scholarly journals Spatiotemporal Variations of Precipitation over Iran Using the High-Resolution and Nearly Four Decades Satellite-Based PERSIANN-CDR Dataset

2020 ◽  
Vol 12 (10) ◽  
pp. 1584 ◽  
Author(s):  
Hamidreza Mosaffa ◽  
Mojtaba Sadeghi ◽  
Negin Hayatbini ◽  
Vesta Afzali Gorooh ◽  
Ata Akbari Asanjan ◽  
...  
Keyword(s):  
Kendall Test ◽  
Rain Gauge ◽  
Annual Precipitation ◽  
Management Decision ◽  
Monthly Precipitation ◽  
Precipitation Trend ◽  
Climate Data ◽  
Suitable Alternative ◽  
Precipitation Estimation ◽  
Precipitation Trends

Spatiotemporal precipitation trend analysis provides valuable information for water management decision-making. Satellite-based precipitation products with high spatial and temporal resolution and long records, as opposed to temporally and spatially sparse rain gauge networks, are a suitable alternative to analyze precipitation trends over Iran. This study analyzes the trends in annual, seasonal, and monthly precipitation along with the contribution of each season and month in the annual precipitation over Iran for the 1983–2018 period. For the analyses, the Mann–Kendall test is applied to the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) estimates. The results of annual, seasonal, and monthly precipitation trends indicate that the significant decreases in the monthly precipitation trends in February over the western (March over the western and central-eastern) regions of Iran cause significant effects on winter (spring) and total annual precipitation. Moreover, the increases in the amounts of precipitation during November in the south and south-east regions lead to a remarkable increase in the amount of precipitation during the fall season. The analysis of the contribution of each season and month to annual precipitation in wet and dry years shows that dry years have critical impacts on decreasing monthly precipitation over a particular region. For instance, a remarkable decrease in precipitation amounts is detectable during dry years over the eastern, northeastern, and southwestern regions of Iran during March, April, and December, respectively. The results of this study show that PERSIANN-CDR is a valuable source of information in low-density gauge network areas, capturing spatiotemporal variation of precipitation.

scholarly journals Precipitation Trends in the Ganges-Brahmaputra-Meghna River Basin, South Asia: Inconsistency in Satellite-Based Products

Atmosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1155
Author(s):  
Muna Khatiwada ◽  
Scott Curtis
Keyword(s):  
River Basin ◽  
Kendall Test ◽  
Precipitation Trend ◽  
Climate Data ◽  
Monsoon Precipitation ◽  
Precipitation Estimation ◽  
Z Value ◽  
The Ganges ◽  
Increasing Precipitation ◽  
Precipitation Trends

The Ganges-Brahmaputra-Meghna (GBM) river basin is the world’s third largest. Literature show that changes in precipitation have a significant impact on climate, agriculture, and the environment in the GBM. Two satellite-based precipitation products, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Multi-Source Weighted-Ensemble Precipitation (MSWEP), were used to analyze and compare precipitation trends over the GBM as a whole and within 34 pre-defined hydrological sub-basins separately for the period 1983–2019. A non-parametric Modified Mann-Kendall test was applied to determine significant trends in monsoon (June–September) and pre-monsoon (March–May) precipitation. The results show an inconsistency between the two precipitation products. Namely, the MSWEP pre-monsoon precipitation trend has significantly increased (Z-value = 2.236, p = 0.025), and the PERSIANN-CDR monsoon precipitation trend has significantly decreased (Z-value = −33.071, p < 0.000). However, both products strongly indicate that precipitation has recently declined in the pre-monsoon and monsoon seasons in the eastern and southern regions of the GBM river basin, agreeing with several previous studies. Further work is needed to identify the reasons behind inconsistent decreasing and increasing precipitation trends in the GBM river basin.

scholarly journals Performance Evaluation of Satellite-Based Rainfall Products over Nigeria

