Search Results (136)

Accurate runoff estimation in ungauged basins is critical for water resource management but often relies on static parameters like the runoff threshold (P₀), derived from the Soil Conservation Service Curve Number method, which fail to capture spatiotemporal soil moisture variability. This study proposes an automated methodology utilising Google Earth Engine to dynamically adjust P₀ by integrating daily soil moisture data from SMAP L4, land cover from MODIS, and precipitation from GSMaP. Unlike traditional approaches that use antecedent precipitation as a proxy, this method classifies moisture conditions using historical percentiles to update the threshold daily. The methodology was validated in two sub-basins within the Guadiana River basin (Spain). The results highlight a stark contrast between methods: while static regulatory values remained invariant (36 and 48 mm), the proposed dynamic model revealed significant fluctuations, with P₀ values ranging from over 50 mm in dry periods down to less than 14 mm during saturation. Conversely, the proposed dynamic method effectively captures real-time soil saturation, exhibiting adaptability with reductions in P₀ of up to 72% immediately following rainfall events. This satellite-based approach provides a scalable, physically consistent alternative for assessing runoff potential in data-scarce regions, significantly enhancing the reliability of hydrological modelling compared to conventional regulatory standards. Full article

Accurate characterization of precipitation in complex terrain is essential for hydrological modeling and climate studies. This study uses daily observations from 156 rain gauges in Sichuan Province (2015–2020) to evaluate two high-resolution satellite products (GSMaP-GNRT and IMERG-Early) and to develop a Transformer-based fusion framework at the gauge scale. All three datasets reproduce the regional seasonal cycle with more rainfall in summer and less in winter. At the daily scale, the fused product attains correlation comparable to GSMaP, while GSMaP and the fusion slightly overestimate precipitation (Bias = 6.24% and 5.21%), and IMERG shows stronger underestimation (Bias = −11.46%). At the monthly scale, the fused dataset achieves the best overall performance in terms of correlation, bias and RMSE. Spatially, the fusion reduces bias and RMSE and yields more homogeneous patterns over Sichuan’s complex terrain. Detection metrics indicate that the fused product increases the probability of detection and slightly improves the critical success index, while the false alarm ratio remains relatively high and comparable to the original products. This implies a gain in event sensitivity and spatial consistency rather than substantially reduced false alarms. Overall, the Transformer-based fusion provides a useful compromise between GSMaP and IMERG, adding value particularly for bias reduction, monthly statistics and event detection. The fused dataset offers a promising input for precipitation monitoring, hydrological simulation and disaster-risk analysis in Sichuan and similar mountainous regions. Full article

Satellite precipitation products (SPPs) generally exhibit varying accuracy and error characteristics, which influence their applicability in hydrological modeling. Based on gauge-observed precipitation and streamflow data, as well as runoff simulations from the SWAT model, this study evaluates the data accuracy, hydrological utility, and error propagation characteristics of eight SPPs derived from the GSMaP, IMERG, and PERSIANN algorithms in the Yellow River Source Region (YRSR), an alpine mountainous watershed. Results show that for estimating precipitation amounts and detecting precipitation events, post-processed GSMaP_Gauge (GGauge) performs best, followed by IMERG Final run data. These two datasets present good substitutability for gauge-based observations and demonstrate considerable potential in streamflow modeling. Specifically, after parameter recalibration, the performance of GGauge is comparable to that of gauge-derived simulations. Most propagation ratios of systematic bias (γ_RB) exceed one, while the ratios of random error (γ_ubRMSE) are below 1, indicating that, through hydrological simulation, systematic bias in precipitation data is more likely to be amplified, whereas random error is generally suppressed. Additionally, γ_ubRMSE exhibits more pronounced autocorrelation than γ_RB, with hotspots in the central region and cold spots in the western part of the YRSR, which is highly related to the basin slope. The statistical features and spatial patterns of error propagation indices help to identify zones that are sensitive to precipitation errors in the study area and highlight the need for targeted strategies to address different types of data error in the modification of SPPs for hydrological application. Full article

