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

Impacts of precipitation uncertainty on hydrological ensemble simulations over the Ganjiang River basin

Study regionThe Ganjiang River basin (113°−117°E, 24°−30°N; 83,374 km2) is a large watershed with complex topography in the Poyang Lake basin in Jiangxi province, China. Study focusThis study evaluates three quantitative precipitation estimates (QPEs) over the Ganjiang River basin, namely the China Gauged-Based Daily Precipitation Analysis (CGDPA) data, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) data, and the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE). The study also investigates the impacts of precipitation uncertainty on hydrological ensemble streamflow simulations using the three QPEs as precipitation inputs of the Variable Infiltration Capacity (VIC) hydrological model. New hydrological insights for the regionAPHRODITE underestimates precipitation compared to CGDPA, while PERSIANN-CDR shows greater spatiotemporal variability. The ensemble mean streamflow demonstrates greater improvement compared to the results obtained from a single parameter set. Among the three QPEs, the simulations forced by CGDPA show the best deterministic and probabilistic verification scores, followed by APHRODITE. PERSIANN-CDR tends to underestimate evaporation and leads to the lowest score of ensemble streamflow simulations, but shows advantages in simulating extremely low streamflow. The study highlights that high-density gauge-based QPEs remain the most accurate source of precipitation inputs for reliable hydrological simulations, while satellite-gauge merged QPEs can provide valuable inputs for hydrological simulations over the basins where meteorological stations are scarce.

Comprehensive Analysis of PERSIANN Products in Studying the Precipitation Variations over Luzon

This study evaluated the capability of satellite precipitation estimates from five products derived from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-CCS-CDR, and PDIR-Now) to represent precipitation characteristics over Luzon. The analyses focused on monthly and daily timescales from 2003–2015 and adopted surface observations from the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) platform as the evaluation base. Among the five satellite precipitation products (SPPs), PERSIANN-CDR was observed to possess a better ability to qualitatively and quantitatively estimate spatiotemporal variations of precipitation over Luzon for the majority of the examined features with the exception of the extreme precipitation events, for which PERSIANN-CCS-CDR is superior to the other SPPs. These results highlight the usefulness of the addition of the cloud patch approach to PERSIANN-CDR to produce PERSIANN-CCS-CDR to depict the characteristics of extreme precipitation events over Luzon. A similar advantage of adopting the cloud patch approach in producing extreme precipitation estimates was also revealed from the comparison of PERSIANN, PERSIANN-CCS, and PDIR-Now. Our analyses also highlighted that all PERSIANN-series exhibit improved skills in regard to detecting precipitation characteristics over west Luzon compared to that over east Luzon. To overcome this weakness, we suggest that an adjustment in the cloud patch approach (e.g., using different cloud temperature thresholds or different brightness temperature and precipitation rate relationships) over east Luzon may be helpful.

Performance of gridded precipitation products in the Black Sea region for hydrological studies

Gridded precipitation products are becoming good alternative data sources for regions with limited weather gauging stations. In this study, four climate gridded precipitation products were utilized, namely Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim/land, the Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), and Multi-Source Weighted-Ensemble Precipitation (MSWEP). The key novelty of this study is to fill the gap in one of important areas of the transcontinental region of Eurasia, namely Rize province in the Black Sea region being selected as a study area since it has complex topography and climatology in addition to a limited number of gauging stations. A set of precipitation products were assessed for performance with the observed precipitation data before using a hydrological model (SWAT) to evaluate the basin response for the climate products. Three methods were considered in this study: (i) spatial comparison and (ii) hydrological and (iii) statistical evaluations. Along with precipitation forcing, the SWAT model simulations were analyzed in conjunction with streamflow observations. In an overall evaluation, the percentage bias of ERA-Interim/land, CFSR, APHRODITE, and MSWEP mean monthly precipitation is 19.9%, 33.4%, 41.4%, and 85.0% respectively. For the flow simulations, the CFSR and MSWEP have resulted in exaggerated peak flows in the high flow season due to overestimated precipitation forcing (Nash Sutcliffe efficiency [NS] equal to 0.22 and −0.73, respectively). On the contrary, the APHRODITE underestimated the peak flows due to lower precipitation estimates (NS = 0.38). The ERA-Interim land showed good agreement with the observed flows (NS = 0.53). From these readings, we stated that the ERA-Interim land exhibited improved performance with the observed precipitation whereas the CFSR showed the worst performance. The study suggests that gridded precipitation products could supplement observed precipitation data for observational data scarcity in mountainous regions.

