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The Jiaozhou Bay ecosystem, a crucial marine ecosystem in China, has been plagued by frequent harmful algal blooms as due to deteriorating water quality and eutrophication. This study analyzed the temporal and spatial changes of harmful algal blooms in Jiaozhou Bay from 2000 to 2022 using the Floating Algae Index (FAI) calculated from MODIS (2000–2022) and Sentinel-2 (2015–2022) satellite image datasets. The calculation results of the image datasets were compared. The frequency of planktonic algal outbreaks was low and constant until 2017, but has increased annually since then. Algae blooms are most common in the summer and primarily concentrated along the bay’s coast, middle, and mouth, with obvious seasonal and spatial distribution characteristics. Several factors influencing algal outbreaks were identified, including sea surface temperature, wind speed, air pressure, dissolved oxygen, nitrogen and phosphorus ratios, chemical oxygen demand, and petroleum pollutants. Algal bloom outbreaks in Jiaozhou Bay are expected to remain high in 2023. The findings provide crucial information for water quality management and future algal outbreak prediction and prevention in Jiaozhou Bay.

Flood forecasting is regarded as the most important basic non-engineering measure, and its accuracy is the key to scientific flood control and regulation. The conceptual rainfall–runoff model (CRR) is widely applied to flood forecasting. The major difficulty associated with the use of CRR models in hydrology is their calibration since most of these models involve a large number of parameters. In order to calibrate the parameters of the CRR model, an improved quadratic interpolation optimization algorithm (IQIO) was proposed. The tent chaos mapping was used to initialize the population, adaptive optimizer probability based on individual adaptation value was used to balance algorithm’s global exploration and local exploitation ability. Thirteen mathematical benchmark functions were used to test the IQIO algorithm. The results showed that the IQIO algorithm exhibited strong exploration capability and fast convergence speed. The CRR model parameters optimized by the IQIO algorithm exhibited high performance, with Nash–Sutcliffe efficiency (NSE) values reaching 0.951 during the calibration period and 0.913 during the validation period. The relative error of runoff in each year was less than 20%, which satisfied the calculation accuracy requirements.

To quantitatively access the effects of drought stress during different growth stages of soybean on development process and yield, a pot-culture experiment was conducted in China’s Huaibei Plain with different irrigation treatments over two seasons (2015 and 2016). Two drought stress levels (mild and severe) were applied at four growth stages for the experiment (S: seedling stage; B: branching stage; FPS: flowering and pod-setting stage; and PF: pod-filling stage). The effects of drought stress at different stages on growth and yield were evaluated and compared. Results of this two-year study showed that all growth and yield parameters were significantly affected by the water deficit during the sensitive FPS. Compared to the full irrigation treatment, severe drought stress during FPS caused a 22% loss of final plant height, 61% loss of the leaf area per plant (LAP), and 67% loss of final aboveground dry matter (ADM). Yield components also declined dramatically with water deficits during FPS and PF. Significant seed yield losses of 73–82% per plant were observed in the plants exposed to drought stress during FPS, and were also associated with the highest nonviable pod percentage of 13%. The greatest losses in 100-seed weight (42–48%) were observed under drought stress during PF. A rising trend in response to increasing soil water deficit (SWD) was observed for LAP, yield, and ADM losses. The slope (k) values of these fitting curves varied at different treatments, the highest value of k (7.37 and 8.47 in two years, respectively) was also observed in the sensitive FPS.

Satellite remote-sensing products with high spatial and time resolution are expected to provide alternative data sources for data-sparse regions. This study clarifies if the satellite–gauge product outperforms the satellite-only product by comparing remote-sensing precipitation products: one that incorporates rain gauge data (GSMaP-Gauge) and one that uses satellite only (GSMaP-MVK). The appropriateness of those two commonly used high-resolution products as the input to the conceptual hydrological model Hydrologiska Byråns Vattenbalansavdelning for stream flow prediction was also investigated. In addition, we also analyzed the deviations of model parameters due to the bias in remote-sensing precipitation inputs compared to standard ground measurements. The results indicated that GSMaP-Gauge was superior, with satisfactory to good performances in predicting stream flow in both temperate and subtropical basins (Hyeonsan, Fuji, and Da). However, its performance was slightly worse than GSMaP-MVK in the Upper-Cau basin, which was explained by the poor quality of the adjusted data source due to sparse data and the satellite–gauge blending algorithm of GSMaP-Gauge. Better parameter agreements with the observations of GSMaP-Gauge than GSMaP-MVK were found in the Hyeonsan and Da river basins where GSMaP-Gauge showed almost consistent relationship of monthly rainfall compared to ground measurements.

