Search Results (1,899)

Conventional evaluations of rainfall prediction models rely on average accuracy, often masking the conditions, locations and causes of model failure and reduced reliability. This study proposes a paradigm shift from conventional average-accuracy benchmarking toward failure-aware forecast-error diagnosis in the Bengawan Solo River Basin, a tropical monsoon river basin in Indonesia with moderate topographic gradients (grid elevations span ≈ 300–650 m). Methodologically, forecasts from previously published models are treated as fixed inputs and their errors are modelled as the response variable, so the analysis diagnoses when and where models fail rather than retraining them. By treating forecast errors as response variables, rather than as random residuals, this study analyses 345,180 model–grid records–month records from ten individual models (RF, XGB, LGBM, SVR, MLP, LSTM, GRU, TCN, CNN, Transformer) and one best ensemble model (Ensemble_Q, a stacking of RF, XGB, SVR, MLP, LGBM, LSTM, GRU, TCN, CNN, Transformer) against observed CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation, seasonal phase, ENSO and IOD regimes (El Niño–Southern Oscillation and Indian Ocean Dipole, respectively), the MJO index (Madden–Julian Oscillation) as an additional analysis, and elevation as a topographic control, using log-error models, high-error logistic regression, interaction tests, and block bootstrap validation (N = 1000), false discovery rate, and spatial statistics. Results indicate that prediction errors are not random but are systematically controlled: the Transition II phase increases log-error by 245% (pooled log-error model) and raises the odds of a high-error event roughly 40-fold relative to the dry season; La Niña conditions amplify errors by 41% and the odds of a high-error event by 3.3 times (though this ENSO signal is largely entangled with co-occurring Negative-IOD months), and every 100 m increase in elevation increases errors by 26%, with errors forming distinct spatial clusters (Moran’s I = 0.78; p = 0.001). Ensemble_Q outperforms the baseline on an aggregate basis (mean absolute error, MAE = 54.10 mm) but still experiences error amplification under these conditions, while spatial deep-learning architectures (TCN, CNN, Transformer) prove most vulnerable to elevation gradients. All major patterns persisted across variations in thresholds, model subsets, ENSO definitions, multiplicity corrections, and bootstrapping. These findings confirm that superior mean accuracy does not guarantee operational reliability, and that conditional failure diagnosis is an essential complement to benchmarking rainfall predictions in tropical monsoon regions. Full article

This study investigates the relationship between key circulation factors and ensemble forecast uncertainty during an atypical extreme heavy-rainfall event under weak synoptic forcing that affected the Guangzhou–Foshan megacity cluster in the Pearl River Delta (PRD) on 8 September 2022. Here, “atypical” refers to a localized extreme event occurring over the low-elevation urban river network without strong synoptic-scale drivers. Framed as an event-specific retrospective diagnostic analysis, the study used the China Land Multi-source Precipitation Analysis System version 2.1 (CMPAS-V2.1), ERA5 reanalysis, and 3-km (R3) and 9-km (R9) ensemble forecasts from the CMA Tropical Regional Atmospheric Model Ensemble Prediction System (CMA-TRAMS EPS). Spearman rank correlation and Monte Carlo field-significance tests were first applied to identify environmental variables closely associated with hourly precipitation variations, pinpointing the 700-hPa U-wind, 500-hPa V-wind, and 850-hPa V-wind as the most significant circulation predictors. Spatial anomaly fields of these key predictors were then subjected to hierarchical clustering, with clustering robustness evaluated using the cophenetic correlation coefficient (CCC) and bootstrap resampling. Because the clustering structure of the 925-hPa V-wind was comparatively weak, it was excluded from the final member-selection procedure. Finally, circulation-consistent ensemble members were selected using a multi-predictor consensus criterion (retaining 13 R3 members and 7 R9 members), and their 24 h accumulated precipitation was averaged to obtain a circulation-conditioned subset mean. The results show that persistent high temperatures, abundant moisture in the middle and lower troposphere, and a favorable multilayer circulation configuration provided suitable conditions for convective instability accumulation and localized heavy rainfall development. The precipitation forecasts exhibited substantial member-to-member variability. Under a consistent evaluation threshold, the 9-km configuration demonstrated superior overall ensemble-mean spatial skill compared to the 3-km configuration, indicating that increasing horizontal resolution does not necessarily improve forecast skill for weakly forced extreme rainfall. The resulting circulation-conditioned subset means successfully shifted the predicted heavy-rainfall center toward the observed Guangzhou–Foshan region and reduced the overestimated heavy-rainfall magnitudes over northern Guangzhou. Rather than serving as a purely objective score-maximizing post-processing algorithm, this approach extracts physically indicative spatial scenarios from ensemble spread. These findings provide a practical diagnostic framework for conditional forecast correction of localized heavy rainfall under weak synoptic forcing, though validation across multiple independent cases and consistent quantitative verification are necessary to assess its operational generalizability. Full article

