Stream temperature modelling using artificial neural networks: application on Catamaran Brook,New Brunswick,Canada

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.     

Hydrometeorological relationships in a glacierized catchment in the Canadian high arctic

Runoff from a small glacierized catchment in the Canadian high Arctic was monitored throughout one melt season. The stream discharge record is one aspect of a larger project involving glacier mass balance, superimposed ice formation and local climate on a glacier in the Sawtooth Range, Ellesmere Island, Northwest Territories, Canada. To better understand the main factors influencing the production of runoff on the glacier during the period of main summer melt, regression analyses were performed relating daily air temperature, shortwave incoming and net radiation, absorptivity and wind speed to daily glacier discharge. Air temperature at the glacier meteorological station on rain-free days is the element with the greatest correlation with runoff (r² = 0.57; n = 34). A multiple regression of discharge with air temperature, shortwave incoming radiation, net radiation hours and wind speed achieved the best fit (r² = 0.84; n = 34). Rain events (> 10mmd^?1) can dominate daily discharge when they occur during the period of ice melt, creating more runoff per unit area than can be produced by melt alone, and significantly reduce the accuracy of runoff predictions.     

Temperature in lowland Danish streams: contemporary patterns,empirical models and future scenarios

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.     

Estimating reference evapotranspiration using numerical weather modelling

Evapotranspiration is an important hydrological process and its estimation usually needs measurements of many weather variables such as atmospheric pressure, wind speed, air temperature, net radiation and relative humidity. Those weather variables are not easily obtainable from in situ measurements in practical water resources projects. This study explored a potential application of downscaled global reanalysis weather data using mesoscale modelling system 5 (MM5). The MM5 is able to downscale the global data down to much finer resolutions in space and time for use in hydrological investigations. In this study, the ERA‐40 reanalysis data are downscaled to the Brue catchment in southwest England. The results are compared with the observation data. Among the studied weather variables, atmospheric pressure could be derived very accurately with less than 0·2% error. On the other hand, the error in wind speed is about 200–400%. The errors in other weather variables are air temperature (<10%), relative humidity (5–21%) and net radiation (4–23%). The downscaling process generally improves the data quality (except wind speed) and provides higher data resolution in comparison with the original reanalysis data. The evapotranspiration values estimated from the downscaled data are significantly overestimated across all the seasons (27–46%) based on the FAO Penman–Monteith equation. The dominant weather variables are net radiation (during the warm period) and relative humidity (during the cold period). There are clear patterns among some weather variables and they could be used to correct the biases in the downscaled data from either short‐term in situ measurements or through regionalization from surrounding weather stations. Artificial intelligence tools could be used to map the downscaled data directly into evapotranspiration or even river runoff if rainfall data are available. This study provides hydrologists with valuable information on downscaled weather variables and further exploration of this potentially valuable data source by the hydrological community should be encouraged. Copyright © 2010 John Wiley & Sons, Ltd.     

Water temperature dynamics in High Arctic river basins

Despite the high sensitivity of polar regions to climate change and the strong influence of temperature upon ecosystem processes, contemporary understanding of water temperature dynamics in Arctic river systems is limited. This research gap was addressed by exploring high‐resolution water column thermal regimes for glacier‐fed and non‐glacial rivers at eight sites across Svalbard during the 2010 melt season. Mean water column temperatures in glacier‐fed rivers (0.3–3.2 °C) were lowest and least variable near the glacier terminus but increased downstream (0.7–2.3 °C km^–1). Non‐glacial rivers, where discharge was sourced primarily from snowmelt runoff, were warmer (mean: 2.9–5.7 °C) and more variable, indicating increased water residence times in shallow alluvial zones and increased potential for atmospheric influence. Mean summer water temperature and the magnitude of daily thermal variation were similar to those of some Alaskan Arctic rivers but low at all sites when compared with alpine glacierized environments at lower latitudes. Thermal regimes were correlated strongly (p < 0.01) with incoming short‐wave radiation, air temperature, and river discharge. Principal drivers of thermal variability were inferred to be (i) water source (i.e. glacier melt, snowmelt, groundwater); (ii) exposure time to the atmosphere; (iii) prevailing meteorological conditions; (iv) river discharge; (v) runoff interaction with permafrost and buried ice; and (vi) basin‐specific geomorphological features (e.g. channel morphology). These results provide insight into the potential changes in high‐latitude river systems in the context of projected warming in polar regions. We hypothesize that warmer and more variable temperature regimes may prevail in the future as the proportion of bulk discharge sourced from glacial meltwater declines and rivers undergo a progressive shift towards snow water and groundwater sources. Importantly, such changes could have implications for aquatic species diversity and abundance and influence rates of ecosystem functioning in high‐latitude river systems. Copyright © 2012 John Wiley & Sons, Ltd.     

