Streamflow Measurements Research Articles

Key Planning Questions to Consider in Small Stream Hydrometric Monitoring

This article provides key questions to consider when planning and operating a small stream hydrometric station. Office planning components include defining hydrometric monitoring objectives; the availability of hydrometric expertise; resource availability; safety plans and standard operating procedures; equipment availability (hydrometric station installation and streamflow measurement); sampling frequency and data availability; permissions and permitting requirements; data processing, access, and archiving; and metadata requirements. Key parts of field-based hydrometric planning include site safety; site accessibility; flow variability; channel control features; flow containment, diversions and additions; low flow considerations; high flow considerations; flow measurement challenges in small streams; benchmarks and survey criteria; and public safety and vandalism. Theoverall goal of the article is to help non-professionals collect better hydrometric data and to highlight the varied planning aspects of typical hydrometric installations and operations.

River Bathymetry Estimate and Discharge Assessment from Remote Sensing

AbstractThe NASA Surface Water Ocean Topography (SWOT) mission and ESA Sentinel‐1, inter‐alia, propose to monitor inland waters from satellites. The radar altimeter and the microwave radiometer can be used to monitor water surface level and assess the cross‐sectional mean flow velocity, respectively. However, for most river sites bathymetric data are lacking, preventing the direct computation of discharge. In this context, a new methodology for simulating the bathymetry and estimating the discharge is proposed. The approach is based on entropy theory and can be applied using ground and satellite observations. Four parameters are needed and include channel roughness, water surface slope, channel bottom elevation, and an entropy parameter of flow depth. These parameters are estimated using a genetic algorithm by minimizing the error in the observed maximum surface velocity. Parameter uncertainty is considered through 1,000 random realizations based on different initial values. Eighteen streamflow measurements recorded from three different gauged river sites are used to benchmark the ground observations. The method included ENVISAT altimetry and Moderate Resolution Imaging Spectroradiometer (MODIS) data (2002‐2010) at one river site. Relative to ground measurements, the method provides good results with an error in channel area and discharge that, on average, does not exceed 10% on the 50th percentile. Relative to satellite measurements, channel area is well simulated with an error, on average, lower than 9%; discharge is less well simulated and represented by an error greater than 30% on the 50th percentile.

A novel algorithm for feature selection based on geographic distance metric: a case study of streamflow forecasting of Austria’s water resources

This paper focuses on input variable selection—feature selection—methods with the artificial neural network for the streamflow forecasting of large basins that have a variety of numerous stations. The feature selection methods in the current hydrology research community are not able to handle the problem in such basins. The paper proposes a novel feature selection algorithm—Bubble Selection—based on the idea of utilizing geographic distance as a metric. Evaluation of the performance of the algorithm is carried out by applying the Bubble Selection, to the case study of modeling Austria’s water resources of 540 stations in a single run mode. The aim is to select features for each station among 2412 stations, streamflow, precipitation, snow, snow depth, and water level measurements are available. The proposed algorithm allows considerably reducing the dimension of features. The Bubble Selection algorithm is further combined with the Sequential Forward Selection algorithm. Performance of the hybrid model is compared with the performance of Feature Ranking method in terms of the coefficient of determination, Nash–Sutcliffe Efficiency, and percent bias. The results show the superiority of the proposed hybrid algorithm over the Feature Ranking. The paper introduces a methodology to model a large basin and it reveals some skills that a feature selection algorithm should have.

Learning to cope with water variability through participatory monitoring: The case study of the Mountainous region, Nepal

Participatory monitoring allows communities to understand the use and management of local water resources and at the same time develop a sense of ownership of environmental information. The data generated through participatory monitoring of stream flow and rainfall generate evidences to corroborate local people’s experiences with changing water resources patterns. In this study we evaluate the potential of participatory monitoring of hydrological variables to improve scarce water supply utilization in agriculture. The case study site is the Mustang district in Nepal, which is located in the Upper Kaligandaki River Basin in the Himalayas with unique and complex geographical and climatic features. This region is characterized by a semi-arid climate with total annual precipitation of less than 300 mm. Water supply, agricultural land, and livestock grazing are the key ecosystem services that underpin livelihood security of the local population, particularly socio-economically vulnerable groups. An analysis of the measured stream flow data indicate that annual flow of water in the stream can meet the current crop irrigation water needs for the agricultural land of the research site. The data provide local farmers a new way of understanding local water needs. Participatory monitoring would contribute to an optimization of the use of ecosystem services to support economic development and livelihood improvement.

