3-month Standardized Precipitation Index Research Articles

Assessing Direction of desertification changes in an Arid Region (A Case study: Semnan County, Iran)

Desertification is a serious ecological, environmental, and socio-economic threat to the world, and there is an urgent need to develop rational methods for its evaluation at different spatio-temporal scales. This study aimed to address the main directions of desertification changes in Semnan County, Iran. Monthly precipitation was used to calculate a 12-month SPI (Standardized Precipitation Index) and the normalized difference vegetation index (NDVI) obtained by LandsatThematicMapper (TM) sensor of April month were used for the three years, viz., 1987, 1998, and 2011. Five major categories were identified at level-I and they were subdivided into 11 categories including, 1. Residential areas, 2. Vegetation, 3. Bare and rocky areas, 4. Salt lands and 5. Sand areas. The results indicated that the reduction of the NDVI values was consistent with the changes in land use/land cover. This progress was taken place in some areas with the development of bare soil to salt land, and in some areas with the expansion of sand land, particularly from 1987 to 1998. The results indicated that the salt lands increased 348.24 and 721.57 square kilometers from 1987 to 1998 and from 1998 to 2011, respectively. The results revealed that desertification occurred at a rapid rate, especially towards the east and southeast directions in the study area. That was, towards the Dasht-e-Kavir and Damghan City. The results showed that besides using NDVI and SPI for drought monitoring, the meteorological variables such as temperature, sunshine hours and evaporation could improve the accuracy of interpretation of the results.

Monitoring drought using composite drought indices based on remote sensing

Drought is one of the most frequent disasters occurring in North China and has a great influence on agriculture, ecology and economy. To monitor drought of typical dry areas in North China, Shandong Province, this paper proposed composite drought indices using multivariable linear regression (MCDIs) to integrate Tropical Rainfall Measuring Mission (TRMM) derived precipitation, Global Land Data Assimilation System Version 2.1 (GLDAS-2.1) derived soil moisture, Moderate Resolution Imaging Spectroradiometer (MODIS) derived land surface temperature (LST) and normalized difference vegetation index (NDVI) from 2013 to 2017 (March to September). Pearson correlation analyses were performed between single remote sensing drought indices and in-situ drought indices, standardized precipitation evapotranspiration index (SPEI), in different time scales to assess the capability of single indices over Shandong Province. The multivariable linear regression method was used to established MCDIs, and mediator and moderator variables were introduced to optimize the model. The correlation coefficients (r) between MCDIs and SPEIs was higher than that between each single index and SPEIs. Additionally, when we investigate the correlations of different MCDIs with both standardized precipitation index (SPI) and moisture index (MI), the highest r values with both 1-month SPI and MI were acquired by the MCDI based on 1-month SPEI (MCDI-1). This suggested MCDI-1 was suitable to monitor meteorological drought. Also, the comparison between MCDI based on 9-month SPEI (MCDI-9) and soil moisture showed MCDI-9 was a good indicator for agricultural drought. Therefore, multivariable linear regression and MCDIs were recommended to be an effective method and indices for monitoring drought across Shandong Province and similar areas.

Frequency change of future extreme summer meteorological and hydrological droughts over North America

This paper describes projected frequency changes in extreme summer meteorological and hydrological droughts over North American catchments. It uses two large ensemble climate models (50-member CanESM2 and 40-member CESM1) under the RCP 8.5 scenario to robustly assess frequency changes up to the 100-year drought, relative to the 1980–2009 reference period. Meteorological droughts, linked solely to precipitation deficit, are represented by the 1-month Standardized Precipitation Index (SPI), whereas hydrological droughts are characterized by the 1-month Streamflow Drought Index (SDI), based on hydrological simulation using a lumped hydrological model. Results show widely different patterns for future changes in extreme meteorological versus hydrological droughts. Future meteorological droughts show patterns of increasing and decreasing frequency that roughly match future expected changes of mean summer precipitation, although some regions are nonetheless projected to see more frequent extreme meteorological droughts despite increases in mean summer precipitation. The frequency changes for the 100-year meteorological droughts can be quite severe, with up to a 20-fold increase observed over some watersheds. On the other hand, hydrological droughts, which combine the effect of precipitation and temperature changes, show a mostly uniform pattern of large to very large increases in drought frequency. This shows that the projected temperature increase is a main driver of future extreme hydrological droughts, enough to overcome the projected increase in mean summer precipitation for many North American catchments. Projected changes in both meteorological and hydrological droughts get consistently worse for the longer considered return periods. In other words, frequency changes for the 100-year droughts are more significant than those expected for the 2- and 20-year droughts. This gradual worsening toward larger extremes has potentially large societal and economic impacts. The large projected increases in the frequency of extreme hydrological drought frequency (up to 27 times) are likely to severely stress water management systems across North America.