In recent years, several drought indices have been developed and used to monitor local to regional scale droughts on various temporal scales. However, to our knowledge, these indices do not possess generalized criteria to define a threshold in which to declare a national-scale drought. We present a statistical methodology to identify national-scale meteorological drought years in India. We implement a Superposed Epoch Analysis and bootstrap analysis to estimate annual cereal crop production losses as a result of widespread meteorological drought events. For this purpose, the meteorological definition of drought based on the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), in combination with the country's cropland area and cereal crops production, is used. The results demonstrate that a national-scale meteorological drought is defined if approximately 19% or more of India's cropland is affected by meteorological drought (SPI3 and SPEI3 equal to or less than −1.00) throughout the monsoon season (June-September). According to this analysis, depending on the indicator data used, a total of 18to 20 national-scale meteorological droughts were identified in India during 1964-2015, causing a 3.61% to 3.93% composite decrease in cereal crops production. The years which were commonly identified as national scale meteorological droughts over cropland by using different approaches are 1965, 1972, 1987, 2002, and 2009. A similar statistical approach can also be used to define drought thresholds at various spatial scales using the drought indices most applicable to the purpose and scale of study.

The Global Precipitation Mission (GPM) Core Observatory that was launched on 27 February 2014 ushered in a new era for estimating precipitation from satellites. Based on their high spatial–temporal resolution and near global coverage, satellite-based precipitation products have been applied in many research fields. The goal of this study was to quantitatively compare two of the latest GPM-era satellite precipitation products (GPM IMERG and GSMap-Gauge Ver. 6) with a network of 840 precipitation gauges over the Chinese mainland. Direct comparisons of satellite-based precipitation products with rain gauge observations over a 20 month period from April 2014 to November 2015 at 0.1° and daily/monthly resolutions showed the following results: Both of the products were capable of capturing the overall spatial pattern of the 20 month mean daily precipitation, which was characterized by a decreasing trend from the southeast to the northwest. GPM IMERG overestimated precipitation by approximately 0.09 mm/day while GSMap-Gauge Ver. 6 underestimated precipitation by −0.04 mm/day. The two satellite-based precipitation products performed better over wet southern regions than over dry northern regions. They also showed better performance in summer than in winter. In terms of mean error, root mean square error, correlation coefficient, and probability of detection, GSMap-Gauge was better able to estimate precipitation and had more stable quality results than GPM IMERG on both daily and monthly scales. GPM IMERG was more sensitive to conditions of no rain or light rainfall and demonstrated good capability of capturing the behavior of extreme precipitation events. Overall, the results revealed some limitations of these two latest satellite-based precipitation products when used over the Chinese mainland, helping to characterize some of the error features in these datasets for potential users.

Precipitation plays a vital role in the hydrological cycle, directly affecting water resource management and influencing flood and drought risk prediction. This study proposes a Bayesian Model Averaging (BMA) framework to integrate multiple precipitation datasets. The framework enhances estimation accuracy for hydrological simulations. The BMA framework synthesizes four precipitation products—Climate Hazards Group Infrared Precipitation with Station (CHIRPS), the fifth-generation ECMWF Atmospheric Reanalysis (ERA5), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals (IMERG)—over China’s Ganjiang River Basin from 2008 to 2020. We evaluated the merged dataset’s performance against its constituent datasets and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) at daily, monthly, and seasonal scales. Evaluation metrics included the correlation coefficient (CC), root mean square error (RMSE), and Kling–Gupta efficiency (KGE). The Variable Infiltration Capacity (VIC) hydrological model was further applied to assess how these datasets affect runoff simulations. The results indicate that the BMA-merged dataset substantially improves precipitation estimation accuracy when compared with individual inputs. The merged product achieved optimal daily performance (CC = 0.72, KGE = 0.70) and showed superior seasonal skill, notably reducing biases in autumn and winter. In hydrological applications, the BMA-driven VIC model effectively replicated observed runoff patterns, demonstrating its efficacy for regional long-term predictions. This study highlights BMA’s potential for optimizing hydrological model inputs, providing critical insights for sustainable water management and risk reduction in complex basins.