To apply distributed optical fiber strain sensing (DOFSS) to the monitoring of rainfall-induced landslide deformation of landslide-prone soil slopes, this study proposes a deployment scheme and installation technique for strain-sensing optical fiber cables in soil slopes based on existing engineering experience and validates them through laboratory-scale physical model tests. The results show that direct burial of optical fiber cables in boreholes is a suitable installation method. A 2.0 mm tight-buffered optical fiber cable anchored with 5.0 mm heat-shrink-tube anchors was adopted as the fixed sensing configuration in the laboratory tests. In the laboratory model tests, the locations of strain peaks measured by the optical fiber cables were spatially associated with the visibly deformed regions, indicating that the strain anomalies can be used to approximately locate internal strain-concentration zones. The slope toe was identified as the location where localized failure was first observed, and the second rapid increase in strain at the slope toe may be regarded as a potential precursor of accelerated localized deformation under the tested condition. Borehole spacing has a significant influence on monitoring accuracy and is recommended to be controlled within 20–48% of the horizontal length of the potentially unstable slope zone. The variations in volumetric water content, earth pressure, and wetting front migration at different locations of the slope are highly correlated with rainfall infiltration and the evolution of slope surface erosion. Full article

Karst groundwater systems are characterized by highly heterogeneous flow networks, resulting in complex responses of groundwater levels to precipitation variability. The Jinan Spring Basin, one of the most representative karst spring systems in northern China, has experienced substantial changes in spring discharge due to variations in precipitation, groundwater exploitation, and hydrogeological conditions. However, the temporal scales at which precipitation signals control groundwater-level fluctuations and the mechanisms governing their transmission within the karst aquifer remain poorly understood. In this study, daily precipitation data from 30 meteorological stations and groundwater-level records at Baotu Spring during 2016–2018 were analyzed using global wavelet spectrum (GWS) and wavelet transform coherence (WTC) approaches. The dominant precipitation cycles, scale-dependent precipitation–groundwater relationships, and phase-derived groundwater response lags were quantified to reveal the hydrological response characteristics of the Jinan karst system. Three prominent precipitation periods were identified at 17.37, 29.22, and 330.57 days. Groundwater responses presented clear temporal-scale dependence, with short-period signals showing rapid but localized responses, while intermediate and long-period signals demonstrated stronger and more persistent coherence. The percentage of significant coherence area (PASC) increased from 23.50% at the 0–17.37 day scale to 64.28% at the 29.22–330.57 day scale, indicating that accumulated precipitation rather than individual rainfall events exerts the dominant control on groundwater-level variations. The spatial distribution of response lags revealed that rapid responses (17.37 days) mainly occurred in the southern recharge areas, reflecting preferential recharge through well-developed karst conduits. Intermediate responses (29.22 days) showed a progressive increase in lag time from south to north, indicating the influence of regional groundwater flow and aquifer storage. Long-period responses (330.57 days) were locally enhanced near major faults, suggesting structural controls on deeper groundwater circulation. This study reveals that precipitation signals in the Jinan Spring Basin are transmitted through multiple groundwater circulation pathways with distinct temporal characteristics. The identified multi-scale response patterns provide new insights into the internal structure and hydrological functioning of karst aquifers and offer scientific support for sustainable management of spring water resources. Full article

