Fluctuations in Monthly and Annual Rainfall Trend in the Limbang River Basin, Malaysia: A Statistical Assessment to Detect the Influence of Climate Change

Bhadar is one of the major rivers of Kathaiwar (Saurashtra) peninsula in Gujarat, India. It originates near Vaddi (Aniali Village) about 26 km north – west of Jasdan in Rajkot district of the state of Gujarat, India at an elevation of 261 m above mean sea level. Impact assessment of climate change over Bhadar river basin is carried out using two statistical methods of Trend Analysis i.e. linear Regression, and Innovative Trend method. Effect of climate change on annual rainfall and monthly rainfall are studied. Results show that there is an overall increase in annual rainfall trend in Bhadar river basin/catchment area at all stations except one station. The results for monthly rainfall show that the rainfall in the month of July and September shows increasing trend at all stations. The results obtained using Linear Regression and Innovative Trend method are found to be consistent.

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Climate Change Impact in Flores Island, a Dry Region in Indonesia

Civil Engineering Dimension ◽

10.9744/ced.23.1.20-27 ◽

2021 ◽

Vol 23 (1) ◽

pp. 20-27

Author(s):

Cilcia Kusumastuti ◽

Dicky Gode ◽

Yobella Febe Kurnianto ◽

Frederik Jones Syaranamual

Keyword(s):

Climate Change ◽

Climate Change Impacts ◽

Annual Rainfall ◽

Rainfall Trend ◽

Simple Linear Regression ◽

Adaptation Measures ◽

Pattern Change ◽

Increasing Trend ◽

Change Impact ◽

Potential Climate Change

Climate change impacts have gained great attention to be studied in various fields. In this paper, an investigation of rainfall pattern change is performed using three statistical methods, i.e., simple linear regression, t-test, and Mann-Kendall’s test. The analysis is performed at 10- and 20-year time scales of daily, monthly, and annual rainfall in Flores Island, a dry region in Indonesia. In general, an increasing monthly rainfall trend is detected in the rainy season (October – April) at a 20-year period, using all three methods. Specifically, a significant increasing trend in March 1989 – 2008 is observed, and it contributes to the significant increasing trend of annual rainfall. The findings presented in this paper should be an alert for potential climate change impacts in the region. The positive consideration of having more rainfall in a dry region might turn into a negative reality when adaptation measures are not well-prepared.

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A framework for quantifying the impacts of future climate and land use/cover changes on runoff in the Han River basin, China

10.5194/egusphere-egu2020-1979 ◽

2020 ◽

Author(s):

Jing Tian ◽

Shenglian Guo ◽

Chong-Yu Xu

Keyword(s):

Climate Change ◽

Land Use ◽

Water Resources ◽

River Basin ◽

Future Climate ◽

Annual Rainfall ◽

Base Period ◽

Han River ◽

The Future ◽

Han River Basin

<p>As a link between the atmosphere and the earth&#8217;s surface, the hydrological cycle is impacted by both climate change and land use/cover change (LUCC). For most basins around the world, the co-variation of climate change and LUCC will continue in the future, which highlights the significance to explore the temporal-spatial distribution and variation mechanism of runoff and to improve our ability in water resources planning and management. Therefore, the purpose of this study is to propose a framework to examine the response of runoff to climate change and LUCC under different future scenarios. Firstly, the future climate scenarios under BCC-CSM1.1 and BNU-ESM are both downscaled and bias-corrected by the Daily bias correction (DBC) method, meanwhile, the future LUCC scenarios are predicted by the Cellular Automaton-Markov (CA-Markov) model according to the integrated basin plans of future land use. Then, based on the baseline scenario S0 (meteorological data from 1966 to 2005 and current situation LUCC2010), the following three scenarios are set with different combinations of future climate land-use situations, i.e., S1: only climate change scenario; S2: only the LUCC scenario; S3: climate and LUCC co-variation scenario. Lastly, the Soil and Water Assessment Tool (SWAT) model is used to simulate the hydrological process and quantify the impacts of climate change and LUCC on the runoff yield. The proposed framework is applied to the Han River basin in China. Results show that: (1) compared with the base period (1966-2005), the annual rainfall, daily maximum, and minimum air temperature during 2021-2060 will have an increase of 4.0%, 1.8&#8451;, 1.6&#8451; in RCP4.5 while 3.7%, 2.5&#8451;, 2.3&#8451; in RCP8.5, respectively; (2) from 2010 to 2050, the forest land and construction land in the Han River basin will have an increase of 2.8% and 1.2%, respectively, while that of farmland and grassland will have a decrease of 1.5% and 2.5%, respectively; (3) comparing with the single climate change or LUCC scenario, the co-variation scenario possesses the largest uncertainty in runoff projection. Under the two concentration paths, there is a consistent upward change in future runoff (2021-2060) of the studied basin compared with that in the base period, furthermore, the increase rate in RCP4.5 (+5.10%) is higher than that in RCP8.5 (+2.67%). The results of this study provide a useful reference and help for water resources and land use management in the Han River basin.</p>

