The occurrence and development of drought involve multiple interacting processes within the atmosphere-land surface system. Traditional drought indices usually characterize drought conditions from a single perspective, making it difficult to comprehensively reflect regional drought conditions. To address this limitation, this study proposes a Standardized Composite Drought Index (SCDI) from a water balance perspective based on a threedimensional copula model, in which precipitation (P), potential evapotranspiration (PET), and soil moisture (SM) are jointly modeled to provide a comprehensive characterization of regional drought conditions. Based on SCDI, the spatiotemporal evolution characteristics of drought over Northern China (NC) from 1982 to 2023 were analyzed, and multivariate cross-wavelet transform (WTC) was employed to explore the relationship between SCDI and atmospheric circulation factors (ACFs). The results indicated that: (1) SCDI can effectively characterize the evolution of drought onset, persistence, and alleviation; (2) During the study period, drought conditions in NC generally intensified, whereas the Qinghai-Tibet Plateau (Subregion IV) exhibited a certain humidification tendency; temporally, NC demonstrated pronounced drought intensification in March and spring; (3) Regional drought variability is mainly influenced by the coupled effects of ACFs. Among these, the Atlantic Multidecadal Oscillation (AMO) plays a key regulating role in drought evolution, while the combined influence of AMO, the Western Pacific (WP), and the Pacific Decadal Oscillation (PDO) exhibits a more pronounced coupled influence on regional drought variability. These findings provide insights into the spatiotemporal evolution of drought in NC and offer scientific support for regional drought risk management.

In complex hydrological regions, single drought indices, such as the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Vegetation Condition Index (VCI), and the Vegetation Health Index (VHI) may not be fully sufficient for monitoring composite drought events. Therefore, in this study, a new composite drought index (CDI) that uses nested copula functions to integrate precipitation, the normalized difference vegetation index (NDVI), and temperature was proposed, and its reliability was tested and applied in Liaoning Province, China. The results indicated that: (1) The CDI correlates well with SPI, SPEI, VCI, and VHI, showing high sensitivity, specificity, and over 87

Global warming has intensified extreme drought events in both frequency and severity, posing severe threats to ecosystems and human societies. Consequently, accurate assessment of drought conditions is essential. However, traditional drought indices inadequately represent hydrological cycle processes. This study constructed two comprehensive drought indices—CDIPRE (precipitation-based) and CDIRUN (runoff-based)—for Shandong Province, China (1959–2023), using the C-vine Copula framework to integrate precipitation (input) and runoff (output) variables within regional hydrological cycles. The findings reveal that: (1) Both indices effectively integrate meteorological, hydrological, and agricultural drought information, thereby overcoming the limitations of conventional univariate indices. (2) CDIRUN outperforms CDIPRE in capturing the characteristics of different drought types and assessing agricultural drought impacts. (3) Run theory analysis revealed that CDIRUN exhibited longer total drought durations in the north-central parts of the study area, while other drought characteristics showed scale-dependent spatial distributions. (4) Geodetector analysis identified the input variables underlying CDIRUN (PRE, RUN, PET, SM) as dominant drivers of spatial heterogeneity in drought. (5) During the study period, the North Pacific Index (NP) showed the strongest statistical correlation with CDIRUN; NP-NOI (Northern Oscillation Index) and NP-NOI-SOI (Southern Oscillation Index) were identified as the statistically optimal bivariate and multivariate combinations, respectively. Thus, CDIRUN index thus provides a robust tool for drought monitoring, water resource management, and agricultural planning in the context of climate change.

