An ensemble modeling framework to study the effects of climate change on the trophic state of shallow reservoirs

Zhang, Chen; Huang, Yixuan; Javed, Aisha; Arhonditsis, George B.

doi:10.1016/j.scitotenv.2019.134078

Cited by 39 publications

(22 citation statements)

References 95 publications

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“…Focusing on precipitation, Park et al ( 2016 ) found that the Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) has a slight advantage in simulating precipitation extremes in East Asia among CORDEX-EA historical runs. Using a score-based method, Zhang et al ( 2019a ) also declared that HadGEM3-RA is better than other CORDEX-EA runs in simulating precipitation as well as temperature. Moreover, HadGEM3-RA provides a longer output (1981–2100) than other RCMs, meaning that climate change signals can be captured to a large extent.…”

Section: Study Area and Datamentioning

confidence: 99%

“…Even the COVID-19 pandemic, which occurred along with a sudden reduction in greenhouse gas emissions, has not seemed to change the trend of global warming (Forster et al 2020 ). As a vital component of the climate system, precipitation also changes in its intensity and frequency with rising temperatures, thus bringing challenges to watershed adaptation/elimination management (Miralha et al 2020 ; Zhang et al 2019a ). Moreover, recent attention has been given to the temporal unevenness of precipitation because this unevenness can change the potential risks, e.g., both the rainfall duration and antecedent dry period can influence the first-flush effect of nonpoint source pollution (NPS; e.g., Chen 2020 ; Feng et al 2013 ).…”

Section: Introductionmentioning

confidence: 99%

“…Although regional climate models (RCMs) nested in GCMs improve the resolution of GCMs, systematic biases still remain, limiting the direct use of raw GCM or RCM outputs. To reduce these systematic biases and provide more realistic climate simulations, bias correction (BC) techniques have become an imperative postprocessing procedure for almost all watershed climate impact studies (Guo et al 2019 ; Nguyen et al 2020 ; Zhang et al 2019a ). Statistical BC techniques are widely used in climate change research; in these methods, the statistical relationships between observed climate variables and their corresponding historical simulations are transplanted to the projected periods of the model (Albek et al 2019 ; Ouyang et al 2019 ; Shalby et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Gao

Liu

et al. 2021

Environ Sci Pollut Res

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Understanding dynamic future changes in precipitation can provide prior information for nonpoint source pollution simulations under global warming. However, the evolution of the dependence structure and the unevenness characteristics of precipitation are rarely considered. This study applied a two-stage bias correction to daily precipitation and max/min temperature data in the Daqing River Basin (DQRB) with the HadGEM3-RA climate model. Validated from 1981 to 2015, future scenarios under two emission paths covering 2031–2065 and 2066–2100 were projected to assess variations in both the amount and unevenness of precipitation. The results suggested that, overall, the two-stage bias correction could reproduce the marginal distributions of variables and the evolution process of the dependence structure. In the future, the amount of precipitation in the plains is expected to increase more than that in the mountains, while precipitation unevenness, as measured by relative entropy, shows a slight increase in the mountains and a decrease in the plains, with enhanced seasonality. Conditioned on rising temperatures, high-/low-intensity precipitation tends to intensify/weaken precipitation unevenness. Additionally, the potential application of the bias correction method used herein and the possible impacts of uneven precipitation on nonpoint source pollution are given for further analyses. This study can provide useful information for future nonpoint source pollution simulations in the DQRB. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-16066-9.

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Section: Study Area and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Gao

Liu

et al. 2021

Environ Sci Pollut Res

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“…The function of the water availability estimation module is estimating the water availability under historical and future scenarios. As the outputs of the General Circulation Model (GCM) have been widely used across the world for projecting the future climate [30,31], they are inputted into Soil and Water Assessment Tool (SWAT) hydrological model for predicting the water availability in the future. In order to adjust the outputs of large scale GCM for satisfying the inputs of the small-scale SWAT model, an effective statistical downscaling method (i.e., Daily Bias Correction (DBC) method) is adopted in this module [32].…”

Section: Water Availability Estimation Modulementioning

confidence: 99%

Impacts of Water Resources Allocation on Water Environmental Capacity under Climate Change

Zeng

Liu

Guo

et al. 2021

Water

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Water environmental capacity (WEC) is an essential indicator for effective environmental management. The designed low water flow condition is a prerequisite to determine WEC and is often based on the stationarity assumption of low water flow series. As the low water flow series has been remarkably disturbed by climate change as well as reservoirs operation and water acquisition, the stationarity assumption might bring risk for WEC planning. As the reservoir operation and water acquisition under climate change can be simulated by a water resources allocation model, the low water flow series outputted from the model are the simulations of the disturbances and often show nonstationary conditions. After estimating the designed low water flow through nonstationary frequency analysis from these low water flow series, the WEC under the nonstationary conditions can be determined. Thus, the impacts of water resources allocation on WEC under climate change can be quantitatively assessed. The mid-lower reaches of the Hanjiang River basin in China were taken as a case study due to the intensive reservoir operation and water acquisition under the climate change. A representative concentration pathway scenario (RCP4.5) was employed to project future climate, and a Soil and Water Assessment Tool (SWAT) model was employed to simulate water availability for driving the Interactive River-Aquifer Simulation (IRAS) model for allocating water. Water demand in 2016 and 2030 were selected as baseline and future planning years, respectively. The results show that water resources allocation can increase the amount of WEC due to amplifying the designed low water flow through reservoir operation. Larger regulating capacities of water projects can result in fewer differences of WEC under varied water availability and water demand conditions. The increasing local water demand will decrease WEC, with less regulating capacity of the water projects. Even the total available water resources will increase over the study area under RCP4.5. More water deficit will be found due to the uneven temporal-spatial distribution as well as the increasing water demand in the future, and low water flow will decrease, which further leads to cut down WEC. Therefore, the proposed method for determining the WEC can quantify the risk of the impacts of water supply and climate change on WEC to help water environmental management.

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“…These studies have confirmed the applicability of physical models and mathematical models in watershed hydrological simulation. [ 12,13,14,15 ] However, due to the differences in principle, modeling process and data requirements of various hydrological models, it becomes a problem to select the appropriate model in practical application. Therefore, comparing different hydrological models becomes a meaningful study.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Different Types of Hydrological Models Applied to Hydrological Simulation

Gui

Wu²,

Zhang

2021

CLEAN Soil Air Water

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The hydrological model is one of the key methods in hydrological research and management, which can be divided into physical model and mathematical model. In order to explore the applicability of different models in hydrological simulation, the physical model Hydrologic Simulation Program‐FORTRAN (HSPF), the mathematical models Autoregressive Integrated Moving Average (ARIMA) and Back Propagation Neural Network (BPNN) are built to simulate the hydrological process of Sanya River, China. First, the result shows that all three models have good performance, but the HSPF model has the best performance followed by the ARIMA and the BPNN model with the lowest performance. Next, the effect of time step on the performance of the BPNN and ARIMA models is obviously greater than for the HSPF model. Finally, the applicability of various models is discussed by combining the modeling process and data requirements of the model, and the applicability of the mathematical model in the hydrological simulation of watersheds with insufficient data is confirmed.

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An ensemble modeling framework to study the effects of climate change on the trophic state of shallow reservoirs

Cited by 39 publications

References 95 publications

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Impacts of Water Resources Allocation on Water Environmental Capacity under Climate Change

Comparative Study of Different Types of Hydrological Models Applied to Hydrological Simulation

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