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Integrating the Budyko framework with the emerging hot spot analysis in local land use planning for regulating surface evapotranspiration ratio
Abstract
Land use planning regulates surface hydrological processes by adjusting land properties with varied evapotranspiration ratios. However, a dearth of empirical spatial information hampers the regulation of place-specific hydrological processes. Therefore, this study proposed a Local Land Use Planning framework for EvapoTranspiration Ratio regulations (ETR-LLUP), which was tested for the developments of spatially-varied land use strategies in the Dongjiang River Basin (DRB) in Southern China. With the first attempt at integrating the Emerging Hot Spots Analysis (EHSA) with the Budyko framework, the spatiotemporal trends of evapotranspiration ratios based on evaporative index and dryness index, from 1992 to 2018, were illustrated. Then, representative land-cover types in each sub-basin were defined using Geographically Weighted Principal Component Analysis, in two wet years (1998 and 2016) and three dry years (2004, 2009, and 2018), which in turn were identified using the Standard Precipitation Index. Finally, Geographically Weighted Regressions (GWRs) were used to detect spatially-varied relationships between land-cover proportions and evaporative index in both dry and wet climates. Results showed that the DRB was consistently a water-limited region from 1992 to 2018, and the situation was getting worse. We also identified the upper DRB as hotspots for hydrological management. Forests and croplands experienced increasingly water stress compared to other vegetation types. More importantly, the spatial results of GWR models enabled us to adjust basin land use by 1) expanding and contracting a combination of ‘mosaic natural vegetation’ and ‘broadleaved deciduous trees’ in the western and eastern parts of the basin, respectively; and 2) increasing ‘broadleaved evergreen trees’ in the upstream parts of the basin. These spatially-varied land use strategies based on the ETR-LLUP framework allow for place-specific hydrological management during both dry and wet climates.
... Many similar studies have been conducted in different regions, including the Yellow River Basin in China [17], the Upper Upatoi Watershed in the USA [19], and the semi-arid forested watershed in Iran [20]. However, the limitations of these previous studies are using the mean values (multiyear averages are used in describing spatial variation, and spatial averages are used in describing temporal variation), and failing to include all WCFs in each timestep and spatialgrid observation [21]. To address these limitations, emerging hot spot analysis (EHSA) is an effective method that can deal with these limitations, and it has been used to investigate spatiotemporal variations in multiple fields, including hydrological drought risk [22], surface evapotranspiration ratios [21], fire occurrences [23], and surface deformation [24]. ...
... However, the limitations of these previous studies are using the mean values (multiyear averages are used in describing spatial variation, and spatial averages are used in describing temporal variation), and failing to include all WCFs in each timestep and spatialgrid observation [21]. To address these limitations, emerging hot spot analysis (EHSA) is an effective method that can deal with these limitations, and it has been used to investigate spatiotemporal variations in multiple fields, including hydrological drought risk [22], surface evapotranspiration ratios [21], fire occurrences [23], and surface deformation [24]. The EHSA integrates temporal and spatial patterns and could present the spatial nonstationarity of the WCF, describe the location and pattern of historical changes more accurately, and identify different patterns through trend significance [22]. ...
... EHSA is a geospatial tool, which integrates temporal column information into general hot spot analysis [21,45], that could identify the spatial and temporal heterogeneity of WCF more accurately and comprehensively. EHSA requires a space-time cube, which is a netCDF file containing x, y, and z (time) dimensions. ...
The water conservation function (WCF), as one of the most critical ecosystem services, has an important impact on the ecological sustainability of a region. Accurately characterizing the spatiotemporal heterogeneity of WCF and further exploring its driving factors are of great significance for river basin management. Here, the WCF of the upper Yangtze River basin (UYRB) from 1991 to 2020 was calculated using the water yield module in the Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) model. Also, we innovatively applied emerging hot spot analysis (EHSA), which could describe the location and pattern of historical changes more accurately, to investigate the spatiotemporal heterogeneity and evolution of WCF. Based on the Geographical Detector Model (GDM), the main driving factors of WCF and their interactions were revealed. The results showed the following: (1) the WCF in the UYRB experienced a temporal increase at a growth rate of 1.48 mm/a, while remarkable differences were observed across the change rates of sub-watersheds. (2) The spatial variation of the WCF showed a gradual increase from northwest to southeast. Interestingly, the Jinshajing River upstream (JSJU) source area with a low WCF showed an increasing trend (with diminishing cold spots). On the contrary, the downstream regions of the JSJU watershed (with intensifying cold spots) underwent a weakening WCF. (3) Among all driving factors, precipitation (q = 0.701) exhibited the most remarkable prominent impact on the spatial heterogeneity of the WCF. Additionally, the interaction of factors exhibited more explanatory power than each factor alone, such as precipitation and saturated soil hydraulic conductivity (q = 0.840). This research study is beneficial to water resource management and provides a theoretical basis for ecological restoration.
... To overcome these limitations, Emerging Hot Spot Analysis (EHSA) is considered a viable approach. Currently, EHSA has been utilized in studying temporal and spatial changes in diverse fields, such as surface evapotranspiration rate [22], prediction of hydrological drought risk [23], and surface deformation [24]. Furthermore, Liu [25] investigated the spatial heterogeneity of WCF in the Yangtze River Basin using EHSA. ...
... The space-time cube is a form of multidimensional spatiotemporal data that integrates time and space [31], enabling visualization of spatiotemporal data, time-series forecasting, and analysis of spatiotemporal patterns [22]. In this study, the space-time cube takes the form of a netCDF file with three dimensions: x, y, and z. ...
Precisely delineating the spatiotemporal heterogeneity of water conservation services function (WCF) holds paramount importance for watershed management. However, the existing assessment techniques exhibit common limitations, such as utilizing only multi-year average values for spatial changes and relying solely on the spatial average values for temporal changes. Moreover, traditional research does not encompass all WCF values at each time step and spatial grid, hindering quantitative analysis of spatial heterogeneity in WCF. This study addresses these limitations by utilizing an improved water balance model based on ecosystem type and soil type (ESM-WBM) and employing the EFAST and Sobol’ method for parameter sensitivity analysis. Furthermore, a space–time cube of WCF, constructed using remote-sensing data, is further explored by Emerging Hot Spot Analysis for the expression of WCF spatial heterogeneity. Additionally, this study investigates the impact of two core parameters: neighborhood distance and spatial relationship conceptualization type. The results reveal that (1) the ESM-WBM model demonstrates high sensitivity toward ecosystem types and soil data, facilitating the accurate assessment of the impacts of ecosystem and soil pattern alterations on WCF; (2) the EHSA categorizes WCF into 17 patterns, which in turn allows for adjustments to ecological compensation policies in related areas based on each pattern; and (3) neighborhood distance and the type of spatial relationships conceptualization significantly impacts the results of EHSA. In conclusion, this study offers references for analyzing the spatial heterogeneity of WCF, providing a theoretical foundation for regional water resource management and ecological restoration policies with tailored strategies.
