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1.
Stream water temperature plays a significant role in aquatic ecosystems where it controls many important biological and physical processes. Reliable estimates of water temperature at the daily time step are critical in managing water resources. We developed a parsimonious piecewise Bayesian model for estimating daily stream water temperatures that account for temporal autocorrelation and both linear and nonlinear relationships with air temperature and discharge. The model was tested at 8 climatically different basins of the USA and at 34 sites within the mountainous Boise River Basin (Idaho, USA). The results show that the proposed model is robust with an average root mean square error of 1.25 °C and Nash–Sutcliffe coefficient of 0.92 over a 2‐year period. Our approach can be used to predict historic daily stream water temperatures in any location using observed daily stream temperature and regional air temperature data.  相似文献   

2.
Jason A. Leach  Dan Moore 《水文研究》2017,31(18):3160-3177
Stream temperature controls a number of biological, chemical, and physical processes occurring in aquatic environments. Transient snow cover and advection associated with lateral throughflow inputs can have a dominant influence on stream thermal regimes for headwater catchments in the rain‐on‐snow zone. Most existing stream temperature models lack the ability to properly simulate these processes. We developed and evaluated a conceptual‐parametric catchment‐scale stream temperature model that includes the role of transient snow cover and lateral advection associated with throughflow. The model consists of routines for simulating canopy interception, snow accumulation and melt, hillslope throughflow runoff and temperature, and stream channel energy exchange processes. The model was used to predict discharge and stream temperature for a small forested headwater catchment near Vancouver, Canada, using long‐term (1963–2013) weather data to compute model forcing variables. The model was evaluated against 4 years of observed stream temperature. The model generally predicted daily mean stream temperature accurately (annual RMSE between 0.57 and 1.24 °C) although it overpredicted daily summer stream temperatures by up to 3 °C during extended low streamflow conditions. Model development and testing provided insights on the roles of advection associated with lateral throughflow, channel interception of snow, and surface–subsurface water interactions on stream thermal regimes. This study shows that a relatively simple but process‐based model can provide reasonable stream temperature predictions for forested headwater catchments located in the rain‐on‐snow zone.  相似文献   

3.
J.J. Dick  D. Tetzlaff  C. Soulsby 《水文研究》2015,29(14):3098-3111
We monitored temperatures in stream water, groundwater and riparian wetland surface water over 18 months in a 3.2‐km2 moorland catchment in the Scottish Highlands. The stream occupies a glaciated valley, aligned east–west. It has three main headwater tributaries with a large north facing catchment, a south facing catchment and the smallest east facing headwater. The lower catchment sampling locations begin after the convalescence of all three headwaters. Much of the stream network is fringed by riparian peatlands. Stream temperatures are mainly regulated by energy exchanges at the air–water interface. However, they are also influenced by inflows from the saturated riparian zone, where surface water source areas are strongly connected with the stream network. Consequently, the spatial distribution of stream temperatures exhibits limited variability. Nevertheless, there are significant summer differences between the headwaters, despite their close proximity to each other. This is consistent with aspect (and incident radiation), given the south and east facing headwaters having higher temperatures. The largest, north‐facing sub‐catchment shows lower summer diurnal temperature variability, suggesting that lower radiation inputs dampen temperature extremes. Whilst stream water temperature regimes in the lower catchment exhibit little change along a 1‐km reach, they are similar to those in the largest headwater; probably reflecting size and comparable catchment aspect and hydrological flow paths. Our results suggest that different parts of the channel network and its connected wetlands have contrasting sensitivity to higher summer temperatures. This may be important in land management strategies designed to mitigate the impacts of projected climatic warming. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

