Computational improvements to multi-scale geographically weighted regression

ABSTRACT

Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Multi-scale Geographically Weighted Regression (MGWR) is a recent advancement to the classic GWR model. MGWR is superior in capturing multi-scale processes over the traditional single-scale GWR model by using different bandwidths for each covariate. However, the multiscale property of MGWR brings additional computation costs. The calibration process of MGWR involves iterative back-fitting under the additive model (AM) framework. Currently, MGWR can only be applied on small datasets within a tolerable time and is prohibitively time-consuming to run with moderately large datasets (greater than 5,000 observations). In this paper, we propose a parallel implementation that has crucial computational improvements to the MGWR calibration. This improved computational method reduces both memory footprint and runtime to allow MGWR modelling to be applied to moderate-to-large datasets (up to 100,000 observations). These improvements are integrated into the mgwr python package and the MGWR 2.0 software, both of which are freely available to download.     

A chironomid-based salinity inference model from lakes on the Tibetan Plateau

Previous studies have shown chironomids to be excellent indicators of environmental change and training sets have been developed in order to allow these changes to be reconstructed quantitatively from subfossil sequences. Here we present the results of an investigation into the relationships between surface sediment subfossil chironomid distribution and lake environmental variables from 42 lakes on the Tibetan Plateau. Canonical correspondence analysis (CCA) revealed that of the 11 measured environmental variables, salinity (measured as total dissolved solids TDS) was most important, accounting for 10.5% of the variance in the chironomid data. This variable was significant enough to allow the development of quantitative inference models. A range of TDS inference models were developed using Weighted Averaging (WA), Partial Least Squares (PLS), Weighted Averaging–Partial Least Squares (WA–PLS), Maximum Likelihood (ML), Modern Analogues Technique (MAT) and Modern Analogues Techniques weighted by similarity (WMAT). Evaluation of the site data indicated that four lakes were major outliers, and after omitting these from the training set the models produced jack-knifed coefficients of determination (r ²) between 0.60 and 0.80, and root-mean-squared errors of prediction (RMSEP) between 0.29 and 0.44 log₁₀ TDS. The best performing model was the two-component WA–PLS model with r ² _jack = 0.80 and RMSEP_jack = 0.29 log₁₀ TDS. The model results were similar to other chironomid-salinity models developed in different regions, and they also showed similar ecological groupings along the salinity gradient with respect to freshwater/salinity thresholds and community diversity. These results therefore indicate that similar processes may be controlling chironomid distribution across salinity gradients irrespective of biogeographical constraints. The performance of the transfer functions illustrates that chironomid assemblages from the Tibetan Plateau lakes are clearly sensitive indicators of salinity. The models will therefore allow the quantification of long-term records of past water salinity for lacustrine sites across the Tibetan Plateau, which has important implications for future hydrological research in the region.     

Midges (Chironomidae,Ceratopogonidae, Chaoboridae) as a temperature proxy: a training set from Tasmania,Australia

Chironomid and ceratopogonid head capsules, along with Chaoborus mandibles, were used to model mean temperature of the warmest quarter (TWARM) in Tasmania. Our transfer function is based on midge assemblages and 21 environmental variables sampled from 47 lakes. Canonical correspondence analysis (CCA) revealed seven variables that account for a significant (P ≤ 0.05) portion of the explainable variance. In order of explanatory power, these were pH, TWARM, annual radiation, magnesium, annual precipitation, SiO₂, and depth. TWARM was modeled using weighted averaging partial least squares (WA-PLS) and generated a model with and RMSEP = 0.94. Advances in chironomid paleoecology are progressing very quickly in the Southern Hemisphere. Chironomid identification guides and autecological data are available for many regions, highlighting the potential for developing midge-based quantitative models to address hemispheric and interhemispheric climate hypotheses.     

