X. W. Zhuang

Assessment of climate change impacts on watershed in cold-arid region: an integrated multi-GCM-based stochastic weather generator and stepwise cluster analysis method

An integrated multi-GCM-based stochastic weather generator and stepwise cluster analysis (MGCM-SWG–SCA) method is developed, through incorporating multiple global climate models (MGCM), stochastic weather generator (SWG), and stepwise-clustered hydrological model (SCHM) within a general framework. MGCM-SWG–SCA can investigate uncertainties of projected climate changes as well as create watershed-scale climate projections from large-scale variables. It can also assess climate change impacts on hydrological processes and capture nonlinear relationship between input variables and outputs in watershed systems. MGCM-SWG–SCA is then applied to the Kaidu watershed with cold-arid characteristics in the Xinjiang Uyghur Autonomous Region of northwest China, for demonstrating its efficiency. Results reveal that the variability of streamflow is mainly affected by (1) temperature change during spring, (2) precipitation change during winter, and (3) both temperature and precipitation changes in summer and autumn. Results also disclose that: (1) the projected minimum and maximum temperatures and precipitation from MGCM change with seasons in different ways; (2) various climate change projections can reproduce the seasonal variability of watershed-scale climate series; (3) SCHM can simulate daily streamflow with a satisfactory degree, and a significant increasing trend of streamflow is indicated from future (2015–2035) to validation (2006–2011) periods; (4) the streamflow can vary under different climate change projections. The findings can be explained that, for the Kaidu watershed located in the cold-arid region, glacier melt is mainly related to temperature changes and precipitation changes can directly cause the variability of streamflow.

An inexact joint-probabilistic programming method for risk assessment in water resources allocation

In this study, an inexact joint probabilistic programming (IJPP) approach is developed for risk assessment and uncertainty reflection in water resources management systems. IJPP can dominate random parameters in the model’s left- and right-hand sides of constraints and interval parameters in the objective function. It can also help examine the risk of violating joint probabilistic constraints, which allows an increased robustness in controlling system risk in the optimization process. Moreover, it can facilitate analyses of various policy scenarios that are associated with different levels of economic consequences when the promised targets are violated within a multistage context. The IJPP method is then applied to a case study of planning water resources allocation within a multi-reservoir and multi-period context. Solutions of system benefit, economic penalty, water shortage, and water-allocation pattern vary with different risks of violating water-demand targets from multiple competitive users. Results also demonstrate that different users possess different water-guarantee ratios and different water-allocation priorities. The results can be used for helping water resources managers to identify desired system designs against water shortage and for risk control, and to determine which of these designs can most efficiently accomplish optimizing the system objective under uncertainty.

A Bayesian-based two-stage inexact optimization method for supporting stream water quality management in the Three Gorges Reservoir region

In this study, a Bayesian-based two-stage inexact optimization (BTIO) method is developed for supporting water quality management through coupling Bayesian analysis with interval two-stage stochastic programming (ITSP). The BTIO method is capable of addressing uncertainties caused by insufficient inputs in water quality model as well as uncertainties expressed as probabilistic distributions and interval numbers. The BTIO method is applied to a real case of water quality management for the Xiangxi River basin in the Three Gorges Reservoir region to seek optimal water quality management schemes under various uncertainties. Interval solutions for production patterns under a range of probabilistic water quality constraints have been generated. Results obtained demonstrate compromises between the system benefit and the system failure risk due to inherent uncertainties that exist in various system components. Moreover, information about pollutant emission is accomplished, which would help managers to adjust production patterns of regional industry and local policies considering interactions of water quality requirement, economic benefit, and industry structure.

A hybrid factorial stepwise-cluster analysis method for streamflow simulation – a case study in northwestern China

ABSTRACT In this study, a hybrid factorial stepwise-cluster analysis (HFSA) method is developed for modelling hydrological processes. The HFSA method employs a cluster tree to represent the complex nonlinear relationship between inputs (predictors) and outputs (predictands) in hydrological processes. A real case of streamflow simulation for the Kaidu River basin is applied to demonstrate the efficiency of the HFSA method. After training a total of 24 108 calibration samples, the cluster tree for daily streamflow is generated based on a stepwise-cluster analysis (SCA) approach and is then used to reproduce the daily streamflows for calibration (1995–2005) and validation (2008–2010) periods. The Nash-Sutcliffe coefficients for calibration and validation are 0.68 and 0.65, respectively, and the deviations of volume are 1.68% and 4.11%, respectively. Results show that: (i) the HFSA method can formulate a SCA-based hydrological modelling system for streamflow simulation with a satisfactory fitting; (ii) the variability and peak value of streamflow in the Kaidu River basin can be effectively captured by the SCA-based hydrological modelling system; (iii) results from 26 factorial experiments indicate that not only are minimum temperature and precipitation key drivers of system performance, but also the interaction between precipitation and minimum temperature significantly impacts on the streamflow. The findings are useful in indicating that the streamflow of the study basin is a mixture of snowmelt and rainfall water. EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR G. Thirel