... On the continent scale, the products of MODIS (Mu et al. 2007) are widely used in the research of ET, such as East Asia (Hwang & Choi 2013), conterminous United States (Velpuri et al. 2013) and Siberia (Shi et al. 2022). Some experts use other remote sensing data for ET mapping like Landsat (Yang et al. 2017;Tan et al. 2019) or ERA5 (Fan et al. 2022;Li et al. 2022b). However, many research did not consider the annual and seasonal change of ET, which is more available on the regional scale. ...
Evapotranspiration (ET) is a crucial parameter in the process of the hydrological cycle. It is vital for water resource management in the Xiangjiang River Basin (XRB) within Hunan Province of China to explore the spatial and temporal dynamic characteristics of ET. Based on MOD16, this study revealed the characteristics of spatiotemporal patterns of ET in the XRB from 2000 to 2020. We first applied land use data and change rate for overall trend analysis on ET. Then, we obtained migration routes of ET through standard deviation ellipse (SDE). Furthermore, we demonstrated the effects of monsoon and urban expansion on ET change. The results showed: (1) while the ET of artificial surfaces decreased the change rate in most regions of the XRB was 8.83%, indicating that the overall trend of ET in the XRB was increasing. (2) At 10-year intervals, the SDE center of ET all migrated in a clockwise direction. (3) The correlation between precipitation and ET is more obvious than that between temperature and ET. (4) With the influence of monsoon on precipitation in East Asia, the changes in precipitation are consistent with the ET change.
HIGHLIGHTS
Revealing the spatiotemporal characteristics of evapotranspiration (ET) in a river basin with the typical East Asian monsoon climate.;
Using the standard deviation ellipse method to access the spatiotemporal migration routes of ET, which provides a new insight for ET mapping.;
Demonstrating the implications of the monsoon and urban expansion on ET changes.;
The evolution of land use/land cover (LULC) patterns significantly influences the dynamics of carbon storage (CS) in terrestrial ecosystems. In response to future environmental changes, however, most studies fail to synthesize the effects of policy pathways and evolving core driving factors on LULC projections. This article presents a systematic framework to assess the dynamic response of the terrestrial ecosystem CS to future LULC changes. After investigating spatiotemporal characteristics and driving forces, policy effects and future core driving factors are integrated into the improved Markov–future land use simulation model to project LULC across diverse scenarios. Then the Integrated Valuation of Ecosystem Service and Tradeoff model is coupled to explore CS dynamics with LULC changes. This framework was applied to the Weihe River Basin. The finding reveals that the overall proportion of cultivated land, forestland and grassland is above 85% and is significantly influenced by policy effects. Precipitation, temperature, population density and gross domestic product are core driving factors of LULC changes. Equal-interval projection is a viable approach to mitigate policy impacts by avoiding error propagation while coupling future core driving factors to improve LULC projection accuracy. Ecological protection should be emphasized in the future. The rate of increase in CS is 1.25 and 1.63 times higher than the historical trend and economic development scenario, respectively, which alleviates carbon loss from the expansion of built-up land. This research provides a valuable reference for future insight and optimization of ecological conservation strategies.
Spatially-invariant land use and cover changes (LUCC) are not suitable for managing non-stationary drought conditions. Therefore, developing a spatially varying framework for managing land resources is necessary. In this study, the Dongjiang River Basin in South China is used to exemplify the significance of spatial heterogeneity in land planning optimization for mitigating drought risks. Using ERA5 that is the 5th major atmospheric reanalysis from the European Centre for Medium-Range Weather Forecast, we computed the Standardized Runoff Index (SRI) to quantify the hydrologic drought during 1992 to 2018. Also, based on Climate Change Initiative land use product, The Geographically Weighted Principal Component Analysis was used to identify the most dominant land types in the same period. Then, we used the Emerging Hot Spots Analysis to characterize the spatiotemporal evolution of historical LUCC and SRI. The spatially varying coefficients of Geographically and Temporally Weighted Regression models were used to reveal the empirical relationships between land types and the SRI. Results indicated that rainfed cropland with herbaceous cover, mosaic tress and shrub, shrubland, and grassland were four land types having statistical correlations with drought conditions over 27 years. Moreover, since 2003, the DRB was becoming drier, and the northern areas generally experienced severer hydrologic drought than the south. More importantly, we proposed region-specific land-use strategies for drought risk reductions. At a basin scale, we recommended to 1) increase rainfed herbaceous cropland and 2) reduce mosaic tree and shrub. At a sub-basin scale, the extents of shrub and grassland were suggested to increase in the northern DRB but to reduce in the south. Region-specific land use planning, including suitable locations, scales, and strategies, will contribute to handling current ‘one-size-fits-all’ LUCC. Planners are suggested to integrate spatial characteristics into future LUCC for regional hydrologic management.
Spatial patterns in long-term average evapotranspiration (ET) represent a unique source of information for evaluating the spatial pattern performance of distributed hydrological models on a river basin to continental scale. This kind of model evaluation is getting increased attention, acknowledging the shortcomings of traditional aggregated or timeseries-based evaluations. A variety of satellite remote sensing (RS)-based ET estimates exist, covering a range of methods and resolutions. There is, therefore, a need to evaluate these estimates, not only in terms of temporal performance and similarity, but also in terms of long-term spatial patterns. The current study evaluates four RS-ET estimates at moderate resolution with respect to spatial patterns in comparison to two alternative continental-scale gridded ET estimates (water-balance ET and Budyko). To increase comparability, an empirical correction factor between clear sky and all-weather ET, based on eddy covariance data, is derived, which could be suitable for simple corrections of clear sky estimates. Three RS-ET estimates (MODIS16, TSEB and PT-JPL) and the Budyko method generally display similar spatial patterns both across the European domain (mean SPAEF = 0.41, range 0.25–0.61) and within river basins (mean SPAEF range 0.19–0.38), although the pattern similarity within river basins varies significantly across basins. In contrast, the WB-ET and PML_V2 produced very different spatial patterns. The similarity between different methods ranging over different combinations of water, energy, vegetation and land surface temperature constraints suggests that robust spatial patterns of ET can be achieved by combining several methods.