The Utility of Infrequent Snow Depth Images for Deriving Continuous Space‐Time Estimates of Seasonal Snow Water Equivalent

AbstractSnow water equivalent (SWE), particularly in mountains regions, has been an elusive hydrologic measurement. We examine the utility of a data assimilation approach to generate space‐time continuous estimates of SWE from more readily available snow depth (SD) measurements. A multitemporal lidar data set provides a unique opportunity to assimilate single SD images and verify posterior estimates against SD images at nonassimilation times. Application over three water years shows significant improvement in the posterior estimates with an average correlation between estimated and measured SD fields of 0.88 compared to 0.52 for prior estimates that do not benefit from the assimilated SD data. We also show that posterior estimates are consistent with independent in situ SWE and streamflow measurements. This work demonstrates that using high‐resolution/high‐accuracy, but infrequent, SD measurements combined with a data assimilation framework could make significant inroads toward the goal of spatially distributed SWE and snowmelt estimates at the global scale.

Estimating minimum streamflow from measurements at ungauged sites in regions with streamflow‐gauging networks

AbstractEstimation of low flows in rivers continues to be a vexing problem despite advances in statistical and process‐based hydrological models. We develop a method to estimate minimum streamflow at seasonal to annual timescales from measured streamflow based on regional similarity in the deviations of daily streamflow from minimum streamflow for a period of interest. The method is applied to 1,019 gauged sites in the Western United States for June to December 2015. The gauges were clustered into six regions with distinct timing and magnitude of low flows. A gamma distribution was fit each day to the deviations in specific discharge (daily streamflow divided by drainage area) from minimum specific discharge for gauges in each region. The Kolmogorov–Smirnov test identified days when the gamma distribution was adequate to represent the distribution of deviations in a region. The performance of the gamma distribution was evaluated at gauges by comparing daily estimates of minimum streamflow with estimates from area‐based regression relations for minimum streamflow. Each region had at least 8 days during the period when streamflow measurements would provide better estimates than the regional regression equation, but the number of such days varied by region depending on aridity and homogeneity of streamflow within the region. Synoptic streamflow measurements at ungauged sites have value for estimating minimum streamflow and improving the spatial resolution of hydrological model in regions with streamflow‐gauging networks.

Hydrological Modelling and Water Resources Assessment of Chongwe River Catchment using WEAP Model

The Chongwe River Catchment (CRC) is located in Zambia. It receives a mean annual precipitation of 889 mm. The catchment is facing growing anthropogenic and socio-economic activities leading to severe water shortages in recent years, particularly from July to October. The objective of this study was to assess the available water resources by investigating the important hydrological components and estimating the catchment water balance using the Water Evaluation and Planning (WEAP) model. The average precipitation over a 52 year period and a 34 year period of streamflow measurement data for four stations were used in the hydrological balance model. The results revealed that the catchment received an estimated mean annual precipitation of 4603.12 Mm3. It also released an estimated mean annual runoff and evapotranspiration of 321.94 Mm3 and 4063.69 Mm3, respectively. The estimated mean annual total abstractions in the catchment was 119.87 Mm3. The average annual change in the catchment storage was 120.18 Mm3. The study also determined an external inflow of 22.55 Mm3 from the Kafue River catchment. The simulated mean monthly streamflow at the outlet of the CRC was 10.32 m3/s. The estimated minimum and maximum streamflow volume of the Chongwe River was about 1.01 Mm3 in September and 79.7 Mm3 in February, respectively. The performance of the WEAP model simulation was assessed statistically using the coefficient of determination (R2 = 0.97) and the Nash–Sutcliffe model efficiency coefficient (NSE = 0.64). The R2 and NSE values indicated a satisfactory model fit and result. Meeting the water demand of the growing population and associated socio-economic development activities in the CRC is possible but requires appropriate water resource management options.