Evaluation of six gauge-based gridded climate products for analyzing long-term historical precipitation patterns across the Lancang-Mekong River Basin

Freshwater plays a vital role in global sustainability by improving human lives and protecting nature. In the Lancang-Mekong River Basin (LMRB), sustainable development is principally dependent upon precipitation that predominantly controls freshwater resources availability required for both life and livelihood of ∼70 million people. Hence, this study comprehensively analyzed long-term historical precipitation patterns (in terms of trends, variability, and links to climate teleconnections) throughout the LMRB as well as its upper (Lancang River Basin, LRB) and lower (Mekong River Basin, MRB) parts employing six gauge-based gridded climate products: Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Climate Prediction Center (CPC), Climate Research Unit (CRU), Global Precipitation Climatology Center (GPCC), Precipitation Reconstruction over Land (PRECL), and University of Delaware (UDEL). Accordingly, annual and seasonal (dry and wet) precipitation time series were calculated for three study periods: century-long outlook (1901-2010), mid-past (1951-2010), and recent decades (1981-2010). However, the role of climate teleconnections in precipitation variability over the LMRB was only identified during their available temporal coverages: mid-past and recent decades. The results generally showed that: (i) both annual and seasonal precipitation increased across all three basins in 1981-2010; (ii) wet and dry seasons got drier and wetter, respectively, in all basins in 1951-2010; (iii) all such changes were fundamentally attributed to increases in precipitation variability on both annual and seasonal scales over time; (iv) these variations were most strongly associated with the Pacific Decadal Oscillation (PDO), Atlantic Multi-decadal Oscillation (AMO) and East Pacific/North Pacific (EP/NP) pattern in the LMRB and the MRB during 1951-2010, but with the North Sea-Caspian Pattern (NCP) and the Southern Annular Mode (SAM) in the LRB; (v) such relationships got stronger in 1981-2010, while the Southern Oscillation Index (SOI) became the most influential teleconnection for dry season precipitation variability across all basins; and (vi) GPCC (APHRODITE) provided the most reliable gauge-based gridded precipitation time series over the LMRB for the years before (after) 1951. These findings lay a foundation for further studies focusing on water resources and sustainable development in the LMRB.

Observed changes in extreme precipitation over the Tienshan Mountains and associated large-scale climate teleconnections

Under global warming, extreme hydrological events are experiencing increasingly violent fluctuations. Investigating changes in the intensity and frequency of extreme precipitation (EP) events is particularly critical for understanding the hydrological response to climate change. Based on high-precision and long-term daily grid precipitation data obtained from Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), 25 EP indices were examined for the Tienshan Mountains region of Central Asia (TMCA). Here, the relationship between EP and associated large-scale climate teleconnections is revealed by using a series of approaches such as trend analysis and the geographical detector method (GMD), a statistical tool to measure and attribute spatial stratified heterogeneity. The results show an overall increase in EP during 1951–2014, as reflected in the 25 indices. Furthermore, the number of consecutive dry days (CDD) decreased from 87.02 to 69.35 while the number of consecutive wet days (CWD) increased from 3.89 to 4.61. Meanwhile, the increasing trend of total precipitation (PRCPTOT) was 18.43 mm/10a, and changes in EP frequency were shown to increase with event rareness. For R95p, the observed changes in frequency are 34.46%, but these jump to 96.58% for R99p. Moreover, the study also notes that changes in EP are elevation-dependent, with middle altitude areas (1500–3500 m) being most sensitive to change rates. As well, the study reveals that the occurrence of EP responds non-linearly to climatic teleconnections, and that the combined effect of two factors generally make much larger contributions to EP than the summation of individual factors. Further analyses indicate strong zonal circulation at 500 hPa, 1000 hPa potential height increases airflow from west to east. And the weakening of the East Asian Summer Monsoon accompanied by the westward extension of the western Pacific subtropical high and the increase in Mongolian anticyclone activity all bring sufficient exogenous water vapor from the North Atlantic and Indian Ocean to the TMCA.

