Top PDF The Spatiotemporal Variations of Runoff in the Yangtze River Basin under Climate Change

The Spatiotemporal Variations of Runoff in the Yangtze River Basin under Climate Change

The Spatiotemporal Variations of Runoff in the Yangtze River Basin under Climate Change

The spatiotemporal variations of runoff have been exten- sively studied in Yangtze River Basin (YRB). Chen et al. analyzed annual mean runoff from Yichang, Wuhan, and Datong stations. They examined runoff variability of the YRB based on hydrological data from the three stations and found that the runoff in the middle and lower reaches tends to increase by about 50% over that in the upper reach above Yichang [11]. Zhang and Wen analyzed annual runoff of the upper YRB, showing that the annual runoff discharge in Jinsha recorded at Pingshan had normal variation during the past decades [12]. Xu et al. used the distributed hydrological model to analyze the spatial-temporal variation of runoff in the upper YRB; their results indicated that the annual river discharges at hydrological gauges on the mainstream show no significant trend [13]. Zhang and Wei analyzed the variation of runoff in the upper reaches of the YRB on the basis of monitoring data series from 1881 to 2006. They found that the average annual runoff after 1990 is obviously higher than
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Quantifying uncertainty in the impacts of climate change on river discharge in sub catchments of the Yangtze and Yellow River Basins, China

Quantifying uncertainty in the impacts of climate change on river discharge in sub catchments of the Yangtze and Yellow River Basins, China

Mean annual precipitation in the Yangtze River basin is about 1070 mm and mean annual river discharge is ∼ 976 km 3 , equivalent to a specific discharge of 542 mm. An- nual per capita water availability decreased from 2700 m 3 in 1980 to 2100 m 3 in 2005. Previous studies (Zhang et al., 2006, 2008; Jiang et al., 2007) show that there has not been a significant change in annual precipitation but an increase in the number of extreme (10th percentile) precipitation events is observed (Su et al., 2008). Greater variability in precip- itation has intensified floods and prolonged droughts. Spa- tial and seasonal changes in precipitation have also been ob- served. Increased precipitation has been detected in middle and lower reaches of the Yangtze River in summer whereas a decrease in precipitation is observed in the upper reaches of the basin near the Three Gorges Dam site in autumn (Xu et al., 2008). Although no significant trend was detected for annual runoff in the Yangtze River basin during 1961–2000, a significant positive trend in flood discharges was found in the middle and lower basin over the same period.
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Hydrological Implications of Climate Change on River Basin Water Cycle: Case Studies of the Yangtze River and Yellow River Basins, China

Hydrological Implications of Climate Change on River Basin Water Cycle: Case Studies of the Yangtze River and Yellow River Basins, China

Abstract: Climate change is a global issue that draws widespread attention from the international society. As an important component of the climate system, the water cycle is directly affected by climate change. Thus, it is very important to study the influences of climate change on the basin water cycle with respect to maintenance of healthy rivers, sustainable use of water resources, and sustainable socioeconomic development in the basin. In this study, by assessing the suitability of multiple General Circulation Models (GCMs) recommended by the Intergovernmental Panel on Climate Change, Statistical Downscaling Model (SDSM) and Automated Statistical Downscaling model (ASD) were used to generate future climate change scenarios. These were then used to drive distributed hydrologic models (Variable Infiltration Capacity, Soil and Water Assessment Tool) for hydrological simulation of the Yangtze River and Yellow River basins, thereby quantifying the effects of climate change on the basin water cycle. The results showed that suitability assessment adopted in this study could effectively reduce the uncertainty of GCMs, and that statistical downscaling was able to greatly improve precipitation and temperature outputs in global climate mode. Compared to a baseline period (1961–1990), projected future periods (2046–2065 and 2081– 2100) had a slightly decreasing tendency of runoff in the lower reaches of the Yangtze River basin. In particular, a significant increase in runoff was observed during flood seasons in the southeast part. However, runoff of the upper Yellow River basin decreased continuously. The results provide a reference for studying climate change in major river basins of China.
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Analysis of long term terrestrial water storage variations in the Yangtze River basin

Analysis of long term terrestrial water storage variations in the Yangtze River basin