The Yellow River Basin (YRB), located in the mid-latitude region of China, encompasses diverse ecosystem types and is highly sensitive to climate change. However, the temporal patterns and environmental drivers of net ecosystem CO2 exchange (NEE) across multiple time scales remain poorly understood. Using eddy covariance observations from the ChinaFLUX network collected between 2003 and 2020, this study investigated the temporal dynamics of NEE and its primary environmental controls in five representative ecosystem types within the YRB and its adjacent 100-km buffer zone: cropland, forest, grassland, shrubland, and wetland ecosystems. The results showed that all five ecosystems exhibited a generally U-shaped diurnal pattern from May to September, characterized by net CO2 uptake during the daytime and net CO2 release at night. At the daily scale, cropland displayed a typical bimodal carbon uptake pattern, whereas forest ecosystem exhibited the greatest day-to-day variability in NEE. In contrast, grassland, shrubland, and wetland ecosystems showed relatively smooth daily fluctuations. The net CO2 source-sink functions derived from NEE differed substantially among ecosystem types. Forest ecosystems acted as the most stable and persistent carbon sinks, whereas croplands exhibited short-term but high-intensity carbon uptake. Wetlands showed pronounced interannual variability, including an extreme net CO2 release event at the Haibei wetland site in 2007. Grassland and shrubland ecosystems were more susceptible to environmental stress and could shift from net CO2 sinks to net CO2 sources during drought years. The environmental controls on NEE exhibited clear time-scale dependence. At the half-hourly scale, photosynthetically active radiation (PAR) was the dominant driver of NEE variability, the influence of temperature increased progressively from the daily to monthly scales. These findings improve the understanding of regional carbon dynamics in the YRB, provide insights into the net CO2 source-sink status of different ecosystem types, and elucidate the mechanisms regulating ecosystem CO2 exchange across multiple temporal scales.

Drought is a complex phenomenon involving multiple interrelated processes, including meteorological, hydrological, and agricultural components. Traditional drought indices typically focus on a single aspect of drought, limiting their ability to fully characterize its multifaceted nature. In this study, a composite drought index (CDI) is developed based on the C-vine copula modeling framework, coupling key hydrological cycle variables including precipitation, potential evapotranspiration, soil moisture, and runoff. Two CDI are constructed: CDI-P, which takes precipitation as the central variable, and CDI-R, which takes runoff as the central variable. The results indicate that the CDIs effectively capture drought events and exhibit strong correlations with traditional drought indices across the study area. They also maintain relatively low false alarm and omission rates. Moreover, they demonstrate sensitivity and responsiveness to various drought types. By analyzing drought events across varying patch-area thresholds, the optimal thresholds were identified as 6% for CDI-P and 5.5% for CDI-R. Compared with traditional indices, the CDIs offer improved capabilities in revealing the interrelationships among various drought processes and capturing their compound impacts. Since 2000, the North China Plain has experienced increasing trends in drought duration, intensity, and severity. Drought migration paths are predominantly aligned along the northeast-southwest orientation.

ABSTRACT Liaoning Province, a major grain production base in China, has faced increasingly frequent extreme drought events under global climate change, impacting local economic and social sustainability. Effective prevention requires comprehensive risk assessments. However, existing risk assessment studies often suffer from low spatial resolution and limited integration of geographic big data. This study integrates multi-source geographic big data, using 10 indicators across risk, vulnerability, and exposure dimensions. A comprehensive drought disaster risk assessment model was established by combining the analytic hierarchy process (AHP) and the entropy weight method. Theil–Sen median analysis evaluated drought risks from 2001 to 2021 and predicted future trends. Results revealed spatial heterogeneity in drought risks, with ‘higher in the west and north, lower in the east and south’ distribution. Chaoyang City, in the western hilly region, had the highest risk, with a vulnerability index above 0.65, while Panjin City in the east showed lower risk and a vulnerability index below 0.45. Over 20 years, the overall risk declined across the province. This method aligns with actual drought losses, validating its effectiveness and enhancing understanding of drought risk patterns to mitigate impacts.

Environmental management, particularly in ecologically vulnerable areas sensitive to climate change, such as Northern China, depends crucially on understanding the mechanisms governing changes in vegetation greening. However, the extent of climate change and human activity’s impact on vegetation change remains unclear, hindering effective management for environmental sustainability. In this study, we characterized spatiotemporal dynamics of vegetation from 1982 to 2022 using the kernel normalized difference vegetation index (kNDVI), and quantified the relative contributions of climate change and human activity to vegetation changes. Results showed that kNDVI increased at 0.008/10 a from 1982 to 2022, with a notably accelerated rise after 2000. Human activities. dominated the kNDVI variations area accounting for 52.45