Diatom-based total phosphorus (TP) and pH transfer functions for the Irish Ecoregion

A 72-lake diatom training set was developed for the Irish Ecoregion to examine the response of surface sediment diatom assemblages to measured environmental variables. A variety of multivariate data analyses was used to investigate environmental and biological data structure and their inter-relationships. Of the variables used in determining a typology for lakes in the Irish Ecoregion, alkalinity was the only one found to have a significant effect on diatom assemblages. A total of 602 diatom taxa were identified, with 233 recorded at three or more sites with abundances ≥1%. Generally diatom data displayed a high degree of heterogeneity at the species level and non-linear ecological responses. Both pH and total phosphorus (TP) (in the ranges of 5.1–8.5 and 4.0–142.3 μg l⁻¹ respectively) were shown to be the most significant variables in determining the surface sediment diatom assemblages. The calibration models for pH and TP were developed using the weighted averaging (WA) method; data manipulation showed strong influences on model performances. The optima WA models based on 70 lakes produced a jack-knifed coefficient of determination (r ² _jack) of 0.89 with a root mean squared error (RMSEP) of 0.32 for pH and r ² _jack of 0.74 and RMSEP of 0.21 (log₁₀ μg l⁻¹) for TP. Both models showed strong performances in comparison with existing models for Ireland and elsewhere. Application of the pH and TP transfer functions developed here will enable the generation of quantitative water quality data from the expanding number of palaeolimnological records available for the Irish Ecoregion, and thus facilitate the use of palaeolimnological approaches in the reconstruction of past lake water quality, ecological assessment and restoration.     

Paleoenvironmental inferences from diatom assemblages of the middle Miocene Shanwang Formation,Shandong, China

Diatoms were analyzed in a laminated sediment sequence from the middle Miocene, lacustrine Shanwang Formation, Shandong Province, eastern China, to reconstruct past conditions in the lake and evaluate relationships between inferred changes in the aquatic and terrestrial environments. Changes in the diatom assemblages over the 22.9-m-long sediment sequence were used to assign 19 lithologic layers to five zones. In Zone 1, Aulacoseira cf. distans and Melosira youngi were dominant in diatomaceous laminations. In Zone 2, only a few Aulacoseira spp. and Cymbella spp. were found in the yellow-green mudstone samples. In Zone 3, benthic pennate taxa, such as Fragilaria, Pinnularia, and Cymbella dominated in parts of the laminites. In Zone 4, Aulacoseira taxa regained dominance, and in Zone 5, benthic diatoms were found in only one sample. Shifts in the diatom assemblages and other sedimentological evidence indicate a change in the water level from a relatively deep system (>8–12 m) to a mudflat, then fluctuating water levels (8–12 m), shallower conditions (4–8 m), and finally a terrestrial environment. Abundant Aulacoseira indicate not only cold water, but also wind-induced turbulence. Water depth fluctuations coincided with the aridity index, reflected by terrestrial plant fossils in the sediments. Water pH, total phosphorus (TP), and total organic carbon (TOC) reconstructions were undertaken using the European Diatom Database (EDDI), and results showed a correlation between fluctuating water levels and volcanic activity in Zone 3.     

Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations

Geographically Weighted Regression (GWR) is a widely used tool for exploring spatial heterogeneity of processes over geographic space. GWR computes location-specific parameter estimates, which makes its calibration process computationally intensive. The maximum number of data points that can be handled by current open-source GWR software is approximately 15,000 observations on a standard desktop. In the era of big data, this places a severe limitation on the use of GWR. To overcome this limitation, we propose a highly scalable, open-source FastGWR implementation based on Python and the Message Passing Interface (MPI) that scales to the order of millions of observations. FastGWR optimizes memory usage along with parallelization to boost performance significantly. To illustrate the performance of FastGWR, a hedonic house price model is calibrated on approximately 1.3 million single-family residential properties from a Zillow dataset for the city of Los Angeles, which is the first effort to apply GWR to a dataset of this size. The results show that FastGWR scales linearly as the number of cores within the High-Performance Computing (HPC) environment increases. It also outperforms currently available open-sourced GWR software packages with drastic speed reductions – up to thousands of times faster – on a standard desktop.     

