Determining hydrologic pathways of streamflow using geochemical tracers in a claypan watershed

Despite the low permeability of claypan soils, groundwater has been heavily contaminated by nitrate in agricultural watersheds dominated by claypan soils. However, it is unclear how nitrate concentrations in groundwater affect stream water quality. In this study, streamflow pathways were investigated using natural geochemical tracers in the 73-km² Goodwater Creek Experimental Watershed in northeastern Missouri. Samples were collected from 2011 to 2017 from stream water (weekly-biweekly), precipitation (event-based), groundwater in 25 wells with screened depths varying from 2 to 16 m (bimonthly–seasonal) and interflow above the claypan in 7 shallow piezometers (weekly–monthly). The results of endmember mixing analysis using major ions indicate that streamflow was dominated by near-surface runoff (59 ± 20%), followed by interflow (25 ± 16%) and groundwater (16 ± 13%). Analysis of endmember distances using the mixing space defined by stream water chemistry suggests that groundwater contributions to streamflow came primarily from the intermediate to deep glacial till aquifer near and below 8 m. Near-surface runoff was persistent and dominant even after isolated precipitation events during a prolonged dry period. It is hypothesised that the alluvial aquifer near stream banks acts as a mixing zone to receive and store various source waters, resulting in persistent delivery of runoff to the stream. Groundwater, even though its contribution was limited, plays a significant role in regulating streamflow NO₃⁻ concentrations. This study significantly improves our understanding of claypan hydrology and will lead to the development of models and decision support tools for implementation of management practices that improve groundwater and stream water quality in restrictive layer watersheds.     

Reference hydrologic networks II. Using reference hydrologic networks to assess climate-driven changes in streamflow

Abstract

Reference hydrologic networks (RHNs) can play an important role in monitoring for changes in the hydrological regime related to climate variation and change. Currently, the literature concerning hydrological response to climate variations is complex and confounded by the combinations of many methods of analysis, wide variations in hydrology, and the inclusion of data series that include changes in land use, storage regulation and water use in addition to those of climate. Three case studies that illustrate a variety of approaches to the analysis of data from RHNs are presented and used, together with a summary of studies from the literature, to develop approaches for the investigation of changes in the hydrological regime at a continental or global scale, particularly for international comparison. We present recommendations for an analysis framework and the next steps to advance such an initiative. There is a particular focus on the desirability of establishing standardized procedures and methodologies for both the creation of new national RHNs and the systematic analysis of data derived from a collection of RHNs.

Editor Z.W. Kundzewicz; Associate editor K. Hamed

Citation Burn, D. H., et al., 2012 Whitfield, P.H. 2012. Reference hydrologic networks, I. The status of national reference hydrologic networks for detecting trends and future directions. Hydrological Sciences Journal, 57(8) this issue[Taylor & Francis Online] , [Google Scholar]. Reference hydrologic networks II. Using reference hydrologic networks to assess climate-driven changes in streamflow. Hydrological Sciences Journal, 57 (8), 1580–1593.     

Detailed streamflow data for understanding hydrologic responses in the Logan River Observatory

The Logan River watershed, located in Northern Utah, USA, consists of a relatively pristine, mountainous area that drains to a lower elevation, valley area influenced by both urban development and agriculture. The Logan River Observatory has been collecting aquatic (streamflow and water quality) and climate data throughout the Logan River watershed since 2014. While streamflow measurements are commonly made at the outlets of research watersheds, the Logan River watershed consists of diverse hydrologic, topographic, and geologic settings that require a detailed understanding of streamflow variability over time at many locations. Here, we illustrate: (a) the importance of collecting streamflow time series throughout complex watersheds, and (b) how simple flow balances can provide much needed hydrologic insight into the locations and timing of gains and losses over reaches to guide future investigations.     

Evolutionary assimilation of streamflow in distributed hydrologic modeling using in-situ soil moisture data

This study has applied evolutionary algorithm to address the data assimilation problem in a distributed hydrological model. The evolutionary data assimilation (EDA) method uses multi-objective evolutionary strategy to continuously evolve ensemble of model states and parameter sets where it adaptively determines the model error and the penalty function for different assimilation time steps. The assimilation was determined by applying the penalty function to merge background information (i.e., model forecast) with perturbed observation data. The assimilation was based on updated estimates of the model state and its parameterizations, and was complemented by a continuous evolution of competitive solutions.The EDA was illustrated in an integrated assimilation approach to estimate model state using soil moisture, which in turn was incorporated into the soil and water assessment tool (SWAT) to assimilate streamflow. Soil moisture was independently assimilated to allow estimation of its model error, where the estimated model state was integrated into SWAT to determine background streamflow information before they are merged with perturbed observation data. Application of the EDA in Spencer Creek watershed in southern Ontario, Canada generates a time series of soil moisture and streamflow. Evaluation of soil moisture and streamflow assimilation results demonstrates the capability of the EDA to simultaneously estimate model state and parameterizations for real-time forecasting operations. The results show improvement in both streamflow and soil moisture estimates when compared to open-loop simulation, and a close matching between the background and the assimilation illustrates the forecasting performance of the EDA approach.     

