Drought Statistics Research Articles

Probabilistic analysis of drought impact on wheat yield and climate change implications

Drought is projected to intensify under warming climate and will continuously threaten global food security. Assessing the risk of yield loss due to drought is key to developing effective agronomic options for farmers and policymakers. However, little has been known about determining the likelihood of reduced crop yield under different drought conditions and defining thresholds that trigger yield loss at the regional scale in Australia. Here, we estimated the dependence of yield variation on drought conditions and identified drought thresholds for 12 Australia's key wheat producing regions with historical yield data by developing bivariate models based on copula functions. These identified drought thresholds were used to investigate drought statistics under climate change with an ensemble of 36 climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that drought-induced yield loss was region-specific. The drought thresholds leading to the same magnitude of wheat yield reduction were smaller in regions of southern Queensland and larger in Western Australia mainly due to different climate and soil conditions. Drought will be more frequent and affect larger areas under future warming climates. Based on our results, we advocate for more effective crop management options, particularly in regions where wheat yield is vulnerable to drought in Australia. This will mitigate potential drought impacts on crop production and safeguard global food security.

Spatial and temporal patterns of agricultural drought in China during 1960–2020 characterized by use of the crop water deficit Abnormal Index

Agricultural drought intensifies crop water stress and limits photosynthetic productivity, thereby threatening food security and socio-economic development. However, current drought indices from model simulations or remote sensing have limitations with regard to fully considering the complex interactions between various climate factors and crop water demands. Furthermore, estimates from remote sensing-based drought indices are subject to significant uncertainties due to the coarse spatiotemporal resolution of the data sources, inherent systematic errors in methodologies, and the limited number of ground observations. This study aimed to bridge these gaps by employing the nationwide Agricultural Drought Monitoring Network covering over 2000 in-situ observations and the Crop Water Deficit Abnormal Index (CWDIa) to quantify the spatiotemporal characteristics of agricultural drought from 1960 to 2020. The duration and severity of drought in northern China were significantly greater than in the southern regions, especially in the North China Plain, and the northern Tibetan Plateau. The occurrence of severe drought in southwestern China and in the southern Tibetan Plateau during the spring and winter seasons was driven by persistent water deficits. Over the past 60 years, China has experienced an overall alleviation in the severity and duration of agricultural drought. However, the drought situation has continued to worsen in certain regions, such as in southwestern China and the southern North China Plain. Multiscale decomposition of the ensemble empirical mode has further strengthened this finding (i.e., the drying trend in southwestern China since 1990). As seen in authoritative drought statistics, the integration of CWDIa and dense observations in the drought monitoring framework have demonstrated robust efficacy in capturing over 80 % of the actual drought-covered and drought-affected area. These findings highlight the importance of incorporating climate and crop water demand in agricultural drought monitoring to provide reliable and long-term spatiotemporal estimates of drought characteristics based on a nationwide observation network.

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.

A generalized framework for drought monitoring across Central European grassland gradients with Sentinel-2 time series

Fractional cover time series of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil from remote sensing provide essential detail to understand how grasslands are affected by recent and future drought periods in the 21st century. In this regard, Sentinel-2A/B offer frequent large-area observations, which have not yet been fully exploited for a spatially continuous drought monitoring of highly dynamic Central European grasslands. In this study, we developed a generalized drought monitoring framework for Central European grasslands linking Sentinel-2 data, field survey information, and spectral unmixing. We first implemented a consistent and repeatable strategy to obtain a grassland spectral library supported by the Europe-wide Land Use/Cover Area frame statistical Survey (LUCAS) and multitemporal Sentinel-2 data. Our library captured the spectral variability of PV, NPV, and soil cover from 12 grassland areas distributed along typical environmental and land use gradients of Central Europe. We trained a generalized regression-based unmixing model with synthetic data generated from the spectral library and compared fractional cover estimates to a multitemporal reference dataset. PV, NPV, and soil were estimated with good accuracy, achieving MAEs of 6.54%, 13.7%, and 12.2%, respectively. Local unmixing models trained on area-specific library subsets were overall outperformed by the generalized model highlighting the value of a comprehensive grassland library for generalized spectral unmixing. Based on fractional cover time series from 2017 to 2021, we calculated time series of the grassland-specific Normalized Difference Fraction Index (NDFI) capturing proportions of NPV and soil relative to PV. Comparison of annual growing season drought metrics derived from the NDFI to annual meteorological drought statistics from the Standardized Precipitation Evapotranspiration Index (SPEI) as well as the Soil Moisture Index (SMI) revealed widespread drought impacts on grasslands during the persistent drought period in Central Europe from 2018 to 2020. While impacts on grasslands overall closely followed meteorological and soil drought conditions, regionally varying drought metrics underline that local to regional environmental and hydrological conditions shaped the drought response of Central European grasslands. Our study emphasizes the value of combining Sentinel-2 data, field survey information, and spectral unmixing to enable drought monitoring across grassland gradients of Central Europe with Sentinel-2 time series.

