Estimation of potential evapotranspiration in the mountainous Panama Canal watershed

Spatially distributed hydrometeorological and plant information within the mountainous tropical Panama Canal watershed is used to estimate parameters of the Penman–Monteith evapotranspiration formulation. Hydrometeorological data from a few surface climate stations located at low elevations in the watershed are complemented by (a) typical wet‐ and dry‐season fields of temperature, wind, water vapour and pressure produced by a mesoscale atmospheric model with a 3 × 3 km² spatial and hourly temporal resolution, and (b) leaf area index fields estimated over the watershed during a few years using satellite data with two different spatial and temporal resolutions. The mesoscale model estimates of spatially distributed surface hydrometeorological variables provide the basis for the extrapolation of the surface climate station data to produce input for the Penman–Monteith equation. The satellite information and existing digital spatial databases of land use and land cover form the basis for the estimation of Penman–Monteith spatially distributed parameter values. Spatially distributed 3 × 3 km² potential evapotranspiration estimates are obtained for the 3300 km² Panama Canal watershed. Estimates for Gatun Lake within the watershed are found to reproduce well the monthly and annual lake evaporation obtained from submerged pans. Sensitivity analysis results of potential evapotranspiration estimates with respect to cloud cover, dew formation, leaf area index distribution and mesoscale model estimates of surface climate are presented and discussed. The main conclusion is that even the limited spatially distributed hydrometeorological and plant information used in this study contributes significantly toward explaining the substantial spatial variability of potential evapotranspiration in the watershed. These results also allow the determination of key locations within the watershed where additional surface stations may be profitably placed. Copyright © 2006 John Wiley & Sons, Ltd.     

Applicability of the Modified Universal Soil Loss Equation for prediction of sediment yield in Khanmirza watershed,Iran

Abstract

The present study aims to estimate the sediment yield due to storm rainfall and runoff at the outlet of the Khanmirza watershed (395 km²) located in western Iran. The estimation was made for six storm events using the Modified Universal Soil Loss Equation (MUSLE). All the inputs required for the application of the model were determined through runoff and sediment concentration monitoring at the time of storm events, and field surveys in the study area. The applicability of the model to the study area was then evaluated by comparison of its estimates with those calculated using the measured sediment data. The results of the study demonstrated the efficiency of the MUSLE in estimating storm-associated sediment yield except one storm event in the study area with a high level of agreement and non-significant differences between mean estimated and measured values in the study storm events.     

Rainfall‐runoff modelling using artificial neural networks technique: a Blue Nile catchment case study

A rainfall‐runoff model based on an artificial neural network (ANN) is presented for the Blue Nile catchment. The best geometry of the ANN rainfall‐runoff model in terms of number of hidden layers and nodes is identified through a sensitivity analysis. The Blue Nile catchment (about 300 000 km²) in the Nile basin is selected here as a case study. The catchment is classified into seven subcatchments, and the mean areal precipitation over those subcatchments is computed as a main input to the ANN model. The available daily data (1992–99) are divided into two sets for model calibration (1992–96) and for validation (1997–99). The results of the ANN model are compared with one of physical distributed rainfall‐runoff models that apply hydraulic and hydrologic fundamental equations in a grid base. The results over the case study area and the comparative analysis with the physically based distributed model show that the ANN technique has great potential in simulating the rainfall‐runoff process adequately. Because the available record used in the calibration of the ANN model is too short, the ANN model is biased compared with the distributed model, especially for high flows. Copyright © 2005 John Wiley & Sons, Ltd.     

The Daily Erosion Project – daily estimates of water runoff,soil detachment,and erosion

Water runoff and sediment transport from agricultural uplands are substantial threats to water quality and sustained crop production. To improve soil and water resources, farmers, conservationists, and policy‐makers must understand how landforms, soil types, farming practices, and rainfall interact with water runoff and soil erosion processes. To that end, the Iowa Daily Erosion Project (IDEP) was designed and implemented in 2003 to inventory these factors across Iowa in the United States. IDEP utilized the Water Erosion Prediction Project (WEPP) soil erosion model along with radar‐derived precipitation data and government‐provided slope, soil, and management information to produce daily estimates of soil erosion and runoff at the township scale (93 km² [36 mi²]). Improved national databases and evolving remote sensing technology now permit the derivation of slope, soil, and field‐level management inputs for WEPP. These remotely sensed parameters, along with more detailed meteorological data, now drive daily WEPP hillslope soil erosion and water runoff estimates at the small watershed scale, approximately 90 km² (35 mi²), across sections of multiple Midwest states. The revisions constitute a substantial improvement as more realistic field conditions are reflected, more detailed weather data are utilized, hill slope sampling density is an order of magnitude greater, and results are aggregated based on surface hydrology enabling further watershed research and analysis. Considering these improvements and the expansion of the project beyond Iowa it was renamed the Daily Erosion Project (DEP). Statistical and comparative evaluations of soil erosion simulations indicate that the sampling density is adequate and the results are defendable. The modeling framework developed is readily adaptable to other regions given suitable inputs. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