Climate change in the High Mountain Asia in CMIP6

Abstract. Climate change over High Mountain Asia (HMA, including the Tibetan Plateau) is investigated over the period 1979–2014 and in future projections following the four Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. The skill of 26 Coupled Model Intercomparison Project phase 6 (CMIP6) models is estimated for near-surface air temperature, snow cover extent and total precipitation, and 10 of them are used to describe their projections until 2100. Similarly to previous CMIP models, this new generation of general circulation models (GCMs) shows a mean cold bias over this area reaching −1.9 [−8.2 to 2.9] ∘C (90 % confidence interval) in comparison with the Climate Research Unit (CRU) observational dataset, associated with a snow cover mean overestimation of 12 % [−13 % to 43 %], corresponding to a relative bias of 52 % [−53 % to 183 %] in comparison with the NOAA Climate Data Record (CDR) satellite dataset. The temperature and snow cover model biases are more pronounced in winter. Simulated precipitation rates are overestimated by 1.5 [0.3 to 2.9] mm d−1, corresponding to a relative bias of 143 % [31 % to 281 %], but this might be an apparent bias caused by the undercatch of solid precipitation in the APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) observational reference. For most models, the cold surface bias is associated with an overestimation of snow cover extent, but this relationship does not hold for all models, suggesting that the processes of the origin of the biases can differ from one model to another. A significant correlation between snow cover bias and surface elevation is found, and to a lesser extent between temperature bias and surface elevation, highlighting the model weaknesses at high elevation. The models with the best performance for temperature are not necessarily the most skillful for the other variables, and there is no clear relationship between model resolution and model skill. This highlights the need for a better understanding of the physical processes driving the climate in this complex topographic area, as well as for further parameterization developments adapted to such areas. A dependency of the simulated past trends on the model biases is found for some variables and seasons; however, some highly biased models fall within the range of observed trends, suggesting that model bias is not a robust criterion to discard models in trend analysis. The HMA median warming simulated over 2081–2100 with respect to 1995–2014 ranges from 1.9 [1.2 to 2.7] ∘C for SSP1-2.6 to 6.5 [4.9 to 9.0] ∘C for SSP5-8.5. This general warming is associated with a relative median snow cover extent decrease from −9.4 % [−16.4 % to −5.0 %] to −32.2 % [−49.1 % to −25.0 %] and a relative median precipitation increase from 8.5 % [4.8 % to 18.2 %] to 24.9 % [14.4 % to 48.1 %] by the end of the century in these respective scenarios. The warming is 11 % higher over HMA than over the other Northern Hemisphere continental surfaces, excluding the Arctic area. Seasonal temperature, snow cover and precipitation changes over HMA show a linear relationship with the global surface air temperature (GSAT), except for summer snow cover which shows a slower decrease at strong levels of GSAT.

Evaluation of Extreme Precipitation Based on Three Long-Term Gridded Products over the Qinghai-Tibet Plateau

Accurate estimates of extreme precipitation events play an important role in climate change studies and natural disaster risk assessments. This study aimed to evaluate the capability of the China Meteorological Forcing Dataset (CMFD), Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), and Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) to detect the spatiotemporal patterns of extreme precipitation events over the Qinghai-Tibet Plateau (QTP) in China, from 1981 to 2014. Compared to the gauge-based precipitation dataset obtained from 101 stations across the region, 12 indices of extreme precipitation were employed and classified into three categories: fixed threshold, station-related threshold, and non-threshold indices. Correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), and Kling–Gupta efficiency (KGE), were used to assess the accuracy of extreme precipitation estimation; indices including probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were adopted to evaluate the ability of gridded products’ to detect rain occurrences. The results indicated that all three gridded datasets showed acceptable representation of the extreme precipitation events over the QTP. CMFD and APHRODITE tended to slightly underestimate extreme precipitation indices (except for consecutive wet days), whereas CHIRPS overestimated most indices. Overall, CMFD outperformed the other datasets for capturing the spatiotemporal pattern of most extreme precipitation indices over the QTP. Although CHIRPS had lower levels of accuracy, the generated data had a higher spatial resolution, and with correction, it may be considered for small-scale studies in future research.

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

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.

