Changsen Zhao

Spatial and temporal change in the potential evapotranspiration sensitivity to meteorological factors in China (1960–2007)

Potential evapotranspiration (E0), as an estimate of the evaporative demand of the atmosphere, has been widely studied in the fields of irrigation management, crop water demand and predictions in ungauged basins (PUBs). Analysis of the sensitivity of E0 to meteorological factors is a basic research on the impact of climate change on water resources, and also is important to the optimal allocation of agricultural water resources. This paper dealt with sensitivity of E0 over China, which was divided into ten drainage systems, including Songhua River basin, Liaohe River basin, Haihe River basin, Yellow River basin, Yangtze River basin, Pearl River basin, Huaihe River drainage system, Southeast river drainage system, Northwest river drainage system and Southwest river drainage system. In addition, the calculation method of global radiation in Penman-Monteith formula was improved by optimization, and the sensitivities of Penman-Monteith potential evapotranspiration to the daily maximum temperature (STmax), daily minimum temperature (STmin), wind speed (SU2), global radiation (SRs) and vapor pressure (SVP) were calculated and analyzed based on the long-term meteorological data from 653 meteorological stations in China during the period 1960–2007. Results show that: (1) the correlation coefficient between E0 and pan evaporation increased from 0.61 to 0.75. E0 had the decline trends in eight of ten drainage systems in China, which indicates that “pan evaporation paradox” commonly exists in China from 1960 to 2007. (2) Spatially, Tmax was the most sensitive factor in Haihe River basin, Yellow River basin, Huaihe River drainage system, Yangtze River basin, Pearl River basin and Southeast river drainage system, and VP was the most sensitive factor in Songhua River Basin, Liaohe River basin, Northwest river drainage system while Rs was the most sensitive factor in Southwest river drainage system. For the nation-wide average, the most sensitive factor was VP, followed by Tmax, Rs, U2 and Tmin. In addition, the changes in sensitivity coefficients had a certain correlation with elevation. (3) Temporally, the maximum values of STmax and SRs occurred in July, while the maximum values of STmin, SVP and SU2 occurred in January. Moreover, trend analysis indicates that STmax had decline trends, while STmin, SU2, SRs and SVP had increasing trends.

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.

Separation of the impacts of climate change and human activity on runoff variations

Abstract A practical approach to separate the impact of the processes of climate change (CC) and human activities (HA) on streamflow is presented. A non-parametric Mann-Kendall-Sneyers test, combined with moving t test and Yamamoto methods, was used to recognize abrupt change points in the runoff time series to determine a baseline period. A new algorithm to separate CC and HA influence on streamflow was deduced based on the climate elasticity concept. Application to the Chao River, China, shows that CC imposed a positive impact on streamflow in this region (25%, on average), while HA exerted a continuous negative impact of –75% in the period after the 1950s. These results are of great use in understanding the variation of CC and HA impacts under different human development patterns. Editor Z.W. Kundzewicz; Associate editor H. Aksoy

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.

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.

Bioindicator Assessment Framework of River Ecosystem Health and the Detection of Factors Influencing the Health of the Huai River Basin, China

River ecosystem health assessments are a hot topic in global water resources management. The Huai River Basin is a representative basin faced with serious environmental problems due to high water pollution and numerous water projects. Comprehensive investigation of the factors affecting river ecosystem health is an urgent precursor to the successful management of the Huai River Basin. Based on a series of aquatic ecosystem indicators, monitoring, and standards, such as phytoplankton, zooplankton, and benthic macroinvertebrate data, an assessment framework of river ecosystem health is proposed using a multistep gray correlation evaluation method. Environmental impact factors including climate, land cover, edaphic factors, hydrology, water projects, and water quality were identified using the Kruskal-Wallis test and distance-based redundancy analysis method. The ecosystem was found to be subhealthy with 44% (31/71) of sites in a healthy condition, 51% (36/71) subhealthy, and 5% (4/71) unhealthy. Climate and hydrology were the dominant factors that together contributed 37.5, 24.1, and 6.9% of the variation in phytoplankton, zooplankton, and benthic macroinvertebrates, respectively. The main variables affecting variation were minimum temperature, relative humidity, solar radiation, and the magnitude, frequency, duration, and timing of monthly runoff. Together, these variables explained 24.5% of the variation in the ecosystem health of the whole river. Thus, to improve river ecosystem health in the basin, the regulation of dams and sluices should give priority to ecological outcomes, and the threat of climate change should not be underestimated. This paper provides a blueprint for ongoing ecological monitoring and restoration programs in China, and is expected to be a successful case and reference to transition from water quantity management to river health management in the Huai River Basin.

Linking fish tolerance to water quality criteria for the assessment of environmental flows: A practical method for streamflow regulation and pollution control

The survival of aquatic biota in stream ecosystems depends on both water quantity and quality, and is particularly susceptible to degraded water quality in regulated rivers. Maintenance of environmental flows (e-flows) for aquatic biota with optimum water quantity and quality is essential for sustainable ecosystem services, especially in developing regions with insufficient stream monitoring of hydrology, water quality and aquatic biota. Few e-flow methods are available that closely link aquatic biota tolerances to pollutant concentrations in a simple and practical manner. In this paper a new method was proposed to assess e-flows that aimed to satisfy the requirements of aquatic biota for both the quantity and quality of the streamflow by linking fish tolerances to water quality criteria, or the allowable concentration of pollutants. For better operation of water projects and control of pollutants discharged into streams, this paper presented two coefficients for streamflow adjustment and pollutant control. Assessment of e-flows in the Wei River, the largest tributary of the Yellow River, shows that streamflow in dry seasons failed to meet e-flow requirements. Pollutant influx exerted a large pressure on the aquatic ecosystem, with pollutant concentrations much higher than that of the fish tolerance thresholds. We found that both flow velocity and water temperature exerted great influences on the pollutant degradation rate. Flow velocity had a much greater influence on pollutant degradation than did the standard deviation of flow velocity. This study provides new methods to closely link the tolerance of aquatic biota to water quality criteria for e-flow assessment. The recommended coefficients for streamflow adjustment and pollutant control, to dynamically regulate streamflow and control pollutant discharge, are helpful for river management and ecosystems rehabilitation. The relatively low data requirement also makes the method easy to use efficiently in developing regions, and thus this study has significant implications for managing flows in polluted and regulated rivers worldwide.