scholarly journals Assessing watershed hydrological response to climate change based on signature indices

2021 ◽  
Author(s):  
Atiyeh Fatehifar ◽  
Mohammad Reza Goodarzi ◽  
Seyedeh Sima Montazeri Hedesh ◽  
Parnian Siahvashi Dastjerdi
Keyword(s):  
Climate Change ◽  
Discharge Rate ◽  
Baseline Period ◽  
Future Climate Change ◽  
Hydrological Response ◽  
High Discharge ◽  
Discharge Rates ◽  
Segment Volume ◽  
The Mean ◽  
Hydrological Signatures

Abstract Due to the fact that one of the important ways of describing the performance of basins is to use the hydrological signatures, the present study is to investigate the effects of climate change using the hydrological signatures in Azarshahr Chay basin, Iran. To this end, Canadian Earth system model (CanESM2) is first used to predict future climate change (2030–2059) under two Representative Concentration Pathways (RCP2.6 and RCP8.5). Six signature indices were extracted from flow duration curve (FDC) as follows: runoff ratio (RR), high-segment volume (FHV), low-segment volume (FLV), mid-segment slope (FMS), mid-range flow (FMM), and maximum peak discharge (DiffMaxPeak). These signature indices act as sorts of fingerprints representing differences in the hydrological behavior of the basin. The results indicate that the most significant changes in the future hydrological response are related to the FHV and FLV and FMS indices. The BiasFHV index indicates an increase in high discharge rates under RCP8.5 scenario, compared to the baseline period and the RCP2.6 scenario, as well. The mean annual discharge rate, however, is lower than the discharge rate under this scenario. Generally, for the RCP8.5 scenario, the changes in the signature indices in both high discharges and low discharges are significant.

scholarly journals Reduced global warming from CMIP6 projections when weighting models by performance and independence

2020 ◽  
Vol 11 (4) ◽  
pp. 995-1012
Author(s):  
Lukas Brunner ◽  
Angeline G. Pendergrass ◽  
Flavio Lehner ◽  
Anna L. Merrifield ◽  
Ruth Lorenz ◽  
...  
Keyword(s):  
Climate Change ◽  
Retrospective Analysis ◽  
Climate Models ◽  
Cmip5 Models ◽  
Future Climate Change ◽  
Historical Period ◽  
Climate Change Scenarios ◽  
Uncertainty Range ◽  
Future Warming ◽  
The Mean

Abstract. The sixth Coupled Model Intercomparison Project (CMIP6) constitutes the latest update on expected future climate change based on a new generation of climate models. To extract reliable estimates of future warming and related uncertainties from these models, the spread in their projections is often translated into probabilistic estimates such as the mean and likely range. Here, we use a model weighting approach, which accounts for the models' historical performance based on several diagnostics as well as model interdependence within the CMIP6 ensemble, to calculate constrained distributions of global mean temperature change. We investigate the skill of our approach in a perfect model test, where we use previous-generation CMIP5 models as pseudo-observations in the historical period. The performance of the distribution weighted in the abovementioned manner with respect to matching the pseudo-observations in the future is then evaluated, and we find a mean increase in skill of about 17 % compared with the unweighted distribution. In addition, we show that our independence metric correctly clusters models known to be similar based on a CMIP6 “family tree”, which enables the application of a weighting based on the degree of inter-model dependence. We then apply the weighting approach, based on two observational estimates (the fifth generation of the European Centre for Medium-Range Weather Forecasts Retrospective Analysis – ERA5, and the Modern-Era Retrospective analysis for Research and Applications, version 2 – MERRA-2), to constrain CMIP6 projections under weak (SSP1-2.6) and strong (SSP5-8.5) climate change scenarios (SSP refers to the Shared Socioeconomic Pathways). Our results show a reduction in the projected mean warming for both scenarios because some CMIP6 models with high future warming receive systematically lower performance weights. The mean of end-of-century warming (2081–2100 relative to 1995–2014) for SSP5-8.5 with weighting is 3.7 ∘C, compared with 4.1 ∘C without weighting; the likely (66%) uncertainty range is 3.1 to 4.6 ∘C, which equates to a 13 % decrease in spread. For SSP1-2.6, the weighted end-of-century warming is 1 ∘C (0.7 to 1.4 ∘C), which results in a reduction of −0.1 ∘C in the mean and −24 % in the likely range compared with the unweighted case.

