Regional Drought Research Articles

Nested Patterns of Phytoplankton and Zooplankton and Seasonal Characteristics of Their Mutualistic Networks: A Case Study of the Upstream Section of the Diannong River in Yinchuan City, China

The Diannong River, a valuable river and lake resource of the northern Ningxia Yellow River Irrigation Area, plays an instrumental role in regional flood control, drought resistance, climate regulation, and biodiversity conservation. Phytoplankton and zooplankton, as crucial elements of the aquatic ecosystem, have their distribution patterns evaluated and potential influencing factors identified, thereby enhancing the understanding of community distribution patterns. Nested structures and interspecies interaction relationships bear significant implications for community distribution patterns, functions, and stability. The upstream section of the Diannong River in Yinchuan City was chosen as the study object. Water samples were collected in January, April, July, and October 2021, and the community composition of phytoplankton and zooplankton was analyzed using relative abundance, density, and biomass. The distribution matrix temperature and bipartite network methodologies were deployed to investigate their nested pattern and interaction network seasonal characteristics. The findings indicate that the water environment of the Diannong River’s upstream section displays pronounced spatiotemporal heterogeneity, characterized by weak alkalinity and high fluoride content. The plankton community composition and relative abundance showed marked differences among the distinct sampling periods. The temperature of the random distribution matrix shows a significant difference compared to the zero-sum model, revealing a notable nested pattern in plankton in the Diannong River’s upstream section. The bipartite network suggests that the plankton composition was the simplest in January and the most complex in July, with the fiercest species competition observed in January and the lowest levels of species specificity, vulnerability, and generality. Water temperature (WT), dissolved oxygen (DO), total phosphorus (TP), available phosphorus (AP), CODCr, F−, and Cl− constitute the environmental parameters influencing the overall structure of the phytoplankton community in the Diannong River’s upstream section, whereas zooplankton did not present a significant correlation with water environmental factors.

Monitoring agricultural drought in Peshawar Valley, Pakistan using long -term satellite and meteorological data.

Monitoring agricultural drought across a large area is challenging, especially in regions with limited data availability, like the Peshawar Valley, which holds great agricultural significance in Pakistan. Although remote sensing provides biophysical variables such as precipitation (P), land surface temperature (LST), normalized difference vegetation index (NDVI), and relative soil moisture (RSM) to assess drought conditions at various spatiotemporal scales, these variables have limited capacity to capture the complex nature of agricultural drought and associated crop responses. Here, we developed a composite drought index named "Temperature Vegetation ET Dryness Index" (TVEDI) by modifying the Temperature Vegetation Precipitation Dryness Index (TVPDI) and integrating NDVI, LST, and remotely sensed evapotranspiration (ET) using 3D space and Euclidean distance. Several statistical techniques were employed to examine TVPDI and TVEDI trends and relationships with other commonly used drought indices such as the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized soil moisture index (SSI), as well as crop yield, to better understand how these indices captured the spatial and temporal distribution of agricultural drought in the Peshawar valley between 1986 and 2018. Results indicated that while the temporal patterns of the 3-month SPI, SPEI, and SSI generally align with those of TVEDI and TVPDI, TVEDI was more strongly correlated with these indices (e.g., correlation coefficient, r = 0.78-0.84 from TVEDI and r = 0.73-0.79 from TVPDI). Moreover, the crop yield, a measure of crop response to agricultural drought, demonstrated a significant positive correlation with TVEDI (r = 0.60-0.80), much higher than its correlation with TVPDI (r = 0.30-0.48). These outcomes indicate that the inclusion of ET in TVEDI effectively captured changes in soil moisture, crop water status, and their impact on crop yield. Overall, TVEDI exhibited enhanced capability to identify drought impacts compared to TVPDI, showing its potential for characterizing agricultural drought in regions with limited data availability.