Monitoring Drought Impact on Annual Forage Production in Semi-Arid Grasslands: A Case Study of Nebraska Sandhills

Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000–2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha−1*yr−1, was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R2 = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses.

Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

Satellite-based remote sensing techniques have been widely used to monitor droughts spanning large areas. Various agricultural drought indices have been developed to assess the intensity of agricultural drought and to detect damaged crop areas. However, to better understand the responses of agricultural drought to meteorological drought, agricultural management practices should be taken into consideration. This study aims to evaluate the responses to drought under different forms of agricultural management for the extreme drought that occurred on the Korean Peninsula in 2014 and 2015. The 3-month standardized precipitation index (SPI3) and the 3-month vegetation health index (VHI3) were selected as a meteorological drought index and an agricultural drought index, respectively. VHI3, which comprises the 3-month temperature condition index (TCI3) and the 3-month vegetation condition index (VCI3), differed significantly in the study area during the extreme drought. VCI3 had a different response to the lack of precipitation in South and North Korea because it was affected by irrigation. However, the time series of TCI3 were similar in South and North Korea. These results meant that each drought index has different characteristics and should be utilized with caution. Our results are expected to help comprehend the responses of the agricultural drought index on meteorological drought depending on agricultural management.

A combined drought monitoring index based on multi-sensor remote sensing data and machine learning

The occurrence of drought is related to complicated interactions between many factors, such as precipitation, temperature, evapotranspiration and vegetation. In this study, the relationships between drought and precipitation, temperature, vegetation and evapotranspiration were investigated with a random forest (RF), and a new combined drought monitoring index (CDMI) was constructed. The effectiveness of the CDMI in monitoring drought in Shaanxi Province was verified by the in situ 1 ∼ 12-month standardized precipitation index (SPI); relative soil moisture (RSM) and four other commonly used remote sensing drought monitoring indices. The results show that CDMI is more correlated with the SPI and RSM than the four indices. Moreover, the spatial distributions of drought for the CDMI and RSM are similar. Therefore, the CDMI can be used to monitor droughts in Shaanxi Province, and machine learning can explore the relationships between various factors and establish a drought index without knowledge of the causal mechanisms of these factors.

An example of principal component analysis application on climate change assessment

Climate change assessment is usually based upon air temperature and precipitation changes on an annual and seasonal basis, but there are more levels to their significance as presented by parameters derived from these two basic parameters. In order to define their relevance for climate changes, the principal component analysis (PCA) was performed. In this case, ten meteorological parameters and climate change indicators were defined for two meteorological stations located in geographically completely opposite parts of the country; station Osijek is in continental region of Croatia, and Dubrovnik station is located in the Mediterranean region. Analyses were done for the period 1985–2016 on an annual and seasonal basis. All defined indicators present basic climate change characteristics on annual and seasonal basis as follows: precipitation sum, mean air temperature, air temperature sum, standard deviation of daily air temperature, maximum daily air temperature, maximum daily precipitation, number of days with precipitation > 30 mm, number of days with no precipitation, 1-month standardized precipitation index, and aridity index. In the first step, it was applied on the set of linear regression coefficients defined for 10 climate change indicators. During the second step, PCA was applied on the computed Mann–Kendall test statistic, ZMK.in order to determine the existence of significant temporal tendencies in the indicator values. The provided research proves PCA is a very useful tool for implementing this approach, particularly in the Mediterranean region which shows high sensitivity to many variables important for climate characterization.

