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The historical rise of irrigation has profoundly mitigated the effect of drought on agriculture in many parts of the United States. While irrigation directly alters soil moisture, meteorological drought indices ignore the effects of irrigation, since they are often based on simple water balance models that neglect the irrigation input. Reanalyses also largely neglect irrigation. Other approaches estimate the evaporative fraction (EF), which is correlated with soil moisture under water-limited conditions typical of droughts, with lower values corresponding to drier soils. However, those approaches require satellite observations of land surface temperature, meaning they cannot be used to study droughts prior to the satellite era. Here, we use a recent theory of land-atmosphere coupling--surface flux equilibrium (SFE) theory--to estimate EF from readily available observations of near-surface air temperature and specific humidity with long historical records. In contrast to EF estimated from a reanalysis that largely neglects irrigation, the SFE-predicted EF is greater at irrigated sites than at nonirrigated sites during droughts, and its historical trends are typically consistent with the spatial distribution of irrigation growth. Two sites at which SFE-predicted EF unexpectedly rises in the absence of changes in irrigation can be explained by increased flooding due to human interventions unrelated to irrigation (river engineering and the expansion of fish hatcheries). This work introduces a new method for quantifying agricultural drought prior to the satellite era. It can be used to provide insight into the role of irrigation in mitigating drought in the United States over the twentieth century. [ABSTRACT FROM AUTHOR]

Soil moisture--precipitation (SM-PPT) feedbacks at the mesoscale represent a major challenge for numerical weather prediction, especially for subtropical regions that exhibit large variability in surface SM. How does surface heterogeneity, specifically mesoscale gradients in SM and land surface temperature (LST), affect convective initiation (CI) over South America? Using satellite data, we track nascent, daytime convective clouds and quantify the underlying antecedent (morning) surface heterogeneity. We find that convection initiates preferentially on the dry side of strong SM/LST boundaries with spatial scales of tens of kilometers. The strongest alongwind gradients in LST anomalies at 30-km length scale underlying the CI location occur during weak background low-level wind (<2.5 m s-1), high convective available potential energy (>1500 J kg-1), and low convective inhibition (<250 J kg-1) over sparse vegetation. At 100-km scale, strong gradients occur at the CI location during convectively unfavorable conditions and strong background flow. The location of PPT is strongly sensitive to the strength of the background flow. The wind profile during weak background flow inhibits propagation of convection away from the dry regions leading to negative SM--PPT feedback whereas strong background flow is related to longer life cycle and rainfall hundreds of kilometers away from the CI location. Thus, the sign of the SM-PPT feedback is dependent on the background flow. This work presents the first observational evidence that CI over subtropical South America is associated with dry soil patches on the order of tens of kilometers. Convection-permitting numerical weather prediction models need to be examined for accurately capturing the effect of SM heterogeneity in initiating convection over such semiarid regions. [ABSTRACT FROM AUTHOR]

In this study, we investigate the air temperature response to land-use and land-cover change (LULCC; cropland expansion and deforestation) using subgrid land model output generated by a set of CMIP6 model simulations. Our study is motivated by the fact that ongoing land-use activities are occurring at local scales, typically significantly smaller than the resolvable scale of a grid cell in Earth system models. It aims to explore the potential for a multimodel approach to better characterize LULCC local climatic effects. On an annual scale, the CMIP6 models are in general agreement that croplands are warmer than primary and secondary land (psl; mainly forests, grasslands, and bare ground) in the tropics and cooler in the mid–high latitudes, except for one model. The transition from warming to cooling occurs at approximately 40°N. Although the surface heating potential, which combines albedo and latent heat flux effects, can explain reasonably well the zonal mean latitudinal subgrid temperature variations between crop and psl tiles in the historical simulations, it does not provide a good prediction on subgrid temperature for other land tile configurations (crop vs forest; grass vs forest) under Shared Socioeconomic Pathway 5–8.5 (SSP5–8.5) forcing scenarios. A subset of simulations with the CESM2 model reveals that latitudinal subgrid temperature variation is positively related to variation in net surface shortwave radiation and negatively related to variation in the surface energy redistribution factor, with a dominant role from the latter south of 30°N. We suggest that this emergent relationship can be used to benchmark the performance of land surface parameterizations and for prediction of local temperature response to LULCC. [ABSTRACT FROM AUTHOR]

Parameterization schemes such as soil thermal conductivity (STC) have an important impact on precipitation simulation. The precipitation in the rainy season (April–September) is the main factor affecting aridification in northern China. However, it is unclear how STC affects precipitation simulation during the rainy season. In this study, comparative experiments were conducted using the regional climate model RegCM4.6 coupled with the third-generation land surface model NCAR CLM4.5 to assess the effect of the Johansen and Lu–Ren STC schemes on precipitation. The results show that the STC had a significant effect on the simulation of rainy season precipitation and its variation in northern China. The precipitation variation characteristics simulated by the Lu–Ren scheme were closer than that of the Johansen scheme to the observation. The difference in land surface temperatures (LSTs) simulated by the two STC schemes could be a major cause of the sensitivity in the simulated precipitation. When the local LST increases by 1 K, precipitation decreases by 5–30 mm in most areas of northern China. The numerical experiments revealed that the rise of LST increases the longwave radiation, reduces the surface net radiation, and causes the redistribution of sensible and latent heat flux, forming local water vapor and thermal conditions that are not conducive to precipitation. Moreover, the difference of LST significantly changes the 500-hPa large-scale circulation field, the 700-hPa vapor transportation, and its divergence. The combined action of local heat, water vapor, and large-scale circulation factors reduces the precipitation in the rainy season. On the other side, the variation of the East Asia summer monsoon (EASM) affects the soil water content. In addition, a new STC scheme was added to NCAR CLM4.5, promoting the development of this land surface model. [ABSTRACT FROM AUTHOR]

Soil moisture is an important variable of the climate system and is used to measure dry–wet change in hydroclimate. The warming trend has slowed in China over the past 20 years since 1998, and how the soil moisture changes in this period deserves our attention. With North China as a research region, this study uses the Global Land Data Assimilation System and ground observations to investigate the causes of changes in soil moisture during 1998–2017 versus 1961–97. The results show that 1) annual mean soil moisture experienced an almost continued decrease from the 1960s to 2010s, and no pause in the decrease of soil moisture over the regional warming slowdown of the past 20 years could be detected; 2) with the stabilization or even increase in solar radiation and wind speed as well as the continuous increase of land surface air temperature, the impact of potential evapotranspiration on soil moisture gradually became prominent, and the impact of precipitation decreased, since 1998; 3) the percent contribution of annual potential evapotranspiration to soil moisture variation increased by 26% during 1998–2017 relative to that in 1961–97, and the percent contribution of summer potential evapotranspiration even increased by 45%. Our results will provide insight into the land surface water budget and mechanism involved in drought development in North China. [ABSTRACT FROM AUTHOR]

