that the range of variation of LAI is narrow and even narrower for albedo, which demonstrates that land surface response is more influenced by soil reflectivity rather than the characteristic sparse vegetation coverage (LAI ranging from 0.04 to 0.4 in the Caatinga biome), which can barely alter albedo. Finally, the effects of LD on ET anomalies were assessed by Granger causality and impulse-response analyses as means to link land surface feature changes to the hydrological cycle. Albedo had a slightly weaker impulse than LAI on ET whereas precipitation played a major role. These relations are site-specific and, land surface features (biomass and albedo) showed to have a more substantial influence on ET in severely degraded areas. We concluded that drought led to trends indicating LD prone areas in NEB and the degradation cycle has positive feedback derived from ET reduction resulting in an increased net moisture deficit, although the latter statement has yet to be further investigated. The study warns of the desertification risk that NEB is facing and the need for the authorities to take action to mitigate degradation and drought effects on both traditionally surveyed (desertification nuclei) and newfound LD prone areas. We also highlight the limitation of confirming LD, as to date there is no post-drought data available and, lessons learned from the Sahel case make us cautious about claiming that an area is in fact degraded.

The overarching objective of this study was to evaluate the performance of nine precipitation-based and twelve temperature-based climatic indices derived from four regional climate models (CRCM5-UQUAM, CanRCM4, RCA4 and HIRHAM5) driven by three global circulation models (CanESM2, EC-EARTH and MPI-ESM-LR) and their ensemble mean for the reference period of 31 years (1975–2005). The absolute biases, pattern correlation, the reduction of variance (RV) and the Standardized Precipitation Evapotranspiration Index (SPEI at 3-, 6- and 12-month aggregate periods) techniques were used to evaluate the climate model simulations. The result, in general, shows each climate model has a skill in reproducing at least one of the climatic indices considered in this study. Based on the pattern correlation result, however, EC-EARTH.HIRHAM5 and MPI-ESM-LR.CRCM5-UQAM RCMs showed a relatively good skill in reproducing the observed climatic indices as compared to the other climate model simulations. EC-EARTH.RCA4, CanESM2.RCA4 and MPI-ESM-LR.CRCM5-UQAM RCMs showed a good skill when evaluated using the reduction of variance. The ensemble mean of the RCMs showed relatively better skill in reproducing the observed temperature-based climatic indices as compared to the precipitation-based climatic indices. There were no exceptional differences observed among the performance of the climate models compared to the SPEI, but CanESM2.CRCM5-UQAM, EC-EARTH.RCA4 and the ensemble mean of the RCMs performed relatively good in comparison to the other climate models. The good performance of some of the RCMs has good implications for their potential application for climate change impact studies and future trend analysis of extreme events. They could help in developing an early warning system to mitigate and prepare for possible future impacts of climate extremes (e.g., drought) and vulnerability to climate change across Florida.

South Korea has experienced severe droughts and water scarcity problems that have influenced agriculture, food prices, and crop production in recent years. Traditionally, climate-based drought indices using point-based meteorological observations have been used to help quantify drought impacts on the vegetation in South Korea. However, these approaches have a limited spatial precision when mapping detailed vegetation stress caused by drought. For these reasons, the development of a drought index that provides detailed spatial-resolution information on drought-affected vegetation conditions is essential to improve the country’s drought monitoring capabilities, which are needed to help develop more effective adaptation and mitigation strategies. The objective of this study was to develop a satellite-based hybrid drought index called the vegetation drought response index for South Korea (VegDRI-SKorea) that could improve the spatial resolution of agricultural drought monitoring on a national scale. T...

Improved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. The intercomparison and evaluation of multiple land surface models (LSMs) associated with different meteorological forcing and precipitation datasets can offer a moderate range of water budget variable estimates. In this context, two LSMs, Noah version 3.3 (Noah3.3) and Catchment LSM version Fortuna 2.5 (CLSMF2.5) coupled with the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme are used to produce hydrological estimates over the region. The two LSMs were forced with different combinations of two reanalysis-based meteorological datasets from the Modern-Era Retrospective analysis for Research and Applications datasets (i.e., MERRA-Land and MERRA-2) and three observation-based precipitation datasets, generating a total of 16 experiments. Modeled evapotranspiration (ET), streamflow, and terrestrial water storage estimates were evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) ET, in-situ streamflow observations, and NASA Gravity Recovery and Climate Experiment (GRACE) products, respectively. Results show that CLSMF2.5 provided better representation of the water budget variables than Noah3.3 in terms of Nash-Sutcliffe coefficient when considering all meteorological forcing datasets and precipitation datasets. The model experiments forced with observation-based products, the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), outperform those run with MERRA-Land and MERRA-2 precipitation. The results presented in this paper would suggest that the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System incorporate CLSMF2.5 and HyMAP routing scheme to better represent the water balance in this region.