Amidst the ongoing challenges of climate change, droughts are projected to become more frequent and intense, posing significant risks to water resources and food security. The occurrence and progression of seasonal droughts and their driving mechanisms are becoming increasingly complex. This study examines the seasonal characteristics of meteorological droughts in the North China Plain (NCP) using the Standardized Precipitation Evapotranspiration Index (SPEI). Wavelet coherence (WTC) and multivariate wavelet coherence (MWC) were used to explore the coupling effects of atmospheric circulation factors (ACFs) on seasonal droughts. The results reveal pronounced seasonal differences. Drought events predominantly occur in spring and summer, with spring droughts being particularly severe. Seasonal drought exhibits distinct spatial patterns, with elevated spring drought risk in the northeast affecting the central region, intensified summer drought in the northwest, heightened autumn drought risk in the east with pronounced severity in Henan, and winter droughts are most substantial in the southern region. Seasonal droughts are influenced by different ACFs in each season. In spring, the negative phase of the West Pacific Index (WP), in conjunction with the Pacific Decadal Oscillation (PDO) and Indian Ocean Dipole (IOD), is associated with more frequent or severe droughts. During summer, drought conditions are intensified under the positive phase of the PDO combined with WP and El Niño (Niño3.4). In autumn, drought occurrence is more likely under the combined effects of the negative phase of the Southern Oscillation Index (SOI) and the IOD. In winter, the Niño3.4–SOI–Sunspot Count (Sunspot) combination, especially under the negative phase of Niño3.4, corresponds to increased drought risk. These findings provide critical insights into the mechanisms driving seasonal droughts and provide a valuable foundation for enhancing agricultural productivity and water resource management strategies in the NCP.

Study region: The South-to-North Water Diversion Eastern Route Project section from the Nansihu-Dongpinghu pumping station cluster.Study focus: An integrated framework for prediction and sensitivity analysis of water levels in front of pumping stations is proposed to obtain more accurate predictive surrogate models and to simplify surrogate model inputs. The results show that among the three different water transport models, the Firefly-Support Vector Machine model has a smaller mean absolute error (<2.38 %), root mean square error (<4.76 %), and mean absolute percentage error (<0.07 %) with higher linear correlation (>0.85). The Firefly-Support Vector Machine model is more suitable for water level prediction than other models. The water level in front of the target pumping station and the t-ahead flow were the most sensitive parameters, and the longer the foresight period, the higher the importance.New hydrological insight for the region: Three water transportation modes are proposed according to the characteristics of regional hydrological connectivity in the long-distance water transportation system. This enables the water level prediction surrogate model to adapt to the complex connectivity of pumping stations and lakes in the region, improving the accuracy of water level prediction. Subsequently, the parameter sensitivity of the water level prediction surrogate model for each water transport mode was also tested.

In order to improve the applicability of the CMIP6 climate model in the North China Plain, the quantile mapping method was used to correct the daily temperature and precipitation data of five typical GCMs (CanESM5, FGOALS-g3, IPSL-CM6A-LR, GFDL-CM4, and MRI-ESM2-0, denoted as M1–M5, respectively) and their multi-model ensembles (MME). Based on the historical observational data from 1979 to 2021, the performance of the models before and after correction in terms of temperature, precipitation distribution, spatial deviation and Taylor diagram was evaluated. The results show that after the deviation correction, the simulation results of the models in the main temperature range (0-15°C and 15-30°C) and low precipitation range (<1mm and 1-3mm) are significantly improved, especially for the M1, M2 and M5 models. Taylor diagram analysis shows that the correlation coefficient between temperature and precipitation of the corrected model is generally more than 0.95, the standardized standard deviation is close to 1, the root mean square error is significantly reduced, and the MME performs the best. In summary, deviation correction can effectively improve the simulation accuracy of temperature and precipitation of GCMs in the North China Plain, and the combination of multi-model ensemble and bias correction is an important means for regional climate prediction in the future.

The increasing frequency of droughts under global climate change is a major threat to agricultural production. A thorough understanding of the impact of meteorological drought (MD) on agricultural drought (AD) is essential for developing effective mitigation strategies. As the primary method for assessing MD and AD relationships, the three-dimensional drought framework effectively captures the spatiotemporal continuity of drought events, but most existing matching approaches overlook the cumulative effects of meteorological drought events (MDEs). In this study, we used the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Soil Moisture Index (SSI) to represent MD and AD, respectively, in Shandong Province (SD), China. We further refined the three-dimensional framework to better assess the response of AD to MD. The results show that: (1) the improved framework accounts for the cumulative effects of MDEs while ensuring alignment between agricultural drought events (ADEs) and their corresponding MDEs, reinforcing that AD is primarily driven by MD; (2) the severity of ADEs and the migration of drought centers are closely linked to MDEs, with clear geographic similarities; (3) the thresholds for MDEs to trigger mild, moderate, severe, and exceptional ADEs are 114.87 × 10 ^4 km ^2· months, 148.12 × 10 ^4 km ^2· months, 158.62 × 10 ^4 km ^2· months, and 202.98 × 10 ^4 km ^2· months, respectively. These findings provide a more reliable basis for monitoring agricultural drought and formulating proactive drought mitigation policies.

