Xiuzhi Chen

Global pattern for the effect of climate and land cover on water yield

Research results on the effects of land cover change on water resources vary greatly and the topic remains controversial. Here we use published data worldwide to examine the validity of Fuhs equation, which relates annual water yield (R) to a wetness index (precipitation/potential evapotranspiration; P/PET) and watershed characteristics (m). We identify two critical values at P/PET=1 and m=2. m plays a more important role than P/PET when m<2, and a lesser role when m>2. When P/PET<1, the relative water yield (R/P) is more responsive to changes in m than it is when P/PET>1, suggesting that any land cover changes in non-humid regions (P/PET<1) or in watersheds of low water retention capacity (m<2) can lead to greater hydrological responses. m significantly correlates with forest coverage, watershed slope and watershed area. This global pattern has far-reaching significance in studying and managing hydrological responses to land cover and climate changes.

Study on the cooling effects of urban parks on surrounding environments using Landsat TM data: a case study in Guangzhou, southern China

The temperature cooling effects of ten urban parks on surrounding environments in Guangzhou, southern China, are analysed and quantified using Landsat Thematic Mapper data. The results show that there is a temperature rise (about 1.74°C) between green spaces of parks and bare-ground areas of the surroundings. For those parks whose green area percentage is more than 69% and length:width ratio is close to 1, the average temperature differences between boundaries and surrounding sites of parks have linear relationships with the green areas of parks (R 2 > 0.82). Moreover, the nonlinear relationship between the average cooling distance of parks and green areas can be simulated very well using a logarithmic curve (R 2 > 0.93). When the green areas of parks are smaller than 10 566 m2, parks will have no temperature cooling effects on their surrounding environments. When the green areas of parks reach 740 000 m2, the increase of temperature cooling distance is less than 1 m per 10 000 m2 increase of the green area. The most appropriate size of green areas of urban parks should fall between 10 566 and 740 000 m2. For those parks with water areas larger than 128 889 m2, the temperature cooling effects are usually more remarkable. When the length:width ratios of the green areas of urban parks are more than or equal to 2, their temperature cooling distances are always larger than those with length:width ratios equal to 1 given similar green area. Parks with larger green areas (37 163 m2) or larger water areas (>128 889 m2) will have more significant temperature cooling effects in June than in October.

A new method for extracting built-up urban areas using DMSP-OLS nighttime stable lights: a case study in the Pearl River Delta, southern China

The nighttime stable light (NSL) images on board the Operational Line-scan System (OLS) of the Defense Meteorological Satellite Program (DMSP) are useful for extracting large-scale built-up urban areas. However, most NSL-based studies are presently empirical threshold-based approaches. They always overestimate the areas of built-up land in urban regions because of the ‘blooming’ effect of NSL; and overlook small patches in developing towns where the NSL is much lower. In this study, a neighborhood statistics analysis (NSA) method is developed on the basis of the relative spatial variations between neighboring built-up and non-built-up pixels in DMSP-OLS images. It is applied to extract the built-up areas of eight cities in the Pearl River Delta in 1996, 2000, 2005, and 2009. The validations indicate that the total areas of the NSA-mapped results are highly correlated with those from Landsat TM/ETM+ data (R2 = 0.94; p < 0.001). The comparison results, which are evaluated by landscape indices (the landscape shape index (LSI), the contiguity index (CONTIG), and the perimeter area ratio (PARA)), also show good correlations (R2 > 0.46; p < 0.001). In addition, the total NSL of the built-up urban areas is significantly correlated with the statistical population data (R2 = 0.62; p < 0.001), which indirectly confirms the effectiveness of our proposed method.

An enhanced MODIS remote sensing model for detecting rainfall effects on sediment plume in the coastal waters of Apalachicola Bay

Mapping of total suspended solids (TSS) was conducted to investigate rainstorm-induced characteristic of sediment plume in the coastal waters of Apalachicola Bay. An improved TSS quadratic polynomial regression model (Calibration: R2=0.8586, N=32; validation: RMSE of 4.76 mg/l, N=30) for MODIS remote sensing was presented in this study. TSS mapping of MODIS before and after a rainstorm event showed distinct temporal-spatial variability of TSS concentration. Driven by ocean tidal current, a storm plume of width at about 40-50 km was formed flowing towards southwest of study area. The distinct boundary separating the highly turbid (west side) and relatively clear water (east side) was found near Sikes Cut. Further, by taking the TSS mapping under the low river discharge condition due to a local rainstorm as a reference of background TSS, two thresholds of TSS (25 and 45 mg/l respectively) were used to estimate the range of rainstorm plume and the central area of sediment load from surrounding land, and the spatial extent and evolution of the sediment plume during the local rainstorm. Besides, it was found that the storm plume concentration of TSS at east side of Sites Cut was quickly diluted under 25 mg/L, forming a storm plume towards east with width at about 7-8 km. The method developed in this study may be used to support coastal storm water research and management activities.

