Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation Research Articles

Drought, temperature, and moisture availability: understanding the drivers of isotopic decoupling in native pine species of the Nepalese Himalaya.

The Himalayas experienced long-term climate changes and recent extreme weather events that affected plant growth and the physiology of tree species at high-elevation sites. This study presents the first statistically robust δ18OTR chronologies for two native pine species, Pinus roxburghii, and Pinus wallichiana, in the lower Nepalese Himalaya. The isotope chronologies exhibited 0.88‰ differences in overall mean isotope values attributed to varying elevations (460-2000m asl). Comparative analysis of climate response using data sets from different sources and resolutions revealed the superiority of the APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data set calibrated for the South Asian Summer Monsoon (SASM)-dominated region. Both species exhibited negative correlations with monsoon precipitation and positive correlations with temperature. However, during the peak monsoon season (July-August), daily resolved climate data disentangled statistically insignificant relationships, and revealed that δ18OTR is influenced by atmospheric moisture. Both congeneric species showed a decoupling between the chronologies after 1995. However, no significant change in air moisture origin and monsoon regime between the study sites was observed, indicating a consistent dominant moisture source during different monsoon seasons. Besides, we also observed the decreased inter-series correlation of both δ18OTR chronologies after 1995, with P. wallichiana experiencing a steeper decrease than P. roxburghii. The weakening correlations between and within the chronologies coincided with a regional drought during 1993-1995 in both sites, highlighting the strong regulation of local climate on the impact of regional extreme climate events. Our findings emphasise the importance of employing climate data with optimal spatial and temporal resolution for improved δ18OTR-climate relationships at the intra-annual scale while considering the influence of site-specific local environmental conditions. Assessing climate data sets with station data is vital for accurately interpreting climate change's impact on forest response and long-term climate reconstructions.

Assessment of extreme rainfall events over the Indian subcontinent during the historical and future projection periods based on CMIP6 simulations

AbstractThe impact of climatic change on the summer monsoon season is studied to understand the rainfall pattern towards the end of the century utilizing the Coupled Model Intercomparison Project, Phase 6 (CMIP6) released by the World Climate Research Programme (WCRP). The analysis of model simulations from CMIP6 was carried out using 64 years of the historical period (1951–2014) and future projections till the end of the century (2015–2100). The models are compared with observed daily rainfall data from the APHRODITE (Asian Precipitation‐Highly‐Resolved Observational Data Integration Towards Evaluation). The analysis revealed that most of the models show an overestimation in the annual cycle of rainfall in the historical period; however, a few of them underestimate the values. The majority of them capture the onset signal of the summer monsoon in early June, along with a good seasonality in the daily rainfall climatology. The simulations that are coherent with the observational data sets are selected on the basis of the Taylor diagram for future projections in four scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). The projections of the aforementioned scenarios are taken from the model outputs of EC‐Earth3‐Veg‐LR, INM‐CM4‐8, INM‐CM5‐0, MIROC‐ES2L and MPI‐ESM1‐2‐HR. The selected models exhibit far greater agreement among the 30 models when it comes to the features of rainfall during the summer monsoon. We have given more emphasis on summer monsoon rainfall in the historical and future projection periods since the trends are becoming more chaotic, as reported in observational studies. Over the Indian subcontinent, all of the chosen scenarios show an increased frequency of intense rainfall events with varying decadal and multidecadal features. Central India and west coastal belts are showing positive trends in extreme rainfall events towards the end of the century. At the turn of the century, the southern peninsular region experienced a decline in monsoon precipitation, whereas central India experienced an increase. The severity of rainfall variations during the monsoon season and trends in extremes are increasing as we move from the low‐emission scenario to the high‐emission scenario.