In this study, we focus on the analysis of the long-term variation in TWS of the Yangtze River basin. The Yangtze River, the longest river in China, forms one of the world’s top ten rivers basins as far as water shortage is concerned. This shortage is caused by intensive human water use, and is despite the large volume of runoff the river basin re- ceives (Wong et al., 2007). During the past three decades, the Yangtze River basin has experienced fundamental changes, e.g. a marked increase in temperature, population growth, economic development, water consumption, as well as the dam construction. The Three Gorges Dam (TGD) is the largest hydroelectric dam, and has created the largest man- made lake (more than 600 km 2 of former land) in the world. Such sizeable land use changes alter many factors, such as albedo, regional climate, and the hydrological cycle. In re- cent years, the basin has experienced an increasing trend in the frequency of extreme events, i.e. low runoff in drought years, and floods during intense rainfall (Dai et al., 2008; IPCC, 2001). A better understanding of the changes occur- ring in the Yangtze River basin and its hydrological state vari- ables is thus important. However, previous work has mostly focused on the interaction between runoff, precipitation, and evapotranspiration in the basin, while little attention has been
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Fifty-year climate change and its effect on annual runoff in the Tarim River Basin, China

Fifty-year climate change and its effect on annual runoff in the Tarim River Basin, China

Available online 24 December 2008 a b s t r a c t Based on the hydrologic and meteorological data in the Tarim River basin from 1958 to 2004, the trend, characteristics and spatial variation of climate change in the upper reaches of the Tarim River were examined in the study. The long-term trend of climate change and hydrological variations were deter- mined by using both Mann–Kendall and Mann–Whitney nonparametric tests. The results showed that the temperature and precipitation had significantly increased in the drainage basin in the mid-1980s. The climate was the warmest in 1990s among the recent 50 years. The increase of temperature in the tributaries of the Aksu River and Kaidu-Kongque River is higher than that in the tributaries of the Yar- kand River and Hotan River. The streamflow at Aksu River showed a significant increasing monotonic trend. The annual runoff in the Aksu River had increased by 10.9% since 1990. The independence test of temperature and precipitation with c 2 of the El Nino event reveals that there is no significant effect of the
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Responses of natural runoff to recent climatic variations in the Yellow River basin, China

Responses of natural runoff to recent climatic variations in the Yellow River basin, China

sunshine duration (n) from 1955 to 2011. The monthly nat- uralized streamflow data from 1960 to 2000 were obtained from the Yellow River Conservancy Committee (YRCC), while the recent data (from 2001 to 2011) were unavail- able. The naturalized streamflow is the streamflow record adjusted to remove the impacts of water management. The YRCC has developed a mature technology to reconstruct the naturalized streamflow of the Yellow River (Dong et al., 2001). The naturalized flow was directly comparable with the simulated natural streamflow. The digital elevation model (DEM) with a spatial resolution of 1 km × 1 km was gener- ated from the International Center for Tropical Agriculture (CIAT) product (Reuter et al., 2007) archived at the Com- puter Network Information Center, Chinese Academy of Sci- ences (http://datamirror.csdb.cn). The land cover/use map of the 1980s was taken from the Institute of Geographical Sci- ences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS) (Liu et al., 2002). The fixed land cover map was used throughout the study period. The land surface vegetation change may affect the surface water and energy partitioning (Zhang et al., 2012), and influence the hydrological cycle (Tang et al., 2008a, 2012). The previ- ous studies suggested that comparing climatic changes with land cover change might be a less significant factor in runoff change above the Huayuankou station in the Yellow River basin (Tang et al., 2008a; Cong et al., 2009). There are com- plicated interactions among land cover change, human activ- ities, regional climate, and hydrological cycles. The impact of land use change, a large part of which may be attributed to human influences, is out of the scope of this study, which focuses on natural runoff responses to climatic variations. The soil parameters were estimated by the Soil Water Char- acteristics application of the Soil–Plant–Air–Water (SPAW) model (Saxton and Rawls, 2006), based on the soil texture and organic matter data provided in the China Soil Scientific Database (http://www.soil.csdb.cn).
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Association analysis between spatiotemporal variation of vegetation greenness and precipitation/temperature in the Yangtze River Basin (China)

Association analysis between spatiotemporal variation of vegetation greenness and precipitation/temperature in the Yangtze River Basin (China)