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Rainfall trend analysis and weather forecast accuracy in selected parts of Khyber Pakhtunkhwa, Pakistan

SN Applied Sciences ◽

10.1007/s42452-021-04457-z ◽

2021 ◽

Vol 3 (5) ◽

Author(s):

Fakhri Alam ◽

Muhammad Salam ◽

Nasir Ahmad Khalil ◽

Owais khan ◽

Masaud Khan

Keyword(s):

Climate Change ◽

Trend Analysis ◽

Forecast Accuracy ◽

Weather Forecast ◽

Kendall Test ◽

Annual Rainfall ◽

Rainfall Trend ◽

Khyber Pakhtunkhwa ◽

Temperature Records ◽

Nonparametric Techniques

AbstractClimate change is a multidimensional phenomenon, which has various effects on people's environmental and socioeconomic conditions. In the agricultural economy that is susceptible to natural changes, its impact is more profound. Therefore, climate change directly affects society in different ways, and society must pay a price. Climate change, especially the changes in annual temperature and rainfall, has attracted widespread attention worldwide. The variability of these factors or the magnitude of fluctuations varies according to location. Therefore, in the context of climate change, especially in countries dominated by rainfed agriculture, studying the trend of meteorological variables is essential to assess climate-induced variations and propose feasible adaptation approaches. Focusing on this fact is the main goal of this research study was to determine the rainfall trend and the accuracy of predicted temperature at three particular stations of Khyber Pakhtunkhwa (Kp) Province, Pakistan. For this purpose, rainfall and temperature data were provided by Pakistan Meteorological Department (PMD), Islamabad, for the period 1960–2020. Two types of nonparametric techniques, Sen’s slope estimate and the Mann–Kendall test, were applied to determine a trend in the average monthly and annual rainfall. The results of the annual rainfall trend analysis showed that Peshawar and Dera Ismail Khan stations showed a positive increasing trend, while the monthly rainfall trend showed a negative decreasing trend for all stations. The trend was statistically significant for Peshawar and Saidu Sharif stations. The accuracy of predicted and actual temperature and rainfall indicated that mostly over-forecast occurred at Saidu Sharif and Peshawar. Most of the precipitation and temperature records showed under forecast for Dera Ismail Khan, but some over-prediction has also occurred. Graphical abstract

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Estimating monthly rainfall in rural river basins under climate change: an improved bias-correcting statistical downscaling approach

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-6847-2013 ◽

2013 ◽

Vol 10 (6) ◽

pp. 6847-6896

Author(s):

D. L. Jayasekera ◽

J. J. Kaluarachchi

Keyword(s):

Climate Change ◽

General Circulation Model ◽

River Basin ◽

Bias Correction ◽

General Circulation ◽

Circulation Model ◽

Correction Method ◽

Annual Rainfall ◽

Bias Correction Method ◽

Time Space

Abstract. This study extended the work of Kim et al. (2008) to generate future rainfall under climate change using a discrete-time/space Markov chain based on historical conditional probabilities. A bias-correction method is proposed by fitting suitable statistical distributions to transform rainfall from the general circulation model (GCM) scale to watershed scale. The demonstration example used the Nam Ngum River Basin (NNRB) in Laos which is a rural river basin with high potential for hydropower generation and significant rain-fed agriculture supporting rural livelihoods. This work generated weekly rainfall for a 100 yr period using historical rainfall data from 1961 to 2000 for ten selected weather stations. The bias-correction method showed the ability to reduce bias of the mean values of GCMs when compared to the observed mean amount at each station. The simulated rainfall series is perturbed using the delta change estimated at each station to project future rainfall for the Special Report on Emission Scenarios (SRES) A2. GCMs consisting of third generation coupled general circulation model (CGCM3.1 T63) and European center Hamburg model (ECHAM5) projected an increasing trend of mean annual rainfall in the NNRB. Seasonal rainfall percent changes showed an increase in the wet and dry seasons with the highest increase in the dry season mean rainfall of about 31% from 2051 to 2090. While the GCM projections showed good results with appropriate bias corrections, the Providing REgional Climates for Impacts Studies (PRECIS) regional climate model significantly underestimated historical behavior and produced higher mean absolute errors compared to the corresponding GCM predictions.

