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Given the significant momentum of developing water quality models to simulate water quality variables and support decision-making, the literature recognizes the importance of addressing uncertainties embedded in the water quality models, such as inherent, parametric, and hydrological uncertainties. However, previous studies have not extensively examined the magnitude of hydrological events in terms of their intensity. Therefore, this paper aims to propose an adjusted Monte Carlo event-based scenarios framework that considers four scenarios (1- 35 years of flow rate records, 2- events with ARI ≤ 10 years, 3- events with ARI ≤ 5 years, 4- events with ARI ≤ 1) to evaluate hydrological variabilities and quantify embedded uncertainties. The study employs a Qual2k model that simulates five water quality variables in the Zaroub River, Iran, as a case study. The model's uncertainty boundaries are quantified using five statistical metrics: Plevel, ARIL, SU, CU, and EU. The study's findings reveal that when considering the flow rate time histories during extreme hydrological events, there is a significant increase in uncertainty in the water quality model, highlighting the challenge of incorporating various scenarios within the framework such as high computational demand, integrating different scenarios, sparse data, and its temporal resolution. On the other hand, reducing the ARI values of hydrological events not only minimizes the uncertainty boundaries but also improves the accuracy of the model simulations. These results underscore the critical importance of selecting hydrological scenarios based on the water quality variable under investigation. Furthermore, the proposed framework can be applied to any water quality model and water body. The study's outcomes suggest that the presented methodology reduces uncertainty and provides more reliable simulations for decision-making in water resources conundrums. [ABSTRACT FROM AUTHOR]

This paper combines a risk-based two-stage stochastic robust programming (RTSRP) model into an agricultural-ecological system dynamics (AESD) to conduct agricultural-ecological water resources management. It integrates the chance- constrained programming, conditional risk-at-value, two-stage stochastic robust programming with the AESD model. The RTSRP model improves upon the TSRP model by introducing the risk control measurements. The agricultural-ecological dynamic system (AEDS) model can build water connections amid wheat, field corn, seed corn, economic crops, woodland, meadow, and city greening and farmland shelterbelts. Besides, the ecological service value is transformed into the irrigation benefit of ecological sector to guide water allocation amid sub-ecological sectors at a more applicable detailed scale. It can tradeoff relationships between economic benefit and ecological recovery by formulating two enlarging ecological areas scenarios. The combinations of the RTSRP model and the AEDS model can get optimal water allocation schemes, and simulate future sustainable degree (SD) of optimal water allocation schemes and explore relationships amid water allocation, economic benefit, and SD and risk and robustness of model. It is used to the middle reaches of the Heihe River Basin in China to verify its application. The results show that water allocation schemes have different responses on various risk scenarios. Higher water allocation corresponds to bigger water shortage risk and higher robustness of the model. The economic benefit positive with water allocation is contradictory with SD. Enlarging ecological areas contributes to improvements of economic benefit and ecological recovery, but not the SD under the condition of enough water resources. [ABSTRACT FROM AUTHOR]

The Huaihe River Basin (HRB) is located in the north-south climate transition zone of China and is highly susceptible to climate change. Analysis of the evolution pattern and potential causes of extreme hydrometeorological events is of great significance for flood prevention, mitigation and water resource planning in the basin. Based on the measured meteorological data in the HRB for 1963–2018, statistical analysis methods were used to test variations on extreme hydrometeorological events (precipitation and drought). Possible effects of global warming, atmospheric circulation patterns and sunspots on the spatiotemporal variability of hydrological events were also explored, effectively identifying the common influencing factors of extreme precipitation and drought events. The results show that (1) Extreme hydrometeorological events in the upper reaches are serious, but extreme precipitation tends to slow with time; the increase in extreme precipitation indices in the middle reaches deserves attention, while drought conditions are relatively mild; the drought trend in the lower reaches is significant, and extreme precipitation has improved. (2) Each inactive image for different periods is loaded onto the canvas, and a dynamic graph is used to display the temporal and spatial evolution pattern of extreme hydrometeorological events. (3) Global mean near-surface temperature anomalies (GTEM) are positively correlated with most extreme precipitation indices but negatively correlated with drought indices. The increase in the number of sunspots eased extreme precipitation and worsened drought. [ABSTRACT FROM AUTHOR]

