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In this paper, the development and global application of a new approach to large-scale river routing is described. It differs from previous methods by the extent to which the information content of high-resolution global digital elevation models is exploited in a computationally efficient framework. The model transports runoff directly from its source of generation in a land model cell to its sink on a continental margin or in an internally draining basin (and hence is referred to as source-to-sink routing) rather than from land cell to land cell (which we call cell-to-cell routing). It advances the development of earlier source-to-sink models by allowing for spatially distributed flow velocities, attenuation coefficients, and loss parameters. The method presented here has been developed for use in climate system models, with a specific goal of generating hydrographs at continental margins for input into an ocean model. However, the source-to-sink approach is flexible and can be applied at any space-time scale and in a number of other types of large-scale hydrological and Earth system models. Hydrographs for some of the world's major river basins resulting from a global application, as well as hydrographs for the Nile River from a more detailed application, are discussed.

The Parameterization for Land–Atmosphere–Cloud Exchange (PLACE), a typical surface–vegetation–atmosphere transfer (SVAT) parameterization, was used in a case study of a 2500 km2 area in southwestern Oklahoma for 9–16 July 1997. The research objective was to assess PLACE’s simulation of the spatial variability and temporal evolution of soil moisture and heat fluxes without optimization for this case study. Understanding PLACE’s performance under these conditions may provide perspective on results from more complex coupled land–atmosphere simulations involving similar land surface schemes in data-poor environments. Model simulations were initialized with simple initial soil moisture and temperature profiles tied to soil type and forced by standard meteorological observations. The model equations and parameters were not adjusted or tuned to improve results.For surface soil moisture, 5- and 10-cm soil temperature, and surface fluxes, the most accurate simulation (5% error for soil moisture and 2 K for 5- and 10-cm soil temperature) occurred during the 48 h following heavy rainfall on 11 and 15 July. The spatial pattern of simulated soil moisture was controlled more strongly by soil texture than was observed soil moisture, and the error was correlated with rainfall. The simplifications of the subsurface soil moisture, soil texture, and vegetation cover initialization schemes and the uncertainty in the rainfall data (>10%) could account for differences between modeled and observed surface fluxes that are on the order of 100 W m−2 and differences in soil moisture that are greater than 5%. It also is likely that the soil thermal conductivity scheme in PLACE damped PLACE’s response to atmospheric demand after 13 July, resulting in reduced evapotranspiration and warmer but slower-drying soils. Under dry conditions, the authors expect that SVATs such as PLACE that use a similar simple initialization also would demonstrate a strong soil texture control on soil moisture and surface fluxes and limited spatial variability.

The 1993 summer rainfall over the Midwestern United States caused major flooding in the Upper Mississippi and Lower Missouri River basins. In this paper, a monthly comparative study of the analysis products from the European Centre for Medium-Range Weather Forecasts (ECMWF) with observations is conducted for the period September 1992 to August 1993. The model products include the ECMWF analysis for the Tropical Ocean and Global Atmosphere (TOGA) project and ECMWF reanalysis (ERA) project. Overall, the large-scale hydrological components (precipitation, evaporation, precipitable water, surface temperature, and relative humidity) appear partially improved from TOGA to ERA. Some internal inconsistencies exist in the assimilation of the ERA data from October 1992 to March 1993, in association with the dramatic prevalence of large-scale moisture divergence at 0000 UT. The separation of moisture flux into meridional and zonal components indicates that the correct description of the meridional moisture flow is of crucial importance for simulation of anomalously wet conditions. While the spatially averaged monthly fields in ERA and TOGA are reasonable (in particular, the models maintain very good mean flow), the fine-scale extremes are less accurate. The models do not reproduce observed mesoscale convective complexes due to limitations on resolution and the parameterization of precipitation processes. The new soil hydrology and boundary layer schemes of ERA work better in warm months but with no consistent improvement over TOGA. The terrestrial water budget in ERA is unrealistic, with accumulated model runoff being largely deviated from the observed streamflow. In addition to modeling internal water balance, the lack of the routing procedure and the low spatial resolution of surface hydrology are the other major reasons for this imbalance.

This study compiled a database of precipitating cloud clusters from 85-GHz data in 10 regions of the wet Tropics for a calendar year (November 1992–October 1993). The cloud clusters were grouped into four classes of basic system types, based on size (closed 250 K contour greater or less than 2000 km2) and minimum enclosed 85-GHz brightness temperature (greater or less than 225 K) to indicate the presence or absence of large areas of active, deep convection. For each cloud cluster, instantaneous volumetric rain rates (mm km2 h−1) were calculated using an 85-GHz ice-scattering-based rain-rate retrieval algorithm. Because the ice-scattering signature is linearly related to but does not directly measure rain rate, the methodology was appropriate for estimating relative contributions rather than quantifying tropical rainfall.For the 3-month wet season of each study region, the rainfall contributions with respect to system type, size, and intensity were calculated. Regional differences were small among the contributions with respect to system type and to precipitating area. Although mesoscale convective systems constituted 10%–20% of the regional populations, they contributed 70%–80% of the rainfall. With respect to cloud cluster area, the top 10% of cloud cluster areas contributed more than 70% of the rainfall, and the top 1% (greater than 20 000 km2) contributed about 35% of the total rainfall. Regional differences were apparent in the distributions of rainfall contribution with respect to minimum brightness temperature. The Amazon’s distribution more closely resembled the oceanic distributions than the continental distributions. The distributions of the oceanic regions peaked at 200 K, and over half of the rain in the oceanic regions was contributed by the fewer than 20% of the cloud clusters colder than 210 K. Distributions in the continental regions peaked at 175 K. A total of 70%–80% of the rain was contributed by the 20%–30% of continental cloud clusters colder than 200 K, with nonnegligible contributions from a small number of cloud clusters colder than 120 K. Sub-Saharan Africa had the largest contribution from cloud clusters colder than 120 K.

