scholarly journals Spatial–temporal characterization of rainfall in Pakistan during the past half-century (1961–2020)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ghaffar Ali ◽  
Muhammad Sajjad ◽  
Shamsa Kanwal ◽  
Tingyin Xiao ◽  
Shoaib Khalid ◽  
...  
Keyword(s):  
Environmental Planning ◽  
Hydrological Modeling ◽  
Resource Planning ◽  
Annual Rainfall ◽  
Optimal Method ◽  
Empirical Bayesian Kriging ◽  
Past Half Century ◽  
Rainfall Trends ◽  
Khyber Pakhtunkhwa Province ◽  
Rainfall Map

AbstractSpatial–temporal rainfall assessments are integral to climate/hydrological modeling, agricultural studies, and water resource planning and management. Herein, we evaluate spatial–temporal rainfall trends and patterns in Pakistan for 1961–2020 using nationwide data from 82 rainfall stations. To assess optimal spatial distribution and rainfall characterization, twenty-seven interpolation techniques from geo-statistical and deterministic categories were systematically compared, revealing that the empirical Bayesian kriging regression prediction (EBKRP) technique was best. Hence, EBKRP was used to produce and utilize the first normal annual rainfall map of Pakistan for evaluating spatial rainfall patterns. While the largest under-prediction was estimated for Hunza (− 51%), the highest and lowest rainfalls were estimated for Malam Jaba in Khyber Pakhtunkhwa province (~  1700 mm), and Nok-kundi in Balochistan province (~  50 mm), respectively. A gradual south-to-north increase in rainfall was spatially evident with an areal average of 455 mm, while long-term temporal rainfall evaluation showed a statistically significant (p = 0.05) downward trend for Sindh province. Additionally, downward inter-decadal regime shifts were detected for the Punjab and Sindh provinces (90% confidence). These results are expected to help inform environmental planning in Pakistan; moreover, the rainfall data produced using the optimal method has further implications in several aforementioned fields.

scholarly journals Rainfall trend analysis using Mann-Kendall and Sen’s slope estimator test in West Kalimantan

2021 ◽  
Vol 893 (1) ◽  
pp. 012006
Author(s):  
F Aditya ◽  
E Gusmayanti ◽  
J Sudrajat
Keyword(s):  
Climate Change ◽  
Rainfall Variability ◽  
Annual Rainfall ◽  
Sen’S Slope Estimator ◽  
West Kalimantan ◽  
Rainfall Analysis ◽  
Sen’S Slope ◽  
Rainfall Trends ◽  
Slope Estimator ◽  
The Impact

Abstract Climate change has been a prominent issue in the last decade. Climate change on a global scale does not necessarily have the same effect in different regions. Rainfall is a crucial weather element related to climate change. Rainfall trends analysis is an appropriate step in assessing the impact of climate change on water availability and food security. This study examines rainfall variations and changes at West Kalimantan, focusing on Mempawah and Kubu Raya from 2000-2019. The Mann-Kendall (MK) and Sen's Slope estimator test, which can determine rainfall variability and long-term monotonic trends, were utilized to analyze 12 rainfall stations. The findings revealed that the annual rainfall pattern prevailed in all locations. Mempawah region tends to experience a downward trend, while Kubu Raya had an upward trend. However, a significant trend (at 95% confidence level) was identified in Sungai Kunyit with a slope value of -33.20 mm/year. This trend indicates that Sungai Kunyit will become drier in the future. The results of monthly rainfall analysis showed that significant upward and downward trends were detected in eight locations. Rainfall trends indicate that climate change has occurred in this region.

scholarly journals Rainfall Trend and Its Relationship with Normalized Difference Vegetation Index in a Restored Semi-Arid Wetland of South Africa

2020 ◽  
Vol 12 (21) ◽  
pp. 8919
Author(s):  
Florence M. Murungweni ◽  
Onisimo Mutanga ◽  
John O. Odiyo
Keyword(s):  
South Africa ◽  
Quantile Regression ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rainfall Data ◽  
Ecosystem Change ◽  
Annual Rainfall ◽  
Rainfall Trend ◽  
Vegetation Productivity ◽  
Rainfall Trends