Field test of the superconducting gravimeter as a hydrologic sensor

We report on a field test of a transportable version of a superconducting gravimeter (SG) intended for groundwater storage monitoring. The test was conducted over a 6-month period at a site adjacent to a well in the recharge zone of the karstic Edwards Aquifer, a major groundwater resource in central Texas. The purpose of the study was to assess requirements for unattended operation of the SG in a field setting and to obtain a gravimetric estimate of aquifer specific yield. The experiment confirmed successful operation of the SG, but water level changes were small (<0.3 m) leading to uncertainty in the estimate of specific yield. Barometric pressure changes were the dominant cause of both water level variations and non-tidal gravity changes. The specific yield estimate (0.26) is larger than most published values and dependent mainly on low frequency variations in residual gravity and water level time series.     

Enhanced identification of a hydrologic model using streamflow and satellite water storage data: A multicriteria sensitivity analysis and optimization approach

Hydrologic model development and calibration have continued in most cases to focus only on accurately reproducing streamflows. However, complex models, for example, the so‐called physically based models, possess large degrees of freedom that, if not constrained properly, may lead to poor model performance when used for prediction. We argue that constraining a model to represent streamflow, which is an integrated resultant of many factors across the watershed, is necessary but by no means sufficient to develop a high‐fidelity model. To address this problem, we develop a framework to utilize the Gravity Recovery and Climate Experiment's (GRACE) total water storage anomaly data as a supplement to streamflows for model calibration, in a multiobjective setting. The VARS method (Variogram Analysis of Response Surfaces) for global sensitivity analysis is used to understand the model behaviour with respect to streamflow and GRACE data, and the BORG multiobjective optimization method is applied for model calibration. Two subbasins of the Saskatchewan River Basin in Western Canada are used as a case study. Results show that the developed framework is superior to the conventional approach of calibration only to streamflows, even when multiple streamflow‐based error functions are simultaneously minimized. It is shown that a range of (possibly false) system trajectories in state variable space can lead to similar (acceptable) model responses. This observation has significant implications for land‐surface and hydrologic model development and, if not addressed properly, may undermine the credibility of the model in prediction. The framework effectively constrains the model behaviour (by constraining posterior parameter space) and results in more credible representation of hydrology across the watershed.     

Rediscovering Signal Complexity as a Teleseismic Discriminant

We re-examine the utility of teleseismic seismic complexity discriminants in a multivariate setting using United Kingdom array data. We measure a complexity discriminant taken on array beams by simply taking the logarithm of the ratio of the P-wave coda signal to that of the first arriving direct P wave (β_CF). The single station complexity discriminant shows marginal performance with shallow earthquakes having more complex signatures than those from explosions or deep earthquakes. Inclusion of secondary phases in the coda window can also degrade performance. However, performance improves markedly when two-station complexity discriminants are formed showing false alarm rates similar to those observed for network m_b − M_s. This suggests that multistation complexity discriminants may ameliorate some of the problems associated with m_b – M_s discrimination at lower magnitudes. Additionally, when complexity discriminants are combined with m_b – M_s there is a tendency for explosions, shallow earthquakes and deep earthquakes to form three distinct populations. Thus, complexity discriminants may follow a logic that is similar to m_b – M_s in terms of the separation of shallow earthquakes from nuclear explosions, although the underlying physics of the two discriminants is significantly different.     

