scholarly journals Trend characteristics of rainy days and evaporation at a tropical rainforest region in East Malaysia, Borneo

2020 ◽  
Vol 24 (3) ◽  
pp. 305-315
Author(s):  
Ninu Krishna MV ◽  
Prasanna MV ◽  
Vijith H
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Atmospheric Temperature ◽  
Vital Role ◽  
Annual Rainfall ◽  
Statistical Characterization ◽  
Increasing Trend ◽  
Hydrological System ◽  
Statistical Trends ◽  
Rainy Days

Impact of climate change over the hydrological system in a region can be identified through statistical characterization of hydrometerological parameters such as rainfall, temperature, humidity and evaporation. In order to understand the influence of climate change, statistical trend characteristics of rainy days, non-rainy days and evaporation rate in the Limbang River Basin (LRB) in Sarawak, Malaysia, Northern Borneo was assessed in the present research. Annual rainfall and monthly evaporation data, over a period of 46 years, (1970 - 2015) corresponding to three rain gauging stations, the Limbang DID, Ukong and Long Napir were used in the research. Linear regression model, Mann Kendall and Sen’s slope estimator techniques were applied to detect the statistical trends in rainy days and evaporation. A statistically significant increasing trend in annual rainy days was found at all the stations. Non-rainy days showed a statistically significant decreasing trend at Limbang DID and Ukong. Monthly evaporation rates showed an overall increasing trend and the greatest increasing trend in evaporation was observed in September (2.55 mm/year) for the Limbang DID and in December (2.61 mm/year) for Ukong. Evaporation measured at the Ukong station also showed a non-significant decrease during June and September. A comparison of the evaporation controlling meteorological variables such as rainfall, temperature and relative humidity indicates inter-influence at various strengths. Along with local precipitation characteristics, wind and fluctuation of atmospheric temperature over the region plays a vital role in increased rate of evaporation from the region. Overall, the analysis identified a statistically significant increasing trend in rainy days and evaporation in the LRB. The results of the present research can be used as critical planning data for micro and macro hydroelectric projects in the river basin.

scholarly journals Climate Change Impact on Annual and Monthly Rainfall on Bhadar River Basin, India using Linear Regression and Innovative Trend Method

2019 ◽  
Vol 9 (1) ◽  
pp. 1120-1123
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
River Basin ◽  
Monthly Rainfall ◽  
Annual Rainfall ◽  
Rainfall Trend ◽  
North West ◽  
Method Effect ◽  
Increasing Trend ◽  
Change Impact

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.

scholarly journals An Analysis of Long-Term Rainfall Trends and Variability in the Uttarakhand Himalaya Using Google Earth Engine

2020 ◽  
Vol 12 (4) ◽  
pp. 709 ◽  
Author(s):  
Abhishek Banerjee ◽  
Ruishan Chen ◽  
Michael E. Meadows ◽  
R.B. Singh ◽  
Suraj Mal ◽  
...  
Keyword(s):  
River Basin ◽  
Temporal Trends ◽  
Google Earth ◽  
Monthly Rainfall ◽  
Annual Rainfall ◽  
Google Earth Engine ◽  
Spatio Temporal ◽  
Uttarakhand Himalaya ◽  
Rainy Days

This paper analyses the spatio-temporal trends and variability in annual, seasonal, and monthly rainfall with corresponding rainy days in Bhilangana river basin, Uttarakhand Himalaya, based on stations and two gridded products. Station-based monthly rainfall and rainy days data were obtained from the India Meteorological Department (IMD) for the period from 1983 to 2008 and applied, along with two daily rainfall gridded products to establish temporal changes and spatial associations in the study area. Due to the lack of more recent ground station rainfall measurements for the basin, gridded data were then used to establish monthly rainfall spatio-temporal trends for the period 2009 to 2018. The study shows all surface observatories in the catchment experienced an annual decreasing trend in rainfall over the 1983 to 2008 period, averaging 15.75 mm per decade. Analysis of at the monthly and seasonal trend showed reduced rainfall for August and during monsoon season as a whole (10.13 and 11.38 mm per decade, respectively); maximum changes were observed in both monsoon and winter months. Gridded rainfall data were obtained from the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). By combining the big data analytical potential of Google Earth Engine (GEE), we compare spatial patterns and temporal trends in observational and modelled precipitation and demonstrate that remote sensing products can reliably be used in inaccessible areas where observational data are scarce and/or temporally incomplete. CHIRPS reanalysis data indicate that there are in fact three significantly distinct annual rainfall periods in the basin, viz. phase 1: 1983 to 1997 (relatively high annual rainfall); phase 2: 1998 to 2008 (drought); phase 3: 2009 to 2018 (return to relatively high annual rainfall again). By comparison, PERSIANN-CDR data show reduced annual and winter precipitation, but no significant changes during the monsoon and pre-monsoon seasons from 1983 to 2008. The major conclusions of this study are that rainfall modelled using CHIRPS corresponds well with the observational record in confirming the decreased annual and seasonal rainfall, averaging 10.9 and 7.9 mm per decade respectively between 1983 and 2008, although there is a trend (albeit not statistically significant) to higher rainfall after the marked dry period between 1998 and 2008. Long-term variability in rainfall in the Bhilangana river basin has had critical impacts on the environment arising from water scarcity in this mountainous region.

