Shengtian Yang

Evaluating the suitability of TRMM satellite rainfall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China

The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission (TRMM) data with rain gauge data and further to use this TRMM data to drive a Distributed Time-Variant Gain Model (DTVGM) to perform hydrological simulations in the semi-humid Weihe River catchment in China. Before the simulations, a comparison with a 10-year (2001–2010) daily rain gauge data set reveals that, at daily time step, TRMM rainfall data are better at capturing rain occurrence and mean values than rainfall extremes. On a monthly time scale, good linear relationships between TRMM and rain gauge rainfall data are found, with determination coefficients R2 varying between 0.78 and 0.89 for the individual stations. Subsequent simulation results of seven years (2001–2007) of data on daily hydrological processes confirm that the DTVGM when calibrated by rain gauge data performs better than when calibrated by TRMM data, but the performance of the simulation driven by TRMM data is better than that driven by gauge data on a monthly time scale. The results thus suggest that TRMM rainfall data are more suitable for monthly streamflow simulation in the study area, and that, when the effects of recalibration and the results for water balance components are also taken into account, the TRMM 3B42-V7 product has the potential to perform well in similar basins.

Evaluating nitrogen removal by vegetation uptake using satellite image time series in riparian catchments.

Nitrogen (N) removal by vegetation uptake is one of the most important functions of riparian buffer zones in preventing non-point source pollution (NSP), and many studies about N uptake at the river reach scale have proven the effectiveness of plants in controlling nutrient pollution. However, at the watershed level, the riparian zones form dendritic networks and, as such, may be the predominant spatially structured feature in catchments and landscapes. Thus, assessing the functions of riparian system at the basin scale is important. In this study, a new method coupling remote sensing and ecological models was used to assess the N removal by riparian vegetation on a large spatial scale. The study site is located around the Guanting reservoir in Beijing, China, which was abandoned as the source water system for Beijing due to serious NSP in 1997. SPOT 5 data was used to map the land cover, and Landsat-5 TM time series images were used to retrieve land surface parameters. A modified forest nutrient cycling and biomass model (ForNBM) was used to simulate N removal, and the modified net primary productivity (NPP) module was driven by remote sensing image time series. Besides the remote sensing data, the necessary database included meteorological data, soil chemical and physical data and plant nutrient data. Pot and plot experiments were used to calibrate and validate the simulations. Our study has proven that, by coupling remote sensing data and parameters retrieval techniques to plant growth process models, catchment scale estimations of nitrogen uptake rates can be improved by spatial pixel-based modelling.

Evaluation of soil nitrogen emissions from riparian zones coupling simple process-oriented models with remote sensing data.

Riparian ecosystems have critical impacts on controlling the non-point source pollution and maintaining the health of aquatic ecosystems. In this study, a process oriented soil denitrification model was extended with algorithms from a simple nitrogen (N) cycle model and coupled to land surface remote sensing data to enhance its performance in spatial and temporal prediction of gaseous N emissions from soils in the riparian buffer zone surrounding the Guanting reservoir (China). The N emission model is based on chemical and physical relationships that govern the heat budget, soil moisture variations and nitrogen movement in soils. Besides soil water and heat processes, it includes nitrification, denitrification and ammonia (NH(3)) volatilization. SPOT-5 and Landsat-5 TM satellite data were used to derive spatial land surface information and the temporal variation in land cover parameters was also used to drive the model. A laboratory-scale anaerobic incubation experiment was used to estimate the soil denitrification model parameters for the different soil types. An in situ field-scale experiment was conducted to calibrate and validate the soil temperature, moisture and nitrogen sub-models. An indirect method was used to verify simulated N emissions, resulting in a coefficient of determination of R(2)=0.83 between simulated and observed values. Then the model was applied to the whole riparian buffer zone catchment, using the spatial resolution (10m) of the SPOT-5 image. Model sensitivity analysis showed that soil moisture was the most sensitive parameter for gaseous N emissions and soil denitrification was the main process affecting N losses to the atmosphere in the riparian area. From the aspect of land use management around the Guanting reservoir, the spatial structure and distribution of land cover and land use types in the riparian area should be adapted, to enhance faster ecological restoration of the wetland ecological system surrounding this strategically important water resource.

A Distributed Hydrological Model Driven by Multi-Source Spatial Data and Its Application in the Ili River Basin of Central Asia

Hydrological simulation in ungauged regions is a popular topic in water resource and environmental research, and is also an important part of the international research initiative Predictions in Ungauged Basins (PUB). In this study, a multi-spatial data-based DTVGM (MS-DTVGM), combining multi-source spatial data (MS-spatial data) with the Distributed Time-Variant Gain Model (DTVGM), was built in order to reduce dependence on conventional observation, and was applied to the Ili River basin where traditional data sets are scarce. Because it utilizes MS-spatial data to measure precipitation, potential evapotranspiration, air temperature, vegetation parameters, and soil parameters, the model is driven purely by data from common platforms, thus overcoming the disadvantage of the large amounts of data typically required for distributed hydrological models. The inputs and simulation results were calibrated and validated using station or field observations. The results indicate that: 1) the MS-DTVGM is feasible in the Ili River basin; all model inputs can be acquired from multi-source spatial data and the key parameters are accurate; 2) the MS-DTVGM has good performance on a monthly time scale, and its simulation results can be used for a longer time-scale water resource analysis; and (3) daily runoff generation correlated strongly with snowmelt, the R2 is about 0.69 indicating that the latter is an important contributor to water resources and suggesting that a snowmelt module is indispensable this area. The potential of distributed models for hydrological simulation in data-scarce regions using MS-spatial data was clearly demonstrated.

