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Endorheic and arid regions around the world are suffering from serious drought problems. In this study, a drought forecasting system based on eight state-of-the-art climate models from the North American Multi-Model Ensemble (NMME) and a Distributed Time-Variant Gain Hydrological Model (DTVGM) was established and assessed over the upstream and midstream of Heihe River basin (UHRB and MHRB), a typical arid endorheic basin. The 3-month Standardized Precipitation Index (SPI3) and 1-month Standardized Streamflow Index (SSI1) were used to capture meteorological and hydrological drought, and values below -1 indicate drought events. The skill of the forecasting systems was evaluated in terms of anomaly correlation (AC) and Brier score (BS) or Brier skill score (BSS). The predictability for meteorological drought was quantified using AC and BS with a “perfect model" assumption, referring to the upper limit of forecast skill. The hydrological predictability was to distinguish the role of initial hydrological conditions (ICs) and meteorological forcings, which was quantified by root-mean-square error (RMSE) within the ESP (Ensemble Streamflow Prediction) and reverse ESP framework. The UHRB and MHRB showed season-dependent meteorological drought predictability and forecast skill, with higher values during winter and autumn than that during spring. For hydrological forecasts, the forecast skill in the UHRB was higher than that in MHRB. Predicting meteorological droughts more than 2 months in advance became difficult because of complex climate mechanisms. However, the hydrological drought forecasts could show some skills up to 3-6 lead months due to memory of ICs during cold and dry seasons. During wet seasons, there are no skillful hydrological predictions from lead month 2 onwards because of the dominant role of meteorological forcings. During spring, the improvement of hydrological drought predictions was the most significant as more streamflow was generated by seasonal snowmelt. Besides meteorological forcings and ICs, human activities have reduced the hydrological variability and increased hydrological drought predictability during the wet seasons in the MHRB. [ABSTRACT FROM AUTHOR]

Better quantification of the spatiotemporal distribution of soil moisture across different spatial scales contributes significantly to the understanding of land surface processes on the Earth as an integrated system. While observational data for root-zone soil moisture (RZSM) often have sparse spatial coverage, model-simulated soil moisture may provide a useful alternative. TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) has been widely studied and actively modified in recent years, while a detailed regional application with evaluation currently is still lacking. Thus, TOPLATS was used to generate high-resolution (30 arc s) RZSM based on coarse-scale (0.125°) forcing data over part of the Arkansas–Red River basin. First, the simulated RZSM was resampled to coarse scale to compare with the results of Mosaic, Noah, and VIC from NLDAS. Second, TOPLATS performance was assessed based on the spatial absolute difference among the models. The comparison shows that TOPLATS performance is similar to VIC, but different from Mosaic and Noah. Last, the simulated RZSM was compared with in situ observations of 16 stations in the study area. The results suggest that the simulated spatial distribution of RZSM is largely consistent with the distribution of topographic index (TI) in most instances, as topography was traditionally considered a major, but not the only, factor in horizontal redistribution of soil moisture. In addition, the finer-resolution RZSM can reflect the in situ soil moisture change at most local sites to a certain degree. The evaluation confirms that TOPLATS is a useful tool to estimate high-resolution soil moisture and has great potential to provide regional soil moisture estimates. [ABSTRACT FROM AUTHOR]

Extreme weather and climate events, especially short-term excessive drought and wet periods over agricultural areas, have received increased attention. The Southern Great Plains (SGP) is one of the largest agricultural regions in North America and features the underlying Ogallala-High Plains Aquifer system worth great economic value in large part due to production gains from groundwater. Climate research over the SGP is needed to better understand complex coupled climate-hydrology-socioeconomic interactions critical to the sustainability of this region, especially under extreme climate scenarios. Here the authors studied growing-season extreme conditions using the Weather Research and Forecasting (WRF) Model. The six most extreme recent years, both wet and dry, were simulated to investigate the impacts of land surface model and cumulus parameterization on the simulated hydroclimate. The results show that under short-term climate extremes, the land surface model plays a more important role modulating the land-atmosphere water budget, and thus the entire regional climate, than the cumulus parameterization under current model configurations. Between the two land surface models tested, the more sophisticated land surface model produced significantly larger wet bias in large part due to overestimation of moisture flux convergence, which is attributed mainly to an overestimation of the surface evapotranspiration during the simulated period. The deficiencies of the cumulus parameterizations resulted in the model's inability to depict the diurnal rainfall variability. Both land surface processes and cumulus parameterizations remain the most challenging parts of regional climate modeling under extreme climates over the SGP, with the former strongly affecting the precipitation amount and the latter strongly affecting the precipitation pattern. [ABSTRACT FROM AUTHOR]

Seasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July-September (JAS)] to late autumn [October-December (OND)] and from winter [December-February (DJF)] to spring [March-May (MAM)]. The skill of CFSv2 precipitation forecasts is low during summer [June-August (JJA)] and winter (DJF) over all of China because of low potential predictability of the East Asian summer monsoon and the East Asian winter monsoon for China. As expected, temperature predictive skill is much higher than precipitation predictive skill in all regions. As observed precipitation shows significant correlation with the Oceanic Niño index over western, southwestern, and central China, the authors found that CFSv2 precipitation forecasts generally show similar correlation pattern, suggesting that CFSv2 precipitation forecasts can capture ENSO signals. This evaluation suggests that using CFSv2 forecasts for seasonal hydrological prediction over China is promising and challenging. [ABSTRACT FROM AUTHOR]

Soil temperature plays an important role in hydrology, agriculture, and meteorology. In order to improve the accuracy of soil temperature simulation, a soil temperature data assimilation system was developed based on the Ensemble Particle Filter (EnPF) and the Common Land Model (CLM), and then applied in the Walnut Gulch Experimental Watershed (WGEW) in Arizona, United States. Surface soil temperature in situ observations and Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST) data were assimilated into the system. In this study, four different assimilation experiments were conducted: (1) assimilating in situ observations of instantaneous surface soil temperature each hour, (2) assimilating in situ observations of instantaneous surface soil temperature once per day, (3) assimilating verified MODIS LST once per day, and (4) assimilating original MODIS LST once per day. These four experiments reflect a transition from high-quality and more frequent in situ observations to lower quality and less frequent remote sensing data in the data assimilation system. The results from these four experiments show that the assimilated results are better than the simulated results without assimilation at all layers except the bottom layer, while the superiority gradually diminishes as the quality and frequency of the observations decrease. This demonstrates that remote sensing data can be assimilated using the ensemble particle filter in poorly gauged catchments to obtain highly accurate soil variables (e.g., soil moisture, soil temperature). Meanwhile, the results also demonstrate that the ensemble particle filter is effective in assimilating soil temperature observations to improve simulations, but the performance of the data assimilation method is affected by the frequency of assimilation and the quality of the input data. [ABSTRACT FROM AUTHOR]

Improving flood modeling accuracy is crucial for real-time flood monitoring and early warning systems. Knowing the sources, patterns and driving factors of model uncertainty aids the development of more accurate flood predictions. This study investigates the consistency of two global flood inundation products, i.e., the Soil Moisture Active Passive (SMAP) satellite based fractional water (Fw) cover and the Global Flood Monitoring System (GFMS) modeled flood inundation. Using Pearson's correlation coefficient (r) as the indicator of the SMAP-GFMS model consistency, this research documents the spatial and temporal patterns of the correlations between the two flood products, and investigates factors affecting these relationships, including climate, land cover, hydrology and terrain distributions. Results reveal that globally, 64% locations have moderate to strong SMAP-GFMS correlation (r ≥ 0.4). Locations that are dry and have low biomass and high seasonal flood variability tend to have high correlation; for example, 47% locations with r ≥ 0.4 occur in tropical and arid climate zones, and 43% locations with r ≥ 0.4 are observed in Barren , Evergreen Broadleaf Forest , Grasslands , Open Shrubland and Savannahs. Also, larger rivers have higher correlation, and in each Strahler stream order there are 60% to 65% locations having r ≥ 0.4. Larger watersheds show higher SMAP-GFMS consistency in particular watersheds between 1000 and 40,000 km2. Regions with greater urban infrastructure tend to have lower correlation, while locations with lower elevations and relatively flat topography have higher SMAP-GFMS consistency. This study indicates that GFMS and SMAP provide complementary information on surface water storage variations influencing precipitation driven runoff and flooding, which may enable enhanced global flood predictions. • Consistency of SMAP-derived and GFMS-modeled flood inundation is investigated. • 64% locations have moderate to strong SMAP-GFMS correlation. • Rivers with high Strahler stream orders show high SMAP-GFMS consistency. • Areas with dry climate, low/flat topography, low biomass show high consistency. • GFMS and SMAP provide complementary information on surface water dynamics. [ABSTRACT FROM AUTHOR]