Water use efficiency (WUE), the ratio of gross primary production (GPP) over evapotranspiration (ET), is a critical index for understanding and predicting terrestrial ecosystem carbon-water interactions and their responses to climate change. However, the spatiotemporal variation in WUE and its controlling mechanism are poorly understood till now at regional/global scales, due to either limited data availability or uncertainties in current data streams. For overcoming such limitations, a remote sensing (RS)-based two-leaf Jarvis-type canopy conductance (RST-Gc) model was incorporated into a revised distributed hydrological model ESSI (version 3) to simulate ET. The hydrological model was validated against ET estimates from eddy covariance (EC) data and streamflow observations. A well validated global GPP dataset from Vegetation Photosynthesis Model (VPM) was then incorporated to estimate WUE for the period 2000- 2016 over Northeast China. Variations of the simulated ET, GPP and WUE were quantified and their responses to potential drivers (e.g., precipitation, temperature, net radiation, vapor pressure deficit, and lead area index) were analyzed at different timescales (monthly and inter-annual) across various ecosystems.
The Variable Infiltration Capacity (VIC) hydrologic model was adopted for investigating spatial and temporal variability
of hydrologic impacts of climate change over the Nenjiang River Basin (NRB) based on a set of gridded forcing dataset
at 1/12th degree resolution from 1970 to 2013. Basin-scale changes in the input forcing data and the simulated
hydrological variables of the NRB, as well as station-scale changes in discharges for three major hydrometric stations
were examined, which suggested that the model was performed fairly satisfactory in reproducing the observed
discharges, meanwhile, the snow cover and evapotranspiration in temporal and spatial patterns were simulated
reasonably corresponded to the remotely sensed ones. Wetland maps produced by multi-sources satellite images
covering the entire basin between 1978 and 2008 were also utilized for investigating the responses and feedbacks of
hydrological regimes on wetland dynamics. Results revealed that significant decreasing trends appeared in annual, spring
and autumn streamflow demonstrated strong affection of precipitation and temperature changes over the study
watershed, and the effects of climate change on the runoff reduction varied in the sub-basin area over different time
scales. The proportion of evapotranspiration to precipitation characterized several severe fluctuations in droughts and
floods took place in the region, which implied the enhanced sensitiveness and vulnerability of hydrologic regimes to
changing environment of the region. Furthermore, it was found that the different types of wetlands undergone quite
unique variation features with the varied hydro-meteorological conditions over the region, such as precipitation,
evapotranspiration and soil moisture. This study provided effective scientific basis for water resource managers to
develop effective eco-environment management plans and strategies that address the consequences of climate changes.
KEYWORDS: Data modeling, Calibration, Remote sensing, Modeling, Thermal weapon sites, Climatology, Data processing, Process modeling, Soil science, Data storage
As the tenth-largest river basin in the world and one of the largest in the Russian Federation, the Amur River basin’s
water resources have changed greatly in the last decades. More comprehensive understanding of hydrological process in
the Amur River basin based on hydrological model is needed. With the increased availability of remotely sensed
information, some hydrological variables assessed through remote measurements can be used to complement discharge
data and a different respect of hydrological observations into the modelling process. In this paper, the calibration and
validation of a semi-distributed hydrological model in the Amur River basin using remote sensing data were presented.
The long-term hydrological processes of the Amur River basin for 2000-2013 was simulated based on Soil and Water
Assessment Tool (SWAT) and the changes of the hydrological variables were analyzed. The total water storage change
(TWSC) derived from the Gravity Recovery And Climate Experiment (GRACE), the actual evapotranspiration (ET)
calculated using Moderate Resolution Imaging Spectroradiometer (MODIS) and advanced very high resolution
radiometer (AVHRR) data, and multi-site river discharge data were used in the model calibration and validation. This
study showed that the streamflow, evapotranspiration, surface runoff, soil water content and groundwater discharge into
reach had all changed to varying degrees in Amur River basin during the period 2000-2013 under the influence of
climate changes and human activities. Remotely sensed information was demonstrated useful in successful application of
the model calibration and validation, and especially in reducing the equifinality for different parameters.
