Meteorological Drought Indices Research Articles

Assessment of Drought and Desertification Outlook in China Using Meteorological Drought Indices

Changing land use patterns in China may result in adverse impacts on the climate in Northeast Asia. In particular, desertification of the Chinese mainland can also have a significant influence on the climate in South Korea, such as the occurrence of yellow dust storms. This study aimed to forecast the desertification in China through a comprehensive drought analysis. Three drought indices namely, SPI, GEVI, and SPEI, were determined for eight cities in China namely, Beijing, Guangzhou, Shanghai, Xianyang, Urumqi, Chongqing, Wuhan, and Weihai. There was a strong correlation between SPI and SPEI, while GEVI showed a slightly lower correlation. The long-term droughts that can trigger desertification exhibited high correlations among the considered indices. To assess the impact of climate change, periods of severe drought occurrence were compared in ten-year intervals since 1970. It was observed that severe droughts occurred throughout China in the 1970s and 1990s. However, there was no apparent trend in increasing drought days or worsening drought conditions. Desertification in China can be influenced not only by the rainfall but also by the changes in land use pattern and exploitation of water resources. Therefore, additional research is deemed necessary to evaluate desertification in China.

Drought characterization: A systematic literature review

<p>This study examined the worsening severity of global droughts caused by climate change. However, the multiple definitions and varied range of drought indices pose challenges in effectively monitoring and assessing the prevalence and severity of droughts. This study aims to give a comprehensive overview of the various drought definitions found in the literature and how they have evolved based on their applications. Specifically, the focus was to shed light on the dynamic nature of drought characterization and offer insights into the factors that shaped its conceptualization over time. Within this context, this study explored three primary categories of drought indices: climatic, remote sensing, and composite. Each category was discussed in relation to its utility in specific fields, such as meteorological, agricultural, and hydrological drought assessments, along with an analysis of their strengths and limitations. Furthermore, this study presents modified meteorological drought indices that have been adapted to better monitor agricultural droughts. Additionally, the authors used geographic information systems to create a map showing the distribution of drought-related publications globally over the past decade. The findings showed that countries with arid and semi-arid climates are more actively involved in drought research, highlighting their particular interest and concern regarding the subject matter. The implications of this study emphasize the urgent need for immediate and coordinated efforts to address the escalating issue of droughts caused by climate change. By improving monitoring and assessment methods and focusing on tailored strategies in vulnerable regions, it is possible to mitigate the far-reaching consequences of drought and to build more resilient communities and ecosystems.</p>

Recent European drying and its link to prevailing large-scale atmospheric patterns

The extreme 2018 and 2022 droughts pose as recent examples of a series of drought events that have hit Europe over the last decades with wide ranging social, environmental and economic impacts. Although the link between atmospheric circulation and meteorological drought is clear and often highlighted during major drought events, there is a lack of in-depth studies linking historical changes in meteorological drought indices and prevailing large-scale atmospheric patterns in Europe. To meet this shortfall, we investigated the relation between changes in large-scale atmospheric patterns and meteorological drought, as indicated by the geopotential height at 500mb (Z500) and the Standardised Precipitation-Evapotranspiration Index (SPEI), respectively. Calculations were done separately for four climate regions (North, West, Central-East and Mediterranean) over the growing season (March–September). Coherent patterns of significant changes towards higher pressure (increasing Z500) and drier conditions (decreasing SPEI) over 1979–2021 are found over West in spring and Central-East in summer. Z500 and SPEI are strongly linked, reflected by both significant (1979–2021) correlations and high co-occurrences (69-96%) between meteorological drought and high-pressure anomaly occurrences since 1900. North shows the most heterogeneous trend patterns and weakest links, but constitutes a hotspot of significantly increasing Z500 in September. Finally, we performed an ensemble-based, European wide analysis of future Z500, based on CMIP6 low-end (SSP126) and high-end (SSP585) 21st century emission scenarios. According to the projected changes, anomalously high-pressure systems will be the new normal regardless of scenario, and well exceeding the 2018 and 2022 levels in the case of the high-end emission scenario. However, due to the limitations of the model ensemble to represent the spatial heterogeneity in historical Z500 variability and trends (1979–2014), projected changes in large-scale circulation, and associated meteorological droughts, are highly uncertain. This paper provides new insight into significant trends in atmospheric circulation over Europe, their strong links to the observed drying trends, and the inability of a CMIP6 ensemble to reproduce the spatial heterogeneity of the circulation changes.