The Biscuit Brook and Neversink Reservoir watersheds: Long-term investigations of stream chemistry,soil chemistry and aquatic ecology in the Catskill Mountains,New York,USA, 1983–2020

This data note describes the Biscuit Brook and Neversink Reservoir watershed long-term monitoring data that includes: 1) stream discharge, (1983–2020 for Biscuit Brook and 1937–2020 for the Neversink Reservoir watershed), 2) stream water chemistry, 1983–2020, at 4 stations, 3) fish survey data from 16 locations in the watershed 1990–2019, 4) soil chemistry data from 2 headwater sub-watersheds, 1993–2012 and 5) periodic stream water chemistry sampling data from 364 locations throughout the watershed, 1983–2020. The Neversink Reservoir watershed in the Catskill Mountains of New York, USA drains an area of 172.5 km². The watershed feeds one of six reservoirs in New York City's West of Hudson water supply, which accounts for about 90% of the city's water supply. Biscuit Brook is a 9.63 km² tributary sub-watershed within the Neversink Reservoir watershed.     

High resolution water body mapping for SWAT evaporative modelling in the Upper Oconee watershed of Georgia,USA

Technological improvements in remote sensing and geographic information systems have demonstrated the abundance of artificially constructed water bodies across the landscape. Although research has shown the ubiquity of small ponds globally, and in the southeastern United States in particular, their cumulative impact in terms of evaporative alteration is less well quantified. The objectives of this study are to examine the hydrologic and evaporative importance of small artificial water bodies in the Upper Oconee watershed in the northern Georgia Piedmont, USA, by mapping their locations and modelling these small reservoirs using the Soil Water Assessment Tool. Comparative Soil Water Assessment Tool models were run with and without the inclusion of small reservoir surface area and volume. The models used meteorological inputs from 1990–2013 to represent years with drought, high precipitation, and moderate precipitation for both the calibration and evaluation periods. Statistical comparison of streamflow indicated that the calibration methodology produced results where the default model simulation without reservoirs fit observed flows more closely than the modified model with small reservoirs included (e.g., Nash–Sutcliffe efficiency of 0.72 vs. 0.64, r² of 0.73 vs. 0.66, and percent bias of 11.4 vs. 21.6). In addition, Penman–Monteith, Hargreaves, and Priestley–Taylor evapotranspiration equations were used to estimate actual evaporation from 2,219 small water bodies identified throughout the 1,936.8 km² watershed. Depending on the evaporation equation used, water bodies evaporated an average of 0.03–0.036 km³/year for the period 2003–2013. Using Penman–Monteith further, if the reservoirs were not considered and average actual evapotranspiration rates from the rest of the basin were applied, only 0.016 km³ of water would have left the basin as a result of evapotranspiration. This finding suggests construction of small reservoirs increased evaporation by an average of 0.017 km³ per year (approximately 46,500 m³/day). As the construction of small reservoirs continues and high resolution image data used to map these water bodies becomes increasingly available, watershed models that evolve to address the cumulative impacts of small water bodies on evaporation and other hydrologic processes will have greater potential to benefit the water resource management community.     

