Search Results (90)

Atmospheric water vapor is a major driver of Earth’s climate, yet despite its vital role in driving extreme weather and informing Numerical Weather Prediction (NWP) models, precise quantification of Precipitable Water Vapor (PWV) remains a challenge due to its high spatial and temporal variability. To provide an upgraded evaluation reflecting the most recent data and next-generation instrumentation, this study evaluates the accuracy, relative to radiosonde measurements, in calculating PWV values across six different instruments: Microwave Radiometer (MWR), NOAA-21, TROPOMI, Pandora spectrometer, AERONET sun-photometer, and GNSS/GPS against the bias-corrected Vaisala RS-92 and RS-41 radiosondes over Beltsville, Maryland, utilizing an updated 2024–2025 database. As a certified GRUAN site, HUBC adheres to strict international observation standards designed specifically to provide reference-quality data and comprehensive corrections for systematic errors. This rigorous framework justifies their application as the definitive ‘referent truth’ benchmark for remote sensing validation. RMSE and bias were the primary metrics used to assess relative accuracy. GPS measurements provided the highest level of relative accuracy, yielding the lowest RMSE (1.50 mm) and a near-unity linear fit (y = 0.98x). NOAA-21 and TROPOMI exhibit higher random noise when compared to ground-based instrumentation, yet both obtain high relative accuracy retrievals with negligible biases. AERONET and Pandora also showed strong performance with low RMSEs and R² values of 0.986 and 0.988, respectively, while slightly underestimating PWV. In contrast, the Radiometer performed with the lowest relative accuracy, characterized by the highest RMSE (6.19 mm) and a significant negative bias (−5.55 mm). Although all instruments maintained high correlation coefficients (R² ≥ 0.904), these results indicate that satellite and ground-based remote sensing provide reliable PWV retrievals, while GPS presents the most robust benchmark for high-accuracy PWV retrievals relative to radiosonde measurements. The findings also underscore the need for instrument-specific calibration constants to better align remote sensing retrievals with in situ observations. Full article

Extreme climate events, particularly precipitation extremes, pose significant risks to ecosystems, infrastructure, and socio-economic systems globally. In Peru, the diversity of its climate, driven by its complex topography, makes it highly vulnerable to such events, especially in the Andes and Amazon regions. This study evaluates the performance of 25 CMIP6 GCMs in simulating extreme precipitation events during both the wet and dry seasons at the national level. Gridded precipitation data from the PISCO product and CMIP6 model simulations for the period 1981–2014 were used to estimate extreme precipitation indices, including Rx1day, Rx5day, SDII, CDD, CWD, R10mm, and PRCPTOT. Performance was assessed using statistical metrics such as PBIAS, NRMSE, and the Pattern Correlation Coefficient (PCC), integrated through a TOPSIS ranking. Results indicate that NorESM2-MM, MPI-ESM1-2-LR, and CESM2 exhibit the best performance, achieving TOPSIS scores above 0.8. These models show high spatial correlation (PCC frequently >0.8) and relatively low biases. In contrast, models like FGOALS-g3 and CanESM5 show significant limitations, with PBIAS exceeding 80% in Rx1day and Rx5day and TOPSIS scores below 0.5. The ensemble reveals a persistent ‘drizzle bias,’ with wet day frequency (R1mm) generally overestimated by 20–40% in the wet season and by 40–80% during the dry season across most CMIP6 models. Furthermore, indices of temporal persistence (CWD and CDD) remain the most challenging, with CWD overestimations often exceeding 100–200%. These findings highlight the critical need for statistical or dynamical downscaling, together with bias correction, before using CMIP6 projections for local adaptation strategies in the Andes and Amazon regions. Full article

Climate change is shifting building thermal demand from heating toward cooling, yet quantitative, design-relevant projections for semi-arid continental cities remain scarce. This study projects heating and cooling degree days (HDDs and CDDs) for Ankara, Türkiye, using ERA5-Land as the reference, and a quantile delta mapping (QDM) bias-corrected CMIP6 ensemble under SSP2-4.5 and SSP5-8.5, across eight base temperatures (HDD 15–18 °C; CDD 23–26 °C) to 2100. By the far future, HDD18 decreases by 23–37% whereas CDD24 increases by +205 to +498 °C·day/year (with wide inter-model spread), and the heating degree days factor declines from 0.984 to 0.765, marking a shift toward cooling within the degree-day budget; the cooling season extends from 25 to as many as 106 days. In a deterministic −1 to +2 °C local-temperature-offset test, the direction of change persists, and the cooling design dry-bulb temperature rises by up to +7.3 °C, raising peak cooling loads. Independent 2020–2022 station observations support the HDD projections (CDD projections were not independently validated). Because a single grid cell and station cannot resolve urban canyon or local climate zone contrasts, the conclusions apply to the campus vicinity. Overall, the study translates the climate signal into HVAC-sizing and retrofit-relevant metrics for a warming semi-arid capital. Full article