An approach to identify the best climate models for the assessment of climate change impacts on meteorological and hydrological droughts

Abstract. This paper describes the benefits of using more reliable local climate scenarios to analyse hydrological responses. It assumes that Regional Climate Model (RCM) simulations are more reliable when they provide better approximations to the historical basic and drought statistics after applying a bias correction to them. We have investigated whether the best solutions in terms of their approximation to the local meteorology may also provide the best hydrological assessments. We have carried out a classification of the corrected RCM simulations used for both approximations. This has been applied in the Cenajo basin (south-eastern Spain), where we show that the best approximations of the historical meteorological statistics also provide the best approximations for the hydrological statistics. The selected RCMs were used to generate future (2071–2100) local scenarios under the RCP8.5 emission scenario. The two selected RCMs predict significant changes in mean precipitation (−31.6 % and −44.0 %) and mean temperature (+26.0 % and +32.2 %). They also predict higher frequency (from 5 events in the historical period to 20 and 22 in the future), length (4.8 to 7.4 and 10.5 months), magnitude (2.53 to 6.56 and 9.62 SPI) and intensity (0.48 to 1.00 and 0.94 SPI) of extreme meteorological droughts. These two RCMs also predict higher changes in mean streamflow (−43.5 % and −57.2 %) and hydrological droughts. The two RCMs also predict worrying changes in streamflow (−43.5 % and −57.2 %) and hydrologically extreme droughts: frequency (from 3 to 11 for the first model and 8 events for the second model), length (8.3 to 15.4 and 29.6 months), magnitude (from 3.98 to 11.84 and 31.72 SSI), and intensity (0.63 to 0.90 and 1.52 SSI).

The impact of climate change scenarios on droughts and their propagation in an arid Mediterranean basin. A useful approach for planning adaptation strategies

Areas where there is a scarcity of water frequently experience significant drought periods, which may become exacerbated in the future due to climate change. In this paper we propose a novel and integrated method for a semi-distributed analysis of the impact on potential future meteorological, hydrological, agronomical and operational droughts within a basin. We analyse the propagation and correlation of the different types of droughts, and then this analysis can be used to plan sustainable adaptation strategies. The proposed method is based on sequential applications of different statistical techniques and mathematical models. We have applied several statistical downscaling techniques to generate consistent local future climate scenarios considering both basic and drought statistics. This allows us to analyse the sensitivity of the results to the applied technique and the spatial distribution. A chain of models has been used to propagate climate scenarios to analyse the hydrological, agricultural, and operational impact. We have applied a clustering analysis to historical data to identify homogeneous hydro-climate areas used to analyse the spatial distribution of the impact. The approach has been applied in the Segura basin (in south-eastern Spain). The simulations of the impact in the 3 generated ensemble scenarios on the whole Segura Basin system for the horizon 2071–2100 under the RCP8.5 emission scenario show a significant mean reduction (40.9–59.1%) of the available resources, an increase in pumping rates in aquifers (36.4–42.7%) and lower guarantees (96.3% in the historical period and 75.0–77.6% in the future scenarios) for demand supply. The spatial distribution of the impact is heterogeneous, with the hydro-climate areas near to the coast for agricultural and operational droughts being more affected. An analysis of correlation between the meteorological and operational droughts shows the maximum correlation for a time delay of around 4 months. This information could help to identify when measures to reduce the operational impact should start to be applied when a meteorological drought starts.