Sources of bias in weighted average inferences of environmental conditions

Weighted averaging is widely used for inferring environmental conditions from an observed species assemblage. However, weighted average inferences are known to be systematically biased, and linear corrections (i.e., deshrinking functions) are commonly applied to adjust for this bias. In this analysis, the magnitude of the biases in weighted average inferences (and therefore the values of the deshrinking coefficients) are shown to depend upon the range of conditions sampled in the calibration data set and the true optima and niche breadths of the species observed in the calibration data set. Since the range of conditions and the observed species can differ between the calibration data set and the new data set for which environmental conditions are inferred, the coefficients for the deshrinking function derived using the calibration data may not be applicable to inferences computed using a new data set. Thus, environmental inferences may still exhibit systematic errors even after application of the linear correction. The findings from the theoretical analysis are demonstrated using stream temperature and macroinvertebrate data collected from wadeable streams in the western United States.     

Paleohydrology inferred from diatoms in northern latitude regions

Several recent studies have successfully applied diatom-based paleolimnological techniques to infer past hydrological changes in arctic and subarctic regions. For example, we summarize arctic studies that attempt to determine changes in peat water content, flood frequency, river discharge, effective moisture and ice cover in northern regions. Some of the investigations are still in preliminary stages, but represent innovative approaches to study arctic and subarctic paleohydrology. New data demonstrate that lake depth, which may be related to changing hydrological conditions, is a significant variable influencing the distributions of diatom taxa in lake surface sediment calibration sets from Wood Buffalo National Park (WBNP), on the border of Alberta and the Northwest Territories, Canada, and from Fennoscandia (mainly northwest Finland). Weighted averaging regression and calibration methods were used to develop quantitative inference models for lake depth using diatom assemblages preserved in surface sediments. The predictive abilities of the transfer functions were relatively high (for WBNP r² = 0.70 and RMSE = 2.6 m, and for Fennoscandia r² = 0.88 and RMSE = 1.8 m). However, evaluating the transfer functions using jack-knifing procedures indicated lower predictive abilities, possibly reflecting the relatively small sample size and/or short gradients used in these calibration sets. Such transfer functions can be used to track overall trends in lake levels, and provide an objective assessment as to directions of changing lake levels. Any interpretations of inferred lake levels, especially those related to climate change, must be made cautiously and must include some understanding of the local, present-day hydrological system.     

Mineral Potential Modelling for the Greater Nahanni Ecosystem Using GIS Based Analytical Methods

Mineral potential within the Greater Nahanni Ecosystem (GNE) was modelled in a Geographic Information System (GIS) for four different deposit types: (1) SEDEX (stratiform shale-hosted sedimentary exhalative Zn–Pb–Ag), (2) ‘Carbonate-Fault’ (carbonate-hosted zinc–lead–silver associated with major faults), (3) ‘Intrusion-Related’ (includes skarn, rare metals and gemstones) and (4) Carlin-Type gold as lode and/or derived placer deposits. This mineral potential modelling study integrates data collected during the Nahanni Mineral and Energy Resource Assessment (MERA) undertaken from 2003 to 2007. The results have contributed to the process of determining the geographic boundaries of the proposed expansion of the Nahanni National Park Reserve. Four mineral potential maps were produced (one for each deposit type) using a knowledge-driven approach. A weighting scheme based on integrated mineral deposit and regional geological knowledge was derived for the various evidence maps for each deposit model using expert opinion. The four potential maps were then combined into a final potential map using a maximum operator. Plots showing the efficiency of the models (mineral potential maps) for predicting the known occurrences of the four deposit types show that partial data sets provide reasonable predictions of the remaining known mineral prospects, occurrences and deposits. Hydrocarbon potential from Nahanni MERA 1 was added to the final potential map to ensure that both mineral and energy potential data were incorporated into the park configuration modelling.     