Defining the Multiscalar Index Timescale—Soil Water Depth Continuum for the Southwestern United States

AbstractThe lack of long‐term reliable in‐situ soil moisture data sets creates unique drought monitoring challenges for the semi‐arid Southwestern United States (“Southwest”). Land managers use multiscalar meteorological drought indices, like the Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI), as proxies for soil water availability; however, objectively identifying the index and timescale that best represents soil water availability in semi‐arid environments remains a significant gap for applying available climate information to land management action. Here, we couple site‐specific soil modeling with high resolution, spatially continuous meteorological data sets to define the relationship between index timescale length and soil water availability of different depths. By creating new matric potential indices (MPI) from 0 to 200 cm, we correlate MPI time series with the SPI and SPEI at different timescale lengths to identify the best index‐timescale combination for each depth. Results indicate the general relationship between highest correlating index‐timescale and MPI‐depth operates roughly on a 1‐month:5 cm step progression at shallow depths (<80 cm). Analysis by soil texture class shows that soils with higher clay content produce shallower sloped relationships (>1‐month:5 cm) than sandy soils (<1‐month:5 cm). Overall, the SPI produced higher correlations and less error with the MPI compared to the SPEI across all texture classes and depths. Thus, the SPI is a good indicator of soil water availability at shallow depths (<80 cm) and should be considered for soil drought monitoring on Southwestern drylands. However, land managers should consult local soils information, if available, given the impacts of texture class on timescale‐depth relationships.

Early crop yield prediction for agricultural drought monitoring using drought indices, remote sensing, and machine learning techniques

Abstract The unpredictability of crop yield due to severe weather events such as drought and extreme heat continues to be a key worry. The present study evaluated six meteorological and three Landsat satellite-based vegetation drought indices from 1986 to 2019 in the drought-prone-semi-arid Saurashtra region of Gujarat (India). Cotton and groundnut crop yield prediction models were developed using multiple linear regression (MLR), artificial neural network with MLP, and random forest (RF). The models performed crop yield estimation at two timescales, i.e., 75 days after sowing and 105 days after sowing. The standardized precipitation evapotranspiration index/reconnaissance drought index among meteorological drought indices, normalized difference vegetation anomaly index/vegetation condition index, and normalized difference water index anomaly were chosen as best highest correlations with crop yields. The RF-based models were found most efficient in predicting the cotton and groundnut yield of Saurashtra with R2 ranging from 0.77 to 0.92, Nash–Sutcliffe efficiency ranging from 71 to 90%, and root-mean-square error ranging from 80 to 133 kg/ha for cotton and 299 to 453 kg/ha for groundnut. This study demonstrated the method for making several decisions based on early crop yield prediction including timely drought mitigation measures.

Spatiotemporal variability of vegetation response to meteorological drought on the Korean Peninsula

Abstract To assess vegetation drought, it is important to understand the relationship between climate and vegetation and to accurately measure the response of vegetation activity to meteorological drought. In this study, we used the vegetation health index (VHI) to investigate the propagation time and time-lag of vegetation response to different meteorological drought indices, including the standardized precipitation index (SPI), evaporative demand drought index (EDDI), standardized precipitation–evapotranspiration index (SPEI), and copula-based joint drought index (CJDI). Using correlation analyses of meteorological drought indices with different time-scales and time-lags and VHIs with different weights, we determined which meteorological drought indices and their corresponding time-scales and time-lags best represent the effects of meteorological drought on vegetation activity on the Korean Peninsula. We also evaluated the relative roles of normalized difference vegetation index (NDVI) and land surface temperature (LST) in quantifying vegetation response to meteorological drought. The meteorological drought index for monitoring vegetation response to meteorological drought on the Korean Peninsula was best applied using EDDI in January–May and SPEI in June–December. Vegetation health was dominated by LST in January–September, with a higher impact of NDVI in November–December. We expect these results to provide useful information for vegetation drought monitoring.