Numerical weather simulations and forecasts are highly sensitive to environmental conditions. This is especially true in Taiwan, an ocean-surrounded island, during its transition season. Atmospheric temperature and moisture profiles retrieved from spaceborne sounders provide essential environmental information in regions lacking in situ observations. However, infrared sounders are sensitive to clouds and may induce uncertainties related to cloud properties. The present study analyzed 1 year of soundings from the National Oceanic and Atmospheric Administration’s Unique Combined Atmospheric Processing System (NUCAPS) to investigate the effects of clouds on the retrievals. The results indicated that the retrieved temperature profiles over land and under clouds had greater uncertainty than over oceans or in clear skies. In addition, the moisture profiles often exhibited a bias against cloud-top pressure. Therefore, this study proposed an objective quality control and bias correction method based on cloud effects. Excluding temperature observations affected by clouds and those over land reduced the root mean square difference from 3.3 K to 1.3 K. The relative cloud-top pressure level was used to conduct water vapor bias correction, which achieved effective correction for dry bias in the retrieved moisture profiles. After appropriate constraint criteria were applied, the bias-corrected profiles demonstrated a reduction in moisture bias from −4% to nearly 0%. That is, we assimilated sounding and radiance data into the regional Weather Research and Forecasting model and evaluated their effects, and we discovered that the retrieved profiles and direct observations positively contributed to the forecast of a spring frontal system. However, experiments using objective-bias-corrected sounding data improved skill scores in precipitation forecasts compared with using original sounding data or radiance data under a standard global operational baseline bias correction. Full article

Climate change and increasing water demand threaten water security in strategic high-Andean reservoirs by altering seasonal water availability, increasing evaporative losses, and intensifying pressure on regulated storage. This study assesses the hydrological vulnerability of the Pañe Reservoir, Arequipa, Peru, under CMIP6 scenarios SSP2-4.5 and SSP5-8.5 and three water-demand trajectories projected to 2100. A reservoir-scale water-balance model was calibrated using a genetic algorithm with CHIRPS precipitation and ERA5-Land temperature data, achieving satisfactory performance in calibration (NSE = 0.886; $R^2$ = 0.907) and validation (NSE = 0.888; $R^2$ = 0.891). Climate projections from MPI-ESM1-2-LR, EC-Earth3-Veg-LR, and EC-Earth3 were bias-corrected using Quantile Delta Mapping. Under baseline demand, active storage maintains positive trends of +1.3 $\times {10}^6$ to +2.2 $\times {10}^6$ m³/decade; however, a 100% demand increase produces negative trends of up to −2.8 $\times {10}^6$ m³/decade under SSP2-4.5. Effective precipitation increases by +0.123 and +0.283 mm/day under SSP2-4.5 and SSP5-8.5, respectively, while Rx5day and Rx10day rise by +5.43/+8.78 and +12.13/+19.42 mm/decade. These trends suggest greater wet-season concentration of water inputs, but not necessarily improved dry-season security under rising demand. Reservoir warming reaches +0.60 °C/decade under SSP5-8.5, which may increase evaporative losses and could create conditions more favorable to water-quality deterioration. Overall, future vulnerability in the Pañe system reflects the imbalance between increasing withdrawals and the reservoir’s capacity to regulate highly seasonal high-Andean inflows, highlighting the need for demand control, loss reduction, bofedal protection and complementary wet-season storage. Full article

