Drought Projections Research Articles

Robust ecological drought projections for drylands in the 21st century.

Dryland ecosystems may be especially vulnerable to expected 21st century increases in temperature and aridity because they are tightly controlled by moisture availability. However, climate impact assessments in drylands are difficult because ecological dynamics are dictated by drought conditions that are difficult to define and complex to estimate from climate conditions alone. In addition, precipitation projections vary substantially among climate models, enhancing variation in overall trajectories for aridity. Here, we constrain this uncertainty by utilizing an ecosystem water balance model to quantify drought conditions with recognized ecological importance, and by identifying changes in ecological drought conditions that are robust among climate models, defined here as when >90% of models agree in the direction of change. Despite limited evidence for robust changes in precipitation, changes in ecological drought are robust over large portions of drylands in the United States and Canada. Our results suggest strong regional differences in long-term drought trajectories, epitomized by chronic drought increases in southern areas, notably the Upper Gila Mountains and South-Central Semi-arid Prairies, and decreases in the north, particularly portions of the Temperate and West-Central Semi-arid Prairies. However, we also found that exposure to hot-dry stress is increasing faster than mean annual temperature over most of these drylands, and those increases are greatest in northern areas. Robust shifts in seasonal drought are most apparent during the cool season; when soil water availability is projected to increase in northern regions and decrease in southern regions. The implications of these robust drought trajectories for ecosystems will vary geographically, and these results provide useful insights about the impact of climate change on these dryland ecosystems. More broadly, this approach of identifying robust changes in ecological drought may be useful for other assessments of climate impacts in drylands and provide a more rigorous foundation for making long-term strategic resource management decisions.

Statistical Downscaling for the Projection of the Keetch Byram Drought Index in the Barito Basin

Over the last few decades, prolonged drought in Indonesia has led to a catastrophic wildfire hazard, including on Kalimantan Island. The Barito River basin is one of the major river basins on the island, located in South and Central Kalimantan Provinces. According to The Indonesian National Board for Disaster Management (BNPB), the drought hazard index in the southern part of Kalimantan is mostly at the medium to high-risk level. In terms of Integrated Water Resources (IWRM), more detailed drought risk analysis needs to be conducted at the river basin level, so that drought adaptation and mitigation strategies can be integrated into long-term river basin management plans. In this study, a drought projection of the Barito River basin was simulated by using the Coupled Model Intercomparison Project 5 (CMIP5). A coarse grid of CMIP5 data was statistically downscaled to a smaller grid over the basin area. Data from climatology observation stations and Climate Forecast System Reanalysis (CFSR) were used to calibrate the bias correction function of the CMIP5 data. This function for rainfall data was developed based on the rainfall probability curve, while the bias correction function for temperature data was developed based on the elevation-temperature relation. The bias-corrected rainfall and temperature data were used as input for the Keetch-Byram Drought Index (KBDI) analysis. The study shows that the potential for drought hazard may increase in the future. Drought projection in the Barito basin for 2050 using KBDI shows that the potential areas with medium and high drought risk may cover around 50% and 2%, respectively, or about 35,000km2 and 1,400km2. The occurrence of wildfires also has a strong correlation with the drought index. A comparison between 1998 and 2015 fire hotspot data shows that most hotspots were located in areas in the medium and high drought risk categories. The study shows the importance of climate change impact analysis to prevent more catastrophic hazards in the future, especially in the Barito River basin, Kalimantan Island.

Future Global Meteorological Drought Hot Spots: A Study Based on CORDEX Data

AbstractTwo questions motivated this study: 1) Will meteorological droughts become more frequent and severe during the twenty-first century? 2) Given the projected global temperature rise, to what extent does the inclusion of temperature (in addition to precipitation) in drought indicators play a role in future meteorological droughts? To answer, we analyzed the changes in drought frequency, severity, and historically undocumented extreme droughts over 1981–2100, using the standardized precipitation index (SPI; including precipitation only) and standardized precipitation-evapotranspiration index (SPEI; indirectly including temperature), and under two representative concentration pathways (RCP4.5 and RCP8.5). As input data, we employed 103 high-resolution (0.44°) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), based on a combination of 16 global circulation models (GCMs) and 20 regional circulation models (RCMs). This is the first study on global drought projections including RCMs based on such a large ensemble of RCMs. Based on precipitation only, ~15% of the global land is likely to experience more frequent and severe droughts during 2071–2100 versus 1981–2010 for both scenarios. This increase is larger (~47% under RCP4.5, ~49% under RCP8.5) when precipitation and temperature are used. Both SPI and SPEI project more frequent and severe droughts, especially under RCP8.5, over southern South America, the Mediterranean region, southern Africa, southeastern China, Japan, and southern Australia. A decrease in drought is projected for high latitudes in Northern Hemisphere and Southeast Asia. If temperature is included, drought characteristics are projected to increase over North America, Amazonia, central Europe and Asia, the Horn of Africa, India, and central Australia; if only precipitation is considered, they are found to decrease over those areas.

