Peak Streamflow Research Articles

Hydrologic impacts of climate change in relation to Ontario’s source water protection planning program

Water management activities are currently predicated on the assumption of a stationary climate, despite the reality of climate change. Hydrologic impacts of climate change for three sub-watersheds north of Toronto for 2041–70 were investigated using the precipitation-runoff modeling system to model six global climate model projections from each of representative concentration pathway (RCP) 2.6, RCP 4.5, and RCP 8.5. Annual groundwater recharge, evapotranspiration, and the 7Q20 low streamflow statistic were projected to change from 1976–2005 conditions by −2.2% to +20.5%, +0.9% to +14.4%, and −25.5% to +9.8%, respectively. Seasonal shifts included an earlier date of peak streamflow for the majority of simulations and a +14.0% to +103.9% increase in winter recharge. A steady-state MODFLOW model was employed as a preliminary assessment into the effects of climate change on source water protection outputs. The results of this research further the understanding of climate change impacts on human and ecological systems in southern Ontario.

A Framework to Quantify the Uncertainty Contribution of GCMs Over Multiple Sources in Hydrological Impacts of Climate Change

AbstractThe quantification of climate change impacts on hydrology is subjected to multiple uncertainty sources. Large ensembles of hydrological simulations based on multimodel ensembles (MMEs) have been commonly applied to represent overall uncertainty of hydrological impacts. However, as increasing numbers of global climate models (GCMs) are being developed, how many GCMs in MMEs are sufficient to characterize overall uncertainty is not clear. Therefore, this study investigates the influences of GCM quantity on quantifying overall uncertainty and uncertainty contributions of multiple sources in hydrological impacts. Large ensembles of hydrological simulations are obtained through the permutation of 3 greenhouse gas emission scenarios, 22 GCMs, 6 downscaling techniques, 5 hydrological models (HMs), and 5 sets of HM parameters, which enables to decompose uncertainty components using analysis of variance. The influences of GCM quantity are investigated by repeatedly conducting uncertainty decomposition for hydrological simulations from subsets with different numbers of GCMs. The results show that GCMs are the leading uncertainty sources in evaluating changes in annual and peak streamflows, while for changes in low flow, other uncertainty sources except HM parameters also have large contributions to overall uncertainty. Furthermore, on the condition of using no more than five GCMs, there are large possibilities that the overall uncertainty and GCMs' uncertainty contribution are underestimated. Using around 10 GCMs can ensure that the median of different combinations generates similar uncertainty components as the whole ensemble. Therefore, it is recommended to use at least 10 GCMs in studies of climate change impacts on hydrology to thoroughly quantify uncertainty.

The role of spatial and temporal model resolution in a flood event storyline approach in western Norway

We apply a physical climate storyline approach to an autumn flood event in the West Coast of Norway caused by an atmospheric river to demonstrate the value and challenges of higher spatial and temporal resolution in simulating flood impacts. We use a modelling chain whose outputs are familiar and used operationally, for example to issue flood warnings. With two different versions of a hydrological model, we show that (1) the higher spatial resolution between the global and regional climate model is necessary to realistically simulate the high spatial variability of precipitation in this mountainous region and (2) only with hourly data are we able to capture the fast flood-generating processes leading to the peak streamflow. The higher resolution regional atmospheric model captures the fact that with the passage of an atmospheric river, some valleys receive high amounts of precipitation and others not, while the coarser resolution global model shows uniform precipitation in the whole region. Translating the event into the future leads to similar results: while in some catchments, a future flood might be much larger than a present one, in others no event occurs as the atmospheric river simply does not hit that catchment. The use of an operational flood warning system for future events is expected to facilitate stakeholder engagement.

Information content of snow hydrological signatures based on streamflow, precipitation and air temperature

AbstractHydrological signatures that represent snow processes are valuable to gain insights into snow accumulation and snow melt dynamics. We investigated five snow signatures. Considering inter‐annual average of each calendar day, two slopes derived from the relation between streamflow and air temperature for different periods and streamflow peak maxima are used as signatures. In addition, two different approaches are used to compute inter‐annual average and yearly snow storage estimates. We evaluated the ability of these signatures to characterize average (a) snow melt dynamics and (b) snow storage. They were applied in 10 Critical Zone Observatory catchments of the Southern Sierra mountains (USA) characterized by a Mediterranean climate. The relevance and information content of the signatures are evaluated using snow depth and snow water equivalent measurements as well as inter‐catchment differences in elevation. The slopes quantifying the relations between streamflow and air temperature and the date of streamflow peak were found to characterize snow melt dynamics in terms of snow melt rates and snow melt affected areas. Streamflow peak dates were linked to the period of highest snow melt rates. Snow storage could be estimated both on average, considering all years, and for each year. Snow accumulation dynamics could not be characterized due to the lack of streamflow response during the snow accumulation period. The signatures were found potentially valuable to gain insights into catchment scale snow processes. In particular, when comparing catchments or observed and simulated data, they could provide insights into differences in terms of (a) snow melt rate and/or snow melt affected area over the snow melt season and (b) average or yearly snow storage. Requiring only widely available data, these hydrological signatures can be valuable for snow processes characterization, catchment comparison/classification or model development, calibration or evaluation.

Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

In this study, Regression in the Reproducing Kernel Hilbert Space (RRKHS) technique which is a non-linear regression approach formulated in the reproducing kernel Hilbert space (RRKHS) is applied for rainfall-runoff (R-R) modeling for the first time. The RRKHS approach is commonly applied when the data to be modeled is highly non-linear, and consequently, the common linear approaches fail to provide satisfactory performance. The calibration and verification processes of the RRKHS for one- and multi-day ahead forecasting R-R models were demonstrated using daily rainfall and streamflow measurement from a mountainous catchment located in the Black Sea region, Turkey. The efficacy of the new approach in each forecasting scenario was compared with those of other benchmarks, namely radial basis function artificial neural network and multivariate adaptive regression splines. The results illustrate the superiority of the RRKHS approach to its counterparts in terms of different performance indices. The range of relative peak error (PE) is found as 0.009–0.299 for the best scenario of the RRKHS model, which illustrates the high accuracy of RRKHS in peak streamflow estimation. The superior performance of the RRKHS model may be attributed to its formulation in a very high (possibly infinite) dimensional space which facilitates a more accurate regression analysis. Based on the promising results of the current study, it is expected that the proposed approach would be applied to other similar environmental modeling problems.

Simulating Groundwater-Streamflow Connections in the Upper Colorado River Basin.

In mountain, snow driven catchments, snowmelt is supposed to be the primary contribution to river streamflows during spring. In these catchments the contribution of groundwater is not well documented because of the difficulty to monitor groundwater in such complex environment with deep aquifers. In this study we use an integrated hydrologic model to conduct numerical experiments that help quantify the effect of lateral groundwater flow on total annual and peak streamflow in predevelopment conditions. Our simulations focus on the Upper Colorado River Basin (UCRB; 2.8 × 105 km2 ) a well-documented mountain catchment for which both streamflow and water table measurements are available for several important sub-basins. For the simulated water year, our results suggest an increase in peak flow of up to 57% when lateral groundwater flow processes are included-an unexpected result for flood conditions generally assumed independent of groundwater. Additionally, inclusion of lateral groundwater flow moderately improved the model match to observations. The correlation coefficient for mean annual flows improved from 0.84 for the no lateral groundwater flow simulation to 0.98 for the lateral groundwater flow one. Spatially we see more pronounced differences between lateral and no lateral groundwater flow cases in areas of the domain with steeper topography. We also found distinct differences in the magnitude and spatial distribution of streamflow changes with and without lateral groundwater flow between Upper Colorado River Sub-basins. A sensitivity test that scaled hydraulic conductivity over two orders of magnitude was conducted for the lateral groundwater flow simulations. These results show that the impact of lateral groundwater flow is as large or larger than an order of magnitude change in hydraulic conductivity. While our results focus on the UCRB, we feel that these simulations have relevance to other headwaters systems worldwide.

Precision of raw and bias-adjusted satellite precipitation estimations (TRMM, IMERG, CMORPH, and PERSIANN) over extreme flood events: case study in Langat river basin, Malaysia

Abstract Although satellite precipitation products (SPPs) increasingly provide an alternative means to ground-based observations, these estimations exhibit large systematic and random errors which may cause large uncertainties in hydrologic modeling. Three approaches of bias correction (BC), i.e. linear scaling (LS), local intensity scaling (LOCI), and power transformation (PT), were applied on four SPPs (TRMM, IMERG, CMORPH, and PERSIANN) during 2014/2015 extreme floods in Langat river basin, and the performance in terms of rainfall and streamflow were investigated. The results show that the original TRMM had a potential to predict the peak streamflow although CMORPH show the best performance in general. After performing BC, it is found that the LS-IMERG and LOCI-TRMM show the best performance at both rainfall and streamflow analysis. Generally, it is indicated that the current SPP estimations are still imperfect for any hydrological applications. Cross validation of different datasets is required to avoid the calibration effects of datasets.