Calibration of Littoral Diatoms to Water Chemistry in Standing Fresh Waters (Flanders, Lower Belgium): Inference Models for Historical Sediment Assemblages

Relationships between littoral surface-sediment diatom assemblages and ambient limnological conditions were examined in 186 lentic fresh waters throughout lower Belgium (Flanders). Most of these waters were small, unstratified, alkaline and rich in nutrients. Using weighted-averaging techniques, robust and accurate transfer functions were developed for median pH-values ranging from 3.4 to 9.3 and dissolved inorganic carbon concentrations from <1.6 to 63 mg l⁻¹ (jackknifed r ²≈ 0.87, RMSEP <10% of the observed range), while a less precise model was obtained for sodium (2–571 mg l⁻¹; jackknifed r ² 0.69, RMSEP 9.9% of the range). Restricting the data set to circumneutral and alkaline sites (pH≥6.5) revealed the importance of additional variables, including calcium, silica, chemical oxygen demand and potential gross oxygen production (a proxy for metabolic activity and phytoplankton abundance). Calibration models for these variables were strong enough to be useful (jackknifed r ² 0.57–0.59, RMSEP 13.1–16.4% of the observed range), although estimations should not always be considered entirely independent. Except for the predominant pH gradient, removal of all taxa with a distribution unrelated to the variable of interest improved model performance. In general, such taxa were proportionally represented among taxa classified according to their principal habitat. Application of the present models to diatom assemblages of shallow-water sediments obtained from historical samples and, most importantly, herbarium-macrophyte specimens, will improve hindsight into regional freshwater conditions and add to base-line setting of ecological quality standards in a highly impacted region. Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users.     

Chironomid-based inference models for estimating mean July air temperature and water depth from lakes in Yakutia,northeastern Russia

We investigated the subfossil chironomid fauna of 150 lakes situated in Yakutia, northeastern Russia. The objective of this study was to assess the relationship between chironomid assemblage composition and the environment and to develop chironomid inference models for quantifying past regional climate and environmental changes in this poorly investigated area of northern Russia. The environmental data and sediment samples for chironomid analysis were collected in 5 consecutive years, 2003–2007, from several regions of Yakutia. The lakes spanned wide latitudinal and longitudinal ranges and were distributed through several environmental zones (arctic tundra, typical tundra, steppe-tundra, boreal coniferous forest), but all were situated within the zone of continuous permafrost. Mean July temperature (T_July) varied from 3.4°C in the Laptev Sea region to 18.8°C in central Yakutia near Yakutsk. Water depth (WD) varied from 0.1 to 17.1 m. T_July and WD were identified as the strongest predictor variables explaining the chironomid communitiy composition and distribution of the taxa in our data set. Quantitative transfer functions were developed using two unimodal regression calibration techniques: simple weighted averaging (WA) and weighted averaging partial least squares (WA-PLS). The two-component T_July WA-PLS model had the best performance. It produced a strong coefficient of determination (r ² _boot = 0.87), root mean square error of prediction (RMSEP = 1.93), and max bias (max bias_boot = 2.17). For WD, the one-component WA-PLS model had the best performance (r ² _boot = 0.62, RMSEP = 0.35, max bias_boot = 0.47).     

A comparison between diatom-based pH inference models using Artificial Neural Networks (ANN), Weighted Averaging (WA) and Weighted Averaging Partial Least Squares (WA-PLS) regressions