Study of The Impact of Convectively Coupled Equatorial Kelvin and Rossby Waves on Rainfall Extremes in Sulawesi, Indonesia

This study examined the impact of Equatorial Kelvin and Equatorial Rossby (ER) waves on rainfall extremes over Sulawesi Island, Indonesia. Impacts are assessed by changes in the probability of rainfall events that exceeded extreme value and changes in the fraction of wet days. Using Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) rainfall data and Outgoing Longwave Radiation (OLR) from NOAA, impacts are quantified with the respect to wave’s local phase grouping. The local wave phase is based on the standardized OLR anomalies and their temporal tendency. Results showed that Kelvin and ER wave can modulate daily rainfall extremes over Sulawesi Island. Kelvin wave has the strongest impact with changes in the probability of extreme rainfall by up to +100% (-70%) in phases 5 and 6 (1 and 2) and increases the value of wet day fraction by up to > 0.04. Followed by the ER wave with changes in the probability of extreme rainfall by up to +50% (-50%) in phase 4 (1 and 2) and increases the value of the wet day fraction by up to 0.05. The superposition between ER and Kelvin wave occurrence increases the possibility of extreme rainfall occurrence by up to 100 % in almost every part of Sulawesi island in phases 4,5 and 6. The results suggest that Kelvin and ER waves can be another important source of predictability for extreme rainfall on Sulawesi Island.

Robustness of gridded precipitation products for vietnam basins using the comprehensive assessment framework of rainfall

The use of satellite–based precipitation products (SPPs) have become increasingly prevalent as key inputs to provide regional rainfall for improving hydrological simulations in data–spare regions. This study introduces a new approach – Comprehensive Assessment Framework of Rainfall (CAFR) to evaluate six satellite–based precipitation products (SPPs) for eleven basins with different sizes across Vietnam (2007–2015). These SPPs include the Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final run Version 6 (GPM IMERGF V6), Multi–Source Weighted–Ensemble Precipitation (MSWEP) V2.2, Soil Moisture to Rain (SM2RAIN) – Advanced SCATterometer (ASCAT) V1.5, Asian Precipitation–Highly–Resolved Observational Data Integration Towards Evaluation (APHRODITE) V1901, Climate Hazards group Infrared Precipitation with Stations (CHIRPS) V2.0, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) – Climate Data Record (CDR) V1.0. With the proposed CAFR: (1) IMERGF is suggested to have the best performance overall, especially when simulating flood peaks; (2) SM2RAIN–ASCAT demonstrates the best skills in metrics related to the dry season; (3) For streamflow simulations, SPPs' performance is sensitive to basin size, with larger basins showing better performance skills. In this study, we demonstrate the capability of our proposed framework to better understand SPP applications in hydrological modeling.

Exploring the Future Rainfall Characteristics over India from Large Ensemble Global Warming Experiments

We investigated rainfall patterns over India for the period from 1951 to 2010 and predicted changes for the next century (2051–2100) with an assumed 4K warming from large ensemble experiments (190 members). We focused on rainfall patterns during two periods of present-day climate (1951–1980 and 1981–2010) and their projected changes for the near and far future (2051–2080 and 2081–2110). Our analysis found that the northeastern region of India and some southern regions received higher rainfall during the period of 1951–2010, which is consistent with daily observations from the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE). In the warming climate, rainfall events in India are predicted to carry more precipitation, with the northeast and southern regions experiencing stronger rainfall events. The frequency and intensity of these events (with more than 20 mm of rainfall per day, on average) are also expected to increase. Overall, our study suggests that water-related disasters such as flooding and landslides could be much worse in India in the future due to climate warming.