The importance of ADCP alignment with GPS in moving-boat streamflow measurements

This paper addresses the importance of the alignment of an acoustic Doppler current profiler (ADCP) with a global positioning system (GPS) in moving-boat streamflow measurements. It presents a mathematical analysis of the discharge bias induced by a misalignment angle. A small misalignment angle may cause a significant bias in a transect discharge. The bias consists of non-directional and direction components. The directional bias is proportional to the ratio between boat velocity and water velocity. In a normal condition of ADCP streamflow measurements, however, the directional bias in transect discharges can be approximately cancelled in the average discharge of reciprocal transects, even if heading-dependent errors are involved in the misalignment. This paper also presents a trial-and-error method for estimating the misalignment angle. We analyzed the transect discharge data obtained from a field measurement on the Yangtze River at the Huangling Temple hydrology station located about 5 km downstream from the Three Gorges Dam to gain insights on the effect of misalignment. Results of this case study suggest that the data for transect discharges must be processed to remove directional bias prior to the Type A evaluation of the random uncertainty of the measured discharge. Otherwise, the estimated Type A uncertainty would be false and misleading.

L-Band Passive Microwave Data from SMOS for River Gauging Observations in Tropical Climates

The Global Flood Detection Systems (GFDS) currently operated at the European Commission’s Joint Research Centre (JRC) is a satellite-based observation system that provides daily stream flow measurements of global rivers. The system was initially established using NASA Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) Ka-band passive microwave satellite data. Since its initiation in 2006, the methodology and the GFDS database have been further adapted for data acquired by the Tropical Rainfall Measuring Mission (TRMM) GOES Precipitation Index (GPI), the AMSR2 sensor onboard the Global Change Observation Mission – Water satellite (GCOM-W1), and the Global Precipitation Measurement (GPM) GPM Microwave Imager (GMI) sensor. This paper extends the same flow monitoring methodology to low frequency (L-band) passive microwave observations obtained by the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) sensor that was launched in 2009. A primary focus is tropical climate regions with dense rainforest vegetation (the Amazon, the Orinoco, and the Congo basins) where high-frequency microwave observations from GFDS reveal a significant influence of vegetation cover and atmospheric humidity. In contrast, SMOS passive microwave signatures at the much lower L-band frequency exhibit deeper penetration through the dense vegetation and minimal atmospheric effects, enabling more robust river stage retrievals in these regions. The SMOS satellite river gauging observations are for 2010–2018 and are compared to single-sensor GFDS data over several river sites. To reduce noise, different filtering techniques were tested to select the one most suitable for analysis of the L-band time series information. In-situ water level (stage) measurements from the French Observation Service SO Hybam database were used for validation to further evaluate the performance of the SMOS data series. In addition to GFDS data, water stage information from Jason-2 and Jason-3 altimetry was compared to the microwave results. Correlation of SMOS gauging time series with in-situ stage data revealed a good agreement (r = 0.8–0.94) during the analyzed period of 2010–2018. Moderate correlation was found with both high frequency GFDS data series and altimetry data series. With lower vegetation attenuation, SMOS signatures exhibited a robust linear relationship with river stage without seasonal bias from the complex hysteresis effects that appeared in the Ka-band observations, apparently due to different attenuation impacts through dense forests at different seasonal vegetation stages.

Quantifying Implications of Deposit Aging from Crude Refinery Preheat Train Data

Heat exchanger fouling has been studied for some time in the petroleum industry. As understanding of fouling dynamics and mitigation methods improves, refinery fouling mitigation strategies are changing. The implications of deposit aging in refinery units have not been addressed in detail: aging refers to where the deposit undergoes physical and chemical conversion over time. In the 2009 Heat Exchanger Fouling and Cleaning conference, Wilson et al. [Ageing: Looking back and looking forward] presented a simple framework illustrating how deposit aging impacts heat exchanger thermal and hydraulic performance. This paper presents insights into deposit aging gained from analysis of refinery monitoring data. Two case studies are presented: (i) one from the Preem refinery in Sweden where stream temperature, flow and gauge pressure measurements indicated a higher deposit thermal conductivity in exchangers located in the hotter section of the preheat train. (ii) US refinery stream temperature, flow and plant cleaning log data, showing an increased resistance to cleaning when deposits are exposed to high temperature for a prolonged period. The use of deposit aging analysis to improve exchanger operation is discussed.