Measurements are presented from a purpose-built outdoor test facility for the assurance of situational awareness sensors for Autonomous Vehicles (AVs). The testbed included meteorological instrumentation for an accurate high-resolution picture of weather traversing the site. Static targets at ranges up to 250 m were observed simultaneously with multiple radar, LiDAR and camera systems exposed to the natural weather events over a two-year period. The aim of the testbed was to collect data over a prolonged period of time to ensure the maximum amount of variation in weather was encountered. Uniquely, the testbed treats the measurement of weather as an equal metrology challenge to that of measuring the CAV sensor response with the aim of understanding the extent to which it is possible to quantitatively correlate sensor performance degradation with weather parameters. In this paper case study examples of the testbed data for rainfall, fog and ‘ideal’ neutral conditions have been analysed. Correlations between sensor performance degradation and weather parameters were observed in several cases, including comparisons with rain rate and MOR (visibility). However, large uncertainties and complex interactions between weather-related performance reduction and secondary weather effects, like sensor window and target surface wetting, make an explicit determination of the severity at which a weather condition causes a sensor to become untrustworthy, problematic. Full article

This study develops an interpretable and validated XGBoost–SHAP framework integrating multisource geospatial data and historical flood observations, with model performance evaluated using independent validation and future projections driven by bias-corrected CMIP6 climate scenarios to characterize the spatiotemporal variations and contributions of flood driving factors across three regions of Kazakhstan (2000–2025). The results demonstrate pronounced spatial differences in flood-driving factors: delayed snowmelt coupled with orographic rainfall dominates flood variability in the mountainous Almaty Region; hydrological memory effects regulate flood responses in the Akmola plains; and socioeconomic exposure shows an increasing contribution to flood risk evolution in Turkestan. Future multi-scenario simulations indicate that the flood-affected area in the Almaty Region is projected to increase by 10.8% under SSP2-4.5, which is associated with enhanced snowmelt processes, whereas the Akmola Region may experience a 23.2% reduction under SSP5-8.5, which is associated with changes in evaporation–soil moisture interactions. The interaction between socioeconomic development and natural hazards results in divergent risk trajectories: urban expansion in Akmola and Turkestan may offset declining hydroclimatic hazards, creating a potential risk paradox, whereas mountainous regions remain sensitive to concurrent increases in hazard intensity and exposure. These findings indicate that flood risk evolution in the studied regions of arid Central Asia is being increasingly influenced by socioeconomic dynamics in addition to natural hazards, highlighting the importance of differentiated adaptive planning strategies. Full article

Temperature and precipitation variations during the freeze–thaw period affect snowmelt and accompanying nitrogen export in a complex manner. These influences can be long-lasting, superimposed, and strengthened. Daily discharge and nitrate-nitrogen NO₃⁻-N concentrations were monitored during the snowmelt periods of 2015 and 2016 in an agricultural watershed in northeastern China. The SWAT model was used to simulate the water and NO₃⁻-N export during the snowmelt period of 1951–2014 to identify the controlling climate factors and the combinations associated with enhanced snowmelt water and NO₃⁻-N export. Our results show that the SWAT model performs well for Re values in simulating the daily snowmelt runoff and NO₃⁻-N export, but poorly for NSE and R² values in simulating NO₃⁻-N export. This is attributed to the absence of snowmelt water refreezing and hysteresis modules. The number of days and precipitation of the stable freezing period and the starting day of the snowmelt period were the factors most strongly associated with daily snowmelt runoff, while daily NO₃⁻-N export is mostly affected by precipitation during the snowmelt period. The combinations of climatic factors favored by snowmelt runoff and NO₃⁻-N export were different. Years with longer stable freezing periods, later snowmelt period starting days, and higher rainfall during snowmelt more readily generated high snowmelt runoff. Correspondingly, under the present model configuration, later-appearing, higher-magnitude and more concentrated rainfall events, and higher temperatures between these rainfall and snowmelt events were linked to elevated NO₃⁻-N export. Finally, this study is of great importance for the prevention of spring floods and water pollution during snowmelt periods. Full article

