Fan Li

This study investigates the applicability and accuracy of Copula models constructed using the Principle of Maximum Entropy (POME) for simulating flood peak discharge and flood volume, and compares their performance with that of traditional Copula models to assess the potential of POME in developing objective probability models under multiple statistical constraints. Using annual maximum daily mean discharge (Q) and the corresponding maximum three-day flood volume (W) data from two hydrological stations as case studies, four modeling schemes were designed: M1 (traditional marginals + traditional Copula), M2 (MaxEnt marginals + MaxEnt Copula), M3 (traditional marginals + MaxEnt Copula), and M4 (MaxEnt marginals + traditional Copula). Model performance was comprehensively assessed using the Akaike Information Criterion (AIC), Root Mean Square Error (RMSE), and relative errors of statistical parameters. Results indicate that for marginal distribution fitting, the MaxEnt distribution exhibits superior performance in terms of RMSE, particularly in the upper tail region of the cumulative distribution function. For Copula fitting, the Gaussian Copula demonstrates the best performance in joint probability distribution modeling, whereas the MaxEnt Copula shows relatively weaker performance. Among the four schemes, M4 achieves the optimal simulation results for statistical parameters and linear correlation coefficients. By incorporating multiple statistical constraints, the POME offers an objective modeling framework for both marginal and joint distributions without the need for preassumed distributional forms, making it particularly suitable for capturing the complexity and uncertainty of hydrological events and enabling flexible stochastic modeling of interdependent hydrological variables.

Storm waves and rising sea levels pose significant threats to low-lying coastal areas, particularly sandy beaches, which are especially vulnerable. The research on the long-time-scale changes in sandy coasts, especially the identification of tipping points in the shoreline-retreat rate, is limited. Vung Tau beach, characterized by its low terrain and rapid tourism-driven economic growth, was selected as a typical study area to quantify the shoreline retreat throughout the 21st century under various sea-level rise (SLR) scenarios, and to identify the existence of tipping points by investigating the projected annual change in shoreline retreat (m/yr). This study employs the Probabilistic Coastline Recession (PCR) model, a physics-based tool specifically designed for long-term coastline change assessments. The results indicate that shoreline retreat accelerates over time, particularly after a tipping point is reached around 2050 in the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Under the SSP5-8.5 scenario, the median retreat distance is projected to increase from 19 m in 2050 to 89 m by 2100, nearly a fourfold rise. In comparison, the retreat distances are smaller under the SSP1-2.6 and SSP2-4.5 scenarios, but the same accelerating trend is observed beyond 2050. These findings highlight the growing risks associated with sea-level rise, especially the rapid increase in exceedance probabilities for retreat distances by the end of the century. By 2100, the probability of losing the entire beach at Vung Tau is projected to be 22% under SSP5-8.5. The approach of identifying tipping points based on the PCR model presented here can be applied to other sandy coastal regions, providing critical references for timely planning and the implementation of adaptation measures.

Introduction: Soil testing formula fertilization using organic fertilizer (STFFOF)could increase grain yields and protect the ecological environment but the potential risks of STFFOF remains unclear.Methods: In order to assess the risk on rice stem lodging, a STFFOF field experiment is conducted continuously for 11 years.Results: After 11 years of continuous STFFOF treatment, the stem lodging rate of rice substantially increases by 81.1%*, which completely overweigh its increase in yield. Further research found that STFFOF greatly decreases the concentration of Ca, SiO2, K, Mg, and non-structural carbohydrates in basal internodes, dramatically increases that of N, P, and weight per ear, but slightly affects the structural carbohydrates. The strong correlations imply the increasement in weight per ear, N, and P concentrations, and the significant decrease in starch in the basal internodes might directly increase the brittleness of stem internodes and further cause severe stem lodging and yield loss of rice.Discussion: Results suggest that the potential risks of rice production including stem lodging must be considered when adopting the excessive exploration mode of productivity technology of paddy fields.

Droughts are natural catastrophes that cost the health and wealth of humans due to their harmful effects on the natural environment, ecology, hydrology, and agriculture in particular. Droughts are recurring incidents that last for prolonged periods of time in the northern part of Burundi. Despite the region being prone to drought and often suffering from dry conditions, drought has not been widely investigated. For the quantification and characterization of dryness conditions, this research utilized two drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI), at 2-, 6-, 24-, and 48-month timescales, where 2-, 6-, 24-, and 48-months correspond to agricultural and hydrological droughts, respectively. The two drought indices were compared, and the difference between SPEI and SPI was illustrated by quantifying and characterizing drought incidents. The findings revealed that different types of droughts threatened the northern part of Burundi during the periods of 1993–2000 and 2002–2009. Both indices illustrated that 2005, 2006, and 2007 were extremely dry years. The drought incidents detected by the SPEI index were classified into moderate and severe categories, characterized by long duration and greater magnitude. In contrast, the drought incidents detected by SPI were classified into the “extremely dry” category, characterized by limited duration and lower magnitude but with higher intensities. This research highlighted that SPEI differs from SPI in quantifying and characterizing droughts and highly suggests the use of both SPEI and SPI when assessing droughts. The outcome of this study will be useful in drought prevention and mitigation strategies across Burundi, specifically for agricultural purposes.

