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Texas is subject to severe droughts, including the record-breaking one in 2011. To investigate the critical hydrometeorological processes during drought, we use a land surface model, Noah-MP, to simulate water availability and investigate the causes of the record drought. We conduct a series of experiments with runoff schemes, vegetation phenology, and plant rooting depth. Observation-based terrestrial water storage, evapotranspiration, runoff, and leaf area index are used to compare with results from the model. Overall, the results suggest that using different parameterizations can influence the modeled water availability, especially during drought. The drought-induced vegetation responses not only interact with water availability but also affect the ground temperature. Our evaluation shows that Noah-MP with a groundwater scheme produces a better temporal relationship in terrestrial water storage compared with observations. Leaf area index from dynamic vegetation is better simulated in wet years than dry years. Reduction of positive biases in runoff and reduction of negative biases in evapotranspiration are found in simulations with groundwater, dynamic vegetation, and deeper rooting zone depth. Multiparameterization experiments show the uncertainties of drought monitoring and provide a mechanistic understanding of disparities in dry anomalies. [ABSTRACT FROM AUTHOR]

We examine the drought variability over the conterminousUnited States (CONUS) for 1915-2018 using theNoah-MP land surfacemodel. We examine different model options on drought reconstruction, including optional representation of groundwater and dynamic vegetation phenology. Over our 104-yr reconstruction period, we identify 12 great droughts that each covered at least36% of CONUS and lasted for at least 5 months. The great droughts tend to have smaller areas when groundwater and/or dynamic vegetation are included in the model configuration. We detect a small decreasing trend in dry area coverage over CONUS in all configurations. We identify 45 major droughts in the baseline (with a dry area coverage greater than 23.6% of CONUS) that are, on average, somewhat less severe than great droughts. We find that representation of groundwater tends to increase drought duration for both great andmajor droughts, primarily by leading to earlier drought onset (some due to short-lived recovery froma previous drought) or later demise (groundwater anomalies lag precipitation anomalies). In contrast, representation of dynamic vegetation tends to shortenmajor droughts duration, primarily due to earlier drought demise (closed stoma or dead vegetation reducesET loss during droughts). On a regional basis, the U.S. Southwest (Southeast) has the longest (shortest)major drought durations. Consistent with earlier work, dry area coverage in all subregions except the Southwest has decreased. The effects of groundwater and dynamic vegetation vary regionally due to differences in groundwater depths (hence connectivity with the surface) and vegetation types. [ABSTRACT FROM AUTHOR]

A detailed, physically based one dimensional, column snowpack model (Crocus) has been incorporated into the hydrological model WRF-Hydro. This allows for direct surface mass balance simulation of glaciers and subsequent modeling of meltwater discharge from glaciers. To evaluate the new system (WRF-Hydro/Glacier), WRF model simulations were downscaled to 1 km grid spacing to provide meteorological forcing data to the WRF-Hydro/Glacier system at 100 m grid spacing. Evaluation of the WRF downscaling showed that it compared well with in situ meteorological observations for most of the simulation period. The WRF-Hydro/Glacier system reproduced the glacier surface winter/summer and net mass balance, snow depth, surface albedo and glacier runoff well compared to observations. The WRF-Hydro/Glacier system is only activated over a priori designated glacier areas. This glacier area is initialized with observed glacier thickness and assumed to be pure ice (with corresponding ice density). This allows for melt of the glacier to continue after all accumulated snow has melted. Furthermore, the simulation of surface albedo over the glacier is more realistic as surface albedo is represented by snow where there is accumulated snow, and glacier ice when all accumulated snow is melted. The improved estimation of albedo has an appreciable impact on the discharge from the glacier during late summer. We have shown that the integrated snow pack system allows for improved glacier surface mass balance studies as well as hydrological studies. [ABSTRACT FROM AUTHOR]

Snow cover in the Qinghai-Tibet Plateau (QTP) is a critical component in the water cycle and regional climate of East Asia. Fractional snow cover (FSC) derived from five satellite sources [the three satellites comprising the multisensor synergy of FengYun-3 (FY-3A/B/C), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Interactive Multisensor Snow and Ice Mapping System (IMS)] were intercompared over the QTP to examine uncertainties in mean snow cover and interannual variability over the last decade. A four-step cloud removal procedure was developed for MODIS and FY-3 data, which effectively reduced the cloud percentage from about 40% to 2%-3% with an error of about 2% estimated by a random sampling method. Compared to in situ snow-depth observations, the cloud-removed FY-3B data have an annual classification accuracy of about 94% for both 0.048 and 0.018 resolutions, which is higher than other datasets and is recommended for use in QTP studies. Among the five datasets analyzed, IMS has the largest snow extent (22% higher than MODIS) and the highest FSC (4.7% higher than MODIS), while the morning-overpass MODIS and FY-3A/C FSC are similar and are around 5% higher than the afternoonoverpass FY-3B FSC. Contrary to MODIS, IMS shows increasing variability in snow cover and snow duration over the last decade (2006-17). Differences in variabilities of FSC and snow duration between products are greater at 5-6 km than lower elevations, with seasonal snow-cover change showing the largest uncertainty in snowmelt date. [ABSTRACT FROM AUTHOR]

