Drought Early Warning Research Articles

A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1

Abstract. Efficient methods for predicting weather-related hazards are crucial for the effective management of environmental risk. Many environmental hazards depend on the evolution of meteorological conditions over protracted periods, requiring assessments that account for evolving conditions. The TAMSAT-ALERT approach addresses this challenge by combining observational monitoring with a weighted multi-year ensemble. In this way, it enhances the utility of existing systems by enabling users to combine multiple streams of monitoring and meteorological forecasting data into holistic hazard assessments. TAMSAT-ALERT forecasts are now used in a number of regions in the Global South for soil moisture forecasting, drought early warning and agricultural decision support. The model presented here, General TAMSAT-ALERT, represents a significant scientific and functional advance on previous implementations. Notably, General TAMSAT-ALERT is applicable to any variable for which time series data are available. In addition, functionality has been introduced to account for climatological non-stationarity (for example due to climate change), large-scale modes of variability (for example El Niño) and persistence (for example of land-surface conditions). In this paper, we present a full description of the model, along with case studies of its application to the prediction of central England temperature, Pakistan vegetation conditions and African precipitation.

Agricultural drought monitoring and early warning at the regional scale using a remote sensing-based combined index

Early detection of agricultural drought can alert farmers and authorities, enhancing the resilience of the food sector. A framework is proposed for developing a novel regional agricultural drought index (RegCDI) by combining remotely sensed vegetation health, soil moisture and crop water stress via a transparent Shannon’s entropy weighting method. The framework consists of the selection of suitable datasets based on their regional performance, the aggregation of selected drought indicators, the validation of the combined index against crop yield, and the testing of predictive capabilities. The creation and performance of RegCDI are demonstrated for the drought prone Indian state of Odisha. MODIS surface reflectance is selected for crop water stress and GLDAS-2 for assessing soil moisture deficits and vegetation health. Three selected indicators (SMCI, TCI, and SIWSI-1) are combined into RegCDI for Odisha. The performance of RegCDI is evaluated (a) against other popular drought indices and (b) by comparing with seasonal crop yields. RegCDI is used to identify drought hotspots based on drought severity, duration, and propensity over the study area. A reforecast evaluation of RegCDI (up to three months ahead) showed that the indicators based on soil moisture deficit and crop water stress could predict drought conditions up to two months ahead with no less than 80% accuracy. This demonstrated the potential of the RegCDI framework and its component indicators for early warning of drought in Odisha.

Regional agricultural drought vulnerability prediction based on interpretable Random Forest.

Drought is one of the leading natural disasters in the world. It severely restricts the normal functioning of societies and economies. The purpose of this paper is to predict the drought vulnerability index in Henan Province so that timely measures can be taken to mitigate the impact of drought and to ensure the continued stability of people's lives and social development. First, the drought vulnerability index of Henan Province for 2010-2022 was calculated based on relevant literature and the regional situation of Henan Province. In addition, the main factors of sensibility and resilience are selected, the prediction indicator system is established, and the grey correlation analysis model is applied to select the top 10 main factors of drought vulnerability. Second, the Random Forest model was trained and simulated, and the drought vulnerability index of Henan Province was predicted for 2023-2025. Finally, the SHapley Additive exPlanations model was used to explain the specific effects of the influences of the Random Forest model. The interpretable Random Forest model not only accurately predicts the drought vulnerability index in Henan Province but also alleviates concerns about the "black box" problem of indirect interpretation of the Random Forest model. The results of the study show that the Average Percentage Error of the drought vulnerability index in Henan Province from 2010 to 2022 simulated by the Random Forest model is as low as 3.0457%, and the simulation accuracy is as high as 96.9643%. Drought vulnerability prediction for Henan Province was also made for the period 2023-2025, showing an increasing trend. Relevant sectors should enhance awareness and early warning of drought, improve resilience, and take effective measures to reduce the socio-economic impact of drought.