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

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.

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

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’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℃, 1.6℃ in RCP4.5 while 3.7%, 2.5℃, 2.3℃ 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>

scholarly journals Spatiotemporal Variability and Trends of Extreme Precipitation in the Huaihe River Basin, a Climatic Transitional Zone in East China

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Zhengwe Ye ◽  
Zonghua Li
Keyword(s):  
River Basin ◽  
Extreme Precipitation ◽  
Transitional Zone ◽  
Spatiotemporal Variability ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
The North ◽  
Increasing Trend ◽  
The Huaihe River ◽  
Rainy Days

Precipitation data from 30 stations in the Huaihe River basin (HRB), a climatic transitional zone in east China, were used to investigate the spatiotemporal variability and trends of extreme precipitation on multitimescales for the period 1961–2010. Results indicated that (1) the spatial pattern of the annual precipitation, rainy days, extreme precipitation, and maximum daily precipitations shows a clear transitional change from the south (high) to the north (low) in the HR; it confirmed the conclusion that the HRB is located in the transitional zone of the 800 mm precipitation contour in China, where the 800 mm precipitation contour is considered as the geographical boundary of the south and the north. (2) Higher value of the extreme precipitation intensity mainly occurs in the middle of the east and the central part of the basin; it reveals a relatively distinct west-east spatial disparity, and this is not in line with the spatial pattern of the extreme precipitation total, the sum of the precipitation in 95th precipitation days. (3) Annual precipitation of 22 stations exhibits increasing trend, and these 22 stations are located from the central to the northern part. There is no significant trend detected for the seasonal precipitation. The summer precipitation exhibits a larger change range; this might cause the variation of the flood and drought in the HBR. However, the increasing trend in winter precipitation may be beneficial to the relief of winter agricultural drought. Rainy days in 12 stations, mostly located in and around the central northeastern part, experienced significant decreasing trend. Extreme precipitation days and precipitation intensity have increasing trends, but no station with significant change trend is detected for the maximum precipitation of the basin. (4) The spatiotemporal variability in the HRB is mainly caused by the geographic differences and is largely influenced by the interdecadal variations of East Asian Summer Monsoon in eastern China. The output of this paper could provide references for the practical decision making and integrated basin management of Huaihe River basin.

scholarly journals Analyzing the future climate change of Upper Blue Nile River Basin (UBNRB) using statistical down scaling techniques

2016 ◽  
Author(s):  
Dagnenet Fenta Mekonnen ◽  
Markus Disse
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Future Climate ◽  
Maximum Temperature ◽  
Future Climate Change ◽  
Mean Annual Precipitation ◽  
Nile River ◽  
Climate Variables ◽  
Increasing Trend ◽  
Blue Nile River Basin

Abstract. Climate change is becoming one of the most arguable and threatening issues in terms of global context and their responses to environment and socio/economic drivers. Its direct impact becomes critical for water resource development and indirectly for agricultural production, environmental quality, economic development, social well-being. However, a large uncertainty between different Global Circulation Models (GCM) and downscaling methods exist that makes reliable conclusions for a sustainable water management difficult. In order to understand the future climate change of the Upper Blue Nile River Basin, two widely used statistical down scaling techniques namely LARS-WG and SDSM models were applied. Six CMIP3 GCMs for LARS-WG (CSIRO-MK3, ECHAM5-OM, MRI-CGCM2.3.2, HaDCM3, GFDL-CM2.1, CCSM3) model while HadCM3 GCM and canESM2 from CMIP5 GCMs for SDSM were used for climate change analysis. The downscaled precipitation results from the prediction of the six GCMs by LARS WG showed inconsistency and large inter model variability, two GCMs showed decreasing trend while 4 GCMs showed increasing in the range from −7.9 % to +43.7 % while the ensemble mean of the six GCM result showed increasing trend ranged from 1.0 % to 14.4 %. NCCCS GCM predicted maximum increase in mean annual precipitation. However, the projection from HadCM3 GCM is consistent with the multi-model average projection, which predicts precipitation increase from 1.7 % to 16.6 %. Conversely, the result from all GCMs showed a similar continuous increasing trend for maximum temperature (Tmax) and minimum temperature (Tmin) in all three future periods. The change for mean annual Tmax may increase from 0.4 °c to 4.3 °c whereas the change for mean annual Tmin may increase from 0.3 °c to 4.1 °c. Meanwhile, the result from SDSM showed an increasing trend for all three climate variables (precipitation, minimum and maximum temperature) from both HadCM3 and canESM2 GCMs. The relative change of mean annual precipitation range from 2.1 % to 43.8 % while the change for mean annual Tmax and Tmin may increase from 0.4 °c to 2.9 °c and from 0.3 °c to 1.6 °c respectively. The change in magnitude for precipitation is higher in RCP8.5 scenarios than others as expected. The present result illustrate that both down scaling techniques have shown comparable and good ability to simulate the current local climate variables which can be adopted for future climate change study with high confidence for the UBNRB. In order to see the comparative downscaling results from the two down scaling techniques, HadCM3 GCM of A2 scenario was used in common. The result obtained from the two down scaling models were found reasonably comparable and both approaches showed increasing trend for precipitation, Tmax and Tmin. However, the analysis of the downscaled climate data from the two techniques showed, LARS WG projected a relatively higher increase than SDSM.