Accurate rainfall estimates are essential for managing water resources and planning for climate risks in semi-arid regions, yet long-term gauge networks in these environments are often extremely limited. In this study, we evaluate three widely used multi-source precipitation datasets—CHIRPS, IMERG, and ERA5-Land—against long-term observations from Ed Dueim and Kosti, the two main reference stations in White Nile State, central Sudan. The assessment covers monthly and annual scales across each product’s available record (1952–2022) and uses a broad set of metrics, including Pearson and Spearman correlations, NSE, KGE, RMSE, MAE, percent bias, and categorical detection scores (POD, FAR, CSI). All three datasets capture the region’s single-peak June–October monsoon pattern, but their accuracy differs sharply when it comes to rainfall amounts and year-to-year variability. CHIRPS performs best overall, with the strongest monthly efficiency scores of any product and a consistent, operationally correctable dry bias of 5–13%. IMERG shows strong monthly correlations but consistently overestimates rainfall by 25–42%, which leads to unreliable annual totals. ERA5-Land performs worst across nearly all metrics, with monthly NSE near or below zero, and frequent false alarms during the dry season. Taken together, the evidence points to CHIRPS as the most reliable dataset for routine hydro-climatic monitoring in White Nile State, while IMERG and ERA5-Land may still be useful in more specialized or time-specific applications. Full article

The escalating global threat of antimicrobial resistance (AMR) has intensified efforts to identify safe, effective, and sustainable alternatives to in-feed antibiotics in livestock production. The bovine gastrointestinal microbiome plays a central role in host immunity, nutrient utilization, and disease resilience, positioning microbiome-modulating interventions as promising candidates for antimicrobial stewardship. Despite growing experimental interest, a systematic synthesis of the available evidence in cattle is lacking. This systematic review aimed to evaluate the efficacy of microbiome-modulating interventions, including probiotics, prebiotics, postbiotics, phytogenic feed additives, essential oils, organic acids, and native rumen microbial supplements, as strategies to reduce antimicrobial use in cattle, and to characterize their effects on gut microbial diversity, fermentation characteristics, and host health and performance outcomes. A systematic search of Scopus, Web of Science, and EBSCOhost (including Academic Search Ultimate, MEDLINE with full text, and CAB Abstracts with Full text) was conducted in accordance with PRISMA guidelines. Studies were eligible if they used cattle (dairy cattle, beef cattle, calves, or mixed production systems), employed a microbiome-modulating intervention, and reported at least one microbiological or host outcome. Seventeen peer-reviewed studies published between 2010 and 2025 were included after full-text screening. Risk of bias was assessed using an adapted SYRCLE tool, which identified moderate overall study quality; the majority of included studies were randomized controlled trials or controlled experiments, though reporting of allocation concealment and blinding was inconsistent across studies. Across the 17 included studies, five broad categories of interventions were evaluated: probiotics (n = 5 studies), prebiotics (n = 2), postbiotics and organic acids (n = 4), phytogenic additives and essential oils (n = 4), and native rumen microbial supplements (n = 2). Animals spanned neonatal dairy calves, weaned Holstein calves, dairy heifers, lactating dairy cows, and Bos indicus feedlot beef cattle. Probiotics and organic acids most consistently improved growth performance: benzoic acid supplementation increased average daily gain by 8.4% (p < 0.05) and fructo-oligosaccharide prebiotics elevated body weight at weaning by 6.7% (p < 0.01). Native rumen microbial supplements improved energy-corrected milk yield by up to 3.1% without increasing dry matter intake. Polyphenols and bile acids demonstrated the strongest immunological and disease-preventive effects, reducing calf mortality by approximately 40% and disease severity by approximately 35%, respectively. Microbiome analyses revealed intervention-dependent increases in microbial diversity and shifts toward taxa associated with improved fermentation efficiency, including enrichment of propionate-producing Prevotellaceae, butyrate-associated Ruminococcus, and hindgut Bifidobacterium. Rumen fermentation outcomes included reductions in the acetate:propionate ratio and ammonia-N concentrations and improvements in fiber digestibility of 3.6–4.4 percentage units in dairy cows. Phytogenic additives preserved microbial diversity without inducing broad-spectrum suppression, functioning primarily as microbiome stabilizers rather than direct antimicrobial replacements. This systematic review provides evidence that gut microbiome modulation may enhance growth performance, improve fermentation efficiency, and reduce disease susceptibility in cattle, thereby supporting antimicrobial use reduction across dairy, beef, and mixed production systems. Effect magnitudes varied substantially across intervention categories and production contexts, and study quality was moderate, underscoring the need for larger, pre-registered trials with standardized outcome reporting and direct antibiotic comparator arms. Probiotics, prebiotics, and bile acid metabolites showed the greatest potential as components of integrated antimicrobial stewardship strategies in cattle production. Full article