Droughts are the most destructive catastrophes in the world. The persistence of drought is considered to cause many challenges for both humans and animals and ruins the ecosystem. These challenges have encouraged scientists to search for innovative methods and models that are effective for assessing and predicting drought events. The use of drought indices has been extensively employed in many regions across the globe and their effectiveness demonstrated. This review illustrates the effectiveness of drought indices in the assessment of droughts, with a focus on drought management and mitigation measures. Additionally, several ways of managing drought risk and proactive strategies that need to be implemented to mitigate droughts have been illustrated. In conclusion, this article suggests that drought mitigation should be done more naturally, in ways that strongly protect the environment rather than involve engineering projects which might cause the degradation of rivers and land, and damage the ecosystem.

Drought is a global issue with profound implications worldwide on biodiversity, equal safety, poverty eradication, sustainable development, and social-economic stability due to the significant role that water resources play in the growth of the world economy. Burundi is one of the highly vulnerable countries to the effects of climate change-related events because of its low adaptive capacity and high dependence on rainfed agriculture for livelihood. This research aimed to identify drought incidents and their characteristics in terms of duration, severity, and intensity and analysis of their trends in Burundi using the Standardized Precipitation Evapotranspiration Index (SPEI). Monthly precipitation and temperature data of seven meteorological stations across Burundi (1981–2020) were obtained from Burundi Geographical Institute (IGEBU). The SPEI index of -2, -6 and -24 month timescales were generated to understand drought characteristics and the Mann–Kendall (MK) test combined with Sen’s slope estimator were utilized to analyze the drought trend based on the calculated SPEI values. The overall analysis of the computed SPEI time series demonstrated that drought is more frequent and severe in Burundi, especially in the northern and eastern parts. The results showed that the most prolonged and severe drought occurred during the 1990–2010 decades, and 1993, 1994, 1995, 1999, 2000, 2001, 2004, 2005, 2006, 2009, and 2016 were extremely dry years. Proactive drought mitigation activities, climate change mitigation and water resources management policy are highly recommended in order to reduce drought risk and its effect on both population and the natural environment.

The drip fertigation technique is a modern, efficient irrigation method to alleviate water scarcity and fertilizer surpluses in crop production, while the precise quantification of water and fertilizer inputs is difficult for drip fertigation systems. A field experiment of maize (Zea mays L.) in a solar greenhouse was conducted to meet different combinations of four irrigation rates (I₁₂₅, I₁₀₀, I₇₅ and I₅₀) and three nitrogen (N) fertilizer rates (N₁₂₅, N₁₀₀ and N₇₅) under surface drip fertigation (SDF) systems. The Root Zone Water Quality Model (RZWQM2) was used to assess the response of soil volumetric water content (VWC), leaf area index (LAI), plant height and maize yield to different SDF managements. The model was calibrated by the I₁₀₀N₁₀₀ scenario and validated by the remaining five scenarios (i.e., I₁₂₅N₁₀₀, I₇₅N₁₀₀, I₅₀N₁₀₀, I₁₀₀N₁₂₅ and I₁₀₀N₇₅). The predictions of VWC, LAI and plant height were satisfactory, with relative root mean square errors (RRMSE) < 9.8%, the percent errors (PBIAS) within ±6%, indexes of agreement (IoA) > 0.85 and determination of coefficients (R²) > 0.71, and the relative errors (RE) of simulated yields were in the range of 1.5–7.2%. The simulation results showed that both irrigation and fertilization had multiple effects on water and N stresses. The calibrated model was subsequently used to explore the optimal SDF scenarios for maximizing yield, water use efficiency (WUE) or nitrogen use efficiency (NUE). Among the SDF managements of 21 irrigation rates × 31 N fertilizer rates, the optimal SDF scenarios were I₁₂₀N₁₃₀ for max yield (10516 kg/ha), I₅₀N₇₀ for max WUE (47.3 kg/(ha·mm)) and I₁₂₅N₇₅ for max NUE (30.2 kg/kg), respectively. The results demonstrated that the RZWQM2 was a promising tool for evaluating the effects of SDF management and achieving optimal water and N inputs.

River flow regulations and thermal regimes have been altered by human-induced interventions (such as dam construction) or climate change (such as air temperature variations). It is of great significance to adopt a well-performed data-driven model to accurately quantify the impact of human-induced interventions or climate change over river water temperature (WT), which can help understand the underlying evolution mechanism of the river thermal regimes by dam operation or climate change. This research applied the Bayesian network-based model (BNM), which can easily identify inherently stronger associated variables with a target variable from multiple influencing variables to predict the daily WT and make a quantitative assessment of the effect produced by Three Gorges Reservoir (TGR) construction in the Yangtze River, China. A comparative study between the proposed model and two other models was implemented to verify the predicted accuracy of the BNM. With the help of the BNM model, the impact of reservoir impoundment over water temperature was quantitatively analyzed by calculating the difference between reconstructed water temperature series and observed series during the post-TGR period. The construction of the TGR posed more impact on variations in WT than the impact induced by the climate change according to results. The effect of TGR on WT can be concluded as follows: WT from October to January in post-TGR showed a remarkable warming tendency and an increase in released warmer water volumes than before, while WT showed a cooling tendency during March to June because of the hysteretic effect of WT response to increasing air temperature. The proposed BNM model shows great potential for WT prediction and ecological risk management of rivers.