Performance evaluation of satellite-based approaches for the estimation of daily air temperature and reference evapotranspiration     

H. R. Shwetha 《水文科学杂志》2018,63(9):1347-1367

Different satellite-based radiation (Makkink) and temperature (Hargreaves-Samani, Penman-Monteith temperature, PMT) reference evapotranspiration (ET_o) models were compared with the FAO56-PM method over the Cauvery basin, India. Maximum air temperature (T_max) required in the ET_o models was estimated using the temperature–vegetation index (TVX) and an advanced statistical approach (ASA), and evaluated with observed T_max obtained from automatic weather stations. Minimum air temperature (T_min) was estimated using ASA. Land surface temperature was employed in the ET_o models in place of air temperature (T_a) to check the potency of its applicability. The results suggest that the PMT model with T_a as input performed better than the other ET_o models, with correlation coefficient (r), averaged root mean square error (RMSE) and mean bias error (MBE) of 0.77, 0.80 mm d^?1 and ?0.69 for all land cover classes. The ASA yielded better T_max and T_min values (r and RMSE of 0.87 and 2.17°C, and 0.87 and 2.27°C, respectively).  相似文献   

Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches     

《水文科学杂志》2012,57(15):1824-1842

ABSTRACT

In this research, five hybrid novel machine learning approaches, artificial neural network (ANN)-embedded grey wolf optimizer (ANN-GWO), multi-verse optimizer (ANN-MVO), particle swarm optimizer (ANN-PSO), whale optimization algorithm (ANN-WOA) and ant lion optimizer (ANN-ALO), were applied for modelling monthly reference evapotranspiration (ET_o) at Ranichauri (India) and Dar El Beida (Algeria) stations. The estimates yielded by hybrid machine learning models were compared against three models, Valiantzas-1, 2 and 3 based on root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC) and Willmott index (WI). The results of comparison show that the ANN-GWO-1 model with five input variables (T_min, T_max, RH, U_s, R_s) provides better estimates at both study stations (RMSE = 0.0592/0.0808, NSE = 0.9972/0.9956, PCC = 0.9986/0.9978, and WI = 0.9993/0.9989). Also, the adopted modelling strategy can build a truthful expert intelligent system for estimating the monthly ET_o at study stations.  相似文献   

Regional recharge assessment in the crystalline bedrock aquifer of the Kenogami Uplands,Canada     

Romain Chesnaux 《水文科学杂志》2013,58(2):421-436

Abstract

Estimates of trends of climatic changes at basin and state scales are required for developing adaptation strategies related to planning, development and management of water resources. In the present study, seasonal and annual trends of changes in maximum temperature (T _max), minimum temperature (T _min), mean temperature (T _mean), temperature range (T _range), highest maximum temperature (H _max) and lowest minimum temperature (L _min) have been examined at the basin scale. The longest available records over the last century, for 43 stations covering nine river basins in northwest and central India, were used in the analysis. Of the nine river basins studied, seven showed a warming trend, whereas two showed a cooling trend. The Narmada and Sabarmati river basins experienced the maximum warming and cooling, respectively. The majority of basins in the study area show increasing trend in T _range, H _max and L _min. Seasonal analysis of different variables shows that the greatest changes in T _max and T _mean were observed in the post-monsoon season, while T _min experienced the greatest change in the monsoon season. This analysis provides scenarios of temperature changes which may be used for sensitivity analysis of water availability for different basins, and accordingly in planning and implementation of adaptation strategies.  相似文献   

Quantifying and modelling urban stream temperature: a central US watershed study          下载免费PDF全文

Sean Zeiger  Jason A. Hubbart  Stephen H. Anderson  Michael C. Stambaugh 《水文研究》2016,30(4):503-514