Chemical weathering and CO2 consumption rate in a multilayered‐aquifer dominated watershed under intensive farming: The Orgeval Critical Zone Observatory,France

The impact of intensive farming on chemical weathering in the Critical Zone is still an open question. Extensively instrumented and monitored over the last 50 years, the Orgeval Critical Zone Observatory (CZO) in France is an observation site impacted by intensive farming since the 1960s. The Orgeval observatory represents an ideal place to study the response and resilience capability of the Critical Zone under agricultural stress. This paper investigates the chemical composition of different water bodies in two nested catchments of the Orgeval CZO, including rainfall, springs, rivers, and rocks, over one and half hydrological year. We show that elemental and strontium isotopic ratios are powerful to constrain the origin of the elements. The results show that the river chemistry at the outlet of the two nested catchments is dominated by rain inputs (particularly atmospheric dust dissolution) and the chemical weathering of limestone and gypsum. Fertilizer input is clearly visible, although the distinction between gypsum dissolution and fertilizer inputs needs more investigation. The mixtures of water masses inferred from our data are in good agreement with the hydrological context of the watershed, that is, a multilayered aquifer structure. At the main outlet of the CZO, we estimate that the input of ocean‐derived solutes through rainfall represents 7 t km^?2 year^?1, on the same order of magnitude as the net fertilizer input (10 t km^?2 year^?1), and that rock weathering releases 50 t km^?2 year^?1. Including previously published physical erosion rates, we estimate that the total denudation rate (physical and chemical) of the Orgeval CZO is 20 mm (1,000 year)^?1, which, along with the entire Seine watershed, is among the lowest chemical denudation rates for carbonate terrains under temperate climate. Chemical denudation is about 10 times higher than physical erosion in the Orgeval CZO. The consumption of CO₂ by rock weathering is estimated to be between 265.10³ and 360.10³ molC km² year^?1, similar for the two nested catchments. Compared with the rivers, the springs show a higher CO₂ consumption rate that suggests, as pointed out earlier, a enhancement of carbonate dissolution linked to nitrification and thus fertilizer application. The hyporheic zone appears to be a hot spot in the carbon cycle at the Orgeval CZO. This study sheds light on the complex, anthropocenic, interplay between geology, climate, and human activities that characterize and that take place in intensive agriculture regions.     

Hydrological model comparison and assessment: criteria from catchment scales and temporal resolution

ABSTRACT

This study examines the performance of three hydrological models, namely the artificial neural network (ANN) model, the Hydrologiska Byråns Vattenbalansavdelning-D (HBV-D) model, and the Soil and Water Integrated Model (SWIM) over the upper reaches of the Huai River basin. The assessment is done by using databases of different temporal resolution and by further examining the applicability of SWIM for different catchment sizes. The results show that at monthly scale the performance of the ANN model is better than that of HBV-D and SWIM. The ANN model can be applied at any temporal scale as it establishes an artificial precipitation–runoff relationship for various time scales by only using monthly precipitation, temperature and runoff data. However, at daily scale the performance of both HBV-D and SWIM are similar or even better than the ANN model. In addition, the performance of SWIM at a small catchment size (less than 10 000 km²) is much better than at a larger catchment size. In view of climate change modelling, HBV-D and SWIM might be integrated in a dynamical atmosphere-water-cycle modelling rather than the ANN model due to their use of observed physical links instead of artificial relations within a black box.

Editor D. Koutsoyiannis; Associate editor D. Hughes     

Watershed structural influences on the distributions of stream network water and solute travel times under baseflow conditions

Watershed structure influences the timing, magnitude, and spatial location of water and solute entry to stream networks. In turn, stream reach transport velocities and stream network geometry (travel distances) further influence the timing of export from watersheds. Here, we examine how watershed and stream network organization can affect travel times of water from delivery to the stream network to arrival at the watershed outlet. We analysed watershed structure and network geometry and quantified the relationship between stream discharge and solute velocity across six study watersheds (11.4 to 62.8 km²) located in the Sawtooth Mountains of central Idaho, USA. Based on these analyses, we developed stream network travel time functions for each watershed. We found that watershed structure, stream network geometry, and the variable magnitude of inputs across the network can have a pronounced affect on water travel distances and velocities within a stream network. Accordingly, a sample taken at the watershed outlet is composed of water and solutes sourced from across the watershed that experienced a range of travel times in the stream network. We suggest that understanding and quantifying stream network travel time distributions are valuable for deconvolving signals observed at watershed outlets into their spatial and temporal sources, and separating terrestrial and in‐channel hydrological, biogeochemical, and ecological influences on in‐stream observations. Copyright © 2016 John Wiley & Sons, Ltd.     