Coupling the WRF model with a temperature index model based on remote sensing for snowmelt simulations in a river basin in the Altay Mountains,north‐west China

Snowmelt water is an important freshwater resource in the Altay Mountains in north‐west China; however, warming climate and rapid spring snowmelt can cause floods that endanger both public and personal property and safety. This study simulates snowmelt in the Kayiertesi River catchment using a temperature index model based on remote sensing coupled with high‐resolution meteorological data obtained from National Centers for Environmental Prediction (NCEP) reanalysis fields that were downscaled using the Weather Research Forecasting model and then bias corrected using a statistical downscaled model. Validation of the forcing data revealed that the high‐resolution meteorological fields derived from the downscaled NCEP reanalysis were reliable for driving the snowmelt model. Parameters of the temperature index model based on remote sensing were calibrated for spring 2014, and model performance was validated using Moderate Resolution Imaging Spectroradiometer snow cover and snow observations from spring 2012. The results show that the temperature index model based on remote sensing performed well, with a simulation mean relative error of 6.7% and a Nash–Sutcliffe efficiency of 0.98 in spring 2012 in the river of Altay Mountains. Based on the reliable distributed snow water equivalent simulation, daily snowmelt run‐off was calculated for spring 2012 in the basin. In the study catchment, spring snowmelt run‐off accounts for 72% of spring run‐off and 21% of annual run‐off. Snowmelt is the main source of run‐off for the catchment and should be managed and utilized effectively. The results provide a basis for snowmelt run‐off predictions, so as to prevent snowmelt‐induced floods, and also provide a generalizable approach that can be applied to other remote locations where high‐density, long‐term observational data are lacking. Copyright © 2016 John Wiley & Sons, Ltd.     

Solar radiation,longwave radiation and emergent wetland evapotranspiration estimates from satellite data in Florida,USA / Estimations à partir de données satellitales du rayonnement solaire,du rayonnement de grande longueur d’onde et de l’évapotranspiration d’une zone humide de Floride (EUA)

Abstract

Abstract Routine estimates of daily incoming solar radiation from the GOES-8 satellite were compared to locally measured values in Florida. Longwave radiation estimates corrected using GOES-derived cloud amount and cloud top temperature products improved net radiation estimates as compared to a clear sky longwave approach. The Penman-Monteith, Turc, Hargreaves and Makkink models were applied using GOES-derived estimates of solar radiation and net radiation to predict daily evapotranspiration and were compared to evapotranspiration measured with an eddy-correlation system in an emergent wetland experimental site in north-central Florida under unstressed conditions. While the Penman-Monteith model provided the best estimates of evapotranspiration (R ² = 0.92), the empirical Makkink method demonstrated nearly comparable agreement (R ² = 0.90) using only the GOES solar radiation and measured temperature. The results show that it is possible to generate spatially distributed daily potential evapotranspiration estimates using GOES-derived solar radiation and net radiation with limited additional surface measurements.     

Hydrologic impact of regional climate change for the snowfed and glacierfed river basins in the Republic of Tajikistan: hydrological response of flow to climate change