Given the importance and complexity of crop evapotranspiration estimation under drought stress, an experiment tailored for maize under drought stress was completed using six sets of large-scale weighing lysimeters at the Xinmaqiao Comprehensive Experimental Irrigation and Drainage Station, Anhui Province, China. Our aim was to analyze maize evapotranspiration under different drought conditions. Based on estimates of maize evapotranspiration under no drought stress using the dual crop coefficient approach, we optimized and calibrated basic crop coefficients Kcbini, Kcbmid, Kcbend, and the maximum crop coefficient Kcmax using a genetic algorithm. Measurements of solar radiation at the experimental station were used to derive the empirical parameters a and b from the Angstrom formula through the genetic algorithm, and then evapotranspiration was calculated for the reference crop (ET0). We then estimated the maize evapotranspiration under drought using the dual crop coefficient approach. The results indicated that a slight water deficit during the earlier stage of vegetative growth may stimulate the maize homeostatic mechanism and increase tolerance to drought stress in later growth periods. Maize evapotranspiration significantly decreased if drought stress continued into the elongation stage, and the same degree of drought stress had a greater influence on the middle and later stages of vegetative and reproductive growth. The calibrated results for Kcbini, Kcbmid, Kcbend, and Kcmax were 0.155, 1.218, 0.420 and 1.497 respectively. We calculated the root-mean-square error (RMSE), mean absolute error (MAE), and mean relative error (MRE) of maize evapotranspiration under no drought stress over the full growing season using a dual crop coefficient approach, and the results were 1.33 mm/day, 0.99 mm/day, and 1.30%, respectively, or 18.40%, 17.50%, and 91.11% lower than results using the recommended coefficients. The RMSE, MAE, and MRE results for maize under drought stress during two full growth periods were 1.18 mm/day, 0.98 mm/day, and 13.92%, respectively. These results were higher than maize without drought stress, but better than the estimated results based on FAO-56 recommended values. Therefore, maize evapotranspiration estimation under drought stress using the dual crop coefficient approach and genetic algorithm was reasonable and reliable. This study provides a theoretical basis for developing suitable regional irrigation programs and decreasing losses due to agricultural drought.

Bridge foundation settlement monitoring is crucial for infrastructure safety management, as uneven settlement can lead to stress redistribution, structural damage, and potentially catastrophic collapse. While traditional contact sensors provide reliable measurements, their deployment is labor-intensive and costly, especially for long-span bridges. Current remote sensing methods have not been thoroughly evaluated for their capability to detect and analyze complex foundation settlement patterns in challenging environments with multiple influencing factors. Here, we applied Small Baseline Subsets Synthetic Aperture Radar Interferometry (SBAS-InSAR) technology to monitor foundation settlement of a long-span bridge. Our analysis revealed distinct deformation patterns: uplift in the north bank approach bridge foundation and the left-side main bridge foundation (maximum rate: 36.97 mm/year), concurrent with subsidence in the right-side main bridge foundation and south bank approach bridge foundation (maximum rate: 35.59 mm/year). We then investigated the relationship between these settlement patterns and various environmental factors, including geological conditions, Sediment Transport Index (STI), Topographic Wetness Index (TWI), precipitation, and temperature. The observed settlement patterns were attributed to the combined effects of stratigraphic heterogeneity, dynamic hydrological conditions, and seasonal climate variations. These findings demonstrate that SBAS-InSAR technology can effectively capture complex bridge foundation deformation processes, offering a cost-effective alternative to traditional monitoring methods. This advancement in bridge monitoring technology could enable more widespread and frequent assessment of bridge foundation stability, ultimately improving infrastructure safety management.

The present study comprehensively analyzes error characteristics and performance of the two latest GPM-era satellite precipitation products over eastern China from April 2014 to March 2016. Analysis results indicate that the two products have totally different spatial distributions of total bias. Many of the underestimations for the GSMap-gauged could be traced to significant hit bias, with a secondary contribution from missed precipitation. For IMERG, total bias illustrates significant overestimation over most of the eastern part of China, except upper reaches of Yangtze and Yellow River basins. GSMap-gauged tends to overestimate light precipitation (<16 mm/day) and underestimate precipitation with rain rate larger than 16 mm/day; however, IMERG underestimates precipitation at rain rate between 8 and 64 mm/day and overestimates precipitation at rain rate more than 64 mm/day. IMERG overestimates extreme precipitation indices (RR99P and R20TOT), with relative bias values of 17.9% and 11.5%, respectively. But GSMap-gauged shows significant underestimation of these indices. In addition, both products performed well in the Huaihe, Liaohe, and Yangtze River basins for extreme precipitation detection. At basin scale comparisons, the GSMap-gauged data has a relatively higher accuracy than IMERG, especially at the Haihe, Huaihe, Liaohe, and Yellow River basins.