The Propagation Characteristics of Meteorological Drought to Vegetation Drought Based on Three-Dimensional Clustering Algorithm in China

The spatiotemporal continuity characteristics of drought are the basis for analyzing its spatial migration and evolution, which is significant for mitigation and early warning of drought. The aim of this paper is to identify meteorological and vegetation drought events in China from 1982 to 2022, reveal the dynamic changes of typical drought events, and elucidate the propagation characteristics of meteorological and vegetation drought. The results showed that (1) based on a three-dimensional spatiotemporal clustering algorithm, China experienced 138 meteorological drought events and 76 vegetation drought events; (2) the severity of the meteorological drought event No. M138 (2022.03–2022.11) reached 667.58 × 104 km2·month, and the severity of the vegetation drought event No. V68 (2019.06–2020.04) reached 572.89 × 104 km2·month; (3) a total of 40 meteorological-vegetation drought event pairs had been identified, which was divided into three main types: “single”, “simple”, and “complexity”; and (4) in the typical drought event pair No. P-34, the area difference was 16.45 × 104 km2, and the severity difference was 3.89 × 104 km2. The research results can provide a new perspective for identifying the dynamic changes and propagation characteristics of drought events from a three-dimensional perspective, which is of great significance for predicting vegetation drought and protecting the ecological environment.

Quantifying the effects of direct human activities and climate change on the spatial propagation of hydrological drought in the Yellow River Basin, China

The spatio-temporal evolution and propagation of flood and drought events are important in predicting their occurrences. This study attempted to investigate into the spatial propagation characteristics of hydrological droughts and their dynamics by identifying the spatial propagation probability (PP) and propagation time (PT) of different drought levels in the Yellow River Basin (YRB), China, based on copula functions and conditional probability. Then, the effects of direct human activities and climate change on the hydrological drought propagation were quantified by comparing the propagation characteristics of hydrological drought under observed and natural runoff. Results indicated that: (1) overall, the propagation of hydrological drought in the YRB showed a generally decreasing trend in PP and an increasing trend in PT with the increase of distance, presenting a gradually attenuating propagation along the river channel, the spatial propagation process of hydrological drought is similar to the domino effect; (2) when extreme hydrological drought occurred in the area represented by the Tangnaihai station located in the upstream basin, the probability of downstream areas experiencing moderate to severe hydrological drought showed an approximately decreasing trend along the river channel, with the PP of the observed sequence being slightly less than the natural sequence and the fluctuation of the hydrological drought propagation being more apparent; (3) taking 1986 as the dividing year, the average relative contribution rates of direct human activities to the changes in drought PT in spring, summer, autumn, and winter were 39.7%, 62.9%, 55.3%, and −67.2%, respectively, indicating a significant regulatory effect of reservoirs on hydrological drought propagation. In general, this study provided new insights into the spatial propagation mechanism of hydrological drought and offered a promising pathway for drought early warning and mitigation.

Prediction of DEDI index for meteorological drought with the VMD-CBiLSTM hybrid model

Drought can cause great harm and loss, so accurate and efficient drought prediction has certain research significance. In previous studies, a single machine learning model is often used to predict the factors related to drought, such as precipitation and temperature, to indirectly explain the influence degree of drought, but the overall prediction accuracy is low, which can not fully and effectively predict the nonlinear and non-stationary drought characteristic information. In this paper, the variational modal decomposition model (VMD) is used to decompose the meteorological drought time series, and the convolution neural network (CNN) and the improved bidirectional long short-term memory neural network (BiLSTM) are combined to construct the meteorological drought hybrid prediction model (VMD-CBiLSTM). The research results show that using VMD-CBiLSTM model to forecast the monthly daily evapotranspiration deficit index (DEDI) of three weather stations, compared with the results predicted by VMD-LSTM, VMD-BiLSTM and VMD-ARIMA models, the average prediction accuracy R2 is increased by 0.076, 0.034 and 0.328 respectively, and the average RMSE is decreased by 0.178, 0.094 and 1.373 respectively. Compared with single model, VMD-CBiLSTM can not only reduce the uncertainty of meteorological drought prediction caused by climate model heterogeneity, but also improve the accuracy of drought prediction in arid and semi-arid regions, which can provide reference for coping with drought occurrence and drought early warning in advance.