Climate change and large-scale afforestation characterize the conditions in the Upper Dongjiang River Basin (UDRB), which is one of the most important headwater basins in southern China. It is important to understand whether, and to what extent, the observed runoff change can be attributed to forest and/or climate change. Using process- and relation-based methods, we found precipitation in spring (March–May) decreased notably, while precipitation in summer (June–August) showed an increase from the reference period (1961–1990) to the afforestation period (1991–2010). In comparison, annual averaged potential evapotranspiration did not change much. Both of the methods indicated forest had a positive effect while climate change exerted a negative impact on annual averaged runoff in the UDRB. As a result, the observed annual averaged runoff only showed a little decrease from the reference period to the afforestation period. The climate change impact on monthly averaged runoff basically followed the pattern of precipitation change. Except in July and August, climate change exerted negative or little impact on runoff in most of other months. In comparison, the forest effects on monthly averaged runoff change showed a totally different pattern. Except in May and June, forest exerted positive impact on runoff in other months. As a result, the observed monthly averaged runoff in May and June experienced notable reduction, while those in other months experienced increase or no change. The UDRB provides evidence that additional forest cover would not injure but even increase runoff, especially dry season runoff. The study has important implications for sustainable water management and afforestation in this subtropical region and for similar river basins.
This paper investigates the role of spatial dependence, spatial heterogeneity and spatial scale in principal component analysis for geographically distributed data. It considers spatial heterogeneity by adopting geographically weighted principal component analysis at a fine spatial resolution. Moreover, it focuses on dependence by introducing a novel approach based on spatial filtering. These methods are applied in order to derive a composite indicator of socioeconomic deprivation in the Italian province of Rome while considering two spatial scales: municipalities and localities. The results show that considering spatial information uncovers a range of issues, including neighbourhood effects, which are useful in order to improve local policies.
Actual evapotranspiration (Ea) plays a key role in the global water and energy cycles. The accurate quantification of the impacts of different factors on Ea change can help us better understand the driving mechanisms of Ea in a changing environment. Climate change and vegetation variations are well known as two main factors that have significant impacts on Ea change. Our study used three differential Budyko-type equations to quantify the contributions of climate change and vegetation variations to Ea change. First, in order to establish the relationship between the parameter n, which usually presents the land surface characteristics in the Budyko-type equations, with basic climatic variables and the Normalized Difference Vegetation Index (NDVI), the stepwise linear regression method has been used. Then, elasticity and contribution analyses were performed to quantify the contributions of different numbers of climatic factors and NDVI to Ea change. The North and South Panjiang basin in China was selected to investigate the efficiency of the modified Budyko-type equations and quantify the impacts of climate change and vegetation variations on Ea change. The empirical formal of the parameter n established in this study can be used to simulate the parameter n and Ea for the study area. The results of the elasticity and contribution analyses suggest that climate change contributed (whose average contribution is 149.6%) more to Ea change than vegetation variation (whose average contribution is −49.4%). Precipitation, NDVI and the maximum temperature are the major drivers of Ea change, while the minimum temperature and wind speed contribute the least to Ea change.
Introduction
Landscapes and water are closely linked. Water shapes landscapes, and landscape heterogeneity in turn determines water storage, partitioning, and movement. Understanding hydrological processes from an ecological perspective is an exciting and fast-growing field of research.
Objectives
The motivation of this paper is to review advances in the interaction between landscape heterogeneity and hydrological processes, and propose a framework for synthesizing and moving forward.
Methods
Landscape heterogeneity, mainly topography and land cover, has been widely incorporated into existing hydrological models, but not in a systematic way. Topography, as one of the most important landscape traits, has been extensively used in hydrological models, but mostly to drive water flow downhill. Land cover heterogeneity, represented mostly by vegetation, is usually linked with evaporation and transpiration rather than runoff generation. Moreover, the proportion of different land cover types is usually the only index involved in hydrological models, leaving the influence of vegetation patterns and structure on hydrologic connectivity still largely unexplored. Additionally, moving from “what heterogeneity exists” to “why-type” questions probably offers us new insights into the nexus of landscape and water.
Conclusions
We believe that the principles of self-organization and co-evolution of landscape features shed light on the possibility to infer subsurface heterogeneity from a few observable landscapes, allowing us to simplify complexity to a few quantifiable metrics, and utilizing these metrics in models with sufficient heterogeneity but limited complexity. Landscape-based models can also be beneficial to improve our ability of prediction in ungauged basins and prediction in a changing environment (Panta Rhei, everything flows).
Vegetation change is a critical factor that profoundly affects the terrestrial water cycle. Here, we derive an analytical solution for the impact of vegetation changes on hydrological partitioning within the Budyko framework. This is achieved by deriving an analytical expression between leaf area index (LAI) change and the Budyko land surface parameter (n) change, through the combination of a steady-state ecohydrological model with an analytical carbon cost-benefit model for plant rooting depth. Using China where vegetation coverage has experienced dramatic changes over the past two decades as a study case, we quantify the impact of LAI changes on the hydrological partitioning during 1982-2010 and predict the future influence of these changes for the 21st century using climate model projections. Results show that LAI change exhibits an increasing importance on altering hydrological partitioning as climate becomes drier. In semi-arid and arid China, increased LAI has led to substantial streamflow reductions over the past three decades (on average -8.5% in 1990s and -11.7% in 2000s compared to the 1980s baseline), and this decreasing trend in streamflow is projected to continue towards the end of this century due to predicted LAI increases. Our result calls for caution regarding the large-scale revegetation activities currently being implemented in arid and semi-arid China, which may result in serious future water scarcity issues here. The analytical model developed here is physically based and suitable for simultaneously assessing both vegetation changes and climate change induced changes to streamflow globally.
Actual evapotranspiration (ET) is a major water use flux in a basin water balance with crucial significance for water resources management and planning. Mapping ET with good accuracy has been the subject of ongoing research. Such mapping is even more challenging in heterogeneous and data-scarce regions. The main objective of our research is to estimate ET using daily Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and Global Land Data Assimilation System (GLDAS) weather datasets based on the operational simplified surface energy balance (SSEBop) algorithm at a 1-km spatial scale and 8-day temporal resolution for the Mara Basin (Kenya/Tanzania). Unlike previous studies where the SSEBop algorithm was used, we use a seasonally-varying calibration coefficient for determining the “cold” reference temperature. Our results show that ET is highly variable, with a high inter-quartile range for wetlands and evergreen forest (24% to 29% of the median) and even up to 52% of the median for herbaceous land cover and rainfed agriculture. The basin average ET accounts for about 66% of the rainfall with minimal inter-annual variability. The basin scale validation using nine-years of monthly, gridded global flux tower-based ET (GFET) data reveals that our ET is able to explain 64% of the variance in GFET while the MOD16-NB (Nile Basin) explains 72%. We also observe a percent of bias (PBIAS) of 1.1% and 2.8%, respectively for SSEBop ET and MOD16-NB, indicating a good reliability in the ET estimates. Additionally, the SSEBop ET explains about 52% of the observed variability in the Normalized Difference Vegetation Index (NDVI) for a 16-day temporal resolution and 81% for the annual resolution, pointing to an increased reliability for longer aggregation periods. The annual SSEBop ET estimates are also consistent with the underlying primary (i.e., water and energy) and secondary (i.e., soil, topography, geology, land cover, etc.) controlling factors across the basin. This paper demonstrated how to effectively estimate and evaluate spatially-distributed and temporally-varying ET in data-scarce regions that can be applied elsewhere in the world where observed hydro-meteorological variables are limited.
