scholarly journals Evolution of the hydrological regime in relation to climate change: Case of the Bouregreg River basin, Morocco

2021 ◽  
Vol 94 (1) ◽  
pp. 131-147
Author(s):  
Rajae El Aoula ◽  
Gil Mahe ◽  
Nadia Mhammdi ◽  
Abdellatif Ezzahouani ◽  
Ilias Kacimi ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
River Basin ◽  
Hydrological Regime ◽  
Mountainous Area ◽  
Rainfall Time Series ◽  
Plateau Area ◽  
Middle Atlas ◽  
Very High ◽  
Semi Arid

The objective of this study is to investigate the evolution of rainfall and flow in the Bouregreg watershed, and to identify the most vulnerable regions to climate change over a period of 36 years from 1977 to 2013. Results show significant variations between these sub-regions in term of monthly flows and monthly regimes. January and February rainfall amounts are the most affected by the reduction of rainfall since the drought started end of the 1970’s, inducing a reduction of flows at all hydrological stations mainly since 1979. The year 1996 shows very high precipitations over all sub-basins, and also separates two periods with different rainfall time series variations according to two regions over the basin: the region of the Tsalat sub-basin in the Southeast wet and mountainous area (Middle Atlas) shows a durable decrease of rainfall compared to the Ain Loudah sub-basin in the Center-West semi-arid plateau area.

Long-term (1900-2100) Hydrometeorological and Snow Water Equivalent reconstructions in the French Southern Alps (Mercantour Natural Parc).

2021 ◽  
Author(s):  
Thibault Mathevet ◽  
Cyril Thébault ◽  
Jérôme Mansons ◽  
Matthieu Le Lay ◽  
Audrey Valery ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Water Resources ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Hydrological Regime ◽  
Southern Alps ◽  
Snow Line ◽  
Snow Water

<p>The aim of this communication is to present a study on climate variability and change on snow water equivalent (SWE) and streamflow over the 1900-2100 period in a mediteranean and moutainuous area.  It is based on SWE and streamflow observations, past reconstructions (1900-2018) and future GIEC scenarii (up to 2100) of some snow courses and hydrological stations situated within the French Southern Alps (Mercantour Natural Parc). This has been conducted by EDF (French hydropower company) and Mercantour Natural Parc.</p><p>This issue became particularly important since a decade, especially in regions where snow variability had a large impact on water resources availability, poor snow conditions in ski resorts and artificial snow production or impacts on mountainous ecosystems (fauna and flora). As a water resources manager in French mountainuous regions, EDF developed and managed a large hydrometeorological network since 1950. A recent data rescue research allowed to digitize long term SWE manual measurements of a hundred of snow courses within the French Alps. EDF have been operating an automatic SWE sensors network, complementary to historical snow course network. Based on numerous SWE observations time-series and snow modelization (Garavaglia et al., 2017), continuous daily historical SWE time-series have been reconstructed within the 1950-2018 period. These reconstructions have been extented to 1900 using 20 CR (20<sup>th</sup> century reanalyses by NOAA) reanalyses (ANATEM method, Kuentz et al., 2015) and up to 2100 using GIEC Climate Change scenarii (+4.5 W/m² and + 8.5 W/m² hypotheses). In the scope of this study, Mercantour Natural Parc is particularly interested by snow scenarii in the future and its impacts on their local flora and fauna.</p><p>Considering observations within Durance watershed and Mercantour region, this communication focuses on: (1) long term (1900-2018) analyses of variability and trend of hydrometeorological and snow variables (total precipitation, air temperature, snow water equivalent, snow line altitude, snow season length, streamflow regimes) , (2) long term variability of snow and hydrological regime of snow dominated watersheds and (3) future trends (2020 -2100) using GIEC Climate Change scenarii.</p><p>Comparing old period (1950-1984) to recent period (1984-2018), quantitative results within these regions roughly shows an increase of air temperature by 1.2 °C, an increase of snow line height by 200m, a reduction of SWE by 200 mm/year and a reduction of snow season duration by 15 days. Characterization of the increase of snow line height and SWE reduction are particularly important at a local and watershed scale. Then, this communication focuses on impacts on long-term time scales (2050, 2100). This long term change of snow dynamics within moutainuous regions both impacts (1) water resources management, (2) snow resorts and artificial snow production developments or (3) ecosystems dynamics.Connected to the evolution of snow seasonality, the impacts on hydrological regime and some streamflow signatures allow to characterize the possible evolution of water resources in this mediteranean and moutianuous region This study allowed to provide some local quantitative scenarii.</p>

IMPACT OF FUTURE CLIMATE CHANGE (2020–2059) ON THE HYDROLOGICAL REGIME IN THE HEIHE RIVER BASIN IN SHAANXI PROVINCE, CHINA