In the context of population growth and increasingly scarce resources, the sustainability of water-energy-food nexus (WEFN) has been considered as a major global challenge. This study presented the evaluation index system of WEFN security based on pressure-state-response framework. Then the set pair analysis and variable fuzzy sets were supplied to evaluate the WEFN security in Northwest China from 2010 to 2019. Finally, obstacle degree model was adopted to diagnose the main factors affecting the WEFN security. The results indicated that over the years, in addition to response-subsystem, the safety level of the Northwest China WEFN had obvious spatio-temporal differentiation, which was always between critically safe and unsafe, but the overall trend was positive. In the pressure-subsystem, the WEFN security only in Gansu and Inner Mongolia was at a safe level; in the state-subsystem, the safe level of Ningxia and Shanxi was the worst; in the response-subsystem, the whole region was almost at an unsafe level. The annual average obstacle degree of three subsystems was compared as follows: pressure-subsystem > state-subsystem > response-subsystem. And, their obstacle factors were almost all related to water. The key to improving WEFN security of Northwest China in the future is to deal with the serious mismatch between water and energy resources. [ABSTRACT FROM AUTHOR]

Pressure reducing valves (PRVs) are widely used to regulate pressures in the supply and distribution parts of water networks, by reducing the upstream pressure to a set outlet pressure (i.e., downstream of the PRV), usually referred to as set point. As all types of mechanical equipment, PRVs may exhibit malfunctions affecting pressure regulation, such as high frequency fluctuations around the set point and/or prolonged systematic deviations from the set point, allowing their detection to be approached in a statistical context. In this study, we develop a statistical framework for detection of PRV malfunctions in water supply and water distribution networks, which uses: (a) the root mean squared error as a proper statistical metric for monitoring the performance of PRVs by detecting individual malfunctions in high-resolution pressure time series, and (b) the hazard function concept to identify a proper duration of sequential events from (a) to issue alerts. The suggested methodology is implemented using pressure data at 1-min temporal resolution from pressure management area Diagora of the water distribution network of the city of Patras in Greece, for the 3-year period from 01/Jan./2017 to 31/Dec./2019. The obtained results show that the developed statistical approach effectively detects major PRV malfunctions as the issuance of alerts agrees well with the reported repair dates by the Municipal Enterprise of Water Supply and Sewerage of the City of Patras, allowing it to be used for operational purposes, while making it suitable for possible extensions to continuous monitoring and fault diagnosis of other types of mechanical equipment. [ABSTRACT FROM AUTHOR]

A two-stage joint chance-constrained programming is proposed based on the two-stage stochastic programming, the robust interval programming and the joint chance-constrained programming. In this study, the previous method combining interval order relations and interval possibility degrees is interpreted from the view of robust programming and then is rewritten according to the frame of robust stochastic programming and robust fuzzy programming. Based on the concept of interval possibility degree, the interval chance constrained programming is proposed and a joint chance-constrained method is generated by combining interval with the random and fuzzy chance-constrained programming. The established model is applied in agricultural water planning in an arid region. The results show that the model can provide decision makers with various planting schemes under different risk levels. According to the response of the results in different risk scenarios, decision makers can get the general impression on the influence degree of uncertainties. The analysis on the results can also show the advantage of the established model in handling multiple uncertainties and making tradeoff among different factors like the unit net benefits, penalty and water demand of crops as well as water supply. According to the comparison with the traditional two-step-method-based interval two-stage chance-constrained method, the optimality robustness and the high reference value of the established model can be proved. [ABSTRACT FROM AUTHOR]

The extrapolation of quantiles beyond or below the largest or smallest observation plays an important role in hydrological practice, design of hydraulic structures, water resources management, or risk assessment. Traditionally, extreme quantiles are obtained using parametric methods that require to make an a priori assumption about the distribution that generated the data. This approach has several limitations mainly when applied to the tails of the distribution. Semiparametric or nonparametric methods, on the other hand, allow more flexibility and they may overcome the problems of the parametric approach. Therefore, we present here a comparison between three selected semi/nonparametric methods, namely the methods of Hutson (Stat and Comput, 12(4):331–338, 2002) and Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) and kernel density estimation. While the first and third methods have already applications in hydrology, Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) is proposed in this context for the first time. After describing the methods and their applications in hydrology, we compare their performance for different sample lengths and return periods. We use synthetic samples extracted from four distributions whose maxima belong to the Gumbel, Weibull, and Fréchet domain of attraction. Then, the same methods are applied to a real precipitation dataset and compared with a parametric approach. Eventually, a detailed discussion of the results is presented to guide researchers in the choice of the most suitable method. None of the three methods, in fact, outperforms the others; performances, instead, vary greatly with distribution type, return period, and sample size. [ABSTRACT FROM AUTHOR]

