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In North America, the occurrence of extreme drought events has increased significantly in number and severity over the last decades. Past droughts have contributed to lower agricultural productivity in major farming and ranching areas across the US.We evaluated the relationship between drought indices and crop yields across the US for the period 1961−2014. In order to assess the correlations with yields from the major cash crops in the country, we calculated several drought indices commonly used to monitor drought conditions, including 4 Palmer-based and 3 multiscalar indices (Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Standardized Precipitation Drought Index). The 3 multiscalar drought indices were aggregated at 1 to 12mo timescales. Besides the quantification of the similarities or differences be - tween these drought indices using Pearson correlation coefficients, we identified spatial patterns illustrating this relationship. The results demonstrate that the flexiblemultiscalar indices can identify drought impacts on different types of crops for a wide range of time periods. The differences in spatial and temporal distribution of the correlations depend on the crop and timescale analyzed, but can also be found within the same type of crop. The moisture conditions during summer and shorter timescales (1 to 3mo) turn out to be a determining factor for barley, corn, cotton and soybean yields. Therefore, the use of multiscalar drought indices based on both precipitation and the atmospheric evaporative demand (SPEI and SPDI) seems to be a prudent recommendation.

While most models project large increases in agricultural drought frequency and severity in the 21st century, significant uncertainties exist in these projections. Here, we compare the model-simulated changes with observation-based estimates since 1900 and examine model projections from both the Coupled Model Inter-comparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5). We use the self-calibrated Palmer Drought Severity Index with the Penman-Monteith potential evapotranspiration (PET) (sc_PDSI_pm) as a measure of agricultural drought. Results show that estimated long-term changes in global and hemispheric drought areas from 1900 to 2014 are consistent with the CMIP3 and CMIP5 model-simulated response to historical greenhouse gases and other external forcing, with the short-term variations within the model spread of internal variability, despite that regional changes are still dominated by internal variability. Both the CMIP3 and CMIP5 models project continued increases (by 50–200 % in a relative sense) in the 21st century in global agricultural drought frequency and area even under low-moderate emissions scenarios, resulting from a decrease in the mean and flattening of the probability distribution functions (PDFs) of the sc_PDSI_pm. This flattening is especially pronounced over the Northern Hemisphere land, leading to increased drought frequency even over areas with increasing sc_PDSI_pm. Large differences exist in the CMIP3 and CMIP5 model-projected precipitation and drought changes over the Sahel and northern Australia due to uncertainties in simulating the African Inter-tropical convergence zone (ITCZ) and the subsidence zone over northern Australia, while the wetting trend over East Africa reflects a robust response of the Indian Ocean ITCZ seen in both the CMIP3 and CMIP5 models. While warming-induced PET increases over all latitudes and precipitation decreases over subtropical land are responsible for mean sc_PDSI_pm decreases, the exact cause of its PDF flattening needs further investigation.

Several studies have been conducted on droughts, precipitation, and temperature, whereas none have addressed the underlying relationship between nonlinear dynamic properties and patterns of two main hydrological parameters, precipitation and temperature, and meteorological and hydrological droughts. Monthly datasets of Midlands in the UK between 1921 and 2019 were collected for analysis. Subsequent to apply a multifractal approach to attain the nonlinear features of the datasets, the relationship between two hydrological parameters and droughts was investigated through the cross-correlation technique. A similar process was performed to analyze the relationship between multifractal strength variations in time series of precipitation and temperature and droughts. The nonlinear dynamic results indicated that droughts (meteorological and hydrological) were substantially affected by precipitation than temperature. In other words, droughts were more sensitive to precipitation fluctuations than temperature fluctuations. Concerning temperature, meteorological, and hydrological droughts were dependent on the minimum and maximum temperatures (Tmin and Tmax), respectively. The correlation between precipitation and meteorological drought was more long-range persistence than precipitation and hydrological drought. Besides, the correlation between Tmax and droughts was more long-range persistence than Tmin and droughts. Analysis of nonlinear dynamic patterns proved that the multifractal strength of meteorological drought depended on the multifractal strength of precipitation and Tmax, whereas the multifractal strength of hydrological drought depended on the multifractal strength of the Tmin. The correlation between precipitation and drought indices exhibited more multifractal strength than temperature and drought indices. Finally, the pivotal role of maximum temperature on drought events was quite alerting due to global warming intensification.