COMPARISON AND EVALUATION OF MACHINE-LEARNING-BASED SPATIAL DOWNSCALING APPROACHES ON SATELLITE-DERIVED PRECIPITATION DATA

Abstract. Precipitation estimation with high accuracy and resolution is crucial for hydrological and meteorological applications, particularly in ungauged river basins and regions with scarce water resources. Many machine learning (ML) algorithms have been employed in the downscaling of precipitation, however, it remains unclear which algorithm can outperform others. To address this issue, this study evaluates the performance of four ML based downscaling methods to generate high-resolution precipitation estimates at an annual scale. The satellite-derived precipitation data, environmental variables, such as, latitude, longitude, normalized difference vegetation index (NDVI), digital elevation model (DEM), and land surface temperature (LST), as well as the observations from rainfall gauges were used to constructed the regression models. The performance of the four ML algorithms including the Support Vector Regression (SVR), Random Forest (RF), Spatial Random Forest (SRF), and Extreme Gradient Boosting (XGBoost) algorithms was compared with three conventional methods: Multiple Linear Regression (MLR), geographically weighted regression (GWR) and Kriging interpolation model. Results showed that ML-based method generally outperformed traditional interpolation methods in precipitation downscaling, as they had higher accuracy and were better at reproducing the spatial distribution of rainfall. Out of ML approaches, XGBoost received the best performance, followed by SRF, RF and SVR, indicating its robustness of capturing nonlinear relationships. After the XGBoost, better performance of SRF than RF and SVR was found. This might be because the SRF just introduced the spatial autocorrelation into the RF models, which illustrated the importance of capturing spatial variations in ML algorithms. These findings regarding the comparison and assessment provided a novel downscaling method for generating high-resolution precipitation data, which could benefit regional flood forecasting, drought monitoring, and irrigation planning.

Analysis of spring drought in Northeast China from the perspective of atmosphere, snow cover, and soil

Northeast China is an important grain-producing area, and spring is the sowing time for crops. Drought hinders crop germination and leads to crop failure. Spring drought analysis helps to understand the characteristics of regional spring drought and provides a reference for formulating disaster prevention and reduction measures. Considering the characteristics of seasonally frozen soil regions in northeastern China, sampling at a resolution of 0.5 degrees, accounting for the snow cover factors in the early spring, as well as precipitation, temperature, evapotranspiration, and soil moisture during the spring planting period, and uses the projection pursuit information entropy model to analyze the spatial and temporal variability of spring drought in Northeast China. In the model, a new method was proposed to calculate the cutoff radius by adjusting the radius size with the parameter. The optimal parameters were ultimately determined using the Shapiro-Wilk test, Kolmogorov-Smirnov test, and Epps-Pulley test. The results are as follows: drought is relatively severe in the eastern parts of Inner Mongolia, as well as in the northwest of Liaoning. The central and northern parts of Inner Mongolia, the northern and northeastern parts of Heilongjiang, the southeastern parts of Jilin, and the eastern parts of Liaoning are relatively humid, while the drought situation in other regions is relatively moderate. Regional spring drought shows an increasing trend, and most regions show statistical significance. Areas with high temperatures and low precipitation, as well as low snow cover in the early spring, are prone to spring drought. Moist regions are characterized by high precipitation, low evapotranspiration, and abundant snow resources.

Amplification of Coupled Hot‐Dry Extremes Over Eastern Monsoon China

AbstractHigh air temperatures and low atmospheric humidity can result in severe disasters such as flash droughts in regions characterized by high humidity (monsoon regions). However, it remains unclear whether responses of hot extremes to warming temperature are amplified on dry days as well as the response of dry extremes on hot days. Here, taking eastern monsoon China (EMC) as a typical monsoon region, we find a faster increase in air temperature on drier summer days, and a faster decrease in atmospheric humidity on hotter days, indicating “hotter days get drier” and “drier days get hotter” (i.e., coupling hotter and drier extremes), especially in southern EMC. The southern EMC is also a hotspot where the coupling hot‐dry extremes has become significantly stronger during the past six decades. The stronger hot‐dry coupling in southern EMC is associated with anomalies in large‐scale circulations, such as reduced total cloud cover, abnormal anticyclones in the upper atmosphere, intense descending motion, and strong moisture divergence over this region. Land‐atmosphere feedback enhance the hot‐dry coupling in southern EMC by increasing land surface dryness (seen as a decrease in the evaporation fraction). The decreasing evaporation fraction is associated with drying surface soil moisture, controlled by decreases in pre‐summer 1‐m soil moisture and summer‐mean precipitation. Given hot extremes are projected to increase and atmospheric humidity is predicted to decrease in the future, it is very likely that increasing hot‐dry days and associated disasters will be witnessed in monsoon regions, which should be mitigated against by adopting adaptive measures.

