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 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 Trend Analysis of Precipitation in the Jinsha River Basin in China

2013 ◽  
Vol 14 (1) ◽  
pp. 290-303 ◽  
Author(s):  
Shunjiu Wang ◽  
Xinli Zhang ◽  
Zhigang Liu ◽  
Deming Wang
Keyword(s):  
River Basin ◽  
Annual Precipitation ◽  
Precipitation Trend ◽  
Precipitation Series ◽  
Jinsha River ◽  
Increasing Trend ◽  
Precipitation Area ◽  
Increasing Trends ◽  
Increasing Precipitation ◽  
Precipitation Trends

Abstract According to the mean seasonal and annual precipitation from 30 meteorological stations in the periods of 1961–2008, the precipitation trends are analyzed by using the Mann–Kendall (MK) test in the Jinsha River basin (JRB). Both the temporal and spatial distribution characteristics of precipitation trends in different regions in the JRB are studied for the first time in this paper. There is a slight and insignificant increasing trend in seasonal and annual precipitation except for autumn precipitation, and the annual precipitation has increased by 0.7634 mm yr−1 during the last 48 years. The increasing precipitation trends in spring seem more significant than those in the other three seasons, and autumn is the only season showing a slight and insignificant decreasing precipitation trend. There are more than 80% of stations exhibiting increasing trends for annual precipitation, and it goes to 90% for spring precipitation. The increasing precipitation trends in the headwater and the middle reaches are more dominant than those in the upper and lower reaches. The largest increase magnitude occurred in the less precipitation area, while the largest decrease magnitude occurred in the more precipitation area. The increasing trend of minimum precipitation series and decreasing trend of maximum precipitation series could result in a decreasing trend for the range series in the JRB. In general, the increasing trends of precipitation in the tributary (the Yalong River) are more significant than those in the mainstream (the Jinsha River). The results of this study will provide further knowledge for understanding on the climate change in the JRB.

scholarly journals Error Correction of Multi-Source Weighted-Ensemble Precipitation (MSWEP) over the Lancang-Mekong River Basin

2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Xiongpeng Tang ◽  
Jianyun Zhang ◽  
Guoqing Wang ◽  
Gebdang Biangbalbe Ruben ◽  
Zhenxin Bao ◽  
...  
Keyword(s):  
Error Correction ◽  
River Basin ◽  
Regional Scale ◽  
Mekong River ◽  
Precipitation Data ◽  
Climate Data ◽  
Hydrological Simulation ◽  
Mekong River Basin ◽  
Precipitation Estimation ◽  
Simulation Results

The demand for accurate long-term precipitation data is increasing, especially in the Lancang-Mekong River Basin (LMRB), where ground-based data are mostly unavailable and inaccessible in a timely manner. Remote sensing and reanalysis quantitative precipitation products provide unprecedented observations to support water-related research, but these products are inevitably subject to errors. In this study, we propose a novel error correction framework that combines products from various institutions. The NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), the Multi-Source Weighted-Ensemble Precipitation Version 1.0 (MSWEP), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Records (PERSIANN) were used. Ground-based precipitation data from 1998 to 2007 were used to select precipitation products for correction, and the remaining 1979–1997 and 2008–2014 observe data were used for validation. The resulting precipitation products MSWEP-QM derived from quantile mapping (QM) and MSWEP-LS derived from linear scaling (LS) are evaluated by statistical indicators and hydrological simulation across the LMRB. Results show that the MSWEP-QM and MSWEP-LS can better capture major annual precipitation centers, have excellent simulation results, and reduce the mean BIAS and mean absolute BIAS at most gauges across the LMRB. The two corrected products presented in this study constitute improved climatological precipitation data sources, both time and space, outperforming the five raw gridded precipitation products. Among the two corrected products, in terms of mean BIAS, MSWEP-LS was slightly better than MSWEP-QM at grid-scale, point scale, and regional scale, and it also had better simulation results at all stations except Strung Treng. During the validation period, the average absolute value BIAS of MSWEP-LS and MSWEP-QM decreased by 3.51% and 3.4%, respectively. Therefore, we recommend that MSWEP-LS be used for water-related scientific research in the LMRB.

scholarly journals Spatiotemporal Pattern Assessment of Precipitation for the Wainganga Sub-Basin

2020 ◽  
Vol 15 (3) ◽  
pp. 515-525
Author(s):  
Aradhana Thakur ◽  
Prabhash Kumar Mishra ◽  
A K Nema ◽  
Souranshu Prasad Sahoo
Keyword(s):  
India Meteorological Department ◽  
Kendall Test ◽  
Spatiotemporal Pattern ◽  
Precipitation Trend ◽  
Monsoon Precipitation ◽  
Sen’S Slope ◽  
Mk Test ◽  
Shift Analysis ◽  
Hydrologic System ◽  
Slope Estimator