Evaluating the functionality and streamflow impacts of explicitly modelling forest–snow interactions and canopy gaps in a distributed hydrologic model

Many plot‐scale studies have shown that snow‐cover dynamics in forest gaps are distinctly different from those in open and continuously forested areas, and forest gaps have the potential to alter the magnitude and timing of snowmelt. However, the watershed‐level impacts of canopy gap treatment on streamflows are largely unknown. Here, we present the first research that explicitly assesses the impact of canopy gaps on seasonal streamflows and particularly late‐season low flows at the watershed scale. To explicitly model forest–snow interactions in canopy gaps, we made major enhancements to a widely used distributed hydrologic model, distributed hydrology soil vegetation model, with a canopy gap component that represents physical processes of snowpack evolution in the forest gap separately from the surrounding forest on the subgrid scale (within a grid typically 10–150 m). The model predicted snow water equivalent using the enhanced distributed hydrology soil vegetation model showed good agreement (R² > 0.9) with subhourly snow water equivalent measurements collected from open, forested, and canopy gap sites in Idaho, USA. Compared with the original model that does not account for interactions between gaps and surrounding forest, the enhanced model predicted notably later melt in small‐ to medium‐size canopy gaps (the ratio of gap radius (r) to canopy height (h) ≤ 1.2), and snow melt rates exhibited great sensitivity to changing gap size in medium‐size gaps (0.5 ≤ r/h ≤ 1.2). We demonstrated the watershed‐scale implications of canopy gaps on streamflow in the snow‐dominated Chiwawa watershed, WA, USA. With 24% of the watershed drainage area (about 446 km²) converted to gaps of 60 m diameter, the mean annual 7‐day low flow was increased by 19.4% (i.e., 0.37 m³/s), and the mean monthly 7‐day low flows were increased by 13.5% (i.e., 0.26 m³/s) to 40% (i.e., 1.76 m³/s) from late summer through fall. Lastly, in practical implementation of canopy gaps with the same total gap areas, a greater number of distributed small gaps can have greater potential for longer snow retention than a smaller number of large gaps.     

Minimum relative entropy theory for streamflow forecasting with frequency as a random variable

This paper develops a minimum relative entropy theory with frequency as a random variable, called MREF henceforth, for streamflow forecasting. The MREF theory consists of three main components: (1) determination of spectral density (2) determination of parameters by cepstrum analysis, and (3) extension of autocorrelation function. MREF is robust at determining the main periodicity, and provides higher resolution spectral density. The theory is evaluated using monthly streamflow observed at 20 stations in the Mississippi River basin, where forecasted monthly streamflows show the coefficient of determination (r ²) of 0.876, which is slightly higher in the Upper Mississippi (r ² = 0.932) than in the Lower Mississippi (r ² = 0.806). Comparison of different priors shows that the prior with the background spectral density with a peak at 1/12 frequency provides satisfactory accuracy, and can be used to forecast monthly streamflow with limited information. Four different entropy theories are compared, and it is found that the minimum relative entropy theory has an advantage over maximum entropy (ME) for both spectral estimation and streamflow forecasting, if additional information as a prior is given. Besides, MREF is found to be more convenient to estimate parameters with cepstrum analysis than minimum relative entropy with spectral power as random variable (MRES), and less information is needed to assume the prior. In general, the reliability of monthly streamflow forecasting from the highest to the lowest is for MREF, MRES, configuration entropy (CE), Burg entropy (BE), and then autoregressive method (AR), respectively.     

Integrating downscaled CMIP5 data with a physically based hydrologic model to estimate potential climate change impacts on streamflow processes in a mixed‐use watershed

Climatic changes have altered surface water regimes worldwide, and climate projections suggest that such alterations will continue. To inform management decisions, climate projections must be paired with hydrologic models to develop quantitative estimates of watershed scale water regime changes. Such modeling approaches often involve downscaling climate model outputs, which are generally presented at coarse spatial scales. In this study, Coupled Model Intercomparison Project Phase 5 climate model projections were analyzed to determine models representing severe and conservative climate scenarios for the study watershed. Based on temperature and precipitation projections, output from GFDL‐ESM2G (representative concentration pathway 2.6) and MIROC‐ESM (representative concentration pathway 8.5) were selected to represent conservative (Δ_C) and severe (Δ_S) change scenarios, respectively. Climate data were used as forcing for the soil and water assessment tool to analyze the potential effects of climate change on hydrologic processes in a mixed‐use watershed in central Missouri, USA. Results showed annual streamflow decreases ranging from ?5.9% to ?26.8% and evapotranspiration (ET) increases ranging from +7.2% to +19.4%. During the mid‐21st century, sizeable decreases to summer streamflow were observed under both scenarios, along with large increases of fall, spring, and summer ET under Δ_S. During the late 21st century period, large decreases of summer streamflow under both scenarios, and large increases to spring (Δ_S), fall (Δ_S) and summer (Δ_C) ET were observed. This study demonstrated the sensitivity of a Midwestern watershed to future climatic changes utilizing projections from Coupled Model Intercomparison Project Phase 5 models and presented an approach that used multiple climate model outputs to characterize potential watershed scale climate impacts.     