Exploring the effect of data assimilation by WRF‐3DVar for numerical rainfall prediction with different types of storm events

The mesoscale Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community in providing high‐resolution rainfall forecasts at the catchment scale. Although the performance of the model has been verified in capturing the physical processes of severe storm events, the modelling accuracy is negatively affected by significant errors in the initial conditions used to drive the model. Several meteorological investigations have shown that the assimilation of real‐time observations, especially the radar data can help improve the accuracy of the rainfall predictions given by mesoscale NWP models. The aim of this study is to investigate the effect of data assimilation for hydrological applications at the catchment scale. Radar reflectivity together with surface and upper‐air meteorological observations is assimilated into the Weather Research and Forecasting (WRF) model using the three‐dimensional variational data‐assimilation technique. Improvement of the rainfall accumulation and its temporal variation after data assimilation is examined for four storm events in the Brue catchment (135.2 km²) located in southwest England. The storm events are selected with different rainfall distributions in space and time. It is found that the rainfall improvement is most obvious for the events with one‐dimensional evenness in either space or time. The effect of data assimilation is even more significant in the innermost domain which has the finest spatial resolution. However, for the events with two‐dimensional unevenness of rainfall, i.e. the rainfall is concentrated in a small area and in a short time period, the effect of data assimilation is not ideal. WRF fails in capturing the whole process of the highly convective storm with densely concentrated rainfall in a small area and a short time period. A shortened assimilation time interval together with more efficient utilisation of the weather radar data might help improve the effectiveness of data assimilation in such cases. Copyright © 2012 John Wiley & Sons, Ltd.     

Simulation of spring flows from a karst aquifer with an artificial neural network

In China, 9·5% of the landmass is karst terrain and of that 47,000 km² is located in semiarid regions. In these regions the karst aquifers feed many large karst springs within basins of thousands of square kilometres. Spring discharges reflect the fluctuation of ground water level and variability of ground water storage in the basins. However, karst aquifers are highly heterogeneous and monitoring data are sparse in these regions. Therefore, for sustainable utilization and conservation of karst ground water it is necessary to simulate the spring flows to acquire better understanding of karst hydrological processes. The purpose of this study is to develop a parsimonious model that accurately simulates spring discharges using an artificial neural network (ANN) model. The karst spring aquifer was treated as a non‐linear input/output system to simulate the response of karst spring flow to precipitation and applied the model to the Niangziguan Springs, located in the east of Shanxi Province, China and a representative of karst springs in a semiarid area. Moreover, the ANN model was compared with a previous time‐lag linear model and it was found that the ANN model performed better. Copyright © 2007 John Wiley & Sons, Ltd.     

Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed

This paper reports on an evaluation of the use of artificial neural network (ANN) models to forecast daily flows at multiple gauging stations in Eucha Watershed, an agricultural watershed located in north‐west Arkansas and north‐east Oklahoma. Two different neural network models, the multilayer perceptron (MLP) and the radial basis neural network (RBFNN), were developed and their abilities to predict stream flow at four gauging stations were compared. Different scenarios using various combinations of data sets such as rainfall and stream flow at various lags were developed and compared for their ability to make flow predictions at four gauging stations. The input vector selection for both models involved quantification of the statistical properties such as cross‐, auto‐ and partial autocorrelation of the data series that best represented the hydrologic response of the watershed. Measured data with 739 patterns of input–output vector were divided into two sets: 492 patterns for training, and the remaining 247 patterns for testing. The best performance based on the RMSE, R² and CE was achieved by the MLP model with current and antecedent precipitation and antecedent flow as model inputs. The MLP model testing resulted in R² values of 0·86, 0·86, 0·81, and 0·79 at the four gauging stations. Similarly, the testing R² values for the RBFNN model were 0·60, 0·57, 0·58, and 0·56 for the four gauging stations. Both models performed satisfactorily for flow predictions at multiple gauging stations, however, the MLP model outperformed the RBFNN model. The training time was in the range 1–2 min for MLP, and 5–10 s for RBFNN on a Pentium IV processor running at 2·8 GHz with 1 MB of RAM. The difference in model training time occurred because of the clustering methods used in the RBFNN model. The RBFNN uses a fuzzy min‐max network to perform the clustering to construct the neural network which takes considerably less time than the MLP model. Results show that ANN models are useful tools for forecasting the hydrologic response at multiple points of interest in agricultural watersheds. Copyright © 2008 John Wiley & Sons, Ltd.     