Reproduction could be the most important control point for the spread of hamadryas baboons (Papio hamadryas). Understanding the drivers of reproduction is necessary to determine which interventions may work best, in which locations, and at what time of year. We analyzed monthly variation in the proportion of estrous females and black infants in two troops inhabiting two different sites (Dam and Al Hada) in Saudi Arabia over the course of one year (one week per month; ~720 h per site-year; non-contemporaneous). The effects of season, rainfall, natural food diversity, and anthropogenic foods were studied using generalized linear models with additive nonlinear terms. At the hotter and drier Dam site, seasonality explained most variation, with a higher proportion of estrous females observed during the hot season. At the cooler and wetter Al Hada site, infant proportions and estrous females responded nonlinearly to rainfall, natural food diversity, and human food subsidies, with less seasonality. Patterns in the proportion of infants broadly aligned with the estrous data and provided insight into the temporal distribution of births across the two troops. The two-troop contrast in this study suggests reproductive flexibility. Specifically, there may be a dual control system with an intrinsic seasonal control modulated by a flexible ecological response. Further work is needed to understand the scales and conditioning of environmental drivers of baboon reproduction to inform wildlife management. Full article

In seasonally arid systems, where water is a key limiting resource, unseasonal precipitation events may promote landscape greening. Because herbivores track spatial variation in fresh plant growth, dry-season precipitation could be an important catalyst of out-of-season foraging opportunities in seasonal arid environments. The eastern escarpment of California’s Sierra Nevada Mountains in the western United States features an intense ecoclimatic gradient from the Owens Valley’s cold desert to the High Sierra’s alpine tundra, in which patterns of temperature and precipitation vary seasonally and elevationally, and along which critically endangered Sierra Nevada bighorn sheep (“Sierra bighorn”) migrate seasonally. Immediately preceding the unusually warm 2021–2022 winter, the eastern Sierra experienced an historic autumn precipitation event, presenting an opportunity to investigate how unseasonal weather events shape patterns of plant green-up, and whether these conditions stand to impact herbivore space use. Here, we report on landscape greenness, indexed using the green chromatic coordinate, measured from 26,222 images collected by an in situ array of 20 time-lapse cameras deployed across the desert–alpine gradient of the eastern Sierra, over 5 years centered on the 2021–2022 fall-winter season. We compare greenness trends between time windows following rainfall and random timepoints to identify how unseasonal precipitation impacts plant greening using hierarchical linear models with a continuous first-order autocorrelation structure. Greenness trends were significantly more positive after rain than at random timepoints for low-elevation perennial grasses (p < 0.001), indicating that these plants readily uptake moisture regardless of the season. We additionally compare in situ greenness measurements with NDVI derived from satellite instrumentation to examine whether fine-scale patterns of green-up following unseasonal rains are detectable from space. Although in situ camera greenness and satellite NDVI were positively correlated, unseasonal species-specific greening was not detectable at the comparatively coarse scale of satellite analysis. Finally, we use GPS collar data from a population of Sierra bighorn to explore whether migratory behaviors could be associated with unseasonal landscape greening. Together, this work reveals that dry-season precipitation coupled with cold-season warm spells can lead to unseasonal landscape greenness and suggests that this process may facilitate herbivore access to high-quality forage during a normally barren time of year. Full article

Conventional forest inventories provide standardized but temporally discrete DBH observations, whereas some research and management applications require continuous observations of diameter dynamics between remeasurement campaigns. This study designed and implemented a Smart Tree Monitoring Network based on Internet of Things (IoT) and cloud storage technologies as a complementary intensive-monitoring approach for selected forest plots. The system enables automatic, continuous, networked observation of standing-tree diameter growth and consists of Tree Sensor Nodes (TSNs), Stand Gateways (SGs), and a cloud management platform. The independently designed tree diameter growth monitoring instrument senses micro-variations in DBH and conducts scheduled data acquisition. Low-power wireless transmission from TSNs to gateways is achieved through LoRa/LoRaWAN, while stand gateways aggregate multi-node data and environmental parameters and upload them to the cloud platform via a 4G network. Field deployment involved 426 devices in 10 sample plots with different terrain and climatic conditions in Hubei Province. The results showed that (1) with a 3.6 V, 19,000 mAh lithium battery and a 5 min sampling interval, daily power consumption was 5.37 mAh, corresponding to a theoretical battery-life estimate of 9.69 years under the tested duty-cycle assumptions; (2) at initial deployment, device-measured DBH showed strong agreement with manual measurements, with R² = 0.9996, RMSE = 0.215 cm, MAE = 0.170 cm, and Bias = −0.089 cm, while subgroup analyses indicated larger underestimation for large-diameter trees; and (3) monthly mean RSSI and SNR remained above the adopted reference thresholds throughout 2025, while rainfall and temperature were associated with limited variation in signal quality. These results support the technical feasibility of the system for high-frequency DBH monitoring in selected plots, while long-term measurement drift, end-to-end data completeness, battery life under field aging, and physical durability require further validation. Full article