Owing to the complexity of droughts, detailed assessments of drought events have become a key issue in water resource management and planning. In this study, three-dimensional copula models at Standard Precipitation Evapotranspiration Index (SPEI)-1, SPEI-3, SPEI-6, and SPEI-12 were used to assess drought risks in the Haihe River Basin (HRB) of China from 1961–2020. Drought duration, severity, and peak, as indicated by SPEI, were extracted based on run theory and fitted with suitable marginal distributions. The difference between the joint return period (Tor) and the co-occurrence return period (Tand) could explain the intrinsic correlation between drought characteristics. The smaller the difference, the stronger the correlation. The results showed that droughts in the north-western region of the HRB were characterized by high peak, intense severity, and long duration. In contrast, the eastern region exhibited a higher frequency of drought occurrence. Furthermore, the decreasing trend in precipitation dominated droughts, and topography of the northwest region creates the features of low annual precipitation with more days of precipitation. The drought events in the HRB were influenced by the phase shift between El Niño and La Niña. There was a strong negative phase coupling between SPEI-12 and Niño3.4 (R2 ≥ 0.77). The transition from La Niña to El Niño was responsible for severe droughts in the HRB. The El Niño-Southern Oscillation could predict droughts with lag times of 0.15–4.35 mon in mountainous areas.

Spatial and temporal land-use patterns in the Songhua River Basin (SRB) over the past 20 years were analyzed; the influence of natural geographic, socioeconomic, and anthropogenic factors was considered. Using spatial analysis and geodetector modeling, we assessed various indicators to comprehensively analyze land-use changes in the SRB in a long time series (2001 to 2021). Our goal was to determine the extent to which each factor influences land-use change and the mechanisms of interaction. We found that natural geographic factors and anthropogenic factors, particularly elevation and population density, had a greater influence on land-use changes than climatic and socio-economic factors. Despite a positive trend in land use indicated by the composite index, the SRB is experiencing a decrease in undeveloped land resources annually. We also identified that interactions between factors had varying effects, with the superposition of multiple factors potentially exacerbating conflicts between different land-use types. These findings provide valuable insights for strategic planning, policy formulation, and optimization of land resources in the Songhua River Basin.

Objective and methods In this study, the hydrochemical method and self-organized neural network (SOM-KM) coupling method were employed to identify recharge sources and reveal the water-rich mechanism in the karst groundwater-rich area of the Changxiao karst water system in Jinan City. The contribution ratio of karst groundwater recharge sources in the karst groundwater-rich area was quantitatively calculated using the end-element mixed model. The enrichment mechanism of karst groundwater is explored by combining with topography, geological structure, stratigraphic lithology, and catchment conditions. Results The results showed that the karst groundwater in the catchment drainage area had similar water chemistry to that in the southern recharge area, the karst groundwater in the lateral runoff area, and the Yellow River, indicating a close hydraulic connection. This implies that the karst groundwater in the catchment drainage area is recharged by three sources: The southern mountain area, the karst groundwater in the lateral runoff area, and the Yellow River. The contribution ratios of the three components are 75.09%, 21.02%, and 3.89%, respectively. Carbonate rocks are widely distributed, and fissured karst is well developed in the accumulation and discharge areas, especially in the Maji-Xiaoli-Guide area. Moreover, there are abundant karst groundwater recharge sources in this area. During the runoff process of karst groundwater from southeast to northwest, it is impeded by sandstone and mudstone in the north. As a result, it accumulates in the contact zone between soluble rock and insoluble rock, thus forming impeded-type karst groundwater-rich structures. Conclusion Revealing the enrichment mechanism of karst groundwater in the Changxiao karst water system can provide scientific support for accurate calculations of recoverable resources and the protection of the springs in Jinan.