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

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.

scholarly journals Spatiotemporal Variability of Actual Evapotranspiration and the Dominant Climatic Factors in the Pearl River Basin, China

Atmosphere ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 340 ◽  
Author(s):  
Weizhi Gao ◽  
Zhaoli Wang ◽  
Guoru Huang
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climatic Factors ◽  
Actual Evapotranspiration ◽  
Pearl River ◽  
Pearl River Basin ◽  
Sunshine Hours ◽  
Increasing Trend ◽  
The Pearl River ◽  
The Pearl River Basin

Evapotranspiration is a vital component of the land surface process, thus, a more accurate estimate of evapotranspiration is of great significance to agricultural production, research on climate change, and other activities. In order to explore the spatiotemporal variation of evapotranspiration under global climate change in the Pearl River Basin (PRB), in China, this study conducted a simulation of actual evapotranspiration (ETa) during 1960–2014 based on the variable infiltration capacity (VIC) model with a high spatial resolution of 0.05°. The nonparametric Mann–Kendall (M–K) test and partial correlation analysis were used to examine the trends of ETa. The dominant climatic factors impacting on ETa were also examined. The results reveal that the annual ETa across the whole basin exhibited a slight but not significant increasing trend during the 1960–2014 period, whereas a significant decreasing trend was found during the 1960–1992 period. At the seasonal scale, the ETa showed a significant upward trend in summer and a significant downward trend in autumn. At the spatial scale, the ETa generally showed a decreasing, but not significant, trend in the middle and upper stream of the PRB, while in the downstream areas, especially in the Pearl River Delta and Dongjiang River Basin, it exhibited a significant increasing trend. The variation of the ETa was mainly associated with sunshine hours and average air pressure. The negative trend of the ETa in the PRB before 1992 may be due to the significant decrease in sunshine hours, while the increasing trend of the ETa after 1992 may be due to the recovery of sunshine hours and the significant decrease of air pressure. Additionally, we found that the “paradox” phenomenon detected by ETa mainly existed in the middle-upper area of the PRB during the period of 1960–1992.

scholarly journals Evaluation of water productivity under climate change in irrigated areas of the arid Northwest China using an assemble statistical downscaling method and an agro-hydrological model

2018 ◽  
Vol 379 ◽  
pp. 393-402
Author(s):  
Liu Liu ◽  
Zezhong Guo ◽  
Guanhua Huang
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Arid Region ◽  
Meteorological Data ◽  
Water Productivity ◽  
Northwest China ◽  
Heihe River ◽  
Climate Change Scenarios ◽  
Increasing Trend

Abstract. The Heihe River Basin (HRB) is the second largest inland river basin, located in the arid region of Northwest China with a serious water shortage. Evaluation of water productivity will provide scientific implications for agricultural water-saving in irrigated areas of the arid region under climate change. Based on observed meteorological data, 23 GCMs outputs and the ERA-40 reanalysis data, an assemble statistical downscaling model was developed to generate climate change scenarios under RCP2.6, RCP4.5, RCP8.5 respectively, which were then used to drive the SWAP-EPIC model to simulate crop growth in the irrigated areas of the middle HRB for the future period from 2018 to 2047. Crop yield showed an increasing trend, while crop water consumption decreased gradually in Gaotai and Ganzhou irrigated areas. The water productivity in future 30 years showed an increasing trend in both Gaotai and Ganzhou areas, with the most significant increase under RCP4.5 scenario, which were both larger than 2 kg m−3. Compared with that of the period from 2012 to 2015, the water productivity during 2018–2047 under three RCP scenarios increased by 9.2, 14.3 and 11.8 % in the Gaotai area, and 15.4, 21.6, 19.9 % in the Ganzhou area, respectively.

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

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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