Hydrologic models that rely on site specific linear and non‐linear regression water temperature (T_w) subroutines forced solely with observed air temperature (T_a) may not accurately estimate T_w in mixed‐use urbanizing watersheds where hydrogeological and land use complexity may confound common T_w regime assumptions. A nested‐scale experimental watershed study design was used to test T_w model predictions in a representative mixed‐use urbanizing watershed of the central USA. The linear regression T_w model used in the Soil and Water Assessment Tool (SWAT), a non‐linear regression T_w model, and a process‐based T_w model that accounts for watershed hydrology were evaluated. The non‐linear regression T_w model tested at a daily time step performed significantly (P < 0.01) better than the linear T_w model currently used in SWAT. Both regression T_w models overestimated T_w in lower temperature ranges (T_w < 10.0 °C) with percent bias (PBIAS) values ranging from ?28.2% (non‐linear T_w model) to ?66.1% (linear regression T_w model) and underestimated T_w in the higher temperature range (T_w > 25.0 °C) by 3.2%, and 7.2%, respectively. Conversely, the process‐based T_w model closely estimated T_w in lower temperature ranges (PBIAS = 4.5%) and only slightly underestimated T_w in the higher temperature range (PBIAS = 1.7%). Findings illustrate the benefit of integrating process‐based T_w models with hydrologic models to improve model transferability and T_w predictive confidence in urban mixed‐land use watersheds. The findings in this work are distinct geographically and in terms of mixed‐land use complexity and are therefore of immediate value to land‐use managers in similarly urbanizing watersheds globally. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

Estimation of monthly evaporative loss using relevance vector machine,extreme learning machine and multivariate adaptive regression spline models   总被引：1，自引：0，他引：1  

Ravinesh C. Deo  Pijush Samui  Dookie Kim 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(6):1769-1784

The forecasting of evaporative loss (E) is vital for water resource management and understanding of hydrological process for farming practices, ecosystem management and hydrologic engineering. This study has developed three machine learning algorithms, namely the relevance vector machine (RVM), extreme learning machine (ELM) and multivariate adaptive regression spline (MARS) for the prediction of E using five predictor variables, incident solar radiation (S), maximum temperature (T _max), minimum temperature (T _min), atmospheric vapor pressure (VP) and precipitation (P). The RVM model is based on the Bayesian formulation of a linear model with appropriate prior that results in sparse representations. The ELM model is computationally efficient algorithm based on Single Layer Feedforward Neural Network with hidden neurons that randomly choose input weights and the MARS model is built on flexible regression algorithm that generally divides solution space into intervals of predictor variables and fits splines (basis functions) to each interval. By utilizing random sampling process, the predictor data were partitioned into the training phase (70 % of data) and testing phase (remainder 30 %). The equations for the prediction of monthly E were formulated. The RVM model was devised using the radial basis function, while the ELM model comprised of 5 inputs and 10 hidden neurons and used the radial basis activation function, and the MARS model utilized 15 basis functions. The decomposition of variance among the predictor dataset of the MARS model yielded the largest magnitude of the Generalized Cross Validation statistic (≈0.03) when the T _max was used as an input, followed by the relatively lower value (≈0.028, 0.019) for inputs defined by the S and VP. This confirmed that the prediction of E utilized the largest contributions of the predictive features from the T _max, verified emphatically by sensitivity analysis test. The model performance statistics yielded correlation coefficients of 0.979 (RVM), 0.977 (ELM) and 0.974 (MARS), Root-Mean-Square-Errors of 9.306, 9.714 and 10.457 and Mean-Absolute-Error of 0.034, 0.035 and 0.038. Despite the small differences in the overall prediction skill, the RVM model appeared to be more accurate in prediction of E. It is therefore advocated that the RVM model can be employed as a promising machine learning tool for the prediction of evaporative loss.  相似文献   

Trends in rainfall and temperature in the Peruvian Amazon–Andes basin over the last 40 years (1965–2007)     

Waldo Sven Lavado Casimiro  David Labat  Josyane Ronchail  Jhan Carlo Espinoza  Jean Loup Guyot 《水文研究》2013,27(20):2944-2957