Interannual Variability of Extreme Precipitation during the Boreal Summer over Northwest China

Herein, we investigated the characteristics and mechanisms of interannual variability of extreme summer precipitation in northwest China (NWC). The four high-resolution precipitation predicting products under assessment indicated that both the accumulation of summer daily precipitation ≥95th percentile, and the summer maxima of daily precipitation generally decreased in a southeast—northwest direction, while relatively high values were observed in the Tienshan and Qilian Mountain areas. In turn, the Tropical Rainfall Measuring Mission (TRMM) satellite dataset underestimated extreme precipitation in mountainous areas, while Asian precipitation highly—resolved observational data integration towards evaluation (APHRODITE) and Climate Prediction Center (CPC) captured the characteristics of extreme precipitation in NWC. AMIP-type simulations of the interannual variability of extreme summer precipitation in NWC were quite unsuccessful. However, all of them can capture the increasing trends. Therefore, we further found that the interannual increase in extreme precipitation in NWC is strongly correlated with the weakened South Asian high, strengthened Western Pacific Subtropical high, the enhanced westerly jet, and the mid- to high-latitude Rossby wave trains, whose formation and sustenance can be traced back to sea surface temperature-anomalies in the western Pacific in May, June, and July. An increased sea surface temperature promotes convection and induces diabatic heating, which stimulates anticyclonic anomalies that disturb the enhanced westerly jet, resulting in a barotropic Rossby wave train via the Gill-type response. Additionally, it guides more water vapor convergence to NWC and enhances upward motion via anticyclonic anomalies over western Europe and Eastern Asia, and cyclonic anomalies over Central Asia.

Application of bias corrected FGOALS-g3 model products for detecting changes in extreme precipitation in the Tienshan Mountains, Central Asia

This paper investigates the performance of bias-corrected Flexible Global Ocean-Atmosphere-Land System-g3 (FGOALS-g3) model products and then detects changes in extreme precipitation (EP) in the Tienshan Mountains, Central Asia (TMCA), as reflected by 25 EP indices. The reliability of the FGOALS-g3 model outputs is assessed systematically based on multiple statistical indicators against multi-source precipitation datasets and the bias-corrected FGOALS-g3 model products are applied to project EP variations under different global warming levels. Using the geographical detector method, a novel statistical method for detecting spatial heterogeneity and elucidating the underlying causes, the explanatory power of 20 atmospheric circulation factors related to EP is examined. The findings indicate that while the FGOALS-g3 products can detect the spatial pattern of multi-year average precipitation in the TMCA, there is an obvious overestimation in magnitude, especially in the West and Middle Tienshan Mountains. These biases are significantly reduced, however, after downscaling and bias correction. Compared with the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) data, the grid-by-grid error in bias-corrected FGOALS-g3 simulated mean precipitation is between −7.64% and 10.95%. In terms of EP, the corrected FGOALS-g3 products not only reproduce the spatial distribution, but also reasonably simulate their magnitudes, with some overestimation in light EP and underestimation in heavy EP. Overall, across the historical period, EP has increased. The intensity and frequency of EP are projected to generally increase under different scenarios. At 1.5 °C warming levels, annual total precipitation in wet days (PRCP) increases by 5.74% (7.74%) under the SSP245 (SSP585). Additionally, as an EP becomes rarer, its rate of change rises. The main driving factors in EP are detected to be 30 hPa zonal wind (30ZW), relative number of sunspots (SF), south Asian summer monsoon (SAM), sea surface temperature anomaly in the region of 5°S-5°N, 170°-120°W (NINO 3.4), and mean surface temperature (T).

Distributional Validation of Precipitation Data Products with Spatially Varying Mixture Models

The high mountain regions of Asia contain more glacial ice than anywhere on the planet outside of the polar regions. Because of the large population living in the Indus watershed region who are reliant on melt from these glaciers for fresh water, understanding the factors that affect glacial melt along with the impacts of climate change on the region is important for managing these natural resources. While there are multiple climate data products (e.g., reanalysis and global climate models) available to study the impact of climate change on this region, each product will have a different amount of skill in projecting a given climate variable, such as precipitation. In this research, we develop a spatially varying mixture model to compare the distribution of precipitation in the High Mountain Asia region as produced by climate models with the corresponding distribution from in situ observations from the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) data product. Parameter estimation is carried out via a computationally efficient Markov chain Monte Carlo algorithm. Each of the estimated climate distributions from each climate data product is then validated against APHRODITE using a spatially varying Kullback–Leibler divergence measure. Supplementary materials accompanying this paper appear online.