As sources of data for global forest monitoring grow larger, more complex and numerous, data analysis and interpretation become critical bottlenecks for effectively using them to inform land use policy discussions. Here in this paper, we present a method that combines big data analytical tools with Emerging Hot Spot Analysis (ArcGIS) to identify statistically significant spatiotemporal trends of forest loss in Brazil, Indonesia and the Democratic Republic of Congo (DRC) between 2000 and 2014. Results indicate that while the overall rate of forest loss in Brazil declined over the 14-year time period, spatiotemporal patterns of loss shifted, with forest loss significantly diminishing within the Amazonian states of Mato Grosso and Rondônia and intensifying within the cerrado biome. In Indonesia, forest loss intensified in Riau province in Sumatra and in Sukamara and West Kotawaringin regencies in Central Kalimantan. Substantial portions of West Kalimantan became new and statistically significant hot spots of forest loss in the years 2013 and 2014. Similarly, vast areas of DRC emerged as significant new hot spots of forest loss, with intensified loss radiating out from city centers such as Beni and Kisangani. While our results focus on identifying significant trends at the national scale, we also demonstrate the scalability of our approach to smaller or larger regions depending on the area of interest and specific research question involved. When combined with other contextual information, these statistical data models can help isolate the most significant clusters of loss occurring over dynamic forest landscapes and provide more coherent guidance for the allocation of resources for forest monitoring and enforcement efforts.
Most Earth system models are based on grid-averaged soil columns that do not communicate with one another, and that average over considerable sub-grid heterogeneity in land surface properties, precipitation (P), and potential evapotranspiration (PET). These models also typically ignore topographically driven lateral redistribution of water (either as groundwater or surface flows), both within and between model grid cells. Here, we present a first attempt to quantify the effects of spatial heterogeneity and lateral redistribution on grid-cell-averaged evapotranspiration (ET) as seen from the atmosphere over heterogeneous landscapes. Our approach uses Budyko curves, as a simple model of ET as a function of atmospheric forcing by P and PET. From these Budyko curves, we derive a simple sub-grid closure relation that quantifies how spatial heterogeneity affects average ET as seen from the atmosphere. We show that averaging over sub-grid heterogeneity in P and PET, as typical Earth system models do, leads to overestimations of average ET. For a sample high-relief grid cell in the Himalayas, this overestimation bias is shown to be roughly 12 %; for adjacent lower-relief grid cells, it is substantially smaller. We use a similar approach to derive sub-grid closure relations that quantify how lateral redistribution of water could alter average ET as seen from the atmosphere. We derive expressions for the maximum possible effect of lateral redistribution on average ET, and the amount of lateral redistribution required to achieve this effect, using only estimates of P and PET in possible source and recipient locations as inputs. We show that where the aridity index P/PET increases with altitude, gravitationally driven lateral redistribution will increase average ET (and models that overlook lateral redistribution will underestimate average ET). Conversely, where the aridity index P/PET decreases with altitude, gravitationally driven lateral redistribution will decrease average ET. The effects of both sub-grid heterogeneity and lateral redistribution will be most pronounced where P is inversely correlated with PET across the landscape. Our analysis provides first-order estimates of the magnitudes of these sub-grid effects, as a guide for more detailed modeling and analysis.
This article proposes a systematic analysis of water management and allocation on the scale of a river basin, considering climate impacts and stakeholder networks in the Dongjiang River basin in South China. Specific approaches are integrated to explore various subtopics. Findings indicate a slight increase of precipitation in the basin and strong fluctuations in this century due to climate extremes, which may lead to seasonal or quality-related water shortages. It is highlighted that alternative options for holistic water management are needed in the basin, and participatory water allocation mechanisms and establishment of a basin-wide management framework could be helpful.
There has been growing evidence that vegetation greenness has been increasing in many parts of the northern middle and high latitudes including China during the last three to four decades. However, the effects of increasing vegetation greenness particularly afforestation on the hydrological cycle have been controversial. We used a process-based ecosystem model and a satellite-derived leaf area index (LAI) dataset to examine how the changes in vegetation greenness affected annual evapotranspiration (ET) and water yield for China over the period from 2000 to 2014. Significant trends in vegetation greenness were observed in 26.1% of China's land area. We used two model simulations driven with original and detrended LAI, respectively, to assess the effects of vegetation 'greening' and 'browning' on terrestrial ET and water yield. On a per-pixel basis, vegetation greening increased annual ET and decreased water yield, while vegetation browning reduced ET and increased water yield. At the large river basin and national scales, the greening trends also had positive effects on annual ET and had negative effects on water yield. Our results showed that the effects of the changes in vegetation greenness on the hydrological cycle varied with spatial scale. Afforestation efforts perhaps should focus on southern China with larger water supply given the water crisis in northern China and the negative effects of vegetation greening on water yield. Future studies on the effects of the greenness changes on the hydrological cycle are needed to account for the feedbacks to the climate.
Information about changes in, and causes of, land use/land cover (LULC) is crucial for land use resource planning. We investigated the processes involved in LULC change (LUCC) in the Dongjiang Watershed, in Southern China, over a 15-year period to gain a better understanding of the causes of the main types of LUCC. Using a depth transition matrix and redundancy analysis (RDA), the major types and causes of LUCC for each LULC type over the past 15 years were identified. LUCC exhibited obvious net change, relatively low persistence, and high swap change. The swap changes in most LULC types were considered as a strong signal of LULC transformations. The driving forces behind swap changes were quantified and identified through RDA. The results showed that all driving forces played important roles in explaining swap changes of LULC, although the relative effects of these drivers varied widely with both LULC type and time period. Swap changes of the LULC types were generally classified into two categories. Some, e.g., built-up land and wetland, were affected mostly by landform and/or distance factors, while others, e.g., grassland and woodland, were modulated mostly by climate and/or socioeconomic factors. Selected spatial driving forces and local land use policies played important roles in explaining the dominant LUCC types, but on different timescales. These findings may improve understanding of the detailed processes involved in LUCC, landscape transformation, and the causes of LUCC in other areas with extensive LUCC and could help managers plan, design, and implement land resource management.
