Influence of Climate Variability and Reservoir Operation on Streamflow in the Yangtze River.
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ABSTRACT:
SUBMITTER: Chai Y
PROVIDER: S-EPMC6433911 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Five severe floods occurred in the Yangtze River Basin, China, between July and August 2020, and the Three Gorges Reservoir (TGR) located in the middle Yangtze River experienced the highest inflow since construction. The world's largest cascade-reservoir group, which counts for 22 cascade reservoirs in the upper Yangtze River, cooperated in real time to control floods. The cooperation prevented evacuation of 600,000 people and extensive inundations of farmlands and aquacultural areas. In addition, no water spillage occurred during the flood control period, resulting in a world-record annual output of the TGR hydropower station. This work describes decision making challenges in the cooperation of super large reservoir groups based on a case-study, controlling the 4th and 5th floods (from Aug-14 to Aug-22), the efforts of technicians, multi-departments, and the state, and reflects on these. To realize the full potential of reservoir operation for the Yangtze River Basin and other basins with large reservoir groups globally, we suggest: (i) improve flood forecast accuracy with a long leading time; (ii) strengthen and further develop ongoing research on reservoir group cooperation; and (iii) improve and implement institutional mechanisms for coordinated operation of large reservoir groups.
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Project description:The construction of multifunction reservoirs is important for flood control, agriculture irrigation, navigation, and hydropower generation, but dam construction will inevitably affect the downstream flow and sediment regimes, which can cause some environmental and ecological consequences. Therefore, this paper aims to propose a framework for assessing the multiobjective reservoir operation model based on environmental flows for sustaining the suspended sediment concentration (SSC) requirements in the turbidity maximum zone (TMZ). The Yangtze River Estuary was used as a case study. Through using an analytical model, a quantitative correlation between SSC and water flow rate was established. Then, the quantitative correlation and the SSC requirements were applied to determine the environmental flows for the estuarine TMZ. Subsequently, a multiobjective reservoir operation model was developed for the Three Gorges Reservoir (TGR), and an improved nondominated sorting genetic algorithm III based on elimination operator was applied to the model. An uncertainty analysis and a comparative analysis were used to assess the model's performance. The results showed that the proposed multiobjective reservoir operation model can reduce ecological deficiency under wet, normal, and dry years by 33.65%, 35.95%, and 20.98%, with the corresponding hydropower generation output lost by 3.37%, 3.88%, and 2.95%, respectively. Finally, we discussed ecological satiety rates under optimized and practical operation of the TGR in wet, normal, and dry years. It indicated that the multiobjective-optimized runoff performs better at maintaining the TMZ in the Yangtze River Estuary than practical runoff. More importantly, the results can offer guidance for the management of the TGR to improve the comprehensive development and protection of the estuarine ecological environment.
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Project description:Accurate long-term streamflow and flood forecasting have always been an important research direction in hydrology research. Nowadays, climate change, floods, and other anomalies occurring more and more frequently and bringing great losses to society. The prediction of streamflow, especially flood prediction, is important for disaster prevention. Current hydrological models based on physical mechanisms can give accurate predictions of streamflow, but the effective prediction period is only about 1 month in advance, which is too short for decision making. The artificial neural network (ANN) has great potential for predicting runoff and is not only good at handling non-linear data but can also make long-period forecasts. However, most of ANN models are unstable in their predictions when faced with raw flow data, and have excessive errors in predicting extreme flows. Previous studies have shown a link between the El Niño-Southern Oscillation (ENSO) and the streamflow of the Yangtze River. In this paper, we use ENSO and the monthly streamflow data of the Yangtze River from 1952 to 2018 to predict the monthly streamflow of the Yangtze River in two extreme flood years and a small flood year by using deep neural networks. In this paper, three deep neural network frameworks are used: stacked long short-term memory, Conv long short-term memory encoder-decoder long short-term memory and Conv long short-term memory encoder-decoder gate recurrent unit. The results show that the use of ConvLSTM improves the stability of the model and increases the accuracy of the flood prediction. Besides, the introduction of ENSO to the experimental data resulted in a more accurate prediction of the time of the occurrence of flood peaks and flood flows. Furthermore, the best results were obtained on the convolutional long short-term memory + encoder-decoder gate recurrent unit model.