Accurate prediction of dry matter intake (DMI) is essential for ration formulation, nutrient supply, and evaluation of production efficiency in lactating dairy cows. Several DMI prediction models are currently used, but most comparative studies have emphasized overall accuracy rather than whether model bias changes across biologically relevant production contexts. The objective of this study was to evaluate the context-dependent bias of widely used DMI prediction models in lactating dairy cows across classes of energy-corrected milk (ECM) and lactation stage. A literature-derived database was assembled from 135 studies consisting of 436 treatments from 6985 Holstein cows, reporting observed DMI and the variables required to implement five prediction models and evaluate their prediction error (PE): NRC2001, the Cornell Net Carbohydrate and Protein System (CNCPS), NASEM2021, Agroscope2021, and GfE2023. PE was calculated as predicted DMI minus observed DMI, such that positive values indicated overprediction and negative values indicated underprediction. Observations were classified according to ECM and days in milk (DIM). Mixed models were fitted separately for the ECM class and the lactation-stage class, with the study fitted as a random effect. PE differed among models, and the pattern of bias depended on both the ECM and the lactation-stage classes. The interaction between the ECM class and the model was significant, indicating that productive level modified model bias. The interaction between lactation-stage class and model was also significant and more pronounced, indicating marked changes in model bias across lactation stages. Across classes, NASEM2021 generally remained closest to zero, whereas GfE2023 and CNCPS showed more negative PE values in most contexts. Agroscope2021 showed a more context-sensitive pattern, and NRC2001 remained comparatively moderate across several classes. These findings indicate that the evaluation of DMI prediction models based only on global mean bias may conceal an important biological structure in PE. Context-specific evaluation, particularly across the lactation stage, may provide a more informative basis for selecting DMI prediction models for research and practical ration formulation. Full article

Per-tree geolocation in closed tropical canopies has typical uncertainties of 5–15 m with GNSS receivers, preventing automated linking of field inventories to point-cloud stem data. We propose an anchor-based matching framework that does not require per-tree GNSS. A handheld SLAM LiDAR scanner maps stems and girths within ≈40 min; field crews record species, girth, and serial numbers without physical markers or tools. Dataset linkage uses a small subset of reflective-tape anchor trees (35 and 43 per hectare, roughly one per 400–500 m²) with approximate GNSS locations. Species identity is transferred using median-based GNSS bias correction and quadrant-partitioned Hungarian matching with global deduplication; accuracy is validated by leave-one-anchor-out (LOAO) tests and exact binomial statistics. Tested in two 1-ha plots of open Dry Dipterocarp Forest (DDF; 280 trees/ha) and dense Dry Evergreen Forest (DEF; ~1054 trees/ha) at the Sakaerat Biosphere Reserve, Thailand, SLAM girth matched tape data with R² = 0.997, RMSE = 1.82 cm in DDF; in DEF, after correcting a 6.38 m GNSS bias, R² = 0.986 and RMSE = 7.01 cm, with ≥99% detection for stems ≥30 cm girth (99.2% DDF; 100% DEF). LOAO accuracy was 35/35 in DDF and 40/43 in DEF. Retroreflective-tape anchors were additionally detected automatically from the SLAM intensity channel in 71.4% of DDF anchors (95% CI 53.7–85.4) and 76.7% of DEF anchors (95% CI 61.4–88.2) at intensity ≥ 150 DN, with up to 59-fold enrichment over matched non-anchor controls at I ≥ 250 in DEF (Fisher’s exact p < 1 × 10⁻¹⁵), enabling a fully automated anchor-detection pipeline. Full article

Reliable precipitation projections are essential for water-resource management, flood-risk assessment, and drought preparedness in hydroclimatically complex regions such as Mexico, where uncertainty remains high due to monsoon dynamics, complex topography, and tropical moisture transport. This study evaluates paired CMIP5 and CMIP6 global climate models in simulating the historical (1940–2005) precipitation annual cycle across four regions of Mexico (NW, NE, SW, SE). Model outputs were compared against ERA5 and cross-validated with CRU using complementary metrics assessing error magnitude, variability, temporal phase, and spatial coherence. Results indicate that CMIP6 provides moderate but regionally heterogeneous improvements rather than a uniform advance. The most consistent gains occur in NE and SE Mexico, where dry biases are reduced and seasonal amplitude is better represented. In contrast, SW Mexico exhibits persistent summer wet biases linked to monsoon–topography interactions, while improvements in NW Mexico are mainly confined to selected individual CMIP6 models and are not consistently reflected in the ensemble median. A marked SW–SE summer dipole bias highlights ongoing deficiencies in representing moisture transport and convection. These findings demonstrate that increased model complexity does not guarantee improved regional skill and that ensemble medians may mask individual model performance, underscoring the need for targeted model selection, multi-dataset validation, and bias-correction strategies. Full article