Spatial assessment of hydrologic alteration across the Pearl River Delta,China, and possible underlying causes

The alterations of the water level across the Pearl River Delta (PRD) were investigated using a ‘range of variability approach’ (RVA) based on monthly water level datasets extracted from 17 gauging stations. A mapping method was used to illustrate the spatial patterns in the degrees of alteration of water levels. The results indicated that more stations showing moderate and high alterations in monthly mean maximum and minimum water levels when compared with monthly maximum and minimum water levels. River channels characterized by higher alterations of water levels were observed mainly in the regions north of 22° 30′N. Alterations of water levels across the PRD were a consequence of various influencing factors. However, changed hypsography due to extensive and intensive human activities, particularly the large‐scale dredging and excavation of the river sand, may be taken as one of the major causes for the substantial hydrologic alteration. This study indicated that the river channels characterized by altered water levels are mostly those characterized by highly and moderately intensive sand dredging. The changed ratio of the streamflow between Makou and Sanshui stations, the major upstream flow control stations, also influenced the water level alterations of the Pearl River delta. The results of this study will be of great significance in water resources management and better human mitigation of the natural hazards due to the altered water level under the changing environment. Copyright © 2009 John Wiley & Sons, Ltd.     

Quantitative assessment of the contribution of climate variability and human activity to streamflow alteration in Dongting Lake,China

Climate variability and human activity were regarded as two contributors to streamflow alteration. However, the contributions of the two factors were still unclear in Dongting Lake. Therefore, it was crucial to quantify the relative impact of climate variability and human activity on streamflow alteration. The time series (1961–2010) was divided into three periods, namely, natural period (1961–1980), change period I (1981–2002) and change period II (2003–2010). Sensitivity analysis based on Budyko‐type equations was applied to reveal the contributions of climate variability and human activity in those two change periods, respectively. The results showed that during the change period I, climate variability was the main factor responsible for streamflow alteration in most parts of Dongting Lake, accounting for 60.07–67.27%. However, the impact of climate variability was slightly smaller than that of human activity in West Dongting Lake (the former accounting for 43.20% while the latter accounting for 56.80%). For the change period II, human activity was the dominate factor for streamflow alteration, accounting for 58.89–78.33%. The impact of climate variability gradually decreased while the impact of human activity gradually increased. Along with the intensification of the human activity, the impact of it became more dominant. The results could provide a reference for water resources planning and management decisions. Under the condition of uncontrollable climatic factor, effective measures should be put forward in controlling human activity, such as reservoir/dam operation, closed management of protected area and so on. Besides, it is essential to study the impact of climate variability on future water resources and water resource management under different climate change scenarios. Copyright © 2016 John Wiley & Sons, Ltd.     

The effects of dam construction and precipitation variability on hydrologic alteration in the Lancang River Basin of southwest China

This paper presents an evaluation of the spatio-temporal patterns of hydrologic alteration induced by dam construction and precipitation variability in the Lancang River Basin of southwest China from 1957 to 2000. Analyses were conducted using the linear regression method, the Mann–Kendall test, and the Range of Variability Approach. The results indicate that there was considerable variation in the average monthly precipitation between the pre- and post-dam periods in the Lancang River Basin. Second, the magnitude of monthly runoff was strongly related to precipitation, which showed an up-down annual variation, and was significantly altered by dam construction and precipitation variability. In the modified series (hydrologic series with the precipitation impacts removed), runoff deviations between the pre- and post-dam periods became larger. Third, the extreme runoff cycles were influenced by dam construction and precipitation variability downstream from the dam, and the monthly maximum runoff increased from the pre-dam to post-dam period at all hydrologic stations. Fourth, the degree of hydrologic alteration (DHA) indicates that the precipitation variability not only affected the hydrologic regime of unregulated river reach but also modified the negative impacts of dam construction, which could provide a modest mitigation of the hydrologic alterations induced by dam construction, possibly decreasing the level of DHA. Last, the overall degree of hydrologic alteration in the observed series reached 25.2, 25.3, and 29.1 % for the upstream, midstream, and downstream areas, respectively. These results show that the hydrologic regimes of the Lancang River during the 1957–2000 period were affected by damming and precipitation variability, but the hydrologic alteration was relatively low in the upstream areas of the river without a dam.     