The Amazon Basin experienced an abrupt transition from extreme drought to flood during 2010–2012, causing significant loss and damage to the property of thousands of families. We used datasets derived from the latest products of the Gravity Recovery and Climate Experiment (GRACE), Tropical Rainfall Measuring Mission (TRMM), and the Global Land Data Assimilation System (GLDAS) to assess the extent, intensity, and dynamics of the 2010–2012 abrupt transition from extreme drought to flood in the Amazon. The monthly developing processes during the abrupt transition from extreme drought to flood between 2010 and 2012 were reproduced and examined by comparisons between GRACE terrestrial water storage anomaly and the precipitation derived from TRMM satellite estimates and GLDAS datasets. Accumulated precipitation during the peak of 2010 drought and 2012 flood looks very much similar to terrestrial water storage deficit and surplus, both at the temporal and spatial scales. Furthermore, strong correlations between the 2010 and 2012 extreme drought/flood events over the Amazon and El Niño-Southern Oscillation were also detected. This study can be helpful for archiving historical information on disasters that can contribute to the elaboration of regional scale drought/flood disaster prevention and mitigation strategies in the Amazon.
Water yielding in the hydrologic cycle is a temporally and spatially varied process. However, water yielding mechanics expressed in hydrological simulations seldom accurately characterize such dynamic processes thus weakens the simulation capabilities of present hydrological modeling systems. In this study a conceptual distributed hydrological model entitled ESSI (infiltration Excess and Saturation excess Soil-water Integration model for hydrology) was developed for flooding simulation and long term water resource management studies by means of RS, GIS and data mining techniques. This distributed hydrological modeling system has three significant characteristics: 1) capable of determining temporally and spatially varied water yielding mechanics over the most basic simulated grid by comparing with real-time computed rainfall and soil water variables; 2) excellent weather adoptability to ensure the model perform excellently for either wet and dry watershed conditions; 3) fully distributed simulating capabilities enable the model output about 20 distributed hydrological process components in different time scales, i.e. evapotranspiration (potential and actual), canopy storage, and soil moisture contents in different soil depth etc. Calibration and validation of the modeling system was conducted on two carefully selected climatologically typical watersheds in China, one located in the typical humid climate condition of upper stream of the Hanjiang river Basin, gauged by the Jiangkou hydrometric station (drainage area: 2413 km2), and another the Yingluoxia watershed (drainage area: 10029 km2), situated in typical cold and arid Heihe Mountainous region. With the calibrated model parameters and the appropriate combination of hydrological simulating module, ESSI successfully reproduced the flooding events and long term hydrological processes for the both experiment watershed, which implies the model an excellent hydrological simulation tool under various weather conditions.
The objective of this study is to improve and modify the evapotranspiration module designed with Penman-Monteith (PM) approach embedded in SWAT2000 distributed hydrological model so as to improve the precision of evapotranspiration (ET) estimation in hydrological process simulation. For PM approach in SWAT2000 ET0 simulation, some of its daily parameters such as air pressure, albedo and soil heat flux were simplified, for overcoming this shortcoming, an improved Weather Generator (WGEN) were proposed. By means of the observed and WGEN simulated meteorological parameters, systematic study on the ET estimations with PM approach for arid, semi-arid Heihe River Basin and humid, precipitation-rich Hanjiang River Basin was conducted. It was found that the computed ET with PM approach fairly correlates with the field observed ones, but biased to be a little lower in quantity than the observed ones. Further study indicated a strong linear function between the slope of the linear function and the elevation of the station, which lead us to correct the PM approach yielded result with the elevation of the study plot. Consequently, the modified PM approach with improved WGEN was applied to the estimation of ET for the area where observed meteorological data were absent or missing. In annual time scale the relative error between simulated observed ET was found less than 3% and 20% respectively in both monthly time scale and annual totals, which suggested the reliability of the modifications for both WGEN and PM ET0 module in hydrological process studies. But in daily time scale, further improvements are required.
Leaf area index (LAI) is a critical vegetation parameter for the global and regional scale studies of the climatic and environmental change. There are many methods that can be used to get LAI. In this paper, the method, developed by Qi et al. (2000) was selected. The process includes three steps: the first step is model inversion, using BRDF model to produce LAI with pixels chose randomly in one vegetation type region; the second step is quality control, removing the outliers, fitting equations using the LAI from second step and satellite data NDVI; the third step is LAI mapping, selecting the best equation and applying it to the whole region to mapping spatial LAI distribution. The main objective of this paper is to get one method that can be used in Arid and Semi-arid Northwestern China to derive LAI in the case of lack of LAI measurements. The results derived by the above approach were compared with ones derived from the empirical method (Sellers et al. 1996) and the LAI measured in field. The results suggested that the method can get good result and R2 was 0.7947, though they were greater than field measurements. The results from empirical method were closer to the measurements than ones from Qi's method, but the higher the values of NDVI were, the greater the values of estimated LAI were than LAI measurements, when the values of NDVI were greater than a certain values (here 0.74). However, the result derived from Qi's method is closer to the LAI measured in field. In general, this method was feasible in arid and semi-arid northwestern China and can get satisfactory results.
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