4.
High‐resolution, spatially extensive climate grids can be useful in regional hydrologic applications. However, in regions where precipitation is dominated by snow, snowmelt models are often used to account for timing and magnitude of water delivery. We developed an empirical, nonlinear model to estimate 30‐year means of monthly snowpack and snowmelt throughout Oregon. Precipitation and temperature for the period 1971–2000, derived from 400‐m resolution PRISM data, and potential evapotranspiration (estimated from temperature and day length) drive the model. The model was calibrated using mean monthly data from 45 SNOTEL sites and accurately estimated snowpack at 25 validation sites: R2 = 0·76, Nash‐Sutcliffe Efficiency (NSE) = 0·80. Calibrating it with data from all 70 SNOTEL sites gave somewhat better results (R2 = 0·84, NSE = 0·85). We separately applied the model to SNOTEL stations located < 200 and ≥ 200 km from the Oregon coast, since they have different climatic conditions. The model performed equally well for both areas. We used the model to modify moisture surplus (precipitation minus potential evapotranspiration) to account for snowpack accumulation and snowmelt. The resulting values accurately reflect the shape and magnitude of runoff at a snow‐dominated basin, with low winter values and a June peak. Our findings suggest that the model is robust with respect to different climatic conditions, and that it can be used to estimate potential runoff in snow‐dominated basins. The model may allow high‐resolution, regional hydrologic comparisons to be made across basins that are differentially affected by snowpack, and may prove useful for investigating regional hydrologic response to climate change. Published in 2011 by John Wiley & Sons, Ltd.  相似文献   

5.
Prem B. Parajuli 《水文研究》2010,24(26):3785-3797
The climatic processes such as changes in precipitation, temperature and atmospheric CO2 concentration can intensify the effects on water resources. An assessment of the effects of long‐term climate change on water resources is essential to the development of water quality improvement programs. This study was conducted in the Upper Pearl River Watershed (UPRW) in east‐central Mississippi to assess the effects of long‐term potential future climate change on average mean monthly stream flow from the five spatially distributed U. S. Geological Survey (USGS) gage stations in the UPRW using the Soil and Water Assessment Tool. The model was calibrated (January 1981 to December 1994) and validated (January 1995 to September 2008) using monthly measured stream flow data. The calibrated and validated model determined good to very good performance for stream flow prediction (R2 and E from 0·60 to 0·86) between measured and predicted stream flow values. The root mean square error values (from 14 to 37 m3 s?1) were estimated at similar levels of errors during model calibration and validation. The results showed that long‐term (50 years) average monthly stream flow sensitivity due to climate change effects was found the greatest as a result of percentage change in the precipitation followed by carbon dioxide (CO2) concentration and temperature. The long‐term model simulation scenarios as compared with the base scenario for all five spatially distributed USGS gage stations in the UPRW estimated an average monthly stream flow decrease (from 54 to 67%) and average monthly stream flow increase (from 67 to 79%) depending on the spatial characteristics of the USGS gage stations. Overall, the results indicate that the UPRW hydrology is very sensitive to potential future climate changes and that these changes could stimulate increased streamflow generation from the watershed. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

6.
Fish habitat and aquatic life in rivers are highly dependent on water temperature. Therefore, it is important to understand andto be able to predict river water temperatures using models. Such models can increase our knowledge of river thermal regimes as well as provide tools for environmental impact assessments. In this study, artificial neural networks (ANNs) will be used to develop models for predicting both the mean and maximum daily water temperature. The study was conducted within Catamaran Brook, a small drainage basin tributary to the Miramichi River (New Brunswick, Canada). In total, eight ANN models were investigated using a variety of input parameters. Of these models, four predicted mean daily water temperature and four predicted maximum daily water temperature. The best model for mean daily temperature had eight input parameters: minimum, maximum and mean air temperatures of the current day and those of the preceding day, the day of year and the water level. This model had an overall root‐mean‐square error (RMSE) of 0·96 °C, a bias of 0·26 °C and a coefficient of determination R2 = 0·971. The model that best predicted maximum daily water temperature was similar to the first model but excluded mean daily air temperature. Good results were obtained for maximum water temperatures with an overall RMSE of 1·18 °C, a bias of 0·15 °C and R2 = 0·961. The results of ANN models were similar to and/or better than those observed from the literature. The advantages of artificial neural networks models in modelling river water temperature lie in their simplicity of use, their low data requirement and their good performance, as well as their flexibility in allowing many input and output parameters. Copyright © 2008 Crown in the right of Canada and John Wiley & Sons, Ltd.  相似文献   