The hydroclimatology of the Peruvian Amazon–Andes basin (PAB) which surface corresponding to 7% of the Amazon basin is still poorly documented. We propose here an extended and original analysis of the temporal evolution of monthly rainfall, mean temperature (T_mean), maximum temperature (T_max) and minimum temperature (T_min) time series over two PABs (Huallaga and Ucayali) over the last 40 years. This analysis is based on a new and more complete database that includes 77 weather stations over the 1965–2007 period, and we focus our attention on both annual and seasonal meteorological time series. A positive significant trend in mean temperature of 0.09 °C per decade is detected over the region with similar values in the Andes and rainforest when considering average data. However, a high percentage of stations with significant T_mean positive trends are located over the Andes region. Finally, changes in the mean values occurred earlier in T_max (during the 1970s) than in T_min (during the 1980s). In the PAB, there is neither trend nor mean change in rainfall during the 1965–2007 period. However, annual, summer and autumn rainfall in the southern Andes presents an important interannual variability that is associated with the sea surface temperature in the tropical Atlantic Ocean while there are limited relationships between rainfall and El Niño‐Southern Oscillation (ENSO) events. On the contrary, the interannual temperature variability is mainly related to ENSO events. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

A multimodel comparison for assessing water temperatures under changing climate conditions via the equilibrium temperature concept: case study of the Middle Loire River,France          下载免费PDF全文

Vincent Bustillo  Florentina Moatar  Agnès Ducharne  Dominique Thiéry  Alain Poirel 《水文研究》2014,28(3):1507-1524

This paper investigates three categories of models that are derived from the equilibrium temperature concept to estimate water temperatures in the Loire River in France and the sensitivity to changes in hydrology and climate. We test the models' individual performances for simulating water temperatures and assess the variability of the thermal responses under the extreme changing climate scenarios that are projected for 2081–2100. We attempt to identify the most reliable models for studying the impact of climate change on river temperature (T_w). Six models are based on a linear relationship between air temperatures (T_a) and equilibrium temperatures (T_e), six depend on a logistic relationship, and six rely on the closure of heat budgets. For each category, three approaches that account for the river's thermal exchange coefficient are tested. In addition to air temperatures, an index of day length is incorporated to compute equilibrium temperatures. Each model is analysed in terms of its ability to simulate the seasonal patterns of river temperatures and heat peaks. We found that including the day length as a covariate in regression‐based approaches improves the performance in comparison with classical approaches that use only T_a. Moreover, the regression‐based models that rely on the logistic relationship between T_e and T_a exhibit root mean square errors comparable (0.90 °C) with those obtained with a classical five‐term heat budget model (0.82 °C), despite a small number of required forcing variables. In contrast, the regressive models that are based on a linear relationship T_e = f(T_a) fail to simulate the heat peaks and are not advisable for climate change studies. The regression‐based approaches that are based on a logistic relationship and the heat balance approaches generate notably similar responses to the projected climate changes scenarios. This similarity suggests that sophisticated thermal models are not preferable to cruder ones, which are less time‐consuming and require fewer input data. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

Variability of tree transpiration across three zones in a southeastern U.S. Piedmont watershed     

Johnny L. Boggs  Ge Sun  Jean-Christophe Domec  Steven G. McNulty 《水文研究》2021,35(10):e14389

Quantifying the spatial variability of species-specific tree transpiration across hillslopes is important for estimating watershed-scale evapotranspiration (ET) and predicting spatial drought effects on vegetation. The objectives of this study are to (1) assess sap flux density (J_s) and tree-level transpiration (T_s) across three contrasting zones a (riparian buffer, mid-hillslope and upland-hillslope, (2) determine how species-specific J_s responds to vapour pressure deficit (VPD) and (3) estimate watershed-level transpiration (T_w) using T_s derived from each zone. During 2015 and 2016, we measured J_s in eight tree species in the three topographic zones in a small 12-ha forested watershed in the Piedmont region of central North Carolina. In the dry year of 2015, loblolly pine (Pinus taeda), Virginia pine (Pinus virginiana) and sweetgum (Liquidambar styraciflua) J_s rates were significantly higher in the riparian buffer when compared to the other two zones. In contrast, J_s rates in tulip poplar (Liriodendron tulipifera) and red maple (Acer rubrum) were significantly lower in the buffer than in the mid-hillslope. Daily T_s varied by zone and ranged from 10 to 93 L/day in the dry year and from 9 to 122 L/day in the wet year (2016). J_s responded nonlinearly to VPD in all species and zones. Annual T_w was 447, 377 and 340 mm based on scaled-J_s data for the buffer, mid-hillslope and upland-hillslope, respectively. We conclude that large spatial variability in J_s and scaled T_w was driven by differences in soil moisture at each zone and forest composition. Consequently, spatial heterogeneity of vegetation and soil moisture must be considered when accurately quantifying watershed level ET.  相似文献   