Quantitative Assessment of Impact of Climate Change and Human Activities on Streamflow Changes Using an Improved Three-Parameter Monthly Water Balance Model

Understanding the impact of climate change and human activities on the hydrological cycle of any watershed can provide a scientific basis for regional water resource planning, flood management, and disaster mitigation. An improved three-parameter hydrological model (CM) based on monthly water balance using an exponential equation to depict the distribution of groundwater storage capacity was developed and evaluated. The model uses Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) rainfall data as input, with the Zhejiang Province as the case application, and the effects of climate change and human activities on streamflow changes were assessed by separating environmental variables in this study. The results indicate that APHRODITE data has excellent monthly accuracy, with a mean correlation coefficient (CC) of more than 0.96 and an average absolute percentage bias (Pbais) of less than 5%. The three models are relatively close in their ability to simulate high flows, but the CM simulated low flow is better than the other two models. Positive and negative Pbais phenomena occur in the CM model in each catchment, and absolute levels are regulated by 5%. Furthermore, the CM model’s average Nash efficiency coefficient (NSE) is greater than 0.9, indicating that it can correctly fulfill the water balance. The results are more consistent throughout multiple catchments in each watershed using Budyko-based and hydrological model technique to evaluate the influence of climate change and human activities on streamflow. Climate change dominated streamflow variations in 18 of the 21 catchments in Zhejiang Province, whereas human activities dominated the rest. The findings of the study will be used to influence the management, development, and usage of water resources in the watershed.

Performance evaluation of CORDEX South Asia models for projections of precipitation over the Kabul basin, Afghanistan

Precipitation is one of the key input data for hydrological modelling and water resources assessment. Climate projections are regionally available at a finer resolution, but it is still necessary to validate such data against the observations before any application. In this study the precipitation data available from the Coordinated Regional Downscaling Experiment (CORDEX) program of the South Asian domain was examined against APHRODITE (Asian Precipitation – Highly-Resolved Observational Data Integration Towards Evaluation) precipitation, which is used as observations in the absence of in situ measurements for the data-scarce Kabul basin in Afghanistan. The evaluation methods include verification statistics as well as visual verification by mapping the data over the basin. The evaluation results showed that some of the RCMs (Regional Climate Models) are following an entirely different precipitation pattern both temporally and spatially; however, their driving GCMs might be the same. Six of the 17 RCMs examined in this study performed well for the Kabul basin. The ensemble results of these selected RCMs suggest a decreasing trend in the future precipitation under both RCPs (Representative Concentration Pathways), with variations ranging from −14% to +6% under RCP 4.5 and −17% to +8% under RCP 8.5 for most areas, especially in the central parts of the basin.

Multimodel quantification of green and blue water components and its error propagations through parameter transferability approach across input choices