Quantitative Analyses of Dynamic Strain Sensitivity in Human Skin Mechanoreceptors

2004 ◽  
Vol 92 (6) ◽  
pp. 3233-3243 ◽  
Author(s):  
Benoni B. Edin
Keyword(s):  
Discharge Rate ◽  
Receptive Fields ◽  
Dynamic Strain ◽  
Strain Sensitivity ◽  
Power Functions ◽  
Discharge Rates ◽  
Response Onset ◽  
Quantitative Analyses ◽  
The Mean ◽  
Skin Mechanoreceptors

Microneurographical recordings from 24 slowly adapting (SA) and 16 fast adapting (FA) cutaneous mechanoreceptor afferents were obtained in the human radial nerve. Most of the afferents innervated the hairy skin on the back of the hand. The afferents' receptive fields were subjected to controlled strains in a ramp-and-hold fashion with strain velocities from 1 to 64% · s−1, i.e., strain velocities within most of the physiological range. For all unit types, the mean variation in response onset approached 1 ms for strain velocities >8% · s−1. Except at the highest strain velocities, the first spike in a typical SAIII unit was evoked at strains <0.5% and a typical SAII unit began to discharge at <1% skin strain. Skin strain velocity had a profound effect on the discharge rates of all classes of afferents. The “typical” peak discharge rate at the highest strain velocity studied was 50–95 imp/s−1 depending on unit type. Excellent fits were obtained for both SA and FA units when their responses to ramp stretches were modeled by simple power functions ( r2 > 0.9 for 95% of the units). SAIII units grouped with SAII with respect to onset latency and onset variation but with SAI units with respect to dynamic strain sensitivity. Because both SA and FA skin afferents respond strongly, quickly, and accurately to skin strain changes, they all seem to be able to provide useful information about movement-related skin strain changes and therefore contribute to proprioception and kinesthesia.

scholarly journals Evaluation of hydrological response under future climate change by revising meteorological data in the source region of the Yangtze River, China

2021 ◽  
Author(s):  
Xiaohong Chen ◽  
Haoyu Jin ◽  
Pan Wu ◽  
Wenjun Xia ◽  
Ruida Zhong ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Yangtze River ◽  
Meteorological Data ◽  
Source Region ◽  
Future Climate ◽  
Future Climate Change ◽  
Hydrological Response ◽  
The Yangtze River ◽  
Data Correction

Abstract The source region of the Yangtze River (SRYR) is located in the hinterland of the Tibetan Plateau (TP). The natural environment is hash, and the hydrological and meteorological stations are less distributed, making the observed data are relatively scarce. In order to overcome the impact of lack of data, the China Meteorological Forcing Dataset (CMFD) was used to correct the meteorological data, to make the data more closer to the real distribution on the SRYR surface. This paper used the Soil and Water Assessment Tool (SWAT) to verify interpolation effect. Since the SRYR is an important water resource protection area, have a great significance to study the hydrological response under future climate change. The Back Propagation (BP) neural network algorithm was used to integrate data extracted from the six Global Climate Models (GCMs), and then the SWAT model was used to predict runoff changes in the future status. The results show that the CMFD data set has a high precision in the SRYR, and can be used for meteorological data correction. After the meteorological data correction, the Nash-Sutcliffe efficiency increased from 0.64 to 0.70. Under the future climate change, the runoff in the SRYR shows a decreasing trend, and the distribution of runoff during the year changes greatly. This reflects the amount of water resources in the SRYR will be decreased, which will brings challenges to water resources management in the SRYR.

scholarly journals Evaluation of five hydrological models across Europe and their suitability for making projections under climate change