Climate ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 103
Author(s):  
Kingsley N. Ogbu ◽  
Nina Rholan Hounguè ◽  
Imoleayo E. Gbode ◽  
Bernhard Tischbein
Keyword(s):  
Agricultural Development ◽  
Rain Gauge ◽  
Local Economy ◽  
Climate Data ◽  
Climatic Zones ◽  
Rainfall Characteristics ◽  
Precipitation Estimation ◽  
Climate Data Record ◽  
Local Rainfall

Understanding the variability of rainfall is important for sustaining rain-dependent agriculture and driving the local economy of Nigeria. Paucity and inadequate rain gauge network across Nigeria has made satellite-based rainfall products (SRPs), which offer a complete spatial and consistent temporal coverage, a better alternative. However, the accuracy of these products must be ascertained before use in water resource developments and planning. In this study, the performances of Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Precipitation estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT), were evaluated to investigate their ability to reproduce long term (1983–2013) observed rainfall characteristics derived from twenty-four (24) gauges in Nigeria. Results show that all products performed well in terms of capturing the observed annual cycle and spatial trends in all selected stations. Statistical evaluation of the SRPs performance show that CHIRPS agree more with observations in all climatic zones by reproducing the local rainfall characteristics. The performance of PERSIANN and TAMSAT, however, varies with season and across the climatic zones. Findings from this study highlight the benefits of using SRPs to augment or fill gaps in the distribution of local rainfall data, which is critical for water resources planning, agricultural development, and policy making.

scholarly journals Precipitation Trends over Time Using Mann-Kendall and Spearman’s rho Tests in Swat River Basin, Pakistan

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Ijaz Ahmad ◽  
Deshan Tang ◽  
TianFang Wang ◽  
Mei Wang ◽  
Bakhtawar Wagan
Keyword(s):  
River Basin ◽  
Statistical Tests ◽  
Annual Precipitation ◽  
Positive Trend ◽  
Monthly Precipitation ◽  
Significance Level ◽  
Spearman's Rho ◽  
Spearman’S Rho ◽  
Swat River ◽  
Precipitation Trends

Accurately predicting precipitation trends is vital in the economic development of a country. This research investigated precipitation variability across 15 stations in the Swat River basin, Pakistan, over a 51-year study period (1961–2011). Nonparametric Mann-Kendall (MK) and Spearman’s rho (SR) statistical tests were used to detect trends in monthly, seasonal, and annual precipitation, and the trend-free prewhitening approach was applied to eliminate serial correlation in the precipitation time series. The results highlighted a mix of positive (increasing) and negative (decreasing) trends in monthly, seasonal, and annual precipitation. One station in particular, the Saidu Sharif station, showed the maximum number of significant monthly precipitation events, followed by Abazai, Khairabad, and Malakand. On the seasonal time scale, precipitation trends changed from the summer to the autumn season. The Saidu Sharif station revealed the highest positive trend (7.48 mm/year) in annual precipitation. In the entire Swat River basin, statistically insignificant trends were found in the subbasins for the annual precipitation series; however, the Lower Swat subbasin showed the maximum quantitative increase in the precipitation at a rate of 2.18 mm/year. The performance of the MK and SR tests was consistent at the verified significance level.

scholarly journals Precipitation Trends in Greenland During the Past Thirty Years

1958 ◽  
Vol 3 (23) ◽  
pp. 177-180
Author(s):  
Marvin Diamond
Keyword(s):  
Curve Analysis ◽  
Annual Precipitation ◽  
Precipitation Trend ◽  
Residual Mass ◽  
The Past ◽  
Large Fluctuations ◽  
Precipitation Trends ◽  
Made In

AbstractThe record of annual precipitation as obtained from stratigraphic studies on snow profiles in the interior of northern Greenland made in 1954 by SIPRE personnel shows a decreasing precipitation trend since 1920 with the largest decrease occurring since 1932. A residual mass curve analysis of the data indicates that, in spite of large fluctuations in the accumulated precipitation, the decreasing trend may be considered valid over a period of several years.