Development of multivariate standardized drought index for large lake basins based on the copula function: A case study of Hongze Lake Basin, China

Study regionThe Hongze Lake Basin, China. Study focusA drought index is an essential tool for monitoring and prediction, and the construction of a comprehensive drought index is at the cutting edge of current drought research. Considering the regulation effect of lakes, a new drought index aimed at large lake catchment areas, namely the Multivariate Standardized Drought Index for Lakes Basin (MSDILB), can comprehensively characterise meteorological and hydrological drought. Here, the Standardised Precipitation Evapotranspiration Index (SPEI), Standardised Runoff Index (SRI), and MSDILB drought indices were used to evaluate the evolution of drought in the study area. Moreover, the response and conversion relationships between the basin’s hydro-meteorological elements and the regulation of the lake were explored. New hydrological insights for the RegionThe results indicate that: 1) in a dry year, the spatial-temporal heterogeneous runoff in the basin is redistributed by the storage and regulation of large lakes, thereby reducing the frequency of drought events; 2) the MSDILB drought index merges the characteristics of hydrological drought and meteorological drought indices and can monitor the evolution of drought events in lake basins; 3) the MSDILB drought index can sensitively capture the effects of lake hydrological regime change on drought events in the basin. Thus, it has the advantage of comprehensively characterising the drought conditions in large lake basins.

Dynamic Characteristics of Meteorological Drought and Its Impact on Vegetation in an Arid and Semi-Arid Region

Under the background of global climate warming, meteorological drought disasters have become increasingly frequent. Different vegetation types exhibit varying responses to drought, thus, exploring the heterogeneity of the impact of meteorological drought on vegetation is particularly important. In this study, we focused on Inner Mongolia (IM) as the research area and employed Standardized Precipitation Evapotranspiration Index (SPEI) and Vegetation Health Index (VHI) as meteorological drought and vegetation indices, respectively. The Breaks for Additive Seasons and Trend algorithm (BFAST) was utilized to reveal the dynamic characteristics of both meteorological drought and vegetation changes. Additionally, the Pixel-Based Trend Identification Method (PTIM) was employed to identify the trends of meteorological drought and vegetation during spring, summer, autumn, and the growing season. Subsequently, we analyzed the correlation between meteorological drought and vegetation growth. Finally, the response of vegetation growth to various climate factors was explored using the standardized multivariate linear regression method. The results indicated that: (1) During the study period, both SPEI and VHI exhibited a type of interrupted decrease. The meteorological drought was aggravated and the vegetation growth was decreased. (2) Deserts and grasslands exhibited higher sensitivity to meteorological drought compared to forests. The strongest correlation between SPEI-3 and VHI was observed in desert and grassland regions. In forest areas, the strongest correlation was found between SPEI-6 and VHI. (3) The r between severity of meteorological drought and status of vegetation growth was 0.898 (p < 0.01). Vegetation exhibits a more pronounced response to short-term meteorological drought events. (4) Evapotranspiration is the primary climatic driving factor in the IM. The findings of this study provide valuable insights for the rational utilization of water resources, the formulation of effective irrigation and replenishment policies, and the mitigation of the adverse impacts of meteorological drought disasters on vegetation growth in the IM.