Evapotranspiration (ET) over a diverse land use area in northern Thailand was successfully estimated by long-term eddy covariance measurements. Some measurement gaps due to instrumentation problems and administrative difficulties were unavoidable. Monthly ET trends revealed a maximum of 150 ± 10 mm in June and a minimum of 60 ± 10 mm in January. The annual mean ET was estimated to be 1300 ± 140 mm. The interannual variation in ET reflects the response of the land surface to meteorological events and land use/cover changes (LUCC); however, the effect of rainfall variation on ET was greater than that of LUCC. Effective heterogeneity was evaluated using the Bowen ratio; such information will be useful for understanding the effect of land surface heterogeneity on latent and sensible heat fluxes.
The impact of future climate change on the runoff for the Dongjiang River basin, South China, has been investigated with the Soil and Water Assessment Tool (SWAT). First, the SWAT model was applied in the three sub-basins of the Dongjiang River basin, and calibrated for the period of 1970-1975, and validated for the period of 1976-1985. Then the hydrological response under climate change and land use scenario in the next 40 years (2011-2050) was studied. The future weather data was generated by using the weather generators of SWAT, based on the trend of the observed data series (1966-2005). The results showed that under the future climate change and LUCC scenario, the annual runoff of the three sub-basins all decreased. Its impacts on annual runoff were -6.87%, -6.54%, and -18.16% for the Shuntian, Lantang, and Yuecheng sub-basins respectively, compared with the baseline period 1966-2005. The results of this study could be a reference for regional water resources management since Dongjiang River provides crucial water supplies to Guangdong Province and the District of Hong Kong in China.
In many physical geography settings, principal component analysis (PCA) is applied without consideration for important spatial effects, and in doing so, tends to provide an incomplete understanding of a given process. In such circumstances, a spatial adaptation of PCA can be adopted, and to this end, this study focuses on the use of geographically weighted principal component analysis (GWPCA). GWPCA is a localized version of PCA that is an appropriate exploratory tool when a need exists to investigate for a certain spatial heterogeneity in the structure of a multivariate data set. This study provides enhancements to GWPCA with respect to: (i) finding the scale at which each localized PCA should operate; and (ii) visualizing the copious amounts of output that result from its application. An extension of GWPCA is also proposed, where it is used to detect multivariate spatial outliers. These advancements in GWPCA are demonstrated using an environmental freshwater chemistry data set, where a commentary on the use of preprocessed (transformed and standardized) data is also presented. The study is structured as follows: (1) the GWPCA methodology; (2) a description of the case study data; (3) the GWPCA application, demonstrating the value of the proposed advancements; and (4) conclusions. Most GWPCA functions have been incorporated within the GWmodel R package.
This article considers critically how one of the oldest and most widely applied statistical methods, principal components analysis (PCA), is employed with spatial data. We first provide a brief guide to how PCA works: This includes robust and compositional PCA variants, links to factor analysis, latent variable modeling, and multilevel PCA. We then present two different approaches to using PCA with spatial data. First we look at the nonspatial approach, which avoids challenges posed by spatial data by using a standard PCA on attribute space only. Within this approach we identify four main methodologies, which we define as (1) PCA applied to spatial objects, (2) PCA applied to raster data, (3) atmospheric science PCA, and (4) PCA on flows. In the second approach, we look at PCA adapted for effects in geographical space by looking at PCA methods adapted for first-order nonstationary effects (spatial heterogeneity) and second-order stationary effects (spatial autocorrelation). We also describe how PCA can be used to investigate multiple scales of spatial autocorrelation. Furthermore, we attempt to disambiguate a terminology confusion by clarifying which methods are specifically termed “spatial PCA” in the literature and how this term has different meanings in different areas. Finally, we look at a further three variations of PCA that have not been used in a spatial context but show considerable potential in this respect: simple PCA, sparse PCA, and multilinear PCA.
In this study, we present a collection of local models, termed geographically
weighted (GW) models, that can be found within the GWmodel R package. A GW
model suits situations when spatial data are poorly described by the global
form, and for some regions the localised fit provides a better description. The
approach uses a moving window weighting technique, where a collection of local
models are estimated at target locations. Commonly, model parameters or outputs
are mapped so that the nature of spatial heterogeneity can be explored and
assessed. In particular, we present case studies using: (i) GW summary
statistics and a GW principal components analysis; (ii) advanced GW regression
fits and diagnostics; (iii) associated Monte Carlo significance tests for
non-stationarity; (iv) a GW discriminant analysis; and (v) enhanced kernel
bandwidth selection procedures. General Election data sets from the Republic of
Ireland and US are used for demonstration. This study is designed to complement
a companion GWmodel study, which focuses on basic and robust GW models.
Variability and availability of water resources under changing environment in a regional scale have been hot topics in recent years, due to the vulnerability of water resources associated with social and economic development. In this paper, four subbasins in the Dongjiang basin with a significant land use change were selected as case study. Runoffs of the four subbasins were simulated using the SCS monthly model to identify the quantitative impacts of land use and climate change. The results showed that (1), in the Dongjiang basin, temperature increased significantly, evaporation and sunlight decreased strongly, while precipitation showed a nonsignificant increase; (2) since the 1980s, land uses in the Dongjiang basin have experienced a significant change with a prominent increase in urban areas, a moderate increase in farmlands, and a great decrease in forest areas; (3) the SCS monthly model performed well in the four subbasins giving that the more significant land use change in each subbasin, the more runoff change correspondingly; (4) overall, runoff change was contributed half and half by climate change and human activities, respectively, in all the subbasins, in which about 20%~30% change was contributed by land use change.
The growth of vegetation is affected by water availability, while
vegetation growth also feeds back to influence regional water balance. A
better understanding of the relationship between vegetation state and
water balance would help explain the complicated interactions between
climate change, vegetation dynamics, and the water cycle. In the present
study, the impact of vegetation coverage on regional water balance was
analyzed under the framework of the Budyko hypothesis by using data from
99 catchments in the nonhumid regions of China, including the Inland
River basin, the Hai River basin, and the Yellow River basin. The
distribution of vegetation coverage on the Budyko curve was analyzed,
and it was found that a wetter environment (higher P/E0) had
a higher vegetation coverage (M) and was associated with a higher
evapotranspiration efficiency (E/E0). Moreover, vegetation
coverage was related not only to climate conditions (measured by the
dryness index DI = E0/P) but also to landscape conditions
(measured by the parameter n in the coupled water-energy balance
equation). This suggests that the regional long-term water balance
should not vary along a single Budyko curve; instead, it should form a
group of Budyko curves owing to the interactions between vegetation,
climate, and water cycle. A positive correlation was found between water
balance component (E/P) and vegetation coverage (M) for most of the
Yellow River basin and for the Inland River basin, while a negative
correlation of M ˜ E/P was found in the Hai River basin.