Evaluation of Meteorological Drought and Flood Scenarios over Kenya, East Africa

This work examines drought and flood events over Kenya from 1981 to 2016 using the Standardized Precipitation–Evapotranspiration Index (SPEI). The spatiotemporal analysis of dry and wet events was conducted for 3 and 12 months. Extreme drought incidences were observed in the years 1987, 2000, 2006, and 2009 for SPEI-3, whilst the SPEI-12 demonstrated the manifestation of drought during the years 2000 and 2006. The SPEI showed that the wettest periods, 1997 and 1998, coincided with the El Nino event for both time steps. SPEI-3 showed a reduction in moderate drought events, while severe and extreme cases were on the increase tendencies towards the end of the twentieth century. Conversely, SPEI-12 depicted an overall increase in severe drought occurrence over the study location with ab observed intensity of −1.54 and a cumulative frequency of 64 months during the study period. Wet events showed an upward trend in the western and central highlands, while the rest of the regions showed an increase in dry events during the study period. Moreover, moderate dry/wet events predominated, whilst extreme events occurred least frequently across all grid cells. It is apparent that the study area experienced mild extreme dry events in both categories, although moderately severe dry events dominated most parts of the study area. A high intensity and frequency of drought was noted in SPEI-3, while the least occurrences of extreme events were recorded in SPEI-12. Though drought event prevailed across the study area, there was evidence of extreme flood conditions over the recent decades. These findings form a good basis for next step of research that will look at the projection of droughts over the study area based on regional climate models.

Increased Drought Risk in South Asia under Warming Climate: Implications of Uncertainty in Potential Evapotranspiration Estimates

AbstractObserved and projected changes in potential evapotranspiration (PET) and drought are not well constrained in South Asia. Using five PET estimates [Thornthwaite (PET-TH), Hargreaves–Samani (PET-HS), Penman–Monteith (PET-PM), modified Penman–Monteith (PET-MPM), and energy (PET-EN)] for the observed (1979–2018, from ERA5) and future warming climate, we show that significant warming has occurred in South Asia during 1979–2018. PET changes show considerable uncertainty depending on the method used. For instance, PET-TH has increased significantly while all the other four methods show a decline in PET in the majority of South Asia during the observed period of 1979–2018. The increase in PET-TH is substantially higher than PET-HS, PET-PM, and PET-MPM due to a higher (3–4 times) sensitivity of PET-TH to warming during the observed period. Under the 1.5°, 2.0°, and 2.5°C warming worlds, global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5 GCMs) project increases in PET and drought frequency over the majority of the regions. Drought estimates based on PET-EN and PET-MPM are consistent with soil moisture–based drought estimates and project a substantial increase in the frequency of severe droughts under warming climate in South Asia. In addition, the projected frequency of severe drought based on PET-TH, which is an outlier, is about 5 times higher than PET-EN and PET-MPM. Methods to estimate PET contribute the most in the overall uncertainty of PET and drought projections in South Asia, primarily due to PET-TH. Drought estimates based on PET-TH are not reliable for the observed and projected future climate. Therefore, future drought projections should be either based on PET-EN/PET-MPM or soil moisture.