Improving seasonal forecasts in East Africa has great implications for food security and water resources planning in the region. Dynamically based seasonal forecast systems have much to contribute to this effort, as they have demonstrated ability to represent and, to some extent, predict large-scale atmospheric dynamics that drive interannual rainfall variability in East Africa. However, these global models often exhibit spatial biases in their placement of rainfall and rainfall anomalies within the region, which limits their direct applicability to forecast-based decision-making. This paper introduces a method that uses objective climate regionalization to improve the utility of dynamically based forecast-system predictions for East Africa. By breaking up the study area into regions that are homogenous in interannual precipitation variability, it is shown that models sometimes capture drivers of variability but misplace precipitation anomalies. These errors are evident in the pattern of homogenous regions in forecast systems relative to observation, indicating that forecasts can more meaningfully be applied at the scale of the analogous homogeneous climate region than as a direct forecast of the local grid cell. This regionalization approach was tested during the July–September (JAS) rain months, and results show an improvement in the predictions from version 4.5 of the Max Plank Institute for Meteorology’s atmosphere–ocean general circulation model (ECHAM4.5) for applicable areas of East Africa for the two test cases presented.

Drought is the meteorological phenomenon with the greatest impact on agriculture. Accordingly, drought forecasting is vital in lessening its associated negative impacts. Utilizing remote exploration in the agricultural sector allows for the collection of large amounts of quantitative data across a wide range of areas. In this study, we confirmed the applicability of drought assessment using the evaporative stress index (ESI) in major East Asian countries. The ESI is an indicator of agricultural drought that describes anomalies in actual/reference evapotranspiration (ET) ratios that are retrieved using remotely sensed inputs of land surface temperature (LST) and leaf area index (LAI). The ESI is available through SERVIR Global, a joint venture between the National Aeronautics and Space Administration (NASA) and the United States Agency for International Development (USAID). This study evaluated the performance of ESI in assessing drought events in South Korea. The evaluation of ESI is possible because of the availability of good statistical data. Comparing drought trends identified by ESI data from this study to actual drought conditions showed similar trends. Additionally, ESI reacted to the drought more quickly and with greater sensitivity than other drought indices. Our results confirmed that the ESI is advantageous for short and medium-term drought assessment compared to vegetation indices alone.

Drought is one of the least understood and complex natural hazards often characterized by a significant decrease in water availability for a prolonged period. It can be manifested in one or more forms as meteorological, agricultural, hydrological, and/or socio-economic drought. The overarching objective of this study is to demonstrate and characterize the different forms of droughts and to assess the multidimensional nature of drought in the Abbay/ Upper Blue Nile River (UBN) basin and its national and regional scale implications. In this study, multiple drought indices derived from in situ and earth observation-based hydro-climatic variables were used. The meteorological drought was characterized using the Standardized Precipitation Index (SPI) computed from the earth observation-based gridded CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) rainfall data. Agricultural and hydrological droughts were characterized by using the Soil Moisture Deficit Index (SMDI) and Standardized Runoff-discharge Index (SRI), respectively. The monthly time series of SMDI was derived from model-based gridded soil moisture and SRI from observed streamflow data from 1982 to 2019. The preliminary result illustrates the good performance of the drought indices in capturing the historic severe drought events (e.g., 1984 and 2002) and the spatial extents across the basin. The results further indicated that all forms of droughts (i.e., meteorological, agricultural, and hydrological) occurred concurrently in Abbay/Upper Blue Nile basin with a Pearson correlation coefficient ranges from 0.5 to 0.85 both Kiremt and annual aggregate periods. The concurrent nature of drought is leading to a multi-dimensional socio-economic crisis as indicated by rainfall, and soil moisture deficits, and drying of small streams. Multi-dimensional drought mitigation necessitates regional cooperation and watershed management to protect both the common water sources of the Abbay/Upper Blue Nile basin and the socio-economic activities of the society in the basin. This study also underlines the need for multi-scale drought monitoring and management practices in the basin.

Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.

Drought is a natural climatic phenomenon that occurs throughout the world and impacts many sectors of society. To help decision-makers reduce the impacts of drought, it is important to improve moni...

