Search Results (70)

Diurnally asymmetric warming under global climate change is reshaping terrestrial ecosystems, with important implications for vegetation productivity, biodiversity, and carbon sequestration. However, the mechanisms underlying the delayed and differentiated vegetation responses to daytime and nighttime warming, particularly under interacting precipitation regimes, remain insufficiently understood, limiting accurate assessments of ecosystem resilience under future climate scenarios. Clarifying how vegetation responds dynamically to asymmetric temperature changes and precipitation, including their lagged effects, is therefore essential. Here, we analyzed the spatiotemporal evolution of growing-season Normalized Difference Vegetation Index (NDVI) across the Yellow River Basin from 2001 to 2022 using Theil–Sen median trend estimation and the Mann–Kendall test. We further quantified the lagged responses of NDVI to daytime maximum temperature (Tmax), nighttime minimum temperature (Tmin), and precipitation, and identified their dominant controls using partial correlation analysis and an XGBoost–SHAP framework. Results show that (1) growing-season climate in the YRB experienced pronounced diurnal warming asymmetry: Tmax, Tmin, and precipitation all increased, but Tmin rose substantially faster than Tmax. (2) NDVI exhibited an overall increasing trend, with declines confined to only 2.72% of the basin, mainly in Inner Mongolia, Ningxia, and Qinghai. (3) NDVI responded to Tmax, Tmin, and precipitation with distinct lag times, averaging 43, 16, and 42 days, respectively. (4) Lag times were strongly modulated by topography, soil properties, and hydro-climatic background. Specifically, Tmax lag time shortened with increasing elevation, soil silt content, and slope, while showing a decrease-then-increase pattern with potential evapotranspiration. Tmin lag time lengthened with elevation, soil sand content, and soil pH, but shortened with higher potential evapotranspiration. Precipitation lag time increased with soil silt content and net primary productivity, decreased with soil pH, and varied nonlinearly with elevation (decrease then increase). By explicitly linking diurnal warming asymmetry to vegetation response lags and their environmental controls, this study advances process-based understanding of climate–vegetation interactions in arid and semi-arid regions. The findings provide a transferable framework for improving ecosystem vulnerability assessments and informing adaptive vegetation management and conservation strategies under ongoing asymmetric warming. Full article

Climate change is altering hydrological regimes across the North Aegean and Marmara regions of Türkiye, with increasing relevance for both drought occurrence and flood generation. This study examines long-term variability in temperature, precipitation, and evaporation using meteorological observations over a long time series and relates these changes to urban water management issues. Daily records from 12 meteorological stations, with data availability varying by station and extending back to 1926, were analysed using the non-parametric Mann–Kendall trend test and Sen’s slope estimator. The results indicate statistically significant warming trends across all stations, with several locations recording daily maximum temperatures exceeding 44 °C. Precipitation trends exhibit pronounced spatial heterogeneity: while most stations show decreasing long-term tendencies, others display unchanging or non-significant trends. Nevertheless, extreme daily rainfall events exceeding 200 mm are observed at multiple coastal and island stations, indicating a tendency toward high-intensity precipitation. Evaporation trends also vary across the region, with increasing rates at stations such as Tekirdağ and Çanakkale and decreasing trends at Bandırma and Yalova, reflecting the influence of local atmospheric conditions. Taken together, these findings point to a coupled risk of intensified flooding during short-duration rainfall events and increasing water stress during warm and dry periods. Such conditions challenge the effectiveness of conventional grey infrastructure. The results are therefore interpreted within the framework of the Sponge City approach, which emphasizes permeable surfaces, decentralized storage, infiltration, and the integration of green and blue infrastructure. By linking long-term hydroclimatic trends with urban design considerations, this study provides a quantitative basis for informing adaptive urban water management and planning strategies in Mediterranean-type climate regions. Full article