Data assimilation provides a practical way to improve the accuracy of soil moisture simulation by integrating a land surface model and satellite data. This study establishes a multisource remote sensing data assimilation framework by incorporating a simultaneous state and parameter estimation method to acquire an accurate estimation of the soil moisture over the Tibetan Plateau. The brightness temperature of the Advanced Microwave Scanning Radiometer 2 (AMSR2) is directly assimilated into the coupled system of the Common Land Model (CoLM) and a microwave radiative transfer model (RTM) to improve the soil moisture simulation. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature product and the Beijing Normal University (BNU) leaf area index product are employed to not only improve the estimation of temperature and vegetation variables from the CoLM, but they also provide more accurate background information for the RTM during the brightness temperature assimilation. In situ measurements from the Naqu network are used to evaluate the results. The model simulation showed an obvious underestimation of soil moisture and overestimation of soil temperature, which was alleviated by the assimilation experiments, particularly in the shallow soil layers. The estimated parameters also showed advantages in the soil moisture simulation when compared with the default parameters. The assimilation experiment presents promising results in the combination of model and multisource remote sensing data for estimating soil moisture over the complex mountainous region in Tibet. Significance Statement: We wanted to obtain accurate and spatiotemporal continuous soil moisture over the Tibetan Plateau. Therefore, we established a multisource remote sensing data assimilation framework in which AMSR2 brightness temperature and MODIS land surface temperature were assimilated into a land surface model by a simultaneous state and parameter estimation method. The results showed the soil moisture we obtained has better accuracy than both model simulation and satellite retrieval. Compared with in situ measurements, the model simulations underestimated soil moisture and overestimated soil temperature. With assimilation, the reduction of the mean bias error (MBE) (RMSE) for shallow-layer soil moisture at different sites ranges from 20% (19%) up to 98% (71%). [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) significantly modulates the precipitation and temperature over land, with important consequences on land surface processes such as snowpack. Compared to the impact of remote SST, the effect of nearshore/local SST is less well understood. In this study, the impact of local SST on the mountain snowpack of the U.S. West Coast is investigated using two 6-km regional climate simulations driven by the same lateral boundary conditions but with time-varying versus time-invariant and warmer local SSTs during 2003–15. Results show that local SST warming leads to warmer winter with more precipitation over the mountains. Meanwhile, the removal of SST temporal variability results in reduced temperature variability but increased precipitation variability. As a result, winter snow accumulation decreases by ~200 mm per season in the Cascade Mountains in the north but increases by ~100 mm per season in the Sierra Nevada in the south. Such a dipole response results from the competing effects of precipitation and temperature change at different elevations and are amplified by the enhanced atmospheric river moisture transport. To further delineate the relative contributions of different meteorological factors to the snowpack response, two neural network models were developed to predict the snow behaviors at seasonal and monthly scales. These models reveal the dominant influence of the total amount and the average temperature of precipitation on the snowpack response. These findings highlight the sensitivity of mountain snowpack to local SST in the western United States and underscore the importance of local SST and atmospheric rives to accurate snowpack estimations for water management. [ABSTRACT FROM AUTHOR]

Convection over the Tibetan Plateau (TP) has been linked to heavy rain and flooding in down stream parts of China. Understanding processes which influence the development of convection on the TP could contribute to better forecasting of these extreme events. TP scale (~1000 km) soil moisture gradients have been shown to influence formation of convective systems over the eastern TP. The importance of smaller-scale (~10 km) variability has been identified in other regions (including the Sahel and Mongolia) but has yet to be investigated for the TP. In addition, compared to studies over flat terrain, much less is known about soil moisture--convection feedbacks above complex topography. In this study we use satellite observations of cold cloud, land surface temperature, and soil moisture to analyze the effect of mesoscale soil moisture heterogeneity on the initiation of strong convection in the complex TP environment. We find that strong convection is favored over negative (positive) land surface temperature (soil moisture) gradients. The signal is strongest for less vegetation and low topographic complexity, though still significant up to a local standard deviation of 300m in elevation, accounting for 65%of cases. In addition, the signal is dependent on background wind. Strong convective initiation is only sensitive to local (tens of kilometers) soil moisture heterogeneity for light wind speeds, though large-scale (hundreds of kilometers) gradients may still be important for strong wind speeds. Our results demonstrate that, even in the presence of complex topography, local soil moisture variability plays an important role in storm development. [ABSTRACT FROM AUTHOR]

In the past decade, a variety of algorithms have been introduced to downscale passive microwave soil moisture observations. Some exploit the soil moisture information from optical/thermal sensing of land surface temperature (LST) and vegetation dynamics while others use active microwave (radar) observations. In this study, downscaled soil moisture data at 9- or 1-km resolution from several algorithms are intercompared against in situ soil moisture measurements to determine their reliability in an operational system. The finescale satellite data used here for downscaling the coarse-scale SMAP data are observations of LST from the Geostationary Operational Environmental Satellite (GOES) and vegetation index (VI) from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) for the warm seasons in 2015 and 2016. Three recently developed downscaling algorithms are evaluated and compared: a simple regression algorithm based on 9-km thermal inertial data, a data mining approach called regression tree based on 9- and 1-km LST and VI, and the NASA SMAP enhanced 9-km soil moisture product algorithm. Seven sets of in situ soil moisture data from intensive networks were used for validation, including 1) the CREST-SMART network in Millbrook, New York; 2)WalnutGulchWatershed in Arizona; 3) LittleWashitaWatershed inOklahoma; 4) Fort Cobb Reservoir Experimental Watersheds in Oklahoma; 5) Little River Watershed in Georgia; 6) the Tibetan Plateau network in China, and 7) the OzNet inAustralia. Soilmoisturemeasurements of the in situ networkswere upscaled to the corresponding SMAP reference pixels at 9km and used to assess the accuracy of downscaled products at a 9-km scale. Results revealed that the downscaled 9-km soil moisture products generally outperform the 36-km product for most in situ datasets. The linear regression algorithm using the thermal sensing based evaporative stress index (ESI) had the best agreement with the in situ measurements from networks in the contiguous United States according to the site-by-site comparison. In addition, the inertial thermal linear regression method demonstrated the lowest unbiased RMSE when comparing to thematched-up in situ datasets as well. In general, thismethod is promising for operational generation of fine-resolution soilmoisture data product. [ABSTRACT FROM AUTHOR]

Surface heat fluxes are vital to hydrological and environmental studies, but mapping them accurately over a large area remains a problem. In this study, brightness temperature (TB) observations or soil moisture retrievals from the NASA Soil Moisture Active Passive (SMAP) mission and land surface temperature (LST) product from the Geostationary Operational Environmental Satellite (GOES) are assimilated together into a coupled water and heat transfer model to improve surface heat flux estimates. A particle filter is used to assimilate SMAP data, while a particle smoothing method is adopted to assimilate GOES LST time series, correcting for both systematic biases via parameter updating and for short-term error via state updating. One experiment assimilates SMAP TB at horizontal polarization and GOES LST, a second experiment assimilates SMAP TB at vertical polarization and GOES LST, and a third experiment assimilates SMAP soil moisture retrievals along with GOES LST. The aim is to examine if the assimilation of physically consistent TB and LST observations could yield improved surface heat flux estimates. It is demonstrated that all three assimilation experiments improved flux estimates compared to a no-assimilation case. Assimilating TB data tends to produce smaller bias in soil moisture estimates compared to assimilating soil moisture retrievals, but the estimates are influenced by the respective bias correction approaches. Despite the differences in soil moisture estimates, the flux estimates from different assimilation experiments are in general very similar. [ABSTRACT FROM AUTHOR]