Watershed modelling of hydrology and water quality in the Sacramento River watershed,California

Agricultural pollutant runoff is a major source of water contamination in California's Sacramento River watershed where 8500 km² of agricultural land influences water quality. The Soil and Water Assessment Tool (SWAT) hydrology, sediment, nitrate and pesticide transport components were assessed for the Sacramento River watershed. To represent flood conveyance in the area, the model was improved by implementing a flood routing algorithm. Sensitivity/uncertainty analyses and multi‐objective calibration were incorporated into the model application for predicting streamflow, sediment, nitrate and pesticides (chlorpyrifos and diazinon) at multiple watershed sites from 1992 to 2008. Most of the observed data were within the 95% uncertainty interval, indicating that the SWAT simulations were capturing the uncertainties that existed, such as model simplification, observed data errors and lack of agricultural management data. The monthly Nash–Sutcliffe coefficients at the watershed outlet ranged from 0.48 to 0.82, indicating that the model was able to successfully predict streamflow and agricultural pollutant transport after calibration. Predicted sediment loads were highly correlated to streamflow, whereas nitrate, chlorpyrifos and diazinon were moderately correlated to streamflow. This indicates that timing of agricultural management operations plays a role in agricultural pollutant runoff. Best management practices, such as pesticide use limits during wet seasons, could improve water quality in the Sacramento River watershed. The calibrated model establishes a modelling framework for further studies of hydrology, water quality and ecosystem protection in the study area. Copyright © 2012 John Wiley & Sons, Ltd.     

Patuxent Landscape Model. III. Model calibration

Using the LHEM/SME the Patuxent Landscape Model (PLM) was built to simulate fundamental ecological processes in the watershed scale driven by temporal (nutrient loadings, climatic conditions) and spatial (land use patterns) forcings. The model addresses the effects of both the magnitude and spatial patterns of land use change and agricultural practices on hydrology, plant productivity, and nutrient cycling in the landscape. The spatial resolution for the full Patuxent watershed is 1 km², while subwatersheds are analyzed at a 200 × 200 m resolution to allow adequate depiction of the pattern of ecosystems and human settlement on the landscape. The temporal resolution is different for various components of the model. We used a modular, multiscale approach to calibrate and test the model. Model results show good agreement with data.     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.     

Bootstrap based artificial neural network (BANN) analysis for hierarchical prediction of monthly runoff in Upper Damodar Valley Catchment

Estimation of runoff is a prerequisite for many applications involving conservation and management of water resources. This study is undertaken in the Upper Damodar Valley Catchment (UDVC) having a drainage area of 17513.08 km² for prediction of monthly runoff. Thirty one microwatersheds and 15 sub-watersheds were selected from a total of 716 microwatersheds in the catchment area for this study. The feasibility of using different soil attributes (particle size distribution, organic matter content and apparent density), topographic attributes (primary, secondary and compound), geomorphologic attributes (basin, relief and network indices) and vegetation attribute as Normalized Difference Vegetation Index (NDVI), on prediction of monthly runoff were explored in this study. Principal Component Analysis (PCA) was applied to minimize the data redundancy of the input variables. Ten significant input variables namely; watershed length (km), elongation ratio, bifurcation ratio, area ratio, coarse sand (%), fine sand (%), elevation (m), slope (°), profile curvature (rad/m) and NDVI were selected. The selected input variables were added in hierarchy with monthly rainfall (mm) as inputs for prediction of monthly runoff (mm) using Bootstrap based artificial neural networks (BANN). The performance of the models was tested using Spearman’s correlation coefficient (r), coefficient of efficiency (COE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Best performance was observed for model with monthly rainfall, slope, coarse sand, bifurcation ratio and Normalized Difference Vegetation Index (NDVI) as inputs (r = 0.925 and COE = 0.839). Increase in number of input variables did not necessarily yield better performances of the BANN models. Selection of relevant inputs and their combinations were found to be key elements in determining the performance of BANN models. Annual runoff map was generated for all the microwatersheds utilizing the weights of the best performing BANN model. This study reveals that the specific combinations of soil, topography, geomorphology and vegetation inputs can be utilized for better prediction of monthly runoff.     