Accurate assimilation of satellite-derived precipitation data remains a critical challenge in regional numerical weather prediction (NWP), particularly for convective-scale rainfall. Conventional observation operators rely on radiative transfer models or simplified moist physics, introducing substantial uncertainty at convective scales. This study develops a machine learning-based observation operator and implements a “one-dimensional variational (1D-Var) + three-dimensional variational (3D-Var)” framework to assimilate FY-3G/PMR precipitation data into the CMA-MESO. Results show the machine learning-based operator demonstrates high accuracy for light, moderate, and heavy rain (BIAS < 0.5 mm/h, RMSE < 2.5 mm/h) and exhibits good generalization capability across different weather systems. Through the two-step assimilation, the retrieved humidity profiles improve initial moisture fields. Both the single case study and the one-month continuous cycling experiment consistently show that the assimilation yields measurable improvements in short-term precipitation forecasts, particularly for extreme precipitation events, with a maximum TS improvement of 19.9% for severe torrential rain. This work demonstrates that the machine learning-based observation operator exhibits potential in precipitation data assimilation, offering a viable pathway to enhance NWP. Full article

Rainfall-induced seepage instability is a major concern for internal waste dump slopes in cold-region open-pit coal mines, where slope performance is influenced by groundwater conditions, moisture state, and seasonal temperature variations. This study investigates the internal waste dump slope of the Chaoyang open-pit coal mine and evaluates its seepage and stability responses under prescribed moisture and temperature states before and after rainfall, together with the effectiveness of drainage control. Soil specimens with moisture contents of 14%, 17.6% (natural), 23%, and 26% were tested at ambient temperature, −5 °C, and −15 °C by uniaxial compression and direct shear tests. The mechanical parameters measured under the prescribed moisture and temperature states were assigned to a GTS NX seepage–stability model. Twenty-four parametric cases, comprising four moisture contents, three temperature states, and pre- and post-rainfall conditions, were evaluated using the strength-reduction method, and an HDPE perforated drainage scheme was subsequently assessed. Under the ambient-temperature parameter state, increasing specimen moisture content from 14% to 26% reduced the pre-rainfall factor of safety from 1.41 to 1.18 and the post-rainfall value from 1.38 to 1.17. Parameter sets obtained from low-temperature-conditioned specimens produced higher calculated factors of safety; however, these cases represent prescribed mechanical states rather than the actual winter condition of the full-scale slope. Under the idealized drainage boundary, the pre- and post-rainfall factors of safety increased from 1.22 and 1.18 to 1.40 and 1.39, respectively. The results demonstrate the relative effects of laboratory-derived mechanical parameters, rainfall-induced seepage, and idealized drainage under the prescribed scenarios. Full article

The shear behavior of loess is closely linked to its microstructural evolution, and understanding this relationship is essential for deciphering the mechanisms of loess hazards. In this study, consolidated-drained (CD) triaxial tests were conducted on intact Q₃ Malan loess from the Dongzhi tableland, China, under varying water contents and confining pressures. Scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) analyses were performed on specimens before and after shearing to quantitatively and qualitatively characterize the changes in pore and particle properties and their connection to shear deformation. The results reveal three failure modes, including shear, homogeneous, and plastic failure. They are governed by the combined effects of microstructural variation and microcrack development, depending on confining pressure and water content. Quantitatively, as water content increases from 9% to 20%, cohesion decreases by 86.8% and peak shear strength reduces by 68.4%, while the internal friction angle decreases only slightly. Water-induced strength deterioration is governed primarily by cohesion loss rather than friction angle reduction. Thus, 20% water content was identified as the critical threshold marking the transition from cohesion-dominated to friction-dominated strength degradation. A critical threshold at approximately 27% water content is identified, beyond which about 70% of mesopore and macropore volumes undergo collapse, after which the strength is almost entirely sustained by interparticle friction. Based on these findings, the water-induced strength decay mechanism is categorized into three stages: rapid cement degradation, friction-dominated transition, and slow attenuation. These macroscopic phenomena are closely linked to the continuous adjustment of the microstructure, manifested by the softening, dispersion, and disintegration of cementations, particle movement and rearrangement, and the reduction and mutual transformation of inter-aggregate pores under loading and wetting. The three-stage mechanism and threshold characteristics of loess strength degradation upon wetting revealed in this study can provide theoretical support for early slope-instability warning in loess irrigation and heavy rainfall regions, as well as engineering reinforcement prioritizing the recovery of cohesion. Full article