Quantile Mapping Bias Correction on Rossby Centre Regional Climate Models for Precipitation Analysis over Kenya, East Africa

This study uses the quantile mapping bias correction (QMBC) method to correct the bias in five regional climate models (RCMs) from the latest output of the Rossby Center Climate Regional Model (RCA4) over Kenya. The outputs were validated using various scalar metrics such as root-mean-square difference (RMSD), mean absolute error (MAE), and mean bias. The study found that the QMBC algorithm demonstrates varying performance among the models in the study domain. The results show that most of the models exhibit reasonable improvement after corrections at seasonal and annual timescales. Specifically, the European Community Earth-System (EC-EARTH) and Commonwealth Scientific and Industrial Research Organization (CSIRO) models depict remarkable improvement as compared to other models. On the contrary, the Institute Pierre Simon Laplace Model CM5A-MR (IPSL-CM5A-MR) model shows little improvement across the rainfall seasons (i.e., March–May (MAM) and October–December (OND)). The projections forced with bias-corrected historical simulations tallied observed values demonstrate satisfactory simulations as compared to the uncorrected RCMs output models. This study has demonstrated that using QMBC on outputs from RCA4 is an important intermediate step to improve climate data before performing any regional impact analysis. The corrected models may be used in projections of drought and flood extreme events over the study area.

Anthropogenic land cover change impact on climate extremes during the 21st century

Anthropogenic land cover change (LCC) can have significant impacts at regional and seasonal scales but also for extreme weather events to which socio-economical systems are vulnerable. However, the effects of LCC on extreme events remain either largely unexplored and/or without consensus following modelling over the historical period (often based on a single model), regional or idealized studies. Here, using simulations performed with five earth system models under common future global LCC scenarios (the RCP8.5 and RCP2.6 Representative Concentration Pathways) and analyzing 20 extreme weather indices, we find future LCC substantially modulates projected weather extremes. On average by the end of the 21st century, under RCP8.5, future LCC robustly lessens global projections of high rainfall extremes by 22% for heavy precipitation days (>10 mm) and by 16% for total precipitation amount of wet days (PRCPTOT). Accounting for LCC diminishes their regional projections by >50% (70%) in southern Africa (northeastern Brazil) but intensifies projected dry days in eastern Africa by 29%. LCC does not substantially affect projections of global and regional temperature extremes (<5%), but it can impact global rainfall extremes 2.5 times more than global mean rainfall projections. Under an RCP2.6 scenario, global LCC impacts are similar but of lesser magnitude, while at regional scale in Amazon or Asia, LCC enhances drought projections. We stress here that multi-coupled modelling frameworks incorporating all aspects of land use are needed for reliable projections of extreme events.

Droughts’ projections in homogeneous climatic regions using Standardized Precipitation Index in Pakistan

This study is conducted to develop homogenous climatic regions (HCR) based on Standardized Precipitation Index (SPI) by combining cluster analysis and L-moment approach. Initially, cluster analysis was performed using site characteristics which resulted in five subjective homogenous regions. To check homogeneity, discordancy and heterogeneity measures were implied based on at-site statistics of the stations using L-moments approach. These statistical measures established validity of five HCR for Pakistan and showed that elevation has a key role in the construction of regions. Best-fit distributions were selected in the form of Generalized Pareto distribution and Pearson type-III distribution using L-Moment Ratio Diagram (LMRD) and goodness-of-fit (GOF) test for drought risk assessment. Projections of drought were obtained for each region in the form of regional quantiles at various return periods using regional frequency analysis (RFA). Finally, at-site frequency analysis (ASFA) was performed to calculate quantiles at different return periods and project drought at each individual site using best-fit distributions. The uncertainty of both types of quantile results was assessed using Monte Carlo Simulation which showed similarity at lower while increasing uncertainty at higher return periods. From our analysis, we found that RFA is statistically better due to many values from sites of the region as compared to ASFA for future drought risk assessment. According to the results, regions one and two are enriched of water resources due to rainfall and glaciers, region five has enough rain in monsoon season while regions three and four include most vulnerable and drought-prone areas of Sindh and Baluchistan provinces and have chances of severe to extreme drought in the near future. The findings of this study are helpful for policymakers, monitoring droughts and its projections, water resource planners, etc. in the study area.