2019 ◽  
Vol 01 (01) ◽  
pp. 1950003 ◽  
Author(s):  
AIDI HUO ◽  
XIAOFAN WANG ◽  
YUXIANG CHENG ◽  
CHUNLI ZHENG ◽  
CHENG JIANG
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
River Basin ◽  
Hydrological Regime ◽  
Annual Runoff ◽  
Heihe River Basin ◽  
Shaanxi Province ◽  
Future Climate Change ◽  
Heihe River ◽  
The Heihe River Basin

Assessing the impacts of climate change on hydrological regime and associated social and economic activities (such as farming) is important for water resources management in any river basin. In this study, we used the popular Soil and Water Assessment Tool (SWAT) to evaluate the impacts of future climate change on the availability of water resources in the Heihe River basin located within Shaanxi Province, China, in terms of runoff and streamflow. The results show that over the next 40 years (starting in 2020 till 2059), changes in the averaged annual runoff ratio are approximately [Formula: see text]11.0%, [Formula: see text]6.4%, 7.2%, and 20.4% for each of the next four consecutive decades as compared to the baseline period (2010–2019). The predicted annual runoff demonstrates an increase trend after a reduction and may result in increased drought and flood risk in the Heihe River basin. To minimize or mitigate these impacts, various adaptation methods have been proposed for the study area, such as stopping irrigation, flood control operation; reasonable development and utilization of regional underground water sources should be implemented in Zhouzhi county and Huyi region in the lower reaches of Heihe River basin.

scholarly journals CLIMATE CHANGE IMPACT ASSESSMENT ON THE HYDROLOGY OF A SEMI-ARID RIVER BASIN

2014 ◽  
Vol 70 (4) ◽  
pp. I_121-I_126
Author(s):  
Asif M. BHATTI ◽  
Toshio KOIKE ◽  
Patricia Ann JARANILLA-SANCHEZ ◽  
Mohamed RASMY ◽  
Kohei YOSHIMURA ◽  
...  
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
River Basin ◽  
Climate Change Impact ◽  
Climate Change Impact Assessment ◽  
Change Impact ◽  
Semi Arid

scholarly journals Climate Change Effects on Hydropower Potential in the Alcantara River Basin in Sicily (Italy)

2013 ◽  
Vol 17 (19) ◽  
pp. 1-22 ◽  
Author(s):  
G. T. Aronica ◽  
B. Bonaccorso
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Coupled Model ◽  
Daily Rainfall ◽  
Hydrological Regime ◽  
Future Climate Change ◽  
Hydropower Generation ◽  
Energy Potential ◽  
Hydropower Potential ◽  
The Impact

Abstract In recent years, increasing attention has been paid to hydropower generation, since it is a renewable, efficient, and reliable source of energy, as well as an effective tool to reduce the atmospheric concentrations of greenhouse gases resulting from human activities. At the same time, however, hydropower is among the most vulnerable industries to global warming, because water resources are closely linked to climate changes. Indeed, the effects of climate change on water availability are expected to affect hydropower generation with special reference to southern countries, which are supposed to face dryer conditions in the next decades. The aim of this paper is to qualitatively assess the impact of future climate change on the hydrological regime of the Alcantara River basin, eastern Sicily (Italy), based on Monte Carlo simulations. Synthetic series of daily rainfall and temperature are generated, based on observed data, through a first-order Markov chain and an autoregressive moving average (ARMA) model, respectively, for the current scenario and two future scenarios at 2025. In particular, relative changes in the monthly mean and standard deviation values of daily rainfall and temperature at 2025, predicted by the Hadley Centre Coupled Model, version 3 (HadCM3) for A2 and B2 greenhouse gas emissions scenarios, are adopted to generate future values of precipitation and temperature. Synthetic series for the two climatic scenarios are then introduced as input into the Identification of Unit Hydrographs and Component Flows from Rainfall, Evapotranspiration and Streamflow Data (IHACRES) model to simulate the hydrological response of the basin. The effects of climate change are investigated by analyzing potential modification of the resulting flow duration curves and utilization curves, which allow a site's energy potential for the design of run-of-river hydropower plants to be estimated.

scholarly journals Modelling hydrological responses under climate change using machine learning algorithms – semi-arid river basin of peninsular India

2020 ◽  
Vol 3 (1) ◽  
pp. 481-498
Author(s):  
G. Sireesha Naidu ◽  
M. Pratik ◽  
S. Rehana
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
River Basin ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Peninsular India ◽  
Hydrological Responses ◽  
Semi Arid

Abstract Catchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the calibration factors with limited data for training and testing with precipitation, evapotranspiration and uncalibrated runoff based on various performance measures. The developed model was further used to study the runoff response under climate change variability of precipitation and temperatures. A statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector Regression was used to develop the precipitation and temperature projections based on MIROC GCM outputs with the RCP 4.5 scenario. The proposed modelling framework has been demonstrated on a semi-arid river basin of peninsular India, Krishna River Basin (KRB). The basin outlet runoff was predicted to decrease (13.26%) for future scenarios under climate change due to an increase in temperature (0.6 °C), compared to a precipitation increase (13.12%), resulting in an overall reduction in water availability over KRB.

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