Changes in hydrology and streamflow as predicted by a modelling experiment forced with climate models

Changes in precipitation and temperature have direct effects on crop water use, water stress, crop yield, evapotranspiration, water nutrient dynamics and other indicators. This study, built on a modelling framework with the Soil and Watershed Assessment Tool (SWAT) model for the Raccoon River Watershed in central Iowa, a typical US Midwestern agricultural watershed, examines the watershed response to changes in meteorological inputs from an ensemble of ten global climate models under the A1B scenario. Changes in climate were directly applied to observations (the delta change method) assuming that the estimates of climate change are reliable even if the simulated current climate may be biased. The ensemble average for the mid‐century (1946–1965) predicted 0.7% increase in daily precipitation (monthly variation from ?11.3% to +19.5%) and 2.78 °C increase in average temperature over the entire watershed. These predictions were translated through a well‐calibrated SWAT modelling setup into 22% decrease in snowfall, 16% decrease in surface runoff, 18% decrease in baseflow, 8% increase in evapotranspiration and 17% decrease in total water yield. The spatial impact at the subwatershed level revealed a wide variation (but no defined trend) with decrease in water yield that ranged from 10% to 23%. Flow near the watershed outlet (Van Meter, Iowa) is expected to decline by 17% on an average annual basis with the highest impact occurring during summer months with a maximum 39% reduction in August. Changes in climate were found to have a clear and significant impact signal of decreasing streamflow at the watershed outlet with far‐reaching implication for drinking water supplies for the central Iowa communities. Copyright © 2013 John Wiley & Sons, Ltd.     

A spatial assessment of hydrologic alteration caused by dam construction in the middle and lower Yellow River,China

The ‘range of variability approach’ (RVA) and mapping technique are used to investigate the spatial variability of hydrologic alterations (HA) due to dam construction along the middle and lower Yellow River, China, over the past five decades. The impacts of climate variability on hydrological process have been removed during wet and dry periods and the focus is on the impacts of human activities, such as dam construction, on hydrological processes. Results indicate the following: (1) The impacts of the Sanmenxia reservoir on the hydrologic alteration are relatively slight with a mean HA value of 0·48, ranking in the last place among the four large reservoirs. (2) Xiaolangdi reservoir has significantly changed the natural flow regime downstream with mean HA value of 0·56, ranking it in first place among the large reservoirs. (3) The results of ranked median degrees of 33 hydrologic alteration indicators for 10 stations in the Yellow River show that the hydrologic alteration of Huayuankou ranks the highest among 10 stream gauges. (4) Impacts of reservoirs on hydrological processes downstream of the dams are closely associated with the regulating activities of the reservoirs. At the same time, alterations of streamflow regimes resulting from climatic changes (e.g. precipitation variability) make the situation more complicated and more hydrological observations will be necessary for further analysis. The results of the current study will be greatly beneficial to the regional water resources management and restoration of eco‐environmental systems in the middle and lower Yellow River characterized by intensified dam construction under a changing environment. Copyright © 2008 John Wiley & Sons, Ltd.     

Chloride as a signature indicator of soil textural and hydrologic stratigraphies in variable charge deep profiles

Soil properties that influence water movement through profiles are important for determining flow paths, reactions between soil and solute, and the ultimate destination of solutes. This is particularly important in high rainfall environments. For highly weathered deep profiles, we hypothesize that abrupt changes in the distribution of the quotient [QT = (silt + sand)/clay] reflect the boundaries between textural units or textural (TS) and hydrologic (HS) stratigraphies. As a result, QT can be used as a parameter to characterize TS and as a surrogate for HS. Secondly, we propose that if chloride distributions were correlated with QT, under non‐limiting anion exchange, then chloride distributions can be used as a signature indicator of TS and HS. Soil cores to a depth of 12·5 m were taken from 16 locations in the wet tropical Johnstone River catchment of northeast Queensland, Australia. The cores belong to nine variable charge soil types and were under sugarcane (Saccharum officinarum‐S) production, which included the use of potassium chloride, for several decades. The cores were segmented at 1 m depth increments and subsamples were analysed for chloride, pH, soil water content (θ), clay, silt and sand contents. Selected bores were capped to serve as piezometers to monitor groundwater dynamics. Depth incremented QT, θ and chloride correlated, each individually, significantly with the corresponding profile depth increments, indicating the presence of textural, hydrologic and chloride gradients in profiles. However, rapid increases in QT down the profile indicated abrupt changes in TS, suggesting that QT can be used as a parameter to characterize TS and as a surrogate for HS. Abrupt changes in chloride distributions were similar to QT, suggesting that chloride distributions can be used as a signature indicator of QT (TS) and HS. Groundwater data indicated that chloride distributions depended, at least partially, on groundwater dynamics, providing further support to our hypothesis that chloride distribution can be used as a signature indicator of HS. Copyright © 2005 John Wiley & Sons, Ltd.     