An un-mixing model to study watershed erosion processes

An un-mixing model is formulated within a Bayesian Markov Chain Monte Carlo framework for use within land-use fingerprinting to study watershed erosion processes. The model has two new components: (1) An equation and erosion process parameter are used to weight tracer signatures from each erosion process within a land-use. (2) An extra tracer distribution and episodic erosion parameter are used to represent soil eroded throughout the sampling duration and thus include the episodic nature of erosion. To test specification of these new parameters, the un-mixing model is applied in the 15 km² Jerome Creek Watershed in the Palouse Region of Northwestern Idaho. Erosion processes include surface erosion upon mountain slopes due to logging in the forest land-use and rill/interrill erosion on cultivated slopes and headcut erosion in riparian floodplains of the agricultural land-use (winter wheat/peas rotation and hay pasture). Episodic erosion occurs for the event where the model is applied. A sensitivity analysis shows that the smallest Bayesian credible set results when the new parameters are specified using hydrologic data and process-based models. The un-mixing model predicts that 90% of the eroded-soil originated from the agricultural land-use and 10% originated from the forest land-use. A comparative study is performed that estimates 90.5% and 9.5% of eroded-soil originated from the agricultural and forest land-uses. Successful performance of the un-mixing model highlights future application as a standalone probabilistic tool to monitor watershed erosion processes that exhibit non-equilibrium conditions and provide calibration data for process-based watershed models.     

Application of the geographical information systems approach to watershed mass balance studies

This study was undertaken to test the utility of a geographical information systems (GIS) approach to problems of watershed mass balance. This approach proved most useful in exploring the effects that watershed scale, lithology and land use have on chemical weathering rates, and in assessing whether mass balance calculations could be applied to large multilithological watersheds. Water quality data from 52 stations were retrieved from STORET and a complete GIS database consisting of the watershed divide, lithology and land use was compiled for each station. Water quality data were also obtained from 7 experimental watersheds to develop a methodology to estimate annual fluxes from incomplete data sets. The methodology consists of preparing a composite of daily flux data, calculating a best fit sinusoid and integrating the equation to obtain an annual flux. Comparison with annual fluxes calculated from high resolution data sets suggests that this method predicts fluxes within about 10% of the true annual flux. Annual magnesium fluxes (moles km⁻² yr⁻¹) were calculated for all stations and adjusted for fluxes from atmospheric deposition. Magnesium flux was found to be a strong function of the amount of carbonate in the watershed, and silica fluxes were found to increase with the fraction of sandstone present in the watershed. All fluxes were strongly influenced by mining practices, with magnesium fluxes from affected watersheds being 6–10 times higher than fluxes from comparable pristine watersheds. Mining practices enhance chemical weathering by increasing the surface area of unweathered rock to which water has access and by increasing acidity and rate of mineral weathering. Fluxes were also found to increase with watershed size. This scale dependence is most likely caused by the sensitivity of weathering fluxes to even minor quantities of carbonates, which are likely to be found in all lithologies at larger scales. Mass balances were carried out in watersheds where gauged sub-watersheds made up more than 95% of the area. The calculations show large magnesium flux and water balance discrepancies. These errors may be a result of significant groundwater inputs to streams between gauges. The results suggest that improvements in how we measure discharge and estimate fluxes may be required before we can apply mass balance techniques to larger scales. © 1997 John Wiley & Sons, Ltd.     

Lumped surface and sub‐surface runoff for erosion modeling within a small hilly watershed in northern Vietnam

Developing models to predict on‐site soil erosion and off‐site sediment transport at the agricultural watershed scale represent an on‐going challenge in research today. This study attempts to simulate the daily discharge and sediment loss using a distributed model that combines surface and sub‐surface runoffs in a small hilly watershed (< 1 km²). The semi‐quantitative model, Predict and Localize Erosion and Runoff (PLER), integrates the Manning–Strickler equation to simulate runoff and the Griffith University Erosion System Template equation to simulate soil detachment, sediment storage and soil loss based on a map resolution of 30 m × 30 m and over a daily time interval. By using a basic input data set and only two calibration coefficients based, respectively, on water velocity and soil detachment, the PLER model is easily applicable to different agricultural scenarios. The results indicate appropriate model performance and a high correlation between measured and predicted data with both Nash–Sutcliffe efficiency (Ef) and correlation coefficient (r²) having values > 0.9. With the simple input data needs, PLER model is a useful tool for daily runoff and soil erosion modeling in small hilly watersheds in humid tropical areas. Copyright © 2013 John Wiley & Sons, Ltd.