Although satellite sensors provide global observations, factors such as cloud interference and narrow swath widths frequently result in partial data gaps which constrain the continuous spatiotemporal analysis of the column-averaged dry air mole fraction of CO₂ (XCO₂). To address this challenge, this study develops a novel multi-stage fusion framework that integrates GOSAT and OCO-2 data using inverse error variance weighting and a dynamic bias correction technique, generating a seamless monthly XCO₂ dataset for East Asia (2016–2024). Validation against TCCON measurements (RMSE = 1.22 ppm; R² = 0.96) and WDCGG data (RMSE = 2.85 ppm; R² = 0.76) demonstrates the high accuracy of the product. The results show that the growth rate consistently exceeds 2.2 ppm/year, with clear seasonal patterns characterized by spring maxima and summer minima. Spatially, the locus of rapid growth has shifted toward central and western China, reflecting patterns of regional economic development, while substantial concentrations still persist in the industrialized regions of eastern China, Japan, and South Korea. This study provides new insights into regional atmospheric CO₂ dynamics and emphasizes the efficacy of dynamic bias correction in data fusion. Full article

Climate change is expected to impact rainfall amount, seasonality, and dry/wet patterns, with direct implications for rainwater harvesting systems. This study aims to quantify how future rainfall may affect rainwater harvesting systems across Brazil by combining multi-model climate projections with a daily water balance model. A single-family social housing archetype (60 m² roof area; four occupants; 150 L/day/person; non-potable demand equal to 30% of total demand) was simulated for 652 Brazilian cities, using bias-corrected daily rainfall from the CLIMBra dataset and nineteen climate models. Historical conditions were compared with near-future and far-future projections under the SSP2-4.5 and SSP5-8.5 scenarios. Historically, the greater potential for potable water savings has occurred in wetter, less seasonal climates, such as those in the North. In contrast, more seasonal and drought-prone areas, such as the Northeast, showed lower reliability. In future climates, most models indicate relative reductions in the potential for potable water savings in the North, Northeast, and Centre–West, with larger reductions under SSP5-8.5 and in the far-future scenarios. The South shows the most significant divergence between models and may increase the potential for potable water savings in some projections. On the other hand, in the South, the volume of rainwater harvesting system overflow increases under future scenarios. This work contributes to the literature by delivering a national-scale, multi-model, uncertainty-aware evaluation of rainwater harvesting performance under non-stationary rainfall regimes. Full article

Background/Objectives: Therapeutic drug monitoring (TDM) is essential for optimizing immunosuppressive therapy in solid-organ transplant recipients by maintaining efficacy, while minimizing adverse effects. However, conventional TDM relies on venous sampling and separate assays for tacrolimus (TAC) in whole blood and mycophenolic acid (MPA) in plasma, thereby increasing patient burden and procedural complexity. To address these limitations, we investigated the clinical utility of a microvolume, liquid-phase microsampling device (MSW2™) in combination with liquid chromatography–tandem mass spectrometry (LC-MS/MS). Methods: We established and applied an LC-MS/MS method for simultaneous quantification of TAC, MPA, and mycophenolic acid β-D-glucuronide (MPAG) using only 2.8 µL of whole blood collected with MSW2™, which eliminates drying or extraction steps. Hematocrit-based correction was applied to estimate plasma MPA concentrations from whole-blood measurements. The method was evaluated in 60 renal transplant recipients with paired venous samples for comparison. Analytical performance was assessed using regression, Bland–Altman analyses, predictive metrics, and stability testing under different storage conditions. Results: Microsampled and venous concentrations were strongly correlated (R² > 0.95). Estimated plasma MPA concentrations derived from whole blood closely approximated plasma concentrations (bias < 5%). Reducing the sample volume from 5.6 µL to 2.8 µL improved precision and increased the success rate of blood collection from 72.9% to 94.0%. All analytes remained stable for up to 72 h at ≤25 °C. Conclusions: This approach enables accurate, simultaneous quantification of multiple immunosuppressants from trace blood volumes. By reducing sampling burden and simplifying logistics, it provides a clinically feasible and patient-centered strategy for precision TDM, supporting broader implementation of limited sampling strategies and expanding applicability to pediatric, home-based, and telemedicine settings. Full article