7.
Temperature observations at 25 sites in the 2000 km2 Dee catchment in NE Scotland were used, in conjunction with geographic information system (GIS) analysis, to identify dominant landscape controls on mean monthly maximum stream temperatures. Maximum winter stream temperatures are mainly controlled by elevation, catchment area and hill shading, whereas the maximum temperatures in summer are driven by more complex interactions, which include the influence of riparian forest cover and distance to coast. Multiple linear regression was used to estimate the catchment‐wide distribution of mean weekly maximum stream temperatures for the hottest week of the 2‐year observation period. The results suggested the streams most sensitive to high temperatures are small upland streams at exposed locations without any forest cover and relatively far inland, while lowland streams with riparian forest cover at locations closer to the coast exhibit a moderated thermal regime. Under current conditions, all streams provide a suitable thermal habitat for both, Atlantic salmon and brown trout. Using two climate change scenarios assuming 2·5 and 4 °C air temperature increases, respectively, temperature‐sensitive zones of the stream network were identified, which could potentially have an adverse effect on the thermal habitat of Atlantic salmon and brown trout. Analysis showed that the extension of riparian forests into headwater streams has the potential to moderate changes in temperature under climate change. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

8.
The flow magnitude and timing from hydroelectric dams in the Snake River Basin of the Pacific north‐western US is managed in part for the benefit of salmon. The objective of this research was to evaluate the effects of Hells Canyon Dam discharge operations on hydrologic exchange flows between the river and riverbed in Snake River fall Chinook salmon spawning areas. Interactions between river water and pore water within the upper 1 m of the riverbed were quantified through the use of self‐contained temperature and water level data loggers suspended inside of piezometers. The data were recorded at 20 min intervals over a period of 200 days when the mean daily discharge was 218–605 m3 s?1, with hourly stage changes as large as 1·9 m. Differences in head pressure between the river and riverbed were small, often within ± 2 cm. Measured temperature gradients in the riverbed indicated significant interactions between the surface and subsurface water. At the majority of sites, neither hydraulic nor temperature gradients were significantly affected by either short‐ or long‐term changes in discharge operations from Hells Canyon Dam. Only 2 of 14 study sites exhibited acute flux reversals between the river and riverbed resulting from short‐term, large magnitude changes in discharge. The findings suggest that local scale measurements may not be wholly explanatory of the hydrological exchange between the river and riverbed. The processes controlling surface water exchange at the study sites are likely to be bedform‐induced advective pumping, turbulence at the riverbed surface, and large‐scale hydraulic gradients along the longitudinal profile of the riverbed. By incorporating the knowledge of hydrological exchange processes into water management planning, regional agencies will be better prepared to manage the limited water resources among competing priorities that include salmon recovery, flood control, irrigation supply, hydropower production, and recreation. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

9.
This paper presents the development of a multiple‐station neural network for predicting tidal currents across a coastal inlet. Unlike traditional hydrodynamic models, the neural network model does not need inputs of coastal topography and bathymetry, grids, surface and bottom frictions, and turbulent eddy viscosity. Without solving hydrodynamic equations, the neural network model applies an interconnected neural network to correlate the inputs of boundary forcing of water levels at a remote station to the outputs of tidal currents at multiple stations across a local coastal inlet. Coefficients in the neural network model are trained using a continuous dataset consisting of inputs of water levels at a remote station and outputs of tidal currents at the inlet, and verified using another independent input and output dataset. Once the neural network model has been satisfactorily trained and verified, it can be used to predict tidal currents at a coastal inlet from the inputs of water levels at a remote station. For the case study at Shinnecock Inlet in the southern shore of New York, tidal currents at nine stations across the inlet were predicted by the neural network model using water level data located from a station about 70 km away from the inlet. A continuous dataset in May 2000 was used for the training, and another dataset in July 2000 was used for the verification of the neural network model. Comparing model predictions and observations indicates correlation coefficients range from 0·95 to 0·98, and the root‐mean‐square error ranges from 0·04 to 0·08 m s?1 at the nine current locations across the inlet. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