Addis Ababa is expected to experience water supply stress as a result of complex interaction of urbanization and climate change. The aim of this study is to investigate water demand and supply prospects for the City of Addis Ababa by applying the Water Evaluation and Planning (WEAP) hydrological model and using scenarios of population growth trends and climate change. The study includes analysis of water consumption, hydrological information and climate data which is statistically downscaled using approach used to generate climate data available at the Worldclim data center. Bias corrected climate model data of NIMR-HadGEM2-AO under a midrange RCP 4.5 scenario and RCP8.5, high emissions scenario was used for the study. The result shows that the projected population of Addis Ababa city using high population growth rate (3.3%) will be about 7 million by the year 2039. The climate change projections result under RCP 4.5 and RCP 8.5 scenarios on surface water supply shows that the level of reservoirs volume both at Legedadi/Dire and Gefersa reservoirs will be reduced in the projected years between the years 2023 and 2039. The result of the RCP 8.5 scenario with low population growth shows that the unmet water demand will be 257.28 million m3 in 2037. The result of the RCP 4.5 scenario with low population growth shows that the unmet water demand will be 314.91 million m3 in 2037. This indicates that the unmet water demand with the dry climate of RCP 4.5 climate change scenario is higher than RCP 8.5 scenario. Under the RCP 4.5 scenario with high population growth (3.3%) the unmet water demand is 87.42 million m3 in 2030, 158.38 million m3 in 2035 and 380.72 million m3 in 2037. This indicates that the unmet water demand in both high population growth and the dry climate of RCP 4.5 climate change scenario will lead to severe shortage of water in the city. The most effective management options are water tariff increasing, domestic water use technology efficiency improvement and water harvesting which give satisfactory result in mitigating unmet demand of climate change and population growth in the city.

Monitoring crop phenology provides essential information for crop management, as well as for understanding regional to global scale vegetation dynamics. In this study, a hybrid phenology detection method is presented that incorporates the “shape-model fitting” concept of the two-step filtering method and a simulation concept of the crop models to detect the critical vegetative stages and reproductive stages of corn (Zea mays L.) and soybeans (Glycine max L.) from MODIS 250-m Wide Dynamic Range Vegetation Index (WDRVI) time-series data and 1000-m Land Surface Temperature (LST) data. The method was first developed and tested at the field scale over a ten-year period (2003–2012) for three experimental study sites in eastern Nebraska of USA, where the estimated phenology dates were compared to the ground-based phenology observations for both corn and soybeans. The average root mean square error (RMSE) of phenology stage estimation of the individual development stages across all sites ranged from 1.9 to 4.3 days for corn and from 1.9 to 4.9 days for soybeans. The approach was then tested at a regional scale over eastern Nebraska and the state of Iowa to evaluate its ability to characterize the spatio-temporal variation of targeted corn and soybean phenology stage dates over a larger area. Quantitative regional assessments were conducted by comparing the estimated crop stage dates with crop developmental stage statistics reported by the USDA NASS Crop Progress Reports (NASS-CPR) for both eastern Nebraska and Iowa. The accuracy of the regional-scale phenology estimation in Iowa (RMSE ranged from 2.6 to 3.9 days for corn and from 3.2 to 3.9 days for soybeans) was slightly lower than in eastern Nebraska (RMSE ranged from 1.8 to 2.9 days for corn and from 1.7 to 2.9 days for soybeans), However, the estimation accuracy in Iowa was still reasonable with the estimated phenology dates being within 4 days or less of the observed dates for both corn and soybeans.

This study examines the evolution of several model-based and satellite-derived drought metrics sensitive to soil moisture and vegetation conditions during the extreme flash drought event that impacted major agricultural areas across the central U.S. during 2012. Standardized anomalies from the remote sensing based Evaporative Stress Index (ESI) and Vegetation Drought Response Index (VegDRI) and soil moisture anomalies from the North American Land Data Assimilation System (NLDAS) are compared to the United States Drought Monitor (USDM), surface meteorological conditions, and crop and soil moisture data compiled by the National Agricultural Statistics Service (NASS). Overall, the results show that rapid decreases in the ESI and NLDAS anomalies often preceded drought intensification in the USDM by up to 6 wk depending on the region. Decreases in the ESI tended to occur up to several weeks before deteriorations were observed in the crop condition datasets. The NLDAS soil moisture anomalies were similar to those depicted in the NASS soil moisture datasets; however, some differences were noted in how each model responded to the changing drought conditions. The VegDRI anomalies tracked the evolution of the USDM drought depiction in regions with slow drought development, but lagged the USDM and other drought indicators when conditions were changing rapidly. Comparison to the crop condition datasets revealed that soybean conditions were most similar to ESI anomalies computed over short time periods (2–4 wk), whereas corn conditions were more closely related to longer-range (8–12 wk) ESI anomalies. Crop yield departures were consistent with the drought severity depicted by the ESI and to a lesser extent by the NLDAS and VegDRI datasets.