Development and Application of Estimation Method for the Scale of Damage in Domestic Drought

In this study, an estimation method for damage and recovery costs on account of domestic droughts was developed. The estimation method for the scale of damage was applied based on the national drought statistics generated by the Ministry of Public Administration and Security. The damage and recovery costs of domestic droughts were calculated based on the damage data of domestic droughts that occurred in 2018 and 2019. The estimation method was developed for damage costs incurred due to domestic droughts by considering the number of people affected by water supply restrictions, accommodation, and restaurants. The method for estimating recovery costs after domestic droughts was developed considering domestic disaster support, water support, water development support, equipment support, and human resource support for victims. Due to the construction of a DB of limited domestic drought damage status, the damage costs were calculated for the number of people affected by water supply restrictions, and the recovery costs were calculated for the domestic disaster and water support. The total damage costs caused by domestic droughts in 2018 amounted to 612.471 M KRW, and in Gangwon-do and Jeollanam-do, they were 218.616 M KRW in 2019, and more than 90% of Incheon metropolitan city was calculated. The total recovery costs caused by domestic droughts in 2018 were 5,994.555 M KRW, and in Gangwon-do and Jeollanam-do, they amounted to 4,882.024 M KRW in 2019, and more than 90% of Chungcheongnam-do was calculated. The difference between the damage and recovery costs of domestic droughts was approximately tenfold in 2018 and twentyfold in 2019.

Probabilistic Evaluation of Drought in CMIP6 Simulations.

As droughts have widespread social and ecological impacts, it is critical to develop long‐term adaptation and mitigation strategies to reduce drought vulnerability. Climate models are important in quantifying drought changes. Here, we assess the ability of 285 CMIP6 historical simulations, from 17 models, to reproduce drought duration and severity in three observational data sets using the Standardized Precipitation Index (SPI). We used summary statistics beyond the mean and standard deviation, and devised a novel probabilistic framework, based on the Hellinger distance, to quantify the difference between observed and simulated drought characteristics. Results show that many simulations have less than ±10% error in reproducing the observed drought summary statistics. The hypothesis that simulations and observations are described by the same distribution cannot be rejected for more than 80% of the grids based on our H distance framework. No single model stood out as demonstrating consistently better performance over large regions of the globe. The variance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zones. Though the models capture the characteristics of dry spells well, there is considerable bias in low precipitation values. Good model performance in terms of SPI does not imply good performance in simulating low precipitation. Our study emphasizes the need to probabilistically evaluate climate model simulations in order to both pinpoint model weaknesses and identify a subset of best‐performing models that are useful for impact assessments.

RAINWATER REUSE SYSTEM FOR SMALL RESIDENCES (70 m²) IN THE MUNICIPALITY OF RIO VERDE, GOIÁS, CENTRO-WEST, BRAZIL

A escassez da água vem sendo debatida e argumentada em vários lugares, mostrando a necessidade de ações voltadas ao uso eficiente deste recurso. Assim, o objetivo deste trabalho foi realizar a captação, armazenamento e reuso da água captada de telhado para residência do projeto “Minha Casa, Minha Vida” (70 m2) no município de Rio Verde, Goiás. Foram utilizados dados de precipitação média mensal de 19 anos (1996 - 2015). Simulou-se o armazenamento máximo para assegurar o período de estiagem. Estimou-se o consumo médio de água semanal considerando de 1 a 3 pessoas por residência. Considerou-se como consumo de água lavagem de carro, gramado ou jardim, descarga na bacia, máquina de lavar roupa e lavagem de piso. Nos meses de maior precipitação (novembro-março), conseguiu atender à necessidade das pessoas na residência, e ainda armazenar para aproveitar nos períodos de estiagem. Os meses de fevereiro, março, abril e dezembro, atingiu a capacidade de armazenamento do reservatório (20.000 L) apenas para a residência com 1 e 2 pessoas, para 3 pessoas apenas o mês de março a caixa d’água encheu completamente, sendo assim, supriu as necessidades nos meses de estiagem, onde a demanda não atingiu a capacidade do reservatório. Assim o reuso da água do telhado propicia na economia de água nobre e economia a médio prazo, além de assegurar picos de enchente no período chuvoso.