Impacts of hydrometeorological regime shifts on drought Propagation: The meteorological to hydrological perspective

Exploring drought propagation relationships and thresholds is crucial for drought control and mitigation. However, previous studies have overlooked the impacts of regime shifts in trends of hydrometeorological variables over time on drought propagation and its thresholds. This study proposes an integrated framework focusing on drought propagation from meteorological to hydrological drought. The regime shift periods, both increasing and decreasing trends, were identified using the cumulative difference curve-rank test on precipitation and streamflow at an annual time scale. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were used as meteorological and hydrological drought indices, respectively. Additionally, the propagation relationships and thresholds from meteorological drought to hydrological drought were identified and compared across various regime shift periods. The integrated framework’s applicability was verified by applying it to the Three-Rivers Headwaters region of China, which encompasses the Yellow River, Yangtze River, and Lancang River. This region is strategically important for China and has relatively little human activity. The framework revealed the variability in drought propagation and thresholds during different regime shift periods. The response sensitivity of SSI to SPI and the probabilities and thresholds for propagation from meteorological to hydrological drought differed between periods of increasing and decreasing trends in hydrometeorological variables. When regime shift characteristics were considered, the thresholds for meteorological drought propagating to hydrological drought were 2.81 %–16.90 % higher. These results indicated that, when examining drought propagation relationships and thresholds, accounting for regime shifts in hydrometeorological variables can improve early drought warning and enhance drought control and mitigation efforts.

Design of drought early warning system based on standardized precipitation index prediction using hybrid ARIMA-MLP in Banten province

Drought Early Warning System (DEWS) is an effort to disseminate early warning information based on climate and hydrology aspects. The DEWS design uses ARIMA, MLP, and hybrid ARIMA-MLP models to predict drought based on SPI for 1, 3, and 6 months. Predictions were made using ERA5 monthly rainfall data from 1981-2022 corrected based on observation data on 9 grids of observation rain gauges in Banten Province. The design of the ARIMA model is determined by selecting the combination of p and q parameters with the lowest AIC value, while the MLP architecture is determined by referring to the study literature and by trial and error testing. ARIMA models and hybrid models are not able to follow actual data fluctuations and have high error values in both SPI1, SPI3, and SPI6, so they are not recommended in this study. The MLP model has the best prediction ability, namely in SPI6 prediction with NSE value reaching >0.5 and RMSE value.

Antecedent Conditions Mitigate Carbon Loss During Flash Drought Events

AbstractFlash droughts– the rapid drying of land and intensification of drought conditions—have devasting impacts to natural resources, food supplies, and the economy. Less is currently known about the drivers of flash droughts and their impact on landscape carbon losses. We leverage carbon and water cycle data from NASA OCO‐2 and Soil Moisture Active and Passive missions to quantify flash drought impacts on U.S. carbon exchange. On average, pre‐onset carbon uptake fully offsets post‐onset losses, creating a carbon neutral biosphere over a ±3 month period surrounding flash drought onset. This contrasts with ecosystem models, which underestimate pre‐onset uptake and overestimate post‐onset loss. Furthermore, spaceborne observations of solar induced fluorescence (SIF) provide a reliable indicator of flash droughts at lead times of 2–3 months, due to feedbacks between vegetation growth and soil water loss. This study is expected to improve understanding of flash drought impacts on carbon exchange, and facilitate flash drought early warning.

Hydrological drought forecasts using precipitation data depend on catchment properties and human activities

Hydrological Drought Early Warning Systems play a crucial role in effective drought planning and management, as the impacts of drought are more closely associated with hydrological droughts than meteorological ones. However, current systems primarily focus on meteorological drought forecasts due to the limited access to hydrological data. Here we assess the feasibility of forecasting drought in streamflow and groundwater by solely using precipitation data. The results demonstrate that meteorological drought forecasts derived from the Standardized Precipitation Index with 6-month accumulation periods and various lag times hold the potential to predict streamflow and groundwater droughts. This study also highlights the importance of catchment properties in hydrological drought predictions. Our findings present an opportunity for developing hydrological drought early warning system globally to reach the goal of the Sendai framework for disaster risk reduction by 2030 and support the initiative of early warnings for all.