To reduce the impacts of drought, developing an integrated drought monitoring tool and early warning system is crucial and more effective than the crisis management approach that is commonly used in developing countries like Ethiopia. The overarching goal of this study was to develop a higher-spatial-resolution vegetation outlook (VegOut-UBN) model that integrates multiple satellite, climatic, and biophysical input variables for the Upper Blue Nile (UBN) basin. VegOut-UBN uses current and historical observations in predicting the vegetation condition at multiple leading time steps of 1, 3, 6, and 9 dekades. VegOut-UBN was developed to predict the vegetation condition during the main crop-growing season locally called “Kiremt„ (June to September) using historical input data from 2001 to 2016. The rule-based regression tree approach was used to develop the relationship between the predictand and predictor variables. The results for the recent historic drought (2009 and 2015) and non-drought (2007) years are presented to evaluate the model accuracy during extreme weather conditions. The result, in general, shows that the predictive accuracy of the model decreases as the prediction interval increases for the cross-validation years. The coefficient of determination (R2) of the predictive and observed vegetation condition shows a higher value (R2 > 0.8) for one-month prediction and a relatively lower value (R2 ≅ 0.70) for three-month prediction. The result also reveals strong spatial integrity and similarity of the observed and predicted maps. VegOut-UBN was evaluated and compared with the Standardized Precipitation Index (SPI) (derived from independent rainfall datasets from meteorological stations) at different aggregate periods and with a food security status map. The result was encouraging and indicative of the potential application of VegOut-UBN for drought monitoring and prediction. The VegOut-UBN model could be informative in decision-making processes and could contribute to the development of operational drought monitoring and predictive models for the UBN basin.

In this study, we present an investigation of the spatial and temporal variation of meteorological droughts in the Upper Blue Nile (UBN) basin in Ethiopia using longer historical record (1952–2009) of 14 meteorological stations and 23 other stations with relatively shorter records (1975–2009). The influence of using varying record length on drought category was studied by comparing the Standard Precipitation Index (SPI) results from the 14 stations with long record length, by taking out incrementally 1 year record from 1953 to 1975 and the same length of record was withheld incrementally at the middle of data length (1970–88). The results show that the record from 1953 to 2009 gave the same category of drought severity as that of drought category computed from 1975 to 2009 for each station. Moreover, the same result obtained for the data withholding at the middle of the record length. The results of the spatial and temporal analysis revealed that severe to extreme meteorological droughts were not generally a characteristic of the basin except the north and northeast parts.

The Upper Blue Nile (UBN) basin is less-explored in terms of drought studies as compared to other parts of Ethiopia and lacks a basin-specific drought monitoring system. This study compares six drought indices: Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Evapotranspiration Deficit Index (ETDI), Soil Moisture Deficit Index (SMDI), Aggregate Drought Index (ADI), and Standardized Runoff-discharge Index (SRI), and evaluates their performance with respect to identifying historic drought events in the UBN basin. The indices were calculated using monthly time series of observed precipitation, average temperature, river discharge, and modeled evapotranspiration and soil moisture from 1970 to 2010. The Pearson’s correlation coefficients between the six drought indices were analyzed. SPI and SPEI at 3-month aggregate period showed high correlation with ETDI and SMDI (r > 0.62), while SPI and SPEI at 12-month aggregate period correlate better with SRI. The performance of the six drought indices in identifying historic droughts: 1973–1974, 1983–1984, 1994–1995, and 2003–2004 was analyzed using data obtained from Emergency Events Database (EM-DAT) and previous studies. When drought onset dates indicated by the six drought indices are compared with that in the EM-DAT. SPI, and SPEI showed early onsets of drought events, except 2003–2004 drought for which the onset date was unavailable in EM-DAT. Similarly, ETDI, SMDI and SRI-3 showed early onset for two drought events and late onsets in one-drought event. In contrast, ADI showed late onsets for two drought events and early onset for one drought event. None of the six drought indices could individually identify the onsets of all the selected historic drought events; however, they may identify the onsets when combined by considering several input variables at different aggregate periods.