Investigation of seasonal droughts and related large-scale atmospheric dynamics over the Potwar Plateau of Pakistan

As a result of climate change and unsustainable land use management in the recent past, droughts have become one of the most devastating climatic hazards whose impacts may prolong from months to years. This study presents analysis of droughts for two major cropping seasons, i.e., Kharif (May–September) and Rabi (October–April), over the Potwar Plateau of Pakistan. The analysis is performed using various datasets viz. observational, reanalysis, and Regional Climate Models (RCMs), for the past (1981–2010) and future (2011–2100) time periods. The following two methods for the identification of dry and wet years, also referred to as drought and wetness, are applied: (1) the percentile rank approach and (2) the drought indices, Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI). Future projections of droughts are investigated using RCM (RegCM4.4 and RCA4) outputs from CORDEX South Asia domain under two Representative Concentration Pathway (RCP) scenarios, RCP4.5 and RCP8.5. Generally, the indices show non-significant decreasing trends of drought severity in the recent past for all cases; however, significant increasing trends are observed for annual (0.006) and Kharif (0.007) cases under RCP4.5 scenario. The analysis of large-scale atmospheric dynamics suggests the significant role of low-level geopotential height anomalies over Tibetan Plateau (northwest of Pakistan) during Kharif (Rabi) season in controlling drought occurrence by transporting moisture from the Bay of Bengal (Arabian Sea). Moreover, composites of vertically integrated moisture transport, moisture flux convergence/divergence, and precipitable water anomalies show their marked contribution in maintaining the drought/wetness conditions over the Potwar region.

Multi-model drought predictions using temporally aggregated climate indicators

Long term skillful prediction of prolonged hydrologic anomalies is essential for proper planning to reduce the societal risk of extreme hydrological anomalies such as drought. Climate indices estimated from sea surface temperature anomalies (SSTA) of the Pacific and Indian Oceans are often used to predict monthly and seasonal rainfall in Australia and many other places around the world. This study investigates the merit of distorting the time aggregation of such indices before casting them in a predictive model. Aggregated climate indices are used to predict sustained drought and wet anomalies characterised here using a drought index (i.e. Standardize Precipitation Index, SPI) as response and the Australia as the study region of interest. The aim is to enhance the strength of relationships of drought index and climate indices (predictors) by tuning the frequency of climate predictors using an aggregation technique. Result shows that aggregated climate indices provide significant improvement in prediction of SPI over raw climate indices across Australia. As strong spatial variations in optimum aggregation window lengths are evident across Australia suggesting multiple candidate predictive models with similar accuracies, a model combination approach is also adopted. Model combination is found useful in reducing structural uncertainty and further improving the prediction efficiency. Given that the improved predictive accuracy for SSTA the current generation of climate models exhibit, the methodology developed in this study has significant implications for skillful prediction and projection of long term droughts.

Projections of drought characteristics in China based on a standardized precipitation and evapotranspiration index and multiple GCMs

Droughts have destructive impacts on agricultural production; thus, drought projections are vital for the development of future drought mitigation strategies. This work aimed to project a standardized precipitation and evapotranspiration index (SPEI) at 3-, 6- and 12-month timescales for the period 2011–2100 under two representative concentration pathway (RCP) scenarios – RCP 4.5 and RCP 8.5 in mainland China and to assess the changes in various drought indices over a baseline period of 1961–2000. The spatiotemporal variations in drought characteristics (e.g., the drought occurrence time, duration, severity, peak, and frequency and the percentage of stations suffering from drought (PSSD) were estimated by the projected SPEI for the periods 2011–2040, 2041–2070 and 2071–2100. The results showed that mainland China would experience more frequent and severe droughts in the future than in the baseline period, as denoted by SPEI and the generated drought variables. In particular, drier areas of northwestern China were likely to suffer from worse drought conditions than those in other areas, with PSSD values of 60% and 81% by 2100 under the RCP4.5 and RCP 8.5 scenarios, respectively. Although the annual precipitation was projected to increase in most regions, drought conditions would still worsen because of increased the minimum and maximum air temperatures. However, the GCMs contributed more uncertainties to the projection of the SPEI than the stations or the RCPs, because the GCMs made a larger contribution to the variance (>40%). The SPEI performed better than the other indices that only accounted for the influence of a single variable. The relationship between crop yields and the three drought indices varied by month, crop (maize and cotton), and timescale (3- and 6-month). The drought projections from our study can provide invaluable information for stakeholders in developing regionally specific drought adaptation strategies in the face of climate change.