Characterizing the drought events in Yangtze River basin via the insight view of its sub-basins water storage variations

Yangtze River Basin (YRB) is the most important strategic base of water resources in China, which has the characteristics of complex terrain and unbalanced water distribution in spatial and temporal domain. Drought events have occurred frequently in this basin due to the influence of global climate change and human activities. In this context, it is critical to formulate drought policies based on the characteristics of drought events in different sub-basins. Based on the latest HUST-Grace2020 model released by Huazhong University of Science and Technology, we calculate the terrestrial water storage anomaly (TWSAs) in YRB during January 2003 to July 2016, and combine water storage deficit index (WSDI) with a modified run theory to extract precisely drought events in terms of sub-basins. On this basis, the characteristics of drought events are identified in different sub-basins and different seasons. Subsequently, the links between drought events and topography as well as climate factors are explored in the YRB. The results indicate that: (1) Soil moisture storage anomaly (SMSA) is the uppermost component (41%) of TWSA but the largest contributor (59%) to TWSA trend is groundwater storage anomaly (GWSA). Precipitation is the main factor affecting the composition and trend of TWSC. (2) Eight drought events (one severe level, four moderate level and three mild level) are extracted across YRB and the most severe event can be monitored in each sub-basin which begins from May 2006 to August 2007. (3) The frequency of drought events is higher in the lower region of YRB, but serious drought events are more likely to occur in the source region of YRB. (4) Droughts are more frequent in summer and winter, while severe and extreme situations mainly occur in autumn. (5) Drought severity in the YRB is positively correlated with topography, and drought events are influenced by El Niño, especially in the downstream YRB. This study sheds a theoretical basis for drought early warning of different levels in YRB across the various sub-basins.

Climate Changes and Distribution of Vector Born Communicable Disease in the Eastern Region, Afghanistan.

Vector-borne diseases are parasitic, viral, and bacterial diseases continue to contribute significantly to the global burden of disease, and cause epidemics that interrupt health services and cause wider socioeconomic impacts around the world. Aims of this study to describe and measure the epidemiological burden and sessional variation of current Vector- Borne Diseases (VBDs) in the eastern regions of Afghanistan. This was a Retrospective descriptive study in the eastern region of Afghanistan. Data obtained from (Malaria and Leishmaniasis Information System ( MLIS) and Drought Early Warning Decision Support ( DEWS) reports from January 2019 to Dec 2023 of Ministry of Public Health and includes from more than 260 health facilities covering particularly all parts of the east provinces’ territory. In the result identified four known VBDs in the east four provinces, overall incidences of malaria were 20.28%, Leishmania 1.62%, Dengue 0.054% and Crimean- Congo hemorrhagic Fever (CCHF) 0.0063% with low prevalence’s. Province base Laghman had high incidence (27.82%) then Konar (21.36%) and Nooristan (20.28%) but Nangarhar was relatively in lower incidence (15.8%). Dengue fever firstly coming VBD which two cases were diagnosed in December 2022 and up to 30 December 2023 diagnosed 1526 cases all over district of Nangarhar province. In conclusion, four VBDs incidences have been identified in the eastern region of Afghanistan, and their cases has been clearly increasing in recent years. Their incidences mostly occurring in summer and autumn sessions, Dengue is a newly coming VBD to the Nangarhar province, its spreading is fast, therefore, will distribute more parts of the country rapidly.

An Analysis of the Lagged Relationship between Anomalies of Precipitation and Soil Moisture and Its Potential Role in Agricultural Drought Early Warning

Abstract The standardized precipitation index (SPI) is the most commonly used index for detecting and characterizing meteorological droughts, and it is also extensively used as a proxy variable for soil moisture anomalies (SMA) for the purpose of monitoring agricultural drought in absence of long-term soil moisture observations. However, the potential capability of SPI to warn of the time-lagged soil water deficit—following the well-known “drought cascade” effect—is often overlooked in agricultural drought studies. In this research, a time-lagged correlation analysis is used to evaluate the relationship between the SMA dataset, generated as part of the Global Drought Observatory of the European Union’s Copernicus Emergency Management Service, and a set of SPIs derived from the ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts. The possibility to achieve an optimal agreement between SPI and SMA that also preserves the early warning skills of SPI is evaluated. The results suggest that if only the correlation between SPI and SMA is considered, the maximum agreement is usually obtained with a zero lead time (almost 80% of the cases), with SPI-3 representing the best option in about 40% of the grid cells at global scale. By also accounting for the benefits of a positive lead time, short accumulation periods tend to be favored, with SPI-1 being the optimal choice in about one-half of the cases, and 10–20 days of lead time in more than 90% of the grid cells is achieved without any significant reduction in either correlation or skill in drought extreme detection.