The feasibility of synthesizing distributed fields of soil moisture by the novel application of four-dimensional data assimilation (4DDA) applied in a hydrological model is explored. Six 160-km2 push broom microwave radiometer (PBMR) images gathered over the Walnut Gulch experimental watershed in southeast Arizona were assimilated into the Topmodel-based Land-Atmosphere Transfer Scheme (TOPLATS) using several alternative assimilation procedures. Modification of traditional assimilation methods was required to use these high-density PBMR observations. The images were found to contain horizontal correlations that imply length scales of several tens of kilometers, thus allowing information to be advected beyond the area of the image. Information on surface soil moisture also was assimilated into the subsurface using knowledge of the surface- subsurface correlation. Newtonian nudging assimilation procedures are preferable to other techniques because they nearly preserve the observed patterns within the sampled region but also yield plausible patterns in unmeasured regions and allow information to be advected in time.

Evaporation (ET) is one of the crucial components of the water cycle, which serves as the nexus between global water, energy, and carbon cycles. Accurate quantification of ET is, therefore, pivotal in understanding various earth system processes and subsequent societal applications. The prevailing approaches for ET retrievals are either limited in spatiotemporal coverage or largely influenced by choice of input data or simplified model physics, or a combination thereof. Here, using an independent mass conservation approach, we develop water balance-based ET datasets (ET-WB) for the global land and the selected 168 major river basins. We generate 4669 probabilistic unique combinations of the ET-WB leveraging multi-source datasets (23 precipitation, 29 runoff, and 7 storage change datasets) from satellite products, in-situ measurements, reanalysis, and hydrological simulations. We compare our results with the four auxiliary global ET datasets and previous regional studies, followed by a rigorous discussion of the uncertainties, their possible sources, and potential ways to constrain them. The seasonal cycle of global ET-WB possesses a unimodal distribution with the highest (median value: 65.61 mm/month) and lowest (median value: 36.11 mm/month) values in July and January, respectively, with the spread range of roughly ±10 mm/month from different subsets of the ensemble. Auxiliary ET products illustrate similar intra-annual characteristics with some over/under-estimation, which are completely within the range of the ET-WB ensemble. We found a gradual increase in global ET-WB from 2003 to 2010 and a subsequent decrease during 2010–2015, followed by a sharper reduction in the remaining years primarily attributed to the varying precipitation. Multiple statistical metrics show reasonably good accuracy of monthly ET-WB (e.g., a relative bias of ±20 %) in most river basins, which ameliorates at annual scales. The long-term mean annual ET-WB varies within 500–600 mm/yr and is consistent with the for auxiliary ET products (543–569 mm/yr). Observed trend estimates, though regionally divergent, are evidence of the increasing ET in a warming climate. The current dataset will likely be useful for several scientific assessments centering around water resources management to benefit society at large. [ABSTRACT FROM AUTHOR]

The US Northern Great Plains and the Canadian Prairies are known as the world's breadbaskets for its large spring wheat production and exports to the world. It is essential to accurately represent spring wheat growing dynamics and final yield and improve our ability to predict food production under climate change. This study attempts to incorporate spring wheat growth dynamics into the Noah-MP crop model, for a long time period (13-year) and fine spatial scale (4-km). The study focuses on three aspects: (1) developing and calibrating the spring wheat model at point-scale, (2) applying a dynamic planting/harvest date to facilitate large-scale simulations, and (3) applying a temperature stress function to assess crop responses to heat stress amid extreme heat. Model results are evaluated using field observations, satellite leaf area index (LAI), and census data from Statistics Canada and the US Department of Agriculture (USDA). Results suggest that incorporating a dynamic planting/harvest threshold can better constrain the growing season, especially the peak timing and magnitude of wheat LAI, as well as obtain realistic yield compared to prescribing a static province/state-level map. Results also demonstrate an evident control of heat stress upon wheat yield in three Canadian Prairies Provinces, which are reasonably captured in the new temperature stress function. This study has important implications for estimating crop productions, simulating the land atmosphere interactions in croplands, and crop growth's responses to the raising temperatures amid climate change. [ABSTRACT FROM AUTHOR]