The warming of the Earth's atmosphere system is likely to change temperature and precipitation, which may affect the climate, hydrology and water resources at the river basins over the world. The importance of temperature change becomes even greater in snow or glacier dominated basins where it controls the snowmelt processes during the late‐winter, spring and summer months. In this study hydrologic responses of streamflow in the Pyanj and Vaksh River basins to climate change are analysed with a watershed hydrology model, based on the downscaled atmospheric data as input, in order to assess the regional climate change impact for the snowfed and glacierfed river basins in the Republic of Tajikistan. As a result of this analysis, it was found that the annual mean river discharge is increasing in the future at snow and glacier dominated areas due to the air temperature increase and the consequent increase in snow/ice melt rates until about 2060. Then the annual mean flow discharge starts to decrease from about 2080 onward because the small glaciers start to disappear in the glacier areas. It was also found that there is a gradual change in the hydrologic flow regime throughout a year, with the high flows occuring earlier in the hydrologic year, due to the warmer climate in the future. Furthermore, significant increases in annual maximum daily flows, including the 100‐year return period flows, at the Pyanj and Vaksh River basins toward the end of the 21st century can be inferred from flood frequency analysis results. Copyright © 2012 John Wiley & Sons, Ltd.     

Uncertainty in seasonal snow reconstruction: Relative impacts of model forcing and image availability

There are many areas of uncertainty when solving the inverse problems of snow water equivalent (SWE) reconstruction. These include (i) the ability to infer the Final Date of the Seasonal Snow (FDSS) cover, particularly from remote sensing; (ii) errors in model forcing data (such as air temperature or radiation fluxes); and (iii) weaknesses in the snow model used for the reconstruction, associated with both the fidelity of the equations used to simulate snow processes (structural uncertainty) and the parameter values selected for use in the model equations. We investigate the trade-offs among these sources of uncertainty using 10,000 station-years worth of data from the western US SNOTEL network. Model structural and parameter uncertainty are eliminated by using a perfect model scenario i.e. comparing results to modelled control runs. The model was calibrated for each station-year to ensure that the model simulations reflect reality. Results indicate that for a temperature index model, a ±5 days error in FDSS gives a median −25%/+32% error in maximum SWE. A 1 °C air temperature bias produces a SWE error larger than a 5 days error in the FDSS for 50% of the 10,000 cases. Similarly, a 5 days error in FDSS could be accounted for by a net radiation error of 13 W m⁻² or less during the melt period, in 50% of cases. Mean absolute errors of 1 °C or more are typically reported in the literature for air temperature interpolations at high elevations. Observed solar radiation during the melt season can differ by 30 W m⁻² over relatively short distances, while estimates from reanalysis (NARR, ERA-Interim, MERRA, CFSRR) and GOES satellites typically span more than 40 W m⁻². Using data from both MODIS sensors (Terra & Aqua) at all snow covered points in the western US, a consecutive 5 days gap in imagery at time of FDSS is likely to occur only 5–10% of the time. This work shows that errors in model forcing data are at least as important, if not more, than image availability when reconstructing SWE.     

Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application

Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium‐Range Weather Forecasts 40‐year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10‐km grid resolution; stochastically generated data from weather generator; bias‐corrected dynamically downscaled; and bias‐corrected global reanalysis. The reanalysis products are considered as surrogates for large‐scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias‐corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales. Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling the effects of climate change projections on streamflow in the Nooksack River basin,Northwest Washington

The Nooksack River has its headwaters in the North Cascade Mountains and drains an approximately 2000 km² watershed in northwestern Washington State. The timing and magnitude of streamflow in a snowpack‐dominated drainage basin such as the Nooksack River basin are strongly influenced by temperature and precipitation. Projections of future climate made by general circulation models (GCMs) indicate increases in temperature and variable changes in precipitation for the Nooksack River basin. Understanding the response of the river to climate change is crucial for regional water resources planning because municipalities, tribes, and industry depend on the river for water use and for fish habitat. We combine three different climate scenarios downscaled from GCMs and the Distributed‐Hydrology‐Soil‐Vegetation Model to simulate future changes to timing and magnitude of streamflow in the higher elevations of the Nooksack River. Simulations of future streamflow and snowpack in the basin project a range of magnitudes, which reflects the variable meteorological changes indicated by the three GCM scenarios and the local natural variability employed in the modeling. Simulation results project increased winter flows, decreased summer flows, decreased snowpack, and a shift in timing of the spring melt peak and maximum snow water equivalent. These results are consistent with previous regional studies, but the magnitude of increased winter flows and total annual runoff is higher. Increases in temperature dominate snowpack declines and changes to spring and summer streamflow, whereas a combination of increases in temperature and precipitation control increased winter streamflow. Copyright © 2013 John Wiley & Sons, Ltd.     

