A simulation and risk assessment framework for water-energy-environment nexus: A case study in the city cluster along the middle reach of the Yangtze River, China

In the Anthropocene, there is a strong interlinkage among water, energy, and the environment. The water-energy-environment nexus (WEEN) has been vigorously advocated as an emerging development paradigm and a global research agenda. Based on the nexus concept, a framework for the WEEN complex system simulation and risk assessment is developed. The three metropolitan areas of the city cluster along the middle reaches of the Yangtze River (CCMRYR) are taken as the objects. Regional policies are combined with generic shared socio-economic pathways (SSPs) to form a localized SSPs suitable for the research region. The dynamic simulation of the WEEN complex system and the risk analysis are carried out with the combination of system dynamics models and copula functions. Results show that: There are obvious differences in water utilization, energy consumption, air pollutant emissions, and water pollutant emissions among the three metropolitan areas. The issue of high carbon intensity in the Wuhan Metropolitan Coordinating Region needs to be emphasized and solved from the perspective of optimizing the industrial structure. Adhering to current development patterns, there will be successive peaks in water utilization, energy consumption, and carbon emissions in Wuhan, Dongting Lake, and Poyang Lake Metropolitan Coordinating Region by 2030, leading to high synergy risks at the systemic level, with maximum values of 0.84, 0.85, 0.62, respectively. A development path based on conservation priorities indicates that future policymaking needs to prioritize a resource-saving and pollution-control development pattern directed by technological upgrading against the backdrop of scarce natural resource endowments. The localized SSPs are a beneficial extension that enriches the narrative of regional-scale SSPs. The evolutionary trajectories and risk assessments of WEEN complex systems under different localized SSPs provide a sweeping insight into the consequences of policy decisions, thus enabling policymakers to appraise policy rationality and implement appropriate corrective measures.

Risk Field of Rock Instability using Microseismic Monitoringdata in Deep Mining

With the gradual depletion of surface resources, rock instability caused by deep high stressand mining disturbance seriously affects safe mining. To create effective risk management, a rockinstability risk field model using microseismic monitoring data is proposed in this study. Rockinstability risk was presented visually in 3D visualization. The in-situ microseismic monitoringdata was collected and analyzed to make calculation of peak ground velocity (PGV), peak groundacceleration (PGA), energy flux, energy and seismic moment. Indicator weights of PGV, PGA, energyflux are confirmed by using the analytic hierarchy process (AHP) to calculate risk severity. The Copulafunction is then used to solve the joint probability distribution function of energy and seismic moment.Then the spatial distribution characteristics of risk can be obtained by data fitting. Subsequently, thethree-dimensional (3D) risk field model was established. Meanwhile, the established risk field isverified by comparing monitoring data without disturbance and the blasting data with disturbance.It is suggested that the proposed risk field method could evaluate the regional risk of rock instabilityreasonably and accurately, which lays a theoretical foundation for the risk prediction and managementof rock instability in deep mining.

Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters

This research develops an extension of the Model Conditional Processor (MCP), which merges clusters with Gaussian mixture models to offer an alternative solution to manage heteroscedastic errors. The new method is called the Gaussian mixture clustering post-processor (GMCP). The results of the proposed post-processor were compared to the traditional MCP and MCP using a truncated Normal distribution (MCPt) by applying multiple deterministic and probabilistic verification indices. This research also assesses the GMCP’s capacity to estimate the predictive uncertainty of the monthly streamflow under different climate conditions in the “Second Workshop on Model Parameter Estimation Experiment” (MOPEX) catchments distributed in the SE part of the USA. The results indicate that all three post-processors showed promising results. However, the GMCP post-processor has shown significant potential in generating more reliable, sharp, and accurate monthly streamflow predictions than the MCP and MCPt methods, especially in dry catchments. Moreover, the MCP and MCPt provided similar performances for monthly streamflow and better performances in wet catchments than in dry catchments. The GMCP constitutes a promising solution to handle heteroscedastic errors in monthly streamflow, therefore moving towards a more realistic monthly hydrological prediction to support effective decision-making in planning and managing water resources.

Meteorological and Hydrological Drought Risk Assessment Using Multi-Dimensional Copulas in the Wadi Ouahrane Basin in Algeria

A persistent precipitation deficiency (meteorological drought) could spread to surface water bodies and produce a hydrological drought. Meteorological and hydrological droughts are thus closely related, even though they are separated by a time lag. For this reason, it is paramount for water resource planning and for drought risk analysis to study the connection between these two types of drought. With this aim, in this study, both meteorological and hydrological drought were analyzed in the Wadi Ouahrane Basin (Northwest Algeria). In particular, data from six rainfall stations and one hydrometric station for the period 1972–2018 were used to evaluate the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI) at multiple timescales (1, 2, 3, 4, …, 12 months). By means of a copula function, the conditional return period for both types of drought was evaluated. Results evidenced that runoff is characterized by high level of temporal correlation in comparison to rainfall. Moreover, the composite index JDHMI (Joint Deficit Hydro-meteorological Index) was evaluated. This index is able to reflect the simultaneous hydrological and meteorological behavior at different timescales of 1–12 months well and can present the probability of a common hydrological and meteorological deficit situation more accurately and realistically compared to precipitation or runoff-based indicators. It was found that, over the analyzed basin, the average severity of combined hydro-meteorological drought (JDHMI) was 10.19, with a duration of 9 months and a magnitude of 0.93.