Impacts of the Madden–Julian oscillation on precipitation extremes in Indonesia

AbstractThe influence of the Madden–Julian oscillation (MJO) on the precipitation extremes in Indonesia during the rainy season (October to April) has been evaluated using the daily station rain gauge data from 1987 to 2017 and the gridded Asian Precipitation–Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) from 1998 to 2015 for different phases of the MJO. The results show that MJO significantly modulates the frequency of extreme precipitation events in Indonesia, with the magnitude of the impact varying across regions. Specifically, the convectively active (suppressed) MJO increases (decreases) the probability of extreme precipitation events over the western and central parts of Indonesia by up to 70% (40%). In the eastern part of Indonesia, MJO increases (decreases) extreme precipitation probability by up to 50% (40%). We attribute the differences in the probability of extreme precipitation events to the changes in the horizontal moisture flux convergence induced by MJO. The results indicate that the MJO provides the source of predictability of daily extreme precipitation in Indonesia.

Performance of various gridded temperature and precipitation datasets over Northwest Himalayan Region

This study evaluated the performance of 07 gridded datasets viz. Asian Precipitation Highly-resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE), Climate Research Unit Time-Series (CRU-TS), University of Delaware (UDEL), Tropical rainfall Measurement Mission (TRMM)/ TMPA (TRMM Multi-Satellite Precipitation Analysis), Global Precipitation Climatology Centre (GPCC), Princeton Global Forcings Dataset (PGF), and European Reanalysis Interim (ERA-I) in capturing the amount, seasonality and trend of precipitation over different climatic zones of Northwestern Himalaya (NWH) i.e. Lower Himalaya (LH), Greater Himalaya (GH) and Karakoram Himalaya (KH). A similar comparison was also done for the temperature data but only with 05 datasets, viz. APHRODITE, CRU-TS, PGF, UDEL and ERA-I since TMPA and GPCC are precipitation datasets only. This study is a maiden attempt where in situ observation includes the data from elevations above 5000 m amsl (07 observatories) in NWH (Indian sub-region). Results reveal that for precipitation over NWH; ERA-I, GPCC, and TMPA/TRMM were found to be quite reliable datasets. For temperature, all datasets performed quite well but CRU-TS and ERA-I provided more reliable estimates. The mean absolute error ranged from 13.5 mm/month to 150.7 mm/month for precipitation and 0.75°C/month to 9.9°C/month for temperature. High values of the errors underpin the need for bias correction. On the basis of this analysis, monthly correction factors for wintertime temperature and precipitation have also been suggested for each dataset which when multiplied with corresponding datasets would result in closely approximated values for the area of interest. These results can serve as a guide for bias correction and selection of appropriate gridded datasets for use in studies pertaining to hydrological modeling over NWH.

Evaluation of various spatial rainfall datasets for streamflow simulation using SWAT model of Wunna basin, India

ABSTRACT The spatially distributed rainfall datasets are the most appropriate input for distributed/semi-distributed hydrological model, and there is a less probability of uncertainty in this model. The output of rain gauge results in a high probability of uncertainty in models. The focus of the study is to check the feasibility of different rainfall datasets in hydrological modelling. In this current study, satellite-based four rainfall datasets are adopted. The available high resolution, Tropical Rainfall Measuring Mission (TRMM), and National Centers for Environmental Prediction (NCEP) analysis derived Climate Forecast System Reanalysis (CFSR), The Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) and Global Precipitation Climatology Centre (GPCP) rainfall datasets were used to evaluate the performance of the SWAT model. The performance evaluation of the SWAT model was checked with four rainfall datasets using the coefficient of determination (R 2) and the coefficient of Nash-Sitcliff efficiency (NSE). It was found that TRMM, GPCP and APHRODITE rainfall datasets could be an alternative source, where APHRODITE is not suitable for the hydrological process because of weak %wet days. The rainfall data sets are used to forecast accurate surface runoff.

Inter-Annual Variability of Winter Precipitation Over Nepal Coupled With Ocean-Atmospheric Patterns During 1987–2015