The asymmetric impact of abundant preceding rainfall on heat stress in low latitudes

In addition to high temperature, high humidity can have significant consequences on thermal comfort of human beings. The co-occurrence of high temperature and high humidity (so-called ‘oppressive hot days’) often results in heat stress events, but the extent to which it is affected by preceding surface moisture has not been fully understood to date. In this study, we examine the relations between preceding 3-month standardized precipitation index (SPI) and the number of hot days indicated by the surface air temperature (NHD-Tx) and the wet-bulb globe temperature (NHD-Wx) that combines both temperature and humidity in the hottest month in low latitudes. Results show that, in contrast with the negative correlations between SPI and NHD-Tx, which are associated with the previously reported precipitation deficit-temperature feedback, significant positive correlations between SPI and NHD-Wx are found in some low latitude areas. The probability of above-average NHD-Wx could be ∼30% higher after wet conditions than that after dry conditions in areas like southern South America, some parts of Africa, and West Asia. Hotspot analyses further show that abundant preceding rainfall has an asymmetric impact on oppressive hot days by favoring more above-average NHD-Wx. Our analyses imply that a local feedback may exist between surface moisture and oppressive hot extremes, via which the unbearable heat stress over some parts of the tropics is modulated, controlled, and/or caused by changes in the preceding near-surface humidity/soil moisture. The spatially heterogeneous patterns of the relations between preceding rainfall and heat stress confirm the precipitation deficit-temperature feedback in many areas and reveal the coexistence of surface moisture-oppressive heat stress in several low latitude areas. We emphasize the necessity of considering both feedbacks for a better understanding of the distinct roles of preceding rainfall in the consequent development of heat stress in low latitudes.

Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods

East Nusa Tenggara Province is one of the most vulnerable regions in Indonesia to drought. Drought prediction is definitely needed as a mitigation action to minimize the risk of drought. However, a sparse dataset has led to difficulties in accurately predicting future droughts in areas without meteorological stations, and hence a dataset with a finer resolution is required. This research investigates the performance of a 3-month Standardized Precipitation Index (SPI) derived from the Tropical Rainfall Measuring Mission (TRMM) and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) to predict drought. CART and Random Forest are applied as the classification methods. Using several predictors, the analysis finds that CART has lower predictability than Random Forest. The average accuracy of the prediction using Random Forest reaches 100% with an average Area Under Curve (AUC) of about 0.8. The analysis also shows that predictions using the MERRA-2 dataset lead to higher accuracy and AUC than those using the TRMM. Therefore, using the MERRA-2 dataset predicted by Random Forest can be an optimal way to predict drought in East Nusa Tenggara. The methods confirmed that average soil surface temperature (day and night), Multivariate ENSO Index (MEI), Arctic Oscillation Index (AOI) and Normalized Difference Vegetation Index (NDVI) are strong predictors of drought. The performance of CART and Random Forest is improved with the Synthetic Minority Over-Sampling Technique (SMOTE).The techniques described:•translate drought information and predictors of drought into a base classifier that optimizes the AUC;•allow drought to be predicted for many grid points efficiently and with high accuracy; and•are computationally efficient and easy to implement.