We explored the possibility of using artificial neural networks (ANN) to develop quantitative inference models in paleolimnology. ANNs are dynamic computer systems able to learn the relations between input and output data. We developed ANN models to infer pH from fossil diatom assemblages using a calibration data set of 76 lakes in Quebec. We evaluated the predictive power of these models in comparison with the two most commonly methods used in paleolimnology: Weighted Averaging (WA) and Weighted Averaging Partial Least Squares (WA-PLS). Results show that the relationship between species assemblages and environmental variables of interest can be modelled by a 3-layer back-propagation network, with apparent R² and RMSE of 0.9 and 0.24 pH units, respectively. Leave-one-out cross-validation was used to access the reliabilities of the WA, WA-PLS and ANN models. Validation results show that the ANN model (R² _jackknife = 0.63, RMSE_jackknife = 0.45, mean bias = 0.14, maximum bias = 1.13) gives a better predictive power than the WA model (R² _jackknife = 0.56, RMSE_jackknife = 0.5, mean bias = –0.09, maximum bias = –1.07) or WA-PLS model (R² _jackknife = 0.58, RMSE_jackknife = 0.48, mean bias = –0.15, maximum bias = –1.08). We also evaluated whether the removal of certain taxa according to their tolerance changed the performance of the models. Overall, we found that the removal of taxa with high tolerances for pH improved the predictive power of WA-PLS models whereas the removal of low tolerance taxa lowered its performance. However, ANN models were generally much less affected by the removal of taxa of either low or high pH tolerance. Moreover, the best model was obtained by averaging the predictions of WA-PLS and ANN models. This implies that the two modelling approaches capture and extract complementary information from diatom assemblages. We suggest that future modelling efforts might achieve better results using analogous multi-model strategies.     

A northwest North American training set: distribution of freshwater midges in relation to air temperature and lake depth

Freshwater midges, consisting of Chironomidae, Chaoboridae and Ceratopogonidae, were assessed as a biological proxy for palaeoclimate in eastern Beringia. The northwest North American training set consists of midge assemblages and data for 17 environmental variables collected from 145 lakes in Alaska, British Columbia, Yukon, Northwest Territories, and the Canadian Arctic Islands. Canonical correspondence analyses (CCA) revealed that mean July air temperature, lake depth, arctic tundra vegetation, alpine tundra vegetation, pH, dissolved organic carbon, lichen woodland vegetation and surface area contributed significantly to explaining midge distribution. Weighted averaging partial least squares (WA-PLS) was used to develop midge inference models for mean July air temperature (r _boot² = 0.818, RMSEP = 1.46°C), and transformed depth (ln (x+1); r _boot² = 0.38, and RMSEP = 0.58).     

Climatic variability in the Late Copper Age: stable isotope fluctuation of prehistoric Unio pictorum (Unionidae) shells from Lake Balaton (Hungary)

Geochemical records of bivalve shells have been increasingly studied in the last decade to obtain information on climate conditions. In this paper we present stable isotope compositions of living and prehistoric shells of freshwater mussels (Unionidae) and their relationships with climate conditions in a shallow lake environment of Lake Balaton, West-Central Hungary. Physical conditions and stable oxygen isotope compositions of lake water samples were monitored where living bivalves were collected. Comparisons between seasonal variations in ambient temperature, water composition and within-shell isotopic variations indicate that the shells of Unio pictorum do reflect local changes at high resolution and thus can be used to study past conditions. Additionally, shells covering the last two decades were gathered at several locations along the lake in order to determine spatial and temporal variations in the shells’ isotopic compositions as a function of weather conditions. As an application, prehistoric shells collected in archaeological excavations were analysed in order to study past environmental variations. Climate variations during the Late Copper Age (5460–4870 cal. yr BP) have been assumed on the basis of geomorphological and archaeozoological observations at the site Balatonkeresztúr-Réti-dűlő (south of Lake Balaton), that suggested increasing humidity as a cause of changes in settlement location and domestic livestock husbandry. Stable carbon and oxygen isotope compositions of prehistoric bivalve shells were analyzed from excavations representing five archaeological subphases (Boleráz subphase, 5460–5310 cal. yr BP; two transitional subphases around 5310 cal. yr BP; Early Classic subphase, 5310–5060 cal. yr BP; Late Classic subphase, 5040–4870 cal. yr BP). The analyses revealed significant negative C and O isotope shifts in the transitional subphases relative to the earlier and later subphases. The isotopic variations indicate that the local climate became relatively wet and possibly cold around 5310 cal. yr BP, then it returned to drier (and likely warmer) conditions during the Classic subphases. This interpretation is in agreement with previous studies on climate changes related to the “5.3 ky event” in the European continental area and the North Atlantic Region, indicating an Atlantic influence in the Carpathian Basin.     