Validation of satellite soil moisture in the absence of <i>in situ</i> soil moisture: the case of the Tropical Yankin Basin

Soil moisture is known to be important in hydrology, agronomy, flood and drought forecasting. Acquisition of in situ soil moisture data is time consuming, costly, and does not cover the scale required for basin analysis. The consideration of remotely-sensed soil moisture is therefore promising. However, considering the limitations of satellite data, there is a need to check their validity prior to their utilization for impact studies. This, in turn, poses a problem in the absence of in situ soil moisture. The present study suggests a methodology for testing the validity of remotely-sensed soil moisture without in situ soil moisture. Hydrological models with a detailed soil moisture routine are calibrated and validated with measured stream flows. The most behavioural solutions of modelled soil moistures are averaged, and used as proxy measurements. This methodology was applied to the Yankin Basin (8,171 km2), a tributary of the Niger River Basin. The soil moistures of three hydrological models (UHP-HRU, SWAT and WaSiM) used as proxy were compared with the daily ESA-CCI soil moisture for a four year period (2005-2008). The coefficient of determination (R2), bias and visual inspection were used as quality criteria. A rather small bias ranging from -0.01cm3/cm3 (SWAT & UHP-HRU) to -0.04cm3/cm3 (WaSiM &UHP-HRU) was determined as well as good R2 varying between 0.71 (SWAT & UHP-HRU) and 0.81 (WaSiM & SWAT & UHP-HRU). The ESA-CCI soil moisture was therefore judged as reliable for the study area. More important, this research shows that averaging soil moistures from different hydrological models provides valuable proxy measurements for testing the reliability of satellite soil moistures.

Citizen science flow – an assessment of simple streamflow measurement methods

Abstract. Wise management of water resources requires data. Nevertheless, the amount of streamflow data being collected globally continues to decline. Generating hydrologic data together with citizen scientists can help fill this growing hydrological data gap. Our aim herein was to (1) perform an initial evaluation of three simple streamflow measurement methods (i.e., float, salt dilution, and Bernoulli run-up), (2) evaluate the same three methods with citizen scientists, and (3) apply the preferred method at more sites with more people. For computing errors, we used midsection measurements from an acoustic Doppler velocimeter as reference flows. First, we (authors) performed 20 evaluation measurements in headwater catchments of the Kathmandu Valley, Nepal. Reference flows ranged from 6.4 to 240 L s−1. Absolute errors averaged 23 %, 15 %, and 37 % with average biases of 8 %, 6 %, and 26 % for float, salt dilution, and Bernoulli methods, respectively. Second, we evaluated the same three methods at 15 sites in two watersheds within the Kathmandu Valley with 10 groups of citizen scientists (three to four members each) and one “expert” group (authors). At each site, each group performed three simple methods; experts also performed SonTek FlowTracker midsection reference measurements (ranging from 4.2 to 896 L s−1). For float, salt dilution, and Bernoulli methods, absolute errors averaged 41 %, 21 %, and 43 % for experts and 63 %, 28 %, and 131 % for citizen scientists, while biases averaged 41 %, 19 %, and 40 % for experts and 52 %, 7 %, and 127 % for citizen scientists, respectively. Based on these results, we selected salt dilution as the preferred method. Finally, we performed larger-scale pilot testing in week-long pre- and post-monsoon Citizen Science Flow campaigns involving 25 and 37 citizen scientists, respectively. Observed flows (n=131 pre-monsoon; n=133 post-monsoon) were distributed among the 10 headwater catchments of the Kathmandu Valley and ranged from 0.4 to 425 L s−1 and from 1.1 to 1804 L s−1 in pre- and post-monsoon, respectively. Future work should further evaluate uncertainties of citizen science salt dilution measurements, the feasibility of their application to larger regions, and the information content of additional streamflow data.

A GIS Based Assessment of a Dynamic Watershed in Guam

This paper presents an analysis of the potential impact of existing and proposed natural and human activities on the behavior of existing watershed and suggests recommendations to effectively manage such activities to reduce the detrimental impacts on the watershed. The hydrologic behavior of the Piti-Asan watershed in Guam was analyzed through the field measurements of rainfall, stream level, stream flow, and river turbidity over the course of one year. Estimates of the erosion contribution by areas within the watershed and in particular, within 100-meter buffer zone around the major rivers were conducted based on the data collected via field instrumentation and using the geographic information system (GIS) based erosion model. Stage discharge curves were obtained for both watersheds from analyses of field hydrologic data. In addition, the areas contributing the most potential erosion and the major causes of soil erosion in the Piti-Asan watershed were identified. Finally, best management strategies of the watershed system to aid in maximizing the effectiveness and viability of future restoration efforts were suggested.

Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff Estimates

We leverage the frontiers of the Internet of Things technology in a recently developed end-to-end wireless sensor network (WSN) system that samples, collects, stores, and displays mountain hydrology measurements in near real-time. At the core of the system lies an ultra-low power, radio channel-hoping, and self-organizing mesh that allows for remote autonomous sampling of snow. Such properties, combined with a rugged weather-sealed design of the devices and multi-level data replication, provides reliable real-time data at spatial and temporal scales previously impractical to achieve in mountain environments. The system was deployed at three 1 km2 sites across the North Fork of the Feather River basin with a cluster of 12 sensor nodes for each location. Measurements show that existing operational autonomous systems are non-representative spatially, with biases that can reach up to 50%. A comparison between a wet and dry year showed that snow depths exhibit strong multi-scale inter-year spatial stationarity with major rank conservation. Temporally dense analysis using elastic net regression shows that dominant features at the sub-km2 scale are site-dependent and differ from the watershed scale. Newly introduced explanatory variables, based on the nearest neighbor with a Landsat assimilated historical product, consistently explained up to 90% of the variance in the watershed-scale SWE for both years. At two WSN sites, lagged cross-correlation of snowmelt with stream flow measurements showed a significant improvement of up to 100% compared with existing systems, suggesting that WSNs can be instrumental in improving runoff forecasting and water management.

Value of uncertain streamflow observations for hydrological modelling

Abstract. Previous studies have shown that hydrological models can be parameterised using a limited number of streamflow measurements. Citizen science projects can collect such data for otherwise ungauged catchments but an important question is whether these observations are informative given that these streamflow estimates will be uncertain. We assess the value of inaccurate streamflow estimates for calibration of a simple bucket-type runoff model for six Swiss catchments. We pretended that only a few observations were available and that these were affected by different levels of inaccuracy. The level of inaccuracy was based on a log-normal error distribution that was fitted to streamflow estimates of 136 citizens for medium-sized streams. Two additional levels of inaccuracy, for which the standard deviation of the error distribution was divided by 2 and 4, were used as well. Based on these error distributions, random errors were added to the measured hourly streamflow data. New time series with different temporal resolutions were created from these synthetic streamflow time series. These included scenarios with one observation each week or month, as well as scenarios that are more realistic for crowdsourced data that generally have an irregular distribution of data points throughout the year, or focus on a particular season. The model was then calibrated for the six catchments using the synthetic time series for a dry, an average and a wet year. The performance of the calibrated models was evaluated based on the measured hourly streamflow time series. The results indicate that streamflow estimates from untrained citizens are not informative for model calibration. However, if the errors can be reduced, the estimates are informative and useful for model calibration. As expected, the model performance increased when the number of observations used for calibration increased. The model performance was also better when the observations were more evenly distributed throughout the year. This study indicates that uncertain streamflow estimates can be useful for model calibration but that the estimates by citizen scientists need to be improved by training or more advanced data filtering before they are useful for model calibration.

Water Pollution Assessment of Selected Valuable Rivers in Efon Alaaye, Ekiti State

Pollution in relation to river water is the term used to express the level of contamination and the suitability of such river water to sustain various usage. In many developing countries, the main issue, however, is not the physical scarcity of water but poor management which results in the continuous deterioration of water quality. This study conducted an onsite measurement of seasonal stream flow and quality variation of major valuable streams use for non-potable domestic purposes in Efon-Alaaye, Ekiti state. Stream flow data was obtained using volumetric method and the quality analysis was carried out in accordance with Standard Methods for Examination of Water and Wastewater. The result shows that the average stream flow ranges from 0.120 m3/s to 1.04 m3/s. The physicochemical parameters measured fall short of the national and international standards, which indicates that the water is not fit for human consumption. It was also observed that the self-cleansing of the studied rivers is sufficient to meet the quality requirement for non-potable domestic use. It is recommended that all human activities that could lead to further pollution should be avoided around the river catchment while period public enlightenment should be carried out to sensitize the community on the best hygienic practice within the river catchment to promote good water quality. Keywords: Water quality, water pollution, stream flow, water management.

