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Model calibration has always been one major challenge in the hydrological community. Flood scaling properties (FS) are often used to estimate the flood quantiles for data-scarce catchments based on the statistical relationship between flood peak and contributing areas. This paper investigates the potential of applying FS and multivariate flood scaling properties [multiple linear regression (MLR)] as constraints in model calibration. Based on the assumption that the scaling property of flood exists in four study catchments in northern China, eight calibration scenarios are designed with adopting different combinations of traditional indicators and FS or MLR as objective functions. The performance of the proposed method is verified by employing a distributed hydrological model, namely, the Soil and Water Assessment Tool (SWAT) model. The results indicate that reasonable performance could be obtained in FS with fewer requirements of observed streamflow data, exhibiting better simulation of flood peaks than the Nash-Sutcliffe efficiency coefficient calibration scenario. The observed streamflow data or regional flood information are required in the MLR calibration scenario to identify the dominant catchment descriptors, and MLR achieves better performance on catchment interior points, especially for the events with uneven distribution of rainfall. On account of the improved performance on hydrographs and flood frequency curve at the watershed outlet, adopting the statistical indicators and flood scaling property simultaneously as model constraints is suggested. The proposed methodology enhances the physical connection of flood peak among sub-basins and considers watershed actual conditions and climatic characteristics for each flood event, facilitating a new calibration approach for both gauged catchments and data-scarce catchments. [ABSTRACT FROM AUTHOR]

Initial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models. [ABSTRACT FROM AUTHOR]

The objective of the North American Land Data Assimilation System (NLDAS) is to provide best-available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin-averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts. [ABSTRACT FROM AUTHOR]

This paper presents a detailed analysis of how future climate change may affect water availability in a typical arid endorheic river basin, the Heihe River basin (HRB), in northwest China. The analysis is based on the improved Soil Water Assessment Tool (SWAT), which is calibrated and validated with historical streamflow data from the upper HRB and is used to predict future hydrological responses. Six general circulation models (GCMs), under two emission scenarios (RCP4.5 and RCP8.5), are downscaled to construct future climate change scenarios. The results suggest that the climate of the upper HRB will likely become warmer and wetter in the near future (2021-50), with the largest increase in precipitation occurring in the summer. Correspondingly, the basinwide evapotranspiration, snowmelt, and runoff are projected to increase over the same period. The mean temperature in the near future is projected to rise, relative to the recent 30 years (1981-2010), by 1.2°-1.7°C under scenario RCP4.5 and by 1.4°-2.1°C under scenario RCP8.5. The mean precipitation is projected to increase by 10.0%-16.6% under scenario RCP4.5, and by 10.5%-22.0% under scenario RCP8.5. The mean values of evapotranspiration, snowmelt, and runoff are expected to increase by 14.2%, 4.3%, and 11.4%, respectively, under scenario RCP4.5 and to increase by 18.7%, 5.8%, and 12.8%, respectively, under scenario RCP8.5. Though the model simulations forecast an increase in streamflows in the headwater region of the HRB, future water availability varies significantly over space and time. The findings of this study will help to frame more effective water management strategies for the HRB under changing climatic conditions. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between vegetation in the Liangzhou Oasis in the Upper Shiyang River watershed (USRW) of west-central Gansu, China, and within-watershed precipitation, soil water storage, and oasis self-support. Oases along the base of the Qilian Mountains receive a significant portion of their water supply (over 90%) from surface and subsurface flow originating from the Qilian Mountains. Investigation of vegetation control on oasis water conditions in the USRW is based on an application of a process model of soil water hydrology. The model is used to simulate long-term soil water content (SWC) in the Liangzhou Oasis as a function of (i) monthly composites of Moderate Resolution Imaging Spectroradiometer (MODIS) images of land surface and mean air temperature, (ii) spatiotemporal calculations of monthly precipitation and relative humidity generated with the assistance of genetic algorithms (GAs), and (iii) a 80-m-resolution digital elevation model (DEM) of the area. Modeled removal of vegetation is shown to affect within-watershed precipitation and soil water storage by reducing the exchange of water vapor from the land surface to the air, increasing the air’s lifting condensation level by promoting drier air conditions, and causing the high-intensity precipitation band in the Qilian Mountains to weaken and to be displaced upward, leading to an overall reduction of water to the Liangzhou Oasis. [ABSTRACT FROM AUTHOR]

Landscape-driven microclimates in mountainous terrain pose significant obstacles to predicting the response of organisms to atmospheric warming, but few if any studies have documented the extent of such finescale variation over large regions. This paper demonstrates that ground-level temperature regimes in Great Smoky Mountains National Park (Tennessee and North Carolina) vary considerably over fine spatial scales and are only partially linked to synoptic weather patterns and environmental lapse rates. A 120-sensor network deployed across two watersheds in 2005–06 exhibited finescale (<1000-m extent) temperature differences of over 2°C for daily minima and over 4°C for daily maxima. Landscape controls over minimum temperatures were associated with finescale patterns of soil moisture content, and maximum temperatures were associated with finescale insolation differences caused by topographic exposure and vegetation cover. By linking the sensor array data to 10 regional weather stations and topographic variables describing site radiation load and moisture content, multilevel spatial models of 30-m resolution were constructed to map daily temperatures across the 2090-km2 park, validated with an independent 50-sensor network. Maps reveal that different landscape positions do not maintain relative differences in temperature regimes across seasons. Near-stream locations are warmer in the winter and cooler in the summer, and sites of low elevation more closely track synoptic weather patterns than do wetter high-elevation sites. This study suggests a strong interplay between near-ground heat and water balances and indicates that the influence of past and future shifts in regional temperatures on the park’s biota may be buffered by soil moisture surfeits from high regional rainfall. [ABSTRACT FROM AUTHOR]