Performance evaluation and bias correction of gridded precipitation products over Arun River Basin in Nepal for hydrological applications

Dangol, Sabin; Talchabhadel, Rocky; Pandey, Vishnu Prasad

doi:10.1007/s00704-022-04001-y

Cited by 19 publications

(7 citation statements)

References 56 publications

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“…Increase of POD, CSI and FBI over orographic MM and HM region in IMERGV7 against IMERGV6 can be attributed to above facts. Compared to all MPEs, PERSIANN showed the worst performance with severe underestimation, retaining the characteristics of its parent datasets PERSIANN‐CCS and PERSIANN‐CDR as in past studies (Dangol et al., 2022; Derin et al., 2016; Du et al., 2022; Ghomlaghi et al., 2022; Xiao et al., 2020). It is attributed to the fact that, cloud classification scheme of PERSIANN mainly relies on manually defined features to extract cloud information.…”

Section: Discussionsupporting

confidence: 70%

“…Sun et al, 2018), regional (Bytheway et al, 2023;Hu et al, 2016;Satgé et al, 2020), national (Aksu & Akgül, 2020;Du et al, 2022;Li et al, 2022;Liu et al, 2022;Nepal et al, 2021;W. Xie et al, 2022), and basin scales (Adhikari & Behrangi, 2022;Dangol et al, 2022;Gu et al, 2022;J. Chen et al, 2020;Khadka et al, 2022;Kumar et al, 2021;.…”

Section: Introductionunclassified

“…Until now, many evaluation studies of such datasets have been conducted at global (Beck et al., 2017; Derin et al., 2019; Li et al., 2021; Q. Sun et al., 2018), regional (Bytheway et al., 2023; Hu et al., 2016; Satgé et al., 2020), national (Aksu & Akgül, 2020; Y. Chen et al., 2021; Du et al., 2022; Li et al., 2022; Liu et al., 2022; Nepal et al., 2021; Sharma, Khadka, et al., 2020; W. Xie et al., 2022), and basin scales (Adhikari & Behrangi, 2022; Dangol et al., 2022; Gu et al., 2022; J. Chen et al., 2020; Khadka et al., 2022; Kumar et al., 2021; W. Wang et al., 2020). Most of them have uncovered uncertainties in the measurements, particularly when dealing with complex terrain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

Nepal,

Bao,

et al. 2024

JGR Atmospheres

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Sub‐seasonal to seasonal (S2S) prediction provides an extended range of lead time for decision‐makers across multiple sectors. S2S forecast is crucial for a country that are susceptible to hydro‐meteorological disasters like Nepal. However, S2S forecast requires assessment with reliable datasets before its application. Since, Nepal lacks a dense station network, multi‐source precipitation estimates (MPEs) are the obvious alternatives. Therefore, using classical evaluation metrics and extreme precipitation indices, this study assessed eleven high‐resolution datasets against 159‐gauge stations over Nepal for the 2001–2020 period. These datasets are classified as gauge‐interpolated (APHRODITE), merged (TPMFD, MSWEP, CLDAS, and CHIRPS), satellite‐based (IMERGV7, IMERGV6, CMORPH, and PERSIANN), and reanalysis (ERA5‐L and HAR). Satellite datasets (except IMERGV7) failed to capture the spatial pattern of mean annual precipitation, while others broadly captured it. Most MPEs struggled to accurately estimate wet season precipitation compared to dry season. Furthermore, increasing estimation error from light to extreme precipitation and decreasing skill metrics from flat to complex terrain, demonstrate the intensity and terrain‐specific limitations of MPEs. In terms of precipitation extremes, APHRODITE exhibits the highest skill, followed by MSWEP, TPMFD, HAR, ERA5‐L, IMERGV7, CLDAS, IMERGV6, CHIRPS, CMORPH, and PERSIANN. IMERGV7 exhibits increased skill in detecting extreme precipitation and precipitation over orography against IMERGV6. APHRODITE and TPMFD datasets performed consistently well in all scales, including weekly anomaly, with an average anomaly correlation of 0.84 and 0.66 respectively. However, since APHRODITE is not widely available, TPMFD can serve as a benchmark dataset for evaluating high‐resolution S2S forecasts within the study region.

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Section: Discussionsupporting

confidence: 70%

Section: Introductionunclassified

Section: Introductionmentioning

confidence: 99%

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Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

Nepal,

Bao,

et al. 2024

JGR Atmospheres

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“…All available ESMs with outputs of historical and future (SSP126 and SSP585) fuel availability (i.e., biomass of coarse wood debris, vegetation, and litter) and climate variables (Table 2) (Enfield et al, 1999(Enfield et al, , 2021 were selected, including ACCESS-ESM1-5 (Ziehn et al, 2020), CESM2 (Danabasoglu et al, 2020), NorESM2-LM (Seland et al, 2020), NorESM2-MM (Seland et al, 2020), and TaiESM1 (Y. C. Wang et al, 2021). For each ESM, the variable bias was corrected with the mostly used linear scaling method (Maraun, 2016;Dangol et al, 2022;Shrestha et al, 2017), which adjusted the bias in model simulations based on the ratio of modeled-and observed-variable mean value. Then the bias-corrected variables of each ESM were used to drive the AttentionFire model for future burnedarea projection.…”

Section: Datasets and Experimentsmentioning

confidence: 99%

AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics

Zhu

Riley

et al. 2023

Geosci. Model Dev.

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Abstract. African and South American (ASA) wildfires account for more than 70 % of global burned areas and have strong connection to local climate for sub-seasonal to seasonal wildfire dynamics. However, representation of the wildfire–climate relationship remains challenging due to spatiotemporally heterogenous responses of wildfires to climate variability and human influences. Here, we developed an interpretable machine learning (ML) fire model (AttentionFire_v1.0) to resolve the complex controls of climate and human activities on burned areas and to better predict burned areas over ASA regions. Our ML fire model substantially improved predictability of burned areas for both spatial and temporal dynamics compared with five commonly used machine learning models. More importantly, the model revealed strong time-lagged control from climate wetness on the burned areas. The model also predicted that, under a high-emission future climate scenario, the recently observed declines in burned area will reverse in South America in the near future due to climate changes. Our study provides a reliable and interpretable fire model and highlights the importance of lagged wildfire–climate relationships in historical and future predictions.

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“…The extracted rainfall data under SSP2-4.5 and SSP5-8.5 were corrected using linear bias correction factors. Usually, the data extracted from climate models may have some systematic errors [61]. Therefore, the model's extracted climate data are corrected for bias using the ground-measured climate data.…”

Section: F Bias Correctionmentioning

confidence: 99%

Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka

Rathnayake

Chathuranika

et al. 2023

IEEE Access

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The Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFISbased model outperforms the other algorithms. The correlation coefficient of each algorithm's predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.

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Performance evaluation and bias correction of gridded precipitation products over Arun River Basin in Nepal for hydrological applications

Cited by 19 publications

References 56 publications

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics

Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka

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