Climate Hazards Group Infrared Precipitation with Stations data (CHIRPS) rainfall dataset was early evaluated and compared with 29 meteorological stations over the Haihe River basin in China, for the period 1981–2015. Seven statistical and categorical metrics were applied to evaluate the performance of CHIRPS with gauge measurements at multi-time scales (monthly, seasonally and annually). Using the Standardized Precipitation Index (SPI) as the drought indicator, the applicability of this new long-term satellite precipitation product for drought monitoring was investigated in this study. Results indicate that the good performances were performed at multiple temporal scales (monthly, seasonally and annually). Although it tends to overestimate the higher precipitation in this region, CHIRPS demonstrated good agreement (R2 > 0.70) with gauge observations at monthly scale and greater agreements (R2 > 0.78) at seasonal and annual scales. Meanwhile, CHIRPS performed a good score of BIAS and lower error in a majority of months at multi-time scales. Because of its good performance at multi-time scales and the advantages of high spatial resolution and long-time record, CHIRPS was applied to derive the SPI over the Haihe River basin. It is evaluated and compared with stations observations to derive SPI at time scale of 1, 3 and 6 months. The results indicate that it performed good ability to monitor drought (R2 > 0.70) and successfully captured the historical drought years (1981, 1999, 2001 and 2012). Overall, this study concludes that CHIRPS can be a valuable complement to gauge precipitation data for estimating precipitation and drought monitoring in this region.

Long records of tree‐ring maximum latewood density (MXD) measurements in conifers have been successfully employed to reconstruct summer temperature changes globally. Yet, the potential of MXD as a proxy in semi‐arid, low‐latitude regions for reconstructing either temperature or hydroclimate variability remains largely unexplored. Here, we developed a MXD data set of Chinese pine from semi‐arid northern China, and investigated its sensitivity to different climate variables. We found that the annual self‐calibrated Palmer Drought Severity Index from previous August to current July displays the strongest influence on the MXD variation. The entire MXD chronology (covering 1736–2020) is highly consistent with nearby tree‐ring‐based annual precipitation and drought reconstructions at decadal timescales, confirming a temporally stable hydroclimate signal in our MXD record. In particular, the rapid wetting trend during the 2010–2020 period is well captured by the MXD data. This novel study has wide implications for future use of tree‐ring density data to reconstruct past climate changes globally. Plain Language Summary Tree‐ring maximum latewood density (MXD) is normally identified as carrying a temperature signal in cold high‐altitude or high‐latitude regions. Very few studies have been conducted in areas with a semi‐arid climate. Whether MXD also in such regions can be used as a temperature proxy still needs confirmation. We developed a MXD chronology from Chinese pine trees located in the northern China where temperatures show significant warming trends, but precipitation has not increased during the instrumental period. Correlation with various climate parameters indicates that this tree‐ring MXD record contains a significant hydroclimate signal. The strongest correlation is found with the annual self‐calibrated Palmer Drought Severity Index calculated from previous August to current July. We further noticed that other published MXD chronologies, based on different tree species, also can reflect precipitation or hydroclimate signals when growing in drought‐dominated climatic environments. The generally detected temperature signal in MXD data is not supported by our study, meaning caution should be taken before using MXD data as a proxy for temperature in drought‐prone regions. Key Points A Chinese pine maximum latewood density (MXD) chronology covering the 1736–2020 period is developed for a semi‐arid region in northern China Tree‐ring MXD and width show consistent high‐ and low‐frequency variations limited by common hydroclimate variables Chinese pine tree‐ring MXD in semi‐arid northern China is an indicator of hydroclimate rather than temperature

Investigation of quantitative predictions of precipitation amounts and forecasts of drought events are conducive to facilitating early drought warnings. However, there has been limited research into or modern statistical analyses of precipitation and drought over Northeast China, one of the most important grain production regions. Therefore, a case study at three meteorological sites which represent three different climate types was explored, and we used time series analysis of monthly precipitation and the grey theory methods for annual precipitation during 1967–2017. Wavelet transformation (WT), autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) methods were utilized to depict the time series, and a new hybrid model wavelet-ARIMA-LSTM (W-AL) of monthly precipitation time series was developed. In addition, GM (1, 1) and DGM (1, 1) of the China Z-Index (CZI) based on annual precipitation were introduced to forecast drought events, because grey system theory specializes in a small sample and results in poor information. The results revealed that (1) W-AL exhibited higher prediction accuracy in monthly precipitation forecasting than ARIMA and LSTM; (2) CZI values calculated through annual precipitation suggested that more slight drought events occurred in Changchun while moderate drought occurred more frequently in Linjiang and Qian Gorlos; (3) GM (1, 1) performed better than DGM (1, 1) in drought event forecasting.