The Normalized Difference Vegetation Index (NDVI) calcu- lated as the ratio of the difference between the near infrared and red divided by the sum of near infrared and red is an indicator of vegetation greenness (Bannari et al. 1995 ; de Beurs and Henebry 2004 ). A higher NDVI value implies a larger green vegetation density (Sun et al. 2015 ). The variation trend in NDVI has been commonly used to detect vegetation greenness (Jiang et al. 2015 ; Zhang et al. 2017a ). At the global scale, the mean NDVI shows increasing trends over half (56.3%) of land during 1982–2011 and almost half (46.1%) of the significant trends with seasonal variations (Eastman et al. 2013 ). A pro- nounced increasing mean seasonal NDVI trend is observed during 1982 –2010, but the increasing rate is found to decrease in the past decade (Peng et al. 2011 ). The NDVI was found to be susceptible to the climatic variations using traditional statis- tical approaches (Piao et al. 2003 ; Wang et al. 2003 ). Ichii et al. ( 2002 ) have shown that the increase of NDVI in the northern mi- and high-latitude regions was correlated with increasing temperature, while the NDVI is observed to decrease in the southern semi-arid regions due to precipitation during 1982– 1990. Jiang et al. ( 2017 ) have found stronger correlation of mean NDVI with precipitation compared to the temperature in most parts of Central Asia. Piao et al. ( 2015 ) have shown that the mean NDVI increased with temperature in most parts of China during 1982 –1999. Duan et al. ( 2011 ) and Xu et al. ( 2016 ) have observed that in the arid regions of China, vegeta- tion growth was sensitive to climate change due to low precipitation and relatively high evapotranspiration. In addition, human activities are also important driving factors affecting NDVI. Jiang et al. ( 2017 ) have shown that sparse vegetation and the degradation of some shrubs in the southern parts of the Karakum Desert, the southern Ustyurt Plateau, and the wetland delta of the Large Aral Sea were mainly triggered by human activities. Li et al. ( 2011 ) found that the contribution of climate change and human activities to vegetation NDVI was 79.32 and 20.68%, respectively, in the Three-River Headwaters Region (TRHR) during 2000–2010. Li et al. ( 2017 ) have shown that climate change and human activities both contribute to vegetation NDVI in China’s Loess Plateau during 2000– 2015, and human activities account for 55%.
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Quantitative contribution of climate change and human activities to runoff changes in the Wei River basin, China

Quantitative contribution of climate change and human activities to runoff changes in the Wei River basin, China

Decreasing runoff in many rivers in China has been reported in recent years, and the Wei River basin is one of the most serious cases. This paper aims at developing a new approach to quantifying the impact of climate variations and human activities on this decreasing runoff in the Wei River basin. The man-made changes here include land use, vegetation, and other land surface conditions, while climate change and climate variability are reflected in precipitation and potential evapotranspiration. This study uses the Mann–Kendall test to assess the temporal trends in precipitation, potential evapo- transpiration and runoff, and also analyzes the point of abrupt change. On this basis, the original climate elasticity method and improved climate elasticity method are used to analyze the quantitative hydrological effects of climate change and human activities; these findings are then compared to exist- ing results from the hydrological simulation method. This study shows the following:
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Investigating the Complexity of Runoff Series in the Yangtze River Using Sample Entropy

Investigating the Complexity of Runoff Series in the Yangtze River Using Sample Entropy

Taking the measured runoff time series data of Yichang station, Hankou station and Datong station as the sample, the sample entropy value of three stations was calculated according to the sample entropy theory. The entropy values of the three stations of Yichang station, Hankou station and Datong station are 0.13, 0.15 and 0.16 respectively. It can be seen from Figure 3 that the sample entropy of the hydrological station in the Yangtze River station is increasing gradually, and the appearance of the sample entropy series and the emergence of the trough are obviously consistent. Before the 1970s, the entropy of the Hankou station in the middle reaches was significantly higher than that of the Yichang station and the Cuntan station. Since the Hankou station was the largest inflow station in the Hanjiang River, the largest tributary of the Yangtze River Basin, the reason for this phenomenon was related to the storage of Danjiangkou Reservoir. The entropy of the Yichang and Hankou stations increased in recent years, which was related to the change of climate and environment in the Yangtze River Basin, the increase of water intake outside the river and the change of the underlying surface caused by human activities.
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Effects of climate variability and human activity on terrestrial water storage changes at basin scale: a case study of the Yangtze river basin

Effects of climate variability and human activity on terrestrial water storage changes at basin scale: a case study of the Yangtze river basin