Vegetation coverage was successfully incorporated into an empirical
equation for estimating the catchment landscape parameter n in the
coupled water-energy balance equation. It was found that interannual
variability in vegetation coverage could improve the estimation of the
interannual variability in regional water balance.
There is no consensus on how changes in both temperature and precipitation will affect regional vegetation. We investigated controls on hydrologic partitioning at the catchment scale across many different ecoregions, and compared the resulting estimates of catchment wetting and vaporization (evapotranspiration) to remotely sensed indices of vegetation greenness. The fraction of catchment wetting vaporized by plants, known as the Horton index, is strongly related to the ratio of available energy to available water at the Earth's surface, the aridity index. Here we show that the Horton index is also a function of catchment mean slope and elevation, and is thus related to landscape characteristics that control how much and how long water is retained in a catchment. We compared the power of the components of the water and energy balance, as well as landscape characteristics, to predict Normalized Difference Vegetation Index (NDVI), a surrogate for vegetation productivity, at 312 Model Parameter Estimation Experiment (MOPEX) catchments across the United States. Statistical analysis revealed that the Horton index provides more precision in predicting maximum annual NDVI for all catchments than mean annual precipitation, potential evapotranspiration, or their ratio, the aridity index. Models of vegetation productivity should emphasize plant-available water, rather than just precipitation, by incorporating the interaction of climate and landscape. Major findings related to the Horton index are: (1) it is a catchment signature that is relatively constant from year-to-year; (2) it is related to specific landscape characteristics; (3) it can be used to create catchment typologies; and (4) it is related to overall catchment greenness.
Hydrological models have been increasingly used by hydrologists and water resource managers to understand natural processes and human activities that affect watersheds. In this study, we use the physically based model, Soil and Water Assessment Tool (SWAT), to investigate the hydrological processes in the East River Basin in South China, a coastal area dominated by monsoonal climate. The SWAT model was calibrated using 8-year (1973-1980) record of the daily streamflow at the basin outlet (Boluo station), and then validated using data collected during the subsequent 8 years (1981-1988). Statistical evaluation shows that SWAT can consistently simulate the streamflow of the East River with monthly Nash-Sutcliffe efficiencies of 0.93 for calibration and 0.90 for validation at the Boluo station. We analyzed the model simulations with calibrated parameters, presented the spatiotemporal distribution of the key hydrological components, and quantified their responses to different land uses. Watershed managers can use the results of this study to understand hydrological features and evaluate water resources of the East River in terms of sustainable development and effective management.
In Britain, the performance of all state primary schools is assessed by students' attainment levels in a set of standardized tests administered to pupils at the ages of 7 and 11 (the so-called Key Stages 1 and 2, respectively). These data are analysed for 3687 schools in northern England. In particular, school performance is linked to the number of students taking the test at each school and to various socio-economic indicators of the estimated school catchment areas. The latter are based on a geographical weighting function that links census data, an areal coverage, to school locations, a point coverage. Following a traditional global regression analysis, spatial variations in the relationships are examined with geographically weighted regression (GWR) to reveal some interesting geographical variations in the results.
Monthly precipitation data of 42 rain stations over the Pearl River basin for 1960–2005 were analyzed to classify anomalously
wet and dry conditions by using the standardized precipitation index (SPI) and aridity index (I) for the rainy season (April–September) and winter (December–February). Trends of the number of wet and dry months decided
by SPI were detected with Mann-Kendall technique. Furthermore, we also investigated possible causes behind wet and dry variations
by analyzing NCAR/NCEP reanalysis dataset. The results indicate that: (1) the Pearl River basin tends to be dryer in the rainy
season and comes to be wetter in winter. However, different wetting and drying properties can be identified across the basin:
west parts of the basin tend to be dryer; and southeast parts tend to be wetter; (2) the Pearl River basin is dominated by
dry tendency in the rainy season and is further substantiated by aridity index (I) variations; and (3) water vapor flux, moisture content changes in the rainy season and winter indicate different influences
of moisture changes on wet and dry conditions across the Pearl River basin. Increasing moisture content gives rise to an increasing
number of wet months in winter. However, no fixed relationships can be observed between moisture content changes and number
of wet months in the rainy season, indicating that more than one factor can influence the dry or wet conditions of the study
region. The results of this paper will be helpful for basin-scale water resource management under the changing climate.
An in-depth overview of the role of the hydrological cycle within the climate system, including climate change impacts on hydrological reserves and fluxes, and the controls of terrestrial hydrology on regional and global climatology. This book, composed of self-contained chapters by specialists in hydrology and climate science, is intended to serve as a text for graduate and postgraduate courses in climate hydrology and hydroclimatology. It will also be of interest to scientists and engineers/practioners interested in the water cycle, weather prediction and climate change.
Geographically weighted regression (GWR) is an important local technique for exploring spatial heterogeneity in data relationships. In fitting with Tobler's first law of geography, each local regression of GWR is estimated with data whose influence decays with distance, distances that are commonly defined as straight line or Euclidean. However, the complexity of our real world ensures that the scope of possible distance metrics is far larger than the traditional Euclidean choice. Thus in this article, the GWR model is investigated by applying it with alternative, non-Euclidean distance (non-ED) metrics. Here we use as a case study, a London house price data set coupled with hedonic independent variables, where GWR models are calibrated with Euclidean distance (ED), road network distance and travel time metrics. The results indicate that GWR calibrated with a non-Euclidean metric can not only improve model fit, but also provide additional and useful insights into the nature of varying relationships within the house price data set.
The Budyko curve describes the relationship between climate, evapotranspiration and run-off and can be used to model catchment energy and water balances. The curve's underlying framework assumes catchments are at steady-state, a condition dependent on the scales of application, such that its reliability is greatest when applied to large catchments (>10 000 km2) and using long-term averages (≫1 year). At these scales previous experience has shown that the hydrological role of vegetation does not need to be explicitly considered within the framework. By demonstrating how dynamics in the leaf area, photosynthetic capacity and rooting depth of vegetation affect not only annual and seasonal vegetation water use, but also steady-state conditions, we argue that it is necessary to explicitly include vegetation dynamics into the Budyko framework before it is applied at small scales. Such adaptations would extend the framework not only to applications at small timescales and to small catchments but to operational activities relating to vegetation and water management.