Daily pan evaporation modelling using multi‐layer perceptrons and radial basis neural networks

This paper reports on investigations of the abilities of three different artificial neural network (ANN) techniques, multi‐layer perceptrons (MLP), radial basis neural networks (RBNN) and generalized regression neural networks (GRNN) to estimate daily pan evaporation. Different MLP models comprising various combinations of daily climatic variables, that is, air temperature, solar radiation, wind speed, pressure and humidity were developed to evaluate the effect of each of these variables on pan evaporation. The MLP estimates are compared with those of the RBNN and GRNN techniques. The Stephens‐Stewart (SS) method is also considered for the comparison. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE) and determination coefficient (R²) statistics. Based on the comparisons, it was found that the MLP and RBNN computing techniques could be employed successfully to model the evaporation process using the available climatic data. The GRNN was found to perform better than the SS method. Copyright © 2008 John Wiley & Sons, Ltd.     

Simulating hydrologic and hydraulic processes throughout the Amazon River Basin

Presented here is a model framework based on a land surface topography that can be represented with various degrees of resolution and capable of providing representative channel/floodplain hydraulic characteristics on a daily to hourly scale. The framework integrates two models: (1) a water balance model (WBM) for the vertical fluxes and stores of water in and through the canopy and soil layers based on the conservation of mass and energy, and (2) a routing model for the horizontal routing of surface and subsurface runoff and channel and floodplain waters based on kinematic and diffusion wave methodologies. The WBM is driven by satellite‐derived precipitation (TRMM_3B42) and air temperature (MOD08_M3). The model's use of an irregular computational grid is intended to facilitate parallel processing for applications to continental and global scales. Results are presented for the Amazon Basin over the period Jan 2001 through Dec 2005. The model is shown to capture annual runoff totals, annual peaks, seasonal patterns, and daily fluctuations over a range of spatial scales (>1, 000 to < 4·7M km²). For the period of study, results suggest basin‐wide total water storage changes in the Amazon vary by approximately + /? 5 to 10 cm, and the fractional components accounting for these changes are: root zone soil moisture (20%), subsurface water being routed laterally to channels (40%) and channel/floodplain discharge (40%). Annual variability in monthly water storage changes by + /? 2·5 cm is likely due to 0·5 to 1 month variability in the arrival of significant rainfall periods throughout the basin. Copyright © 2009 John Wiley & Sons, Ltd.     

Yukon River Basin long‐term (1977–2006) hydrologic and climatic analysis

In this study, long‐term discharge data and climate records, such as temperature and precipitation during 1977–2006, have been used to define basin climatic and hydrologic regimes and changes. Discharge analyses at four key gauging stations (Eagle, Stevens Village, Nenana, and Pilot Station) in the Yukon River Basin show that the runoff in the cold season (November to April) is low with small variations, whereas it is high (28 500–177 000 ft³/s; 810–5000 m³/s) with high fluctuations in the warm season (May to October). The Stevens Village Station is in the upper basin and has similar changes with the flow near basin outlet. Flow increases in May (61 074 ft³/s; 1729 m³/s) and September (23 325 ft³/s; 660 m³/s); and decreases in July (35 174 ft³/s; 996 m³/s) and August (6809 ft³/s; 193 m³/s). Discharge in May at the Pilot Station (near the basin outlet) shows a positive trend (177 000 ft³/s; 5010 m³/s). Daily flow analyses show high fluctuation during the warm season and very low flow during the cold season; the 10‐year average analyses of daily flow at Pilot Station show a small increase in the peak and its timing shifted to a little earlier date. The annual flow, average of 227 900 ft³/s (6450 m³/s) with high inter‐annual fluctuations, has increased by 18 200 ft³/s (or 8%; 520 m³/s) during 1977–2006. From 1977 to 2006, basin air temperature in June has increased by 3.9 °F (2.2 °C) and decreased by 10.5 °F (5.8 °C) in January. A strong and positive correlation exists between air temperature in April and discharge in May, whereas a strong and negative correlation relates August temperature and September discharge. Negative trend during 1977–2006 is observed for precipitation in June (0.6 in.; 15 mm) with a confidence over 93%. Precipitation in August and September has strong and positive correlations with discharge in September and October at basin outlet; the precipitation in other months has weak correlation with the discharge. The mean annual precipitation during 1977–2006 increased by 1.1 in. (or 8%; 28 mm), which contributes to the annual flow increase during the study period. Copyright © 2012 John Wiley & Sons, Ltd.     