89 and Yang et al. (2010) show that the TGD reservoirs could have a direct impact on the intra-annual changes in the downstream Yangtze discharges, leading to a dumping of the seasonal variations in the Yangtze River discharge in the middle and lower reaches. Miller et al. (2005) and Wu et al. (2006) also documented that the land use change associated with the TGD would alter the regional pattern of precipitation, wind, and temperature. It could also impact the hydrological cycle of the river basin, and may lead to changes in the soil–climate interaction, which would probably alter the current dumping effect of soil wetness on the climate variability. As shown in Figures 6.4 and 6.5, the consistent droughts in recent years and the operation of the TGD have occurred simultaneously. In 2003, the water level of the TGR reached 135 m. Coincidently, in 2004, the driest period of the past 32 years began for the middle and lower Yangtze. Also, the whole basin suffered an abrupt change in 2006, when the TGR raised its water level from 135 to 156 m. This coincidence is very striking and may imply the possible connection between the TGD and the consistent droughts in recent years, even though there has been no irrefutable evidence to prove that the TGR is responsible for the extremely driest period that has occurred in the past several years, as the TGD has only been in operation for a short period. Apart from the TGR, numerous other reservoirs within the Yangtze catchment together reached 200 km 3 (Yang et al., 2005), more than five times the storage capacity of the TGR. The impact of these reservoirs on the TWS should not be ignored. The Yangtze basin has witnessed remarkable changes in land use and cover induced by high population density and rapid but uneven economic growth (Long et al., 2006; Yin et al., 2010). These changes might alter the soil properties and soil–climate interactions, probably having great influence on the TWS and runoff distribution. It should be pointed out that the ERA-Interim TWS could contain significant uncertainties, as it relies heavily on satellite observations and modeling. Further investigation and analysis is needed to assess the significant impact of human activity on the TWS of the Yangtze River basin.
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Annual baseflow variations as influenced by climate variability and agricultural land use change in the Missouri River Basin

Annual baseflow variations as influenced by climate variability and agricultural land use change in the Missouri River Basin

forest, grassland, and shrubland in the Ozark Plateaus, contribute more surface runoff than baseflow to streamflow ( Fig. 4 ). Although this complements previous research which associates high eleva- tion to increased baseflow ( Rumsey et al., 2015 ), higher BFI values in the Plains (i.e. central MORB; Fig. 4 ), where the terrain is rela- tively low comparable to west of the basin, may be due to the influ- ence of concurrent factors, such as land use, soil composition, and elevation ( Price, 2011 ). Soil permeability, underlying aquifers, and hydrologic landscape regions play a major role in baseflow distri- bution in the basin ( Eckhardt, 2005, 2008; Santhi et al., 2008 ); however, more detailed analysis, which is beyond the scope of this study, is needed to examine the influence of these factors on base- flow before drawing definite conclusions. Overall, baseflow was a large proportion of total streamflow in the MORB during the period examined ( Table 2 ), increasing in the western edge followed by downstream east, then the middle ( Figs. 3 and 4 ).
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On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff

On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff

For this purpose, we investigate the impact of bias cor- rection of precipitation and near-surface air temperature on the simulations from four different hydrological models in two natural flow catchments in southern Germany and south- ern Qu´ebec when driven by multiple GCM–RCM data sets for both a reference (1971–2000) and a future period (2041– 2070). Precipitation is corrected by the local intensity scaling (LOCI) method of Schmidli et al. (2006), while air temper- ature is modified by monthly additive correction. The meth- ods were selected for their simplicity and have some inherent flaws: The monthly correction may create jumps in the cor- rected data sets between months, and following Themeßl et al. (2011) LOCI performance is slightly inferior to the quan- tile mapping approach, especially at high precipitation inten- sities. River runoff is simulated both with direct and bias- corrected meteorological drivers produced by RCMs. From the simulated daily runoff, hydrological indicators character-
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Sensitivity of surface runoff to drought and climate change : application for shared river basins

Sensitivity of surface runoff to drought and climate change : application for shared river basins

5. The Fifth Assessment Report of the IPCC [1] suggests that the region where the examined basin is located is likely to face a decline in precipitation amount and an increase in mean air temperature as part of climate change. The IPCC [36] highlighted that the annual average river runoff availability is projected to decrease by between 10% and 30% over some dry regions at mid-latitudes by 2050. Some impacted regions presently have water-stressed areas. Furthermore, an increase in drought spells is also projected for mid-latitudes. The IPCC reports also point out with high confidence (defined by IPCC) that climate change has the potential to exacerbate water resource stresses in most regions of Asia. The regional projections of temperature and precipitation in Asia based on a so-identified A2-forced emission scenario using the Atmosphere-Ocean General Circulation Model (AOGCM) simulations show that the rate of decrease in precipitation could reach − 40% in winter (between December and February) and − 50% in summer (between June and August). The increase in temperature would be in the order of +10% in winter and +6% in summer. It should be noted that these predictions should be considered as valid until the end of the 21st century. The synthetic scenarios for assessing the runoff sensitivity to climate change were formulated through an incremental shift of the historical P and PET values by a 2% step for a P reduction range from 0% to −40% and a PET increase from 0% to +30%. Correspondingly, 336 scenarios were developed, representing the mutual impact of deviations in P and PET values that lie within the aforementioned assortment of scenarios. These scenarios include all possible basin-wide climate change projections, as well as a wide array of drought severity conditions. The Medbasin-M model was repetitively used to simulate the runoff for the 336 scenarios.
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Impact of possible climate changes on river runoff under different natural conditions