Drought Trend Analysis Based on the Standardized Precipitation–Evapotranspiration Index Using NASA’s Earth Exchange Global Daily Downscaled Projections, High Spatial Resolution Coupled Model Intercomparison Project Phase 5 Projections, and Assessment of Potential Impacts on China’s Crop Yield in the 21st Century

Drought is among the costliest natural disasters on both ecosystems and agroeconomics in China. However, most previous studies have used coarse resolution data or simply stopped short of investigating drought projection and its impact on crop yield. Motivated by the newly released higher-resolution climate projection dataset and the crucial need to assess the impact of climate change on agricultural production, the overarching goal of this study was to systematically and comprehensively predict future droughts at unprecedented resolutions over China as a whole. rather than region-specific projections, and then to further investigate its impact on crop yield by innovatively using a soil water deficit drought index. Methodologically, the drought projections were quantified from very high resolution climate data and further predicted impacts on crop yield over China using the standardized precipitation–evapotranspiration index (SPEI) at a relatively high (25 km) spatial resolution from NASA’s Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). The results showed that (1) overall, China is projected to experience a significant decrease in SPEI (−0.15/decade under RCP (representative concentration pathway) 4.5; −0.14/decade under RCP8.5). Seasonally, the decreasing rate of SPEI is projected to be largest in winter (−0.2/decade and −0.31/decade) and the least in summer (−0.08/decade and −0.10/decade) under respective RCPs. (2) Regionally, winter/spring will get drier, especially at high latitudes/altitudes (North China and Tibetan plateau), and summer/autumn will get wetter in southern China. (3) Both the frequency and duration for medium and severe drought are projected to decrease, while extreme drought, particularly in high latitudes/altitudes, is projected to increase. (4) The percentage of the potential crop production affected by drought would increase to 36% (47%) by 2100 under RCP4.5 (RCP8.5). Especially, the ratio impacted by extreme drought is projected to increase over time and with much worse magnitude under RCP8.5; thus, adaptive crop policies are expected to address such a risk.

Climate Change Influences of Temporal and Spatial Drought Variation in the Andean High Mountain Basin

Climate change threatens the hydrological equilibrium with severe consequences for living beings. In that respect, considerable differences in drought features are expected, especially for mountain-Andean regions, which seem to be prone to climate change. Therefore, an urgent need for evaluation of such climate conditions arises; especially the effects at catchment scales, due to its implications over the hydrological services. However, to study future climate impacts at the catchment scale, the use of dynamically downscaled data in developing countries is a luxury due to the computational constraints. This study performed spatiotemporal future long-term projections of droughts in the upper part of the Paute River basin, located in the southern Andes of Ecuador. Using 10 km dynamically downscaled data from four global climate models, the standardized precipitation and evapotranspiration index (SPEI) index was used for drought characterization in the base period (1981–2005) and future period (2011–2070) for RCP 4.5 and RCP 8.5 of CMIP5 project. Fitting a generalized-extreme-value (GEV) distribution, the change ratio of the magnitude, duration, and severity between the future and present was evaluated for return periods 10, 50, and 100 years. The results show that magnitude and duration dramatically decrease in the near future for the climate scenarios under analysis; these features presented a declining effect from the near to the far future. Additionally, the severity shows a general increment with respect to the base period, which is intensified with longer return periods; however, the severity shows a decrement for specific areas in the far future of RCP 4.5 and near future of RCP 8.5. This research adds knowledge to the evaluation of droughts in complex terrain in tropical regions, where the representation of convection is the main limitation of global climate models (GCMs). The results provide useful information for decision-makers supporting mitigating measures in future decades.

Extreme Floods and Droughts under Future Climate Scenarios

Climate projections indicate that in many regions of the world the risk of increased flooding or more severe droughts will be higher in the future. To account for these trends, hydrologists search for the best planning and management measures in an increasingly complex and uncertain environment. The collection of manuscripts in this Special Issue quantifies the changes in projected hydroclimatic extremes and their impacts using a suite of innovative approaches applied to regions in North America, Asia, and Europe. To reduce the uncertainty and warrant the applicability of the research on projections of future floods and droughts, their continued development and testing using newly acquired observational data are critical.