Estimation of land surface evapotranspiration over complex terrain based on multi‐spectral remote sensing data          下载免费PDF全文

Xingran Liu  Yanjun Shen  Hongjun Li  Ying Guo  Hongwei Pei  Wei Dong 《水文研究》2017,31(2):446-461

Land surface evapotranspiration (ET) plays an important role in energy and water balances. ET can significantly affect the runoff yield of a basin and the available water resources in mountainous areas. The existing models to estimate ET are typically applicable to plains, and excessive data are required to calculate the surface fluxes accurately. This study established a simple and practical model capable of depicting the surface fluxes, while using relatively less parameters. Considering the complex terrain, solar radiation was corrected by importing a series of topographic factors. The water deficit index, a measure of land surface wetness, was calculated by applying the f_c (vegetation fractional cover)‐T_rad (land surface temperature) framework in the two‐source trapezoid model for evapotranspiration model to mountainous areas after corrections of temperature based on altitude variations. The model was successfully applied to the Kaidu River Basin, a basin with few gauges located in the east Tien Shan Mountains of China. Based on the time scale extensions, ET was analyzed at different time scales from 2000 to 2013. The results demonstrated that the corrected solar radiation and water deficit index were reasonably distributed in space and that this model is applicable to ungauged catchments, such as the Kaidu River Basin.  相似文献   

Evaporation modelling by heuristic regression approaches using only temperature data   总被引：1，自引：1，他引：0  

Ozgur Kisi  Salim Heddam 《水文科学杂志》2019,64(6):653-672

Accurate estimation of pan evaporation (E_pan) is very important in water resources management, irrigation scheduling and water budget of lakes. This study investigates the accuracy of two heuristic regression approaches, multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) in estimating pan evaporation using only temperature data as input. Monthly minimum temperature, maximum temperature and E_pan data from three Turkish stations were used, with month number (periodicity information) added as input to see its effect on estimation accuracy. The models were compared with the calibrated Hargreaves-Samani (CHS), Stephens-Stewart (SS) and multiple linear regression methods. Three different train-test splitting strategies (50%–50%, 60%–40% and 75%–25%) were employed for better evaluation of the applied methods. The results show that the MARS method generally estimated monthly E_pan with higher accuracy compared to the M5Tree, CHS and SS methods. When extraterrestrial radiation, calculated from Julian date and latitude information, was used as input to the SS instead of solar radiation, satisfactory estimates were obtained. A positive effect on model accuracy was observed when involving periodicity information in inputs and increasing training data length.  相似文献   

A meteorological-based crop coefficient model for estimation of daily evapotranspiration     