Introduction: Traditional approaches to discover teleconnections and quantify uncertainty, such as global sensitivity analysis, Monte Carlo experiments, decomposition analysis, etc., are computationally intractable for large-scale process-based Coupled Human and Natural Systems (CHANS) models. This study hypothesizes that machine-learned emulator models provide “computationally efficient” algorithms for discovering teleconnections and quantifying uncertainty within and across dynamically evolving human and natural systems. Objectives: This study aims to harness machine-learned emulator models to discover the relative contributions of internal- versus external-to-the-lake teleconnected processes driving the emergence of Harmful Algal Blooms (HABs) and trophic regime shifts. Three objectives are pursued: (1) build emulators; (2); quantify uncertainty and (3) identify teleconnections. Methods: Six machine-learned emulator models are trained on ~3.8 million observations for ~52 features derived from 332 scenarios simulated in an integrated process-based CHANS model that predicts water quality in Missisquoi Bay of Lake Champlain under alternate hydro-climatic and nutrient management scenarios for the 2001–2047 timeframe. The regression random forest (RRF), regression LightGBM (RLGBM) and regression XGBoost (RXGB) models predict the average surface mean of ChlA. Further, the classifier random forest (CRF), classifier LightGBM (CLGBM) and classifier XGBoost (CXGB) predict four trophic states of Missisquoi Bay. Relative importance and partial dependence plots are derived from all six emulator models to quantify relative uncertainty and importance of external-to-the-lake (climatic, hydrological, nutrient management) and internal-to-the-lake (P and N sediment release) drivers of HABs. Results: RXGB (R² = 96%, 48 features) outperforms RLGBM (R² = 95%, 37 features) and RRF (R² = 93%, 20 features) in predicting the average surface mean of ChlA. CLGBM (F1 = 96.15, 4 features) outperforms CXGB (F1 = 95.66, 48 features) and CRF (F1 = 93.06, 23 features) in predicting four trophic states. We discovered that predictor variables representing snow, evaporation and transpiration dynamics teleconnect hydro-climatic processes occurring in terrestrial watersheds with the biogeochemical processes occurring in the freshwater lakes. Conclusions: The proposed approach to discover teleconnections and quantify uncertainty through machine-learned emulator models can be scaled up in different watersheds and lakes for informing integrated water governance processes. Full article

Rainfall and streamflow analyses have long been central to hydrological research, yet traditional approaches often overlook the complexity introduced by changing climate signals, land-use dynamics, and human infrastructure. This study applies an integrated, data-driven framework to explore emerging hydroclimatic shifts in the Navasota River Watershed of east-central Texas. By combining autocorrelation analysis, Mann–Kendall and modified Mann–Kendall trend tests, and Pettitt’s change-point detection, we examine more than a century of precipitation and streamflow records alongside post-1978 reservoir operations. Results reveal an accelerating wetting tendency, particularly evident in decadal rolling averages and early-summer precipitation, accompanied by a statistically significant increase in 10-year moving averages of annual peak streamflow. While abrupt regime shifts were not detected, subtle but persistent changes point to evolving watershed memory and heightened flood risk in the post-dam era. This study reframes rainfall and streamflow trend analysis as a dynamic tool for anticipating hydrologic regime shifts, highlighting the urgent need for adaptive water infrastructure and flood management strategies in rapidly urbanizing and climate-sensitive watersheds. Full article

In the context of global warming, hydroclimatic conditions in the monsoon marginal zone are governed by two primary drivers: the East Asian monsoon and the westerly winds. As a sensitive indicator of climatic change, this region experiences disproportionately amplified adverse effects of climate change are markedly amplified, positioning it as a focal area for climatological research. However, the limited temporal coverage of instrumental records poses significant challenges for understanding historical hydroclimatic variability and its underlying mechanisms. To address this limitation, tree-ring width indices derived from 73 cores of Styphnolobium japonicum ((L.) Schott (1830)) are hereby employed to reconstruct summer maximum temperatures over a 433-year period in the central monsoon fringe zone—specifically, the northwestern Yan Mountains. Results confirm a strong correlation between the tree-ring width index of Styphnolobium japonicum and local summer maximum temperatures (r = 0.770, p < 0.01). Compared to the 19th century, the frequency of temperature fluctuations has increased substantially, with four abrupt regime shifts identified in the reconstructed series (1707, 1817, 1878, and 1994). Spectral analysis reveals cyclical patterns at interannual (2–7 years), decadal (10–30 years), and multidecadal (50 years) timescales. These oscillations align closely with known climate modes, including the EI Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the Atlantic Multidecadal Oscillation (AMO). Among them, the AMO presents particularly strong coherence with the reconstructed temperature variability. These outcomes improve insights into long-term temperature dynamics in the region and highlight the value of dendroclimatic proxies in reconstructing past climate conditions. Full article