An Observational, Irrigation-Sensitive Agricultural Drought Record from Weather Data

Abstract The historical rise of irrigation has profoundly mitigated the effect of drought on agriculture in many parts of the United States. While irrigation directly alters soil moisture, meteorological drought indices ignore the effects of irrigation, since they are often based on simple water balance models that neglect the irrigation input. Reanalyses also largely neglect irrigation. Other approaches estimate the evaporative fraction (EF), which is correlated with soil moisture under water-limited conditions typical of droughts, with lower values corresponding to drier soils. However, those approaches require satellite observations of land surface temperature, meaning they cannot be used to study droughts prior to the satellite era. Here, we use a recent theory of land–atmosphere coupling—surface flux equilibrium (SFE) theory—to estimate EF from readily available observations of near-surface air temperature and specific humidity with long historical records. In contrast to EF estimated from a reanalysis that largely neglects irrigation, the SFE-predicted EF is greater at irrigated sites than at nonirrigated sites during droughts, and its historical trends are typically consistent with the spatial distribution of irrigation growth. Two sites at which SFE-predicted EF unexpectedly rises in the absence of changes in irrigation can be explained by increased flooding due to human interventions unrelated to irrigation (river engineering and the expansion of fish hatcheries). This work introduces a new method for quantifying agricultural drought prior to the satellite era. It can be used to provide insight into the role of irrigation in mitigating drought in the United States over the twentieth century. Significance Statement Irrigation grew profoundly in the United States over the twentieth century, increasing the resilience of American agriculture to drought. Yet observational records of agricultural drought, and its response to irrigation, are limited to the satellite era. Here, we show that a common measure of agricultural drought (the evaporative fraction, EF) can be estimated using widespread weather data, extending the agricultural drought record decades further back in time. We show that EF estimated using our approach is both sensitive and specific to the occurrence of irrigation, unlike an alternative derived from a reanalysis.

Projections of drought characteristics based on combined drought index under CMIP6 models

Abstract This study assesses climate change's impact on drought in Iran's Dez Basin. It introduces the Hydro-Meteorological Drought Index (HMDI), integrating the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Runoff Index (SRI). Using Climatic Research Unit Time Series (CRU TS) data (1980-2012) and downscaling forecasted data from three CMIP6 models (2020-2052) for SSP1-2.6 and SSP5-8.5 scenarios, we employ the rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning Hydrological Bureau's Water Balance Model (HBV)-Light model to predict future streamflow. Drought characteristics are analyzed. Under SSP5-8.5, CanEsm5 shows substantial temperature and runoff increases, notably in Bakhtiari and Borujerd sub-basins (63% and 56%). Future droughts are expected to intensify, particularly under SSP5-8.5. The most severe HMDI-derived drought (HMDI 12) in Borujerd station is projected to increase from -43.44 to -44.05. SSP5-8.5 is likelier to cause severe and prolonged HMDI-derived droughts than SSP1-2.6 or the historical period. The analysis suggests that normal drought levels will persist, while mild and severe drought levels will rise in the future.

Identification of dynamic drought propagation from a nonstationary perspective and its application to drought warnings

Accurate drought warnings in changing environments remain challenging since the nonstationary behaviors of drought propagation. The novelty of this study is the presentation of a nonstationary framework for drought propagation analysis and the objective determination of drought propagation thresholds. The calculated propagation probabilities and thresholds are responsive to changing environments. The Generalized Addictive Models in Location, Scale and Shape (GAMLSS) and time-varying Clayton copula function were adopted to establish the nonstationary joint distribution between meteorological and hydrological drought indices. Several indices reflecting the climatic and anthropic impacts on hydrological process were selected as covariates. Combined with the conditional probability distribution function, the time-varying drought propagation probabilities can be estimated to describe the watershed drought tolerance variations. The receiver operator characteristic (ROC) curves were utilized to determine the propagation thresholds for drought warnings. The upper Yellow River Basin (UYRB) was selected as the study area. Results indicate that the nonstationary models show better fitting performance for two drought indices series. The Clayton copula function with dynamic parameters offers a more accurate description of the linkages between two types of drought. The analysis of watershed drought tolerance reveals a declining trend from June to October, contrasting with an increasing trend during other months. In comparison to the stationary approach, the hit rates of the proposed method improve by 0.18, reaching 0.88, while the false alarm rates decrease by 0.11, reaching 0.24. The criteria for propagation threshold determination in the UYRB is established as a conditional probability of 20%. These findings can provide a foundation for drought warning management in a changing environment.