Satellite precipitation products offer great potential for acquiring reliable precipitation data in data-sparse areas, yet they have inherent uncertainties and errors as indirect observations. This study evaluated the accuracy of multi-source satellite precipitation products from daily and precipitation magnitude perspectives and discussed the spatiotemporal variation in their inversion errors. Based on ground rainfall observations, satellite products, and environmental factors, a Transformer-based multi-source precipitation fusion method was proposed, with its effectiveness preliminarily analyzed for daily precipitation in the Jingle River Basin. The main conclusions are as follows: (1) Compared with the observed precipitation data, the GSMaP_Gauge satellite remote sensing precipitation product showed the closest agreement with the observations, ranking first in all indicators except the Probability of Detection (POD). The MSWEP satellite remote sensing precipitation product followed in performance, while the CHIRPS satellite product performed the poorest. Satellite products showed distinct error characteristics across seasons and rainfall intensities, as well as general overestimation of light rain frequency and insufficient heavy rain capture; however, these products also showed better detection capability in flood seasons. Error spatial distribution was consistent with topography, vegetation coverage, and temperature. (2) Verification demonstrated that the Transformer fusion algorithm effectively reduced relative bias and improved correlation with ground data. The scheme which incorporated environmental factors outperformed the other, which only considered precipitation characteristics, achieving higher estimation accuracy and fusion stability. Full article

(1) Background: Urban traffic micro-environments show strong spatial and temporal variability. Short and intensive campaigns remain a practical approach for understanding exposure patterns in complex environments, but they need clear and interpretable summaries that are not limited to simple site or time segmentation. (2) Methods: We carried out a multi-site campaign across five traffic-affected micro-environments, where measurements covered several pollutants, gases, and meteorological variables. A machine learning framework was introduced to learn interpretable operational regimes as recurring multivariate states using clustering with stability checks, and then we evaluated their added explanatory value and cross-site transfer using a strict site hold-out design to avoid information leakage. (3) Results: Five regimes were identified, representing combinations of emission intensity and ventilation strength. Incorporating regime information increased the explanatory power of simple NO₂ models and allowed the imputation of missing H₂S day using regime-aware random forest with an $R^2$ near 0.97. Regime labels remained identifiable using reduced sensor sets, while cross-site forecasting transferred well for NO₂ but was limited for PM, indicating stronger local effects for particles. (4) Conclusions: Operational-regime learning can transform short multivariate campaigns into practical and interpretable summaries of urban air pollution, while supporting data recovery and cautious model transfer. Full article

Satellite precipitation products serve as valuable global data sources for hydrological modeling, yet their applicability across different hydrological models remains insufficiently explored. The distributed physics-informed deep learning model (DPDL), as a representative of emerging differentiable, physics-based hydrological models, requires a systematic evaluation of the suitability of multi-source precipitation products within its modeling framework. This study focuses on the Xiangjiang River Basin in southern China, where both a DPDL model and a Soil and Water Assessment Tool (SWAT) model were constructed. In addition, two model training strategies were designed: S1 (fixed parameters) and S2 (product-specific recalibration). Multiple precipitation products were used to drive both hydrological models, and their streamflow simulation performance was evaluated under different training schemes to analyze the compatibility between precipitation products and hydrological modeling frameworks. The results show that: (1) In the Xiangjiang River Basin of southern China, GSMaP demonstrated the best overall performance with a Critical Success Index of 0.70 and a correlation coefficient (Corr) of 0.79; IMERG-F showed acceptable accuracy with a Corr of 0.75 but had a relatively high false alarm rate (FAR) of 0.32; while CMORPH exhibited the most significant systematic underestimation with a relative bias (RBIAS) of −8.48%. (2) The DPDL model more effectively captured watershed hydrological dynamics, achieving a validation period correlation coefficient of 0.82 and a Nash–Sutcliffe efficiency (NSE) of 0.79, outperforming the SWAT model. However, the DPDL model showed a higher RBIAS of +16.69% during the validation period, along with greater overestimation fluctuations during dry periods, revealing inherent limitations of differentiable hydrological models when training samples are limited. (3) The S2 strategy (product-specific recalibration) improved the streamflow simulation accuracy for most precipitation products, with the maximum increase in the NSE coefficient reaching 15.8%. (4) The hydrological utility of satellite products is jointly determined by model architecture and training strategy. For the DPDL model, IMERG-F demonstrated the best overall robustness, while GSMaP achieved the highest accuracy under the S2 strategy. This study aims to provide theoretical support for optimizing differentiable hydrological modeling and to offer new perspectives for evaluating the hydrological utility of satellite precipitation products. Full article

