Evaluating empirical and machine learning approaches for reference evapotranspiration estimation using limited climatic variables in Nepal

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104254 - 104254

Опубликована: Фев. 1, 2025

Язык: Английский

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Climate change impacts on flood dynamics and seasonal flow variability in central Nepal: the Kaligandaki River Basin case

Theoretical and Applied Climatology, Год журнала: 2025, Номер 156(3)

Опубликована: Фев. 11, 2025

Язык: Английский

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Integrating Satellite-Based Precipitation Analysis: A Case Study in Norfolk, Virginia

Eng—Advances in Engineering, Год журнала: 2025, Номер 6(3), С. 49 - 49

Опубликована: Март 6, 2025

In many developing cities, the scarcity of adequate observed precipitation stations, due to constraints such as limited space, urban growth, and maintenance challenges, compromises data reliability. This study explores use satellite-based products (SbPPs) a solution supplement missing over long term, thereby enabling more accurate environmental analysis decision-making. Specifically, effectiveness SbPPs in Norfolk, Virginia, is assessed by comparing them with from Norfolk International Airport (NIA) using common bias adjustment methods. The applies three different methods correct biases caused sensor limitations calibration discrepancies then identifies most effective based on statistical indicators, detection capability indices, graphical Bias include additive correction (ABC), which subtracts systematic errors; multiplicative (MBC), scales satellite match data; distribution transformation normalization (DTN), aligns observations. Additionally, addresses uncertainties for estimating precipitation, preparing practitioners challenges practical applications. (ABC) method overestimated mean monthly while PERSIANN-Cloud Classification System (CCS), adjusted was found be bias-adjusted model. MBC resulted slight PBias adjustments 0.09% 0.10% (CDR), 0.15% (PERSIANN) estimates, DTN produced larger 21.36% 31.74% 19.27% (PERSIANN), CCS, when corrected MBC, identified SbPP Virginia. case not only provides insights into technical processes but also serves guideline integrating advanced hydrological modeling resilience strategies, contributing improved strategies climate change adaptation disaster preparedness.

Язык: Английский

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Multi-Model Assessment to Analyze Flow Alteration Under the Changing Climate in a Medium-Sized River Basin in Nepal: A Case Study of the Kankai River Basin

Water, Год журнала: 2025, Номер 17(7), С. 940 - 940

Опубликована: Март 24, 2025

The medium river basins (MRBs) in Nepal originate from mid-hills. These medium-range rivers are typically non-snow-fed, relying on rain and other water sources. small, the sizes of vary between 500 5000 km2. MRBs often used for irrigation agricultural purposes. In this analysis, we first set up, calibrated, validated three hydrological models (i.e., HBV, HEC HMS, SWAT) at Kankai River Basin (one MRB eastern Nepal). Then, best-performing SWAT model was forced with cutting-edge climate (CMs) using thirteen CMIP6 under four shared socioeconomic pathways (SSPs). We employed ten bias correction (BC) methods to capture local spatial variability precipitation temperature. Finally, likely streamflow alteration during two future periods, i.e., near-term timeframe (NF), spanning 2031 2060, long-term (FF), covering years 2071 2100, were evaluated against historical period (baseline: 1986–2014), considering uncertainties associated choice CMs, BC methods, or/and SSPs. study results confirm that there will not be any noticeable shifts seasonal variations future. However, magnitude is projected alter substantially. Overall, estimated upsurge upcoming periods. observed less deviation expected April, around +5 +7% more than baseline period. Notably, a higher percentage increment monsoon season (June–August). During NF (FF) period, flow +20% (+40%) lower SSPs, whereas +30% (+60%) SSPs high season. Thus, likelihoods flooding, inundation, discharge quite coming years.

Язык: Английский

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Multidimensional analysis of climate-induced streamflow variability using CMIP6 data and advanced modeling techniques

Geocarto International, Год журнала: 2025, Номер 40(1)

Опубликована: Май 19, 2025

Язык: Английский

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