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Project description:In this work, we have used the Soil & Water Assessment Tool (SWAT) to examine streamflow variability of the Lower Mekong River Basin (LMRB) associated with changes in the Upper Mekong River Basin (UMRB) inflows. Two hypothetical experiments were formulated and evaluated for the LMRB, where we conducted runoff simulations with multiple inflow changes that include upstream runoff yield increase and decrease scenarios. Streamflow variability of the LMRB was quantified by two streamflow metrics that explain flow variability and predictability, and high flow disturbance. The model experiments were performed for the Lower Mekong River Basin with identical climate, soil, and other watershed characteristics data. Remote sensing precipitation (Tropical Rainfall Measurement Mission, TRMM, and Global Precipitation Measurement mission, GPM), meteorological data as well as spatial data that include a digital elevation model, newly developed soil information (Harmonized World Soil Database, HWSD), and land use and land cover were processed as input to the LMRB model simulations. Observed daily streamflow data along the Lower Mekong River from Chiang Sean, Thailand to Kratie, Cambodia were used for calibration and validation. Our work results suggest that the Lower Mekong River streamflow is highly variable and has a low predictability (Colwell index of about 32%). We found that releasing more water from upstream Mekong during rainfall months by 30% would result in a reduction in the Lower Mekong streamflow predictability by about 21%. This reduction in predictability is mainly attributed to a decrease in the Contingency index. Our work shows that the ability to predict floods/droughts at the Lower Mekong River would be reduced if there is any anticipated change (i.e., increase/decrease) from UMRB releases. Our results also show that releasing more flows from the upstream Mekong would also affect flood duration and the frequency of flood occurrences downstream. The results of this work thus help to quantify the sensitivity of streamflow variability at the Lower Mekong River Basin to upstream anthropogenic changes.
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Project description:Climate change-induced spatial and temporal variability of stremflow has significant implications for hydrological processes and water supplies at basin scale. This study investigated the impacts of climate change on streamflow of the Kurau River Basin in Malaysia using a Climate-Smart Decision Support System (CSDSS) to predict future climate sequences. For this, we used 25 reliazations consisting from 10 Global Climate Models (GCMs) and three IPCC Representative Concentration Pathways (RCP4.5, RCP6.0 and RCP8.5). The generated climate sequences were used as input to Soil and Water Assessment Tool (SWAT) to simulate projected changes in hydrological processes in the basin over the period 2021-2080. The model performed fairly well for the Kurau River Basin, with coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) of 0.65, 0.65 and -3.0, respectively for calibration period (1981-1998) and 0.60, 0.59 and -4.6, respectively for validation period (1996-2005). Future projections over 2021-2080 period show an increase in rainfall during August to January (relatively wet season, called the main irrigation season) but a decrease in rainfall during February to July (relatively dry season, called the off season). Temperature projections show increase in both the maximum and minimum temperatures under the three RCP scenarios, with a maximum increase of 2.5?°C by 2021-2080 relative to baseline period of 1976-2005 under RCP8.5 scenario. The model predicted reduced streamflow under all RCP scenarios compared to the baseline period. Compared to 2021-2050 period, the projected streamflow will be higher during 2051-2080 period by 1.5 m3/s except in February for RCP8.5. The highest streamflow is predicted during August to December for both future periods under RCP8.5. The seasonal changes in streamflow range between -2.8% and -4.3% during the off season, and between 0% (nil) and -3.8% during the main season. The assessment of the impacts of climatic variabilities on the available water resources is necessary to identify adaptation strategies. It is supposed that such assessment on the Kurau River Basin under changing climate would improve operation policy for the Bukit Merah reservoir located at downstream of the basin. Thus, the predicted streamflow of the basin would be of importance to quantify potential impacts of climate change on the Bukit Merah reservoir and to determine the best possible operational strategies for irrigation release.
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Project description:Estimating the impact of climate change on energy use across the globe is essential for analysis of both mitigation and adaptation policies. Yet existing empirical estimates are concentrated in Western countries, especially the United States. We use daily data on household electricity consumption to estimate how electricity consumption would change in Shanghai in the context of climate change. For colder days <7 °C, a 1 °C increase in daily temperature reduces electricity consumption by 2.8%. On warm days >25 °C, a 1 °C increase in daily temperatures leads to a 14.5% increase in electricity consumption. As income increases, households' weather sensitivity remains the same for hotter days in the summer but increases during the winter. We use this estimated behavior in conjunction with a collection of downscaled global climate models (GCMs) to construct a relationship between future annual global mean surface temperature (GMST) changes and annual residential electricity consumption. We find that annual electricity consumption increases by 9.2% per +1 °C in annual GMST. In comparison, annual peak electricity use increases by as much as 36.1% per +1 °C in annual GMST. Although most accurate for Shanghai, our findings could be most credibly extended to the urban areas in the Yangtze River Delta, covering roughly one-fifth of China's urban population and one-fourth of the gross domestic product.