A robust quantitative assessment of water security through efficient water accounting concepts like blue and green water footprints has gained a lot of attention for water resources management at river basin scale. However, such water accounting tools and models have dependencies to the choice of precipitation data used to drive the model, as the input bias propagate into simulated streamflow through interaction with different hydrological processes. This study performs a holistic investigation of sensitivity of water accounting fractions like blue water flow (BWF), green water flow (GWF), green water storage (GWS), and their dependencies on secondary precipitation datasets (SPDs) choices using a lumped conceptual rainfall-runoff model [Hydrological Simulation model (HYSIM)] and a physically based semi-distributed hydrological model [Soil and Water Assessment Tool (SWAT)] in the Damodar River basin, India. These models were driven by seven gridded precipitation datasets, including India Meteorological Department (IMD) data, Indian Monsoon Data Assimilation and Analysis (IMDAA) data, Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) data, Watch forcing ERA-Interim data, Climate Hazard Group Infrared Precipitation with Station data (CHIRPS), PRINCETON precipitation data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Records (PERSIANN-CDR) data. The reference IMD based multimodel results revealed that the annual average blue and green water components of the Damoder River were in the range of 558–656 mm/year, 574–728 mm/year and 34–112 mm/year for BWF, GWF, and GWS respectively during 1994–2010 period. The results obtained after applying parameter transferability approach across the input space have revealed exact quantification of choice dependent annual variations in spatial and temporal characteristics of BWF, GWF, and GWS. A further evaluation based on error propagation ratios (γCC, γrRMSE, γB) has revealed a quantification of input choice induced dampening/magnification effects on flow estimations of both the HYSIM and SWAT and these quantifications were free from the compensation effects of recalibration. The findings in this study demonstrate that the change in annual average BWF varies between −46.97% to +41.38% in comparison to benchmark IMD simulations, and the corresponding changes for GWF is between −19.63% and +17.06% and for GWS is between −29.97% to +35.5% with different choices of SPD scenarios. The systematic (γB) and random (γrRMSE) error propagation factors during monsoon season are larger for SWAT model ([0.21–0.78], [0.69–0.89] respectively) than that of HYSIM model ([0.18–0.74], [0.66–0.81], respectively) and it is varying with choice of different SPDs.

Time Series Analysis of Atmospheric Precipitation Characteristics in Western Siberia for 1979–2018 across Different Datasets

A comparative statistical analysis of the spatiotemporal variability of atmospheric precipitation characteristics (mean and extreme values) in Western Siberia was performed based on data acquired from meteorological stations, global precipitation datasets such as the project of Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) and from Global Precipitation Climatology Centre (GPCC), and reanalysis archives, including from National Centers of Environmental Prediction (NCEP-DOE) and the European Center for Medium Range Weather Forecasts (ERA5) for the period 1979–2018. The best agreement of the values from the observational data was observed with the values from GPCC. This archive also represented the periodicities in the time series of observational data from meteorological stations, especially in the short-period part of the spectrum. Underestimated values were revealed for the APHRODITE archive, while overestimated ones were found for the NCEP reanalysis data. In comparison with GPCC, the ERA5 dataset reproduced the general variability but with a smaller amplitude (the correlation coefficient was up to 0.9). In general, the median estimates of the precipitation amount derived from the meteorological stations’ data, as well from the reanalysis data, were in better agreement with each other rather than their extreme values. However, their temporal variability can be effectively described by other datasets.

Flow velocities of the debris-covered Miyar Glacier, western Himalaya, India

ABSTRACT Spatiotemporal surface velocity measurements of the alpine valley type debris-covered Miyar Glacier of the Chandrabhaga (Chenab) basin, western Himalaya, were assessed based on the cross-correlation of Landsat images spanning nearly three decades (1992-2019). Long-term (1950-2015) temperature and precipitation trends were evaluated using Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) datasets. The mean velocity (1992-2019) of the Miyar Glacier is ∼29 m/yr, with spatial patterns revealing that the debris-covered tongue is nearly stagnant (∼5 m/yr) compared to the debris-free up-glacier zone (∼35 m/yr). The transition zone from clean to debris-covered ice in the mid-ablation area shows the highest long-term mean velocities of ∼60 m/yr during the observation period, likely resulting from a steep surface gradient and greater ice thickness than the other regions of this glacier. The slow-moving and nearly stagnant debris-covered area reveals the highest amount of surface lowering due to the expansion of supraglacial ponds. Miyar Glacier experiences summer speed-up of ∼67–80% in seasonal velocity compared to winter, interpreted as a result from enhanced basal sliding during summer months due to warmer temperatures inputting more meltwater into the subsurface drainage system. Inter-annual velocity variations are greatest in the upper glacier, with higher velocities observed more frequently in recent decades. Future work should aim to elucidate the causes of this pattern, considering the overall rising air temperature trend in the western Himalaya.