The Lake Naivasha Basin in Kenya has experienced significant land use cover changes (LUCC) that has been hypothesized to have altered the hydrological regime in recent decades. While it is generally recognized that LUCC will impact evapotranspiration (ET), the precise nature of such impact is not very well understood. This paper describes how land use conversions among grassland and croplands have influenced ET in the Lake Naivasha Basin for the period 2003 to 2012. MODIS data products were used in combination with the European Centre for Medium-Range Weather Forecasts (ECMWF) data sets to model ET using the Surface Energy Balance System (SEBS). The results indicate that conversions from grassland to cropland accounted for increases in ET of up to 12% while conversion from cropland back to grasslands (abandonment) reduced ET by ~4%. This suggests that recently cultivated agricultural lands could increase local water demands, while abandonment of the farms could decrease the water loss and eventually increase the water availability. Also, recovery of ET following re-conversion from cropland to grassland might be impeded due to delayed recovery of soil properties since parts of the catchment are continuously being transformed with no ample time given for soil recovery. The annual ET over the 10 years shows an estimated decline from 724 mm to 650 mm (~10%). This decline is largely explained by a reduction in net radiation, an increase in actual vapour pressure whose net effect also led to decrease in the air-surface temperature difference. These findings suggest that in order to better understand LUCC effects on water resources of Lake Naivasha, it is important to take into account the effect of LUCC and climate because large scale changes of vegetation type from grassland to cropland substantially will increase evapotranspiration with implications on the water balance.
Land surface evapotranspiration (ET) is a central component of the Earth's global energy balance and water cycle. Understanding ET is important in quantifying the impacts of human influences on the hydrological cycle and thus helps improving water use efficiency and strengthening water use planning and watershed management. China has experienced tremendous land use and land cover changes (LUCC) as a result of urbanization and ecological restoration under a broad background of climate change. This study used MODIS data products to analyze how LUCC and climate change affected ET in China in the period 2001–2013. We examined the separate contribution to the estimated ET changes by combining LUCC and climate data. Results showed that the average annual ET in China decreased at a rate of − 0.6 mm/yr from 2001 to 2013. Areas in which ET decreased significantly were mainly distributed in the northwest China, the central of southwest China, and most regions of south central and east China. The trends of four climatic factors including air temperature, wind speed, sunshine duration, and relative humidity were determined, while the contributions of these four factors to ET were quantified by combining the ET and climate datasets. Among the four climatic factors, sunshine duration and wind speed had the greatest influence on ET. LUCC data from 2001 to 2013 showed that forests, grasslands and croplands in China mutually replaced each other. The reduction of forests had much greater effects on ET than change by other land cover types. Finally, through quantitative separation of the distinct effects of climate change and LUCC on ET, we conclude that climate change was the more significant than LULC change in influencing ET in China during the period 2001–2013. Effective water resource management and vegetation-based ecological restoration efforts in China must consider the effects of climate change on ET and water availability.
This study demonstrates the use of a geographically weighted principal components analysis (GWPCA) of remote sensing imagery to improve land cover classification accuracy. A principal components analysis (PCA) is commonly applied in remote sensing but generates global, spatially-invariant results. GWPCA is a local adaptation of PCA that locally transforms the image data, and in doing so, can describe spatial change in the structure of the multi-band imagery, thus directly reflecting that many landscape processes are spatially heterogenic. In this research the GWPCA localised loadings of MODIS data are used as textural inputs, along with GWPCA localised ranked scores and the image bands themselves to three supervised classification algorithms. Using a reference data set for land cover to the west of Jakarta, Indonesia the classification procedure was assessed via training and validation data splits of 80/20, repeated 100 times. For each classification algorithm, the inclusion of the GWPCA loadings data was found to significantly improve classification accuracy. Further, but more moderate improvements in accuracy were found by additionally including GWPCA ranked scores as textural inputs, data that provide information on spatial anomalies in the imagery. The critical importance of considering both spatial structure and spatial anomalies of the imagery in the classification is discussed, together with the transferability of the new method to other studies. Research topics for method refinement are also suggested.
Water-related ecosystem services is a hot topic in ecological research. Water supply services are crucial to regional water cycles and water quantity balance. The Dongjiang Lake basin is a national priority river basin in China where ecological compensation pilot programs concerning water resources and water supply services are top priorities for ecosystem service protection. We analyzed spatial and temporal patterns associated with generation and use of water supply services in the Dongjiang Lake basin using the InVEST model, socio-economic data and water resource data. We found that between 1995 and 2010, water yield in the Dongjiang Lake basin and its beneficiary areas increased before declining, varying 9350–12 400 m3 ha-1 y-1; average water yield peaked in 2000. The spatial distribution patterns of water yield during these years are similar, progressively decreasing from upstream to downstream with a remarkable reduction in surrounding areas of city clusters. Average water consumption of the basin and its beneficiary areas ranged from 2900–4450 m3 ha-1 y-1 between 1995 and 2010; the spatial distribution patterns of water consumption during these years are similar, dropping gradually from urban construction land to its surroundings with a stronger gradient between urban and rural areas. More water was consumed on both banks and surroundings of the lake. From 1995 to 2010, water supply fell short of demand for urban construction land and its proximity as well as areas along the lake. Water supply services were able to satisfy needs in other regions. The Changsha-Zhuzhou-Xiangtan city cluster suffers from the most strained water supply.
Long-term climate is the first-order control on mean annual water
balance, and vegetation and the interactions between climate seasonality
and soil water storage change have also been found to play important
roles. The purpose of this paper is to extend the Budyko hypothesis to
the seasonal scale and to develop a model for interannual variability of
seasonal evaporation and storage change. A seasonal aridity index is
defined as the ratio of potential evaporation to effective
precipitation, where effective precipitation is the difference between
rainfall and storage change. Correspondingly, evaporation ratio is
defined as the ratio of evaporation to effective precipitation. A
modified Turc-Pike equation with a horizontal shift is proposed to model
interannual variability of seasonal evaporation ratio as a function of
seasonal aridity index, which includes rainfall seasonality and soil
water change. The performance of the seasonal water balance model is
evaluated for 277 watersheds in the United States. The 99% of wet
seasons and 90% of dry seasons have Nash-Sutcliffe efficiency
coefficients larger than 0.5. The developed seasonal model can be
applied for constructing long-term evaporation and storage change data
when rainfall, potential evaporation, and runoff observations are
available. On the other hand, vegetation affects seasonal water balance
by controlling both evaporation and soil moisture dynamics. The
correlation between NDVI and evaporation is strong particularly in wet
seasons. However, the correlation between NDVI and the seasonal model
parameters is only strong in dry seasons.
Budyko's framework has been widely used to study basin-scale water and
energy balances and one of the formulations of the Budyko curve is Fu's
equation. The curve shape parameter ? in Fu's equation controls how much
of the available water will be evaporated given the available energy.