Arman Varmaghani  William E. Eichinger  John H. Prueger 《水文研究》2021,35(2):e14025

Estimation of daily evapotranspiration (ET) over cloudy regions highly desires models which rely on meteorological data only. Notwithstanding, the conventional crop coefficient (K_c) method requires detailed knowledge of geo/biophysical properties of the coupled land-vegetation system, precipitation, and soil moisture. Six Eddy Covariance (EC) towers in Iowa, California and New Hampshire of the USA (covering corn, soybeans, prairie, and deciduous forest) were selected. Investigation on 6 years (2007–2012) 15-min micrometeorological records of these sites revealed that there is an indubitable strong interaction between relative humidity (RH), reference ET (ET_o), and actual ET at different timescales. This allowed to bypass the need for the non-meteorological inputs and express K_c as a second-order polynomial function of RH and ET_o, the ambient regression evapotranspiration model (AREM). The coefficients of the empirical function are crop-specific and may require calibration over different soil types. The mean absolute percentage error (MAPE) of the regression against daily EC observations was 17% during the growing season, and 32% throughout the year with root mean square error (RMSE) of 0.74 mm day⁻¹ and coefficient of determination of 0.71. The model was fully operational (MAPE of 34% and RMSE of 0.82 mm day⁻¹) over the four Iowan sites based on inputs from local weather stations and NLDAS-2 forcing data of NASA. AREM was capable of capturing the dynamics of ET at 15-min and daily timescales irrespective of varying complexities associated with biophysical, geophysical and climatological states.  相似文献   

Impacts of Urbanization and Station-relocation on Surface Air Temperature Series in Anhui Province, China   总被引：1，自引：0，他引：1  

Yuan-Jian Yang  Bi-Wen Wu  Chun-e Shi  Jia-Hua Zhang  Yu-Bin Li  Wei-An Tang  Hua-Yang Wen  Hong-Qun Zhang  Tao Shi 《Pure and Applied Geophysics》2013,170(11):1969-1983

Since the 1990s, many meteorological stations in China have passively “entered” cities, which has led to frequent relocation and discontinuity in observational records at many stations. To study the impacts of urbanization on surface air temperature series, 52 meteorological stations in Anhui Province were chosen based firstly on a homogeneity test of the time series, and then their surrounding underlying surfaces during different decades were identified utilizing Landsat Multispectral Scanner images from the 1970s, Landsat Thematic Mapper images from 1980s and 1990s, and Enhanced Thematic Mapper images after 2000, to determine whether or not the station “entered” city, and then these stations were categorized into three groups: urban, suburban, and rural using Landsat-measured land use/land cover (LULC) around the station. Finally, variations in annual mean air temperature (T _mean), maximum air temperature (T _max), and minimum air temperature (T _min) were analyzed in urban-type stations and compared to their surrounding rural-type stations. The results showed that, in Anhui Province over the past two decades, many rural stations experienced urbanization and changed into urban or suburban locations. This process is referred as the “city-entering” phenomena of stations. Consequently, many of the latest stations were relocated and moved to currently rural and suburban areas, which significantly influenced the continuity of observational records and the homogeneity of long-term trends. Based on homogeneous data series, the averaged annual T _mean, T _max, and T _min over Anhui Province increased at a rate of 0.407, 0.383 and 0.432 °C decade^?1 from 1970 to 2008. The strongest effect of urbanization on annual T _mean, T _max, and T _min trends occurred at urban stations, with corresponding contributions of 35.824, 14.286, and 45.161 % to total warming, respectively. This work provides convincing evidences that (1) urban expansion has important impacts on the evaluation of regional climate change, (2) high spatial resolution images of Landsat are very useful for selecting reference climate stations for evaluating the potential urban bias in the surface air temperature data in certain regions of the continents, and (3) meteorological observation adjustments of station-relocation-induced inhomogeneities are essential for the study of regional or global climate change.  相似文献   

Verification of land surface evapotranspiration estimation from remote sensing spatial contextual information     

Ronglin Tang  Zhao‐Liang Li  Kun‐Shan Chen  Yuanjun Zhu  Wenzhao Liu 《水文研究》2012,26(15):2283-2293

Estimation of evapotranspiration (ET) is of great significance in modeling the water and energy interactions between land and atmosphere. Negative correlation of surface temperature (T_s) 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 T_s–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 (R_n) and soil heat flux. In most cases, the estimated instantaneous R_n 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 T_s–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   

Comparison between gene expression programming and traditional models for estimating evapotranspiration under hyper arid Conditions     

Mohamed A. Yassin  Abdulrahman A. Alazba  Mohamed A. Mattar 《Water Resources》2016,43(2):412-427