Long‐wave radiation and heat flux estimates within a small tributary in Catamaran Brook (New Brunswick,Canada)

River water temperature is an important water quality parameter that also influences most aquatic life. Physical processes influencing water temperature in rivers are highly complex. This is especially true for the estimation of river heat exchange processes that are highly dependent on good estimates of radiation fluxes. Furthermore, very few studies were found within the stream temperature dynamic literature where the different radiation components have been measured and compared at the stream level (at microclimate conditions). Therefore, this study presents results on hydrometeorological conditions for a small tributary within Catamaran Brook (part of the Miramichi River system, New Brunswick, Canada) with the following specific objectives: (1) to compare between stream microclimate and remote meteorological conditions, (2) to compare measured long‐wave radiation data with those calculated from an analytical model, and (3), to calculate the corresponding river heat fluxes. The most salient findings of this study are (1) solar radiation and wind speed are parameters that are highly site specific within the river environment and play an important role in the estimation of river heat fluxes; (2) the incoming, outgoing, and net long‐wave radiation within the stream environment (under the forest canopy) can be effectively calculated using empirical formula; (3) at the study site more than 80% of the incoming long‐wave radiation was coming from the forest; (4) total energy gains were dominated by solar radiation flux (for all the study periods) followed by the net long‐wave radiation (during some periods) whereas energy losses were coming from both the net long‐wave radiation and evaporation. Conductive heat fluxes have a minor contribution from the overall heat budget (<3·5%); (5) the reflected short‐wave radiation at the water surface was calculated on average as 3·2%, which is consistent with literature values. Results of this study contribute towards a better understanding of river heat fluxes and water temperature models as well as for more effective aquatic resources and fisheries management. Copyright © 2011 John Wiley & Sons, Ltd.     

Uncertainty analysis of statistical downscaling methods using Canadian Global Climate Model predictors

Three downscaling models, namely the Statistical Down‐Scaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS‐WG) model and an artificial neural network (ANN) model, have been compared in terms of various uncertainty attributes exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperature. The uncertainty attributes are described by the model errors and the 95% confidence intervals in the estimates of means and variances of downscaled data. The significance of those errors has been examined by suitable statistical tests at the 95% confidence level. The 95% confidence intervals in the estimates of means and variances of downscaled data have been estimated using the bootstrapping method and compared with the observed data. The study has been carried out using 40 years of observed and downscaled daily precipitation data and daily maximum and minimum temperature data, starting from 1961 to 2000. In all the downscaling experiments, the simulated predictors of the Canadian Global Climate Model (CGCM1) have been used. The uncertainty assessment results indicate that, in daily precipitation downscaling, the LARS‐WG model errors are significant at the 95% confidence level only in a very few months, the SDSM errors are significant in some months, and the ANN model errors are significant in almost all months of the year. In downscaling daily maximum and minimum temperature, the performance of all three models is similar in terms of model errors evaluation at the 95% confidence level. But, according to the evaluation of variability and uncertainty in the estimates of means and variances of downscaled precipitation and temperature, the performances of the LARS‐WG model and the SDSM are almost similar, whereas the ANN model performance is found to be poor in that consideration. Further assessment of those models, in terms of skewness and average dry‐spell length comparison between observed and downscaled daily precipitation, indicates that the downscaled daily precipitation skewness and average dry‐spell lengths of the LARS‐WG model and the SDSM are closer to the observed data, whereas the ANN model downscaled precipitation underestimated those statistics in all months. Copyright © 2006 John Wiley & Sons, Ltd.     

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

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).     

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

This study challenges the use of three nature‐inspired algorithms as learning frameworks of the adaptive‐neuro‐fuzzy inference system (ANFIS) machine learning model for short‐term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography‐based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R² parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R²), root‐mean‐square error (RMSE), Nash–Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all‐nature‐inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS‐PSO and ANFIS‐BOA provide slightly better results than the other ANFIS models.     