Copula based post-processing for improving the NMME precipitation forecasts

Using reliable and timely precipitation forecasts on a monthly or seasonal scale could be useful in many water resources management planning, especially in countries facing drought challenges. Amongst many, the North American Multi-Model Ensemble (NMME) is one of the most well-known models. In this study, a Bayesian method based on Copula functions has been applied to improve NMME precipitation forecasts. This method is based on the existence of a correlation between the raw forecast and observational data. Two main factors affect the results of rainfall improvement based on the selected method. This research has presented innovative methods in these regards namely; 1) the approach of selecting the appropriate statistical distribution for variables and 2) the selection method of improved data according to the conditional probability distribution functions (CPDF). To evaluate the effectiveness of the statistical distribution, firstly the precipitation forecast improvement model has been developed based on the application of parametric (Exponential, Normal, Gamma, LogNormal and General Exreteme Value (GEV)) and non-parametric distributions (Standard Normal Kernel). Then the novel mixed distribution function based on GEV parametric distribution and Standard Normal Kernel (non-parametric distribution) has been suggested. As the second aim, a new method for selecting improved data based on the center of mass of estimated CPDF is presented. The evaluation of the proposed method for estimating the statistical distribution of data and improving the forecast precipitation by the NMME model has been performed in Sistan and Baluchestan province in Iran. In this regard, the data of 1982–2010 for the calibration period and the data of 2012–2016 for the validation of the results have been used. According to the results, the non-parametric distribution best fitted with the data in the time series and selecting the appropriate bandwidth increased the efficiency of this distribution. Besides, due to the weakness of non-parametric distributions in the boundaries, the use of GEV distribution with a high ability to estimate boundary conditions as semi-parametric distribution, led to improved performance of the proposed distribution. Finally, the selection of the improved data based on the center of the mass method has efficiently provided much improvement compared to the maximum likelihood method commonly used.

The Schaake Shuffle Technique to Combine Solar and Wind Power Probabilistic Forecasting

One way to mitigate the variability of wind and solar power generation is to install the corresponding plants in nearby locations. For example, in Kuwait, the facility at Shagaya Renewable Energy Park is located in a desert area with both photovoltaic panels and wind turbines that allow the continuous generation of renewable energy throughout the day. The National Center for Atmospheric Research (NCAR) has developed a system to generate probabilistic wind and solar predictions for the Shagaya facility. These predictions are based on the analog ensemble technique that post-processes the wind speed and solar irradiance predictions based on a combination of multiple models including the Weather Research and Forecasting (WRF) numerical model. The ensemble forecasts have 20 members and are generated independently at each wind and solar power production facility. Here we present a method based on the Schaake Shuffle (SS) technique to pair the ensemble members from the independent systems to obtain a unique ensemble prediction of the aggregated wind and solar generation. After reordering through the SS technique, the corresponding paired solar and wind power members can be summed to build a unique ensemble of combined generation that is statistically consistent, as verified by the presented metrics.

Projections of Future Climate Change in Singapore Based on a Multi-Site Multivariate Downscaling Approach

Estimates of the projected changes in precipitation and temperature have great significance for adaption planning in the context of climate change. To obtain the climate change information at regional or local scale, downscaling approaches are required to downscale the coarse global climate model (GCM) outputs to finer resolutions in both spatial and temporal dimensions. The multi-site, multi-variate downscaling approach has received considerable attention recently due to its advantage in providing distributed, physically coherent downscaled meteorological fields for subsequent impact modeling. In this study, a newly developed multi-site multivariate statistical downscaling approach based on empirical copula was applied to downscale grid-based, monthly precipitation, maximum and minimum temperature outputs from nine global climate models to site-specific, daily data over four weather stations in Singapore. The advantage of this approach lies in its ability to reflect the at-site statistics, inter-site and inter-variable dependencies, and temporal structure in the downscaled data. The downscaling was conducted for two projection periods (i.e., the 2021–2050 and 2071–2100 periods) under two emission scenarios (i.e., representative concentration pathway (RCP)4.5 and RCP8.5 scenarios). Based on the downscaling results, projected changes in daily precipitation, maximum and minimum temperatures were examined. The results show that there is no consensus on the projected change in average precipitation over the two future periods. The major uncertainty for precipitation projection comes from the GCMs. For daily maximum and minimum temperatures, all downscaled GCMs project an increase of average temperature in the future. These change signals could be different from those of the original GCM data, both in magnitude and in direction. These findings could assist in adaption planning in Singapore in response to emerging climate risks.

Comparative Study on Probabilistic Forecasts of Heavy Rainfall in Mountainous Areas of the Wujiang River Basin in China Based on TIGGE Data

Based on the ensemble precipitation forecast data in the summers of 2014–2018 from the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), a comparative study of two multi-model ensemble methods, the Bayesian model average (BMA) and the logistic regression (LR), was conducted. Meanwhile, forecasts of heavy precipitation from the two models over the Wujiang River Basin in China for the summer of 2018 were compared to verify their performances. The training period sensitivity test results show that a training period of 2 years was the best for BMA probability forecast model. Compared with the BMA method, the LR model required more statistical samples and its optimal length of the training period was 5 years. According to the Brier score (BS), for precipitation events exceeding 10 mm with lead times of 1–7 days, the BMA outperformed the LR and the raw ensemble prediction system forecasts (RAW) except for forecasts with a lead time of 1 day. Furthermore, for heavy rainfall events exceeding 25 and 50 mm, the RAW and the BMA performed much the same in terms of prediction. The reliability diagram of the two multi-model ensembles (i.e., BMA and LR) was more reliable than the RAW for heavy and moderate rainfall forecasts, and the BMA model had the best performance. The BMA probabilistic forecast can produce a highly concentrated probability density function (PDF) curve and can also provide deterministic forecasts through analyzing percentile forecast results. With regard to the heavy rainfall forecast in mountainous areas, it is recommended to refer to the forecast with a larger percentile between the 75th and 90th percentiles. Nevertheless, extreme events with low probability forecasts may occur and cannot be ignored.