This study examines the impacts of assimilating Soil Moisture Active Passive (SMAP) L-band brightness temperatures (TBs) on warm season short-range numerical weather prediction (NWP) forecasts. Focusing upon the summer 2015 period over North America, offline assimilation cycles are run with the Canadian Land Data Assimilation System (CaLDAS) to compare the impacts of assimilating SMAP TB versus screen-level observations to analyze soil moisture. The analyzed soil moistures are quantitatively compared against a set of in situ sparse soil moisture networks and a set of SMAP core validation sites. These surface analyses are used to initialize a series of 48-h forecasts where near-surface temperature and precipitation are evaluated against in situ observations. Assimilation of SMAP TBs leads to soil moisture that is markedly improved in terms of correlation and standard deviation of the errors (STDE) compared to the use of screen-level observations. NWP forecasts initialized with SMAP-derived soil moistures exhibit a general dry bias in 2-m dewpoint temperatures (TD2m), while displaying a relative warm bias in 2-m temperatures (TT2m), when compared to those forecasts initialized with soil moistures analyzed with screen-level temperature errors. Largest impacts with SMAP are seen for TD2m, where the use of screen-level observations leads to a daytime wet bias that is reduced with SMAP. The overall drier soil moisture leads to improved precipitation bias scores with SMAP. A notable deterioration in TD2m STDE scores was found in the SMAP experiments during the daytime over the Northern Great Plains. A reduction in the daytime TD2m wet bias was found when the observation errors for the screen-level observations were increased. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity and temporal variation of soil moisture and surface heat fluxes are key to many geophysical and environmental studies. It has been demonstrated that they can be mapped by assimilating soil thermal and wetness information into surface energy balance models. The aim of this work is to determine whether enhancing the spatial resolution or temporal sampling frequency of soil moisture data could improve soil moisture or surface heat flux estimates. Two experiments are conducted in an area mainly covered by grassland, and land surface temperature (LST) observations from the Geostationary Operational Environmental Satellite (GOES) mission are assimilated together with either an enhanced L-band passive soil moisture product (9 km, 2–3 days) from the Soil Moisture Active Passive (SMAP) mission or a merged product (36 km, quasi-daily) from the SMAP and the Soil Moisture Ocean Salinity (SMOS) mission. The results suggest that the availability of soil moisture observations is increased by 41% after merging data from the SMAP and the SMOS missions. A comparison with results from a previous study that assimilated a coarser SMAP soil moisture product (36 km, 2–3 days) suggests that enhancing the temporal sampling frequency of soil moisture observations leads to improved soil moisture estimates at both the surface and root zone, and the largest improvement is seen in the bias metric (0.008 and 0.007 m3 m−3 on average at the surface and root zone, respectively). Enhancing the spatial resolution, however, does not significantly improve soil moisture estimates, particularly at the surface. Surface heat flux estimates from assimilating soil moisture data of different spatial or temporal resolutions are very similar. [ABSTRACT FROM AUTHOR]

Green vegetation fraction (GVF) plays a crucial role in the atmosphere–land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model. [ABSTRACT FROM AUTHOR]

Floods in the Mississippi basin can have large negative societal, natural, and economic impacts. Understanding the drivers of floods, now and in the future, is relevant for risk management and infrastructure-planning purposes. We investigate the drivers of 100-yr-return lower Mississippi River floods using a global coupled climate model with an integrated surface water module. The model provides 3400 years of physically consistent data from a static climate, in contrast to available observational data (relatively short records, incomplete land surface data, transient climate). In the months preceding the model's 100-yr floods, as indicated by extreme monthly discharge, above-average rain and snowfall lead to moist subsurface conditions and the buildup of snowpack, making the river system prone to these major flooding events. The meltwater from snowpack in the northern Missouri and upper Mississippi catchments primes the river system, sensitizing it to subsequent above-average precipitation in the Ohio and Tennessee catchments. An ensemble of transient forcing experiments is used to investigate the impacts of past and projected anthropogenic climate change on extreme floods. There is no statistically significant projected trend in the occurrence of 100-yr floods in the model ensemble, despite significant increases in extreme precipitation, significant decreases in extreme snowmelt, and significant decreases in less extreme floods. The results emphasize the importance of considering the fully coupled land–atmosphere system for extreme floods. This initial analysis provides avenues for further investigation, including comparison to characteristics of less extreme floods, the sensitivity to model configuration, the role of human water management, and implications for future flood-risk management. [ABSTRACT FROM AUTHOR]

The Colorado River basin (CRB) is one of the most important watersheds for energy, water, and food security in the United States. CRB water supports 15% of U.S. food production, more than 50 GW of electricity capacity, and one of the fastest growing populations in the United States. Energy–water–food nexus impacts from climate change are projected to increase in the CRB. These include a higher incidence of extreme events, widespread snow-to-rain regime shifts, and a higher frequency and magnitude of climate-driven disturbances. Here, we empirically show how the historical annual streamflow maximum and hydrograph centroid timing relate to temperature, precipitation, and snow. In addition, we show how these hydroclimatic relationships vary with elevation and how the elevation dependence has changed over this historical observational record. We find temperature and precipitation have a relatively weak relation (|r| < 0.3) to interannual variations in streamflow timing and extremes at low elevations (<1500 m), but a relatively strong relation (|r| > 0.5) at high elevations (>2300 m) where more snow occurs in the CRB. The threshold elevation where this relationship is strongest (|r| > 0.5) is moving uphill at a rate of up to 4.8 m yr−1 (p = 0.11) and 6.1 m yr−1 (p = 0.01) for temperature and precipitation, respectively. Based on these findings, we hypothesize where warming and precipitation-related streamflow changes are likely to be most severe using a watershed-scale vulnerability map to prioritize areas for further research and to inform energy, water, and food resource management in the CRB. [ABSTRACT FROM AUTHOR]

With the advance of the weather radar technology, dual-polarization (dual-pol) radar data are now available for hydrological studies, which go beyond the traditional rainfall products relying purely on rain gauge data. Previous studies have focused on the evaluation of rainfall products and their hydrological responses using point-based observational data; however, spatial patterns of simulated hydrological variables are equally important to be considered in order to fully address the distributed effect of the precipitation estimates. In the present study, we compare three rainfall estimations based on rain gauge, single-polarization, and dual-pol radar data. Special attention is given to the use of the two radar products and their corresponding hydrological simulations of both surface water and groundwater. Performance of the hydrological simulations is evaluated based first on traditional point-based observations of stream discharge and groundwater head, and second on remotely sensed land surface temperature data. For the latter, the empirical orthogonal function analysis, which quantifies spatial pattern similarities, is employed. The Skjern River catchment in western Denmark is selected as the study site, and the results show that all three models perform equally well in terms of the traditional aggregated evaluation criteria, such as Nash-Sutcliffe efficiency (NSE) andRMSE on time series data. It is found that the differences of simulated hydrological spatial patterns are sensitive to rainfall signal intensity, as well as the simulation scale in space (,100 km2) and time (subdaily). Our study suggests that the currently available observational data have limited capabilities to clearly differentiate the performance of the three applied models due to the low resolution. [ABSTRACT FROM AUTHOR]

The maximum entropy production (MEP) model based on nonequilibrium thermodynamics and the theory of Bayesian probabilities was recently developed to model land surface fluxes, including soil evaporation and vegetation transpiration. This model requires few input data and ensures the closure of the surface energy balance. This study aims to test the capability of such a model to realistically simulate evapotranspiration (ET) over a wide range of climates and vegetation covers. A weighting coefficient is introduced to calculate total ET from soil evaporation and vegetation transpiration over partially vegetated land surfaces, resulting in the MEP-ET model. Using this coefficient, the model outputs are compared with in situ observations of ET at eight FLUXNET sites across the continental United States. Results confirm the close agreement between the MEP-ET predicted daily ET and the corresponding observations at sites characterized by moderately limited water availability. Poor ET results were obtained under high water stress conditions. A regulation parameter was therefore introduced in the MEP-ET model to properly take into account the effects of soil water stress on stomata, yielding the generalized MEP-ET model. This parameter considerably reduced model biases under water stress conditions for various heterogeneous land surface sites. The generalized MEP-ET model outperforms several popular ET models, including Penman-Monteith (PM), modified Priestley-Taylor-Jet Propulsion Laboratory (PT-JPL), and air-relative-humidity-based two-source model (ARTS) at all test sites. [ABSTRACT FROM AUTHOR]