Drought has had large impacts on economies, societies and the environment, and could become even more disruptive given the context of climate change characterized by increasing temperature and more variable and extreme precipitation. Changes in the frequency, duration, and severity of droughts will have enormous impacts on the hydrological cycle, water management and agricultural production. Therefore, one major concern arising from climate change is its potential effects on water resources. Although South Korea has been experiencing serious drought and water scarcity issues in recent years, preparedness for potential changes in the frequency, severity and duration of drought disasters due to climate change effects has received only limited attention. It is important to detect changes in temporal trends of drought at the regional scale. This information will aid understanding the impacts of climate change and its subsequent effects on hydrology and agriculture. In this paper, we have addressed the question of how climate change might influence the impact of drought hazard by estimating the potential changes in temporal trends of drought in South Korea. We have assessed the temporal trends of future drought with drought indices (Standardized Precipitation Index [SPI], Standardized Precipitation Evapotranspiration Index [SPEI], and Self-Calibrating Palmer Drought Severity Index [SC-PDSI]) using past observed data (1981–2010) from 54 meteorological stations maintained by the Korea Meteorological Administration (KMA) and projected climate change scenarios (2011–2100) as depicted by the Representative Concentration Pathways models (RCPs). The drought hazard assessment was quantitatively evaluated by analyzing drought frequency, duration, severity and magnitude using the run theory method based on different timescales of the drought indices. The results demonstrated a significant increase of potential drought impacts in the future. Additionally, significant increases in the drought magnitude and severity were found at different time scales for each drought indicator. The results indicated that the temporal pattern of potential drought progression and recession across South Korea can be used for the development of proactive drought risk management and mitigation strategies.

The vegetation drought response index (VegDRI), which combines traditional climate- and satellite-based approaches for assessing vegetation conditions, offers new insights into assessing the impacts of drought from local to regional scales. In 2011, the U.S. southern Great Plains, which includes Texas, Oklahoma, and New Mexico, was plagued by moderate to extreme drought that was intensified by an extended period of record-breaking heat. The 2011 drought presented an ideal case study to evaluate the performance of VegDRI in characterizing developing drought conditions. Assessment of the spatiotemporal drought patterns represented in the VegDRI maps showed that the severity and patterns of the drought across the region corresponded well to the record warm temperatures and much-below-normal precipitation reported by the National Climatic Data Center and the sectoral drought impacts documented by the Drought Impact Reporter (DIR). VegDRI values and maps also showed the evolution of the drought signal before the Las Conchas Fire (the largest fire in New Mexico’s history). Reports in the DIR indicated that the 2011 drought had major adverse impacts on most rangeland and pastures in Texas and Oklahoma, resulting in total direct losses of more than $12 billion associated with crop, livestock, and timber production. These severe impacts on vegetation were depicted by the VegDRI at subcounty, state, and regional levels. This study indicates that the VegDRI maps can be used with traditional drought indicators and other in situ measures to help producers and government officials with various management decisions, such as justifying disaster assistance, assessing fire risk, and identifying locations to move livestock for grazing.

Air temperature (Ta) is a key input in a wide range of agroclimatic applications. Moderate Resolution Imaging Spectroradiometer (MODIS) Ts (Land Surface Temperature (LST)) products are widely used to estimate daily Ta. However, only daytime LST (Ts-day) or nighttime LST (Ts-night) data have been used to estimate Tmax/Tmin (daily maximum or minimum air temperature), respectively. The relationship between Tmax and Ts-night, and the one between Tmin and Ts-day has not been studied. In this study, both the ability of Ts-night data to estimate Tmax and the ability of Ts-day data to estimate Tmin were tested and studied in the Corn Belt during the growing season (May–September) from 2008 to 2012, using MODIS daily LST products from both Terra and Aqua. The results show that using Ts-night for estimating Tmax could result in a higher accuracy than using Ts-day for a similar estimate. Combining Ts-day and Ts-night, the estimation of Tmax was improved by 0.19–1.85, 0.37–1.12 and 0.26–0.93 °C for crops, deciduous forest and developed areas, respectively, when compared with using only Ts-day or Ts-night data. The main factors influencing the Ta estimation errors spatially and temporally were analyzed and discussed, such as satellite overpassing time, air masses, irrigation, etc.