Estimation of daily average temperature using multisource spatial data in data sparse regions of Central Asia

Abstract The distribution of the daily average air temperature with high spatial resolution is vital for hydro-ecological applications. The air temperature usually recorded at fixed-point stations provides little distribution information and easily suffers from the scarce amount and uneven distribution of the stations in the data sparse regions. In this study, a method based on multisource spatial data was developed to estimate the spatial distribution of daily average temperature, especially for data sparse regions. In this method, the instantaneous temperature was retrieved first using the moderate resolution imaging spectroradiometer data, which was then transformed to a daily value using transformation equations. Second, the global land data assimilation system air temperature data were spatially downscaled and used to improve the data accuracy from step 1 at low temperatures. This method was applied in the Ili River basin in Central Asia, and the results were evaluated against data from two stations’ observations and in situ data from a field test site. The results showed the correlation coefficient varies from 0.90 to 0.94 and the root mean square deviation is ∼ 3 ° C , indicating the generated temperature matched the observations well. This suggests the method is an alternative for data sparse regions.

The effect of environmental factors on spatial variability in land use change in the high-sediment region of China’s Loess Plateau

In areas with topographic heterogeneity, land use change is spatially variable and influenced by climate, soil properties, and topography. To better understand this variability in the high-sediment region of the Loess Plateau in which soil loss is most severe and sediment diameter is larger than in other regions of the plateau, this study builds some indicators to identify the characteristics of land use change and then analyze the spatial variability as it is affected by climate, soil property, and topography. We build two indicators, a land use change intensity index and a vegetation change index, to characterize the intensity of land use change, and the degree of vegetation restoration, respectively. Based on a subsection mean method, the two indicators are then used to assess the spatial variability of land use change affected by climatic, edaphic, and topographic elements. The results indicate that: 1) Land use changed significantly in the period 1998–2010. The total area experiencing land use change was 42,302 km2, accounting for 22.57%of the study area. High-coverage grassland, other woodland, and forest increased significantly, while low-coverage grassland and farmland decreased in 2010 compared with 1998. 2) Land use change occurred primarily west of the Yellow River, between 35 and 38 degrees north latitude. The four transformation types, including (a) low-coverage grassland to medium-coverage grassland, (b) medium-coverage grassland to high-coverage grassland, (c) farmland to other woodland, and (d) farmland to medium-coverage grassland, were the primary types of land use change, together constituting 60% of the area experiencing land use change. 3) The spatial variability of land use change was significantly affected by properties of dryness/wetness, soil conditions and slope gradient. In general, land use changed dramatically in semi-arid regions, remained relatively stable in arid regions, changed significantly in clay-rich soil, remained relatively stable in clay-poor soil, changed dramatically in steeper slopes, and remained relatively stable in tablelands and low-lying regions. The increase in vegetation coincided with increasing changes in land use for each physical element. These findings allow for an evaluation of the effect of the Grain to Green Program, and are applicable to the design of soil and water conservation projects on the Loess Plateau of China.

Spatial evaluation of phosphorus retention in riparian zones using remote sensing data

Riparian zones act as important buffer zones for non-point source pollution, thus improving the health of aquatic ecosystems. Previous research has shown that riparian zones play an important role, and that land use has an important effect, on phosphorus (P) retention. A spatial basin-scale approach for analyzing P retention and land use effects could be important in preventing pollution in riparian zones. In this study, a riparian phosphorus cycle model based on EcoHAT was generated with algorithms from soil moisture and heat models, simplified soil and plant phosphorus models, plant growth models, and universal soil loss equations. Based on remote sensing data, model performance was enhanced for spatial and temporal prediction of P retention in the riparian zone. A modified soil and plant P model was used to simulate the soil P cycle of a riparian zone in a temperate continental monsoon climate in northern China. A laboratory experiment and a field experiment were conducted to validate the P cycle model. High coefficients of determination (R2) between simulated and observed values indicate that the model provides reliable results. P uptake variations were the same as the net primary productivity (NPP) trends, which were affected by soil temperature and moisture in the temperate continental monsoon climate. Beginning in June, the monthly content increased, with the maximum appearing in August, when the most precipitation and the highest temperatures occur. The spatial distribution of P uptake rates from March to September showed that areas near water frequently had relatively high values from May to August, which is contrary to results obtained in March, April, and September. The P uptake amounts for different land uses changed according to expectation. The average monthly P uptake rates for farmlands and grasslands were more than those for orchards and lowlands, which had moderate P uptake rates, followed by shrubs and forests. The spatial distribution of soil erosion demonstrated that the soil erosion came primarily from high-intensity agricultural land in the western and central areas, while the northern and eastern study regions, which were less affected by human activity, experienced relatively slight soil erosion. From the point of view of P pollution prevention, the spatial structure of riparian zones and the spatial distribution of land use around the Guanting reservoir are thus not favorable.