The spatial variability of topsoil water content (SWC) is often expressed through the relationship between its spatial mean 〈θ〉 and standard deviation σθ. The present study tests the concept that a reasonably performing land surface model (LSM) should be able to produce σθ-〈θ〉 data pairs that fall into a polygon, spanned by the cloud of observed data and two anchor points: σθ at the permanent wilting point σθ-〈θwp〉 and σθ at saturation σθ-〈θs〉. A state-of-the-art LSM, Noah-MP, was driven by atmospheric forcing data obtained from eddy covariance field measurements in two regions of southwestern Germany, Kraichgau (KR) and Swabian Alb (SA). KR is characterized with deep loess soils, whereas the soils in SA are shallow, clayey, and stony. The simulations series were compared with SWC data from soil moisture networks operating in the two study regions. The results demonstrate that Noah-MP matches temporal 〈θ〉 dynamics fairly well in KR, but performs poorly in SA. The best match is achieved with the van Genuchten-Mualem representation of soil hydraulic functions and site-specific rainfall, soil texture, green vegetation fraction (GVF) and leaf area index (LAI) input data. Nevertheless, most of the simulated σθ-〈θ〉 pairs are located outside the envelope of measurements and below the lower bound, which shows that the model smooths spatial SWC variability. This can be mainly attributed to missing topography and terrain information and inadequate representation of spatial variability of soil texture and hydraulic parameters, as well as the model assumption of a uniform root distribution. [ABSTRACT FROM AUTHOR]

Water management activities modify water fluxes at the land surface and affect water resources in space and time. Conventional understanding on the role of water management suggests that regulated river flow would be less sensitive to future climate conditions than natural flow in terms of the absolute changes in mean monthly flows. In this study the authors evaluate such an assumption by redefining sensitivity as the difference in the emergence of changes in cumulative distribution functions (CDFs) of future regulated and natural flows in response to climate change with respect to their respective historical regulated and natural flow conditions. The emergence of changes (shift in CDFs) in natural and regulated river flow regimes across the western United States from simulations driven by multiple climate models and scenarios were compared. Forty percent of Hydrologic Unit Codes 4 (HUC4s) over the western United States might perceive such a shift in seasonal regulated flow earlier than they would have seen in natural flow conditions, although the absolute change is smaller than that under natural conditions. About 10% of the regulated HUC4s see a delay and are therefore less sensitive to climate change. In the spring (MAM), the overall sensitivity tends to decrease as the level of river regulation increases, as expected. However, in the winter (DJF) and summer (JJA) seasons, the sensitivity tends to increase with increasing levels of regulation, with changes in smaller magnitudes than under natural conditions. The results could inform integrated assessment studies when designing adaptation strategies in the water-energy-food nexus. [ABSTRACT FROM AUTHOR]

Wetlands cover large areas of the middle and high latitudes and influence the surface water and energy budget, surface hydrology, and the climate system. In this study, a scheme implicitly representing a snow-fed wetland, in which snowmelt can be stored with consideration of subgrid terrain complexity, was implemented in the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) land surface model. An atmospheric general circulation model (AGCM) experiment was conducted using the Model for Interdisciplinary Research on Climate, version 5 (MIROC5), with and without the wetland scheme, with the main aim of reducing the model bias of warm and dry boreal summer at mid- to high latitudes. The experiment showed not only a better surface hydrology but also a weaker land-atmosphere coupling strength and larger (smaller) latent (sensible) heat flux due to the delayed snowmelt runoff. The summer warm and dry bias was partially improved over snowy and flat areas, particularly over much of western Eurasia and North America, without an apparent deterioration of simulated surface hydrology and climate over the rest of the land in the other seasons; the mean absolute error of 2-m air temperature and precipitation over land at 458-908N in summer decreased by 19% and 4%, respectively. The next step of model development will involve implementing an explicit representation of subgrid-scale surface water and related processes. [ABSTRACT FROM AUTHOR]

Southeast Asian tropical rain forests in the Maritime Continent are among the most important biomes in terms of global and regional water cycling. How land use and land cover change (LULCC) relating to deforestation and forest degradation alter the local hydroclimate over the island of Borneo is examined using the Weather Research and Forecasting (WRF) Model with an appropriate land surface model for describing the influence of changes in the vegetation status on the atmosphere. The model was validated against precipitation data from Tropical Rainfall Measuring Mission (TRMM) satellite 3B42 measurements. A main novelty in this analysis is that the diurnal cycle of precipitation over the island, which is a dominant climatic characteristic of the Maritime Continent, was successfully reproduced. To clarify the impact of the LULCC on the precipitation regimes over the island, numerical experiments were performed with the model that demonstrated the following. Deforestation that generates high albedo areas, such as bare lands, would induce a reduction in precipitation because of reductions in evapotranspiration, convection, and horizontal atmospheric moisture inflow. On the other hand, a decrease in evapotranspiration efficiency without changing the surface albedo could increase precipitation due to an increase in convection and horizontal atmospheric moisture inflow in compensation for the decrease in evapotranspiration. In detail, on the Maritime Continent, through changes in the land surface heating process and land–sea breeze circulation, the LULCC would impact the amplitude of the diurnal precipitation cycle in each region as defined according to the distance from the coast, resulting in changes in the precipitation regimes over the island. [ABSTRACT FROM AUTHOR]

MODIS thermal sensors can provide us with global land surface temperature (LST) several times each day, but have difficulty in obtaining information from the land surface in cloudy situations. As a result, the monthly day or night LST products [ Terra monthly day LST (TMD), Terra monthly night LST (TMN), Aqua monthly day LST (AMD), and Aqua monthly night LST (AMN)] are the average LST values calculated over a variable number of clear-sky days in a month. Is it possible to derive an accurate estimate of monthly mean LST based on averaging of the multidaily overpasses of MODIS sensors? In situ ground measurements and ERA-Interim reanalyses data, both of which provide continuous information in either clear or cloudy conditions, have been used to validate the approach. Using LST measurements from 156 ground flux towers, it was found that the three mean values , , and (mean biases of 0.19, 0.59, and 0.40 K, respectively) can all provide a reliable estimate of all-sky monthly mean LST. Of the three means, we recommend the use of for monthly mean LST in climate studies as it provides the most complete coverage. When retrievals from either Terra or Aqua are not available, then either or may be used to fill the gaps. The intrinsic error in the MODIS monthly mean LST cannot be explained from monthly mean view time, view angle, and clear-sky ratio. MODIS monthly LST calculated using this approach (RMSE = 2.65, mean bias < ±1 K) will have wide applicability for climate studies and numerical model evaluation. [ABSTRACT FROM AUTHOR]