Drought Assessment and Monitoring for Rajshahi District Using Meteorological Drought Indices

An analysis for drought condition has been made in the Rajshahi district of Bangladesh. The assessment of drought for the country has become an urgent need. The agriculture of the country is dependent on the amount of precipitation. As drought and precipitation are inversely related so it is desired to know the condition of Rajshahi. The main characteristics that define drought are the absence of precipitation for a lengthy period and a low distribution of rainfall in contrast to the normal climatic condition. The monthly maximum average temperature data, monthly minimum average temperature data and total monthly precipitation data for the last 48 years (1971-2019) from Bangladesh Meteorological Department (BMD) are utilized for the analysis process. An online software named DrinC is preferred due to its open-source platform and its user-friendly interface. The meteorological data serve as inputs for the software. The Deciles Index (DI), the Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) have been used to identify the drought condition of Rajshahi. The values of the DI and SPI are generated from the precipitation data. However, for the generation of the RDI, the precipitation data as well as the potential evapo-transpiration (PET) data, which is an intermediate data that is generated from the two different types of temperature data via DrinC software are used. The values of SPI, RDI and DI values are presented in a tabular as well as in graphical form. From the comparison of these data and from the study of the recurrence interval the state and the repetition of drought in Rajshahi district can be determined.

Impacts of climate change on drought and its consequences on the agricultural crop under worst-case scenario over the Godavari River Basin, India

Extreme dry and hot conditions lead to intense droughts in central India. However, the precise changes in future drought characteristics and their consequences on crop production have not been fully examined in Godavari River Basin. Therefore, this study focuses on the spatiotemporal modelling of climate change impacts on drought over the past, present, and future periods using long-term high spatial gridded data. The past and present observed datasets were obtained from Indian Meteorological Department, whereas the future datasets were obtained from d4PDF Scenarios. The standardized precipitation evapotranspiration index (SPEI) was applied to analyse drought statistics to identify the worst-case drought scenario (i.e. year most affected by severe-to-extreme drought with high drought severity), which was used further to access precise drought risk. Because SPEI considers the effect of both rainfall and temperature and therefore it is the widely recommended meteorological drought indices and best suited for climate change impact assessment. Drought severity was analysed using a Multi-criteria assessment risk matrix, and the areas under higher agriculture drought risk were mapped. The frequency of droughts with higher intensity and magnitude increased in the present decade compared to the past three decades. 75% area showed higher vulnerability to drought risks, whereas only 25% showed lower risks and less vulnerability to drought occurrences. Results demonstrate that drought severity varies from one sub-basin to another. More drought episodes were predicted for the central belt, particularly over the sub-basins of Wardha, Wainganga, Pranitha, and part of Indravati and Lower Godavari. 11% of the area covering six districts in the GRB was identified as highly vulnerable, and rice production was drastically reduced, accounting for 41.02% of production loss during the worst-case drought event. The frequency of severe-to-extreme droughts is expected to increase under future scenarios; therefore, effective mitigation strategies are recommended to minimise the agriculture drought risks and economic loss.

Standardized Precipitation and Evapotranspiration Index Approach for Drought Assessment in Slovakia—Statistical Evaluation of Different Calculations

In the conditions of rising air temperature and changing precipitation regimes in Central Europe and Slovakia over the last two decades, it is necessary to analyse drought, develop high-quality tools for drought detection, and understand its reactions to the emerging drought situation. One of the frequently used meteorological drought indices is the Standardized Precipitation and Evapotranspiration Index (SPEI). Several parameters can be modified in different steps of the calculation process of SPEI. In the article, we analyse the influence of selected adjustable parameters on the index results. Our research has shown that the choice of a statistical distribution (Log-logistic, Pearson III, or Generalized Extreme Value) for fitting water balance can affect the feasibility of calculating distribution parameters (and thus the index) from the provided input data, as well as lead to either underestimation or overestimation of the index. The normality test of SPEI can be used as a tool for the detection and elimination of highly skewed indices and cases when the indices were not well determined by the distribution function. This study demonstrated improved results when using the GEV distribution, despite the common use of the Log-logistic distribution. With the Pearson III distribution, unusually high or low SPEI values (|SPEI| > 6) were detected.