Variation in hydrologic connectivity as a result of microtopography explained by discharge to catchment size relationship

In hydrological terms, raised bogs are often approximated by simple models as in the acrotelm–catotelm concept. However, raised bogs are often characterized by a pronounced surface topography, causing large changes in connectivity of contributing areas on the bog. In this study, daily regression of measured discharges versus catchment areas is used to quantify the impact of surface topography on catchment connectivity within a raised bog. The resulting coefficient of determination shows the strength of the relationship between the discharge and catchment area over time under different hydrological conditions. Monitoring of discharge, water table, transmissivity, and basic weather data on a raised bog (1.9 km²) in eastern central Estonia took place from May 2008 to June 2010. Contributing areas, calculated based on the outlet's discharge volume (V _Q ) divided by the net precipitation volume ( ), of the outlet containing the central pool‐ridge system varied between 1×10^?3 and 0.7 km², suggesting significant differences in connectivity between hydrological events. Correlation between discharge and theoretical catchment size was high (R ²>0.75) when the water table was close to the surface (less than 5 cm below peat surface), and consequently, transmissivities were also high (up to 1,030m²d^?1), which led to connectivity of local storage elements, such as pools and hollows. However, a water table below this threshold resulted in large parts of the catchment being disconnected. The importance of water table depths on catchment connectivity suggests the need to reconsider the hydrological concept of raised bogs; to incorporate these shallow flow components and better understand residence time and consequently transport of solutes, such as DOC, from patterned peatlands.     

Assessment of the impact of climate change on the flow regime of the Han River basin using indicators of hydrologic alteration

Most natural disasters are caused by water‐/climate‐related hazards, such as floods, droughts, typhoons, and landslides. In the last few years, great attention has been paid to climate change, and especially the impact of climate change on water resources and the natural disasters that have been an important issue in many countries. As climate change increases the frequency and intensity of extreme rainfall, the number of water‐related disasters is expected to rise. In this regard, this study intends to analyse the changes in extreme weather events and the associated flow regime in both the past and the future. Given trend analysis, spatially coherent and statistically significant changes in the extreme events of temperature and rainfall were identified. A weather generator based on the non‐stationary Markov chain model was applied to produce a daily climate change scenario for the Han River basin for a period of 2001–2090. The weather generator mainly utilizes the climate change SRES A2 scenario driven by input from the regional climate model. Following this, the SLURP model, which is a semi‐distributed hydrological model, was applied to produce a long‐term daily runoff ensemble series. Finally, the indicator of hydrologic alteration was applied to carry out a quantitative analysis and assessment of the impact of climate change on runoff, the river flow regime, and the aquatic ecosystem. It was found that the runoff is expected to decrease in May and July, while no significant changes occur in June. In comparison with historical evidence, the runoff is expected to increase from August to April. A remarkable increase, which is about 40%, in runoff was identified in September. The amount of the minimum discharge over various durations tended to increase when compared to the present hydrological condition. A detailed comparison for discharge and its associated characteristics was discussed. Copyright © 2010 John Wiley & Sons, Ltd.     

Impulse response of the kinematic diffusion model as a probability distribution of hydrologic samples with zero values

It is hypothesized that the unit impulse response of a linearized kinematic diffusion (KD) model is a probability distribution suitable for frequency analysis of hydrologic samples with zero values. Such samples may include data on monthly precipitation in dry seasons, annual low flow, and annual maximum peak discharge observed in arid and semiarid regions. The hypothesized probability distribution has two parameters, which are estimated using the methods of moments (MOM) and maximum likelihood (MLM). Also estimated are errors in quantiles for MOM and MLM. The distribution shows an equivalency of MOM and MLM with respect to the mean value—an important property for ML-estimation in the case of the unknown true distribution function. The hypothesized KD distribution is tested on 44 discharge data series and compared with the Muskingum-like (M-like) probability distribution function. A comparison of empirical distribution with KD and M-like distributions shows that MOM better reproduces the upper tail of the distribution, while MLM is more robust for higher sample values and more conditioned on the value of the probability of the zero value event. The KD-model is suitable for frequency analysis of short samples with zero values and it is more universal than the M-like model as its modal value cannot be only equaled to zero value but also to any positive value.