Physical Understanding of Human-Induced Changes in U.S. Hot Droughts Using Equilibrium Climate Simulations

AbstractAlthough the link between droughts and heat waves is widely recognized, how climate change affects this link remains uncertain. Here we assess how, and by how much, human-induced climate change affects summertime hot drought compound events over the contiguous United States. Results are derived by comparing hot drought statistics in long simulations of a coupled climate model (CESM1) subjected to year-1850 and year-2000 radiative forcings. Within each climate state, a strong and nonlinear dependency of heat-wave intensity on drought severity is found in water-limited regions of the southern Great Plains and southwestern United States whereas heat-wave intensity is found to be insensitive to drought severity in energy-limited regions of the northern and/or northeastern United States. Applying a statistical model that is based on pair-copula constructions, we find that anthropogenic warming leads to enhanced soil moisture–temperature coupling in water-limited areas of the southern Great Plains and/or southwestern United States and consequently amplifies the intensity of extreme heat waves during severe droughts. This strengthened coupling accounts for a substantial fraction of rising temperature extremes related to the long-term climate change in CESM1, highlighting the importance of changes in land–atmosphere feedback in a warmer climate. In contrast, coupling effects remain weak and largely unchanged in energy-limited regions, thereby yielding no appreciable contribution to heat-wave intensification over the northern and/or northeastern United States apart from the long-term warming effects.

Meteorological Drought Warning Research in Fujian Province, China during 1971-2016

This paper optimizes the meteorological drought assessment method in Fujian Province, China, making it more suitable for long-term serial drought statistics. Using the precipitation data of Pingtan National Basic Weather Station from 1971 to 2016, according to the optimized meteorological drought assessment method, statistics Pingtan meteorological drought process, and its characteristics analysis; through the multi-model integrated forecasting super-aggregate method, the 2011-2014 Fujian precipitation reality and the European Center, T639, WRF, Japan and other numerical forecast products were used for comparative analysis. The use of multiple regression method was used to forecast precipitation, by the localized precipitation correction scheme. The early warning method of single station meteorological drought process based on precipitation reality and precipitation forecast is obtained, and the platformization idea is proposed.

An Integrated Statistical Method to Generate Potential Future Climate Scenarios to Analyse Droughts

The objective of this paper is to investigate different methods to generate future potential climatic scenarios at monthly scale considering meteorological droughts. We assume that more reliable scenarios would be generated by using regional climatic models (RCMs) and statistical correction techniques that produce better approximations to the historical basic and drought statistics. A multi-objective analysis is proposed to identify the inferior approaches. Different ensembles (equifeasible and non-equifeasible) solutions are analysed, identifying their pros and cons. A sensitivity analysis of the method to spatial scale is also performed. The proposed methodology is applied in an alpine basin, the Alto Genil (southern Spain). The method requires historical climatic information and simulations provided by multiple RCMs (9 RCMs are considered in the proposed application) for a future period, assuming a potential emission scenario. We generate future series by applying two conceptual approaches, bias correction and delta change, using five statistical transformation techniques for each. The application shows that the method allows improvement of the definition of local climate scenarios from the RCM simulation considering drought statistics. The sensitivity of the results to the applied approach is analysed.

Prediction Experiment of Regional Drought over Korea Using the Similarity of Spatiotemporal Patterns of Past Droughts

This study investigated a drought prediction method on the basis of similarity of spatiotemporal patterns of past droughts in Korea. The method was implemented in the following steps: First, drought areas in Korea were divided into four drought subregions by means of hierarchical clustering analysis. Second, spatiotemporal drought statistics for each subregion for the period from 1926 to 2008 were established. Temporal statistics involve the drought onset, end dates, duration, and regional drought intensities, measured by the Effective Drought Index (EDI). These statistics were collected over the four subregions, and spatial patterns of drought were examined. Third, the analogous drought events that had spatiotemporal patterns similar to those of the current or subject drought were selected. Fourth, the progress of the subject drought and the selected drought were compared. Finally, the progress pattern of the subject drought was predicted on the basis of the hypothesis that it will progress in a way similar to the selected analogous case. We applied this predicted method to several previous drought cases and evaluated the prediction efficiency. The results showed that this method was efficient in predicting droughts for about 1 year.