Global warming has increased the risk of meteorological drought and associated hazards. Based on daily data from 35 meteorological stations in the Songhua River Basin (SRB) collected from 1960 to 2019, this study applied the standardised precipitation evapotranspiration index (SPEI) and used the ensemble empirical mode decomposition (EEMD) method and Pettitt's mutation test to perform a partitioned segmentation analysis of the multi-temporal and spatial variations in the meteorological droughts affecting the basin in terms of frequency and intensity, and discussed the relationship between the atmospheric circulation index and meteorological drought. The results showed that the overall meteorological drought trends shifted from a drought to a humid trend from 1960 to 2019. The drought in the basin had periods of 3, 6.7, 15, and 30 years, with mutation points in 1968, 1980, 1997, and 2012. The drought trend in Regions I and IV of the entire basin was enhanced, while that in Regions II, III, and V was weakened. Drought in the basin was strongly influenced by the Arctic Oscillation (AO) and Southern Oscillation Index (SOI). HIGHLIGHTS The SRB shifted from a drought trend to a wet trend from 1960 to 2019. High-frequency and high-intensity drought areas are mainly distributed in Regions I and IV. Drought in the basin is greatly influenced by AO and SOI.

The modeling and prediction of air conditioning load in residential buildings has been an indispensable step in energy conservation and cost reduction. This paper proposes a data-driven load prediction model based on machine learning technology. Firstly, the input data is processed by feature engineering according to the Pearson Correlation Coefficient (PCC) between load and features. Secondly, three machine learning algorithms (SVR, MLP, and RF) and Pearson Correlation Coefficient (GTO) optimization algorithm are used for prediction. Finally, the predicted load is output by linear analysis and weighted regression. The model is verified by using residential building simulation data generated by Dest software. The influence of model integration on the overall prediction results and the error distribution of predicted load in different periods are analyzed and discussed. The Mean Absolute Error (EMAE) of predicted load is 1.52kW, and the Mean Absolute Percentage Error (EMAPE) is 10.7%.

ABSTRACT Peak discharge is an essential element of hydrological forecasting. Due to rapid outbreaks of flash floods in hilly areas and the lack of measured data, the fast and accurate estimation of peak discharge is crucial for flash flood hazard management. Three machine learning algorithms were applied to estimate peak discharge; this estimation was compared with the results of hydrological–hydraulic models, and the results were verified with measured watershed data. In this paper, 10 hydrological and geomorphological parameters were selected to predict the flood peak discharge in 103 watersheds in Taiyi Mountain North District. The results show that the particle swarm optimization backpropagation (PSO-BP) neural network model outperforms the BP neural network and random forest regression in prediction performance. PSO-BP has a lower mean absolute error (2.51%), root mean square error (3.74%), and mean absolute percentage error (2.74%) than the other models, which indicates that PSO-BP has high prediction accuracy. Importance analysis revealed that rainfall, early impact rainfall, catchment area, and rain intensity are the key input parameters of PSO-BP. The proposed method was confirmed to be a fast and relatively accurate algorithm for estimating the peak discharge of flash floods in ungauged basins.

Huaihe River Basin (HRB) is located in China’s north-south climatic transition zone, which is very sensitive to global climate change. Based on the daily maximum temperature, minimum temperature, and precipitation data of 40 meteorological stations and nine monthly large-scale ocean-atmospheric circulation indices data during 1959–2019, we present an assessment of the spatial and temporal variations of extreme temperature and precipitation events in the HRB using nine extreme climate indices, and analyze the teleconnection relationship between extreme climate indices and large-scale ocean-atmospheric circulation indices. The results show that warm extreme indices show a significant ( P < 0.05) increasing trend, while cold extreme indices (except for cold spell duration) and diurnal temperature range (DTR) show a significant decreasing trend. Furthermore, all extreme temperature indices show significant mutations during 1959–2019. Spatially, a stronger warming trend occurs in eastern HRB than western HRB, while maximum 5-d precipitation (Rx5day) and rainstorm days (R25) show an increasing trend in the southern, central, and northwestern regions of HRB. Arctic oscillation (AO), Atlantic multidecadal oscillation (AMO), and East Atlantic/Western Russia (EA/WR) have a stronger correlation with extreme climate indices compared to other circulation indices. AO and AMO (EA/WR) exhibit a significant ( P < 0.05) negative (positive) correlation with frost days and diurnal temperature range. Extreme warm events are strongly correlated with the variability of AMO and EA/WR in most parts of HRB, while extreme cold events are closely related to the variability of AO and AMO in eastern HRB. In contrast, AMO, AO, and EA/WR show limited impacts on extreme precipitation events in most parts of HRB.