An experimental drought monitoring tool has been developed that predicts the vegetation condition (Vegetation Outlook) using a regression-tree technique at a monthly time step during the growing season in Eastern Africa. This prediction tool (VegOut-Ethiopia) is demonstrated for Ethiopia as a case study. VegOut-Ethiopia predicts the standardized values of the Normalized Difference Vegetation Index (NDVI) at multiple time steps (weeks to months into the future) based on analysis of “historical patterns” of satellite, climate, and oceanic data over historical records. The model underlying VegOut-Ethiopia capitalizes on historical climate-vegetation interactions and ocean-climate teleconnections (such as El Nino and the Southern Oscillation (ENSO)) expressed over the 24 year data record and also considers several environmental characteristics (e.g., land cover and elevation) that influence vegetation's response to weather conditions to produce 8 km maps that depict future general vegetation conditions. VegOut-Ethiopia could provide vegetation monitoring capabilities at local, national, and regional levels that can complement more traditional remote sensing-based approaches that monitor “current” vegetation conditions. The preliminary results of this case study showed that the models were able to predict the vegetation stress (both spatial extent and severity) in drought years 1–3 months ahead during the growing season in Ethiopia. The correlation coefficients between the predicted and satellite-observed vegetation condition range from 0.50 to 0.90. Based on the lessons learned from past research activities and emerging experimental forecast models, future studies are recommended that could help Eastern Africa in advancing knowledge of climate, remote sensing, hydrology, and water resources.

This study first assesses the performance of regional climate models in the Coordinated Regional Climate Downscaling Experiment (CORDEX) in reproducing observed extreme precipitation indices over the Greater Horn of Africa (GHA) region during 1989–2005. The study then assesses projected changes in these extremes during 2069–2098 compared to 1976–2005. The Regional Climate Model (RCM) simulations are made using two RCMs, with large-scale forcing from four CMIP5 Global limate Models(GCMs) under two Representative Concentration Pathways (RCP4.5 and RCP8.5). We found that RCM simulations have reasonably captured observed patterns of moderate precipitation extreme indices (MPEI). Pattern correlation coefficients between simulated and observed MPEI exceed 0.5 for all except the Simple Daily Intensity Index (SDII). However, significant overestimations or underestimations exist over localized areas in the region. Projected changes in Total annual Precipitation (PRCPTOT) and the annual number of heavy (>10 mm) and very heavy (>20 mm) precipitation days by 2069–2098 show a general north-south pattern, with decreases over the southern half and increases over the northern half of the GHA. These changes are often greatest over parts of Somalia, Eritrea, the Ethiopian highlands and southern Tanzania. Maximum one- and five-day precipitation totals over a year and SDII (ratio of PRCPTOT to rainy days) are projected to increase over a majority of the GHA, including areas where PRCPTOT is projected to decrease, suggesting fewer, but heavier rainy days in the future. Changes in the annual sum of daily precipitation above the 95th and 99th percentiles are statistically significant over a few locations, with the largest projected decrease/increase over Eritrea and northwestern Sudan/Somalia. Projected changes in Consecutive Dry Days (CDD) suggest longer periods of dryness over the majority of the GHA, except the central portions covering northern Uganda, southern South Sudan, southeastern Ethiopia and Somalia. Substantial increases in CDD are located over southern Tanzania and the Ethiopian highlands. The magnitude and the spatial extent of statistically-significant changes in all MPEI increase from RCP4.5 to RCP8.5, and the separation between positive and negative changes becomes clearer under RCP8.5.

The relationship between the El Nino-Southern Oscillation (ENSO) and hydrologic variability in the United States is investigated using Empirical Orthogonal Function (EOF)/Principal Component Analysis (PCA). The multivariate ENSO index (MEI) is utilized to identify strong coherences associated with multiple months (1-, 2-, 4-, 6-, 12-, 24-, 48-month) of the Log-Standardized Hydrologic Drought Index (LSHDI) in the conterminous states for the period 1950–2005. Based on 56 years of monthly streamflow data for 102 forecast climate divisions, this research explores the spatial and temporal variation of hydrologic responses corresponding to ENSO events. Preliminary results show that a potential predictor of the dominant streamflow modes in the northern Great Plains is identified from streamflows in western Arizona. Also, positive relationships between hydrologic drought and El Nino were found in the Pacific Northwest (Washington, Oregon, and northern California), whereas negative relationships were detected in southern California and the northern Great Plains. These findings will provide useful insights to help improve streamflow forecast potential and capabilities, and minimize the impacts of hydrologic events (e.g. floods and droughts) associated with ENSO events.

The development of new tools that provide timely, detailed-spatial-resolution drought information is essential for improving drought preparedness and response. This paper presents a new method for monitoring drought-induced vegetation stress called the Vegetation Drought Response Index (VegDRI). VegDRI integrates traditional climate-based drought indicators and satellite-derived vegetation index metrics with other biophysical information to produce a 1 km map of drought conditions that can be produced in near-real time. The initial VegDRI map results for a 2002 case study conducted across seven states in the north-central United States illustrates the utility of VegDRI for improved large-area drought monitoring.