scholarly journals Spatio-Temporal Trends of Monthly and Annual Precipitation in Aguascalientes, Mexico

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 437
Author(s):  
Osías Ruiz-Alvarez ◽  
Vijay P. Singh ◽  
Juan Enciso-Medina ◽  
Ronald Ernesto Ontiveros-Capurata ◽  
Arturo Corrales-Suastegui
Keyword(s):  
Time Series ◽  
Annual Precipitation ◽  
Irrigated Agriculture ◽  
Monthly Precipitation ◽  
Precipitation Trend ◽  
Precipitation Time Series ◽  
June July August ◽  
Precipitation Decrease ◽  
Weather Stations ◽  
June July

The objective of this research was to analyze the temporal patterns of monthly and annual precipitation at 36 weather stations of Aguascalientes, Mexico. The precipitation trend was determined by the Mann–Kendall method and the rate of change with the Theil–Sen estimator. In total, 468 time series were analyzed, 432 out of them were monthly, and 36 were annual. Out of the total monthly precipitation time series, 42 series showed a statistically significant trend (p ≤ 0.05), from which 8/34 showed a statistically significant negative/positive trend. The statistically significant negative trends of monthly precipitation occurred in January, April, October, and December. These trends denoted more significant irrigation water use, higher water extractions from the aquifers in autumn–winter, more significant drought occurrence, low forest productivity, higher wildfire risk, and greater frost risk. The statistically significant positive trends occurred in May, June, July, August, and September; to a certain extent, these would contribute to the hydrology, agriculture, and ecosystem but also could provoke problems due to water excess. In some months, the annual precipitation variability and El Niño-Southern Oscillation (ENSO) were statistically correlated, so it could be established that in Aguascalientes, this phenomenon is one of the causes of the yearly precipitation variation. Out of the total annual precipitation time series, only nine series were statistically significant positive; eight out of them originated by the augments of monthly precipitation. Thirteen weather stations showed statistically significant trends in the total precipitation of the growing season (May, June, July, August, and September); these stations are located in regions of irrigated agriculture. The precipitation decrease in dry months can be mitigated using shorter cycle varieties with lower water consumption, irrigation methods with high efficiency, and repairing irrigation infrastructure. The precipitation increase in humid months can be used to store water and use it during the dry season, and its adverse effects can be palliated with the use of varieties resistant to root diseases and lodging. The results of this work will be beneficial in the management of agriculture, hydrology, and water resources of Aguascalientes and in neighboring arid regions affected by climate change.

Monthly Precipitation Trend Analysis by Applying Nonparametric Mann- Kendall (MK) and Spearman’s rho (SR) Tests In Dongting Lake, China: 1961-2012

2017 ◽  
Vol 5 (1) ◽  
pp. 41 ◽  
Author(s):  
Muhammad Tayyab ◽  
Jianzhong Zhou ◽  
Rana Adnan ◽  
Aqeela Zahra
Keyword(s):  
Time Scale ◽  
Spatial Scales ◽  
Dongting Lake ◽  
Study Phase ◽  
Positive Trend ◽  
Monthly Precipitation ◽  
Precipitation Trend ◽  
Spearman's Rho ◽  
Spearman’S Rho ◽  
Precipitation Trends

This research highlights the precipitation trends and presents the results of the study in temporal and spatial scales. Precise predictions of precipitation trends can play imperative part in economic growth of a state. This study examined precipitation inconsistency for 23 stations at the Dongting Lake, China, over a 52-years study phase (1961–2012). Statistical, nonparametric Mann- Kendall (MK) and Spearman’s rho (SR) tests were applied to identify trends within monthly, seasonal, and annual precipitation. The trend-free prewhitening method used to exclude sequential correlation in the precipitation time series. The performance of the Mann- Kendall (MK) and Spearman’s rho (SR) tests was steady at the tested significance level. The results showed fusion of increasing (positive) and decreasing (negative) trends at different stations within monthly and seasonal time scale. In case of whole Dongting basin on monthly time scale, significant positive trend is found, while at Yuanjiang River and Xianjiag River both positive and negative significant trends are identified.