2015 ◽  
Vol 12 (10) ◽  
pp. 10289-10330 ◽  
Author(s):  
W. Greuell ◽  
J. C. M. Andersson ◽  
C. Donnelly ◽  
L. Feyen ◽  
D. Gerten ◽  
...  
Keyword(s):  
Climate Change ◽  
Interannual Variability ◽  
Large Scale ◽  
Interannual Variation ◽  
Hydrological Models ◽  
High Discharge ◽  
Annual Cycles ◽  
Model Ranking ◽  
The Mean

Abstract. The main aims of this paper are the evaluation of five large-scale hydrological models across Europe and the assessment of the suitability of the models for making projections under climate change. For the evaluation, 22 years of discharge measurements from 46 large catchments were exploited. In the reference simulations forcing was taken from the E-OBS dataset for precipitation and temperature, and from the WFDEI dataset for other variables. On average across all catchments, biases were small for four of the models, ranging between −29 and +23 mm yr−1 (−9 and +8 %), while one model produced a large negative bias (−117 mm yr−1; −38 %). Despite large differences in e.g. the evapotranspiration schemes, the skill to simulate interannual variability did not differ much between the models, which can be ascribed to the dominant effect of interannual variation in precipitation on interannual variation in discharge. Assuming that the skill of a model to simulate interannual variability provides a measure for the model's ability to make projections under climate change, the skill of future discharge projections will not differ much between models. The quality of the simulation of the mean annual cycles, and low and high discharge was found to be related to the degree of calibration of the models, with the more calibrated models outperforming the crudely and non-calibrated models. The sensitivity to forcing was investigated by carrying out alternative simulations with all forcing variables from WFDEI, which increased biases by between +66 and +85 mm yr−1 (21–28 %), significantly changed the inter-model ranking of the skill to simulate the mean and increased the magnitude of interannual variability by 28 %, on average.

scholarly journals Hydrological response to future climate change in the Agano River basin, Japan

2010 ◽  
Vol 4 ◽  
pp. 25-29 ◽  
Author(s):  
Xieyao Ma ◽  
Takao Yoshikane ◽  
Masayuki Hara ◽  
Yasutaka Wakazuki ◽  
Hiroshi G Takahashi ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Future Climate ◽  
Future Climate Change ◽  
Hydrological Response

scholarly journals Future climate change impacts on runoff of scarcely gauged Jhelum river basin using SDSM and RCPs

2021 ◽  
Author(s):  
Saira Munawar ◽  
Muhammad Naveed Tahir ◽  
Muhammad Hassan Ali Baig
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Change Impacts ◽  
Baseline Period ◽  
Future Climate Change ◽  
Mean Annual Precipitation ◽  
Climatic Data ◽  
Global Issue ◽  
Jhelum River ◽  
Jhelum River Basin

Abstract Climate change is a global issue and causes great uncertainties in runoff and streamflow projections, especially in high-altitude basins. The quantification of climatic indicators remains a tedious job for the scarcely gauged mountainous basin. This study investigated climate change by incorporating GCM (CCSM4) using the SDSM method for RCPs in the Jhelum river basin. Historical climatic data were coupled with Aphrodite data to cope with the scarcity of weather stations. SDSM was calibrated for the period 1976–2005 and validated for the period 2006–2015 using R2 and RMSE. Future climatic indicators were downscaled and debiased using the MB-BC method. The de-biased downscaled data and MODIS data were used to simulate discharge of Jhelum river basin using SRM. Simulated discharge was compared with measured discharge by using Dv% and NSE. The R2 and RMSE for SDSM range between 0.89–0.95 and 0.8–1.02 for temperature and 0.86–0.96 and 0.57–1.02 for precipitation. Projections depicted a rising trend of 1.5 °C to 3.8 °C in temperature, 2–7% in mean annual precipitation and 3.3–7.4% in discharge for 2100 as compared to the baseline period. Results depicted an increasing trend for climatic indicators and discharge due to climate change for the basin.