Effect of snowpack removal on energy balance,melt and runoff in a small supraglacial catchment

Modelling melt and runoff from snow‐ and ice‐covered catchments is important for water resource and hazard management and for the scientific study of glacier hydrology, dynamics and hydrochemistry. In this paper, a distributed, physically based model is used to determine the effects of the up‐glacier retreat of the snowline on spatial and temporal patterns of melt and water routing across a small (0·11 km²) supraglacial catchment on Haut Glacier d'Arolla, Switzerland. The melt model uses energy‐balance theory and accounts for the effects of slope angle, slope aspect and shading on the net radiation fluxes, and the effects of atmospheric stability on the turbulent fluxes. The water routing model uses simplified snow and open‐channel hydrology theory and accounts for the delaying effects of vertical and horizontal water flow through snow and across ice. The performance of the melt model is tested against hourly measurements of ablation in the catchment. Calculated and measured ablation rates show a high correlation (r² = 0·74) but some minor systematic discrepancies in the short term (hours). These probably result from the freezing of surface water at night, the melting of the frozen layer in the morning, and subsurface melting during the afternoon. The performance of the coupled melt/routing model is tested against hourly discharge variations measured in the supraglacial stream at the catchment outlet. Calculated and measured runoff variations show a high correlation (r² = 0·62). Five periods of anomalously high measured discharge that were not predicted by the model were associated with moulin overflow events. The radiation and turbulent fluxes contribute c. 86% and c. 14% of the total melt energy respectively. These proportions do not change significantly as the surface turns from snow to ice, because increases in the outgoing shortwave radiation flux (owing to lower albedo) happen to be accompanied by decreases in the incoming shortwave radiation flux (owing to lower solar incidence angles) and increases in the turbulent fluxes (owing to higher air temperatures and vapour pressures). Model sensitivity experiments reveal that the net effect of snow pack removal is to increase daily mean discharges by c. 50%, increase daily maximum discharges by >300%, decrease daily minimum discharges by c. 100%, increase daily discharge amplitudes by >1000%, and decrease the lag between peak melt rates and peak discharges from c. 3 h to c. 50 min. These changes have important implications for the development of subglacial drainage systems. Copyright © 2002 John Wiley & Sons, Ltd.     

Determination of bare surface soil moisture from combined temporal evolution of land surface temperature and net surface shortwave radiation

Land surface soil moisture (SSM) is an important variable for hydrological, ecological, and meteorological applications. A multi‐linear model has recently been proposed to determine the SSM content from the combined diurnal evolution of both land surface temperature (LST) and net surface shortwave radiation (NSSR) with the parameters TN (the LST mid‐morning rising rate divided by the NSSR rising rate during the same period) and t_d (the time of daily maximum temperature). However, in addition to the problem that all the coefficients of the multi‐linear model depend on the atmospheric conditions, the model also suffers from the problems of the nonlinearity of TN as a function of the SSM content and the uncertainty of determining the t_d from the diurnal evolution of the LST. To address these problems, a modified multi‐linear model was developed using the logarithm of TN and normalizing t_d by the mid‐morning temperature difference instead of using the TN and t_d. Except for the constant term, the coefficients of all other variables in the modified multi‐linear model proved to be independent of the atmospheric conditions. Using the relevant simulation data, results from the modified multi‐linear model show that the SSM content can be determined with a root mean square error (RMSE) of 0.030m³/m³, provided that the constant term is known or estimated day to day. The validation of the model was conducted using the field measurements at the Langfang site in 2008 in China. A higher correlation is achieved (coefficient of determination: R² = 0.624, RMSE = 0.107m³/m³) between the measured SSM content and the SSM content estimated using the modified multi‐linear model with the coefficients determined from the simulation data. Another experiment is also conducted to estimate the SSM content using the modified model with the constant term calibrated each day by one‐spot measurements at the site. The estimation result has a relatively larger error (RMSE = 0.125m³/m³). Additionally, the uncertainty of the determination of the coefficients is analysed using the field measurements, and the results indicate that the SSM content obtained using the modified model accurately characterizes the surface soil moisture condition. Copyright © 2012 John Wiley & Sons, Ltd.