Shallow groundwater in the Prairie Pothole Region (PPR) is recharged predominantly by snowmelt in the spring and may supply water for evapotranspiration through the summer/fall. This two-way exchange is underrepresented in land-surface models. Furthermore, the impacts of climate change on the groundwater recharge are uncertain. In this paper, we use a coupled land and groundwater model to investigate the hydrologic cycle of shallow groundwater in the PPR and study its response to climate change at the end of the 21st century. The results show that the model reasonably simulates the water table depth (WTD) and the timing of recharge processes, but underestimates the seasonal variation of WTD, due to mismatches of the soil types between observations and the model. The most significant change under future climate occurs in the winter, when the warmer temperature changes the rain/snow partitioning, delay the time for snow accumulation/soil freezing while bringing forward early melting/thawing. Such changes lead to an earlier start to a longer recharge season, but with lower recharge rates. Different signals are shown in the eastern and western PPR in the future summer, with reduced precipitation and drier soils in the east but little change in the west. The annual recharge increased by 25% and 50% in the eastern and western PPR, respectively. Additionally, we found the mean and seasonal variation of the simulated WTD are sensitive to soil properties and fine-scale soil information is needed to improve groundwater simulation on a regional scale. [ABSTRACT FROM AUTHOR]

We implement a set of new parameterizations into the widely used SNow, ICe, and Aerosol Radiative (SNICAR) model to account for effects of snow grain shape (spherical versus nonspherical) and black carbon (BC)-snow mixing state (external versus internal). We find that nonspherical snow grains lead to higher pure albedo but weaker BC-induced albedo reductions relative to spherical snow grains, while BC-snow internal mixing significantly enhances albedo reductions relative to external mixing. The combination of snow nonsphericity and internal mixing suggests an important interactive effect on BC-induced albedo reduction. Comparisons with observations of clean and BC-contaminated snow albedo show that model simulations accounting for both snow nonsphericity and BC-snow internal mixing perform better than those using the common assumption of spherical snow grains and external mixing. We further apply the updated SNICAR model with comprehensive in-situ measurements of BC concentrations in the Tibetan Plateau snowpack to quantify the present-day (2000–2015) BC-induced snow albedo effects from a regional and seasonal perspective. The BC concentrations show distinct and substantial sub-regional and seasonal variations, with higher values in the non-monsoon season and low altitudes. As a result, the BC-induced regional mean snow albedo reductions and surface radiative effects vary by up to an order of magnitude across different sub-regions and seasons, with values of 0.7–30.7 (1.4–58.4) W m−2 for BC externally mixed with fresh (aged) snow spheres. The BC radiative effects are further complicated by uncertainty in snow grain shape and BC-snow mixing state. BC-snow internal mixing enhances the mean albedo effects over the plateau by 30–60 % relative to external mixing, while nonspherical snow grains decrease the mean albedo effects by up to 31 % relative to spherical grains. Based on this study, extensive measurements and improved model characterization of snow grain shape and aerosol-snow mixing state are urgently needed in order to precisely evaluate BC-snow albedo effects. [ABSTRACT FROM AUTHOR]

ABSTRACT This study explores the sensitivity of high-resolution mesoscale simulations of urban heat island ( UHI) in the Chicago metropolitan area ( CMA) and its environs to urban physical parameterizations, with emphasis on the role of lake breeze. A series of climate downscaling experiments were conducted using the urban-Weather Research and Forecasting ( uWRF) model at 1-km horizontal resolution for a relatively warm period with a strong lake breeze. The study employed best available morphological data sets, selection of appropriate urban parameters, and estimates of anthropogenic heating sources for the CMA. Several urban parameterization schemes were then evaluated using these parameter values. The study also examined (1) the impacts of land data assimilation for initialization of the mesoscale model, (2) the role of urbanization on UHI and lake breeze, and (3) the effects of sub-grid scale land-cover variability on urban meteorological predictions. Comparisons of temperature and wind simulations with station observations and Moderate Resolution Imaging Spectroradiometer satellite data in the CMA showed that uWRF, with appropriate selection of urban parameter values, was able to reproduce the measured near-surface temperature and wind speeds reasonably well. In particular, the model was able to capture the observed spatial variation of 2-m near-surface temperatures at night, when the UHI effect was pronounced. Results showed that inclusion of sub-grid scale variability of land-use and initializing models with more accurate land surface data can yield improved simulations of near-surface temperatures and wind speeds, particularly in the context of simulating the extent and spatial heterogeneity of UHI effects. [ABSTRACT FROM AUTHOR]

A thick top layer of organic matter is a dominant feature in boreal forests and can impact land-atmosphere interactions. In this study, the multi-parameterization version of the Noah land surface model (Noah-MP) was used to investigate the impact of incorporating a forest-floor organic soil layer on the simulated surface energy and water cycle components at the BERMS Old Aspen site (OAS) field station in central Saskatchewan, Canada. Compared to a simulation without an organic soil parameterization (CTL), the Noah-MP simulation with an organic soil (OGN) improved Noah-MP-simulated soil temperature profiles and soil moisture at 40-100 cm, especially the phase and amplitude (Seasonal cycle) of soil temperature below 10 cm. OGN also enhanced the simulation of sensible and latent heat fluxes in spring, especially in wet years, which is mostly related to the timing of spring soil thaw and warming. Simulated top-layer soil moisture is better in OGN than that in CTL. The effects of including an organic soil layer on soil temperature are not uniform throughout the soil depth and are more prominent in summer. For drought years, the OGN simulation substantially modified the partitioning of water between direct soil evaporation and vegetation transpiration. For wet years, the OGN-simulated latent heat fluxes are similar to CTL except for the spring season when OGN produced less evaporation, which was closer to observations. Including organic soil produced more subsurface runoff and resulted in much higher runoff throughout the freezing periods in wet years. [ABSTRACT FROM AUTHOR]