Clearance of terrestrial wetland vegetation and rainfall variations affect biodiversity. The rainfall trend–NDVI (Normalized Difference Vegetation Index) relationship was examined to assess the extent to which rainfall affects vegetation productivity within Nylsvley, Ramsar site in Limpopo Province, South Africa. Daily rainfall data measured from eight rainfall stations between 1950 and 2016 were used to generate seasonal and annual rainfall data. Mann-Kendall and quantile regression were applied to assess trends in rainfall data. NDVI was derived from satellite images from between 1984 and 2003 using Zonal statistics and correlated with rainfall of the same period to assess vegetation dynamics. Mann-Kendall and Sen’s slope estimator showed only one station had a significant increasing rainfall trend annually and seasonally at p < 0.05, whereas all the other stations showed insignificant trends in both rainfall seasons. Quantile regression showed 50% and 62.5% of the stations had increasing annual and seasonal rainfall, respectively. Of the stations, 37.5% were statistically significant at p < 0.05, indicating increasing and decreasing rainfall trends. These rainfall trends show that the rainfall of Nylsvley decreased between 1995 and 2003. The R2 between rainfall and NDVI of Nylsvley is 55% indicating the influence of rainfall variability on vegetation productivity. The results underscore the impact of decadal rainfall patterns on wetland ecosystem change.

scholarly journals Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand

2016 ◽  
Vol 48 (3) ◽  
pp. 867-882 ◽  
Author(s):  
M. S. Babel ◽  
T. A. J. G. Sirisena ◽  
N. Singhrattna
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Large Scale ◽  
Resource Planning ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Rainfall Forecasting ◽  
Artificial Neural ◽  
Atmospheric Variables

Understanding long-term seasonal or annual or inter-annual rainfall variability and its relationship with large-scale atmospheric variables (LSAVs) is important for water resource planning and management. In this study, rainfall forecasting models using the artificial neural network technique were developed to forecast seasonal rainfall in May–June–July (MJJ), August–September–October (ASO), November–December–January (NDJ), and February–March–April (FMA) and to determine the effects of climate change on seasonal rainfall. LSAVs, temperature, pressure, wind, precipitable water, and relative humidity at different lead times were identified as the significant predictors. To determine the impacts of climate change the predictors obtained from two general circulation models, CSIRO Mk3.6 and MPI-ESM-MR, were used with quantile mapping bias correction. Our results show that the models with the best performance for FMA and MJJ seasons are able to forecast rainfall one month in advance for these seasons and the best models for ASO and NDJ seasons are able do so two months in advance. Under the RCP4.5 scenario, a decreasing trend of MJJ rainfall and an increasing trend of ASO rainfall can be observed from 2011 to 2040. For the dry season, while NDJ rainfall decreases, FMA rainfall increases for the same period of time.

scholarly journals Assessing Groundwater Level with a Unified Seasonal Outlook and Hydrological Modeling Projection

2020 ◽  
Vol 10 (24) ◽  
pp. 8882
Author(s):  
Jing-Ying Huang ◽  
Dong-Sin Shih
Keyword(s):  
Groundwater Level ◽  
Hydrological Modeling ◽  
Seasonal Forecast ◽  
Groundwater Table ◽  
Seasonal Rainfall ◽  
Water Model ◽  
Annual Rainfall ◽  
Efficiency Coefficient ◽  
Hydrological Models ◽  
Temporal And Spatial Distribution