Precipitation is the major component of a hydrologic system, which significantly influences the planning and management of the water resources. The present study is principally concerned with the shifting of precipitation patterns over time. This study attempted to explain the precipitation trend for 65 years (1951-2015) using a Mann-Kendall test (MK test) and trend magnitude by Sen's slope estimator. Daily gridded data procured from India Meteorological Department (IMD) of 0.25º × 0.25º degrees to find the monthly and seasonal variability of precipitation. Eighty-five grids were falling in the basin processed for the periods from 1951 to 2015. The statistical analysis revealed that the average annual precipitation (AAP) and monsoon precipitation following the insignificant downward trend with Z statistics 0.10 and 1.23 and Sen's slope 0.79 and 0.76, respectively, over the basin. The shift analysis shows the AAP and monsoon precipitation increased significantly during 1951-61, whereas during the 1962-2015 rise in precipitation was insignificant. That changes in precipitation over the Wainganga sub-basin (WSB) may occur probably due to a rising trend of temperature. Therefore, nature-based climate solutions are the best way to endure the condition.

scholarly journals Capacity of the PERSIANN-CDR Product in Detecting Extreme Precipitation over Huai River Basin, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1747
Author(s):  
Shanlei Sun ◽  
Jiazhi Wang ◽  
Wanrong Shi ◽  
Rongfan Chai ◽  
Guojie Wang
Keyword(s):  
River Basin ◽  
Extreme Precipitation ◽  
Precipitation Amount ◽  
Climate Data ◽  
Validation Metrics ◽  
Huai River Basin ◽  
Huai River ◽  
Precipitation Estimation ◽  
Precipitation Changes ◽  
Linear Trends

Assessing satellite-based precipitation product capacity for detecting precipitation and linear trends is fundamental for accurately knowing precipitation characteristics and changes, especially for regions with scarce and even no observations. In this study, we used daily gauge observations across the Huai River Basin (HRB) during 1983–2012 and four validation metrics to evaluate the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) capacity for detecting extreme precipitation and linear trends. The PERSIANN-CDR well captured climatologic characteristics of the precipitation amount- (PRCPTOT, R85p, R95p, and R99p), duration- (CDD and CWD), and frequency-based indices (R10mm, R20mm, and Rnnmm), followed by moderate performance for the intensity-based indices (Rx1day, R5xday, and SDII). Based on different validation metrics, the PERSIANN-CDR capacity to detect extreme precipitation varied spatially, and meanwhile the validation metric-based performance differed among these indices. Furthermore, evaluation of the PERSIANN-CDR linear trends indicated that this product had a much limited and even no capacity to represent extreme precipitation changes across the HRB. Briefly, this study provides a significant reference for PERSIANN-CDR developers to use to improve product accuracy from the perspective of extreme precipitation, and for potential users in the HRB.

scholarly journals Model to assess the impacts of external drivers on the hydrology of the Ganges River Basin

2014 ◽  
Vol 364 ◽  
pp. 76-81 ◽  
Author(s):  
L. Muthuwatta ◽  
A. Sood ◽  
B. Sharma
Keyword(s):  
River Basin ◽  
Assessment Tool ◽  
Baseline Period ◽  
Middle Part ◽  
Water Yield ◽  
Ganges River ◽  
Climate Data ◽  
The Future ◽  
The Ganges ◽  
Ganges River Basin

Abstract. Impact of climate change on the hydrology of the Ganges River Basin (GRB) is simulated by using a hydrological model – Soil and Water Assessment Tool (SWAT). Climate data from the GCM, Hadley Centre Coupled Model, version 3 (HadCM3) was downscaled with PRECIS for the GRB under A1B Special Report on Emission Scenarios (SRES) scenarios. The annual average precipitation will increase by 2.2% and 14.1% by 2030 and 2050, respectively, compared to the baseline period (1981–2010). Spatial distribution of the future precipitation shows that in the substantial areas of the middle part of the GRB, the annual precipitation in 2030 and 2050 will be reduced compared to the baseline period. Simulations indicate that in 2050 the total groundwater recharge would increase by 12%, while the increase of evapotranspiration will be about 10% compared to the baseline period. The water yield is also expected to increase in the future (up to 40% by 2050 compared to baseline), especially during the wetter months. The model setup is available for free from IWMI’s modelling inventory.

scholarly journals Hydro-Climatic Variability in the Karnali River Basin of Nepal Himalaya

Climate ◽  
2016 ◽  
Vol 4 (2) ◽  
pp. 17 ◽  
Author(s):  
Kabi Khatiwada ◽  
Jeeban Panthi ◽  
Madan Shrestha ◽  
Santosh Nepal
Keyword(s):  
River Basin ◽  
River Discharge ◽  
Global Climate ◽  
Monsoon Season ◽  
Climatic Variability ◽  
Kendall Test ◽  
Temperature Trend ◽  
Maximum Temperature ◽  
Spatial And Temporal Variability ◽  
Climate Data