This paper examines the Harirud (Harirod, Tejen) River Basin, a vital transboundary water source shared by Afghanistan, Iran, and Turkmenistan. The basin supports farming, energy production, and home supply in a dry area. Despite its ecological, socio-economic, and geopolitical importance, the basin lacks a cooperative governance framework, leaving it vulnerable to unilateral development, institutional weakness, and climate stress. Addressing an important research gap, this study investigates how unilateral water infrastructure and climate change jointly reshape water security and governance between Afghanistan and Iran. A qualitative case study approach integrates insights from hydropolitics, benefit sharing, and environmental security to analyse ecological and political dynamics. Findings show that climate change has disrupted hydrological regimes—average temperatures have increased by about 1.7 °C and rainfall has declined by roughly 150 mm since 1980. Unilateral dam constructions have altered seasonal flows and intensified hydro-political tensions. The study concludes that implementing Integrated Water Resources Management (IWRM), joint hydrological monitoring, climate adaptation, and equitable benefit-sharing can transform the Harirud from a contested river into a foundation for regional stability and sustainable development. Full article

This study examines long-term trends in maximum (Tmax) and minimum (Tmin) near-surface air temperatures and precipitation across South America, focusing on Brazilian biomes and national capitals, using ERA5 reanalysis data for 1979–2024. To isolate the underlying climate signal, seasonal cycles were removed using Seasonal-Trend decomposition based on Loess (STL), which effectively separates short-term variability from long-term trends. Temperature trends were quantified using ordinary least squares (OLS) regression, allowing consistent estimation of linear changes over time, while precipitation trends were assessed using the non-parametric Mann–Kendall test combined with Theil–Sen slope estimation, a robust approach that minimizes the influence of outliers and serial correlation in hydroclimatic data. Results indicate widespread but spatially heterogeneous warming, with Tmax increasing faster than Tmin, consistent with reduced cloudiness and evaporative cooling. A meridional precipitation dipole is evident, with drying across the Cerrado, Pantanal, Caatinga, and Pampa, contrasted by rainfall increases in northern South America linked to ITCZ shifts. The Pantanal emerges as the most vulnerable biome, showing strong warming (+0.51 °C decade⁻¹) and the steepest rainfall decline (−10.45 mm decade⁻¹). Satellite-based fire detections (2013–2024) reveal rising wildfire activity in the Amazon, Pantanal, and Cerrado, aligning with the “hotter and drier” climate regime. In the capitals, persistent Tmax increases suggest enhanced urban heat island effects, with implications for public health and energy demand. Although ERA5 provides coherent spatial coverage, regional biases and sparse in situ observations introduce uncertainties, particularly in the Amazon and Andes, these do not alter the principal finding that the magnitude and persistence of the 1979–2024 warming lie well above the range of interdecadal variability typically associated with the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO). This provides strong evidence that the recent warming is not cyclical but reflects the externally forced secular warming signal. These findings underscore growing fire risk, ecosystem stress, and urban vulnerability, highlighting the urgency of targeted adaptation and resilience strategies under accelerating climate change. Full article

Westerlies Asia, which includes arid Central Asia (ACA) and arid West Asia (AWA), is characterized by water vapor transport primarily controlled by the westerlies. Recent studies have identified a dipole pattern in hydroclimate variability between ACA and AWA during both the Holocene and modern period. However, it remains unclear whether such a dipole pattern persisted over the past millennium. Our findings demonstrate that the PMIP4 multi-model simulations reveal a dipole precipitation pattern between arid Central Asia and arid West Asia over the past millennium. During the Little Ice Age (LIA), annual precipitation increased in ACA but decreased in AWA, while the opposite pattern occurred during the Medieval Climate Anomaly (MCA). This dipole precipitation pattern is attributed to seasonal differences: increased spring precipitation in ACA together with decreased summer precipitation in AWA shaped the annual precipitation anomaly during the Little Ice Age, with a reversed regime during the Medieval Climate Anomaly. Mechanistically, a negative North Atlantic Oscillation (NAO) phase during LIA springs shifted the westerly moisture transport southward, enhancing moisture supply to ACA and increasing the precipitation there. In contrast, during LIA summers, a positive NAO phase displaced the westerly northward, reducing moisture advection to AWA, while a strengthened Azores High promoted moisture outflow and descending motion, suppressing precipitation. These findings offer a paleo-hydroclimatic basis for anticipating alternating dry-wet regimes between subregions, which can inform adaptive water allocation strategies, drought and flood preparedness, and long-term infrastructure planning across Westerlies Asia in a warming world. Full article