Estimation of regional meteorological aridity and drought characteristics in Baluchistan province, Pakistan.

Droughts and prevailing arid conditions have a significant impacts on the natural environment, agriculture, and human life. To analyze the regional characteristics of drought in Baluchistan province, the aridity index (AI) and standardized potential evapotranspiration index (SPEI) were used in. The study analyzed the rainfall, temperature, and potential evapotranspiration (PET) data and the same were used for the calculation of AI as well as SPEI to find out the drought spells during the study period. The linear regression and Mann-Kendall test were applied to calculate the trend in AI as well as in SPEI results. The AI results revealed that most of the meteorological stations are arid and semi-arid, where the highest increasing aridity is noted at Kalat (0.0065/year). The results of the SPEI at 1 and 6-months identified the extreme to severe drought spell during 1998-2002 in all meteorological stations of Baluchistan province. The distinct drought spells identified from the SPEI results were in the years 1998-2003, 2006-2010, 2015-2016 and 2019. The drought frequency results showed highest frequency percentage at Lasbella (46%) of extreme to severe drought. The Mann-Kendall trend results showed negative trend in monthly AI and 1-month SPEI results and most significant trend was observed in April and October months, this shows that aridity and drought in the region are decreasing to some extent except Dalbandin and Lasbella observed increasing trend in winter season (November to January months) and Kalat met-station observed increasing trend in June. Prior investigation and planning of drought situations can help in controlling the far-reaching consequences on environment and human society.

Drought risk assessment: The importance of vulnerability factors interdependencies in regional drought risk management

Recognizing the vulnerability factors is essential in assessing the drought risk of a region. Moreover, considering the interactions and interdependencies between the vulnerability factors will generate a more accurate and realistic analysis. The present study tries to depict the interdependencies between vulnerability factors and their covering effect on each other. Kashkan River basin in the west of Iran has hydrological, physical, social, and cultural diversity; hence, one of the main goals of the current research is to evaluate drought risk and detect its affecting factors in each rural district. Twelve vulnerability factors were selected and standardized based on regional risk management. To model interdependencies of vulnerability factors, the integration of the Fuzzy Analytic Network Process (FANP) and Decision-making Trial and Evaluation Laboratory (DEMATEL) method was compared with the Fuzzy Analytic Hierarchical Process (FAHP). The Drought Vulnerability Index (DVI), a weighted aggregation of factors, was multiplied by the Drought Hazard Index (DHI) to obtain a drought risk map. Based on the results, FANP-DEMATEL modifies the risk severity of 20 % of districts. Both methods introduce the western part of the basin with intense risk levels, indicating the irrefutable effect of the hazard index on drought risk maps. The scarcity of hydrological factors can cause profound risk changes in both methods. Furthermore, comparing weighting scenarios revealed that, in some cases, sophisticated weighting methods can be replaced by logical weighting strategies.

Quantifying Drought Characteristics in Complex Climate and Scarce Data Regions of Afghanistan