Spatio-temporal trend mapping of precipitation and its extremes across Afghanistan (1951–2010)

The civil war, harsh climate, tough topography, and lack of accurate meteorological stations have limited observed data across Afghanistan. To fulfill the gap, this study analyzed the trend in precipitation and its extremes using Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) daily dataset between 1951 and 2010 at the spatial resolution of 0.25° × 0.25°. Non-parametric modified Mann-Kendall test and Sen’s slope estimator were employed to detect the trend and quantify it at the significance level of 5%. Significant decreasing trends were observed only in small clusters of southwestern regions ranging between 0 and −1.5mm/year and the northeastern region between −1.5 and −6 mm/year for the annual time series. A similar trend pattern was observed in the spring season decreasing at the rate of −0.15 and 0.54 mm/year in the northeastern and 0 to −0.15 mm/year southwestern region. A decrease in spring precipitation is expected to affect crop production especially in the northeastern region which hosts 22% of the arable area. An increasing trend in the eastern region at a maximum rate of 0.16 mm/year was observed which could intensify the flooding events. Trend analysis of extreme precipitation indices indicated similar spatial distribution to the mean precipitation, concentrated around southwestern, northeastern, and eastern regions. The increasing frequency of consecutive dry days in the western region and very heavy precipitation (R10mm) and extremely heavy precipitation (R20mm) in the eastern region could be fueling the occurrence of droughts and floods respectively. Taking these findings of the erratic nature of rainfall and extreme events into consideration for sustainable management of water resources would be fruitful.

Systematical Evaluation of Three Gridded Daily Precipitation Products Against Rain Gauge Observations Over Central Asia

Understanding the precipitation variability and extreme precipitation over arid Central Asia (CA) has largely been hampered by the lack of daily precipitation observations. The gridded precipitation datasets over CA are large discrepancies. Here, three gauge-based gridded daily precipitation products from Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Global Precipitation Climatology Center (GPCC), and Climate Prediction Center Based Analysis of Global Daily Precipitation (CPC_global) were assessed and compared with 49 rain gauge daily observations precipitation (OBS) from January 1985 to December 2015 using different time-scales over CA and different climate regimes, specifically Northern CA with temperate continental climate (NCA), Southwestern CA with dry arid desert climate (SWCA), and Southeastern CA with Mediterranean continental climate (SECA). Four accuracy indices [correlation coefficient (R), Bias, root mean square error (RMSE), and relative bias (RBias)] were employed to evaluate the performance of the three products in depicting the spatiotemporal features of precipitation variation over CA at multiple time scales (including daily, monthly, seasonal, and yearly). The mean annual and daily precipitation of OBS and three gridded products exhibit the trend of a gradual precipitation decreased from SECA to NCA and SWCA. The best overall performance was obtained for APHRODITE and GPCC for daily and annual time-scale, whereas CPC shows noticeable underestimation precipitation in SECA. The monthly precipitation depicted distinct features with a bimodal pattern with a peak in March and another in December, include the SECA and SWCA regions. In contrast, precipitation was concentrated in summer with the peak in July over the NCA region. At monthly scale terms, APHRODITE was more accurate in the wet seasons (winter and spring months) in SWCA and SECA. Additionally, GPCC has fairly better capability in summer months in NCA. Considering the spatial distribution, the bias variability was largerly in mountainous areas than in the plains. Temporally, the bias largerly in the dry seasons than in the wet seasons. At the interannual variability scale, GPCC was capable of qualitatively increasing the CA (NCA and SECA) precipitation during the last 21 years, while APHRODITE underestimated the trends. The CPC overestimated the precipitation trends over all regions. This study can serve as a reference for selecting daily precipitation products with low densities of stations, complex topographies, and similar climatic regions.