Climate change increasingly threatens the sustainability of regional water resources; therefore, robust station-scale precipitation projections are essential for basin-level planning. This study aims to develop and evaluate a hybrid, machine-learning-based statistical downscaling framework to generate monthly precipitation projections for the 21st century in the Büyük Menderes Basin, western Türkiye, using the HadGEM3-GC31-LL global climate model from the CMIP6. Monthly observations from 23 rainfall observation stations and ERA5 reanalysis predictors were employed to train station-specific Random Forest (RF) models, with optimal predictor sets identified through a multistage selection procedure (MPSP). Coarse-resolution general circulation model (GCM) fields were harmonized with ERA5 data using a three-stage inverse distance weighting (IDW), Delta, and Variance rescaling approach. The downscaled projections were bias-corrected using Quantile Delta Mapping (QDM) to maintain the climate-change signal. The RF models exhibited strong predictive skill across most stations, with test Nash–Sutcliffe Efficiency (NSE) values ranging from 0.45 to 0.81, RSR values from 0.43 to 0.74, and PBIAS values from −21.99% to +5.29%. Future projections indicate a basin-wide drying trend under both scenarios. Relative to the baseline, mean annual precipitation is projected to decrease by approximately 12.2, 19.6, and 33.7 mm in the near (2025–2050), mid (2051–2075), and late (2076–2099) periods under SSP2-4.5 (Shared Socioeconomic Pathway 2-4.5, a moderate greenhouse gas scenario). Under the high-emission SSP5-8.5 scenario, projected decreases are 25.2, 53.2, and 86.9 mm, respectively. Late-century reductions reach approximately 15–22% in several sub-basins. These findings indicate a substantial decline in future water availability and underscore the value of RF-based hybrid downscaling and trend-preserving bias correction for water resources planning in semi-arid Mediterranean basins. Full article

This study introduces the Hybrid Hydrological Forecast System (HHFS), a dual-stage, data-driven framework for monthly streamflow forecasting at the Santa Branca outlet in the upper Paraíba do Sul River Basin, Brazil. The system combines two nonlinear regressors, Multi-Layer Perceptron (MLP) and extreme Gradient Boosting (XGB), calibrated through a structured four-step evolutionary procedure in GA₁ (hydrological weighting, dual-regime Ridge fusion, rolling bias correction, and monthly mean–variance adjustment) and a hydro-adaptive probabilistic optimization in GA₂. SHAP-based analysis provides physical interpretability of the learned relations. The regressive stage (GA₁) generates a bias-corrected and climatologically consistent central forecast. After the full four-step optimization, GA₁ achieves robust generalization skill during the independent test period (2020–2023), yielding NSE = 0.77 ± 0.05, KGE = 0.85 ± 0.05, R² = 0.77 ± 0.05, and RMSE = 20.2 ± 3.1 m³ s⁻¹, representing a major improvement over raw MLP/XGB outputs (NSE ≈ 0.5). Time-series, scatter, and seasonal diagnostics confirm accurate reproduction of wet- and dry-season dynamics, absence of low-frequency drift, and preservation of seasonal variance. The probabilistic stage (GA₂) constructs a hydro-adaptive prediction interval whose width (max-min streamflow) and asymmetry evolve with seasonal hydrological regimes. The optimized configuration achieves comparative coverage COV = 0.86 ± 0.00, hit rate p = 0.96 ± 0.04, and relative width r = 2.40 ± 0.15, correctly expanding uncertainty during wet-season peaks and contracting during dry-season recessions. SHAP analysis reveals a coherent predictor hierarchy dominated by streamflow persistence, precipitation structure, temperature extremes, and evapotranspiration, jointly explaining most of the predictive variance. By combining regressive precision, probabilistic realism, and interpretability within a unified evolutionary architecture, the HHFS provides a transparent, physically grounded, and operationally robust tool for reservoir management, drought monitoring, and hydro-climatic early-warning systems in data-limited regions. Full article