Multi-Time Scale Analysis on Characteristics of Precipitation in Hebei Province

2015 ◽  
Vol 1092-1093 ◽  
pp. 1165-1170 ◽  
Author(s):  
Meng Wang ◽  
Bao Hong Lu ◽  
Han Wen Zhang ◽  
Cong Fei Zhu
Keyword(s):  
Abrupt Change ◽  
Linear Trend ◽  
Kendall Test ◽  
Hebei Province ◽  
Change Points ◽  
Annual Precipitation ◽  
Precipitation Trend ◽  
Multi Scale ◽  
Weather Stations ◽  
Autumn And Winter

Based on the precipitation data observed monthly of 19 weather stations in Hebei province from 1960 to 2011, three methods, linear trend estimation, Mann-Kendall test as well as Morlet wavelet transformation, were adopted to analyze the characteristics of precipitation trend, abrupt change points and cyclical variations under the circumstance of multi-time scales in the past 52 years. Annual precipitation had a decreasing trend, and precipitation in spring increased dramatically, meanwhile precipitation of summer decreased significantly; however, precipitations in autumn and winter were fluctuated in an acceptable range. There were various abrupt change points both in annual precipitation series and in spring as well as in summer, yet any abrupt change points were found in autumn and winter. Multi-scale periodicities were found by wavelet analysis in annual and seasonal precipitations.

scholarly journals Uncertainty Assessments of Satellite Derived Rainfall Products

2016 ◽  
Author(s):  
Margaret Kimani ◽  
Joost Hoedjes ◽  
Zhongbo Su
Keyword(s):  
Large Scale ◽  
Rainfall Variability ◽  
Tropical Rainfall Measuring Mission ◽  
Global Precipitation Climatology Project ◽  
Rain Gauge ◽  
Climate Data ◽  
Error Distributions ◽  
Precipitation Estimation ◽  
Scatter Plots ◽  
Global Precipitation

Accurate and consistent rainfall observations are vital for climatological studies in support of better planning and decision making. However, estimation of accurate spatial rainfall is limited by sparse rain gauge distributions. Satellite rainfall products can thus potentially play a role in spatial rainfall estimation but their skill and uncertainties need to be under-stood across spatial-time scales. This study aimed at assessing the temporal and spatial performance of seven satellite products (TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Center (CPC) Morphing (CMORPH), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP) and Global Precipitation Climatology Project (GPCP) using gridded (0.05o) rainfall data over East Africa for 15 years(1998-2012). The products&rsquo; error distributions were qualitatively compared with large scale horizontal winds (850 mb) and elevation patterns with respect to corresponding rain gauge data for each month during the &lsquo;long&rsquo; (March-May) and &lsquo;short&rsquo; (October-December) rainfall seasons. For validation only rainfall means extracted from 284 rain gauge stations were used, from which qualitative analysis using continuous statistics of Root Mean Squared Difference, Standard deviations, Correlations, coefficient of determinations (from scatter plots) were used to evaluate the products&rsquo; performance. Results revealed rainfall variability dependence on wind flows and modulated by topographic influences. The products&rsquo; errors showed seasonality and dependent on rainfall intensity and topography. Single sensor and coarse resolution products showed lowest performance on high ground areas. All the products showed low skills in retrieving rainfall during &lsquo;short&rsquo; rainfall season when orographic processes were dominant. CHIRPS, CMORPH and TRMM performed well, with TRMM showing the best performance in both seasons. There is need to reduce products&rsquo; errors before applications.

scholarly journals Characterization of Interannual and Seasonal Variability of Hydro-Climatic Trends in the Upper Indus Basin