New gridded rainfall dataset over the Korean peninsula: Gap infilling, reconstruction, and validation

AbstractThis study describes a new high‐resolution latitude/longitude) gridded daily rainfall dataset (K‐Hidra version 2020) developed from rainfall records of 389 gauge stations irregularly distributed across the Korean peninsula. The observational datasets are often composed of incomplete time series covering different temporal periods with numerous missing values, informing that infilling process is essential. Three supportive but separated evaluation frameworks, which have been paid less attention to, are explicitly addressed to identify the proper infilling model in the study area. After a gridded rainfall dataset is created with infilled dataset, it is validated by comparing the gridded products obtained by observations. Furthermore, K‐Hidra is compared with other gridded rainfall estimates including Climate Prediction Center (CPC), Global Precipitation Climatology Project (GPCP), Asian Precipitation Highly‐Resolved Observational Data Integration Towards Evaluation (APHRODITE), and Tropical Rainfall Measuring Missing (TRMM) Multi‐satellite Precipitation Analysis (TMPA). Our results suggest that the elastic net model algorithm is the most effective in dealing with missing values in the study area. Results also demonstrate that the infilling process is vital to properly produce the reconstructed series although its effects substantially vary based on the size of the available data as well as the spatial and temporal distribution of the observed precipitation. Furthermore, K‐Hidra reveals that the Korean peninsula have experienced significant changes in precipitation‐related characteristics such as annual daily maximum precipitation and total annual dry days, likely leading to frequent hydrologic extremes. Lastly, K‐Hidra is compared with other estimates, CPC has a small bias and high correlations with the new dataset while precipitation variabilities are acceptably represented by all other gridded estimates. From the results demonstrated in this study, when officially released, K‐Hidra is expected to be useful for climate impact and rainfall variability analyses over the Korean peninsula.

Verification of APHRODITE Precipitation Data Set in Bangladesh

We investigated the preformance of Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) of water resources precipitation products in Bangladesh taking rain gauge data as reference for a 3-year period (2003-2005). Various statistical and categorical indices such as coefficient of correlation (CC), bias, relative bias (RB), mean absolute error (MAE), root mean square error (RMSE), probability of detection (POD), and false alarm ratio (FAR), were applied to measure the performance of the product. With CC value of 0.85, bias of 0.91, RB of -9.5%, MAE of 7.7 mm, and RMSE of 15.2 mm the product tended to underestimate rainfall values during the study period. Although, the POD score of 1.00 demonstrated very good skill in detecting the occurrence of rainfall events, FAR value of 0.25 indicated a considerable amount of false alarms. Moreover, as the precipitation threshold increased, the underestimation became more prominent over the study region. Analysis on the basis of location of the rain gauges also showed that APHRODITE consistently underestimated rainfall values with the increase of extreme rainfall thresholds.
 Journal of Engineering Science 12(1), 2021, 51-56

Identification and inter‐comparison of appropriate long‐term precipitation datasets using decision tree model and statistical matrix over China

AbstractThe reliability of long‐term precipitation estimates is vital for climatology and hydrometeorology applications. Different climatic zones and high rain gauge network (more than 800) of China are a suitable topography for performance evaluation of different long‐term precipitation datasets. In this study, seven long‐term precipitation datasets are tested against in situ observations at different time scales (1981–2016) at 813 grid points. Well‐known statistical indicators and Fast‐frugal tree (FFt) decision model are employed to identify the best long‐term datasets. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record is the only datasets that did not perform well in the study area. Asian Precipitation‐Highly‐Resolved Observational Data Integration Towards Evaluation (APHRODITE), Global Precipitation Climatology Centre (GPCC), and Climate Prediction Center (CPC‐Global) estimates are comparable with in situ observations. Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), National Centers for Environmental Prediction (NCEP2) overestimate the precipitation extremes in the region. There exists a difference of 100–250 mm among precipitation datasets at an annual scale. All of the seven long‐term datasets underestimate the rxdays across China. The minimum range of rxdays (maximum precipitation amount in under defined days: 1 day or 5 days) captured by datasets is comparable except PERSIANN‐CDR. The maximum range calculated with PERSIANN‐CDR is 55.01 (rx1day), and 129.67 (rx5day), much less than the in situ rxdays. This analysis shows that datasets failed to capture maximum precipitation intensity in the region as well. FFt decision model results show that APHRODITE ranked first based on calculated consecutive dry days among all six other datasets in the most climatic zones. Overall, results indicate that data assimilation, the spatial coverage of ground stations, and interpolation techniques used to develop the datasets may limit the reliability of precipitation datasets in the study area.

