Evaluating the Present Annual Water Budget of a Himalayan Headwater

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Evaluating the Present Annual Water Budget of a Himalayan Headwater PUBLICATIONS Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE Evaluating the present annual water budget of a Himalayan 10.1002/2016JD026279 headwater river basin using a high-resolution Key Points: atmosphere-hydrology model • A significant disagreement in high-altitude precipitation estimates Lu Li1 , David J. Gochis2 , Stefan Sobolowski1 , and Michel D. S. Mesquita1 is found between gauge observations, TRMM, APHRODITE, and the WRF 1Uni Research Climate, Bjerknes Centre for Climate Research, Bergen, Norway, 2National Center for Atmospheric Research, • A large amount of precipitation in high mountainous areas in Beas Basin Boulder, Colorado, USA is occurring and is not properly accounted for in TRMM or APHRODITE • WRF-Hydro modeling system shows Abstract Understanding the present water budget in Himalayan Basins is a challenge due to poor in situ skill in capturing monthly discharge coverage, incomplete or unreliable records, and the limitations of coarse resolution gridded data set. In the variability and the daily discharge distribution study, a two-way coupled implementation of the Weather Research and Forecasting (WRF) Model and the WRF-Hydro hydrological modeling extension package (WRF/WRF-Hydro) was employed in its offline configuration, over a 10 year simulation period for a mountainous river basin in North India. A triple nest is employed, in which the innermost domain had 3 km for atmospheric model grids and 300 m for hydrological Correspondence to: L. Li, components. Two microphysical parameterization (MP) schemes are quantitatively evaluated to reveal how [email protected] differently MP influences orographic-related precipitation and how it impacts hydrological responses. The WRF-Hydro modeling system shows reasonable skill in capturing the spatial and temporal structure of fl Citation: high-resolution precipitation, and the resulting stream ow hydrographs exhibit a good correspondence Li, L., D. J. Gochis, S. Sobolowski, and with observation at monthly timescales, although the model tends to generally underestimate streamflow M. D. S. Mesquita (2017), Evaluating the amounts. The Thompson Scheme fits better to the observations in the study. More importantly, WRF shows present annual water budget of a “ ” Himalayan headwater river basin using that for high-altitude precipitation, a high bias is exhibited in winter precipitation from WRF, which is about a high-resolution atmosphere-hydrol- double to triple that as estimated from valley-sited rain gauges and remotely sensed precipitation estimates ogy model, J. Geophys. Res. Atmos., 122, from Tropical Rainfall Measuring Mission and Asian Precipitation - Highly-Resolved Observational Data 4786–4807, doi:10.1002/2016JD026279. Integration Towards Evaluation. Given the full annual cycle pattern and amount in high-altitude precipitation Received 21 NOV 2016 and the statistical correspondence in discharge, it is concluded that the WRF-Hydro modeling system shows Accepted 1 APR 2017 potential for explicitly predicting potential changes in the atmospheric-hydrology cycle of ungauged or Accepted article online 7 APR 2017 poorly gauged basins. Published online 3 MAY 2017 Plain Language Summary Understanding the present water budget in Himalayan Basins is a challenge due to poor in situ coverage, incomplete or unreliable records, and the limitations of coarse resolution gridded data set. In a Himalayan headwater river basin, the Weather Research and Forecasting (WRF)-Hydro modeling system shows reasonable skill in capturing the precipitation and the resulting stream flow hydrographs exhibit a good correspondence with observation at monthly timescales. More importantly, WRF shows that for high-altitude precipitation, a high “bias” is exhibited in winter precipitation from WRF, which is about double to triple that as estimated from valley-sited rain gauges and remotely sensed precipitation estimates from both Tropical Rainfall Measuring Mission and Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation. Given the full annual cycle pattern and amount in high-altitude precipitation and the statistical correspondence in discharge, it is concluded that the WRF-Hydro modeling system shows potential for explicitly predicting potential changes in the atmospheric-hydrology cycle of ungauged or poorly gauged basins. 