Droughts cause critical and major risk to ecosystems, agriculture, and social life. While attempts have been made globally to understand drought characteristics, data scarcity in developing countries often challenges detailed analysis, including climatic, environmental, and social aspects. Therefore, this study developed a framework to investigate regional drought analysis (RDA) using regional drought intensity-duration-frequency (RD-IDF) curves and regional drought risk assessment (RDRA) based on the drought hazard indicator (DHI) and drought vulnerability indicator (DVI) for scarce data regions in Afghanistan. The drought characteristics were analyzed using the regional standardized-precipitation-index (SPI), and standardized precipitation-deficit distribution (SPDD). Further, L-moment statistics were used to classify different homogenous regions based on regional frequency analysis (RFA). The historical monthly precipitation data from 23 rainfall stations for the years 1970 to 2016 were collected from the Ministry of Water and Energy of Afghanistan. Based on the analysis performed, the area was classified into six homogeneous regions R-1, R-2, R-3, R-4, R-5, and R-6. The drought was very consistent—almost 50% of the years—irrespective of the homogeneous region classified. R-4, located in the northeast of the country, had a one-year extreme drought with high resiliency and low risk to drought compared to other regions. As R-1, R-3 and R-5 are located in the southwest, center and southeast parts of Afghanistan, they experience moderate drought with low resiliency and high drought risk due to long period of droughts. Moreover, the uniform distribution of precipitation deficit (Dm), was less in arid climate regions. In contrast, the semi-arid climate regions showed higher values of Dm. Furthermore, in the results in all the regions, the IDF curves showed a high drought intensity with increasing drought return periods. In contrast, the intensity significantly decreased when the time scale increased, and fewer were enhanced within the increasing drought return period. However, the outcome of this study may contain essential information for end users to make spatially advanced planning for drought effect mitigation in Afghanistan.

Enhanced growth resistance but no decline in growth resilience under long-term extreme droughts.

The frequency, intensity, and duration of extreme droughts, with devastating impacts on tree growth and survival, have increased with climate change over the past decades. Assessing growth resistance and resilience to drought is a crucial prerequisite for understanding the responses of forest functioning to drought events. However, the responses of growth resistance and resilience to extreme droughts with different durations across different climatic zones remain unclear. Here, we investigated the spatiotemporal patterns in growth resistance and resilience in response to extreme droughts with different durations during 1901-2015, relying on tree-ring chronologies from 2389 forest stands over the mid- and high-latitudinal Northern Hemisphere, species-specific plant functional traits, and diverse climatic factors. The findings revealed that growth resistance and resilience under 1-year droughts were higher in humid regions than in arid regions. Significant higher growth resistance was observed under 2-year droughts than under 1-year droughts in both arid and humid regions, while growth resilience did not show a significant difference. Temporally, tree growth became less resistant and resilient to 1-year droughts in 1980-2015 than in 1901-1979 in both arid and humid regions. As drought duration lengthened, the predominant impacts of climatic factors on growth resistance and resilience weakened and instead foliar economic traits, plant hydraulic traits, and soil properties became much more important in both climatic regions; in addition, such trends were also observed temporally. Finally, we found that most of the Earth system models (ESMs) used in this study overestimated growth resistance and underestimated growth resilience under both 1-year and 2-year droughts. A comprehensive ecophysiological understanding of tree growth responses to longer and intensified drought events is urgently needed, and a specific emphasis should be placed on improving the performance of ESMs.

Development and allocation of improved eucalyptus for biotic an abiotic tolerance

Since the 1960s, Altri Florestal is running a programme for selecting trees based on growth, wood density and cellulose content. Currently Eucalyptus globulus breeding programme is in third generation. Capturing the genetic gain is conducted through vegetative propagation to produce clones, as well as through seed production from a mass-controlled pollination programme for full-sib families. Ongoing climate change raises the importance of considering tolerance to abiotic stress in the Eucalyptus globulus breeding programme. Pursuing this objective has been done by carrying out a Best Linear Unbiased Prediction (BLUP) analysis considering macro climate regions with different exposure to cold and drought. Cold and drought maps were combined in one resulting in four regions of exposure: drought region (R1); cold region (R2); cold and drought region (R3); region with low exposure to cold and drought (R4). BLUP analysis included data from eighty-four field breeding trials with the following climatic distribution: 30 (R1); 19 (R2); 4 (R3), and 31 (R4). Data used comprises 160k genotypes and 210k measurements of height, diameter at breast height, and survival. It was possible to select around four genotypes with an overall superior performance in terms of growth and survival for the four regions considered. Furthermore, within each macro climate region groups of genotypes were selected with an increase of 50% volume per hectare above the original eucalyptus in the breeding programme. Altri is also involved in the development of new eucalyptus hybrids aiming to develop a new generation of eucalyptus hybrids more resilient to drought and cold, as well as pests such as Gonipterus platensis. This ongoing work is carried out via interspecific hybridization between E. globulus and other eucalyptus species. Eucalyptus hybrids are in several stages of development, namely: tested in several field trials, early field selection, being raised in the nursery, and being done through controlled crosses. Selection criteria will be based on performance from growth, survival, wood characteristics in field trials and in trials in controlled conditions. Finally, it is not enough to develop a portfolio of improved eucalyptus to ensure that realised genetic gain will be captured into operations. For this to happen it is necessary to ensure that allocation of improved eucalyptus is carefully planned. A prototype of decision tree for deploying improved eucalyptus has been already developed.