10.
A comparison between half‐hourly and daily measured and computed evapotranspiration (ET) using three models of different complexity, namely, the Priestley–Taylor (P‐T), the reference Penman–Monteith (P‐M) and the Common Land Model (CLM), was conducted using three AmeriFlux sites under different land cover and climate conditions (i.e. arid grassland, temperate forest and subhumid cropland). Using the reference P‐M model with a semiempirical soil moisture function to adjust for water‐limiting conditions yielded ET estimates in reasonable agreement with the observations [root mean square error (RMSE) of 64–87 W m?2 for half‐hourly and RMSE of 0.5–1.9 mm day?1 for daily] and similar to the complex Common Land Model (RMSE of 60–94 W m?2 for half‐hourly and RMSE of 0.4–2.1 mm day?1 for daily) at the grassland and cropland sites. However, the semiempirical soil moisture function was not applicable particularly for the P‐T model at the forest site, suggesting that adjustments to key model variables may be required when applied to diverse land covers. On the other hand, under certain land cover/environmental conditions, the use of microwave‐derived soil moisture information was found to be a reliable metric of regional moisture conditions to adjust simple ET models for water‐limited cases. Further studies are needed to evaluate the utility of the simplified methods for different landscapes. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

11.
Continuous temperature measurements at 11 stream sites in small lowland streams of North Zealand, Denmark over a year showed much higher summer temperatures and lower winter temperatures along the course of the stream with artificial lakes than in the stream without lakes. The influence of lakes was even more prominent in the comparisons of colder lake inlets and warmer outlets and led to the decline of cold‐water and oxygen‐demanding brown trout. Seasonal and daily temperature variations were, as anticipated, dampened by forest cover, groundwater input, input from sewage plants and high downstream discharges. Seasonal variations in daily water temperature could be predicted with high accuracy at all sites by a linear air‐water regression model (r2: 0·903–0·947). The predictions improved in all instances (r2: 0·927–0·964) by a non‐linear logistic regression according to which water temperatures do not fall below freezing and they increase less steeply than air temperatures at high temperatures because of enhanced heat loss from the stream by evaporation and back radiation. The predictions improved slightly (r2: 0·933–0·969) by a multiple regression model which, in addition to air temperature as the main predictor, included solar radiation at un‐shaded sites, relative humidity, precipitation and discharge. Application of the non‐linear logistic model for a warming scenario of 4–5 °C higher air temperatures in Denmark in 2070‐2100 yielded predictions of temperatures rising 1·6–3·0 °C during winter and summer and 4·4–6·0 °C during spring in un‐shaded streams with low groundwater input. Groundwater‐fed springs are expected to follow the increase of mean air temperatures for the region. Great caution should be exercised in these temperature projections because global and regional climate scenarios remain open to discussion. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

12.
A data-driven model is designed using artificial neural networks (ANN) to predict the average onset for the annual water temperature cycle of North-American streams. The data base is composed of daily water temperature time series recorded at 48 hydrometric stations in Québec (Canada) and northern US, as well as the geographic and physiographic variables extracted from the 48 associated drainage basins. The impact of individual and combined drainage area characteristics on the stream annual temperature cycle starting date is investigated by testing different combinations of input variables. The best model allows to predict the average temperature onset for a site, given its geographical coordinates and vegetation and lake coverage characteristics, with a root mean square error (RMSE) of 5.6 days. The best ANN model was compared favourably with parametric approaches.  相似文献   

13.
Estimation of evapotranspiration (ET) is of great significance in modeling the water and energy interactions between land and atmosphere. Negative correlation of surface temperature (Ts) versus vegetation index (VI) from remote sensing data provides diagnosis on the spatial pattern of surface soil moisture and ET. This study further examined the applicability of Ts–VI triangle method with a newly developed edges determination technique in estimating regional evaporative fraction (EF) and ET at MODIS pixel scale through comparison with large aperture scintillometer (LAS) and high‐level eddy covariance measurements collected at Changwu agro‐ecological experiment station from late June to late October, 2009. An algorithm with merely land and atmosphere products from MODIS onboard Terra satellite was used to estimate the surface net radiation (Rn) and soil heat flux. In most cases, the estimated instantaneous Rn was in good agreement with surface measurement with slight overestimation by 12 W/m2. Validation results from LAS measurement showed that the root mean square error is 0.097 for instantaneous EF, 48 W/m2 for instantaneous sensible heat flux, and 30 W/m2 for daily latent heat flux. This paper successfully presents a miniature of the overall capability of Ts–VI triangle in estimating regional EF and ET from limited number of data. For a thorough interpretation, further comprehensive investigation needs to be done with more integration of remote sensing data and in‐situ surface measurements. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