Review of studies on hydrological modelling in Malaysia

Hydrological models are vital component and essential tools for water resources and environmental planning and management. In recent times, several studies have been conducted with a view of examining the compatibility of model results with streamflow measurements. Some modelers are of the view that even the use of complex modeling techniques does not give better assessment due to soil heterogeneity and climatic changes that plays vital roles in the behavior of streamflow. In Malaysia, several public domain hydrologic models that range from physically-based models, empirical models and conceptual models are in use. These include hydrologic modeling system (HEC-HMS), soil water assessment tool (SWAT), MIKE-SHE, artificial neural network (ANN). In view of this, a study was conducted to evaluate the hydrological models used in Malaysia, determine the coverage of the hydrological models in major river basins and to identify the methodologies used (specifically model performance and evaluation). The results of the review showed that 65% of the studies conducted used physical-based models, 37% used empirical models while 6% used conceptual models. Of the 65% of physical-based modelling studies, 60% utilized HEC-HMS an open source models, 20% used SWAT (public domain model), 9% used MIKE-SHE, MIKE 11 and MIKE 22, Infoworks RS occupied 7% while TREX and IFAS occupy 2% each. Thus, indicating preference for open access models in Malaysia. In the case of empirical models, 46% from the total of empirical researches in Malaysia used ANN, 13% used Logistic Regression (LR), while Fuzzy logic, Unit Hydrograph, Auto-regressive integrated moving average (ARIMA) model and support vector machine (SVM) contributed 8% each. Whereas the remaining proportion is occupied by Numerical weather prediction (NWP), land surface model (LSM), frequency ratio (FR), decision tree (DT) and weight of evidence (WoE). Majority of the hydrological modelling studies utilized one or more statistical measure of evaluating hydrological model performance (R, R2, NSE, RMSE, MAE, etc.) except in some few cases where no specific method was stated. Of the 70 papers reviewed in this study, 16 did not specify the type of model evaluation criteria they used in evaluating their studies, 17 utilized only one method while 37 used two or more methods. NSE with 27% was found to be the most widely used method of evaluating model performance; R and RMSE came second with a percentage use 24% each. R2 (20%) was recorded as the third most widely used model evaluation criteria in Malaysia, MAE came fourth with 16% while PBIAS is the least with 11%.The findings of this work will serve as a guide to modelers in identifying the type of hydrological model they need to apply to a particular catchment for a particular problem. It will equally help water resources managers and policy makers in providing them with executive summary of hydrological studies and where more input is needed to achieve sustainable development.

PTV-Stream: A simplified particle tracking velocimetry framework for stream surface flow monitoring

Particle tracking velocimetry (PTV) is a promising image-based approach for remote streamflow measurements in natural environments. However, most PTV approaches require highly-defined round-shaped tracers, which are often difficult to observe outdoors. PTV-Stream offers a versatile alternative to cross-correlation-based PTV by affording the identification and tracking of features of any shape transiting in the field of view. This nearest-neighbor algorithm is inherently thought for estimating surface flow velocity of streams in outdoor conditions. The procedure allows for reconstructing and filtering the trajectories of features that are more likely to pertain to actual objects transiting in the field of view rather than to water reflections. The procedure is computationally efficient and is demonstrated to yield accurate measurements even in case of downsampled image sequences.

Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represent an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity typically do not reflect other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). The Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definitions based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

Utilizing non-traditional data sources for estimating channel seepage losses

Accurate streamflow data, which constitute the basic component in estimating seepage losses or gains in streamflow are in short supply in developing countries. The lack of either financial or technical resources, or both, and at times, the lack of understanding of the importance of accurate streamflow measurements in supporting modeling and water resources decision making process hinder the installation and proper maintenance of streamflow gauges. Therefore, the current study adopted the aim of demonstrating the feasibility of utilizing alternative streamflow data sources in hydrologic studies. In the current study, such sources included effluent from a large wastewater treatment plant as an inflow to the river segment under consideration while the outflow from the same river segment was taken to be the flow discharged into a dam reservoir as calculated from daily dam reservoir water balance. A water balance was established for a 48 km segment on the Zarqa River in Jordan with components covering the inputs of discharge from springs, and wastewater treatment plants and outputs such as direct evaporation, and surface withdrawals for irrigation purposes. Channel seepage was estimated as the difference between inputs and outputs of the river segment water balance. Final results demonstrated the feasibility of utilizing non-traditional data sources, whenever available, to conduct analysis that traditionally has not been possible.