Previous studies have found that land surface characteristics
significantly affect variations in the parameter ?. In this study, we
focus on the vegetation impact and examine the conditions under which
vegetation plays a major role in controlling the variability of ?. Using
data from 26 major global river basins that are larger than 300,000km2,
the basin-specific ? parameter is found to be linearly correlated with
the long-term averaged annual vegetation coverage. A simple
parameterization for the ? parameter based solely on remotely-sensed
vegetation information improves predictions of annual actual
evapotranspiration by reducing the root mean square error (RMSE) from 76
mm to 47 mm as compared to the default ? value used in the Budyko curve
method. The controlling impact of vegetation on the basin-specific ?
parameter is diminished in small catchments with areas less than
50,000km2, which suggests a scale-dependence of the role of vegetation
in affecting water and energy balances. In small catchments, other key
ecohydrological processes need to be taken into account in order to
fully capture the variability of the ? parameter in Fu's equation.
The East River in South China plays a key role in the socio-economic development in the region and surrounding areas. Adequate understanding of the hydrologic response to land use change is crucial to develop sustainable water resources management strategies in the region. The present study makes an attempt to evaluate the possible impacts of land use change on hydrologic response using a numerical model and corresponding available vegetation datasets. The variable infiltration capacity model is applied to simulate runoff responses to several land use scenarios within the basin (e.g., afforestation, deforestation, and reduction in farmland area) for the period 1952–2000. The results indicate that annual runoff is reduced by 3.5 % (32.3 mm) when 25 % of the current grassland area (including grasslands and wooded grasslands, with 46.8 % of total vegetation cover) is converted to forestland. Afforestation results in reduction in the monthly flow volume, peak flow, and low flow, but with significantly greater reduction in low flow for the basin. The simulated annual runoff increases by about 1.4 % (12.6 mm) in the deforestation scenario by changing forestland (including deciduous broadleaf, evergreen needleleaf, and broadleaf, with 15.6 % of total vegetation cover) to grassland area. Increase in seasonal runoff occurs mainly in autumn for converting cropland to bare soil.
Highlights
► A new, simple, process-based ecohydrological model: the Budyko–Choudhury–Porporato (BCP) model. ► Predicts Choudhury’s n parameter as a function of soil properties, rooting depth and storm depth. ► Is analytical; easily assess the hydrological effects of land management and climate variability. ► Runoff predictions more accurate with the BCP model than with Budyko or Choudhury models. ► Describes a new rooting depth model that accounts for the influence of climate seasonality on roots.
Mean annual evapotranspiration from a catchment is determined largely by precipitation and potential evapotranspiration; characteristics of the catchment (e.g., soil, topography, etc.) play only a secondary role. It has been shown that the ratio of mean annual potential evapotranspiration to precipitation (referred as the index of dryness) can be used to estimate mean annual evapotranspiration by using one additional parameter. This study evaluates the effects of climatic and catchment characteristics on the partitioning of mean annual precipitation into evapotranspiration using a rational function approach, which was developed based on phenomenological considerations. Over 470 catchments worldwide with long-term records of precipitation, potential evapotranspiration, and runoff were considered, and results show that model estimates of mean annual evapotranspiration agree well with observed evapotranspiration taken as the difference between precipitation and runoff. The mean absolute error between modeled and observed evapotranspiration was 54 mm, and the model was able to explain 89% of the variance with a slope of 1.00 through the origin. This indicates that the index of dryness is the most significant variable in determining mean annual evapotranspiration. Results also suggest that forested catchments tend to show higher evapotranspiration than grassed catchments and their evapotranspiration ratio (evapotranspiration divided by precipitation) is most sensitive to changes in catchment characteristics for regions with the index of dryness around 1.0. Additionally, a stepwise regression analysis was performed for over 270 Australian catchments where detailed information of vegetation cover, precipitation characteristics, catchment slopes, and plant available water capacity was available. It is shown that apart from the index of dryness, average storm depth, plant available water capacity, and storm arrival rate are also significant.
Spatial nonstationarity is a condition in which a simple “global” model cannot explain the relationships between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. In this paper, a technique is developed, termed geographically weighted regression, which attempts to capture this variation by calibrating a multiple regression model which allows different relationships to exist at different points in space. This technique is loosely based on kernel regression. The method itself is introduced and related issues such as the choice of a spatial weighting function are discussed. Following this, a series of related statistical tests are considered which can be described generally as tests for spatial nonstationarity. Using Monte Carlo methods, techniques are proposed for investigating the null hypothesis that the data may be described by a global model rather than a non-stationary one and also for testing whether individual regression coefficients are stable over geographic space. These techniques are demonstrated on a data set from the 1991 U.K. census relating car ownership rates to social class and male unemployment. The paper concludes by discussing ways in which the technique can be extended.
Natural forests in southern China have been severely logged due to high human demand for timber, food, and fuels during the past century, but are recovering in the past decade. The objective of this study was to investigate how vegetation cover changes in composition and structure affected the water budgets of a 9.6-km2 Dakeng watershed located in a humid subtropical mountainous region in southern China. We analyzed 27 years (i.e., 1967-1993) of streamflow and climate data and associated vegetation cover change in the watershed. Land use/land cover census and Normalized Difference of Vegetation Index (NDVI) data derived from remote sensing were used to construct historic land cover change patterns. We found that over the period of record, annual streamflow (Q) and runoff/precipitation ratio did not change significantly, nor did the climatic variables, including air temperature, Hamon’s potential evapotranspiration (ET), pan evaporation, sunshine hours, and radiation. However, annual ET estimated as the differences between P and Q showed a statistically significant increasing trend. Overall, the NDVI of the watershed had a significant increasing trend in the peak spring growing season. This study concluded that watershed ecosystem ET increased as the vegetation cover shifted from low stock forests to shrub and grasslands that had higher ET rates. A conceptual model was developed for the study watershed to describe the vegetation cover-streamflow relationships during a 50-year time frame. This paper highlighted the importance of eco-physiologically based studies in understanding transitory, nonstationary effects of deforestation or forestation on watershed water balances.
On the importance of including vegetation dynamics in Budyko's hydrological model
- Donohue
Mapping the results of local statistics: using geographically weighted regression
- Matthews
Matthews, S.A., Yang, T.C., 2012. Mapping the results of local statistics: using
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10.4054/DemRes.2012.26.6.
Impact of land use change during 1989-2009 on eco-capacity in Dongjiang watershed
- Peng
Peng, Z., Gu, C., Liu, Z., Lin, W., Zhou, P., 2014. Impact of land use change during 1989-2009 on eco-capacity in Dongjiang watershed. Chin. J. Plant Ecol. 38 (7), 675.
https://doi.org/10.3724/SP.J.1258.2014.00063.
How Emerging Hot Spot Analysis Works
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