Performance of potential evapotranspiration models across different climatic stations in New South Wales, Australia

Study regionNew South Wales, southeast Australia Study focusEstimating potential evapotranspiration (ETp) rates, detecting its temporal trends and analysing its interannual oscillation are critical for long-term assessment of water availability and regional drought. This study aimed to evaluate the comprehensive performance of 12 simplified models in characterising ETp against the benchmark Penman model across different climate sites in southeast Australia. This study used Taylor skill score (S), normalised root mean square error (nRMSE) and relative mean bias error (rMBE) to estimate models’ capability in estimating ETp rates. Then, this study adopted Mann-Kendall test and continuous wavelet transform (CWT) to test temporal trends and periodicity of ETp estimated by all models. New hydrology insights for the regionJensen-Haise model was capable to produce fair (nRMSE ≤ 30%) estimates of daily ETp across all stations. Models except Mak1 were generally able to produce reasonable estimates of ETp at larger time scale. In addition, we found that the 12 alternative ETp models generally agreed with the Penman model on the primary (9.6–12.4-year) and quasi (2.6–3.9-year) periods of ETp, but they did not show matchable ability in detecting ETp temporal trends. The comprehensive investigation on models’ performance will shed light on models’ selection in estimation of drought and hydrological cycle.

Assessment of regional drought risk coupled with drought response capacity considering water supply systems

Assessment of regional drought risk coupled with drought response capacity considering water supply systems

Meteorological drought assessment in northern Bangladesh: A machine learning-based approach considering remote sensing indices

Meteorological drought, driven by inadequate precipitation, has significant repercussions for water resources, agriculture, and human well-being. This study conducted an extensive assessment of meteorological drought in northern Bangladesh, employing remote sensing indices and machine learning techniques. The main aim was to evaluate meteorological drought occurrences in northern Bangladesh from 2010 to 2019, utilizing seven drought parameters and a machine learning model. Utilizing a Random Forest (RF) model, this study employed the Standardized Precipitation Index (SPI) as the dependent variable and seven remote sensing indices as independent variables. Through this methodology, the study assessed the significance of these indices generated by the model and integrated them, culminating in the creation of a meteorological drought distribution map spanning 2010 to 2019. This approach offers novel insights by probing the interplay and collective impacts of these indices, shedding light on previously unexplored aspects of regional drought patterns of northern Bangladesh. The major findings showed that precipitation strongly influenced both short-term and long-term meteorological drought episodes. Moreover, land surface-related indices, such as Evapotranspiration (ET) and Normalized Difference Water Index (NDWI), exhibited a more pronounced impact on short-term drought occurrences, while vegetation-related indices like Normalized Multi-band Drought Index (NMDI) and Normalized Difference Vegetation Index (NDVI) demonstrated greater influence over long-term drought events. During this timeframe, the Rajshahi division experienced frequent extreme and severe drought events. Moderate droughts and abnormally dry conditions were widespread. The Barind tract area consistently faced moderate to extreme droughts, with exceptions in 2011, 2014, and 2019. On average, over 5% of the region had extreme droughts, while more than 12% experienced severe droughts during this decade. Long-term drought indicators (SPI 6 and SPI 9) consistently showed higher frequencies of extreme and severe droughts compared to short-term indicators (SPI 1 and SPI 3), emphasizing the influence of prolonged rainfall deficits on extreme droughts and the relevance of longer time frames for severe drought dynamics. The RF model demonstrated strong performance with accuracy ranging from 81% to 95%. Low prediction errors (RMSE 6% to 31%) and high out-of-bag (OOB) accuracy ranging from 76% to 98% highlighted its accuracy. The F1 score consistently exceeded 76%, indicating high precision and recall. Cross-validation values ranged from 78% to 94%, affirming reliable generalization to new data. Incorporating the main findings, this study contributes valuable insights for the formulation of targeted drought mitigation strategies in northern Bangladesh. It is imperative to note that the scope of this study is confined to the northern region of Bangladesh, and generalizing these findings to other regions should be exercised with caution. Nevertheless, the research methodology and approach can serve as a model for future studies in related fields, advancing knowledge of how to assess droughts using remote sensing and machine learning methods.