2021 ◽  
Author(s):  
Muhammad Usman Liaqat ◽  
Giovanna Grossi ◽  
Shabeh ul Hasson ◽  
Roberto Ranzi
Keyword(s):  
Trend Analysis ◽  
Hydrological Cycle ◽  
Winter Season ◽  
Rain Gauge ◽  
Indus Basin ◽  
Annual Precipitation ◽  
Monthly Precipitation ◽  
Catchment Scale ◽  
Upper Indus Basin ◽  
Annual Scale

Abstract A high resolution seasonal and annual precipitation climatology of the Upper Indus Basin was developed, based on 1995-2017 precipitation normals obtained from four different gridded datasets (Aphrodite, CHIRPS, PERSIANN-CDR and ERA5) and quality-controlled high and mid elevation ground observations. Monthly precipitation values were estimated through the anomaly method at the catchment scale and compared with runoff data (1975-2017) for verification and detection of changes in the hydrological cycle. The gridded dataset is then analysed using running trends and spectral analysis and the Mann–Kendall test was employed to detect significant trends. The nonparametric Pettitt test was also used to identify the change point in precipitation and runoff time series. The results indicated that bias corrected CHIRPS precipitation dataset, followed by ERA5, performed better in terms of RMSE, MAE, MAPE and BIAS in simulating rain gauge-observed precipitation. The running trend analysis of annual precipitation exhibited a very slight increase whereas a more significant increase was found in the winter season (DJF). A runoff coefficient value greater than one, especially in glacierized catchments (Shigar, Shyok and Gilgit) indicate that precipitation was likely underestimated and glacial melt in a warming climate provides excess runoff volumes. As far as the streamflow is concerned, variabilities are more pronounced at the seasonal rather than at the annual scale. At the annual scale, trend analysis of discharge shows slightly significant increasing trend for the Indus River at the downstream Kachura, Shyok and Gilgit stations. Seasonal flow analysis reveals more complex regimes and its comparison with the variability of precipitation favours a deeper understanding of precipitation, snow- and ice-melt runoff dynamics, addressing the hydroclimatic behaviour of the Karakoram region.

scholarly journals Spatial variability and temporal trends of precipitation over the Loess Plateau of China: 1957-2013

2018 ◽  
Vol 246 ◽  
pp. 01120
Author(s):  
Yu Yang ◽  
Zhe Yin ◽  
Zhijie Shan ◽  
Qin Wei ◽  
Bai Li ◽  
...  
Keyword(s):  
Loess Plateau ◽  
Temporal Trends ◽  
Kendall Test ◽  
Accurate Information ◽  
Monthly Precipitation ◽  
The Loess Plateau ◽  
Annual Average ◽  
Study Region ◽  
Average Rainfall ◽  
Precipitation Trends

Accurate information about regional precipitation and trends in spatiotemporal variation is crucial, both from the perspective of quantifying water budgets and to determine appropriate vegetation restoration. The objectives of this study were to evaluate spatiotemporal changes in precipitation and to analyze the monthly, annual, and seasonal precipitation trends of 85 stations in the Loess Plateau during 1957−2013. The Mann−Kendall test and Sen’s slope estimator were applied to analyze the precipitation data. Monthly precipitation trends exhibited apparent regional differences over the Loess Plateau, significant increasing trends in rainfall were found in winter. On the seasonal scale, the magnitude of significant negative trends in seasonal rainfall varied from 0.595 mm/yr2 to 2.732 mm/yr2. The magnitude of significant positive trends varied from 0.010 mm/yr2 to 1.987 mm/yr2. One of the most remarkable findings was that all the stations showed significant positive trends in winter. For annual average rainfall, the magnitude of significant positive trends varied from 2.075 mm/yr2 to 3.427 mm/yr2. No significant negative trends were detected. Although, the annual average rainfall over the Loess Plateau showed a non-significant increasing trend, the seasonal and regional pattern was obvious. Such findings can provide important implications for ecological restoration and farming operations across the study region.

Sign in / Sign up

Export Citation Format

Share Document