Correction and Informed Regionalization of Precipitation Data in a High Mountainous Region (Upper Indus Basin) and Its Effect on SWAT-Modelled Discharge

Khan, Asim Jahangir; Koch, Manfred

doi:10.3390/w10111557

Cited by 41 publications

(42 citation statements)

References 92 publications

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“…Similarly, owing to the complex orography of the UIB region and to the co-action of different hydro-climatic, neither the sparse observed station data or gridded data products based on them, nor the sensors-based climatic datasets, fully represent the precipitation regime of the region [26,6,27,15] This gridded precipitation and temperature data is derived, based on all the available in-situ observations available in the UIB, through reconstruction for the periods before the mid-nineties, interpolation and correction for the orography and elevation-induced effects guided by available data for runoff, actual evapotranspiration and glacier mass-balance [7].…”

Section: Observed Datamentioning

confidence: 99%

“…Δ T (°C) and Δ P (%), the ranking (Skm) was done based on the difference ΔT (°C) or ΔP (%) shown by each member, with the percentile value, relevant to that group, The models were also evaluated for their skills in simulation the past climate during the reference period . The selected models simulations were compared to reference temperature and precipitation gridded dataset [7] and were assigned skill scores. We did not use the same method for assigning skill score to temperature and precipitation.…”

Section: Ranking Based On Changes In Climate Extremesmentioning

confidence: 99%

“…The current study considers a combination of these approaches along with utilizing improved data for the past climate in the UIB [7] and assessment of models skills in simulating the seasonal cycles in the region.…”

Section: Introductionmentioning

confidence: 99%

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Selecting and Downscaling a Set of Climate Models for Projecting Climatic Change for Impact Assessment in the Upper Indus Basin (UIB)

Khan¹,

Koch²

2018

Preprint

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This study focusses on identifying a set of representative future climate projections for the Upper Indus Basin (UIB). Although a large number of GCM’s predictor sets are nowadays available in the CMIP5 archive, the issue of their reliability for specific regions must still be confronted. This situation makes it imperative to sort out the most appropriate, single or small-ensemble set of GCMs for the assessment of climate change impacts in a region. Here a set of different approaches is adopted and applied for a step-wise shortlist and selection of appropriate climate models for the UIB under two RCPs: RCP 4.5 and RCP 8.5, based on, a) range of projected mean changes, b) range of projected extreme changes, and c) skill in reproducing the past climate. Furthermore, because of higher uncertainties in climate projection for high mountainous regions like the UIB, a wider range of future GCM climate projections is considered by using all possible future extreme scenarios (wet-warm, wet-cold, dry-warm, dry-cold). Based on this two-fold procedure, a limited number of climate models is pre-selected, out of which the final selection is done by assigning ranks to the weighted score for each of the mentioned selection criteria. The dynamically downscaled climate projections from the Coordinated Regional Downscaling Experiment (CORDEX) available for the top-ranked GCMs are further statistically downscaled (bias-corrected) over the UIB. The downscaled projections up to year 2100 indicate temperature increases ranging between 2.3 °C and 9.0 °C and precipitation changes that range, from a slight annual increase of 2.2% under the drier scenarios,  to as high as 15.9% for the wet scenarios.  Moreover, for all scenarios, the future precipitation will be more extreme, as the probability of wet days will decrease, while, at the same time, the precipitation intensities will increase. The spatial distribution of the downscaled predictors across the UIB also shows similar patterns for all scenarios, with a distinct precipitation decrease over the south-eastern parts of the basin, but an increase in the northeastern parts. These two features are particularly intense for the “Dry-Warm” and the “Median” scenarios over the late 21st century.

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Section: Observed Datamentioning

confidence: 99%

Section: Ranking Based On Changes In Climate Extremesmentioning

confidence: 99%

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Selecting and Downscaling a Set of Climate Models for Projecting Climatic Change for Impact Assessment in the Upper Indus Basin (UIB)

Khan¹,

Koch²

2018

Preprint

Self Cite

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“…SWAT is a quasi-distributed, physical-based hydrologic model developed by the Agricultural Research Service of the US Department of Agriculture to simulate water, sediment, and agricultural chemical transport at river-basin scale [27,28]. As a quasi-distributed hydrologic model, the spatial heterogeneity of the important physical properties of the watershed is delineated by first partitioning a basin or watershed into sub-basins; then further partitioning each sub-basin into hydrologic response units (HRUs) based on the land use, soil types, and topography maps.…”