Developing a drought-monitoring index for the contiguous US using SMAP

Abstract. Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe (between 85.044∘ N/S) using an L-band (1.4 GHz) microwave radiometer in 2–3 days depending on location. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. However, the short SMAP record length poses a statistical challenge for meaningful assessment of its indices. This study presents initial insights about using SMAP for monitoring drought and pluvial regions with a first application over the contiguous United States (CONUS). SMAP soil moisture data from April 2015 to December 2017 at both near-surface (5 cm) SPL3SMP, or Level 3, at ∼36 km resolution, and root-zone SPL4SMAU, or Level 4, at ∼9 km resolution, were fitted to beta distributions and were used to construct probability distributions for warm (May–October) and cold (November–April) seasons. To assess the data adequacy and have confidence in using short-term SMAP for a drought index estimate, we analyzed individual grids by defining two filters and a combination of them, which could separate the 5815 grids covering CONUS into passed and failed grids. The two filters were (1) the Kolmogorov–Smirnov (KS) test for beta-fitted long-term and the short-term variable infiltration capacity (VIC) land surface model (LSM) with 95 % confidence and (2) good correlation (≥0.4) between beta-fitted VIC and beta-fitted SPL3SMP. To evaluate which filter is the best, we defined a mean distance (MD) metric, assuming a VIC index at 36 km resolution as the ground truth. For both warm and cold seasons, the union of the filters – which also gives the best coverage of the grids throughout CONUS – was chosen to be the most reliable filter. We visually compared our SMAP-based drought index maps with metrics such as the U.S. Drought Monitor (from D0–D4), 1-month Standard Precipitation Index (SPI) and near-surface VIC from Princeton University. The root-zone drought index maps were shown to be similar to those produced by the root-zone VIC, 3-month SPI, and the Gravity Recovery and Climate Experiment (GRACE). This study is a step forward towards building a national and international soil moisture monitoring system without which quantitative measures of drought and pluvial conditions will remain difficult to judge.

Watershed-scale retrospective drought analysis and seasonal forecasting using multi-layer, high-resolution simulated soil moisture for Southeastern U.S

Drought assessment at local scales needs a reliable framework capturing both local landscape processes and the watershed-scale hydrological responses. Any such tool, implementable in a near real-time, semi-automated framework, is largely unavailable for the South Atlantic-Gulf (SAG) region in the Southeastern US (SEUS). In this study, we evaluate a drought monitoring and forecasting approach using multi-layer, high resolution, simulated soil moisture for 50 watersheds in the SEUS. Soil and Water Assessment Tool (SWAT) is integrated with meteorological drivers (precipitation and temperature) from Climate Forecast System Reanalysis data (CFSR) for retrospective simulations (January 1982–December 2013), and Climate forecast system model version-2 (CFSv2) models to obtain the near real-time estimates (January 2014 through mid-March 2017) and 9-month lead forecasts (mid-March through December 2017) of the hydrologic variables at 12-digit Hydrologic Unit Code (HUC-12) resolution. Drought assessment is carried out by combining the drought severity estimates of the weekly percentiles of the surface and total column (TC) soil moisture, aggregated to 4-week and 8-week respectively, following a different severity classification scheme for each layer. Several drought indices and observed drought maps from the U.S. Drought Monitor (USDM) are used to compare the agreement (or disagreement) of the proposed approach with the observed drought conditions in the region. The results show promising application of the proposed approach for near real-time estimation as the drought estimates show high (∼80–90%) Index of Agreement (IOA) with the Palmer Drought Severity Index (PDSI) for all drought categories (mild-exceptional). The retrospective assessment shows that TC soil moisture percentiles have high correlation (>0.7) with long-term drought indices. The surface soil moisture percentiles show high correlation (∼0.6) with Palmer Z Index (ZNDX) and 1-month Standardized Precipitation Index (SPI-1), while longer aggregations increase the association with the long-term drought indices. While the SWAT-CFSv2 based drought estimation is useful in near real-time mode, higher disagreement between the forecasted drought maps and the observed drought severity from the USDM is noted for a forecast window of greater than ∼ 4–6 weeks.

Developing a satellite-based combined drought indicator to monitor agricultural drought: a case study for Ethiopia