Middle and late Holocene climate change and human impact inferred from diatoms,algae and aquatic macrophyte pollen in sediments from Lake Montcortès (NE Iberian Peninsula)

During the middle and late Holocene, the Iberian Peninsula underwent large climatic and hydrologic changes, but the temporal resolution and regional distribution of available palaeoenvironmental records is still insufficient for a comprehensive assessment of the regional variability. The high sedimentation rate in karstic, meromictic Montcortès Lake (Catalan pre-Pyrenees) allows for a detailed reconstruction of the regional palaeoecology over the last 5,340 years using diatom analysis, aquatic pollen, sedimentological data, and historic documentary records. Results show marked fluctuations in diatom species assemblage composition, mainly between dominant Cyclotella taxa and small Fragilariales. We suggest that the conspicuous alternation between Cyclotella comta and C. cyclopuncta reflects changes in trophic state, while the succession of centric and pennate species most likely reflects changes in the hydrology of the lake. The diatom assemblages were used to identify six main phases: (1) high productivity and likely lower lake levels before 2350 BC, (2) lower lake levels and a strong arid phase between 2350 and 1850 BC, (3) lake level increase between 1850 and 850 BC, (4) relatively high lake level with fluctuating conditions during the Iberian and Roman Epochs (650 BC–350 AD), (5) lower lake levels, unfavourable conditions for diatom preservation, eutrophication and erosion triggered by increased human activities in the watershed during the Medieval Climate Anomaly (900–1300 AD), and (6) relatively higher lake levels during the LIA (1380–1850 AD) and afterwards. The combined study of diatoms, algae and pollen provides a detailed reconstruction of past climate, which refines understanding of regional environmental variability and interactions between climate and socio-economic conditions in the Pyrenees.     

Zooplankton distribution and abundance in saline lakes of Alberta and Saskatchewan,Canada

Zooplankton collections were made during 1985, 1986, 1988 and 1989 from 17 lakes in Saskatchewan and 3 in Alberta. Salinity ranged from 2.8 to 269 g L⁻¹ (total filtrable residue). A total of 35 species was present in four taxa: Anostraca (3 species), Cladocera (11), Copepoda (7) and Rotifera (14). Species richness was greatest at salinities <7 g L⁻¹ (15–16 species). Lakes with salinities between 7 and 100 g L⁻¹ generally had 6–8 zooplankton species, while the most saline lakes (>100 g L⁻¹) had 2–5 species. The largest concentrations of zooplankton occurred at <30 g L⁻¹, but some species (Brachionus plicatilis, Hexarthra polyodonta, Artemia franciscana, Diaptomus connexus) were abundant at salinities >50 g L⁻¹. Eurysaline species included the rotifersAsplanchna girodi (3–111 g L⁻¹),Brachionus plicatilis (13–146) andKeratella quadrata (2.8–103).Artemia franciscana (33–269—but absent from Big Quill Lake, 49–82),Daphnia similis (3–104).D. connexus (9–82),Diacyclops thomasi (3–72), andCletocamptus albuquerquensis (17 to 126—but never abundant in the plankton). About half the species were restricted to hyposaline waters (3–20 g L⁻¹), but some (Hexarthra fennica, Moina hutchinsoni, Hexarthra polyodonta) occurred only at intermediate salinities. The latter two species were also only present at high pH values (>9.2). There was a trend of decreasing species richness with increasing salinity. TWINSPAN classification of 94 lake samples (six parameters) based on zooplankton species abundances yielded a dendrogram with 14 ‘indicator’ species characteristic of seven lake groups related partly to a salinity gradient, but with other environmental factors such as water column depth, pH, Secchi disk transparency, water temperature and month sampled also influenced lake separation. *** DIRECT SUPPORT *** A02GG004 00002     