Nepal is a mountainous country located on the southern slope of the central Himalayas, where ~3% of the total annual rainfall occurs in the winter season. The inter-annual variability and shift in the winter precipitation are of great importance in space and time, however, has not been investigated in Nepal. This study sought to investigate the inter-annual variability of winter precipitation (DJF), below- and above-average winter precipitation anomalies over Nepal and their controlling factors associated with ocean-atmospheric circulation patterns during the historical period of 1987–2015. Here, we used Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE), Climate Prediction Center (CPC) database of National Centres for Environmental Prediction (NCEP) and Japan Marine Science and Technology Centre (JAMSTEC) data from different sources to study the Empirical Orthogonal Function (EOF), Wavelet, Composite, and Correlation analysis, respectively. The results show that the leading EOF mode captures 62.1% of the total variance of precipitation exhibiting a single mode of variability. As determined by wavelet analysis, the winter precipitation of Nepal shows 2 to 2.6 year’s power spectrum for 29 years (1987–2015). The time series of winter precipitation anomalies revealed the years 1996, 1998, 2000, 2005, 2007, and 2008 as precipitation deficit years which is supported by the composite analysis showing negative precipitation anomalies with positive outgoing longwave radiation (OLR) and vertical velocity. These phenomena are converse for wet events during 1988, 1995, 1997, 2002, 2012, and 2014. The wet years exhibit moisture convergence with strong south-westerly wind anomalies originated from the Arabian Sea. The dry events reveal moisture divergence with weaker north-westerly wind anomalies. The temperature gradient over Tibetan Plateau is linked with a shift in precipitation regime over Nepal. The further finding indicates that preceding summer monsoon (JJAS) and post-monsoon (ON) warming over the Indo-Pacific Ocean influences the following year's winter precipitation over Nepal. The observed changes in the dry and wet years during winter are useful for disaster preparedness and the planning and monitoring of water resources and agriculture.

Evaluation of corrected APHRODITE estimates for hydrological simulation in the Yarlung Tsangpo–Brahmaputra River Basin

AbstractPrecipitation is one of the most important inputs for hydrological simulation because it controls water balance, and accurate representation of rainfall distribution is essential for hydrological simulation. In the Yarlung Tsangpo–Brahmaputra River Basin (YBRB), gauge‐based gridded datasets have difficulties in representing complex orographic precipitation as the rainfall stations are too sparse to capture accurate precipitation patterns. This study used Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) estimates to drive the Soil and Water Assessment Tool (SWAT) model in the YBRB. Bias correction on APHRODITE estimates was achieved through linear scaling based on a denser network of gauges. In addition, the elevation bands method was implemented in SWAT to reproduce spatial precipitation by considering orographic effects. The results showed that original APHRODITE data tended to systematically underestimate rainy season precipitation with considerable underestimation on windward slopes of the Himalayas. Corrected APHRODITE data increased precipitation estimates and were able to better represent the spatial patterns of precipitation. During the calibration and validation periods, the mean monthly Nash–Sutcliffe efficiency (NSE) of the corrected APHRODITE‐simulation were 0.86 and 0.82, respectively, which were much higher than those of the original APHRODITE‐simulation (0.79 and 0.72), indicating the reliability of corrected APHRODITE estimates in driving hydrological model. In addition, although the elevation bands method has some limitations induced by the constant precipitation lapse rate, it was also found that this method could improve the accuracy of hydrological simulation. In large transboundary sparsely gauged river basins, implementation of bias correction and elevation bands can improve the representation of spatial precipitation of gridded gauge‐based rainfall products, which may be beneficial for hydrological simulation and water resources management.

Reliability of reanalysis and remotely sensed precipitation products for hydrological simulation over the Sefidrood River Basin, Iran

ABSTRACTHydrological models require different inputs for the simulation of processes, among which precipitation is essential. For hydrological simulation, four different precipitation products – Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE); European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim); Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) real time (RT); and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) – are compared against ground-based datasets. The variable infiltration capacity (VIC) model was calibrated for the Sefidrood River Basin (SRB), Iran. APHRODITE and ERA-Interim gave better rainfall estimates at daily time scale than other products, with Nash-Sutcliffe efficiency (NSE) values of 0.79 and 0.63, and correlation coefficient (CC) of 0.91 and 0.82, respectively. At the monthly time scale, the CC between all rainfall datasets and ground observations is greater than 0.9, except for TMPA-RT. Hydrological assessment indicates that PERSIANN is the best rainfall dataset for capturing the streamflow and peak flows for the studied area (CC: 0.91, NSE: 0.80).

Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia

Abstract Reliable prediction of rainfall extremes is vital for disaster management, particularly in the context of increasing rainfall extremes due to global climate change. Physical-empirical models have been developed in this study using three widely used Machine Learning (ML) methods namely, Support Vector Machines (SVM), Random Forests (RF), Bayesian Artificial Neural Networks (BANN) for the prediction of rainfall and rainfall related extremes during Northeast Monsoon (NEM) in Peninsular Malaysia from synoptic predictors. The gridded daily rainfall data of Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) was used to estimate four rainfall indices namely, rainfall amount, average rainfall intensity, days having >95-th percentile rainfall, and total number of dry days in Peninsular Malaysia during NEM for the period 1951–2015. The National Centers for Environmental Prediction (NCEP) reanalysis sea level pressure (SLP) data was used for the prediction of rainfall indices with different lead periods. The recursive feature elimination (RFE) method was used to select the SLP at different NCEP grid points which were found significantly correlated with NEM rainfall indices. The results showed superior performance of BANN among the ML models with normalised root mean square error of 0.04–0.14, Nash-Sutcliff Efficiency of 0.98–1.0, and modified agreement index of 0.97–0.99 and Kling-Gupta efficient index 0.65–0.96 for one-month lead period prediction. The 95% confidence interval (CI) band for BANN was found narrower than the other ML models. Almost all the forecasted values by BANN were also found with 95% CI, and therefore, the p-factor and the r-factor for BANN in predicting rainfall indices were found in the range of 0.95–1.0 and 0.25–0.49 respectively. Application of BANN in prediction of rainfall indices with higher lead time was also found excellent. The synoptic pattern revealed that SLP over the north of South China Sea is the major driver of NEM rainfall and rainfall extremes in Peninsular Malaysia.

Evaluating Suitability of Multiple Precipitation Products for the Lancang River Basin

Global reanalysis precipitation products could provide valuable meteorological information for flow forecasting in poorly gauged areas, helping to overcome a long-standing challenge in the field. But not all data sources are suitable for all regions or perform the same way in hydrological modeling, so it is essential to test the suitability of precipitation products before applying them. In this study, five widely used global high-resolution precipitation products—Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR), Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), China Gauge-based Daily Precipitation Analysis developed by China Meteorological Administration (CMA) and Agricultural Model Intercomparison and Improvement Project based on the NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA)—were evaluated using statistical methods and a hydrological approach for their suitability for the Lancang River Basin. The results indicated that APHRODITE, CMA, AgMERRA and CHIRPS were more accurate precipitation indicators than NCEP-CFSR in terms of the multiyear average and seasonal spatial distribution pattern, all of the CHIRPS, AgMERRA and APHRODITE perform better than CMA and NCEP-CFSR at the small, medium and high precipitation intensities ranges in subbasin11 and sunbabsin46. All five products performed better in subbasin46 (a low-altitude region) than in subbasin11 (a high-altitude region) on the daily and monthly scales. In addition to NCEP-CFSR, the other four products all presented encouraging potential for streamflow simulation at daily (Yunjinghong) and monthly (Yunjinghong, Jiuzhou and Gajiu) scale. Hydrological simulations forced with APHRODITE were the best of the five for the Yunjinghong station in capturing daily and monthly measured streamflow. Except for NCEP-CFSR, all products were very good for hydrological simulations for the Gajiu and Jiuzhou stations.

Ranking of CMIP5 GCM Skills in Simulating Observed Precipitation over the Lower Mekong Basin, Using an Improved Score-Based Method

This study assessed the performances of 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) in reproducing observed precipitation over the Lower Mekong Basin (LMB). Observations from gauge-based data of the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) precipitation data were obtained from 1975 to 2004. An improved score-based method was used to rank the performance of the GCMs in reproducing the observed precipitation over the LMB. The results revealed that most GCMs effectively reproduced precipitation patterns for the mean annual cycle, but they generally overestimated the observed precipitation. The GCMs showed good ability in reproducing the time series characteristics of precipitation for the annual period compared to those for the wet and dry seasons. Meanwhile, the GCMs obviously reproduced the spatial characteristics of precipitation for the dry season better than those for annual time and the wet season. More than 50% of the GCMs failed to reproduce the positive trend of the observed precipitation for the wet season and the dry season (approximately 52.9% and 64.7%, respectively), and approximately 44.1% of the GCMs failed to reproduce positive trend for annual time over the LMB. Furthermore, it was also revealed that there existed different robust criteria for assessing the GCMs’ performances at a seasonal scale, and using multiple criteria was superior to a single criterion in assessing the GCMs’ performances. Overall, the better-performed GCMs were obtained, which can provide useful information for future precipitation projection and policy-making over the LMB.