Precipitation is a key parameter for the study of climate change and variability and the detection and monitoring of natural disaster such as drought. Precipitation datasets that accurately capture the amount and spatial variability of rainfall is critical for drought monitoring and a wide range of other climate applications. This is challenging in many parts of the world, which often have a limited number of weather stations and/or historical data records. Satellite-derived precipitation products offer a viable alternative with several remotely sensed precipitation datasets now available with long historical data records (+30 years), which include the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) datasets. This study presents a comparative analysis of three historical satellite-based precipitation datasets that include Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B43 version 7 (1998-2015), PERSIANN-CDR (1983-2015) and CHIRPS 2.0 (1981-2015) over Chile to assess their performance across the country and evaluate their applicability for agricultural drought evaluation when used in the calculation of commonly used drought indicator as the Standardized Precipitation Index (SPI). In this analysis, 278 weather stations of in-situ rainfall measurements across Chile were initially compared to the satellite-based precipitation estimates. The study area (Chile) was divided into five latitudinal zones: North, North-Central, Central, South-Central and South to determine if there were a regional difference among these satellite-based estimates. Nine statistics were used to evaluate the performance of satellite products to estimate the amount and spatial distribution of historical rainfall across Chile. Hierarchical cluster analysis, k-means and singular value decomposition were used to analyze these datasets to better understand their similarities and differences in characterizing rainfall patterns across Chile. Monthly analysis showed that all satellite products highly overestimated precipitation in the arid North zone. However, there were no major difference between all three products from North to South-Central zones. Though, in the South zone, PERSIANN-CDR shows the lowest fit with high underestimation, further CHIRPS 2.0 and TMPA 3B43 v7 had better agreement with in-situ measurements. The accuracy of satellite products were highly dependent on the amount of monthly rainfall with the best results found during winter seasons and in zones (Central to South) with higher amounts of precipitation. PERSIANN-CDR and CHIRPS 2.0 were used to derive SPI at time-scale of 1, 3 and 6 months, both satellite products presented similar results when it was compared in-situ against satellite SPI's. Because of its higher spatial resolution that allows better characterizing of spatial variation in precipitation pattern, the CHIRPS 2.0 was used to mapping the SPI-3 over Chile. The results of this study show that in order to use the CHIRPS 2.0 and PERSIANN-CDR data sets in Chile to monitor spatial patterns in the rainfall and drought intensity conditions, these products should be calibrated to adjust for the overestimation/underestimation of precipitation geographically specially in the North zone and seasonally during the summer and spring months in the other zones.

Droughts are normal climate episodes, yet they are among the most expensive natural disasters in the world. Knowledge about the timing, severity, and pattern of droughts on the landscape can be incorporated into effective planning and decision-making. In this study, we present a data mining approach to modeling vegetation stress due to drought and mapping its spatial extent during the growing season. Rule-based regression tree models were generated that identify relationships between satellite-derived vegetation conditions, climatic drought indices, and biophysical data, including land-cover type, available soil water capacity, percent of irrigated farm land, and ecological type. The data mining method builds numerical rule-based models that find relationships among the input variables. Because the models can be applied iteratively with input data from previous time periods, the method enables to provide predictions of vegetation conditions farther into the growing season based on earlier conditions. Visualizing the model outputs as mapped information (called VegPredict) provides a means to evaluate the model. We present prototype maps for the 2002 drought year for Nebraska and South Dakota and discuss potential uses for these maps.

Drought is a complex natural hazard that is best characterized by multiple climatological and hydrological parameters. Improving our understanding of the relationships between these parameters is necessary to reduce the impacts of drought. Data mining is a recently developed technique that can be used to interact with large databases and assist in the discovery of associations between drought and oceanic data by extracting information from massive and multiple data archives. In this study, a new data-mining algorithm [i.e., Minimal Occurrences With Constraints and Time Lags (MOWCATL)] has been used to identify the relationships between oceanic parameters and drought indices. Rather than using traditional global statistical associations, the algorithm identifies drought episodes separate from normal and wet conditions and then uses drought episodes to find time-lagged relationships with oceanic parameters. As with all association-based data-mining algorithms, MOWCATL is used to find existing relationships in the data, and is not by itself a prediction tool. Using the MOWCATL algorithm, the analyses of the rules generated for selected stations and state-averaged data for Nebraska from 1950 to 1999 indicate that most occurrences of drought are preceded by positive values of the Southern Oscillation index (SOI), negative values of the multivariate ENSO index (MEI), negative values of the Pacific–North American (PNA) index, negative values of the Pacific decadal oscillation (PDO), and negative values of the North Atlantic Oscillation (NAO). The frequency and confidence of the time-lagged relationships between oceanic indices and droughts at the selected stations in Nebraska indicate that oceanic parameters can be used as indicators of drought in Nebraska.