Section: Swat and Swat-icnmentioning

confidence: 99%

Improved Curve Number Estimation in SWAT by Reflecting the Effect of Rainfall Intensity on Runoff Generation

Zhang

Lin

Chen

et al. 2019

Water

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Determining the amount of rainfall that will eventually become runoff and its pathway is a crucial process in hydrological modelling. We proposed a method to better estimate curve number by adding an additional component (AC) to better account for the effects of daily rainfall intensity on rainfall-runoff generation. This AC is determined by a regression equation developed from the relationship between the AC series derived from fine-tuned calibration processes and observed rainfall series. When incorporated into the Soil and Water Assessment Tool and tested in the Anxi Watershed, it is found, overall, the modified SWAT (SWAT-ICN) outperformed the original SWAT (SWAT-CN) in terms of stream flow, base flow, and annual extreme flow simulation. These models were further evaluated with the data sets of two adjacent watersheds. Similar results were achieved, indicating the ability of the proposed method to better estimate curve number.

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“…Uncertainty in the assessment of glaciers' mass balance in the Himalayas may result from the lack of in situ high-altitude precipitation (and snowfall in the accumulation areas) assessment [8], which is often tackled with proxies, such as lapse rate-based interpolation [29], gridded products (e.g., APHRODITE [32]), remote sensing estimates [21,33], and even "reverse hydrology" exercises [34][35][36]. In less monitored areas, scarce knowledge of precipitation regime in the high altitude may further hamper hydrological modeling and water budget closure [37].…”

Section: Introductionmentioning

confidence: 99%

Field Study of Mass Balance, and Hydrology of the West Khangri Nup Glacier (Khumbu, Everest)

Bocchiola

Bombelli

Camin

et al. 2020

Water

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The depiction of glaciers’ dynamics in the high altitudes of Himalaya and the hydrological fluxes therein is often limited. Although sparse seasonal (snow/ice) melt data may be available, dense precipitation networks are not available everywhere, and especially in the highest area, and the assessment of accumulation processes and mass balance may be difficult. Hydrological fluxes are little measured in the high altitudes, and few studies are available covering flow modeling and flow partitioning. Here, we investigate the snow accumulation, ice melt, and mass balance of West Khangri Nup (WKN) glacier (0.23 km2, mean altitude 5494 m asl), which is a part of the Khumbu glacier in the Everest region, where information of precipitation and hydro-glaciological dynamics in the highest altitudes was made available recently in fulfillment of several research projects. Weather, glaciological, snow pits, hydrologic, and isotopic data gathered during field campaigns (2010–2014) on the glacier and at the EVK2CNR Pyramid site were used to (i) set up the Poli-Hydro glacio-hydrological model to describe ice and snow melt and hydrological flows from the glacier, and (ii) investigate seasonal snow dynamics on this high region of the glacier. Coupling ice ablation data and Poli-Hydro simulation for ca. 5 years (January 2010–June 2014), we estimate that the WKN depleted ca. −10.46 m of ice water equivalent per year m IWE year−1 (i.e., annually ca. −2.32 meter of water equivalent per year m WE year−1). Then, using snowpack density and isotopic (δ18O) profiles on the WKN, we demonstrate that the local snowpack is recent (Fall–Winter 2013–2014) and that significant snow accumulation did not occur recently, so this area has not been a significant one of accumulation recently. Analysis of recent snow cover from LANDSAT images also confirms snow dynamics as depicted. Our study presents original data and results, and it complements present studies covering glaciers’ mass balance as well as an investigation of accumulation zones in the Everest region and the Himalayas, which is also potentially helpful in the assessment of future dynamics under ongoing climate change.

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Correction and Informed Regionalization of Precipitation Data in a High Mountainous Region (Upper Indus Basin) and Its Effect on SWAT-Modelled Discharge

Cited by 41 publications

References 92 publications

Selecting and Downscaling a Set of Climate Models for Projecting Climatic Change for Impact Assessment in the Upper Indus Basin (UIB)

Selecting and Downscaling a Set of Climate Models for Projecting Climatic Change for Impact Assessment in the Upper Indus Basin (UIB)

Improved Curve Number Estimation in SWAT by Reflecting the Effect of Rainfall Intensity on Runoff Generation

Field Study of Mass Balance, and Hydrology of the West Khangri Nup Glacier (Khumbu, Everest)

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