A multivariate conditional model for streamflow prediction and spatial precipitation refinement

The effective prediction and estimation of hydro-meteorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high-dimensional vine copulas, conditional bivariate copula simulations, and a quantile-copula function. The vine copula is employed because of its flexibility in modeling the high-dimensional joint distribution of multivariate data by building a hierarchy of conditional bivariate copulas. We investigate two cases to evaluate the performance and applicability of the proposed approach. In the first case, we generate one-month-ahead streamflow forecasts that incorporate multiple predictors including antecedent precipitation and streamflow records in a basin located in South China. The prediction accuracy of the vine-based model is compared with that of traditional data-driven models such as the support vector regression (SVR) and the adaptive neural-fuzzy inference system (ANFIS). The results indicate that the proposed model produces more skillful forecasts than SVR and ANFIS. Moreover, this probabilistic model yields additional information concerning the predictive uncertainty. The second case involves refining spatial precipitation estimates derived from the tropical rainfall measuring mission precipitation (TRMM) product for the Yangtze River basin by incorporating remotely sensed soil moisture data and the observed precipitation from meteorological gauges over the basin. The validation results indicate that the proposed model successfully refines the spatial precipitation estimates. Although this model is tested for specific cases, it can be extended to other hydro-meteorological variables for predictions and spatial estimations.

Interval-Parameter Robust Minimax-regret Programming and Its Application to Energy and Environmental Systems Planning

Abstract In this study, an interval-parameter robust minimax-regret programming method is developed and applied to the planning of energy and environmental systems. Methods of robust programming, interval-parameter programming, and minimax-regret analysis are incorporated within a general optimization framework to enhance the robustness of the optimization effort. The interval-parameter robust minimax-regret programming can deal with uncertainties expressed as discrete intervals, fuzzy sets, and random variables. It can also be used for analyzing multiple scenarios associated with different system costs and risk levels. In its solution process, the fuzzy decision space is delimited into a more robust one through dimensional enlargement of the original fuzzy constraints; moreover, an interval-element cost matrix can be transformed into an interval-element regret matrix, such that the decision makers can identify desired alternatives based on the inexact minimax regret criterion. The developed method has been applied to a case study of energy and environmental systems planning under uncertainty. The results indicate that reasonable solutions have been generated.

Spatial clusters and temporal trends of seasonal surface soil moisture across China in responses to regional climate and land cover changes

Investigating the spatiotemporal changes of regional surface soil moisture in responses to climate and land cover changes is vital for understanding the underlying mechanisms of hydrological processes. While previous studies mainly attributed the causes of soil moisture changes to climatic factors, few took land cover into consideration. We analyzed the seasonal-differentiated effects of climate and land cover changes on surface soil moisture of Chinas 80 drainage basins using the Essential Climate Variable Soil Moisture (ECV_SM) product spanning the 1979-2010 period. The low-low spatial clusters of annual (Jan ~ Dec), warm-season (Apr ~ Sept) and cool-season (Oct ~ Dec, Jan ~ Mar) surface soil moisture have spread from northwest China to northeast and even central China during past three decades. In cool seasons, significant decreasing trends of surface soil moisture in most drainage basins of northwest, northeast, central and south China were detected. But in warm seasons, the surface soil moisture in central and south China showed significant increasing trends. Precipitation, potential evapotranspiration and the ratio of the two are three driving forces for warm-season surface soil moisture changes in northeast and northwest China. These climatic factors are main contributors to the declining trends of cool-season surface soil moisture in central and south China. Land cover changes showed to be the major factor driving the significant decreasing trends of cool-season surface soil moisture in northwest, central and south China. Due to vegetations self-shading effect, the large-scale reforestation and vegetation growth are believed as main causes of the increasing trends of warm-season surface soil moisture in central and south China.

Water-use efficiency of an old-growth forest in lower subtropical China

Carbon and water fluxes are key properties of ecosystem processes and functions. A better understanding of their temporal dynamics and coupling mechanism between these fluxes will help us improve ecosystem management for mitigation as well as adaption to future climatic change. From 2003 to 2009, carbon and water flux data were obtained by the eddy covariance method over an old-growth forest in the lower subtropical China. The 7 years of observational data indicated that the water-use efficiency (WUE) of the old-growth forest exhibited weak inter-annual variability. The mean annual WUE ranged from 1.70 to 1.98 g C kg−1 H2O. An analysis of the effects of environmental variables on the monthly gross primary productivity (GPP) and evapotranspiration (ET) indicated that solar radiation, air temperature, precipitation and vapor pressure deficit (VPD) produced similar effects on the monthly GPP and ET, which suggests that photosynthesis and ET were similarly driven by the climatic variables. At the monthly scale, the WUE decreased significantly as the precipitation and soil moisture content increased. However, a significant correlation was not detected between the WUE and the VPD at the monthly scale. Moisture conditions tend to be major drivers of the ecosystem WUE.