14.
Water temperature influences most of the physical, chemical and biological properties of rivers. It plays an important role in the distribution of fish and the growth rates of many aquatic organisms. Therefore, a better understanding of the thermal regime of rivers is essential for the management of important fisheries resources. This study deals with the modelling of river water temperature using a new and simplified model based on the equilibrium temperature concept. The equilibrium temperature concept is an approach where the net heat flux at the water surface can be expressed by a simple equation with fewer meteorological parameters than required with traditional models. This new water temperature model was applied on two watercourses of different size and thermal characteristics, but within a similar meteorological region, i.e., the Little Southwest Miramichi River and Catamaran Brook (New Brunswick, Canada). A study of the long‐term thermal characteristics of these two rivers revealed that the greatest differences in water temperatures occurred during mid‐summer peak temperatures. Data from 1992 to 1994 were used for the model calibration, while data from 1995 to 1999 were used for the model validation. Results showed a slightly better agreement between observed and predicted water temperatures for Catamaran Brook during the calibration period, with a root‐mean‐square error (RMSE) of 1·10 °C (Nash coefficient, NTD = 0·95) compared to 1·45 °C for the Little Southwest Miramichi River (NTD = 0·94). During the validation period, RMSEs were calculated at 1·31 °C for Catamaran Brook and 1·55 °C for the Little Southwest Miramichi River. Poorer model performances were generally observed early in the season (e.g., spring) for both rivers due to the influence of snowmelt conditions, while late summer to autumn modelling performances showed better results. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

15.
One of the challenges when modelling a complex variable such as water temperature in rivers is that it can be difficult to determine the sources of error and to ensure that the simulations are truly representative of the reality. Therefore, a heat budget study was completed in a controlled environment, which excluded advection and bottom fluxes but enabled observation of all the other fluxes. A 21.42 m3 pool was installed and insulated to limit heat exchange through the sides and bottom. All the major energy fluxes were monitored for a 50‐day period. Different equations for individual heat budget terms were tested to determine their ability to reproduce the observations. This experiment also permitted to assess the relative importance of each component of the heat budget. Performance of each semi‐empirical equation was determined by comparing predictions and measured values. It was thus possible to choose the formulae that best represented the measured heat exchange processes, while understanding the limits of some of the semi‐empirical representations of heat exchange processes. The results highlight the importance of radiative terms into the heat budget because they controlled the major sources and sinks. The study also showed the importance of the wind function determination into the calculation of latent heat flux. The resulting water temperature model returned simulated hourly water temperature with an overall root mean square error of 0.71 °C/h and a modified Nash–Sutcliffe coefficient of 0.97. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

16.
Successful applications of stochastic models for simulating and predicting daily stream temperature have been reported in the literature. These stochastic models have been generally tested on small rivers and have used only air temperature as an exogenous variable. This study investigates the stochastic modelling of daily mean stream water temperatures on the Moisie River, a relatively large unregulated river located in Québec, Canada. The objective of the study is to compare different stochastic approaches previously used on small streams to relate mean daily water temperatures to air temperatures and streamflow indices. Various stochastic approaches are used to model the water temperature residuals, representing short‐term variations, which were obtained by subtracting the seasonal components from water temperature time‐series. The first three models, a multiple regression, a second‐order autoregressive model, and a Box and Jenkins model, used only lagged air temperature residuals as exogenous variables. The root‐mean‐square error (RMSE) for these models varied between 0·53 and 1·70 °C and the second‐order autoregressive model provided the best results. A statistical methodology using best subsets regression is proposed to model the combined effect of discharge and air temperature on stream temperatures. Various streamflow indices were considered as additional independent variables, and models with different number of variables were tested. The results indicated that the best model included relative change in flow as the most important streamflow index. The RMSE for this model was of the order of 0·51 °C, which shows a small improvement over the first three models that did not include streamflow indices. The ridge regression was applied to this model to alleviate the potential statistical inadequacies associated with multicollinearity. The amplitude and sign of the ridge regression coefficients seem to be more in agreement with prior expectations (e.g. positive correlation between water temperature residuals of different lags) and make more physical sense. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