Accurate precipitation data is vital for hydrological modeling, climate research, and water resource management, especially in arid regions like the United Arab Emirates (UAE), where rainfall is sparse and highly variable. This study assesses the performance of the Multi-Source Weighted-Ensemble Precipitation v2.8 (MSWEP) dataset against ground-based gauge data and three satellite precipitation products—CMORPH, IMERG, and GSMaP—across the UAE from 2004 to 2020. Evaluation metrics include statistical, categorical, and extreme precipitation indices. MSWEP shows a moderate correlation with gauge data (mean CC = 0.62), performing better than CMORPH (0.54) but below IMERG (0.68). It also yields lower RMSE and MAE than CMORPH and GSMaP, indicating improved error metrics. However, MSWEP overestimates light rainfall and underestimates extreme events, reflected in a lower KGE (0.42) and weak performance in the 95th percentile rainfall, especially in coastal and mountainous areas. Seasonal analysis reveals overestimation in winter and underestimation during summer convective storms. While MSWEP offers strong global coverage and temporal consistency, its application in arid environments like the UAE requires bias correction. These findings highlight the need for integrating multiple datasets and regional adjustments to enhance rainfall estimation accuracy for hydrological and climate-related applications. Full article

Real-time precipitation monitoring through satellite remote sensing represents a critical technological frontier for operational hydrology in data-scarce mountainous regions. Following a comprehensive evaluation of reanalysis precipitation products in the downstream Yarlung Zangpo watershed, this investigation advances understanding by systematically assessing eight satellite-based precipitation retrieval algorithms against ground truth observations from 18 meteorological stations (2014–2022). Multi-temporal performance analysis employed statistical metrics including correlation analysis, root mean square error, mean absolute error, and bias assessment to characterize algorithm reliability across annual, monthly, and seasonal scales. Representative monthly spatial analysis (January, April, July) and comprehensive 12 month × 18 station heatmap visualization revealed pronounced seasonal performance variations and elevation-dependent error patterns. Satellite retrieval algorithms demonstrated systematic underestimation tendencies, with observational precipitation averaging 2358 mm/yr, substantially exceeding remote sensing estimates across six of eight products. IMERG_EarlyRun and IMERG_LateRun achieved optimal performance with annual correlation coefficients of 0.41/0.37 and minimal bias (relative bias: −3.0%/1.4%), substantially outperforming other products. Unexpectedly, IMERG_FinalRun exhibited severe deterioration (correlation: 0.37, relative bias: −73.8%) compared to Early/Late Run products despite comprehensive gauge adjustment, indicating critical limitations of statistical correction procedures in data-sparse mountainous environments. Temporal analysis revealed substantial year-to-year performance variability across all products, with algorithm accuracy strongly modulated by annual precipitation characteristics and underlying meteorological conditions. Station-level assessment demonstrated that 100% of stations showed underestimation for IMERG_FinalRun versus balanced patterns for IMERG_EarlyRun/LateRun (53% underestimation, 47% overestimation), confirming systematic gauge-adjustment failures. Supplementary terrain–precipitation analysis indicated GSMaP_MVK_G shows superior spatial pattern representation, while IMERG_LateRun excels in capturing temporal variations, suggesting multi-product integration strategies for comprehensive monitoring. Comparative assessment with previous reanalysis evaluation establishes that satellite products offer superior real-time availability but exhibit greater temporal variability compared to model-based approaches’ consistent performance. IMERG_EarlyRun and IMERG_LateRun are recommended for operational real-time applications, GSMaP_MVK_G for terrain-sensitive spatial analysis, and reanalysis products for seasonal assessment, while IMERG_FinalRun and FY2 require substantial improvement before deployment in high-altitude watershed management systems. Full article