Global climate change has local implications. Focusing on datasets from the topographically-challenging Karnali river basin in Western Nepal, this research provides an overview of hydro-climatic parameters that have been observed during 1981–2012. The spatial and temporal variability of temperature and precipitation were analyzed in the basin considering the seven available climate stations and 20 precipitation stations distributed in the basin. The non-parametric Mann–Kendall test and Sen’s method were used to study the trends in climate data. Results show that the average precipitation in the basin is heterogeneous, and more of the stations trend are decreasing. The precipitation shows decreasing trend by 4.91 mm/year, i.e., around 10% on average. Though the increasing trends were observed in both minimum and maximum temperature, maximum temperature trend is higher than the minimum temperature and the maximum temperature trend during the pre-monsoon season is significantly higher (0.08 °C/year). River discharge and precipitation observations were analyzed to understand the rainfall-runoff relationship. The peak discharge (August) is found to be a month late than the peak precipitation (July) over the basin. Although the annual precipitation in most of the stations shows a decreasing trend, there is constant river discharge during the period 1981–2010.

scholarly journals Assessment and Correction of the PERSIANN-CDR Product in the Yarlung Zangbo River Basin, China

2018 ◽  
Vol 10 (12) ◽  
pp. 2031 ◽  
Author(s):  
Jiangtao Liu ◽  
Zongxue Xu ◽  
Junrui Bai ◽  
Dingzhi Peng ◽  
Meifang Ren
Keyword(s):  
Correlation Coefficient ◽  
River Basin ◽  
Correction Method ◽  
Probability Of Detection ◽  
Climate Data ◽  
Precipitation Estimation ◽  
Original Product ◽  
Lhasa River ◽  
Correction Step ◽  
Unit Hydrographs

Satellite products can provide spatiotemporal data on precipitation in ungauged basins. It is essential and meaningful to assess and correct these products. In this study, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) product was evaluated and corrected using the successive correction method. A simple hydrological model was driven by the corrected PERSIANN-CDR data. The results showed that the accuracy of the original PERSIANN-CDR data was low on a daily scale, and the accuracy decreased gradually from the east to the west of the basin. With one correction step, the accuracy of the corrected PERSIANN-CDR data was significantly higher than that of the initial data. The correlation coefficient increased from 0.58 to 0.73, and the probability of detection (POD) value of the corrected product was 18.2% higher than the original product. The temporal-spatial resolution influenced the performance of the satellite product. As the resolution became coarser, the correlation coefficient between the corrected PERSIANN-CDR data and the gauged data gradually became lower. The Identification of unit Hydrographs and Component flows from Rainfall, Evapotranspiration, and Streamflow (IHACRES) model could be satisfactorily applied in the Lhasa River basin with corrected PERSIANN-CDR data. The successive correction method was an effective way to correct the bias of the PERSIANN-CDR product.

scholarly journals Variability of Trends in Precipitation across the Amazon River Basin Determined from the CHIRPS Precipitation Product and from Station Records

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1244
Author(s):  
Victor Hugo da Motta Paca ◽  
Gonzalo Espinoza-Dávalos ◽  
Daniel Moreira ◽  
Georges Comair
Keyword(s):  
River Basin ◽  
Amazon Basin ◽  
Amazon River ◽  
Precipitation Trend ◽  
Amazon River Basin ◽  
Station Data ◽  
Spatially Distributed ◽  
Madeira River ◽  
Precipitation Trends

The Amazon River Basin is the largest rainforest in the world. Long-term changes in precipitation trends in the basin can affect the continental water balance and the world’s climate. The precipitation trends in the basin are not spatially uniform; estimating these trends only at locations where station data are available has an inherent bias. In the present research, the spatially distributed annual precipitation trends were studied in the Amazon River Basin from the year 1981 to 2017 using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) product. The precipitation trends were also cross-validated at locations where station data were available. The research also identifies clusters within the basin where trends showed a larger increase (nine clusters) or decrease in precipitation (10 clusters). The overall precipitation trend in the Amazon River Basin over 37 years showed a 2.8 mm/year increase, with a maximum of 45.1 mm/year and minimum of −37.9 mm/year. The highest positive cluster was in Cuzco in the Ucayali River basin, and the lowest negative was in Santa Cruz de la Sierra, in the upstream Madeira River basin. The total volume of the incoming precipitation was 340,885.1 km3, with a withdrawal of −244,337.1 km3. Cross-validation was performed using 98 in situ stations with more than 20 years of recorded data, obtaining an R2 of 0.981, a slope of 1.027, and a root mean square error (RMSE) of 363.6 mm/year. The homogeneous, standardized, and continuous long-term time series provided by CHIRPS is a valuable product for basins with a low-density network of stations such as the Amazon Basin.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

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