Impact of possible climate changes on river runoff under different natural conditions

Received: 28 December 2017 – Revised: 13 April 2018 – Accepted: 19 April 2018 – Published: 5 June 2018 Abstract. The present study was carried out within the framework of the International Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) for 11 large river basins located in different continents of the globe under a wide variety of natural conditions. The aim of the study was to investigate possible changes in various characteristics of annual river runoff (mean values, standard deviations, frequency of extreme annual runoff) up to 2100 on the basis of application of the land surface model SWAP and meteorological projections simulated by five General Circulation Models (GCMs) according to four RCP scenarios. Analysis of the obtained results has shown that changes in climatic runoff are different (both in magnitude and sign) for the river basins located in different regions of the planet due to differences in natural (primarily climatic) conditions. The climatic elas- ticities of river runoff to changes in air temperature and precipitation were estimated that makes it possible, as the first approximation, to project changes in climatic values of annual runoff, using the projected changes in mean annual air temperature and annual precipitation for the river basins. It was found that for most rivers under study, the frequency of occurrence of extreme runoff values increases. This is true both for extremely high runoff (when the projected climatic runoff increases) and for extremely low values (when the projected climatic runoff decreases).
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Water allocation under future climate change and socio-economic development : the case of Pearl River Basin

Water allocation under future climate change and socio-economic development : the case of Pearl River Basin

Different new methods to deal with uncertainties in water resources management have been developed in recent years. For example, Lempert and Groves (2010) developed Robust Decision Making (RDM) which uses multiple futures, robustness criteria, and adaptivity to hedge against uncertainty. A large ensemble of monthly temperature and precipitation sequences were generated based on the Atmosphere-Ocean General Circulation Models (AOGCM) using K-nearest neighbour (KNN) bootstrapping technique to represent a plausible range of climate changes. Matrosov et al. (2013) used an information-gap theory to propagate uncertainties, and to rank different infrastructure portfolios for 2035. Climate change uncertainty is represented using monthly climate change perturbation factors that are multiplied by historical river flow time series. Mortazavi-Naeini et al. (2015) used robust optimization to secure urban bulk water supply against extreme drought and uncertainties associated to climate change. They obtained the ranges of future rainfall and potential evapotranspiration (PET) for 23 GCMs from a previous study CSIRO-BoM (2007), then used a stochastic multi-site model to generate 10,000 50-year replicate of daily rainfall and PET based on these ranges. However, only one emission scenario (A1F1) was involved in their study. Culley et al. (2016) developed a bottom-up approach to identify the maximum operational adaptive capacity of water resource systems with respect to a future climate exposure space. The climate exposure space used in their study is generated based on seven general circulation models and six regional climate models under three representative concentration pathways (RCPs).
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Mapping mean and variance of runoff in a river basin

Mapping mean and variance of runoff in a river basin

A comparison of empirical and model correlation func- tions carried out here should not be seen in the light of a for- mal test. The theoretical derivations developed herein are in its infancy and not yet ready for such formal procedures. This comparison is rather to be seen as a first diagnostic to indicate if the assumptions make sense. We will use branch 4 as an illustration when comparing the theoretical derivation with the empirical functions of Fig. 4. Figure 7a thus shows the estimated autocorrelation (Eq. B2) for durations of an hour, a day, five days and a month for the central site on this branch, and Fig. 7b the autocorrelation function for one day duration for all stations along the branch. Figure 7c in a similar way shows the estimated cross-correlation function (Eq. B5) be- tween the central and the outlet sites for different durations and Fig. 7d the cross-correlation function between the outlet site and upstream stations along the same river branch for a duration of one day.
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Understanding the Impacts of Climate Change in the Tana River Basin, Kenya