Gene Expression Programming (GEP) was used to develop new mathematical equations for estimating daily reference evapotranspiration (ET _ref) for the Kingdom of Saudi Arabia. The daily climatic variables were collected by 13 meteorological stations from 1980 to 2010. The GEP models were trained on 65% of the climatic data and tested using the remaining 35%. The generalised Penman-Monteith model was used as a reference target for evapotranspiration (ET) values, with h _c varies from 5 to 105 cm with increment of a centimetre. Eight GEP models have been compared with four locally calibrated traditional models (Hargreaves-Samani, Irmak, Jensen-Haise and Kimberly-Penman). The results showed that the statistical performance criteria values such as determination coefficients (R ²) ranged from as low as 64.4% for GEP-MOD1, where the only parameters included (maximum, minimum, and mean temperature and crop height), to as high as 95.5% for GEP-MOD8 with which all climatic parameters included (maximum, minimum and mean temperature; maximum, minimum and mean humidity; solar radiation; wind speed; and crop height). Moreover, an interesting founded result is that the solar radiation has almost no effect on ET _ref under the hyper arid conditions. In contrast, the wind speed and plant height have a great positive impact in increasing the accuracy of calculating ET _ref. Furthermore, eight GEP models have obtained better results than the locally calibrated traditional ET _ref equations.  相似文献   

Polynomial chaos expansion and response surface method for nonlinear modelling of reference evapotranspiration     

Behrooz Keshtegar  Ozgur Kisi  Mohammad Zounemat-Kermani 《水文科学杂志》2019,64(6):720-730

The feasibility of polynomial chaos expansion (PCE) and response surface method (RSM) models is investigated for modelling reference evapotranspiration (ET₀). The modelling results of the proposed models are validated against the M5 model tree and multi-layer perceptron neural network (MLPNN) methods. Two meteorological stations, Isparta and Antalya, in the Mediterranean region of Turkey, are inspected. Various input combinations of daily air temperature, solar radiation, wind speed and relative humidity are constructed as input attributes for the ET₀. Generally, the modelling accuracy is increased by increasing the number of inputs. Including wind speed in the model inputs considerably increases their accuracy in modelling ET₀. Mean absolute error (MAE), root mean square error (RMSE), agreement index (d) and Nash-Sutcliffe efficiency (NSE) are used as comparison criteria. The PCE is the most accurate model in estimating daily ET₀, giving the lowest MAE (0.036 and 0.037 mm) and RMSE (0.047 and 0.050 mm) and the highest d (0.9998 and 0.9999) and NSE (0.9992 and 0.9996) with the four-input PCE models for Isparta and Antalya, respectively.  相似文献   

Short‐term stream water temperature observations permit rapid assessment of potential climate change impacts          下载免费PDF全文

Peter Caldwell  Catalina Segura  Shelby Gull Laird  Ge Sun  Steven G. McNulty  Maria Sandercock  Johnny Boggs  James M. Vose 《水文研究》2015,29(9):2196-2211

Assessment of potential climate change impacts on stream water temperature (T_s) across large scales remains challenging for resource managers because energy exchange processes between the atmosphere and the stream environment are complex and uncertain, and few long‐term datasets are available to evaluate changes over time. In this study, we demonstrate how simple monthly linear regression models based on short‐term historical T_s observations and readily available interpolated air temperature (T_a) estimates can be used for rapid assessment of historical and future changes in T_s. Models were developed for 61 sites in the southeastern USA using ≥18 months of observations and were validated at sites with longer periods of record. The T_s models were then used to estimate temporal changes in T_s at each site using both historical estimates and future T_a projections. Results suggested that the linear regression models adequately explained the variability in T_s across sites, and the relationships between T_s and T_a remained consistent over 37 years. We estimated that most sites had increases in historical annual mean T_s between 1961 and 2010 (mean of +0.11 °C decade^?1). All 61 sites were projected to experience increases in T_s from 2011 to 2060 under the three climate projections evaluated (mean of +0.41 °C decade^?1). Several of the sites with the largest historical and future T_s changes were located in ecoregions home to temperature‐sensitive fish species. This methodology can be used by resource managers for rapid assessment of potential climate change impacts on stream water temperature. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

