Qiuwen Zhou

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.

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.

Comprehensive analysis of changes to catchment slope properties in the high-sediment region of the Loess Plateau, 1978–2010

To control soil erosion and restore the degraded environment in the Loess Plateau, a large number of measures related to soil and water conservation have been employed that have profoundly affected catchment properties. This study constructed three indicators to characterize changes to the catchment slope, proposed both a method for a regression analysis of adjacent images and a sequence model, and applied multisource remotely sensed images and GIS spatial clustering analysis technologies to extract thematic information and comprehensively analyze the catchment change characteristics. The results indicate that the catchment slope properties changed significantly. At catchment scale, the average values of ARC, DVC and ART were 6.43%, 25.57% and 4.30%, respectively. There were six clustering types of catchment slope property changes. The maximum and minimum of the average similarities of the clustering types were 0.992 and 0.935. Each slope control measures had a distinct effect on catchment slope; the dominating factor of each clustering type was identified as: Type 1: D-VC, Type 2: D-VCLU, Type 3: D-LUVC, Type 4: D-TAVC, Type 5: D-TAC and Type 6: D-MFC. Type 5 and Type 1 covered the largest areas, respectively occupying 37.28% and 31.01%. Catchment slope property changes also had distinct types that depended on their geomorphological conditions. These findings provide a useful basis from which to further study catchment slope hydrological and soil erosion processes.

Insight into runoff characteristics using hydrological modeling in the data-scarce southern Tibetan Plateau: Past, present, and future

Regional hydrological modeling in ungauged regions has attracted growing attention in water resources research. The southern Tibetan Plateau often suffers from data scarcity in watershed hydrological simulation and water resources assessment. This hinders further research characterizing the water cycle and solving international water resource issues in the area. In this study, a multi-spatial data based Distributed Time-Variant Gain Model (MS-DTVGM) is applied to the Yarlung Zangbo River basin, an important international river basin in the southern Tibetan Plateau with limited meteorological data. This model is driven purely by spatial data from multiple sources and is independent of traditional meteorological data. Based on the methods presented in this study, daily snow cover and potential evapotranspiration data in the Yarlung Zangbo River basin in 2050 are obtained. Future (2050) climatic data (precipitation and air temperature) from the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR5) are used to study the hydrological response to climate change. The result shows that river runoff will increase due to precipitation and air temperature changes by 2050. Few differences are found between daily runoff simulations from different Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5 and RCP8.5) for 2050. Historical station observations (1960–2000) at Nuxia and model simulations for two periods (2006–2009 and 2050) are combined to study inter-annual and intra-annual runoff distribution and variability. The inter-annual runoff variation is stable and the coefficient of variation (CV) varies from 0.21 to 0.27. In contrast, the intra-annual runoff varies significantly with runoff in summer and autumn accounting for more than 80% of the total amount. Compared to the historical period (1960–2000), the present period (2006–2009) has a slightly uneven intra-annual runoff temporal distribution, and becomes more balanced in the future (2050).