Against the backdrop of insufficient accuracy and adaptability of satellite precipitation products in complex terrain areas, this study focused on the Min River Basin (MRB) on the eastern edge of the Qinghai–Tibet Plateau. A two-step machine learning fusion framework was established, which integrates precipitation event identification and quantitative intensity estimation in a systematic manner. This framework incorporated 5 precipitation products (PERSIANN-CDR, CMORPH, GSMaP, IMERG, MSWEP), measured data, and environmental variables. The study compared the precipitation estimation performance of Random Forest (RF), Extreme Learning Machine (ELM), eXtreme Gradient Boosting (XGBoost), Bagging, and Double Machine Learning (DML) models, and analyzed the models’ performance under different precipitation intensities and altitudes, as well as their variable sensitivity. The results showed that: (1) DML models outperformed Single Machine Learning (SML) models and original precipitation products, with RF-Bagging being the optimal model. The daily-scale Correlation Coefficient (CC) of RF-Bagging was over 50% higher than that of original products, while the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were reduced by more than 40% and 35%, respectively. (2) For moderate-to-heavy precipitation, the RF-Bagging and RF-RF models maintain a stable Critical Success Index (CSI) of 0.7. In high-altitude regions, their Probability of Detection (POD) approaches 1, and the Heidke Skill Score (HSS) is 30–40% higher than that in mid-altitude areas, significantly outperforming other models and demonstrating strong adaptability to complex terrain. For light precipitation, while the POD values of these two models are comparable to those of other models, their False Alarm Rate (FAR) is reduced by 15–20%, effectively mitigating precipitation false alarms. (3) GSMaP, IMERG, and MSWEP were the core input variables for all models. RF and ELM models were more dependent on environmental variables, while XGBoost and Bagging models relied more on satellite data. This framework can provide technical references for precipitation estimation in complex terrain areas and contribute to watershed water resource management as well as flood prevention and mitigation. Full article

High-resolution Fourier transform infrared (FTIR) spectra of methyl fluoride (CH₃F) were recorded in the mid- and far-infrared regions using the Bruker IFS 125HR spectrometers at GSMA (Reims, France) and at the SOLEIL synchrotron facility (Saint-Aubin, France). The measurements cover both the pure rotational transitions of the ground state (10–100 cm⁻¹) and the vibrational triad region (1950–2450 cm⁻¹), which includes the 2${\nu }_3$, ${\nu }_3+{\nu }_6$, and 2${\nu }_6$ bands. Spectra were recorded under various pressure conditions to optimize line visibility, with a high resolution. Line assignments were performed using predictions from the tensorial effective Hamiltonian implemented in the MIRS package, together with a newly developed automated assignment tool, SpectraMatcher, which facilitates line matching and discrimination of CH₃F transitions from overlapping CO₂ features. More than 5000 transitions (up to $J=52$ in the ground state and up to $J=45$ in the triad and $K=19$) were assigned and included in a global fit. The sixth-order tensorial effective Hamiltonian model yielded excellent agreement with experiment, with root mean square (RMS) deviations better than 7 × 10⁻⁴ cm⁻¹ across all regions. This paper presents the first continuous rovibrational study of CH₃F over both the triad and far-infrared ground state regions. The improved accuracy from previous studies stems from the improved set of effective Hamiltonian parameters which will also form a good basis from future applications in atmospheric modelling and spectroscopic databases. Full article

In drylands, farmers practicing rain-dependent agriculture sow their seeds in accordance with the onset of the rainy season. Thus, the onset of the rainy season is extremely important, but inexperienced farmers cannot determine when it begins. To support farmers in determining the optimal sowing date, we propose a method that combines GSMaP estimates with actual measurements from a mobile application to present rainfall data and evaluate its usefulness. In the proposed method, GSMaP is used to visualize estimated rainfall data, but satellite-based estimates can differ from ground-based actual measurements. If farmers own smartphones, they can use a mobile application to record actual rainfall measurements. This allows farmers to selectively incorporate both estimated and actual rainfall data into their cultivation plans. Full article

The acquisition of high-precision spatiotemporal precipitation data with long-term continuity plays an irreplaceable role in supporting agricultural modeling, hydrological forecasting, disaster prevention, and climate research. This study evaluates and corrects daily precipitation from satellite products (GPM, PERSIANN-CDR, CMORPH, GSMaP), merged datasets (GPCP, MSWEP, CHIRPS), and reanalysis products (ERA5, GLDAS) over the Huang-Huai-Hai Plain from 2000 to 2020. The study proposes a two-stage “error correction and residual correction” optimization framework. The error correction stage integrates machine learning with statistical methods (RF-DQDM), while the residual correction stage uses ground observations to dynamically adjust systematic biases. Results show that all corrected products outperform their original versions in spatial patterns and statistical metrics. Original precipitation data exhibit significant systematic errors modulated by topography, with eastern lowlands showing smaller errors. After correction, correlation coefficients rise above 0.8, and RMSE reductions average 60%. And product responses diverge significantly. CHIRPS improves from weakest to top performer, while model limitations constrain GLDAS enhancements. This framework establishes a transferable monsoon region optimization paradigm. This study provides a transferable bias correction framework for monsoon regions and builds a homogenized high-precision precipitation benchmark. It also recommends using CHIRPS or ERA5 for extreme rainfall analysis and MSWEP or GPCP for hydrological applications. Full article