GRACE-based groundwater drought in the Indochina Peninsula during 1979–2020: Changing properties and possible teleconnection mechanisms

Groundwater is very important for human productivity and daily life, hydrological cycle regulation, and ecosystem stability. However, due to the complex mechanisms of groundwater drought, the spatial and temporal variations of groundwater drought and its driving mechanisms are still not fully understood, especially in Indochina Peninsula. In this work, we used a reconstructed long-term terrestrial water storage dataset from the Gravity Recovery and Climate Experiment (GRACE) emission and a GRACE-based groundwater drought index to investigate the spatial and temporal characteristics of groundwater drought during 1979–2020 in the Indochina Peninsula. The possible teleconnection mechanisms between groundwater drought and the Indian Ocean Dipole (IOD), El Niño-Southern Oscillation (ENSO), and El Niño Modoki (ENSO_M) were also investigated using cross wavelet transform method. The results show that groundwater drought worsens significantly during 1979–2020, and becomes much more frequent and intensified after 2000 in the southern Indochina Peninsula. Both univariate and bivariate (logic ‘or’ and ‘and’) return periods for duration, severity, and peak of groundwater drought are short in the southern Indochina Peninsula, and thus the risk of groundwater drought is high. The IOD, ENSO, and ENSO_M can reduce the intensity of groundwater drought to a certain extent during the warm phases, but only ENSO_M tends to significantly exacerbate the intensity of groundwater drought during the cold phases in the southern Indochina Peninsula. The variations in groundwater drought are dominated by ENSO_M, and are also coupled influenced by the IOD and ENSO in the southern Indochina Peninsula. The results provide valuable information for the sustainable ecological environment and socioeconomic development, especially development of groundwater drought early warning and prediction models in the Indochina Peninsula.

Pairing monitoring datasets with probabilistic forecasts to provide early warning of drought in Australia

Droughts are a cyclical feature of Australia's climate that have compounding impacts on agricultural productivity and the wellbeing of drought-affected communities. Understanding anticipated drought conditions in context of antecedent observations is critical to providing early warning of drought. In this study we paired probabilistic seasonal rainfall forecasts with precipitation, soil moisture and evapotranspiration data that were objectively combined using Principal Component Analysis (PCA). The final Drought Early Warning System (DEWS) maps overlay forecasting information with the multi-variate PCA-weighted maps at 1-, 3- and 6-month timescales over the common period of overlap between all datasets (1982–2018). In this study period, the 1982–1983 Ash Wednesday tinder drought and the 1997–2001 Millennium drought were investigated. We validated PCA-weighted maps with satellite vegetation data and found performance was strongest over the Murray Darling Basin region (R = 0.63, p = 0.009) and poorest over Central interior Australia (insignificant correlations). We also validated PCA-weighted maps using agricultural commodity data from ABARES. Significant negative correlations at 95 %, 99 % and 99.9 % confidence intervals were found between %-Area in drought category and crop cultivation area; export volume/value; crop yield; and rural debt. Our findings indicate that early warning of drought can be categorised by concern – wherein dry antecedent conditions and dry forecasted conditions are of the highest concern. The developed proof-of-concept DEWS contributes to the growing body of proactive drought management research. In a drought vulnerable future, operationalising and communicating drought early warnings will be critical to reducing the harmful impacts of drought on communities, economies, and environments.

Proactive policy options for drought resilience in the Sahel region

In the Sahel region, drought is of serious concern due to its consequences on the livelihoods of the populations. In this paper, opportunities for proactive drought policies are analyzed with qualitative techniques using a series of semi-structured qualitative interviews with key experts from the public, private, and other relevant societal sectors. The results show that adaptation options such as sustainable agricultural practices, migration, diversification of income sources, and social networks are most frequently used as a drought response by rural households in the region. To build resilience to drought in the Sahel, there is a need to disseminate climate-friendly agricultural practices accompanied by strong monitoring and evaluation system. It is also important to provide information on early drought warning and preparedness and mobilize more financial resources in implementing policies for building drought resilience. Finally, a drought policy should be participatory and results-based, and be based on long-term forecasting to ensure sustainability and resilience.

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.