IMPLEMENTATION OF AN AGROFORESTRY SYSTEM AS A REFORESTATION INSTRUMENT IN THE CAATINGA BIOME

The relevance of the agroforestry system not only for farmers, but also for the environment and the growth of their knowledge is the reason for this work, particularly because the agroforestry system has been established and is producing results in the Sertão da Caatinga region. The Caatinga is the dominant biome in the northeast region of Brazil, which has suffered over the years as a result of intensive commercial exploitation. This exploitation converted more than half of its land into desertified regions. The implementation of the agroforestry system was motivated by the desire to preserve the Caatinga and its ecosystem. The regional drought itself, the creation of cisterns to capture water, strategies for less drainage, and the production of food without pesticides can be counted among the challenges overcome, and non-deforestation, the benefits for the Caatinga biome, and the Production of healthy food for subsistence can be counted among the results achieved, all of them culminating in the dissemination with the same environmental characteristics. As a result, studies are being conducted in an effort to reduce the harmful effects of these behaviors. Based on this, the objective of this study is to evaluate the implementation of an Agroforestry System as a strategy for the recovery of degraded regions in an area considered to be in an accelerated phase of desertification in the hinterland biome.

Evaluating the cumulative and time-lag effects of vegetation response to drought in Central Asia under changing environments

Central Asia (CA) is an arid region sensitive to climate change, and with the increase in drought events due to global warming, droughts are posing more severe threats to the vegetation in CA. However, the response of vegetation in CA to the cumulative and time-lag effects of prolonged drought remains unclear. This has limited our understanding of the mechanisms by which vegetation responds to meteorological drought in arid regions. To clarify the response of vegetation to drought in CA, this study analyzed the effects of drought and meteorological factors on vegetation growth dynamics. The extent of vegetation response to the cumulative and time-lag effects from 2001 to 2020 was evaluated using three satellite-derived vegetation indices (NDVI, EVI, NIRv) and the solar-induced chlorophyll fluorescence (SIF) and standardized precipitation evapotranspiration index (SPEI). We used the Copula-Bayes conditional probability method to assess the drought thresholds for vegetation under two scenarios: mild and severe losses. The effects of soil moisture (SM) and vapor pressure deficit (VPD) on vegetation dynamics were analyzed based on an improved partial wavelet coherence (PWC). The results show that the average response time of vegetation to the cumulative and lagged effects of drought was mostly concentrated within 4–6 months, and the lagged effect was stronger than the cumulative effect. The vegetation response to drought is nonlinear along the precipitation gradient. The SPEI values for triggering mild vegetation loss (below the 40th percentile) drought threshold range from −0.7 to −1.0, while the SPEI values for triggering severe vegetation loss (below the 10th percentile) drought threshold range from −2.0 to −2.5. The SM was the main driver affecting vegetation dynamics. This study provides deeper insight into the response of vegetation to drought in the arid zone of CA, which will further provide theoretical support for addressing global climate change and extreme drought events.

Different types of meteorological drought and their impact on agriculture in Central China

Challenges in drought monitoring hinder social development. In this study, a robust drought monitoring framework composed of ten indicators was established based on the standard precipitation actual evapotranspiration (SPAEI) index calculated using the maximum entropy production (MEP) model. Additionally, the relationship between meteorological drought and agricultural development was analyzed. The results indicated that the seasonal SPAEI performed better than the standardized precipitation evapotranspiration index (SPEI) in terms of drought area, vegetation drought, and drought identification in Central China. The annual drought duration was between 3.4 and 4.5 months, and the annual drought frequency ranged from 1.1 to 1.9 times per year. The main drought pattern was divided into persistent light drought and short-term severe drought, with the latter dominating crop yields in Central China. Although persistent light drought contributes more to drought, short-term severe drought has a greater impact on crop yields. Thoroughly investigating regional drought patterns and assessing their impacts on agriculture will contribute to a better understanding of drought characteristics and provide valuable guidance for regional agricultural water resource allocation and management.