Although the annual rainfall in Taiwan is high, water shortages still occasionally occur owing to its nonuniform temporal and spatial distribution. At these times, the groundwater is considered an acceptable alternative water source. Groundwater is of particular value because it is considered a clean and reliable source of fresh water. To prevent water scarcity, this study utilized seasonal forecasting by incorporating hydrological models to evaluate the seasonal groundwater level. The seasonal prospective issued by the Central Weather Bureau of Taiwan (CWB) was combined with weather generator data to construct seasonal weather forecasts as the input for hydrological models. A rainfall-runoff model, HEC-HMS, and a coupled groundwater and surface water model, WASH123D, were applied to simulate the seasonal groundwater levels. The Fengshan Creek basin in northern Taiwan was selected as a study site to test the proposed approach. The simulations demonstrated stability and feasibility, and the results agreed with the observed groundwater table. The calibrations indicated that the average errors of river stage were 0.850 for R2, 0.279 for root-mean-square error (RMSE), and 0.824 for efficiency coefficient (CE). The simulation also revealed that the simulated groundwater table corresponded with observed hydrographs very well (R2 of 0.607, RMSE of 0.282 m, and CE of 0.621). The parameters were verified in this study, and they were deemed practical and adequate for subsequent seasonal assessment. The seasonal forecast of 2018 at Guanxi station indicated that the 25th and 75th percentiles of simulated annual rainfall were within 1921–3285 mm and the actual annual rainfall was 2031 mm. Its seasonal rainfall outlook was around 30% accurate for forecasts of three consecutive months in 2018. Similarly, at Xinpu station, its seasonal rainfall outlook was about 40% accurate, and the amount of annual rainfall (1295 mm) was within the range of the 25th and 75th percentiles (1193–1852 mm). This revealed that the actual annual precipitations at both Guanxi and Xinpu station corresponded with the range of 25th and 75th percentiles of simulated rainfall, even if the accurate rate for the 3 month seasonal forecast had some error. The subsequent groundwater simulations were overestimated because the amount of actual rainfall was far lower than the average of the historical record in some dry season months. However, the amount of rainfall returned to normal values during the wet seasons, where the seasonal forecast and observation results were similar.

scholarly journals Long term effect of climate change on rainfall in northwest Iraq

2014 ◽  
Vol 4 (3) ◽  
Author(s):  
Nadhir Al-Ansari ◽  
Mawada Abdellatif ◽  
Salahalddin Ali ◽  
Sven Knutsson
Keyword(s):  
Climate Change ◽  
Management Strategies ◽  
Daily Rainfall ◽  
Annual Rainfall ◽  
Water Shortages ◽  
Intergovernmental Panel ◽  
Continuous Decrease ◽  
Rainfall Trends ◽  
Semi Arid Region

AbstractMiddle East, like North Africa, is considered as arid to semi-arid region. Water shortages in this region, represents an extremely important factor in stability of the region and an integral element in its economic development and prosperity. Iraq was an exception due to presence of Tigris and Euphrates Rivers. After the 1970s the situation began to deteriorate due to continuous decrease in discharges of these rivers, are expected to dry by 2040 with the current climate change. In the present paper, long rainfall trends up to the year 2099 were studied in Sinjar area, northwest of Iraq, to give an idea about its future prospects. Two emission scenarios, used by the Intergovernmental Panel on Climate Change (A2 and B2), were employed to study the long term rainfall trends in northwestern Iraq. All seasons consistently project a drop in daily rainfall for all future periods with the summer season is expected to have more reduction compared to other seasons. Generally the average rainfall trend shows a continuous decrease. The overall average annual rainfall is slightly above 210 mm. In view of these results, prudent water management strategies have to be adopted to overcome or mitigate consequences of future severe water crisis.

Mathematical Study of Monthly and Annual Rainfall Trends in Nasarawa State, Nigeria

2014 ◽  
Vol 10 (1) ◽  
pp. 56-62 ◽  
Author(s):  
EKWE, Michael Chibuike ◽  
◽  
JOSHUA, Jonah Kunda ◽  
IGWE, Johnson Eze ◽  
OSINOWO, Adekunle Ayodotun
Keyword(s):  
Annual Rainfall ◽  
Mathematical Study ◽  
Rainfall Trends

Rainfall trends in hindsight and in foresight

2020 ◽  
Author(s):  
Theano Iliopoulou ◽  
Demetris Koutsoyiannis
Keyword(s):  
Daily Rainfall ◽  
Predictive Performance ◽  
Predictive Modelling ◽  
Annual Rainfall ◽  
Methodological Framework ◽  
Rainfall Trends ◽  
Out Of Sample ◽  
Linear Trends