Identifying water resources management strategies in adaptation to climate change under uncertainty

In this study, an integrated simulation-based allocation modeling system (ISAMS) is developed for identifying water resources management strategies in response to climate change. The ISAMS incorporates global climate models (GCMs), a semi-distributed land use-based runoff process (SLURP) model, and a multistage interval-stochastic programming (MISP) approach within a general framework. The ISAMS can not only handle uncertainties expressed as probability distributions and interval values but also reveal climate change impacts on water resources allocation under different projections of GCMs. The ISAMS is then applied to the Kaidu-kongque watershed with cold arid characteristics in the Tarim River Basin (the largest inland watershed basin in China) for demonstrating its efficiency. Results reveal that different climate change models corresponding to various projections (e.g., precipitation and temperature) would lead to changed water resources allocation patterns. Variations in water availability and demand due to uncertainties could result in different water allocation targets and shortages. A variety of decision alternatives about water allocations adaptive to climate change are generated under combinations of different global climate models and ecological water release plans. These findings indicate that understanding the uncertainties in water resources system, building adaptive methods for generating sustainable water allocation patterns, and taking actions for mitigating water shortage problems are key adaptation strategies responding to climate change.

A hybrid land-water-environment model for identification of ecological effect and risk under uncertain meteorological precipitation in an agroforestry ecosystem

In this study, a hybrid land-water-environment (LWE) model is developed for identifying ecological effect and risk under uncertain precipitation in an agroforestry ecosystem. A simulation-based fuzzy-stochastic programming with risk analysis (SFSR) method is used into LWE model to reflect the meteorological impacts; meanwhile, it also can quantify artificial fuzziness (e.g., risk attitude of policymaker) and natural vagueness (e.g., ecological function) in decision-making. The developed LWE model with SFSR method is applied to a practical agroforestry ecosystem in China. Results of optimized planting scale, irrigative water schedule, pollution mitigation scheme, and system benefit under changed rainfall, precise risk-adoption and vague ecological function are obtained; meanwhile their corresponding ecological effects and risks are analyzed. It found that current LWE plans could generate massive water deficits (e.g., 23.22×106m3 in crop irrigation and 26.32×106m3 in forest protection at highest) due to over-cultivation and excessive pollution discharges (e.g., the highest excessive TP and TN discharges would reach 460.64 and 15.30×103 ton) due to irrational fertilization, which would increase regional ecological risks. In addition, fifteen scenarios associated with withdrawing cultivation and recovering forest based on regional environment heterogeneity (such as soil types) have been discussed to adjust current agriculture-environment policies. It found that, the excessive pollution discharges (TN and TP) could be reduced 12.95% and 18.32% at highest through ecological expansions, which would generate higher system benefits than that without withdrawing farmland and recovering forest. All above can facilitate local policymakers to modulate a comprehensive LWE with more sustainable and robust manners, achieving regional harmony between socio-economy and eco-environment.

Conjunctive water management under multiple uncertainties: A centroid-based type-2 fuzzy-probabilistic programming approach

Abstract In this study, a centroid-based type-2 fuzzy-probabilistic programming (CT2FP) approach is developed for supporting conjunctive use of surface water and groundwater under multiple uncertainties. CT2FP can not only tackle uncertainties expressed as type-2 fuzzy sets (in both objective function and constraints) but also address complexity with characteristic of randomness and two-layer fuzziness (i.e., type-2 fuzzy random variables). Solution method based on α -plane theory, enhanced Karnik–Mendel algorithm (EKM) and interactive algorithm are proposed to transform type-2 fuzzy-probabilistic constraints into their deterministic equivalents. A case study in Zhangweinan River Basin (China) is used to demonstrate the applicability of the proposed approach. Scenarios associated with different constraint-violation risk levels are examined to generate applicable cropping patterns and water-allocation schemes. The amount of groundwater used for irrigation can be determined (i.e., more than [462.84, 495.78] × 10 6 m 3 in dry season and no more than [470.83, 537.19] × 10 6 m 3 in wet season, respectively) to address the conflict between food security and ecological protection. The relationship among crop area, water allocation, and economic benefit can be reflected to enhance the agricultural sustainable development for the study basin.