The central Amazon forest is projected to experience larger interannual precipitation variability, with uncertain impacts on terrestrial hydrologic fluxes. How surface runoff, groundwater, and evapotranspiration (ET) change as a function of annual precipitation (AP) has large climate and biogeochemical implications. A process-based hydrological model is used to examine the sensitivity of hydrologic budgets and stream discharge Q s generation to AP in an upland Amazon catchment. The authors find that AP strongly controls infiltration, base flow, and surface runoff, but not ET. Hence, AP alone can predict interannual changes in these fluxes except ET. Experiments with perturbed rainfall show the strong control derives from the predominant groundwater component that varies linearly with AP but is insensitive to seasonal rainfall fluctuations. Most rainfall from large storms infiltrates and becomes base flow rather than runoff or ET. Annual baseflow index (BFI; the fraction of stream discharge from base flow) is nearly constant (~0.8) when AP is below ~2500 mm yr−1 and decreases with AP above this value, which represents an inflection point for increased storage-dependent saturation excess. These results indicate that the system is energy limited and groundwater dominated in dry seasons, which implies some resilience of ET to moderate droughts. The results suggest AP is a good predictor for interannual changes in infiltration. Both the seasonal near-surface soil moisture and surface runoff are correlated more strongly to the subsurface fluxes than to precipitation over monthly and annual time scales. Finally, the results confirm the importance of central Amazon groundwater flow and its buffering effect on storms and droughts, implying needed model development in regional to global models. [ABSTRACT FROM AUTHOR]

Comparing streamflow simulations against observations has become a straightforward way to evaluate a land surface model's (LSM) ability in simulating water budget within a catchment. Using a mesoscale river routing scheme (RRS), this study evaluates simulated streamflows over the upper Ouémé River basin resulting from 14 LSMs within the framework of phase 2 of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project (ALMIP2). The ALMIP2 RRS (ARTS) has been used to route LSM outputs. ARTS is based on the nonlinear Muskingum-Cunge method and a simple deep water infiltration formulation representing water-table recharge as previously observed in that region. Simulations are performed for the 2005-08 period during which ground observations are largely available. Experiments are designed using different ground-based rainfall datasets derived from two interpolation methods: the Thiessen technique and a combined kriging-Lagrangian methodology. LSM-based total runoff (TR) averages vary from 0.07 to 1.97 mm day−1, while optimal TR was estimated as ~0.65 mm day−1. This highly affected the RRS parameterization and streamflow simulations. Optimal Nash-Sutcliffe coefficients for LSM-averaged streamflows varied from 0.66 to 0.92, depending on the gauge station. However, individual LSM performances show a wider range. A more detailed rainfall distribution provided by the kriging-Lagrangian methodology resulted in overall better streamflow simulations. The early runoff generation related to reduced infiltration rates during early rainfall events features as one of the main reasons for poor LSM performances. [ABSTRACT FROM AUTHOR]

Soil moisture availability exerts control over the land surface energy partition in parts of Europe. However, determining the strength and variability of this control is impeded by the lack of reliable evaporation observations at the continental scale. This makes it difficult to refine the broad range of soil moisture-evaporation behaviors across global climate models (GCMs). Previous studies show that satellite observations of land surface temperature (LST) during rain-free dry spells can be used to diagnose evaporation regimes at the GCM gridbox scale. This relative warming rate (RWR) diagnostic quantifies the increase in dry spell LST relative to air temperature and is used here to evaluate a land surface model (JULES) both offline and coupled to a GCM (HadGEM3-A). It is shown that RWR can be calculated using outputs from an atmospheric GCM provided the satellite clear-sky sampling bias is incorporated. Both offline JULES and HadGEM3-A reproduce the observed seasonal and regional RWR variations, but with weak springtime RWRs in central Europe. This coincides with sustained bare soil evaporation (Ebs) during dry spells, reflecting previous site-level JULES studies in Europe. To assess whether RWR can discriminate between surface descriptions, the bare soil surface conductance and the vegetation root profile are revised to limit Ebs. This increases RWR by increasing the occurrence of soil moisture-limited dry spells, yielding more realistic springtime RWRs as a function of antecedent precipitation but poorer relationships in summer. This study demonstrates the potential for using satellite LST to assess evaporation regimes in climate models. [ABSTRACT FROM AUTHOR]

Distributed hydrological models simulate states and fluxes of water and energy in the terrestrial hydrosphere at each cell. The predicted spatial patterns result from complex nonlinear relationships and feedbacks. Spatial patterns are often neglected during the modeling process, and therefore a spatial sensitivity analysis framework that highlights their importance is proposed. This study features a comprehensive analysis of spatial patterns of actual evapotranspiration (ET) and land surface temperature (LST), with the aim of quantifying the extent to which forcing data and model parameters drive these patterns. This framework is applied on a distributed model [MIKE Système Hydrologique Européen (MIKE SHE)] coupled to a land surface model [Shuttleworth and Wallace-Evapotranspiration (SW-ET)] of a catchment in Denmark. Twenty-two scenarios are defined, each having a simplified representation of a potential driver of spatial variability. A baseline model that incorporates full spatial detail is used to assess sensitivity. High sensitivity can be attested in scenarios where the simulated spatial patterns differ significantly from the baseline. The core novelty of this study is that the analysis is based on a set of innovative spatial performance metrics that enable a reliable spatial pattern comparison. Overall, LST is very sensitive to air temperature and wind speed whereas ET is rather driven by vegetation. Both are sensitive to groundwater coupling and precipitation. The conclusions may be limited to the selected catchment and to the applied modeling system, but the suggested framework is generically relevant for the modeling community. While the applied metrics focus on specific spatial information, they partly exhibit redundant information. Thus, a combination of metrics is the ideal approach to evaluate spatial patterns in models outputs. [ABSTRACT FROM AUTHOR]

Although soil moisture is an essential variable within the Earth system and has been extensively investigated, there is still a limited understanding of its spatiotemporal distribution and variability. Thus, the objective of this study is to attempt to reproduce the spatial variability of soil moisture and brightness temperature as measured by point-based and airborne remote sensing measurements. To do so, Environment and Climate Change Canada's Surface Prediction System (SPS) is run at very high resolution (100 m) over a region of Manitoba (Canada) where an extensive soil moisture experiment took place in the summer of 2012 [SMAP Validation Experiment 2012 (SMAPVEX12)]. Results show that realistic finescale soil texture improves the quality of SPS outputs. Soil moisture spatial average evolution in time is well simulated by SPS. Simulated spatial variability is underestimated when compared to point-based measurements, although results are improved when examined domainwide versus comparisons using grid points corresponding to measurement sites. SPS brightness temperature fields compare well with remote sensing data in terms of spatial variability. It is shown that during drier periods, factors other than soil texture become important with respect to soil moisture spatial variability. However, during periods with plenty of precipitation, soil texture seems essential in improving simulated soil moisture spatial variability at high resolutions. These results support the conclusion that SPS could provide very high-resolution soil moisture products for research and operational purposes if high-resolution soil texture and vegetation products are made available on a larger scale. [ABSTRACT FROM AUTHOR]

Land surface models, like the Common LandModel component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball-Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspacemethod is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California, Colorado, and Oklahoma, to identifywhich input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball-Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO2 Michaelis-Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C(qe25),maximumrate of carboxylation at 25°C(vcmx25), andmultiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models. [ABSTRACT FROM AUTHOR]

Retrospective forecasts from CFSv2 are evaluated in terms of three elements of land-atmosphere coupling at subseasonal to seasonal time scales: sensitivity of the atmosphere to variations in land surface states, the magnitude of variability of land states and fluxes, and the memory or persistence of land surface anomalies. The Northern Hemisphere spring and summer seasons are considered for the period 1982-2009. Ensembles are constructed from all available pairings of initial land and atmosphere/ocean states taken from the Climate Forecast System Reanalysis at the start of April, May, and June among the 28 years, so that the effect of initial land states on the evolving forecasts can be assessed. Finally, improvement and continuance of forecast skill derived horn accurate land surface initialization is related to the three coupling elements. It is found that soil moisture memory is the most broadly important element for significant improvement of realistic land initialization on forecast skill. However, coupling strength manifested through the elements of sensitivity and variability are necessary to realize the potential predictability provided by memory of initial land surface anomalies. Even though there is clear responsiveness of surface heat fluxes, near-surface temperature, humidity, and daytime boundary layer development to variations in soil moisture over much of the globe, precipitation in CFSv2 is unresponsive. Failure to realize potential predictability from land surface states could be due to unfavorable atmospheric stability or circulation states; poor quality of what is considered realistic soil moisture analyses; and errors in the land surface model, atmospheric model, or their coupled interaction. [ABSTRACT FROM AUTHOR]