Projection of spatiotemporal patterns and possible changes of drought in the Yellow River basin, China

Drought projection is of critical significance for designing long-term drought adaptation strategies to cope with changing climate. This study presents an application of multi-class models for characterizing future droughts in the Yellow River basin (YRB). Using meteorological observations and simulations of three CMIP5 climatic models as input into VIC hydrologic model, the standardized Palmer drought index-based joint drought index (SPDI-JDI) is calculated to investigate spatiotemporal patterns and possible changes of future drought in the YRB, through an approach of bivariate modeling and analysis. It is concluded that moderate drought will be alleviated with decreased duration and severity, but extreme drought risk is likely to increase for future climate scenarios (2021–2050) relative to baseline period (1961–2000). From the perspective of bivariate analyses, the concurrence probability of drought events with extreme duration and severity might rise in the future, while increasing variability and heterogeneity of duration, severity and bivariate joint/concurrent return period would also increase the stochastic occurrence of extreme drought over time and space. These findings can provide insights into projection of climate change impacts on drought trends for the next 30 years, and help to preparations for strict control of socio-economic water consumption as well as eco-environmental protection and restoration in the YRB.

Projecting drought characteristics over East African basins at specific global warming levels

Drought poses a threat to socio-economic activities across most East African river basins. While there are indications that global warming may continue to enhance evaporation from basins and intensify droughts, most drought projections over East Africa are based on rainfall alone. The present study combines rainfall and Potential Evapotranspiration (PET) to examine the impacts of global warming on droughts in East Africa, with a focus on four major river basins. We characterised the drought by means of two drought indices: the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI). While the SPEI is based on the Climate Water Balance (CWB) (precipitation minus evapotranspiration), SPI is based on only rainfall. Multi-ensemble simulations from the CORDEX (the Coordinated Regional Climate Downscaling Experiment) datasets were analysed for the study. The capability of the simulations to reproduce the relevant climate variables (temperature, precipitation, PET and water balance, SPEI and SPI) over the East Africa region and specifically across four river basins (Rufiji, Tana, Juba and Upper-Nile) was evaluated. The impacts of climate change on the drought indices were considered at four specific Global Warming Levels (GWLs) (i.e. 1.5 °C, 2.0 °C, 2.5 °C, and 3.0 °C) under the Representative Concentration Pathway (RCP) 8.5 scenario and a Self-Organizing Map (SOM) was used to classify the SPEI and SPI projections into 12 groups, based on their similarities.Our results show that the CORDEX models give a realistic simulation of the relevant climate variables (including the drought indices) over East Africa and the river basins. For most of the variables, the spatial correlations between the observed and the simulated variables are high (0.6 < r > 0.9). In the annual cycle over these basins, the observations fall within the simulation spreads, and the simulation ensemble mean closely follows the observations. However, the ensemble mean does not accurately reproduce the spatial distribution of the drought frequency in the region. The CORDEX simulations project an increase in the intensity and frequency of SPEI droughts over East Africa. The magnitude of the projection, which varies over the region, increases with increasing GWLs. In contrast to the SPEI projection, however, the SPI projects only a weak change for intensity and frequency of droughts, and the magnitude does not vary with the GWLs. The SOM classification shows that 90% of the simulations project more severe and more frequent droughts with SPEI than they do with SPI. Over the river basins, the SPEI projections are more robust than the SPI. Hence, using only rainfall for drought projections may underestimate the severity of future droughts, while incorporating PET may enhance the robustness of the projections. The results of the study have application in mitigating the impacts of droughts on socio-economic activities across the East African river basins.

Uncertainty and hotspots in 21st century projections of agricultural drought from CMIP5 models

Future climate changes could alter hydrometeorological patterns and change the nature of droughts at global to regional scales. However, there are considerable uncertainties in future drought projections. Here, we focus on agricultural drought by analyzing surface soil moisture outputs from CMIP5 multi-model ensembles (MMEs) under RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios. First, the annual mean soil moisture by the end of the 21st century shows statistically significant large-scale drying and limited areas of wetting for all scenarios, with stronger drying as the strength of radiative forcing increases. Second, the MME mean spatial extent of severe drought is projected to increase for all regions and all future RCP scenarios, and most notably in Central America (CAM), Europe and Mediterranean (EUM), Tropical South America (TSA), and South Africa (SAF). Third, the model uncertainty presents the largest source of uncertainty (over 80%) across the entire 21st century among the three sources of uncertainty: internal variability, model uncertainty, and scenario uncertainty. Finally, we find that the spatial pattern and magnitude of annual and seasonal signal to noise (S/N) in soil moisture anomalies do not change significantly by lead time, indicating that the spreads of uncertainties become larger as the signals become stronger.