Comprehensive Evaluation of Two Successive V3 and V4 IMERG Final Run Precipitation Products over Mainland China

The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Run (IMERGF) product has now been upgraded to Version 4 (V4), which has been available since March 2017. Therefore, it is desirable to evaluate the characteristic differences between the V4 and the previous V3 products. A comprehensive performance evaluation of the errors of the successive V3 and V4 IMERGF products is performed with a comparison of the China daily Precipitation Analysis Products (CPAP) from March 2014 to February 2015. The version 6 Global Satellite Mapping of Precipitation (GSMaP) research product (which is another Global Precipitation Measurement (GPM) based precipitation product) is also used as a comparison in this study. Overall, the IMERGF-V4 product does not exhibit the anticipated improvement for China compared to the IMERGF-V3 product. An analysis of the metrics of annual daily average precipitation over China for the IMERGF-V3 and IMERGF-V4 products indicates a decrease of the relative bias (RB) from 3.70% to −7.18%, a decrease of the correlation coefficient (CC) from 0.91 to 0.89, an increase of the fractional standard error (FSE) from 0.49 to 0.56, and an increase of the root-mean-square error (RMSE) from 0.63 mm to 0.72 mm. Compared to the IMERGF-V3 product, the IMERGF-V4 product exhibits a significant underestimation of precipitation in the Qinghai-Tibetan plateau with a much lower RB of −60.91% (−58.19%, −65.30%, and −63.74%) based on the annual (summer, autumn, and winter) daily average precipitation and an even worse performance during winter (−72.33% of RB). In comparison, the GSMaP product outperforms the IMERGF-V3 and IMERGF-V4 products and has the smallest RMSE (0.47 mm/day), highest CC (0.95), lowest FSE (0.37), and best performance of the RB (−2.39%) in terms of annual daily precipitation over China. However, the GSMaP product underestimates the precipitation more than the IMERGF-V3 product for the arid XJ region.

A Soil Erosion Assessment of the Upper Mekong River in Yunnan Province, China

Abstract This study estimated average annual soil loss and clarified its spatial distribution and impact on reservoirs in the upper Mekong River basin in Yunnan Province, China. A quantitative grid-based estimation was made using a Universal Soil Loss Equation model in a geographic information system framework, along with remote sensing and other source data. The results suggest that the average annual soil loss in most of the area ranged from 0 to 2853 t/ha/y, with a mean value of 19.8 t/ha/y. We estimated that a little more than half (61.0%) of the study area undergoes minimal erosion; this was primarily observed in the south, and more particularly in the southeast portion of the study area. Almost one fifth (19.2%) of the study area was estimated to undergo low erosion; this was primarily found in the central and southwest portions of the study area. Moderate soil erosion was observed in 8.5% of the study area. We estimated 11.3% of the study area to undergo high or extreme erosion; these locations were concentrated in the northern part of the study area. Soil erosion appeared most frequently at the mean elevation and mean slope zone. Dams on the upper reaches were found to be threatened by the presence of sediment.

Detecting and analyzing soil phosphorus loss associated with critical source areas using a remote sensing approach

The detection of critical source areas (CSAs) is a key step in managing soil phosphorus (P) loss and preventing the long-term eutrophication of water bodies at regional scale. Most related studies, however, focus on a local scale, which prevents a clear understanding of the spatial distribution of CSAs for soil P loss at regional scale. Moreover, the continual, long-term variation in CSAs was scarcely reported. It is impossible to identify the factors driving the variation in CSAs, or to collect land surface information essential for CSAs detection, by merely using the conventional methodologies at regional scale. This study proposes a new regional-scale approach, based on three satellite sensors (ASTER, TM/ETM and MODIS), that were implemented successfully to detect CSAs at regional scale over 15years (2000-2014). The approach incorporated five factors (precipitation, slope, soil erosion, land use, soil total phosphorus) that drive soil P loss from CSAs. Results show that the average area of critical phosphorus source areas (CPSAs) was 15,056km2 over the 15-year period, and it occupied 13.8% of the total area, with a range varying from 1.2% to 23.0%, in a representative, intensive agricultural area of China. In contrast to previous studies, we found that the locations of CSAs with P loss are spatially variable, and are more dispersed in their distribution over the long term. We also found that precipitation acts as a key driving factor in the variation of CSAs at regional scale. The regional-scale method can provide scientific guidance for managing soil phosphorus loss and preventing the long-term eutrophication of water bodies at regional scale, and shows great potential for exploring factors that drive the variation in CSAs at global scale.