Assessing reliability of precipitation data over the Mekong River Basin: A comparison of ground‐based, satellite, and reanalysis datasets

Accurate precipitation data are the basis for hydro‐climatological studies. As a highly populated river basin, with the biggest inland fishery in Southeast Asia, freshwater dynamics is extremely important for the Mekong River Basin (MB). This study focuses on evaluating the reliability of existing gridded precipitation datasets both from satellite and reanalysis, with a ground observations‐based gridded precipitation dataset as the reference. Two satellite products (Tropical Rainfall Measuring Mission [TRMM] and the Precipitation Estimation from Remote Sensing Information using an Artificial Neural Network—Climate Data Record [PERSIANN‐CDR]), as well as three reanalysis products (Modern‐Era Retrospective analysis for Research and Applications [MERRA2], the European Centre for Medium‐Range Weather Forecasts interim reanalysis [ERA‐Interim], and the Climate Forecast System Reanalysis [CFSR]) were compared with the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over the MB. The APHRODITE was chosen as the reference for the comparison because it was developed based on ground observations and has also been selected as reference data in previous studies. Results show that most of the assessed datasets are able to capture the major climatological characteristics of precipitation in the MB for the 10‐year study period (1998–2007). Generally, both satellite data (TRMM and PERSIANN‐CDR) show higher reliability than reanalysis products at both spatial and temporal scales across the MB, with the TRMM outperforming when compared to the PERSIANN‐CDR. For the reanalysis products, MERRA2 is more reliable in terms of temporal variability, but with some underestimation of precipitation. The other two reanalysis products CFSR and ERA‐Interim are relatively unreliable due to large overestimations. CFSR is better positioned to capture the spatial variability of precipitation, while ERA‐Interim shows inconsistent spatial patterns but more realistically resembles the daily precipitation probability. These findings have practical implications for future hydro‐climatological studies.

A Decadal Historical Satellite Data and Rainfall Trend Analysis (2001–2016) for Flood Hazard Mapping in Sri Lanka

Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) gridded rainfall data with flood maps derived from Synthetic Aperture Radar (SAR) and multispectral satellite to arrive at holistic spatio-temporal patterns of floods in Sri Lanka. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data were used to map flood extents for emergency relief operations while eight-day Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data for the time period from 2001 to 2016 were used to map long term flood-affected areas. The inundation maps produced for rapid response were published within three hours upon the availability of satellite imagery in web platforms, with the aim of supporting a wide range of stakeholders in emergency response and flood relief operations. The aggregated time series of flood extents mapped using MODIS data were used to develop a flood occurrence map (2001–2016) for Sri Lanka. Flood hotpots identified using both optical and synthetic aperture average of 325 km2 for the years 2006–2015 and exceptional flooding in 2016 with inundation extent of approximately 1400 km2. The time series rainfall data explains increasing trend in the extreme rainfall indices with similar observation derived from satellite imagery. The results demonstrate the feasibility of using multi-sensor flood mapping approaches, which will aid Disaster Management Center (DMC) and other multi-lateral agencies involved in managing rapid response operations and preparing mitigation measures.

Evaluation of five gridded rainfall datasets in simulating streamflow in the upper Dong Nai river basin, Vietnam

ABSTRACTRainfall data with an appropriate spatial resolution is a key input to hydrological models. However, networks of rain gauges are often sparsely and unevenly distributed in large catchments, especially in developing countries. High-resolution rainfall datasets, such as the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Forecast System Reanalysis (CFSR), the Climatic Research Unit Time Series (CRU-TS), the Global Precipitation Climatology Centre (GPCC) and the Tropical Rainfall Measuring Mission (TRMM), have become available to overcome such limitations. The objective of this study was to evaluate the impacts of four land-based rainfall products (APHRODITE, CFSR, CRU-TS, and GPCC) and a satellite-based rainfall product (TRMM) on streamflow of the upper catchment of Tri An reservoir in Vietnam using the Hydrological Modeling System (HEC-HMS). In addition, the available rain gauges data were used for comparison purpose. Result indicates that the TRMM and GPCC data show their best match to rain gauges data in simulating the streamflow in the period 1999–2007. Generally, the results indicate that the TRMM and GPCC data could be alternative solutions.