Multiple rivers flowing into the same bay can be correlated in water quality management and together determine the environmental status of the bay. Nonpoint source pollution management for multi-watershed aiming to alleviate environmental contamination as well as yield considerable economic and environmental benefits can be under additional challenges. In this study, a Bayesian simulation-based multi-watershed effluent trading designing model (BS-METM) is established for multi-watershed nonpoint source pollution management through incorporating techniques of water quality simulation, uncertainty analysis with Bayesian inference, optimal design for effluent trading, as well as mechanism analysis. BS-METM is capable of reflecting parameter uncertainties in nutrient simulation, disclosing the detailed optimal trading schemes under the impact of uncertainties and vital factors, and identifying optimal effluent trading mechanisms through revealing interaction among trading processes of multiple watersheds. BS-METM is applied to a real case of adjacent coastal watersheds (i.e. Daguhe and Moshuihe watersheds), which are identified as major sources of total phosphorus and ammonia nitrogen loadings to Jiaozhou Bay, China. Effluent trading optimization under multiple mechanisms, including intra-watershed trading, cross-watershed trading and non-trading, are conducted. The optimized industry scales and trading processes are obtained. The effects of vital factors on the trading process (i.e. environmental allowance-violation risk level and water availability level) are investigated. The interactions between water availability level and trading mechanism are also analyzed. It is proved that non-trading mechanism would be recommended under low water availability level and cross-watershed trading mechanism would be recommended under medium and high water availability level. The results provide a solid scientific basis for nonpoint source pollution management as well as effective sustainable development for multi-watershed region. [ABSTRACT FROM AUTHOR]

Three drought indices (the Standardized Precipitation Index [SPI], Evaporative Stress Index [ESI], and Soil Moisture Anomaly Index [SMAI]) were integrated using triple collocation (TC) to produce the merged drought index (MDI). The new index was then compared with the Gravity recovery and climate experiment (GRACE)–Drought severity index (DSI), a comprehensive drought index reflecting storage variation in surface, sub-surface, and groundwater levels across East Asia and Australia, from 2003 to 2014. Before merging the three drought indices, their performance was analyzed. The mean correlation between the three drought indices and the GRACE–DSI indicated that the performance of the ESI was superior to the SMAI and SPI over the study areas. In terms of average weight results using the merging approach, the ESI was associated with larger weights (0.372 and 0.359) and contributions (43% and 38%), followed by the SMAI and SPI for East Asia and Australia, respectively. The SMAI achieved a similar weight (0.360) and contribution (39%) as the ESI across Australia. To determine the robustness of the MDI as estimated by TC weights, we evaluated the MDI and the reference GRACE-DSI with respect to documented drought records in the study areas. The MDI produced trends similar to those of the GRACE-DSI in Australia, while MDI and GRACE-DSI trends were not similar in East Asia. The correlation between the MDI and GRACE-DSI in Australia (0.41–0.62) was also higher than in East Asia (0.24–0.32) during the study periods. This discrepancy was due to the conceptual difference in that MDI reflects the near-surface water storage variation while GRACE-DSI reflects the variation of deeper water. Nevertheless, our results showed that the MDI out-performed single drought indices and was able to capture documented drought events across the study regions. This suggests that merging different drought indices into a single tool can better represent droughts, and may be a valuable approach for water resource management. [ABSTRACT FROM AUTHOR]

The reliable agricultural water scarcity risk assessment depends on the accurate agricultural water supply and demand data series, and the crop water requirements (ETc) and effective precipitation (Pe) are the key parameters of natural agricultural water supply and demand. In order to simulate more agricultural water supply and demand data, a stochastic simulation model (MCMP-Copula) was proposed. The MCMP-Copula comprehensively considered the contemporaneous dependence between the measured ETc and Pe by copula and the temporal dependence of the measured ETc or Pe by copula based on Markov process and simulated data by Monte Carlo. Based on the Pe and ETc data during an entire growing season of wheat-rice from 1961 to 2017 in the Sichuan Hilly Area, a typical hilly area of Southwest China, more Pe and ETc data was simulated and the agricultural water resources scarcity risk in nature was analyzed. The results showed the simulated 560 years Pe and ETc data captured the same statistics and dependence characteristics of the measured data. When p (Pe) > 25% and p (ETc) < 62.5%, the Pe was just less than ETc and the irrigation was required to meet crops growth. The irrigation probability and return period were 48.10% and 2.08 years for simulated data, and 47.08% and 2.12 years for measured data. When Pe was poor and ETc was high, the probability of water resources shortage was 15.51% and the return period was 6.45 years for simulated data, whereas the values were 14.32% and 6.98 years for measured data. The encounter probability and return period of simulated data were more conservative than measured data. Therefore, assessing the agricultural water resources shortage risk based on the MCMP-Copula model could provide a more secure and reliable result, which had an important theoretical guidance for further agricultural drought risk decision-making. [ABSTRACT FROM AUTHOR]

