Assessment of IMERG-V06, TRMM-3B42V7, SM2RAIN-ASCAT, and PERSIANN-CDR Precipitation Products over the Hindu Kush Mountains of Pakistan, South Asia

Hamza, Ali; Anjum, Muhammad Naveed; Cheema, Muhammad Jehanzeb Masud; Chen, Xi; Afzal, Arslan; Azam, Muhammad; Shafi, Muhammad Kamran; Gulakhmadov, Aminjon

doi:10.3390/rs12233871

Cited by 36 publications

(26 citation statements)

References 56 publications

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“…Calibrated models could misrepresent the uncertainty as the calibration process may disguise such uncertainty in forcing dataset (Biemans et al., 2009; Schmied et al., 2014). Precipitation is amongst the most important forcing data that is subject to major uncertainties due to climate and topographic complexities (Faridzad et al., 2018; Hamza et al., 2020; Henderson‐Sellers et al., 1993; Tang et al., 2018) and directly affects water balance (Bárdossy & Das, 2008; Nilsson et al., 2005; Wang et al., 2016). Particularly for the MRB, studies have suggested that over 65% of the streamflow variations in parts of the basin can be attributed to the variations in precipitation alone (Fan & He, 2015).…”

Section: Introductionmentioning

confidence: 99%

On the Precipitation‐Induced Uncertainties in Process‐Based Hydrological Modeling in the Mekong River Basin

Kabir

Pokhrel

Felfelani

2022

Water Resources Research

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Numerous studies have examined the reliability of various precipitation products over the Mekong River Basin (MRB) and modeled its basin hydrology. However, there is a lack of comprehensive studies on precipitation‐induced uncertainties in hydrological simulations using process‐based land surface models. This study examines the propagation of precipitation uncertainty into hydrological simulations over the entire MRB using the Community Land Model version 5 (CLM5) at a high spatial resolution of 0.05° (∼5 km) and without any parameter calibration. Simulations conducted using different precipitation datasets are compared to investigate the discrepancies in streamflow, terrestrial water storage (TWS), soil moisture, and evapotranspiration (ET) caused by precipitation uncertainty. Results indicate that precipitation is a key determinant of simulated streamflow in the MRB; peak flow and soil moisture are particularly sensitive to precipitation input. Further, precipitation data with a higher spatial resolution did not improve the simulations, contrary to the common perception that using meteorological forcing with higher spatial resolution would improve hydrological simulations. In addition, since high flow indicators are particularly influenced by precipitation data, the choice of precipitation data could directly impact flood pulse simulations in the MRB. Notable differences are also found among TWS, soil moisture, and ET simulated using different precipitation products. Moreover, TWS, soil moisture, and ET exhibit a varying degree of sensitivity to precipitation uncertainty. This study provides crucial insights on precipitation‐induced uncertainties in process‐based hydrological modeling and uncovers these uncertainties in the MRB.

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Section: Introductionmentioning

confidence: 99%

On the Precipitation‐Induced Uncertainties in Process‐Based Hydrological Modeling in the Mekong River Basin

Kabir

Pokhrel

Felfelani

2022

Water Resources Research

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“…Despite the fact that these most recent global SRPs are freely available at precise spatial and temporal resolutions, their performance differs from one location to the next across the globe [12,13]. The literature review revealed that the performance evaluation of SRPs is essential before their direct application in any region [7,[14][15][16]. In this regard, several researchers have evaluated the accuracy of different SRPs in several countries-for instance, in America [17], Brazil [18], China [19][20][21], Italy [22], Iran [23], Malaysia [24], Pakistan [14], Taiwan [25], and Saudi Arabia [26].…”

Section: Introductionmentioning

confidence: 99%

Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

Anjum

Irfan

Waseem

et al. 2022

Water

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This study compares the performance of four satellite-based rainfall products (SRPs) (PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0) in a semi-arid subtropical region. As a case study, Punjab Province of Pakistan was considered for this assessment. Using observations from in-situ meteorological stations, the uncertainty in daily, monthly, seasonal, and annual rainfall estimates of SRPs at pixel and regional scales during 2010–2018 were examined. Several evaluation indices (Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias, and relative Bias (rBias), as well as categorical indices (Probability of Detection (POD), Critical Success Index (CSI), and False Alarm Ration (FAR)) were used to assess the performance of the SRPs. The following findings were found: (1) CHIRPS-2.0 and SM2RAIN-ASCAT products were capable of tracking the spatiotemporal variability of observed rainfall, (2) all SRPs had higher overall performances in the northwestern parts of the province than the other parts, (3) all SRP estimates were in better agreement with ground-based monthly observations than daily records, and (4) on the seasonal scale, CHIRPS-2.0 and SM2RAIN-ASCAT were better than PERSIANN-CCS and PERSIANN. In all seasons, CHIRPS-2.0 and SM2RAIN-ASCAT outperformed PERSIANN-CCS and PERSIANN-CDR. Based on our findings, we recommend that hydrometeorological investigations in Pakistan’s Punjab Province employ monthly estimates of CHIRPS-2.0 and SM2RAIN-ASCAT products.

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“…Precipitation data with high accuracy and high spatial resolution play significant roles in forcing local and global climate models and hydrological and ecological models. Studies have shown that the performance of IMERG products is better than those of some other satellite-based precipitation datasets [25,50]. However, in some basins and localized areas, the IMERG products are still too coarse to be applied.…”

Section: Discussionmentioning

confidence: 99%

An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed

Zhao

2021

Remote Sensing

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Satellites are capable of observing precipitation over large areas and are particularly suitable for estimating precipitation in high mountains and poorly gauged regions. However, the coarse resolution and relatively low accuracy of satellites limit their applications. In this study, a downscaling scheme was developed to obtain precipitation estimates with high resolution and high accuracy in the Heihe watershed. Shannon’s entropy, together with a semi-variogram, was applied to establish the optimal precipitation station network. A combination of the random forest (RF) method and the residual correction approach with the established rain gauge network was applied to downscale monthly precipitation products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results indicated that the RF model showed little improvement in the accuracy of IMERG-based precipitation downscaling. Including residual modification could improve the results of the RF model. The mean absolute error (MAE) and root mean square error (RMSE) values decreased by 19% and 21%, respectively, after residual corrections were added to the RF approach. Moreover, we found that enough rain gauge records are necessary for and remain an important component of tuning model performance. The application of more rain gauges improves the performance of the combined RF and residual modification methods, with the MAE and RMSE values reduced by 8% and 9%, respectively. Residual correction, together with enough precipitation stations, can effectively enhance the quality of the precipitation patterns and magnitudes obtained in the RF downscaling process. The proposed downscaling scheme is an effective tool for increasing the accuracy and spatial resolution of precipitation fields in the Heihe watershed.

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Assessment of IMERG-V06, TRMM-3B42V7, SM2RAIN-ASCAT, and PERSIANN-CDR Precipitation Products over the Hindu Kush Mountains of Pakistan, South Asia

Cited by 36 publications

References 56 publications

On the Precipitation‐Induced Uncertainties in Process‐Based Hydrological Modeling in the Mekong River Basin

On the Precipitation‐Induced Uncertainties in Process‐Based Hydrological Modeling in the Mekong River Basin

Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

An Efficient Downscaling Scheme for High-Resolution Precipitation Estimates over a High Mountainous Watershed

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