Comparison of CMIP5 models for drought predictions and trend analysis over Mojo catchment, Awash Basin, Ethiopia

Climate change's effects on water, such as floods, droughts, ocean acidification, and increasing sea levels, are expected to worsen in the future years. In the context of the development the subject of mitigating the anticipated effects of drought has become highly important. Droughts in the African mainland are immediately in danger of hunger, particularly in Ethiopia. The creation of Global Climate Models (GCMs) and analysis of impending catastrophic events are two of the finest ways to handle this issue. This study analyzes the future Meteorological and Hydrological Drought with a comparison of GCM models on the Mojo Catchment using the Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), and Streamflow Drought Index (SDI) methodologies, under three slice periods at near (2006-2030), mid (2031-2055) and far (2056-2080) under the Representative Concentration Pathways (RCPs) including RCP4.5 and RCP8.5 scenario data. Overall Meteorological drought results show that at yearly time scale drought incidence is lower than biannual time scale in both indices. MIROC-MIROC5 and MPI-M-MPI-ESM-LR models are conducted for drought analysis. Under SPI6 (biannual period), 22% of the highest drought observed at far future periods at a scenario of RCP4.5 and for time scale SPI12 the frequency of drought decreases. On the catchment, there is no significant trend increase but both the meteorological and hydrological drought indices show the catchment to be highly susceptible to moderate range of drought and then severely drought takes place. Model MPI-M-MPI-ESM-LR is the most significant contributor to the analysis of the size and frequency of droughts and station Ejere is more venerable to drought as compared with other stations under the MPI-M-MPI-ESM-LR model. The occurrence of SDI12 drought on the MPI-M-MPI-ESM-LR model has a direct relation with the anomaly's climate variables (temperature and Precipitation). the maximum driest drought variability occurred at the same GCM model (anomalies up to -3.7mm) in the year 2041. Additionally, the frequency occurrence of drought at the annual timescale (SDI12) under RCP8.5 at the far future period of the catchment is suspected to 22% probability of drought occurrence. Overall, the Mojo catchment is moderately vulnerable to drought, and SPI-6 is more suspected to high frequency on drought under RCP4.5 especially near and mid periods. It is crucial for an organization, individuals, academics, water resource professionals, and disaster risk management in the catchment on alerting and planning water projects.

Trend Analysis of Rainfall and Meteorological Drought Indices over India During 1958–2017

Trend Analysis of Rainfall and Meteorological Drought Indices over India During 1958–2017

Dry and Wet Period Analysis using Meteorological Drought Indices in Sabarkantha district Gujarat, India

Drought assessment is very important to manage water resources in lean period. In the present study, identification of drought years and extent of deficit of annual rainfall is accomplished by use of three meteorological based drought indices like the Standardized Precipitation Evapotranspiration index (SPEI), China-Z index (CZI) and Modified China-Z index (MCZI) on 1, 3, 6, 9 and 12 month (short term and long term) timescales using monthly precipitation and temperature data from 1901 to 2002 at Sabarkantha to specify the drought conditions in present study area. The analysis of multiple time steps in drought indices make it harder to decide the best time step to show the drought conditions. The months of January, March, November and December have been identified 76, 79, 77 and 80 times as drought months respectively in the 20th century, indicating that these months must be provided with assured Irrigation. It is observed that 1904, 1911, 1915, 1923, 1974, 1987 and 2002 affected by severe drought condition in SPEI-12. The China Z Index and Modified China Z index shows that’s 1904, 1911, 1915, 1918, 1923, 1948, 1951, 1965, 1968, 1969, 1972, 1974, 1985, 1987, 1991, 1995 and 2002 affected by extreme drought. The study also reveals that only 1% year obtain as extreme wet conditions. From result it shows that 23% years are categorised into moderate to extreme drought years, which includes 15% Moderate dry, 8% severe dry years.