Using equilibrium temperature to assess thermal disturbances in rivers          下载免费PDF全文

C. Buendia  S. Sabater  A. Palau  R. J. Batalla  R. Marcé 《水文研究》2015,29(19):4350-4360

Flow regulation is widely known to modify the thermal regime of rivers. Here, we examine the sensitivity of an empirical approach, the Equilibrium Temperature Concept (ETC), to detect both the effects of hydraulic infrastructures on the annual thermal cycle and the recovery of the thermal equilibrium with the atmosphere. Analysis was undertaken in a Pyrenean river (the Noguera Pallaresa, Ebro basin) affected by a series of reservoirs and hydropower plants. Equilibrium temperature (T_e) is defined as the water temperature (T_w) at which the sum of all heat fluxes is zero. Based on the assumption of a linear relationship between T_e and T_w, we identified changes in the T_e–T_w regression slope, used as an indicator of a thermal alteration in river flow. We also assessed the magnitude of the alteration by examining the regression slope and its statistical significance. Variations in the regression parameters were used as indicators of the influence of factors other than atmospheric conditions on water temperature. Observed T_w showed a linear relationship with T_e at all river stations. However, the slopes of the T_e–T_w relationship appeared to be lower in the reaches downstream from hydraulic infrastructures, particularly below large dams. A seasonal analysis indicated that T_e–T_w relationships had higher slopes and lower p‐values during autumn, while no significant differences were found at other seasons. Although thermal characteristics did not strongly depend on atmospheric conditions downstream of hydraulic infrastructures, the river recovered to pre‐alteration conditions with distance downstream, indicating the natural tendency of water to attain thermal equilibrium with the atmosphere. Accepting associated uncertainties, mostly because of the quality of the data and the lack of consideration of other factors influencing the thermal regime (e.g. discharge), ETC appears to be a simple and effective method to identify thermal alterations in regulated rivers. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

A simple temperature method for the estimation of evapotranspiration          下载免费PDF全文

Temesgen Enku  Assefa M. Melesse 《水文研究》2014,28(6):2945-2960

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 (ET_o), 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 ET_o 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 ET_o where there are insufficient data. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

Seasonal climate patterns and their influence on calibration of the Hargreaves-Samani equation     

S. Shahidian  R.P. Serralheiro  J.R. Serrano  J.L. Teixeira 《水文科学杂志》2013,58(6):985-996

Abstract

Annual patterns in climate parameters were studied to evaluate how these influence the quality of reference evapotranspiration (ETo) estimates obtained from the Hargreaves-Samani (HS) equation, since the method only uses the measured temperature directly. The work evaluates how these patterns can be used to improve the HS ETo estimates. Ten-year moving averages from a set of California Irrigation Management Information System (CIMIS) stations were used to evaluate the relationships between solar radiation (R_s), temperature (T) and ETo. The results indicate that T treads behind solar radiation and its value peaks some 25 days later. Thus, the main irrigation season in the Mediterranean climate (1 May–30 September) can be divided into three phases: increasing R_s and T; decreasing R_s with increasing T; and decreasing R_s and T. Non-univocal annual cycles were observed between R_s and T, ETo and R_s, and ETo and T. These annual patterns result in important seasonal changes in the ratio between the HS and Penman-Monteith (FAO PM) ETo estimates. The changes are particularly important during the irrigation season, where the FAO PM initially calculates greater ETo values than the HS methodology, and from the end of May to early September, where the HS equation overestimates the ETo values (by 17 mm, or 3%). These patterns obtained from 2000–2009 data were used to calibrate and improve HS ETo estimates at new sites for the 2010–2011 period. Calibration based on the proposed seasonal region-wide FAO PM/HS ETo ratios improved both the bias (decreased from 0.40 to 0.36 mm d^-1) and r² (increased from 0.67 to 0.87) of the ETo estimates for the irrigation season. The proposed methodology can be easily applied to other regions, even when the existing weather stations are sparse.