[1] The Gravity Recovery and Climate Experiment (GRACE) provides quantitative measures of terrestrial water storage (TWS) change. GRACE data show a significant decrease in TWS in the lower (southern) La Plata river basin of South America over the period [2002][2003][2004][2005][2006][2007][2008][2009], consistent with recognized drought conditions in the region. GRACE data reveal a detailed picture of temporal and spatial evolution of this severe drought event, which suggests that the drought began in lower La Plata in around austral spring 2008 and then spread to the entire La Plata basin and peaked in austral fall 2009. During the peak, GRACE data show an average TWS deficit of ∼12 cm (equivalent water layer thickness) below the 7 year mean, in a broad region in lower La Plata. GRACE measurements are consistent with accumulated precipitation data from satellite remote sensing and with vegetation index changes derived from Terra satellite observations. The Global Land Data Assimilation System model captures the drought event but underestimates its intensity. Limited available groundwater-level data in southern La Plata show significant groundwater depletion, which is likely associated with the drought in this region. GRACE-observed TWS change and precipitation anomalies in the studied region appear to closely correlate with the ENSO climate index, with dry and wet seasons corresponding to La Niña and El Niño events, respectively.

Satellite‐based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground‐based observations are limited. However, existing satellite‐based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN‐CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM‐GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN‐CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009–2014. The historical data (satellite and gauge) for 2009–2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN‐CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root‐mean‐square errors and mean biases. The systematic biases of the PERSIANN‐CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground‐based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide.

Abstract. Ecohydrology and hydropedology are two emerging fields that are interconnected. In this study, we demonstrate stemflow hydrology and preferential water flow along roots in two desert shrubs (H. scoparium and S. psammophila) in the south fringe of Mu Us sandy land in North China. Stemflow generation and subsequent movement within soil-root system were investigated during the growing seasons from 2006 to 2008. The results indicated that the amount of stemflow in H. scoparium averaged 3.4% of incident gross rainfall with a range of 2.3–7.0%, while in S. psammophila stemflow averaged 6.3% with a range of 0.2–14.2%. Stemflow was produced from rainfall events with total amount more than 1 mm for both shrubs. The average funneling ratio (the ratio of rainfall amount delivered to the base of the tree to the rainfall that would have reached the ground should the tree were not present) was 77.8 and 48.7 for H. scoparium and S. psammophila, respectively, indicating that branches and stems were fully contributing to stemflow generation and thereby provided sources of water for possible preferential flow into deeper soil layer. Analysis of Rhodamine-B dye distribution under the shrubs showed that root channels were preferential pathways for the movement of most stemflow water into the soil. Distribution of soil water content under the shrubs with and without stemflow ascertained that stemflow was conducive to concentrate and store water in deeper layers in the soil profiles, which may create favorable soil water conditions for plant growth under arid conditions. Accordingly, a clear linkage between aboveground ecohydrology and belowground hydropedology in the desert shrubs is worth noticing, whereby an increase in stemflow would result in an increase in soil hydrologic heterogeneity.

With high spatial‐temporal resolution, Satellite‐based Precipitation Estimates (SPE) are becoming valuable alternative rainfall data for hydrologic and climatic studies but are subject to considerable uncertainty. Effective merging of SPE and ground‐based gauge measurements may help to improve precipitation estimation in both better resolution and accuracy. In this study, a framework for merging satellite and gauge precipitation data is developed based on three steps, including SPE bias adjustment, gauge observation gridding, and data merging, with the objective to produce high‐quality precipitation estimates. An inverse‐root‐mean‐square‐error weighting approach is proposed to combine the satellite and gauge estimates that are in advance adjusted and gridded, respectively. The model is applied and tested with the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Cloud Classification System (PERSIANN‐CCS) estimates (daily, 0.04° × 0.04°) over Chile, for the 6 year period of 2009–2014. Daily observations from about 90% of collected gauges over the study area are used for model calibration; the rest of the gauged data are regarded as ground “truth” for validation. Evaluation results indicate high effectiveness of the model in producing high‐resolution‐precision precipitation data. Compared to reference data, the merged data (daily) show correlation coefficients, probabilities of detection, root‐mean‐square errors, and absolute mean biases that were consistently improved from the original PERSIANN‐CCS estimates. The cross‐validation evidences that the framework is effective in providing high‐quality estimates even over nongauged satellite pixels. The same method can be applied globally and is expected to produce precipitation products in near real time by integrating gauge observations with satellite estimates.

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From the context of this area, combined with the well, core and logging data, the characteristics of heterogeneity of this region can be studied. Through calculating the parameters like porosity, permeability, saturation, permeability coefficient of variation, sudden coefficient and differential, the heterogeneity in the layer and the plane has been studied. The sand groups in the Chang 6 reservoir mainly are delta front sand underwater distributary channel sand body, the heterogeneity in the layer is weak; In the plane, the heterogeneity of distributary channel sand body is weak, while the one in estuary dam, front sheet sands and underwater natural levee is strong. Through comprehensive analysis, Chang 6 reservoir in Hulangmao region is strong heterogeneity reservoir. After comparing permeability coefficient of variation, sudden coefficient and differential of 3 layer and 5 layer in Chang 6,it can conclude that the development effect of 5 layer is better than 3 layer in the process of water-flooding development. This research can be some guidance to some area in the mainland.

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