Robustness of Meteorological Droughts in Dynamically Downscaled Climate Simulations

AbstractWe examine the robustness of a suite of regional climate models (RCMs) in simulating meteorological droughts and associated metrics in present‐day climate (1971‐2003) over the conterminous United States (U.S.). The RCMs that are part of North American Regional Climate Change Assessment Program (NARCCAP) simulations are compared with multiple observations over the climatologically homogeneous regions of the U.S. The seasonal precipitation, climatology, drought attributes, and trends have been assessed. The reanalysis‐based multi‐model median RCM reasonably simulates observed statistical attributes of drought and the regional detail due to topographic forcing. However, models fail to simulate significant drying trend over the Southwest and West. Further, reanalysis‐based NARCCAP runs underestimate the observed drought frequency overall, with the exception of the Southwest; whereas they underestimate persistence in the drought‐affected areas over the Southwest and West‐North Central regions. However, global climate model‐driven NARCCAP ensembles tend to overestimate regional drought frequencies. Models exhibit considerable uncertainties while reproducing meteorological drought statistics, as evidenced by a general lack of agreement in the Hurst exponent, which in turn controls drought persistence. Water resources managers need to be aware of the limitations of current climate models, while regional climate modelers may want to fine‐tune their parameters to address impact‐relevant metrics.

What are the impacts of bias correction on future drought projections?

Summary It is expected that anthropogenic greenhouse gas emissions will continue to change our climate, and in turn the characteristics of future drought. Assessments of the risks of future droughts, when at a global or a continental scale, are often based on simulations from General Circulation Models (GCMs). When raw GCM simulations are used, it is assumed that the future deviations from modelled historical climatology represent the future drought. On the other hand, it is known that raw GCM simulations are significantly biased for the variables that affect hydrology and a correction is needed before assessments can be performed. We investigate here whether drought assessments based on raw GCM simulations are biased and the typical extent of this bias. Our assessment is based on the use of monthly precipitation data from 18 CMIP3 GCMs, two popular bias correction alternatives and Australia as the study domain. A number of different precipitation drought attributes have been assessed. These include the Standardized Precipitation Index (SPI), multi-year rainfall statistics and a drought vulnerability statistic that measures the maximum deviation of a time series from its mean. We find significant differences between droughts assessments using raw GCM simulations and using bias corrected sequences. Large increases in drought frequencies are projected for some parts of Australia. Both bias correction methods moderate these increases. This result is consistent across the three different drought statistics. The bias corrected drought projections also generally have slightly more agreement (smaller range of future changes) across the GCMs compared to the raw projections, which is a promising result for attempting to reduce model structural uncertainty. What this study shows is raw model simulations can lead to incorrect drought assessments even at continental scales and bias corrections should be applied.

A hydrological record extension model for reconstructing streamflows from tree-ring chronologies

Characterizing drought events is an important factor in designing and operating water resource projects, but instrumental hydrologic records are generally short (<100 years). Because estimates of drought statistics can improve when longer records are available, we developed a stochastic model to extend instrumental streamflows based on tree-ring chronologies. This Record Extension plus Noise (or REXTN) model consists of an autoregressive term to account for the temporal persistence of streamflows, predictor variables with longer records, and a noise term. The noise term was included to avoid underestimating the variability of the flows and to generate multiple extensions, which offer the possibility of quantifying the uncertainty of drought statistics such as the critical drought. For cases where having multiple extensions is not desirable, a statistically based algorithm was developed to select a single extended record. Using a simulation experiment, model REXTN was found to perform better than other existing reconstruction methods. The model was then applied to extend streamflows of the Poudre River, CO, USA, based on tree ring-chronologies back to the year 1600, and the reconstructions were used to determine drought statistics such as duration and magnitude. When results based on the classical linear regression model were compared with those calculated by model REXTN, the latter was found to better match flow and drought statistics from the instrumental records, as well as to give a broader range of drought duration and magnitude. The REXTN model provides a useful addition to the range of tools available to hydrologists for coping with the uncertainty associated with water resource management under future climate scenarios. Copyright © 2014 John Wiley & Sons, Ltd.