Understanding the persistence and stability of hydroclimatic trends is essential for climate adaptation and sustainable water resource management, particularly in Mediterranean regions characterized by irregular precipitation regimes. This study examines long-term rainfall variability (1974–2021) at six meteorological stations along the southern coasts of Türkiye using three complementary non-parametric techniques: the Mann-Kendall (MK) test, the Wilcoxon Signed-Rank Test (WT), and the Innovative Trend Analysis (ITA). The three tests were applied with their respective methodological extensions to enhance sensitivity and better capture trend stability. Results show that while most stations exhibit generally stable rainfall regimes, period- and location-specific variations with non-monotonic or oscillatory tendencies are present, revealing patterns that standard trend tests often fail to detect. The WT method was more responsive to short-term fluctuations, whereas ITA and its three-dimensional version (3D-ITA) provided valuable insights into trend persistence and stability. Overall, the findings highlight that trend stability assessment enables the distinction between transient climate variability and sustained hydroclimatic change, offering a stronger scientific basis for adaptive water management and regional sustainability planning under climate uncertainty. Full article

This research, carried out within the framework of the European Space Agency’s second Scout mission (HydroGNSS), seeks to utilize CYGNSS Level 1B products over land for soil moisture estimation. The approach involves a novel physically based algorithm, which inverts a semiempirical forward model of surface reflectivity proposed in the literature. An Artificial Neural Network (ANN) algorithm has also been developed. Both methods are implemented in the frame of the HydroGNSS mission to make the most of the reliability of an approach rooted in a physical background and the power of a data-driven approach that may suffer from limited training data, especially right after launch. The study aims to compare the results and performance of these two methods. Additionally, it intends to evaluate the impact of auxiliary data. The static auxiliary data include topography, Above Ground Biomass (AGB), land cover, and surface roughness. Dynamic auxiliary data include Vegetation Water Content (VWC) and Vegetation Optical Depth (VOD) from Soil Moisture Active Passive (SMAP), as well as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) from Moderate Resolution Imaging Spectroradiometer (MODIS), on enhancing the accuracy of retrievals. The algorithms were trained and validated using target soil moisture values derived from SMAP L3 global daily products and in situ measurements from the International Soil Moisture Network (ISMN). In general, the ANN approach outperformed the semiempirical model with RMSE = 0.047 m³ m⁻³ and R = 0.91. We also introduced a global stratification framework by intersecting land cover classes with climate regimes. Results show that the ANN consistently outperforms the semiempirical model in most strata, achieving around RMSE = 0.04 m³ m⁻³ and correlations above 0.8. The semiempirical model, however, remained more stable in data-scarce conditions, highlighting complementary strengths for HydroGNSS. Full article

Climate-driven hydrological extremes and anthropogenic interventions are increasingly altering streamflow regimes worldwide. While prior studies have explored climate or regulation effects separately, few have integrated multiple teleconnection indices and reservoir chronologies within a cross-basin comparative framework. This study addresses this gap by assessing long-term streamflow nonstationarity and its drivers at two key stations—Khabarovsk on the Amur River and Datong on the Yangtze River—representing distinct hydroclimatic settings. We utilized monthly discharge records, meteorological data, and large-scale climate indices to apply trend analysis, wavelet transform, percentile-based extreme diagnostics, lagged random forest regression, and slope-based attribution. The results show that Khabarovsk experienced an increase in winter baseflow from 513 to 1335 m³/s and a notable reduction in seasonal discharge contrast, primarily driven by temperature and cold-region reservoir regulation. In contrast, Datong displayed increased discharge extremes, with flood discharges increasing by +71.9 m³/s/year, equivalent to approximately 0.12% of the mean flood discharge annually, and low discharges by +24.2 m³/s/year in recent decades, shaped by both climate variability and large-scale hydropower infrastructure. Random forest models identified temperature and precipitation as short-term drivers, with ENSO-related indices showing lagged impacts on streamflow variability. Attribution analysis indicated that Khabarovsk is primarily shaped by cold-region reservoir operations in conjunction with temperature-driven snowmelt dynamics, while Datong reflects a combined influence of both climate variability and regulation. These insights may provide guidance for climate-responsive reservoir scheduling and basin-specific regulation strategies, supporting the development of integrated frameworks for adaptive water management under climate change. Full article