Indian monsoon rainfall has a very strong connection with the Indian economy. Any variation in trend or pattern of Indian summer monsoon rainfall will have serious implications on agronomy, water resources and various associated sectors of the economy in India. In this study, an in-depth investigation of the monsoon rainfall trend is analyzed for 146 years period (1871–2016). Three different spatial scales using a multimethod approach consisting of the Linear Regression Model (LRM), Mann Kendall Test (MKT) and Innovative Trend Analysis (ITA) are analyzed in particular and synchronized way. Monotonic trend with one or the other tests are found in Meteorological Sub-division (MetSD) 3, 4, 14, 11, 10, 19, 20, 27, 8, 29, 28, 23 and 32 (Assam, Meghalaya, Manipur, Mizoram, Nagaland, Tripura, Punjab, Uttar Pradesh, Madhya Pradesh, Chhattisgarh, Jharkhand, Andhra Pradesh, Telangana , Konkan & Goa and Coastal Karnataka). Whereas, no significant monotonic trend was found for India as a unit. Two Homogenous Monsoon Regions (HMR) i.e. Central Northeast and Northeast have the monotonic rainfall trend. Moreover, the synchronized methodology made it possible to identify the most refined significant monotonic trend. It revealed a decreasing monotonic trend in MetSD 4, 20 and 27 (Manipur, Mizoram, Nagaland, Tripura, East Madhya Pradesh and Chhattisgarh) only. ITA based results revealed that MetSD 14, 8, 19 and 29 (Punjab, Jharkhand, West Madhya Pradesh and Telangana) are a new addition to the list of MetSDs with the significant monotonic trend. Changepoint in the trend is obtained for Northeast HMR in the year 1956 and MetSD 4, 20, 23 and 27 in the year 1969, 1961, 1930 and 1961 respectively. This study provides insight into the most refined trend on monsoon rainfall at different spatial scales in India using the updated methods of analysis. [ABSTRACT FROM AUTHOR]

Global warming is already having a negative impact on vital sectors on which human development depends, such as water resource availability. In this study, the changes and abrupt change timing of climatic extreme indices, aridity and drought over the Region of South Aegean are captured using the Mann–Kendall and Pettitt tests, while the latter variables are correlated with the water volume transported by ships to the region as well as the relevant costs. The region's climate is shifting to warmer conditions with less precipitation, since significantly positive trends were noted with regard to the number of tropical nights, warm nights, warm days, the warm spell duration index and the diurnal temperature range; significant negative trends were observed in relation to the number of cool nights, cool days and the cold spell duration index, with the change-point year for the latter variables being 2006. Inaddition, 7/11 precipitation related indices exhibited a downward trend, while significantly negative trends were observed with regard to the number of consecutive dry days, with the timing of the abrupt change being 2001. The Aridity Index (AI) reveals that the region's climate characterization is changing from dry and sub-humid to semi-arid conditions, whilst the Reconnaissance Drought Index standardized (RDIst) and the Standardized Precipitation Index (SPI) indices suggests an amplification of drought phenomena over the Region. The tourism variables illustrated a significant positive trend, with the timing of the abrupt change being registered during 2006–2009, whilst the correlation analysis between tourism variables and water transfers implies that the surge on water transfer by ships to the Region occurred between 1998 and 2008. This can be mainly attributed to the changes in climate patterns. The correlation analysis documents a strong positive correlation between the water transfer dataset and the diurnal temperature range, and a moderately negative association with the precipitation related indices, annual precipitation, drought phenomena and aridity with 7/11. [ABSTRACT FROM AUTHOR]

A copula-measure Me based interval multi-objective multi-stage stochastic chance-constrained programming (CMIMOMSP) model is proposed for water consumption optimization. It can conduct water allocation amid multiple users and multiple stages, and deal with the uncertainties presented as interval numbers, random fuzzy interval numbers, and stochastic variables. It improves upon multi-stage stochastic chance-constrained programming by introducing the multi-objective programming, and it can tradeoff the relationships amid economic benefit, full usage of water resources, and economic loss. It enhances the accuracy of copula function and conditional distribution function through proposing the interval functions. Besides, it can deal with the impact of the decision attitudes of managers on water allocation by formulating the function equation between water demand and the optimistic-pessimistic factor. The CMIMOMSP model is applied to a case study of the Heihe River Basin to verify its application. The results indicate that: (1) the optimistic-pessimistic factors have different degrees of positive influences on water allocation for industrial, domestic and ecological sectors; (2) the joint violated probability and optimistic-pessimistic factor have various range of impacts on agricultural water allocation; (3) tthe objective function values have different variation tendencies with the rise of joint violated probabilities and optimistic-pessimistic factors. Its robustness is enhanced by comparing it with the three single-objective programming models. The CMIMOMSP model can provide various water allocation schemes for managers with different risk attitudes in semi-arid and arid districts. [ABSTRACT FROM AUTHOR]