Changes in Future Drought with HadGEM2-AO Projections

The standardized precipitation index (SPI)—a meteorological drought index—uses various reference precipitation periods. Generally, drought projections using future climate change scenarios compare reference SPIs between baseline and future climates. Here, future drought was projected based on reference precipitation under the baseline climate to quantitatively compare changes in the frequency and severity of future drought. High-resolution climate change scenarios were produced using HadGEM2-AO General Circulation Model (GCM) scenarios for Korean weather stations. Baseline and future 3-month cumulative precipitation data were fitted to gamma distribution; results showed that precipitation of future climate is more than the precipitation of the baseline climate. When future precipitation was set as that of the baseline climate instead of the future climate, results indicated that drought intensity and frequency will decrease because the non-exceedance probability for the same precipitation is larger in the baseline climate than in future climate. However, due to increases in regional precipitation variability over time, some regions with opposite trends were also identified. Therefore, it is necessary to understand baseline and future climates in a region to better design resilience strategies and mechanisms that can help cope with future drought.

More severe hydrological drought events emerge at different warming levels over the Wudinghe watershed in northern China

Abstract. Assessment of changes in hydrological droughts at specific warming levels is important for an adaptive water resources management with consideration of the 2015 Paris Agreement. However, most studies focused on the response of drought frequency to the warming and neglected other drought characteristics, including severity. By using a semiarid watershed in northern China (i.e., Wudinghe) as an example, here we show less frequent but more severe hydrological drought events emerge at 1.5, 2 and 3 ∘C warming levels. We used meteorological forcings from eight Coupled Model Intercomparison Project Phase 5 climate models under four representative concentration pathways, to drive a newly developed land surface hydrological model to simulate streamflow, and analyzed historical and future hydrological drought characteristics based on the standardized streamflow index. The Wudinghe watershed will reach the 1.5, 2 and 3 ∘C warming levels around 2015–2034, 2032–2051 and 2060–2079, with an increase in precipitation of 8 %, 9 % and 18 % and runoff of 27 %, 19 % and 44 %, and a drop in hydrological drought frequency of 11 %, 26 % and 23 % as compared to the baseline period (1986–2005). However, the drought severity will rise dramatically by 184 %, 116 % and 184 %, which is mainly caused by the increased variability in precipitation and evapotranspiration. The climate models and the land surface hydrological model contribute to more than 80 % of total uncertainties in the future projection of precipitation and hydrological droughts. This study suggests that different aspects of hydrological droughts should be carefully investigated when assessing the impact of 1.5, 2 and 3 ∘C global warming.

Climate model projections for future seasonal rainfall cycle statistics in Northwest Costa Rica

Increasing water scarcity due to rising demand and changes in climate and land use are expected to exert significant stress on water resources in many parts of the world. In many areas, distinctive patterns of seasonal precipitation play an important role in regional ecosystems, economies, and food and energy supplies. Given the inconsistency among climate model precipitation projections, this study develops a statistical approach to better characterize seasonal patterns of precipitation and to assess the potential impact of climate change on these patterns. The method is applied to evaluate the bimodal seasonal pattern of precipitation in northwest Costa Rica as a case study. A Gaussian mixture model is used to describe the bimodal pattern and quantify changes in the seasonal precipitation cycle projected by 19 Coupled General Circulation Models. The model simulations for the current period (1979–2005) are compared with observed monthly precipitation data based on four goodness‐of‐fit metrics to select the best performing models. The monthly bias‐corrected and spatially disaggregated (BCSD) climate projections from the selected models are used to investigate the projected change in the bimodal seasonal pattern, seasonal mean precipitation and interannual variability at the late twenty‐first century. Under RCP8.5, the degree of consistency associated with these BCSD climate projections is found to be higher for the projections of the midsummer drought than that for the early portion of the wet season (early season). For the late portion of the wet season (late season) projections, the degree of consistency is found to be the lowest. The proposed method provides (a) an overall characterization of inconsistencies in climate model projections for the region, (b) more information about the possible changes in the seasonal cycle of precipitation, and (c) the possibility of performing detailed comparison tests among climate models over a set of simulations and projections.