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 drought indicator is lacking in areas such as Ethiopia, which is extremely susceptible to this natural hazard. The overarching goal of this study is to develop a combined drought indicator (CDI-E) to monitor the spatial and temporal extents of historic agricultural drought events in Ethiopia. The CDI-E was developed by combining four satellite-based agrometeorological input parameters – the Standardized Precipitation Index (SPI), Land Surface Temperature (LST) anomaly, Standardized Normalized Difference Vegetation Index (stdNDVI) and Soil Moisture (SM) anomaly – for the period from 2001 to 2015. The method used to combine these indices is based on a quantitative approach that assigns a weight to each input parameter using Principal Component Analysis (PCA). The CDI-E results were evaluated using satellite-based gridded rainfall (3-month SPI) and crop yield data for 36 intra-country crop growing zones for a 15-year period (2001 to 2015). The evaluation was carried out for the main rainfall season, Kiremt (June-September), and the short rainfall season, Belg (February-May). The results showed that moderate to severe droughts were detected by the CDI-E across the food insecure regions reported by FEWS NET during Kiremt and Belg rainfall seasons. Relatively higher correlation coefficient values (r > 0.65) were obtained when CDI-E was compared with the 3-month SPI across the majority of Ethiopia. The spatial correlation analyses of CDI-E and cereal crop yields showed relatively good correlations (r > 0.5) in some of the crop growing zones in the northern, eastern and southwestern parts of the country. The CDI-E generally mapped the spatial and temporal patterns of historic drought and non-drought years and hence the CDI-E could potentially be used to develop an agricultural drought monitoring and early warning system in Ethiopia. Moreover, decision makers and donors may potentially use CDI-E to more accurately monitor crop yields across the food-insecure regions in Ethiopia.

Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in northwestern China

Abstract. Endorheic and arid regions around the world are suffering from serious drought problems. In this study, a drought forecasting system based on eight state-of-the-art climate models from the North American Multi-Model Ensemble (NMME) and a Distributed Time-Variant Gain Hydrological Model (DTVGM) was established and assessed over the upstream and midstream of Heihe River basin (UHRB and MHRB), a typical arid endorheic basin. The 3-month Standardized Precipitation Index (SPI3) and 1-month Standardized Streamflow Index (SSI1) were used to capture meteorological and hydrological drought, and values below −1 indicate drought events. The skill of the forecasting systems was evaluated in terms of anomaly correlation (AC) and Brier score (BS) or Brier skill score (BSS). The predictability for meteorological drought was quantified using AC and BS with a “perfect model” assumption, referring to the upper limit of forecast skill. The hydrological predictability was to distinguish the role of initial hydrological conditions (ICs) and meteorological forcings, which was quantified by root-mean-square error (RMSE) within the ESP (Ensemble Streamflow Prediction) and reverse ESP framework. The UHRB and MHRB showed season-dependent meteorological drought predictability and forecast skill, with higher values during winter and autumn than that during spring. For hydrological forecasts, the forecast skill in the UHRB was higher than that in MHRB. Predicting meteorological droughts more than 2 months in advance became difficult because of complex climate mechanisms. However, the hydrological drought forecasts could show some skills up to 3–6 lead months due to memory of ICs during cold and dry seasons. During wet seasons, there are no skillful hydrological predictions from lead month 2 onwards because of the dominant role of meteorological forcings. During spring, the improvement of hydrological drought predictions was the most significant as more streamflow was generated by seasonal snowmelt. Besides meteorological forcings and ICs, human activities have reduced the hydrological variability and increased hydrological drought predictability during the wet seasons in the MHRB.

The Identification of Iran’s Moisture Sources Using a Lagrangian Particle Dispersion Model

Iran has faced many water shortage crises in the past. Iran’s moisture sources for precipitation were identified by Lagrangian approach using the FLEXible PARTicle dispersion model (FLEXPART) v9.0 model. The results demonstrate that Iran receives its moisture from both continental and oceanic sources. During the wet season, moisture uptake from the Arabian Sea, the Persian Gulf, and the Mediterranean Sea is dominant, while during the dry season, the role of the Red Sea, the Caspian Sea, and the Persian Gulf is intensified. Studying drought conditions by comparing 1-month, 6-month, and 12-month standardized precipitation index (SPI) with (E-P) values of oceanic and continental moisture sources (E stands for the evaporation and P the precipitation) using multiregression model demonstrates that among oceanic sources the Arabian Sea, the Persian Gulf, the Mediterranean Sea, and the Indian Ocean affect SPI values and among continental sources, moisture from bare grounds and cultivated lands influences SPI values during wet season. However, no correlation exists between oceanic and continental (E-P) and SPI values during the dry season. The results obtained by this study can be used by meteorologists and hydrology scientists for future water management programmes in Iran.