Application of precipitation products for flood modeling of transboundary river basin: a case study of Jhelum Basin

The main objective of this study is the flood modeling of the River Jhelum Basin in the Jammu and Kashmir using the Integrated Flood Analysis System (IFAS) hydrological model. The region having an area of 33,000 km2 has a limited rain gauge coverage. The data inaccessibility is accentuated due to the mountainous topography, glaciers, and remote areas. This research makes innovative use of the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) dataset to overcome the data scarcity for the basin. For flood modeling, a regionalized approach was adopted, whereby the four main tributaries of the River Jhelum Basin were separately modeled. The parameters, thus, determined, were later used in the modeling of the River Jhelum, the main river of the basin. The model was calibrated on the medium-high flood of 2010 and validated on the very high flood of 2014, obtaining good results as indicated by Nash–Sutcliffe Model Efficiency Coefficient (NSE) of 0.77. Later, the model was executed using the Global Satellite Mapping of Precipitation (GSMaP). It performed adequately for the high flood event of 2014, while for the flood of 2010, it gave poor results. Besides, this study also demonstrates the efficacy of a new generation of computer models that work by accessing the free global datasets available on the web for hydrological modeling.

Evaluating the Performance of Secondary Precipitation Products through Statistical and Hydrological Modeling in a Mountainous Tropical Basin of India

This paper investigates the performance of gridded rainfall datasets for precipitation detection and streamflow simulations in Indiaʼs Tungabhadra river basin. Sixteen precipitation datasets categorized under gauge-based, satellite-only, reanalysis, and gauge-adjusted datasets were compared statistically against the gridded Indian Meteorological Dataset (IMD) employing two categorical and three continuous statistical metrics. Further, the precipitation datasets’ performance in simulating streamflow was assessed by using the Soil and Water Assessment Tool (SWAT) hydrological model. Based on the statistical metrics, Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) furnished very good results in terms of detecting rainfall, followed by Climate Hazards Group Infrared Precipitation (CHIRP), National Centres for Environmental Prediction-Climate Forecast System Reanalysis (NCEP CFSR), Tropical Rainfall Measurement Mission (TRMM) 3B42 v7, Global Satellite Mapping of Precipitation Gauge Reanalysis v6 (GSMaP_Gauge_RNL), and Multisource Weighted Ensemble Precipitation (MSWEP) datasets which had good-to-moderate performances at a monthly time step. From the hydrological simulations, TRMM 3B42 v7, CHIRP, CHIRPS 0.05°, and GSMaP_Gauge_RNL v6 produced very good results with a high degree of correlation to observed streamflow, while Soil Moisture 2 Rain-Climate Change Initiative (SM2RAIN-CCI) dataset exhibited poor performance. From the extreme flow event analysis, it was observed that CHIRP, TRMM 3B42 v7, Global Precipitation Climatology Centre v7 (GPCC), and APHRODITE datasets captured more peak flow events and hence can be further implemented for extreme event analysis. Overall, we found that TRMM 3B42 v7, CHIRP, and CHIRPS 0.05° datasets performed better than other datasets and can be used for hydrological modeling and climate change studies in similar topographic and climatic watersheds in India.