Drought has an impact on many aspects of society. To help decision makers reduce the impacts of drought, it is important to improve our understanding of the characteristics and relationships of atmospheric and oceanic parameters that cause drought. In this study, the use of data mining techniques is introduced to find associations between drought and several oceanic and climatic indices that could help users in making knowledgeable decisions about drought responses before the drought actually occurs. Data mining techniques enable users to search for hidden patterns and find association rules for target data sets such as drought episodes. These techniques have been used for commercial applications, medical research, and telecommunications, but not for drought. In this study, two time-series data mining algorithms are used in Nebraska to illustrate the identification of the relationships between oceanic parameters and drought indices. The algorithms provide flexibility in time-series analyses and identify drought episodes separate from normal and wet conditions, and find relationships between drought and oceanic indices in a manner different from the traditional statistical associations. The drought episodes were determined based on the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI). Associations were observed between drought episodes and oceanic and atmospheric indices that include the Southern Oscillation Index (SOI), the Multivariate ENSO Index (MEI), the Pacific/North American (PNA) index, the North Atlantic Oscillation (NAO) Index, and the Pacific Decadal Oscillation (PDO) Index. The experimental results showed that among these indices, the SOI, MEI, and PDO have relatively stronger relationships with drought episodes over selected stations in Nebraska. Moreover, the study suggests that data mining techniques can help us to monitor drought using oceanic indices as a precursor of drought.

This study investigates the spatial and temporal variation of meteorological droughts in the Upper Blue Nile (UBN) basin in Ethiopia using long historical records (1953–2009) for 14 meteorological stations, and relatively short records (1975–2009) for 23 other stations. The influence of using varying record length on drought category was studied by comparing the Standard Precipitation Index (SPI) results from the 14 stations with long record length, by taking out incrementally 1-year records from 1953 to 1975. These analyses show that the record length from 1953 to 1975 has limited effect on changing the drought category and hence the record length from 1975 to 2009 could be used for drought analysis in the UBN basin. Spatio-temporal analyses of the SPI values show that throughout the UBN basin seasonal or annual meteorological drought episodes occurred in the years 1978/79, 1984/85, 1994/95 and 2003/04. Persistency from seasonal to annual drought, and from one year to the next, has been found. Th...

Drought is a recurring phenomenon in Ethiopia that significantly impacts the socioeconomic sector and various components of the environment. The overarching goal of this study is to assess the spatial and temporal patterns of meteorological drought using a satellite-derived rainfall product for the Upper Blue Nile Basin (UBN). The satellite rainfall product used in this study was selected through evaluation of five high-resolution products (Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) v2.0, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Climatology and Time-series (TARCAT) v2.0, Tropical Rainfall Measuring Mission (TRMM) and Africa Rainfall Estimate Climatology version 2 [ARC 2.0]). The statistical performance measuring techniques (i.e., Pearson correlation coefficient (r), mean error (ME), root mean square error (RMSE), and Bias) were used to evaluate the satellite rainfall products with the corresponding ground observation data at ten independent weather stations. The evaluation was carried out for 1998–2015 at dekadal, monthly, and seasonal time scales. The evaluation results of these satellite-derived rainfall products show there is a good agreement (r > 0.7) of CHIRPS and TARCAT rainfall products with ground observations in majority of the weather stations for all time steps. TARCAT showed a greater correlation coefficient (r > 0.70) in seven weather stations at a dekadal time scale whereas CHIRPS showed a greater correlation coefficient (r > 0.84) in nine weather stations at a monthly time scale. An excellent score of Bias (close to one) and mean error was observed in CHIRPS at dekadal, monthly and seasonal time scales in a majority of the stations. TARCAT performed well next to CHIRPS whereas PERSSIAN presented a weak performance under all the criteria. Thus, the CHIRPS rainfall product was selected and used to assess the spatial and temporal variability of meteorological drought in this study. The 3-month Z-Score values were calculated for each grid and used to assess the spatial and temporal patterns of drought. The result shows that the known historic drought years (2014–2015, 2009–2010, 1994–1995 and 1983–1984) were successfully indicated. Moreover, severe drought conditions were observed in the drought prone parts of the basin (i.e., central, eastern and southeastern). Hence, the CHIRPS rainfall product can be used as an alternative source of information in developing the grid-based drought monitoring tools for the basin that could help in developing early warning systems.