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Project description:In the Upper Colorado River Basin (UCRB), the principal source of water in the southwestern U.S., demand exceeds supply in most years, and will likely continue to rise. While General Circulation Models (GCMs) project surface temperature warming by 3.5 to 5.6°C for the area, precipitation projections are variable, with no wetter or drier consensus. We assess the impacts of projected 21(st) century climatic changes on subbasins in the UCRB using the Soil and Water Assessment Tool, for all hydrologic components (snowmelt, evapotranspiration, surface runoff, subsurface runoff, and streamflow), and for 16 GCMs under the A2 emission scenario. Over the GCM ensemble, our simulations project median Spring streamflow declines of 36% by the end of the 21(st) century, with increases more likely at higher elevations, and an overall range of -100 to +68%. Additionally, our results indicated Summer streamflow declines with median decreases of 46%, and an overall range of -100 to +22%. Analysis of hydrologic components indicates large spatial and temporal changes throughout the UCRB, with large snowmelt declines and temporal shifts in most hydrologic components. Warmer temperatures increase average annual evapotranspiration by ∼23%, with shifting seasonal soil moisture availability driving these increases in late Winter and early Spring. For the high-elevation water-generating regions, modest precipitation decreases result in an even greater water yield decrease with less available snowmelt. Precipitation increases with modest warming do not translate into the same magnitude of water-yield increases due to slight decreases in snowmelt and increases in evapotranspiration. For these basins, whether modest warming is associated with precipitation decreases or increases, continued rising temperatures may make drier futures. Subsequently, many subbasins are projected to turn from semi-arid to arid conditions by the 2080 s. In conclusion, water availability in the UCRB could significantly decline with adverse consequences for water supplies, agriculture, and ecosystem health.
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Project description:While most large river-deltas in the world are facing the risk of subsidence and erosion in the Anthropocene, it is suspected that the Changjiang submerged delta (CSD) could be subjected to the impacts of the world's largest dam, the Three Gorges Dam (TGD). Here we firstly indicate that the CSD went through high accumulation (1958-1978); slight accumulation (1978-1997), slight erosion (1997-2002); and high accumulation (2002-2009), despite the 70% reduction of the sediment load from upstream since the operation of the TGD in 2003. Meanwhile, at the depocenter of the submerged delta, the accumulation maintained a high rate of 10 cm/yr during 1958-2009. This suggests on a longer term, the distal sediment source from the upstream had little effect on the CSD. Within this time frame the changes in the partition of sediment load among the branching channels of the Changjiang Estuary could likely control the shifting of the depocenter of the CSD on a decadal time scale. Episodic extreme floods and storm surges also increased the magnitude of deposition and erosion of the CSD on short-term scales. A re-evaluation of the impacts of TGD on the CSD is urgently needed.
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Project description:River networks are typically treated as conduits of fixed discharge conveyance capacity in flood models and engineering design, despite knowledge that alluvial channel networks adjust their geometry, conveyance, planform, extent and drainage density over time in response to shifts in the magnitude and frequency of streamflows and sediment supply. Consistent relationships between modes of climate variability conducive to wetter-/drier-than-average conditions and changes in channel conveyance have never been established, hindering geomorphological prediction over interannual to multidecadal timescales. This paper explores the relationship between river channel conveyance/geometry and three modes of climate variability (the El Niño-Southern Oscillation, Atlantic Multidecadal Oscillation, and Arctic Oscillation) using two-, five- and ten-year medians of channel measurements, streamflow, precipitation and climate indices over seven decades in 67 United States rivers. We find that in two thirds of these rivers, channel capacity undergoes coherent phases of expansion/contraction in response to shifts in catchment precipitation and streamflow, driven by climate modes with different periodicities. Understanding the sensitivity of channel conveyance to climate modes would enable better river management, engineering design, and flood predictability over interannual to multidecadal timescales.
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Project description:Understanding contributions of climate change and human activities to changes in streamflow is important for sustainable management of water resources in an arid area. This study presents quantitative analysis of climatic and anthropogenic factors to streamflow alteration in the Tarim River Basin (TRB) using the double mass curve method (DMC) and the Budyko methods. The time series (1960~2015) are divided into three periods: the prior impacted period (1960~1972) and the two post impacted periods, 1973~1986 and 1987~2015 with trend analysis. Our results suggest that human activities played a dominant role in deduction in the streamflow in TRB with contribution of 144.6% to 120.68% during the post impacted period I and 228.68% to 140.38% during the post impacted period II. Climatic variables accounted for 20.68%~44.6% of the decrease during the post impacted period I and 40.38% ~128.68% during the post impacted period II. Sensitivity analysis indicates that the streamflow alteration was most sensitive to changes in landscape parameters. The aridity index and all the elasticities showed an obvious increasing trend from the upstream to the downstream in the TRB. Our study suggests that it is important to take effective measures for sustainable development of eco-hydrological and socio-economic systems in the TRB.
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