Understanding the Impacts of Climate Change in the Tana River Basin, Kenya

SWAT is a publically available rainfall–runoff hydrology and water quality model. The model possesses adequate rep- resentation of physical processes governing hydrology and is particularly suitable for application in large river basins. In the SWAT model, a river basin is subdivided into mul- tiple sub-catchments, each sub-catchment consisting of at least one representative stream. The sub-catchments are fur- ther divided into hydrologic response units (HRUs), which are lumped land areas within the catchment comprising unique land cover, soil, and slope combinations. For a de- tailed description of the SWAT model, reference is made to Muthuwatta et al. (2014). Various data sets were accumu- lated from global and local sources. The major data sets used in this study are listed in Table 1.
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Physically based distributed modelling of river runoff under changing climate conditions

Physically based distributed modelling of river runoff under changing climate conditions

Numerical experiments with the physically-distributed model ECOMAG for the Northern Dvina River basin identified two opposite tendencies in reaction of the river basin to changing of climatic characteristics. On the one hand warming leads to decrease of annual runoff and flood runoff volumes due to increase of evapotranspiration; on the other hand, precipitation increase leads to the increasing of the aforementioned runoff characteristics. Notably, the results of modelling are more sensitive to precipitation changes. Which tendencies will dominate in the future will depend on realizations of specific scenarios of climate change. As defined for the last decades, changes of climatic and hydrological characteristics in the Northern Dvina River basin (rising of air temperature by 1.5ºC, precipitation by 3.4%, annual runoff volume by 3%) and generated on the basis of greenhouse gases emissions A2 scenario for the next 50 years, the estimations (rising of air temperature by 3ºC, precipitation by 11%, annual runoff volume by 14%) are good and correspond with the developed scheme of runoff reaction to climate changes in the study basin.
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An uncertainty approach to assessment of climate change impacts on the Zambezi River Basin

An uncertainty approach to assessment of climate change impacts on the Zambezi River Basin

Flooding risk is typically modeled at a daily or sub-daily level, while we are limited by the nature of this analysis to monthly runoff changes. In that case, these results are likely underestimating flood risk and correspond specifically to large-scale flooding events—floods causing river inundation—rather than local flash flooding events. Using an extreme value distribution fitting, we make claims on the recurrence of damaging flood events. We do this by fitting runoff over the period, 1951 to 2000, estimating the recurrence interval of high runoff events. We then use the fitted parameters from the historical fit for the future runoff to calculate future recurrence intervals. Figure 3 shows the occurrence of high-damage flood events, greater than the 50-year event intensity, as a mean over all basins within the country. We did not find changes to flood occurrence in Malawi or Zimbabwe. Considering that, on average, about 1 high-damage flood event occurs in the 50-year base scenario, Mozambique and Zambia are more likely, based on this analysis, to have a significant increase in high- damage flood events in the future under the UCE case than the L1S case. Again, this analysis is limited by the fact that we are only considering changes in the monthly mean climate, not changes in inter-annual variability. These results are primarily driven by persistent seasonal changes in precipitation, where precipitation in high-runoff months increases, while precipi- tation in low-rainfall months decreases.
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Modeling the Effects of Climate Change on Stream Temperature in the Nooksack River Basin

Modeling the Effects of Climate Change on Stream Temperature in the Nooksack River Basin

Late into the 21 st century stream temperatures in the North and Middle forks increase similarly to the South Fork (Table 5; Figure 6). During the first half of the century, higher levels of snowmelt and glacial melt buffer rising stream temperatures during the summer, however later in the century, smaller glaciers and a reduced snowpack and warmer headwater temperatures cause stream temperatures to increase in the summer months late in the century. Murphy (2016) predicts glacier area in the two basins to decrease by up to 80% and the SWE to decrease by up to 75% compared to historical averages for RCP 8.5 scenarios. As such, late century stream temperature in the Middle Fork increase by approximately 71% for the RCP 8.5 scenario. Similarly, in the North Fork, stream temperature increases by up to 82% from the hindcast for the same 2075 time period and RCP 8.5 scenario during the summer. Although the North and Middle forks experience much higher stream temperature increases in the summer under climate change conditions versus the South Fork, their maximum magnitudes are still lower. Mitchell et al. (2017) found that winter snowpack will persist above 1500 m in elevation in all three basins late into the 21 st century. As such, because of the higher elevations in Middle and especially in the North Fork (Table 1) snowmelt and some glacier melt and cooler headwater temperatures will continue to buffer the overall stream temperatures in the summer (Table 5; Figure 6).
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