Drought is one of the most impactful natural disasters, and it significantly impacts three main sectors of a country: the environment, society, and the economy. Therefore, drought assessment and monitoring are essential for reducing vulnerability and risk. However, insufficient and sparse long-term in situ rainfall data limit drought assessment in developing countries. Recently developed satellite-based rainfall products, available at different temporal and spatial resolutions, offer a valuable alternative in data-poor regions like Sri Lanka, where rain gauge networks are sparse and maintenance issues are prevalent. This study evaluates the accuracy of satellite-based rainfall estimates compared to in situ observations for drought assessment within the Mi Oya River Basin, Sri Lanka. We assessed the performance of various satellite-based rainfall products, including IMERG, GSMaP, CHIRPS, PERSIANN, and PERSIANN-CDR, by comparing them with ground-based observations over 20 years, from 2003 to 2022. Our methodology involved checking detection accuracy using the False Alarm Ratio (FAR), Probability of Detection (POD), and Critical Success Index (CSI), and assessing accuracy through metrics such as Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC), Percentage Bias (PBias), and Nash–Sutcliffe Efficiency (NSE). The two best-performing satellite-based rainfall products were used for meteorological and hydrological drought assessment. In the accuracy detection metrics, the results indicate that while products like IMERG and GSMaP generally provide reliable rainfall estimates, others like PERSIANN and PERSIANN-CDR tend to overestimate rainfall. For instance, IMERG shows a CSI range of 0.04–0.25 for moderate and heavy rainfall and 0.10–0.30 for light rainfall. On a monthly scale, IMERG and CHIRPS showed the highest performance, with CC (NSE) values of 0.81–0.94 (0.53–0.83) and 0.79–0.86 (0.54–0.74), respectively. However, GSMaP showed the lowest bias, with a range of −17.1–13.2%. Recorded drought periods over 1981–2022 (1998–2022) were reasonably well captured by CHIRPS (IMERG) products in the Mi Oya River Basin. Our results highlighted uncertainties and discrepancies in the capability of different rainfall products to assess drought conditions. This research provides valuable insights for optimizing the use of satellite rainfall products in hydrological modeling and disaster preparedness in the Mi Oya River Basin. Full article

Volatile Organic Compounds (VOCs) are important contributors to indoor and occupational air pollution, such as environments involving the extensive use of paints and solvents. The routine measurement of VOCs is often limited by resource constraints, creating a need for indirect estimation techniques. This work presents the need for a predictive framework that offers a practical, interpretable alternative to a full-spectrum chemical analysis and supports early exposure detection in resource-limited settings, contributing to environmental health monitoring and occupational risk assessment. This study explores the capability of machine learning to simultaneously predict the concentrations of five paint-related VOCs using other co-emitted VOCs along with demographic variables. Three models—Multi-Output Gaussian Process Regression (MOGP), CatBoost Multi-Output Regressor, and Multi-Output Neural Networks—were calibrated and each achieved a high predictive performance. Further, a feature importance analysis is conducted and showed that certain VOCs and some demographic variables consistently influenced the predictions across all models, pointing to common exposure determinants for individuals, regardless of their specific exposure setting. Additionally, a subgroup analysis identified the exposure disparities across demographic groups, supporting targeted risk mitigation efforts. Full article

This study investigates how to use the gridded satellite datasets of observational precipitation to improve the performance of the climatological simulation by using the method of non-parametric quantile mapping (QM). The precipitation in Southeast Asia is simulated in 2001–2005 using the climate model of Weather Research and Forecasting (WRF). Two satellite datasets of observational precipitation, GSMaP and CHIRPS, are used for model training, simulation evaluation, and cross-validation. The evaluations of simulation and bias correction suggest that QM is able to perfectly correct the overall quantile distributions of the simulated precipitation, which is characterized by overestimation at most quantiles, especially for light and extreme precipitation. After the QM correction based on GSMaP (CHIRPS), the relative bias of the monthly average for all months is reduced from 39.3% to 4.1% (from 57.2% to 4.2%). The biases of spatial patterns are largely narrowed from 43.5% (59.4%) to 4.0% (2.5%) for annual-mean precipitation and from 43.5% (59.4%) to 4.0% (2.5%) for extreme precipitation. The results indicate that the QM correction based on the gridded satellite datasets outperforms the raw model output and greatly improves the estimates of the simulated precipitation. Full article