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Rainfall Trend and Variability Analysis of Sub-Tropical Hills of Arunachal Pradesh in Northeastern Himalayan Region of India

Current World Environment ◽

10.12944/cwe.11.2.35 ◽

2016 ◽

Vol 11 (2) ◽

pp. 631-636

Author(s):

Kaushik Bhagawati ◽

Rupankar Bhagawati ◽

Amit Sen ◽

Kshitiz Shukla ◽

Rajesh Alone

Keyword(s):

Climate Change ◽

Trend Analysis ◽

Himalayan Region ◽

Annual Rainfall ◽

Significant Shift ◽

Rainfall Trend ◽

Arunachal Pradesh ◽

Extreme Rainfall Events ◽

Dry Spell ◽

The Impact

The climate change especially the changes in rainfall pattern is most crucial for Himalayan region as it leads to changes in river runoff and consequently affecting environment, agricultural productivity and human livelihood downstream. Current study aims to evaluate the rainfall trend and variability in the highest rainfall recipient sub-tropical hill regions of Arunachal Pradesh in Northeastern Himalayan region of India. Sen’s estimator is used for trend analysis and Mann-Kendall test to determine significance of the trend. The 37 years (1979-2015) data reveals no clear and consistent trend of average annual rainfall. But a wide inter and intra seasonal variation in the monthly rainfall has been observed. Also a significant shift in rainfall during pre-monsoon and Southwest monsoon was noticed leading to change in forest and agricultural growing seasons, mid-season dry spell during July and increase in extreme rainfall events during August, September and October. The trend analysis of rainfall will help in prediction of future climate scenarios in this Himalayan region and to understand the impact of climate change.

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Multi-Criteria Decision Framework to Evaluate Bias Corrected Climate Change Projections in the Piracicaba River Basin

Revista Brasileira de Meteorologia ◽

10.1590/0102-77863630068 ◽

2021 ◽

Author(s):

Camila Billerbeck ◽

Ligia Monteiro da Silva ◽

Silvana Susko Marcellini ◽

Arisvaldo Méllo Junior

Keyword(s):

Climate Change ◽

River Basin ◽

Climate Models ◽

Regional Climate ◽

Climate Change Impacts ◽

Regional Climate Models ◽

Annual Rainfall ◽

Monthly Precipitation ◽

Climate Change Effects ◽

Piracicaba River

Abstract Regional climate models (RCM) are the main tools for climate change impacts assessment in hydrological studies. These models, however, often show biases when compared to historical observations. Bias Correction (BC) are useful techniques to improve climate projection outputs. This study presents a multi-criteria decision analysis (MCDA) framework to compare combinations of RCM with selected BC methods. The comparison was based on the modified Kling-Gupta efficiency (KGE’). The criteria evaluated the general capability of models in reproducing the observed data main statistics. Other criteria evaluated were the relevant aspects for hydrological studies, such as seasonality, dry and wet periods. We applied four BC methods in four RCM monthly rainfall outputs from 1961 to 2005 in the Piracicaba river basin. The Linear Scaling (LS) method showed higher improvements in the general performance of the models. The RCM Eta-HadGEM2-ES, corrected with Standardized Reconstruction (SdRc) method, achieved the best results when compared to the observed precipitation. The bias corrected projected monthly precipitation (2006-2098) preserved the main signal of climate change effects when compared to the original outputs regarding annual rainfall. However, SdRc produced significant decrease in monthly average rainfall, higher than 45% for July, August and September for RCP4.5 and RCP8.5 scenarios.

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Climate Change Impact on Surface Water and Groundwater Recharge in Northern Thailand

Water ◽

10.3390/w12041029 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1029 ◽

Cited By ~ 3

Author(s):

Chanchai Petpongpan ◽

Chaiwat Ekkawatpanit ◽

Duangrudee Kositgittiwong

Keyword(s):