Reversed asymmetric warming of sub-diurnal temperature over land during recent decades

In the latter half of the twentieth century, a significant climate phenomenon “diurnal asymmetric warming” emerged, wherein global land surface temperatures increased more rapidly during the night than during the day. However, recent episodes of global brightening and regional droughts and heatwaves have brought notable alterations to this asymmetric warming trend. Here, we re-evaluate sub-diurnal temperature patterns, revealing a substantial increase in the warming rates of daily maximum temperatures (Tmax), while daily minimum temperatures have remained relatively stable. This shift has resulted in a reversal of the diurnal warming trend, expanding the diurnal temperature range over recent decades. The intensified Tmax warming is attributed to a widespread reduction in cloud cover, which has led to increased solar irradiance at the surface. Our findings underscore the urgent need for enhanced scrutiny of recent temperature trends and their implications for the wider earth system.

Correspondence of Drought Occurrences at Multi‐Temporal Scales Between North China and Upper Hanjiang River

AbstractUsing the drought and flood grades, the historical co‐drought probabilities of the water source area (i.e., the Upper Hanjiang River or UH) and the receiving area (i.e., North China or NC) of the Middle Route of the South‐to‐North Water Diversion project from 1470 to 2017 were analyzed. It was found that there is relatively high possibility for co‐droughts to occur in the UH and NC at interannual to multi‐decadal time scales. When extreme drought occurred in NC, there was a 52% probability that the UH was on the drier side and even a 40% probability of severe drought. During the three typical severe drought events that occurred mainly in NC (i.e., 1637–1643, 1877–1878, and 1997–2002), severe droughts also appeared in the UH, with 1877–1878 even more severe than in NC. Among the seven driest years of the study period in NC (i.e., 1640, 1641, 1832, 1920, 1965, 1968, and 1997), only 1832 was wet in the UH, and 1920 even experienced the same severe region‐wide drought as NC did. Furthermore, when severe consecutive droughts occurred in NC, there was at least a 66% probability that the UH was also experiencing a drought. In addition, there were obvious cycles of drought and flood variability of around 3–6 years, 10 years, 30 years, and 55–80 years in both the UH and NC. These findings are of importance to in‐depth understanding of the regional drought variability and its mechanism at varied temporal scales and the effective management of the world‐class hydro project.

Drought reconstruction since 1796 CE based on tree-ring widths in the upper Heilongjiang (Amur) River basin in Northeast Asia and its linkage to Pacific Ocean climate variability

Abstract. The economic and environmental impacts of persistent droughts in East Asia are of growing concern, and therefore it is important to study the cyclicity and causes of these regional droughts. The self-calibrating Palmer drought severity index (scPDSI) has been extensively employed to describe the severity of regional drought, and several scPDSI reconstructions based on tree rings have been produced. We compiled a tree-ring chronology for Hailar pine (Pinus sylvestris var. mongolica) from two sites in the Hailar region in the upper Heilongjiang (Amur) River basin. Analysis of the climate response revealed that scPDSI was the primary factor limiting tree ring growth from May to July. The mean May to July scPDSI in the Hailar region since 1796 was reconstructed from the tree-ring width chronology. The results of spatial correlation analysis revealed that the reconstructed scPDSI in this region responded significantly to climate change. Analysis of the synoptic climatology indicated that the drought in the upper Heilongjiang (Amur) River basin is closely related to El Niño–Southern Oscillation (ENSO) and the Silk Road teleconnection. The results of atmospheric water cycle analysis show that water vapor transport processes are the dominant factor in the development of drought in this region.

Groundwater trend analysis and regional groundwater drought assessment of a semi-arid region of Rajasthan, India

Groundwater trend analysis and regional groundwater drought assessment of a semi-arid region of Rajasthan, India