scholarly journals Assessing Soil Organic Carbon Stock Dynamics under Future Climate Change Scenarios in the Middle Qilian Mountains

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1698
Author(s):  
Wei Liu ◽  
Meng Zhu ◽  
Yongge Li ◽  
Jutao Zhang ◽  
Linshan Yang ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Organic Carbon ◽  
Qilian Mountains ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Machine Learning Model ◽  
Soc Stock ◽  
The Mean

Soil organic carbon (SOC) simply cannot be managed if its amounts, changes and locations are not well known. Thus, evaluations of the spatio-temporal dynamics of SOC stock under future climate change are crucial for the adaptive management of regional carbon sequestration. Here, we evaluated the dynamics of SOC stock to a 60 cm depth in the middle Qilian Mountains (1755–5051 m a.s.l.) by combining systematic measurements from 138 sampling sites with a machine learning model. Our results reveal that the combination of systematic measurements with the machine learning model allowed spatially explicit estimates of SOC change to be made. The average SOC stock in the middle Qilian Mountains was expected to decrease under future climate change, while the size and direction of SOC stock changes seemed to be elevation-dependent. Specifically, in comparison with the 2000s, the mean annual precipitation was projected to increase by 18.37, 19.80 and 30.80 mm, and the mean annual temperature was projected to increase by 1.9, 2.4 and 2.9 °C under the Representative Concentration Pathway (RCP) 2.6 (low-emissions pathway), RCP4.5 (low-to-moderate-emissions pathway), and RCP8.5 (high-emissions pathway) scenarios by the 2050s, respectively. Accordingly, the area-weighted SOC stock and total storage for the whole study area were estimated to decrease by 0.43, 0.63 and 1.01 kg m–2 and 4.55, 6.66 and 10.62 Tg under the RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively. In addition, the mid-elevation zones (3100–3900 m), especially the subalpine shrub-meadow Mollic Leptosols, were projected to experience the most intense carbon loss. However, the higher elevation zones (>3900 m), especially the alpine desert zone, were characterized by significant carbon accumulation. As for the low-elevation zones (<2900 m), SOC was projected to be less varied under future climate change scenarios. Thus, the mid-elevation zones, especially the subalpine shrub-meadows and Mollic Leptosols, should be given priority in terms of reducing CO2 emissions in the Qilian Mountains.

scholarly journals Integrated model projections of climate change impacts on water-level dynamics in the large Poyang Lake (China)

2019 ◽  
Author(s):  
Yunliang Li ◽  
Qi Zhang ◽  
Hui Tao ◽  
Jing Yao
Keyword(s):  
Climate Change ◽  
Water Level ◽  
Lake Water ◽  
Hydrological Model ◽  
Poyang Lake ◽  
Baseline Period ◽  
Global Climate Models ◽  
Water Levels ◽  
Future Climate Change ◽  
Increased Risk

Abstract This study outlines a framework for examining potential impacts of future climate change in Poyang Lake water levels using linked models. The catchment hydrological model (WATLAC) was used to simulate river runoffs from a baseline period (1986–2005) and near-future (2020–2035) climate scenarios based on eight global climate models (GCMs). Outputs from the hydrological model combined with the Yangtze River's effects were fed into a lake water-level model, developing in the back-propagation neural network. Model projections indicate that spring–summer water levels of Poyang Lake are expected to increase by 5–25%, and autumn–winter water levels are likely to be lower and decrease by 5–30%, relative to the baseline period. This amounts to higher lake water levels by as much as 2 m in flood seasons and lower water levels in dry seasons in the range of 0.1–1.3 m, indicating that the lake may be wet-get-wetter and dry-get-drier. The probability of occurrence for both the extreme high and low water levels may exhibit obviously increasing trends by up to 5% more than at present, indicating an increased risk in the severity of lake floods and droughts. Projected changes also include possible shifts in the timing and magnitude of the lake water levels.

Sign in / Sign up

Export Citation Format

Share Document