1. Introduction The Himalayan Mountains are the source region of one of the world’s largest supplies of freshwater. More than 1.2 billion people in the surrounding regions are directly or indirectly reliant upon their resources [Kaser et al., 2010; Ménégoz et al., 2013]. Most of these areas are in developing countries with significant rural, subsistence populations that are vulnerable to the hydrological impacts associated with changes in seasonal precipitation patterns associated with the Indian Summer Monsoon [Krishnamurthy et al., 2009; Christensen et al., 2013] and long-term storage associated with glacier growth/retreat [Yao et al., 2007]. ©2017. American Geophysical Union. Despite this urgency, understanding even present-day hydroclimate variability in Himalayan Basins is a All Rights Reserved. challenge due to poor in situ coverage [Maussion et al., 2011], incomplete or unreliable records [Hewitt, LI ET AL. EVALUATE WATER BUDGET IN HIMALAYAN BASIN 4786 Journal of Geophysical Research: Atmospheres 10.1002/2016JD026279 2005; Bolch et al., 2012; Hartmann and Andresky, 2013], and the limitations of coarse resolution dynamical models such as Global Circulation Models over complex terrain [Fyfe and Flato, 1999; Mass et al., 2002; Leung and Qian, 2003; Salathé et al., 2008]. A first step toward addressing these challenges has been the development of gridded precipitation estimates derived from a variety of sources such as satellite- based data, i.e., Tropical Rainfall Measuring Mission (TRMM) [Huffman et al., 2007; Yan et al., 2016; Adjei et al., 2016]; reanalysis data, i.e., ERA-Interim [Dee et al., 2011] and the WATCH Forcing Data Era-Interim [Weedon et al., 2014]; and rain gauge-based data, i.e., Climate Research Unit [Mitchell and Jones, 2005], Climate Prediction Center [Xie et al., 2010] and the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) [Yatagai et al., 2012; Xu et al., 2016]. While the proliferation of remotely sensed, reanalysis and merged products has been a positive development for researchers, many studies find that the aforementioned data sets are often inconsistent with each other [e.g., Palazzi et al., 2013; Yatagai et al., 2012; Ménégoz et al., 2013]. Further, precipitation in the Himalayan region is strongly influenced by terrain, and regional patterns and amounts are not always captured by gridded precipitation data sets, which have grid spacing of 25 km at the high end. High-resolution dynamical simulations have shown promising in addressing some of the issues related to complex terrain. Numerical weather prediction models can provide reliable estimates of precipitation at high spatial and temporal resolutions [Zängl, 2007; Prein et al., 2013; Rasmussen et al., 2011; Collier et al., 2013; Ji and Kang, 2013a]. An added benefit of high-resolution dynamical approaches is that they not only provide data in great detail but also produce a full complement of variables, which allow for investigations of the physics that drive the local climate. This increases credibility and enables the simulation of complex local processes that might otherwise be overlooked [Arritt and Rummukainen, 2011; Pavelsky et al., 2012; Mayer et al., 2015]. Although Regional Climate Models (RCMs) outputs are able to describe the spatial variability of precipitation over the Himalayas, significant biases in precipitation still exist and a corollary uncertainty in the local- regional water budget remains unresolved. At relatively coarse resolution, RCMs (>20 km grid spacing) can represent atmospheric circulation features but they do not always provide adequate precipitation represen- tation and exhibit large differences when compared to reanalyses, rain gauge, and satellite observations [Biskop et al., 2012; Dimri et al., 2013; Ménégoz et al., 2013; Tramblay et al., 2013; Ji and Kang, 2013b]. Conversely, high-resolution, convection permitting (<4 km grid spacing) RCMs have demonstrated reason- able skill in reproducing precipitation distribution and intensity patterns over complex terrain [e.g., Rasmussen et al., 2011, 2014; Collier et al., 2013]. Over the Himalayan region, in particular, Maussion et al. [2011] showed improved representation of precipitation in a high-resolution (2 km) dynamical simulation when compared to satellite products. Cossu and Hocke [2014] compared 13 WRF parameterization schemes over a bell-shaped mountain and found that the choice of microphysical scheme has important conse- quences for water phase component, hydrometeor distribution, and precipitation. At high resolution, and with selection of the proper parameterizations, RCMs have demonstrated the capability to capture the statis- tical features of orographic precipitation such as seasonality, relative intensity, and precipitation phase even if the timing of specific events is off due to internal model variability [Barstad and Caroletti, 2013; Rasmussen et al., 2011, 2014]. For example, an optimally performing WRF model setup, which employed the Thompson microphysical scheme [Thompson et al., 2008], was used to produce the High Asia Reanalysis (HAR) data set at 10 km resolution over the Tibetan Plateau [Maussion et al., 2014].
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