Climate Change ◽

Surface Water ◽

Groundwater Recharge ◽

River Basin ◽

Climate Change Impact ◽

Annual Rainfall ◽

Northern Thailand ◽

Surface Water And Groundwater ◽

Rcp 8.5 ◽

Change Impact

Climate change is progressing and is now one of the most important global challenges for humanities. Water resources management is one of the key challenges to reduce disaster risk. In Northern Thailand, flood and drought have always occurred because of the climate change impact and non-systematic management in the conjunctive use of both sources of water. Therefore, this study aims to assess the climate change impact on surface water and groundwater of the Yom and Nan river basins, located in the upper part of Thailand. The surface water and groundwater regimes are generated by a fully coupled SWAT-MODFLOW model. The future climate scenarios are considered from the Representative Concentration Pathways (RCPs) 2.6 and 8.5, presented by the Coupled Model Intercomparison Project Phase 5 (CMIP5), in order to mainly focus on the minimum and maximum Green House Gas (GHG) emissions scenarios during the near future (2021–2045) periods. The results show that the average annual air temperature rises by approximately 0.5–0.6 °C and 0.9–1.0 °C under the minimum (RCP 2.6) and maximum (RCP 8.5) GHG emission scenarios, respectively. The annual rainfall, obtained from both scenarios, increased by the same range of 20–200 mm/year, on average. The summation of surface water (water yield) and groundwater recharge (water percolation) in the Yom river basin decreased by 443.98 and 316.77 million m3/year under the RCPs 2.6 and 8.5, respectively. While, in the Nan river basin, it is projected to increase by 355 million m3/year under RCP 2.6 but decrease by 20.79 million m3/year under RCP 8.5. These quantitative changes can directly impact water availability when evaluating the water demand for consumption, industry, and agriculture.

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Assessment of Seasonal and Annual Rainfall Trends and Variability in Sharjah City, UAE

Advances in Meteorology ◽

10.1155/2016/6206238 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 8

Author(s):

Tarek Merabtene ◽

Mohsin Siddique ◽

Abdallah Shanableh

Keyword(s):

Climate Change ◽

Temporal Variability ◽

Low Frequency ◽

Annual Rainfall ◽

Spatial And Temporal Variability ◽

Rainfall Trend ◽

Significant Drop ◽

Statistical Measures ◽

Rainfall Trends ◽

The Impact

Although a few studies on rainfall spatial and temporal variability in the UAE have been carried out, evidence of the impact of climate change on rainfall trends has not been reported. This study aims at assessing the significance of long-term rainfall trends and temporal variability at Sharjah City, UAE. Annual rainfall and seasonal rainfall extending over a period of 81 years (1934–2014) recorded at Sharjah International Airport have been analyzed. To this end, several parametric and nonparametric statistical measures have been applied following systematic data quality assessment. The analyses revealed that the annual rainfall trend decreased from −3 mm to −9.4 mm per decade over the study periods. The decreasing annual rainfall trend is mainly driven by the significant drop in winter rainfall, particularly during the period from 1977 to 2014. The results also indicate that high probability extreme events have shifted toward low frequency (12.7 years) with significant variations in monthly rainfall patterns and periodicity. The findings of the present study suggest reevaluating the derivation of design rainfall for infrastructure of Sharjah City and urge developing an integrated framework for its water resources planning and risk under climate change impacts scenarios.

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Assessment of potential impact of climate change on streamflow: a case study of the Brahmani River basin, India

Journal of Water and Climate Change ◽

10.2166/wcc.2018.129 ◽

2018 ◽

Vol 10 (3) ◽

pp. 624-641 ◽

Cited By ~ 5

Author(s):

Kumari Vandana ◽

Adlul Islam ◽

P. Parth Sarthi ◽

Alok K. Sikka ◽

Hemlata Kapil

Keyword(s):

Climate Change ◽

River Basin ◽

Low Flow ◽

Annual Rainfall ◽

Future Climate Change ◽

Distributed Parameter ◽

Climate Change Scenarios ◽

Model Ensemble ◽

Simulation Results ◽

The Impact

Abstract The impact of future climate change on streamflow in the Brahmani River basin, India has been assessed using a distributed parameter hydrological model Precipitation Runoff Modelling System (PRMS) and multi-model ensemble climate change scenarios. The multi-model ensemble climate change scenarios were generated using the Hybrid-Delta ensemble method for A2, A1B, and B1 emission scenarios for three different future periods of the 2020s (2010–2039), 2050s (2040–2069) and 2080s (2070–2099). There is an increase in annual mean temperature in the range of 0.8–1.0, 1.5–2.0 and 2.0–3.3 °C during the 2020s, 2050s, and 2080s, respectively. Annual rainfall is projected to change in the range of −1.6–1.6, 1.6–3.1, and 4.8–8.1% during the 2020s, 2050s and 2080s, respectively. Simulation results indicated changes in annual streamflow in the range of −2.2–2.5, 2.4–4.7, and 7.3–12.6% during the 2020s, 2050s, and 2080s, respectively. Simulation results showed an increase in high flows and reduction in low flows, but the frequency of both high and low flow increases during future periods. The results of this work will be useful in developing a water management adaptation plan in the study basin.

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