A mosaic approach to represent subgrid snow variation in a coupled atmosphere-land surface model (WRF-Noah) is introduced and tested. Solid precipitation is scaled in 10 subgrid tiles based on precalculated snow distributions, giving a consistent, explicit representation of variable snow cover and snow depth on subgrid scales. The method is tested in the Weather Research and Forecasting (WRF) Model for southern Norway at 3-km grid spacing, using the subgrid tiling for areas above the tree line. At a validation site in Finse, the modeled transition time from full snow cover to snow-free ground is increased from a few days with the default snow cover fraction formulation to more than 2 months with the tiling approach, which agrees with in situ observations from both digital camera images and surface temperature loggers. This in turn reduces a cold bias at this site by more than 2°C during the first half of July, with the noontime bias reduced from -5° to -1°C. The improved representation of subgrid snow variation also reduces a cold bias found in the reference simulation on regional scales by up to 0.8°C and increases surface energy fluxes (in particular the latent heat flux), and it resulted in up to 50% increase in monthly (June) precipitation in some of the most affected areas. By simulating individual soil properties tor each tile, this approach also accounts for a number of secondary effects of uneven snow distribution resulting in different energy and moisture fluxes in different tiles also after the snow has disappeared. [ABSTRACT FROM AUTHOR]

In the last decade operational probabilistic ensemble flood forecasts have become common in supporting decision-making processes leading to risk reduction. Ensemble forecasts can assess uncertainty, but they are limited to the uncertainty in a specific modeling system. Many of the current operational flood prediction systems use a multimodel approach to better represent the uncertainty arising from insufficient model structure. This study presents a multimodel approach to building a global flood prediction system using multiple atmospheric reanalysis datasets for river initial conditions and multiple TIGGE forcing inputs to the ECMWF land surface model. A sensitivity study is carried out to clarify the effect of using archive ensemble meteorological predictions and uncoupled land surface models. The probabilistic discharge forecasts derived from the different atmospheric models are compared with those from the multimodel combination. The potential for further improving forecast skill by bias correction and Bayesian model averaging is examined. The results show that the impact of the different TIGGE input variables in the HTESSEL/Catchment-Based Macroscale Floodplain model (CaMa-Flood) setup is rather limited other than for precipitation. This provides a sufficient basis for evaluation of the multimodel discharge predictions. The results also highlight that the three applied reanalysis datasets have different error characteristics that allow for large potential gains with a multimodel combination. It is shown that large improvements to the forecast performance for all models can be achieved through appropriate statistical postprocessing (bias and spread correction). A simple multimodel combination generally improves the forecasts, while a more advanced combination using Bayesian model averaging provides further benefits. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) mission is the successor to the Tropical Rainfall Measuring Mission (TRMM), which orbited Earth for ~17 years. With Core Observatory launched on 27 February 2014, GPM offers global precipitation estimates between 60°N and 60°S at 0.1° × 0.1° resolution every 30 min. Unlike during the TRMM era, the Netherlands is now within the coverage provided by GPM. Here the first year of GPM rainfall retrievals from the 30-min gridded Integrated Multisatellite Retrievals for GPM (IMERG) product Day 1 Final Run (V03D) is assessed. This product is compared against gauge-adjusted radar rainfall maps over the land surface of the Netherlands at 30-min, 24-h, monthly, and yearly scales. These radar rainfall maps are considered to be ground truth. The evaluation of the first year of IMERG operations is done through time series, scatterplots, empirical exceedance probabilities, and various statistical indicators. In general, there is a tendency for IMERG to slightly underestimate (2%) countrywide rainfall depths. Nevertheless, the relative underestimation is small enough to propose IMERG as a reliable source of precipitation data, especially for areas where rain gauge networks or ground-based radars do not offer these types of high-resolution data and availability. The potential of GPM for rainfall estimation in a midlatitude country is confirmed. [ABSTRACT FROM AUTHOR]

A new land surface scheme has been developed at Environment and Climate Change Canada (ECCC) to provide surface fluxes of momentum, heat, and moisture for the Global Environmental Multiscale (GEM) atmospheric model. In this study, the performance of the Soil, Vegetation, and Snow (SVS) scheme in estimating the surface and root-zone soil moisture is evaluated against the Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme currently used operationally at ECCC within GEM for numerical weather prediction. In addition, the sensitivity of SVS soil moisture results to soil texture and vegetation data sources (type and fractional coverage) has been explored. The performance of SVS and ISBA was assessed against a large set of in situ observations as well as the brightness temperature data from the Soil Moisture Ocean Salinity (SMOS) satellite over North America. The results indicate that SVS estimates the time evolution of soil moisture more accurately, and compared to ISBA, results in higher correlations with observations and reduced errors. The sensitivity tests carried out during this study revealed that the SVS soil moisture results are not affected significantly by the soil texture data from different sources. The vegetation data source, however, has a major impact on the soil moisture results predicted by SVS, and accurate specification of vegetation characteristics is therefore crucial for accurate soil moisture prediction. [ABSTRACT FROM AUTHOR]

A method for projecting the water levels of the Laurentian Great Lakes under scenarios of human-caused climate change, used almost to the exclusion of other methods in the past, relies very heavily on the large basin runoff model (LBRM) as a component for determining the water budget for the lake system. This model uses near-surface air temperature as a primary predictor of evapotranspiration (ET); as in previous published work, it is shown here that the model's very high sensitivity to temperature causes it to overestimate ET in a way that is greatly at variance with the fundamental principle of conservation of energy at the land surface. The traditional formulation is characterized here as being equivalent to having several suns in the virtual sky created by LBRM. More physically based methods show, relative to the traditional method, often astoundingly less potential ET and less ET, more runoff from the land and net basin supply for the lake basins, and higher lake water levels in the future. Using various methods of estimating the statistical significance, it is found that, at minimum, these discrepancies in results are significant at the 99.998% level. The lesson for the larger climate impact community is to use caution about whether an impact is forced directly by air temperature itself or is significantly forced by season or latitude independently of temperature. The results here apply only to the water levels of the Great Lakes and the hydrology of its basin and do not affect larger questions of climate change. [ABSTRACT FROM AUTHOR]

Land surface schemes (LSSs) are of potential interest both to hydrologists looking for innovative ways to simulate river flow and the land surface water balance and to atmospheric scientists looking to improve weather and climate predictions. This paper discusses three ideas, which are grounded in hydrological science, to improve LSS predictions of streamflow and latent heat fluxes. These three possibilities are 1) improved representation of lateral flow processes, 2) the appropriate representation of surface heterogeneity, and 3) calibration to streamflow as a way to account for parameter uncertainty. The current understanding of lateral hydrological processes is described along with their representation of a selected group of LSSs. Issues around spatial heterogeneity are discussed, and calibration in hydrologic models and LSSs is examined. A case study of an evapotranspiration-dominated basin with over 10 years of extensive observations in central Canada is presented. The results indicate that in this particular basin, calibration of streamflow presents atmospheric modelers with a unique opportunity to improve upon the current practice of using lookup tables to define parameter values. More studies are needed to determine if model calibration to streamflow is an appropriate method for generally improving LSS-modeled heat fluxes around the globe. [ABSTRACT FROM AUTHOR]