An assessment of the ability of Bartlett–Lewis type of rainfall models to reproduce drought statistics

Abstract. Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e.g. water stage in rivers) or agricultural (e.g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett–Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett–Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as a test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett–Lewis model types studied fail to preserve extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period.

Assessment of Trends and Possible Climate Change Impacts on Summer Moisture Availability in Western Canada based on Metrics of the Palmer Drought Severity Index

Abstract This paper compares three existing Palmer Drought Severity Index (PDSI) formulations for simulating summer moisture variability in western Canada and a preliminary analysis of climate change impacts on summer moisture anomalies. The three models considered are Palmer's original algorithm (orPDSI), the self-calibrating PDSI (scPDSI), and a version modified for Canadian Prairie conditions (cpPDSI). In all formulations, potential evapotranspiration was parameterized by the Penman–Monteith method instead of the traditional Thornthwaite method. The scPDSI was used as a benchmark for evaluation as it is more appropriate for comparing drought severity of diverse climates. The results confirm that orPDSI produces inflated drought statistics as compared to scPDSI, whereas cpPDSI produced more conservative drought statistics than scPDSI. On the basis of results from scPDSI, historical moisture availability in the Canadian Prairies has shown a significant downward trend since 1950 at the 5% level, whereas southern British Columbia has shown a significant increasing trend. No discernible trend was found in the northern parts of the study area. These results were corroborated by trends in annual precipitation and summer temperature over the respective regions. When scPDSI parameters were calibrated using historical climate data, simulations for the 2050s using climate change scenarios from the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) showed increases in summer moisture deficit relative to the 1961–90 baseline. However, projecting the extent to which the frequency of extreme drought and/or wet spell categories will change is not trivial since the computation of scPDSI is tied to the definition of the frequency of extreme events.

Implications of Parameter Uncertainty on Soil Moisture Drought Analysis in Germany

Abstract Simulated soil moisture is increasingly used to characterize agricultural droughts but its parametric uncertainty, which essentially affects all hydrological fluxes and state variables, is rarely considered for identifying major drought events. In this study, a high-resolution, 200-member ensemble of land surface hydrology simulations obtained with the mesoscale Hydrologic Model is used to investigate the effects of the parametric uncertainty on drought statistics such as duration, extension, and severity. Simulated daily soil moisture fields over Germany at the spatial resolution of 4 × 4 km2 from 1950 to 2010 are used to derive a hydrologically consistent soil moisture index (SMI) representing the monthly soil water quantile at every grid cell. This index allows a quantification of major drought events in Germany. Results of this study indicated that the large parametric uncertainty inherent to the model did not allow discriminating major drought events without a significant classification error. The parametric uncertainty of simulated soil moisture exhibited a strong spatiotemporal variability, which significantly affects all derived drought statistics. Drought statistics of events occurring in summer with at most 6 months duration were found to be more uncertain than those occurring in winter. Based on the ensemble drought statistics, the event from 1971 to 1974 appeared to have a 67% probability of being the longest and most severe drought event since 1950. Results of this study emphasize the importance of accounting for the parametric uncertainty for identifying benchmark drought events as well as the fact that using a single model simulation would very likely lead to inconclusive results.

Improved confidence in regional climate model simulations of precipitation evaluated using drought statistics from the ENSEMBLES models

An ensemble of regional climate model simulations from the European framework project ENSEMBLES is compared with observations of low precipitation events across a number of European regions. We characterize precipitation deficits in terms of two drought indices, the Standardized Precipitation Index and the self-calibrated Palmer Drought Severity Index. Models that robustly describe the observations for the period 1961–2000 in given regions are identified and an assessment of the overall performance of the ensemble is provided. The results show that in general, models capture the most severe drought events and that the ensemble mean model also performs well. Some regions that appear to be more problematic to simulate well are also identified. These are relatively small regions and have rather complex topographical features. The analysis suggests that assessment of future drought occurrence based on climate change experiments in general would appear to be robust. But due to the heterogeneous and often fine-scaled structure of drought occurrence, quantitative results should be used with great care, particularly in regions with complex terrain and limited information about past drought occurrence.