Spatio-temporal evaluation of drought characteristics in the Dhasan basin

ABSTRACT. The meteorological drought characteristics including onset, departure, duration, severity as well as intensity have been evaluated mainly for monsoon season at all the three rain gauge stations located in Dhasan basin. The Standardized Precipitation Index (SPI) has been applied to understand and quantify the drought severity on multiple time scale (1, 3, 6, 12 and 24 months). The spatiotemporal analysis of drought based on 3-month SPI has also carried out to identify drought years and the regions of the study area which is under the grip of continuous drought events. Based on the 3-month SPI, major drought events have been identified. The maximum drought severity of -11.17 occurred during November 1991 to August 1992 having the longest duration of 10 months, in the area under Sagar rain gauging station. The onset of most of the drought events in the basin take place during the beginning of Kharif season and terminate by the end of August or September, so affect the agricultural crops severely. The spatial variation indicates that during June 2002, about 55.74% of basin area was experiencing severe drought conditions, followed by 35.29% area under moderate drought condition and only 8.97% area faced mild drought conditions. The inter-relationship among the drought duration, number of drought events, drought severity and time scale have been studied.

Assessing the Spatio-temporal Variability of Meteorological Drought in Jordan

In the last decade, severe drought impacts have been witnessed in Jordan and there is an immense need to develop drought management strategy and policy framework to tackle this problem. This study aimed to investigate the spatio-temporal variability of precipitation based on long-term data using Standardized Precipitation Index (SPI) to assess the trend and intensity of drought in Jordan. In this context, the data of 29 meteorological stations of the last 37 years have been obtained from Jordan Meteorological Department. SPI-3, SPI-6 and SPI-12 have been calculated to assess short- and long-term drought events, and Mann–Kendall and linear regression tests were applied to detect the drought trend in the study region. Drought of varying duration and intensity was detected at each time scale (3-, 6-, and 12-month SPI) as well as at each meteorological station. The SPI results show that Jordan faces an increasing frequency of drought with a probability of occurrence once in every 2 or 3 years. On contrary to this, long-term drought occurs in whole country once in every 15–20 years which have more than 2-year consecutive duration. The Mann–Kendall test result shows significant temporal decrease in the amount of precipitation at all meteorological stations except Madaba. The results of linear regression test indicate significant increase in magnitude of drought with the rate 0.02/annum with a significance of 0.0001 using SPI values. This study concluded that there is an increasing drought trend and the situation will become more severe in future as the amount of rainfall is decreasing gradually in Jordan.

Drought Prediction for Areas with Sparse Monitoring Networks: A Case Study for Fiji

Hybrid drought prediction models were developed for areas with limited monitoring gauges using the APEC Climate Center Multi-Model Ensemble seasonal climate forecast and machine learning models of Extra-Trees and Adaboost. The models provide spatially distributed detailed drought prediction data of the 6-month Standardized Precipitation Index for the case study area, Fiji. In order to overcome the limitation of a sparse monitoring network, both in-situ data and bias-corrected dynamic downscaling of historical climate data from the Weather Research Forecasting (WRF) model were used as reference data. Performance measures of the mean absolute error as well as classification accuracy were used. The WRF outputs reflect the topography of the area. Hybrid models showed better performance than simply bias corrected forecasts in most cases. Especially, the model based on Extra-Trees trained using the WRF model outputs performed the best in most cases.

Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

Meaningful seasonal prediction of drought conditions is key information for end-users and water managers, particularly in Latin America where crop and livestock production are key for many regional economies. However, there are still not many studies of the feasibility of such a forecasts at continental level in the region. In this study, precipitation predictions from the European Centre for Medium Range Weather (ECMWF) seasonal forecast system S4 are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI) for Latin America, and their skill is evaluated over the hindcast period 1981–2010. The value-added utility in using the ensemble S4 forecast to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on their climatological characteristics. As expected, skill of the S4-generated SPI forecasts depends on the season, location, and the specific aggregation period considered (the 3- and 6-month SPI were evaluated). Added skill from the S4 for lead times equaling the SPI accumulation periods is primarily present in regions with high intra-annual precipitation variability, and is found mostly for the months at the end of the dry seasons for 3-month SPI, and half-yearly periods for 6-month SPI. The ECMWF forecast system behaves better than the climatology for clustered grid points in the North of South America, the Northeast of Argentina, Uruguay, southern Brazil and Mexico. The skillful regions are similar for the SPI3 and -6, but become reduced in extent for the severest SPI categories. Forecasting different magnitudes of meteorological drought intensity on a seasonal time scale still remains a challenge. However, the ECMWF S4 forecasting system does capture the occurrence of drought events for the aforementioned regions and seasons reasonably well. In the near term, the largest advances in the prediction of meteorological drought for Latin America are obtainable from improvements in near-real-time precipitation observations for the region. In the longer term, improvements in precipitation forecast skill from dynamical models, like the fifth generation of the ECMWF seasonal forecasting system, will be essential in this effort.

The Temporal-Spatial Characteristics of Drought in the Loess Plateau Using the Remote-Sensed TRMM Precipitation Data from 1998 to 2014

Rainfall gauges are always sparse in the arid and semi-arid areas of Northwest China, which makes it difficult to precisely study the characteristics of drought at a large scale in this region and similar areas. This study used the TRMM (The Tropical Rainfall Measuring Mission) multi-satellite precipitation data to study the spatial-temporal evolution of drought in the Loess Plateau based on the SPI (Standardized Precipitation Index) drought index for the period of 1998–2014. The results indicate that the monthly TRMM precipitation data are well matched with the observed precipitation, indicating that this remotely sensed data set can be reliably used to calculate the SPI drought index. Based on the study findings, the average precipitation in the Loess Plateau is showing a significant increasing trend at the rate of 4.46 mm/year. From the spatial perspective, the average annual precipitation in the Southeast is generally greater than that in the Northwest. However, the annual precipitation in the Southeast area is showing a decreasing trend, whereas, the annual precipitation in the northwest areas is showing an increasing trend. Through the SPI analysis, the 3-month SPI and 12-month SPI were both showing an increasing trend, which indicates that the drought severity in the Loess Plateau was a generally declining trend at a seasonal to annual time scale. From the spatial perspective, the SPI values in the Central and Northwest of the Loess Plateau were significantly increasing, whereas, the SPI values in the southern area of the Loess Plateau were slightly decreasing. From the seasonal characteristics, the high-risk area for drought in the spring was concentrated in the northeast and southwest part, and in the summer and autumn, the high-risk area was transferred to the south part. Through this study, it is concluded that the Loess Plateau was likely getting wetter during the time period since the Grain-for-Green Project (1999–2012) was implemented, which replaced much farmland with forestry. This is a positive signal for vegetation recovery and ecological restoration in the near future.

Spatiotemporal characterization of current and future droughts in the High Atlas basins (Morocco)

Over the past decades, drought has become a major concern in Morocco due to the importance of agriculture in the economy of the country. In the present work, the standardized precipitation index (SPI) is used to monitor the evolution, frequency, and severity of droughts in the High Atlas basins (N’Fis, Ourika, Rheraya, Zat, and R’dat), located south of Marrakech city. The spatiotemporal characterization of drought in these basins is performed by computing the SPI with precipitation spatially interpolated over the catchments. The Haouz plain, located downstream of these basins, is strongly dependent on water provided by the mountain ranges, as shown by the positive correlations between the normalized difference vegetation index (NDVI) in the plain and the 3, 6, and 12-month SPI in the High Atlas catchments. On the opposite, no significant correlations are found with piezometric levels of the Haouz groundwater due to intensified pumping for irrigation in the recent decades. A relative SPI index was computed to evaluate the climate change impacts on drought occurrence, based on the projected precipitation (2006–2100) from five high-resolution CORDEX regional climate simulations, under two emission scenarios (RCP 4.5 and RCP 8.5). These models show a decrease in precipitation towards the future up to − 65% compared to the historical period. In terms of drought events, the future projections indicate a strong increase in the frequency of SPI events below − 2, considered as severe drought condition.