Precipitation and vegetation transpiration variations dominate the dynamics of agricultural drought characteristics in China

Agricultural drought posing a significant threat to agricultural production is subject to the complex influence of ocean, terrestrial and meteorological multi-factors. Nevertheless, which factor dominating the dynamics of agricultural drought characteristics and their dynamic impact remain equivocal. To address this knowledge gap, we used ERA5 soil moisture to calculate the standardized soil moisture index (SSI) to characterize agricultural drought. The extreme gradient boosting model was then adopted to fully examine the influence of ocean, terrestrial and meteorological multi-factors on agricultural drought characteristics and their dynamics in China. Meanwhile, the Shapley additive explanation values were introduced to quantify the contribution of multiple drivers to drought characteristics. Our analysis reveals that the drought frequency, severity and duration in China ranged from 5–70, 2.15–35.02 and 1.76–31.20, respectively. Drought duration is increasing and drought intensity is intensifying in southeast, north and northwest China. In addition, potential evapotranspiration is the most significant driver of drought characteristics at the basin scale. Regarding the dynamic evolution of drought characteristics, the percentages of raster points for drought duration and severity with evapotranspiration as the dominant factor are 30.7 % and 32.7 %, and the percentages with precipitation are 35.3 % and 35.0 %, respectively. Precipitation in northern regions has a positive effect on decreasing drought characteristics, while in southern regions, evapotranspiration dominates the dynamics in drought characteristics due to increasing vegetation transpiration. Moreover, the drought severity is exacerbated by the Atlantic Multidecadal Oscillation in the Yangtze and Pearl River basins, while the contribution of the North Atlantic Oscillation to the drought duration evolution is increasing in the Yangtze River basin. Generally, this study sheds new insights into agricultural drought evolution and driving mechanism, which are beneficial for agricultural drought early warning and mitigation.

A novel comprehensive agricultural drought index accounting for precipitation, evapotranspiration, and soil moisture

Univariate drought indices do not reflect the integrated process through which droughts develop and occur. Therefore, developing a multivariate drought index that accounts for precipitation, evapotranspiration, and soil moisture is crucial for comprehensive drought prediction and evaluation. In this study, a comprehensive agricultural drought index (CADI) was developed using a combination of five commonly used binary copulas by considering evapotranspiration factors based on the Multivariate Standardized Drought Index (MSDI), which is based on both precipitation and soil moisture, and the drought monitoring capability of CADI was evaluated. The relationship between the CADI and the yield anomalies of winter wheat and summer maize in the rainfed and irrigated croplands of the North China Plain (NCP) was analyzed, and the drought evolution characteristics and trends in rainfed and irrigated croplands were examined. The results indicated that compared with the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil Moisture Index (SSI), and MSDI, the proposed CADI accurately reflected the integrated process of meteorological and agricultural drought occurrence and evolution. In addition, the strength of the correlation between the CADI and the yield anomalies of winter wheat and summer maize in the rainfed and irrigated croplands of the NCP surpassed that of the SPEI, SSI, and MSDI, thereby confirming the validity and reliability of the proposed CADI for drought monitoring. Over the past four decades, droughts in the rainfed and irrigated croplands of the NCP have demonstrated a decreasing trend, and severe droughts have been concentrated in winter. Overall, the results of this study can be used in agrometeorological drought early warning and agricultural impact assessment and can provide theoretical guidance for drought monitoring in other regions.

Drought impact prediction across time and space: limits and potentials of text reports

Drought impact prediction can improve early warning and thus preparedness for droughts. Across Europe drought has and will continue to affect environment, society and economy with increasingly costly damages. Impact models are challenged by a lack of data, wherefore reported impacts archived in established inventories may serve as proxy for missing quantitative data. This study develops drought impact models based on the Alpine Drought Impact report Inventory (EDIIALPS) to evaluate the potential to predict impact occurrences. As predictors, the models use drought indices from the Alpine Drought Observatory and geographic variables to account for spatial variation in this mountainous study region. We implemented regression and random forest (RF) models and tested their potential (1) to predict impact occurrence in other regions, e.g. regions without data, and (2) to forecast impacts, e.g. for drought events near real-time. Both models show skill in predicting impacts for regions similar to training data and for time periods that have been extremely dry. Logistic regression outperforms RF models when predicting to very different conditions. Impacts are predicted best in summer and autumn, both also characterised by most reported impacts and therefore highlighting the relevance to accurately predict impacts during these seasons in order to improve preparedness. The model experiments presented reveal how impact-based drought prediction can be approached and complement index-based early warning of drought.