Rivers play a crucial role in the hydrological cycle and serve as essential freshwater resources for both human populations and ecosystems. Climate change significantly alters precipitation patterns and river discharge variability. However, the impact of precipitation patterns (rainfall and snowfall) and air temperature on river discharge in coastal zones remains inadequately understood. This study focused on Toyama Prefecture, located along the Sea of Japan, as a representative coastal area. We analyzed over 30 years of datasets, including air temperature, precipitation, snowfall, and river discharge, to assess the effects of climate change on river discharge. Trends in hydroclimatic datasets were assessed using the rescaled adjusted partial sums (RAPS) method and the Mann–Kendall (MK) non-parametric test. Furthermore, a correlation analysis and the Structural Equation Model (SEM) were applied to construct a relationship between precipitation, temperature, and river discharge. Our findings indicated a significant increase in air temperature at a rate of 0.2 °C per decade, with notable warming observed in late winter (January and February) and early spring (March). The average river fluxes for the Jinzu, Oyabe, Kurobe, Shou, and Joganji rivers were 182.52 m³/s, 60.37 m³/s, 41.40 m³/s, 38.33 m³/s, and 18.72 m³/s, respectively. The tipping point for snowfall decline occurred in 1992, marked by an obvious decrease in snowfall depth. The SEM showed that, although rainfall dominated the changes in river discharge (loading = 0.94), the transition from solid (snow) to liquid (rain) precipitation may alter the river discharge regime. The percentage of flood occurrence increased from 19% (1940–1992) to 41% (1993–2020). These changes highlight the urgent need to raise awareness about the impacts of climate change on river floods and freshwater resources in global coastal regions. Full article

The objective of this study was to analyse the influence of two large-scale climate indices on precipitation in the Danube basin, both separately and in combination. The evolution of the hydroclimatic regime in this area is of particular importance but has received limited attention. One of the indices for these data is the well-known the North Atlantic Oscillation (NAOI) climate index, which has been used in numerous investigations; the aim of using this index is to determine its influence on various hydroclimatic variables in many regions of the globe. The other index, the Greenland–Balkan Oscillation index (GBOI), has been demonstrated to have a greater influence on various hydroclimatic variables in Southeastern Europe compared to the NAOI. First, through different bivariate methods, such as estimating wavelet total coherence (WTC) in the time–frequency domain and applying partial wavelet coherence (PWC), the performance of the GBOI contributing to precipitation in the Danube basin was compared with that of the NAOI in the winter season. Then, by using relatively simple multivariate methods such as multiple linear regression (MLR) and a variant thereof called ridge regression (RR), notable results were obtained regarding the prediction of overall precipitation in the Danube basin in the winter season. The training period was 90 years (1901–1990), and the testing period was 30 years (1991–2020). The used Nash–Sutcliffe (NS) performance criterion varied between 0.65 and 0.94, depending on the preprocessing approach applied for the input data, proving that statistical modelling for the winter season is both simple and powerful compared to modern deep learning methods. Full article

The Dobrogea region, located in southeastern Romania, experiences a semi-arid climate. This study provides a deep analysis of monthly precipitation series from 46 meteorological stations spanning 1965–2005, exploring mean and variance characteristics and detecting structural changes in precipitation patterns. The series normality was assessed using the Lilliefors test, and transformation, such as the Yeo–Johnson method, was used to address skewness. Analyses of mean and variance included parametric (t-tests, ANOVA) and non-parametric (Mann–Whitney U, Fligner–Killeen) tests to address the homogeneity/inhomogeneity of the data series in mean and variance. Change points were detected using a Minimum Description Length (MDL) framework, modeling the series as piecewise linear regressions with seasonal effects and autocorrelated errors. Pairwise comparisons indicate the low similarity of the series means, and variances, so spatial and temporal variability in precipitation is notable. Validation of the proposed MDL approach on synthetic datasets demonstrated high accuracy, and application to real data identified significant shifts in precipitation regimes. Applied to the monthly series collected at the ten main hydro-meteorological stations, a MDL framework provided at least two change points for each. Full article

Climate change is projected to bring substantial changes to hydroclimatic extremes, which will affect natural river regimes and have wide-ranging impacts on human health and ecosystems, particularly in Central Highland Vietnam. This study focuses on understanding and quantifying the projected impacts of climate change on streamflow in the Kon-Ha Thanh River basin, using the Soil and Water Assessment Tool (SWAT) between 2016 and 2099. The study examined projected changes in streamflow across three time periods (2016–2035, 2046–2065, and 2080–2029) under two scenarios, Representative Conversion Pathways (RCPs) 4.5 and 8.5. The model was developed and validated on a daily scale with the model performance, yielding good performance scores, including Coefficient of Determination (R²), Nash-Sutcliffe Efficiency (NSE), and Root Mean Squared Error (RMSE) values of 0.79, 0.77, and 50.96 m³/s, respectively. Our findings are (1) streamflow during the wet season is projected to increase by up to 150%, particularly in December, under RCP 8.5; (2) dry season flows are expected to decrease by over 10%, beginning in May, heightening the risk of water shortages during critical agricultural periods; and (3) shifts in the timing of flood and dry seasons are found toward 2099 that will require adaptive measures for water resource management. These findings provide a scientific foundation for incorporating climate change impacts into regional water management strategies and enhancing the resilience of local communities to future hydroclimatic challenges. Full article