分布式水文模型全局敏感性高效分析方法研究(英文)

Sensitivity analysis of hydrological model is the key for model uncertainty quantification. However, how to effectively validate model and identify the dominant parameters for distributed hydrological models is a bottle-neck to achieve parameters optimization. For this reason, a new approach was proposed in this paper, in which the support vector machine was used to construct the response surface at first. Then it integrates the SVM-based response surface with the Sobol’ method, i.e. the RSMSobol’ method, to quantify the parameter sensitivities. In this work, the distributed time-variant gain model (DTVGM) was applied to the Huaihe River Basin, which was used as a case to verify its validity and feasibility. We selected three objective functions (i.e. water balance coefficient WB, Nash-Sutcliffe efficiency coefficient NS, and correlation coefficient RC) to assess the model performance as the output responses for sensitivity analysis. The results show that the parameters g1 and g2 are most important for all the objective functions, and they are almost the same to that of the classical approach. Furthermore, the RSMSobol method can not only achieve the quantification of the sensitivity, and also reduce the computational cost, with good accuracy compared to the classical approach. And this approach will be effective and reliable in the global sensitivity analysis for a complex modelling system.     

Quantitative reconstruction of lake conductivity in the Quaternary of the Near East (Israel) using ostracods

Surface sediments, water samples and environmental data from 37 lakes, ponds and streams in Israel were analysed to determine the main variables controlling ostracod species distributions. Multivariate statistical analysis revealed that the greatest amounts of variation in the distribution of the ostracod taxa among the 37 water bodies were explained by the host water δD value (12.9%), water temperature (11.0%), mean January air temperature (10.5%), electrical conductivity (9.5%), and the Mg and NO₃ concentrations (7.8 and 7.1%, ion concentrations as % of the anions or cations). A supplementary data set comprising ostracod species composition and electrical conductivity readings for 24 water bodies was available from previous research and was merged with the 37 samples data set to develop an ostracod-based transfer function for the reconstruction of electrical conductivities. A weighted averaging partial least squares regression (WA-PLS) provided the best results with a relatively high coefficient of determination (r ²) between measured and inferred electrical conductivity values of 0.73, a root mean square error of prediction of 0.13 (13.4% of gradient length) and a maximum bias of 0.24 (23.9% of gradient length), as assessed by leave-one-out cross-validation based on 56 water bodies. The application of the EC transfer function onto (sub)fossil ostracod assemblages from Holocene and early to mid Pleistocene lake sediments provided EC values consistent with other proxies and demonstrated that Quaternary ostracod assemblages from subaqueous sediments can now be used to trace the hydrological history of water bodies in the Near East. A better understanding of past hydrological conditions in response to the natural climate variability is crucial in regions that face restricted water resources and rising demands in times of rapid climate and environmental change.     

The land use and land cover change database and its relative studies in China

In the mid-1990s, we established the national operative dynamic information serving systems on natural resources and environment. During building the land-use/land-cover change (LUCC) database for the mid-1990s, 520 scenes of remotely sensed images of Landsat Thematic Mapper (TM) were interpreted into land-use/land-cover categories at scale of 1:100,000 under overall digital software environment after being geo-referenced and ortho-rectified. The vector map of land-use/land-cover in China at the scale of 1:100,000 was recently converted into a 1-km raster database that captures all of the high-resolution land-use information by calculating area percentage for each kind of land use category within every cell. Being designed as an operative dynamic information serving system, monitoring the change in land-use/land-cover at national level was executed. We have completed the updating of LUCC database by comparing the TM data in the mid-1990s with new data sources received during 1999–2000 and 1989–1990. The LUCC database has supported greatly the national LUCC research program in China and some relative studies are incompletely reviewed in this paper.