Spatiotemporal Evolution and Nowcasting of the 2022 Yangtze River Mega-Flash Drought

Flash droughts challenge early warnings due to their rapid onset, which requires a proper drought index and skillful nowcasting system. A few studies have assessed the nowcast skill for flash droughts using a one-dimensional index, but whether the models can capture their spatiotemporal evolution remains unclear. In this study, a three-dimensional meteorological flash drought index based on the percentile of 15-day moving average precipitation minus evapotranspiration (P-ET) is developed. The index is then used to investigate the spatiotemporal evolution of a mega-flash drought that occurred in the Yangtze River basin during the summer of 2022. The results show that the mega-flash drought started at the beginning of July in the upper reaches of the river and expanded to the middle and lower reaches at the beginning of August due to the spread of the high-pressure system. The evolution is well captured by the proposed three-dimensional index. The spatial correlations between the China Meteorological Administration global medium-range ensemble forecast system (CMA-GFS)’s nowcast and reanalysis ranged from 0.58 to 0.85, and the hit rate and equitable threat score are 0.54 and 0.26, respectively. This study shows that the CMA-GFS nowcast of the P-ET index roughly captured the drought’s evolution, which can be used for flash drought early warnings and water resource management.

Exploring dynamic response of agrometeorological droughts towards winter wheat yield loss risk using machine learning approach at a regional scale in Pakistan

ContextCrop yield is a major agriculture sector affected by climate change; especially agrometeorological droughts experienced by south Asian countries in past decades. Research objectiveThe main goals of this research were to explore the spatiotemporal characteristics of seven agrometeorological drought indices at a regional scale in Pakistan. Secondly, to forecast the wheat yield loss risk (YLR) due to droughts under current and future climate scenarios by employing three machine learning (ML) methods; random forest (RF), gradient boosting machine (GBM), and generalized additive model (GAM). MethodThe relationship between detrended wheat yield and a combination of five remote sensing indices Normalized Difference Water Index (NDWI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), and Drought Severity Index (DSI)), and two meteorological drought indices (Palmer’s Drought Severity Index (PDSI), and Standardized Precipitation Evapotranspiration Index (SPEI)) was analyzed. Mann-Kendall trend (MK), Sens’s slope, and Sequential Mann-Kendall (SQMK) tests are applied to explore the trend and trend-changing years for all indices over the historical time of 20 years.The YLR and all indices were projected (2021–2050) from the baseline period (2001–2020) using PROPHET time series forecasting and CMIP6 climatic models. YLR was forecasted on present and future projected time series by employing three non-linear ML regression models. ResultsThe output of the drought analysis revealed that the study area was hit by high to severe drought events in 2001–2004, 2006, 2008, 2010, 2012, and 2017. Trend analysis revealed intersection years breaking the rising trend of drought indices. All drought indices are significantly correlated with meteorological wheat yield with a sequence of NDWI>DSI>VCI>VHI>PDSI>SPEI>TCI. Future projections under high emission scenarios revealed a rise in YLR associated with frequent projected droughts from VHI, DSI, SPEI, and PDSI. YLR forecasting from agrometeorological indices is best predicted by random forest with the lowest RMSE = 0.005314. NDWI (26%) and VCI (19%) are found to be significant relative predictors associated with 51% high YLR in the baseline period and SPEI (20%) and NDWI (17%) as the most important relative predictors associated with 39% high YLR in future. ConclusionThe region is vulnerable to agrometeorological droughts with more susceptibility to less rain and high temperature affecting crop health and a high risk of yield loss in the future. ImplicationThe study provides a direction to stakeholders and policymakers to develop and adapt better strategies to mitigate and prevent drought-related yield loss risk in the future.

Modelling causality between agricultural and meteorological drought indices in the Corong River basin, East Java Indonesia

Modelling causality between agricultural and meteorological drought indices in the Corong River basin, East Java Indonesia