&lt;p&gt;Trends are customarily identified in rainfall data in the framework of explanatory modelling. Little insight however has been gained by this type of analysis with respect to their performance in foresight. In this work, we examine the out-of-sample predictive performance of linear trends through extensive investigation of 60 of the longest daily rainfall records available worldwide. We devise a systematic methodological framework in which linear trends are compared to simpler mean models, based on their performance in predicting climatic-scale (30-year) annual rainfall indices, i.e. maxima, totals, wet-day average and probability dry, from long-term daily records. Parallel experiments from synthetic timeseries are performed in order to provide theoretical insights to the results and the role of parsimony in predictive modelling is discussed. In line with the empirical findings, it is shown that, prediction-wise, simple is preferable to trendy.&lt;/p&gt;

Climate Change Impacts on Jordan River Flow: Downscaling Application from a Regional Climate Model

2010 ◽  
Vol 11 (4) ◽  
pp. 860-879 ◽  
Author(s):  
Rana Samuels ◽  
Alon Rimmer ◽  
Andreas Hartmann ◽  
Simon Krichak ◽  
Pinhas Alpert
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Model ◽  
Local Level ◽  
Regional Climate ◽  
Resource Planning ◽  
Annual Rainfall ◽  
Future Climate Change ◽  
Jordan River ◽  
Response Models

Abstract The integration of climate change projections into hydrological and other response models used for water resource planning and management is challenging given the varying spatial resolutions of the different models. In general, climate models are generated at spatial ranges of hundreds of kilometers, while hydrological models are generally watershed specific and based on input at the station or local level. This paper focuses on techniques applied to downscale large-scale climate model simulations to the spatial scale required by local response models (hydrological, agricultural, soil). Specifically, results were extracted from a regional climate model (RegCM) simulation focused on the Middle East, which was downscaled to a scale appropriate for input into a local watershed model [the Hydrological Model for Karst Environment (HYMKE)] calibrated for the upper Jordan River catchment. With this application, the authors evaluated the effect of future climate change on the amount and form of precipitation (rain or snow) and its effect on streamflow in the Jordan River and its tributaries—the major water resources in the region. They found that the expected changes in the form of precipitation are nearly insignificant in terms of changing the timing of streamflow. Additionally, the results suggest a future increase in evaporation and decrease in average annual rainfall, supporting expected changes based on global models in this region.

scholarly journals Analysis of rainfall trends over Tripura

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 27-36
Author(s):  
RANJAN PHUKAN ◽  
D. SAHA
Keyword(s):  
Heavy Rainfall ◽  
Monsoon Season ◽  
Rainfall Variability ◽  
Kendall Test ◽  
Annual Rainfall ◽  
The North ◽  
Rainfall Trends ◽  
Annual Scale ◽  
Increasing Trends ◽  
Rainy Days

Rainfall in India has very high temporal and spatial variability. The rainfall variability affects the livelihood and food habits of people from different regions. In this study, the rainfall trends in two stations in the north-eastern state of Tripura, namely Agartala and Kailashahar have been studied for the period 1955-2017. The state experiences an annual mean of more than 2000 mm of rainfall, out of which, about 60% occurs during the monsoon season and about 30% in pre-monsoon. An attempt has been made to analyze the trends in seasonal and annual rainfall, rainy days and heavy rainfall in the two stations, during the same period.Non-parametric Mann-Kendall test has been used to find out the significance of these trends. Both increasing and decreasing trends are observed over the two stations. Increasing trends in rainfall, rainy days and heavy rainfall are found at Agartala during pre-monsoon season and decreasing trends in all other seasons and at annual scale. At Kailashahar, rainfall amount (rainy days & heavy rainfall) is found to be increasing during pre-monsoon and monsoon seasons (pre-monsoon season). At annual scale also, rainfall and rainy days show increasing trends at Kailashahar. The parameters are showing decreasing trends during all other seasons at the station. Rainy days over Agartala show a significantly decreasing trend in monsoon, whereas no other trend is found to be significant over both the stations.  

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