Climate change imposes severe constraints on global food security, yet evidence on how crops respond to climate change in irrigation-dominated systems remains limited compared with rainfed agriculture. Building on this gap, we investigated how asymmetric climate shocks influence rice production in a major irrigated setting, using Egypt’s Nile Delta as a case study. Drawing on annual data for 1961–2022, we estimated a nonlinear autoregressive distributed lag (NARDL) model in which harvested area, fertilizer use, and seasonal temperature and precipitation jointly determine rice output, with structural-break tests used to inform model specification and the historical interpretation of major water and agricultural policy reforms. Temperature and precipitation are decomposed into cumulative positive and negative partial sums to isolate the effects of warming versus cooling and of rainfall surpluses versus deficits. The results showed a stable long-run cointegrating relationship with pronounced asymmetries. Autumn temperature shocks were most strongly associated with production variation, with cooling shocks more damaging than warming shocks were beneficial, while precipitation effects were asymmetric in the opposite direction: production gains associated with above average rainfall years exceeded the losses associated with below average rainfall years, consistent with irrigation buffering rainfall shortfalls. This finding does not rule out drainage-related losses from extreme, short-duration rainfall events, which the annual precipitation total used here cannot separately identify. Land expansion and fertilizer intensification remain positively associated with rice output, yet they only partially offset climate-induced losses, underscoring the limits of input-based adaptation amid increasing climatic volatility. Overall, the findings suggest that irrigation systems face distinct climate risks often obscured in symmetric models, highlighting adaptation priorities in drainage and storage, sustainable use of marginal lands, and season-specific climate services for irrigated rice regions. Full article

Drought is a complex and periodic issue that has a significant impact on agriculture and water resources, particularly in semi-arid areas. This study evaluated meteorological and agricultural drought conditions in the Thanjavur district by combining rainfall data with remote-sensing methods. Meteorological drought was analyzed using 33 years of rainfall data and the Standardized Precipitation Index (SPI) (1992–2024) using 20 rainfall stations for the data available between 1992 and 2024. The spatial variation in rainfall was analyzed using Kriging interpolation in GIS. Agricultural droughts were analyzed using the Normalized Difference Vegetation Index (NDVI) and Vegetation Con0dition Index (VCI) using multi-temporal Landsat satellite images (Landsat 5 and Landsat 8). Land Use and Land Cover (LULC) classification was included to determine drought vulnerability across different land types. The NDVI and VCI indices showed an intensification of agricultural drought in 2010. The results demonstrated temporal and spatial differences in drought conditions for the years 1992, 1997, 2004, 2009, 2014, 2019, and 2024 and indicated that the region experienced periodic severe drought conditions of 3%, 3%, 3%, 8%, 19%, 9%, and 11% in the study area, respectively. During the drought period, the vegetation indices showed a strong sensitivity of agricultural areas to changes in rainfall, and low NDVI and VCI values indicated increased vegetation stress. Meteorological and agricultural droughts were integrated using the Analytical Hierarchy Process (AHP) method by combining various indicators to analyze the drought condition across the Thanjavur district. The multiple criteria decision-making (MCDM) method uses pairwise comparisons of various factors, such as giving high importance to SPI and rainfall, followed by vegetation indices and LULC. The consistency ratio validated the reliability of the weighting term. This method shows that combining meteorological and remote sensing indicators advances a robust framework for monitoring and assessing droughts. Conceptual droughts illustrate how meteorological droughts are associated with the development of agricultural droughts. The results of this study can be adopted for effective drought management, irrigation planning, and sustainable agricultural practices in this region. Full article