The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) illustrated the value of prescribing a priori performance targets in model intercomparisons. It showed that the performance of turbulent energy flux predictions from different land surface models, at a broad range of flux tower sites using common evaluation metrics, was on average worse than relatively simple empirical models. For sensible heat fluxes, all land surface models were outperformed by a linear regression against downward shortwave radiation. For latent heat flux, all land surface models were outperformed by a regression against downward shortwave radiation, surface air temperature, and relative humidity. These results are explored here in greater detail and possible causes are investigated. It is examined whether particular metrics or sites unduly influence the collated results, whether results change according to time-scale aggregation, and whether a lack of energy conservation in flux tower data gives the empirical models an unfair advantage in the intercomparison. It is demonstrated that energy conservation in the observational data is not responsible for these results. It is also shown that the partitioning between sensible and latent heat fluxes in LSMs, rather than the calculation of available energy, is the cause of the original findings. Finally, evidence is presented that suggests that the nature of this partitioning problem is likely shared among all contributing LSMs. While a single candidate explanation for why land surface models perform poorly relative to empirical benchmarks in PLUMBER could not be found, multiple possible explanations are excluded and guidance is provided on where future research should focus. [ABSTRACT FROM AUTHOR]

The ability of the Canadian Seasonal to Interannual Prediction System (CanSIPS) to provide realistic forecast initial conditions for snow cover is assessed using in situ measurements and gridded snow analyses. Forecast initial conditions for snow in CanCM3 and CanCM4 employed by CanSIPS are determined by the response of the Canadian Land Surface Scheme (CLASS) used in both models to forcing from model atmospheric fields constrained by assimilation of 6-hourly reanalysis data. These snow initial conditions are found to be representative of the daily climatology of snow water equivalent (SWE) as well as interannual variations in maximum SWE and the timing of snow onset and snowmelt observed at eight in situ measurement sites located across Canada. The level of this agreement is similar to that of three independent gridded snow analyses (MERRA, the European Space Agency's GlobSnow, and an offline forced version of CLASS). Total Northern Hemisphere snow mass generated by the CanSIPS initialization procedure is larger for both models (especially CanCM3) than in MERRA, mostly because of higher SWE in regions of common snow cover. Globally, the interannual variability of initial SWE is found to correlate highly with that of MERRA in locations with appreciable snow. These initial values are compared to SWE in freely running CanCM3 and CanCM4 simulations produced without data assimilation of atmospheric fields. Differences in climatological SWE relative to MERRA are similar in the freely running and assimilating CanCM3 and CanCM4 simulations, suggesting that inherent model biases are a major contributor to biases in CanSIPS snow initial conditions. [ABSTRACT FROM AUTHOR]

In present-day Earth system models, the coupling of land surface and atmosphere is based on simplistic assumptions. Often the heterogeneous land surface is represented by a set of effective parameters valid for an entire model grid box. Other models assume that the surface fluxes become horizontally homogeneous at the lowest atmospheric model level. For heterogeneity above a certain horizontal length scale this is not the case, resulting in spatial subgrid-scale variability in the fluxes and in the state of the atmosphere. The Max Planck Institute for Meteorology's Earth System Model is used with three different coupling schemes to assess the importance of the representation of spatial heterogeneity at the land surface as well as within the atmosphere. Simulations show that the land surface-atmosphere coupling distinctly influences the simulated near-surface processes with respect to different land-cover types. The representation of heterogeneity also has a distinct impact on the simulated gridbox mean state and fluxes in a large fraction of land surface. [ABSTRACT FROM AUTHOR]

Current land surface models (LSMs) tend to largely underestimate the daytime land surface temperature for high-altitude regions. This is partly because of underestimation of heat transfer resistance, which may be resolved through adequate parameterization of roughness lengths for momentum and heat transfer. In this paper, the regional-scale effects of the roughness length parameterizations for alpine grasslands are addressed and the performance of the Noah LSM using the updated roughness lengths compared to the original ones is assessed. The simulations were verified with various satellite products and validated with ground-based observations. More specifically, four experimental setups were designed using two roughness length schemes with two different parameterizations of (original and updated). These experiments were conducted in the source region of the Yangtze River during the period 2005-10 using the Noah LSM. The results show that the updated parameterizations of roughness lengths reduce the mean biases of the simulated daytime in spring, autumn, and winter by up to 2.7 K, whereas larger warm biases are produced in summer. Moreover, model efficiency coefficients (Nash-Sutcliffe) of the monthly runoff results are improved by up to 26.3% when using the updated roughness parameterizations. In addition, the spatial effects of the roughness length parameterizations on the simulations are discussed. This study stresses the importance of proper parameterizations of and for LSMs and highlights the need for regional adaptation of the and values. [ABSTRACT FROM AUTHOR]

Using satellite measurements in microwave bands to retrieve precipitation over land requires proper discrimination of the weak rainfall signals from strong and highly variable background Earth surface emissions. Traditionally, land retrieval methods rely on a weak signal of rainfall scattering on high-frequency channels and make use of empirical thresholding and regression-based techniques. Because of the increased surface signal interference, retrievals over radiometrically complex land surfaces-snow-covered lands, deserts, and coastal areas-are particularly challenging for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken Locally Linear Embedding Algorithm for Retrieval of Precipitation (ShARP) using data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The study focuses on a radiometrically complex region, partly covering the Tibetan highlands, Himalayas, and Ganges-Brahmaputra-Meghna River basins, which is unique in terms of its diverse land surface radiation regime and precipitation type, within the TRMM domain. Promising results are presented using ShARP over snow-covered land surfaces and in the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM 2A12, version 7, product. The results show that ShARP can significantly reduce the rainfall overestimation due to the background snow contamination and markedly improve detection and retrieval of rainfall in the vicinity of coastlines. During the calendar year 2013, compared to TRMM 2A25, it is demonstrated that over the study domain the root-mean-square difference can be reduced up to 38% annually, while the improvement can reach up to 70% during the cold months of the year. [ABSTRACT FROM AUTHOR]

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those it is found that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely because of differences in instrumentation, calibration, and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat-dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory), and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but they poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration, or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison. [ABSTRACT FROM AUTHOR]

Soil moisture plays a fundamental role in regulating the summertime surface energy balance across Europe. Understanding the spatial and temporal behavior in soil moisture and its control on evapotranspiration (ET) is critically important and influences heat wave events. Global climate models (GCMs) exhibit a broad range of land responses to soil moisture in regions that lie between wet and dry soil regimes. In situ observations of soil moisture and evaporation are limited in space, and given the spatial heterogeneity of the landscape, are unrepresentative of the GCM gridbox scale. On the other hand, satelliteborne observations of land surface temperature (LST) can provide important information at the larger scale. As a key component of the surface energy balance, LST is used to provide an indirect measure of surface drying across the landscape. To isolate soil moisture constraints on evaporation, time series of clear-sky LST are analyzed during dry spells lasting at least 10 days from March to October. Averaged over thousands of dry spell events across Europe, and accounting for atmospheric temperature variations, regional surface warming of between 0.5 and 0.8 K is observed over the first 10 days of a dry spell. Land surface temperatures are found to be sensitive to antecedent rainfall; stronger dry spell warming rates are observed following relatively wet months, indicative of soil moisture memory effects on the monthly time scale. Furthermore, clear differences in surface warming rate are found between cropland and forest, consistent with contrasting hydrological and aerodynamic properties. [ABSTRACT FROM AUTHOR]

Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASA's Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land-atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS-WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spinup can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux-PBL-ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research. [ABSTRACT FROM AUTHOR]

This study presents a numerical analysis of the impact of the horizontal resolution on the forecast capability of the Canadian offline land surface prediction system (SPS; formerly known as GEM-Surf) forced by the 15-km Global Environmental Multiscale (GEM) atmospheric model. This system is used to quantify on a statistical basis the subgrid-scale variability of (near-)surface variables for 25-km grid spacing based on the 2.5- or 10-km SPS run at regional scale over the 2012 summer season. The model bias and the distributions characterizing the subgrid-scale variability drastically depend on the geographic areas as well as on the diurnal cycle. These results show the benefits of high-resolution land surface simulations to account for length scales that are more consistent with the scales at which the actual land surface balance is affected by the heterogeneous geophysical fields (i.e., roughness length, land-water mask, glacier mask, and soil texture). The model bias results highlight the potential of an SPS-GEM two-way coupling strategy for refining predictions near the surface through the upscaling of high-resolution surface heat fluxes to the coarser atmospheric grid spacing, with these fluxes being significantly different from those explicitly resolved at 25 km and featuring nonlinear behavior with respect to the horizontal resolution. Since the computational power of meteorological operational centers progressively increases, making it possible to run high-resolution limited-area models, solving the surface at high resolution in a surface-atmosphere fully coupled system becomes a key aspect for improving numerical weather and environmental forecast performance. [ABSTRACT FROM AUTHOR]

This study explores the performance of Environment Canada's Surface Prediction System (SPS) in comparison to in situ observations from the Brightwater Creek soil moisture observation network with respect to soil moisture and soil temperature. To do so, SPS is run at hyperresolution (100 m) over a small domain in southern Saskatchewan (Canada) during the summer of 2014. It is shown that with initial conditions and surface condition forcings based on observations, SPS can simulate soil moisture and soil temperature evolution over time with high accuracy (mean bias of 0.01 m3 m−3 and −0.52°C, respectively). However, the modeled spatial variability is generally much weaker than observed. This is likely related to the model's use of uniform soil texture, the lack of small-scale orography, as well as a predefined crop growth cycle in SPS. Nonetheless, the spatial averages of simulated soil conditions over the domain are very similar to those observed, suggesting that both are representative of large-scale conditions. Thus, in the context of the National Aeronautics and Space Administration's (NASA) Soil Moisture Active Passive (SMAP) project, this study shows that both simulated and in situ observations can be upscaled to allow future comparison with upcoming satellite data. [ABSTRACT FROM AUTHOR]

Soil moisture observations from seven observational networks (spanning portions of seven states) with different biome and climate conditions were used in this study to evaluate multimodel simulated soil moisture products. The four land surface models, including Noah, Mosaic, Sacramento soil moisture accounting (SAC), and the Variable Infiltration Capacity model (VIC), were run within phase 2 of the North American Land Data Assimilation System (NLDAS-2), with a ⅛° spatial resolution and hourly temporal resolution. Hundreds of sites in Alabama, Colorado, Michigan, Nebraska, Oklahoma, West Texas, and Utah were used to evaluate simulated soil moisture in the 0-10-, 10-40-, and 40-100-cm soil layers. Soil moisture was spatially averaged in each state to reduce noise. In general, the four models captured broad features (e.g., seasonal variation) of soil moisture variations in all three soil layers in seven states, except for the 10-40-cm soil layer in West Texas and the 40-100-cm soil layer in Alabama, where the anomaly correlations are weak. Overall, Mosaic, SAC, and the ensemble mean have the highest simulation skill and VIC has the lowest simulation skill. The results show that Noah and VIC are wetter than the observations while Mosaic and SAC are drier than the observations, mostly likely because of systematic errors in model evapotranspiration. [ABSTRACT FROM AUTHOR]

Accurately measuring interannual variability in terrestrial evapotranspiration ET is a major challenge for efforts to detect trends in the terrestrial hydrologic cycle. Based on comparisons with annual values of terrestrial evapotranspiration derived from a terrestrial water balance analysis, past research has cast doubt on the ability of existing products to accurately capture variability. Using a variety of estimates, this analysis reexamines this conclusion and finds that estimates of variations obtained from a land surface model are more strongly correlated with independently acquired from thermal infrared remote sensing than derived from water balance considerations. This tendency is attributed to significant interannual variations in terrestrial water storage neglected by the water balance approach. Overall, results demonstrate the need to reassess perceptions concerning the skill of estimates derived from land surface models and show the value of accurate remotely sensed ET products for the validation of interannual ET. [ABSTRACT FROM AUTHOR]

In recent years, increased attention has been paid to the role of previously neglected water source (e.g., irrigation, direct groundwater extraction, and inland water bodies) and sink (e.g., tile drainage) processes on the surface energy balance. However, efforts to parameterize these processes within land surface models (LSMs) have generally been hampered by a lack of appropriate observational tools for directly observing the impact(s) of such processes on surface energy fluxes. One potential strategy for quantifying these impacts are direct comparisons between bottom-up surface energy flux predictions from a one-dimensional, free-drainage LSM with top-down energy flux estimates derived via thermal infrared remote sensing. The neglect of water source (and/or sink) processes in the bottom-up LSM can be potentially diagnosed through the presence of systematic energy flux biases relative to the top-down remote sensing retrieval. Based on this concept, the authors introduce the Atmosphere-Land Exchange Inverse (ALEXI) Source-Sink for Evapotranspiration (ASSET) index derived from comparisons between ALEXI remote sensing latent heat flux retrievals and comparable estimates obtained from the Noah LSM, version 3.2. Comparisons between ASSET index values and known spatial variations of groundwater depth, irrigation extent, inland water bodies, and tile drainage density within the contiguous United States verify the ability of ASSET to identify regions where neglected soil water source-sink processes may be impacting modeled surface energy fluxes. Consequently, ASSET appears to provide valuable information for ongoing efforts to improve the parameterization of new water source-sink processes within modern LSMs. [ABSTRACT FROM AUTHOR]

The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010-11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation. [ABSTRACT FROM AUTHOR]

The Canadian Land Data Assimilation System (CaLDAS) has been developed at the Meteorological Research Division of Environment Canada (EC) to better represent the land surface initial states in environmental prediction and assimilation systems. CaLDAS is built around an external land surface modeling system and uses the ensemble Kalman filter (EnKF) methodology. A unique feature of CaLDAS is the use of improved precipitation forcing through the assimilation of precipitation observations. An ensemble of precipitation analyses is generated by combining numerical weather prediction (NWP) model precipitation forecasts with precipitation observations. Spatial phasing errors to the NWP first-guess precipitation forecasts are more effective than perturbations to the precipitation observations in decreasing (increasing) the exceedance ratio (uncertainty ratio) scores and generating flatter, more reliable ranked histograms. CaLDAS has been configured to assimilate L-band microwave brightness temperature TB by coupling the land surface model with a microwave radiative transfer model. A continental-scale synthetic experiment assimilating passive L-band TBs for an entire warm season is performed over North America. Ensemble metric scores are used to quantify the impact of different atmospheric forcing uncertainties on soil moisture and TB ensemble spread. The use of an ensemble of precipitation analyses, generated by assimilating precipitation observations, as forcing combined with the assimilation of L-band TBs gave rise to the largest improvements in superficial soil moisture scores and to a more rapid reduction of the root-zone soil moisture errors. Innovation diagnostics show that the EnKF is able to maintain a sufficient forecast error spread through time, while soil moisture estimation error improvements with increasing ensemble size were limited. [ABSTRACT FROM AUTHOR]