The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

Global climate models play an important role in quantifying past and projecting future changes in drought. Previous studies have pointed to shortcomings in these models for simulating droughts, but systematic evaluation of their level of agreement has been limited. Here, historical simulations (1950–2004) for 20 models from the latest Coupled Model Intercomparison Project (CMIP5) were analyzed for a variety of drought metrics and thresholds using a standardized drought index. Model agreement was investigated for different types of drought (precipitation, runoff, and soil moisture) and how this varied with drought severity and duration. At the global scale, climate models were shown to agree well on most precipitation drought metrics, but systematically underestimated precipitation drought intensity compared to observations. Conversely, simulated runoff and soil moisture droughts varied significantly across models, particularly for intensity. Differences in precipitation simulations were found to explain model differences in runoff and soil moisture drought metrics over some regions, but predominantly with respect to drought intensity. This suggests it is insufficient to evaluate models for precipitation droughts to increase confidence in model performance for other types of drought. This study shows large but metric-dependent discrepancies in CMIP5 for modeling different types of droughts that relate strongly to the component models (i.e., atmospheric or land surface scheme) used in the coupled modeling systems. Our results point to a need to consider multiple models in drought impact studies to account for high model uncertainties.

Human influences can affect streamflow drought characteristics and propagation. The question is where, when and why? To answer these questions, the impact of different human influences on streamflow droughts were assessed in England and Wales, across a broad range of climate and catchments conditions. We used a dataset consisting of catchments with near-natural flow as well as catchments for which different human influences have been indicated in the metadata (“Factors Affecting Runoff”) of the UK National River Flow Archive (NRFA). A screening approach was applied on the streamflow records to identify human-influenced records with drought characteristics that deviated from those found for catchments with near-natural flow. Three different deviations were considered, specifically deviations in (1) the relationship between streamflow drought duration and the base flow index, BFI (specifically, BFIHOST, the BFI predicted from the hydrological properties of soils), (2) the correlation between streamflow and precipitation and (3) the temporal occurrence of streamflow droughts compared to precipitation droughts, i.e. an increase or decrease in streamflow drought months relative to precipitation drought months over the period of record. The identified deviations were then related to the indicated human influences. Results showed that the majority of catchments for which human influences were indicated did not show streamflow drought characteristics that deviated from those expected under near-natural conditions. For the catchments that did show deviating streamflow drought characteristics, prolonged streamflow drought durations were found in some of the catchments affected by groundwater abstractions. Weaker correlations between streamflow and precipitation were found for some of the catchments with reservoirs, water transfers or groundwater augmentation schemes. An increase in streamflow drought occurrence towards the end of their records was found for some of the catchments affected by groundwater abstractions and a decrease in streamflow drought occurrence for some of the catchments with either reservoirs or groundwater abstractions. In conclusion, the proposed screening approaches were sometimes successful in identifying streamflow records with deviating drought characteristics that are likely related to different human influences. However, a quantitative attribution of the impact of human influences on streamflow drought characteristics requires more detailed case-by-case information about the type and degree of all different human influences. Given that, in many countries, such information is often not readily accessible, the approaches adopted here could provide useful in targeting future efforts. In England and Wales specifically, the catchments with deviating streamflow drought characteristics identified in this study could serve as the starting point of detailed case study research.

In this study, the effects of elevation and longitude on precipitation and drought on the Yunnan–Guizhou Plateau (YGP) are investigated, and the performance of four precipitation datasets (China Meteorological Forcing Dataset [CMFD], Global Precipitation Climatology Centre [GPCC], Climatic Research Unit [CRU] and Integrated Multi-satellite Retrievals for Global Precipitation Measurement [IMERG]) is evaluated for the YPG. On an intra-annual scale, the precipitation is bimodal and unimodal at 12 and 35 observation stations, respectively. The stations with bimodal precipitation are primarily located on the southeastern YGP. The first peak of the bimodal precipitation regime occurs in May or June and is higher than the second peak that occurs in October. The peak of the unimodal precipitation regime appears in either June or July. On an interannual scale, annual and seasonal precipitation have been decreasing at most stations, significantly so at some stations. The average precipitation on an annual scale and the spring, winter, and drought season gradually decreases from northeast to southwest and is significantly negatively correlated with the elevation and significantly positively correlated with the longitude. The average precipitation for the summer, spring, and rainy season has a relatively complex spatial distribution and is relatively weakly correlated with the elevation and longitude. The standard deviation (SD) of the precipitation is significantly negatively correlated with the elevation and significantly positively correlated with the longitude. The coefficients of variation (Cv) for the annual, seasonal and monthly precipitation are significantly correlated with the elevation and longitude at various time scales. For the spring and drought season, the standardized precipitation index (SPI) has significant negative and positive correlations with elevation and longitude, respectively, whereas the opposite is true for the summer, autumn, and rainy season. The SD of SPI for summer, autumn, rainy, and drought seasons shows a significant negative (positive) correlation with elevation (longitude). The evaluation results show that CMFD performs best, followed by GPCC, with the worst for CRU and IMERG. Different sources of water vapor influence YGP precipitation and thus YGP drought. Overall, the average precipitation and drought and their SD and Cv are more strongly correlated with the longitude than with the elevation.