17.
Accurate estimation of evapotranspiration (ET) is essential in water resources management and hydrological practices. Estimation of ET in areas, where adequate meteorological data are not available, is one of the challenges faced by water resource managers. Hence, a simplified approach, which is less data intensive, is crucial. The FAO‐56 Penman–Monteith (FAO‐56 PM) is a sole global standard method, but it requires numerous weather data for the estimation of reference ET. A new simple temperature method is developed, which uses only maximum temperature data to estimate ET. Ten class I weather stations data were collected from the National Meteorological Agency of Ethiopia. This method was compared with the global standard PM method, the observed Piche evaporimeter data, and the well‐known Hargreaves (HAR) temperature method. The coefficient of determination (R2) of the new method was as high as 0.74, 0.75, and 0.91, when compared with that of PM reference evapotranspiration (ETo), Piche evaporimeter data, and HAR methods, respectively. The annual average R2 over the ten stations when compared with PM, Piche, and HAR methods were 0.65, 0.67, and 0.84, respectively. The Nash–Sutcliff efficiency of the new method compared with that of PM was as high as 0.67. The method was able to estimate daily ET with an average root mean square error and an average absolute mean error of 0.59 and 0.47 mm, respectively, from the PM ETo method. The method was also tested in dry and wet seasons and found to perform well in both seasons. The average R2 of the new method with the HAR method was 0.82 and 0.84 in dry and wet seasons, respectively. During validation, the average R2 and Nash–Sutcliff values when compared with Piche evaporation were 0.67 and 0.51, respectively. The method could be used for the estimation of daily ETo where there are insufficient data. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

18.
ABSTRACT

A new deep extreme learning machine (ELM) model is developed to predict water temperature and conductivity at a virtual monitoring station. Based on previous research, a modified ELM auto-encoder is developed to extract more robust invariance among the water quality data. A weighted ELM that takes seasonal variation as the basis of weighting is used to predict the actual value of water quality parameters at sites which only have historical data and no longer generate new data. The performance of the proposed model is validated against the monthly data from eight monitoring stations on the Zengwen River, Taiwan (2002–2017). Based on root mean square error, mean absolute error, mean absolute percentage error and correlation coefficient, the experimental results show that the new model is better than the other classical spatial interpolation methods.  相似文献   

19.
Monthly mean data from a 90 year period relating to a small catchment (142.4 km2) in north-central Austria were used to provide a long-term perspective on the nature of the air–water temperature relationship. Annual mean values of air and water temperature were related in a relatively insensitive and scattered way (r2 < 55%, b < 0.65), whereas the relationship for monthly mean values was closer and steeper (r2 > 95%, b > 0.65). A separate regression equation was needed to describe the behaviour of monthly mean water temperatures as the air temperatures fell below freezing. Analysis of air–water temperature regressions for individual months revealed a series of relations which were generally more scattered and less, but variously, sensitive than the ensemble relationship of monthly mean values. Monthly mean water temperatures could be predicted from the ensemble air–water temperature relationship and from the relations for individual months with root mean square errors of > 1.0 and < 0.8°C, respectively. Segmentation of air–water temperature regressions according to air temperatures above and below freezing did not significantly improve water temperature prediction. Hysteresis in, and the relatively low slope of, the air–water temperature relationships in the study catchment reflected the importance of snowmelt in the flow regime. © 1997 by John Wiley & Sons Ltd.  相似文献   

20.
In hydrology, the storage‐discharge relationship is a fundamental catchment property. Understanding what controls this relationship is at the core of catchment science. To date, there are no direct methods to measure water storage at catchment scales (101–103 km2). In this study, we use direct measurements of terrestrial water storage dynamics by means of superconducting gravimetry in a small headwater catchment of the Regen River, Germany, to derive empirical storage‐discharge relationships in nested catchments of increasing scale. Our results show that the local storage measurements are strongly related to streamflow dynamics at larger scales (> 100 km2; correlation coefficient = 0.78–0.81), but at small scale, no such relationship exists (~ 1 km2; correlation coefficients = ?0.11). The geologic setting in the region can explain both the disconnection between local water storage and headwater runoff, and the connectivity between headwater storage and streams draining larger catchment areas. More research is required to understand what controls the form of the observed storage‐discharge relationships at the catchment scale. This study demonstrates that high‐precision gravimetry can provide new insights into the complex relationship between state and response of hydrological systems. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

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