The use of support vectors from support vector machines for hydrometeorologic monitoring network analyses

Hydrometeorologic monitoring networks are ubiquitous in contemporary earth-system science. Network stakeholders often inquire about the importance of sites and their locations when discussing funding and monitoring design. Support vector machines (SVMs) can be useful by their assigning each monitoring site as either a support or nonsupport vector. A potentiometric surface was created from synthetic data and 800 random observation locations (sites) as an analog to a groundwater-level network. Using generalized additive models for potentiometric surface prediction, simulations show that a subsample of support vectors from the 800 sites will out perform random samples of sample size equaling the support vector count. Support vector percentages from simulation quantify the recurrence that SVMs assign each site as a support vector, and these percentages in turn measure site importance. An example application of support vector percentages identifies important monitoring sites needed to regionalize the 0.1 annual exceedance probability peak streamflow. The results indicate that 152 of 283 streamgages with support vector percentages equalling 100 percent have not operated since about 2000 and generally have much smaller drainage areas than the greater streamgage network in Texas. The drainage area disparity is an indication of historical imbalance in peak streamflow data acquisition from various stream sizes in Texas.

Flood Size Increases Nonlinearly Across the Western United States in Response to Lower Snow‐Precipitation Ratios

AbstractMany mountainous and high‐latitude regions have experienced more precipitation as rain rather than snow due to warmer winter temperatures. Further decreases in the annual snow fraction are projected under continued global warming, with potential impacts on flood risk. Here, we quantify the size of streamflow peaks in response to both seasonal and event‐specific rain‐fraction using stream gage observations from watersheds across the western United States. Across the study watersheds, the largest rainfall‐driven streamflow peaks are >2.5 times the size of the largest snowmelt‐driven peaks. Using a panel regression analysis of individual precipitation and snowmelt events, we show that the empirical streamflow response grows approximately exponentially as the liquid precipitation input increases, with rain‐dominated runoff leading to proportionately larger streamflow increases than snowmelt or mixed rain‐and‐snow runoff. We find that the response to changes in rain percentage is largest in the wettest watersheds, where wet antecedent conditions are important for increasing runoff efficiency. Similarly, the effect of rain percentage is larger across watersheds in the Northwest and West regions compared to watersheds in the Northern Rockies and Southwest regions. Overall, as a higher percentage of precipitation falls as rain, increases in the size of rainfall‐driven and “rain‐on‐snow”‐driven floods have the potential to more than offset decreases in the size of snowmelt‐driven floods.

Impact Assessment of Urban Flood in Surat City using HEC-HMS and GIS

Flood is natural disaster which is occurring in almost every part of the country. Total of 30 years (1985-2016) hydrological data has been given as an input to the model study. In order to run HEC-HMS model for assessment of flood at Surat outlet of Tapi river basin the input such as Rainfall, soil map and land use/land cover map required are extracted from soil. The hydrology of the area is characterised by high interaction between ground water and surface water. Input files were prepared using HEC-GeoHMS and later imported to HEC-HMS along with the meteorological and hydrological data to calculate run-off at the watershed outlet. Analysis was carried out in HEC-HMS to determine peak stream flow and occurrence time. SCS (Soil Conservation service) method can be used for flood stimulation in HEC-HMS. From the values of R2 (coefficient of determination) and from stimulated Qpeak (discharge) and observed Qpeak the flood model can be derived in HEC-HMS. Satellite image of area near Tapi region is selected and with the help of software we could analyse the flood prone areas. Parameters like population Density, resources availability, Elevation of geographical area, etc. database can be collected using GIS. Software like ArcGIS can be used to get the data of flood. From the data obtained using HEC-HMS and GIS the area of surat are divided into High risk and Low risk zones. Based on this study it will be helpful in identification of low lying & flood prone areas in Surat city and also helpful in Surat urban planning near Tapi River.

Comparing calibration strategies of a conceptual snow hydrology model and their impact on model performance and parameter identifiability

Having a realistic estimation of snow cover by conceptual hydrological models continues to challenge hydrologists. The calibration of the free model parameters is an unavoidable step and the uncertainties resulting from the use of this optimal set remains a source of concern, especially in forecasting applications and climate changes impact assessments. This study seeks to improve the calibration of the conceptual hydrological model GR4J coupled with the Cemaneige snow model, in order to obtain a more realistic simulation of the snow water equivalent (SWE) and to reduce the uncertainty of the free parameters. The performance of the two models was tested over twelve snow-dominated basins in southern Quebec, Canada. Four calibration strategies were adopted and compared. In the first two strategies, the parameters were calibrated against observed streamflow alone using a local and a global algorithm. In the third and fourth strategies the calibration of snow and hydrological parameters was performed against observed streamflow and snow water equivalent (SWE) measured at snow course transects, first separately, and then with a multiobjective approach. An ensemble of equifinal parameters was used to compare the capacity of the global and multiobjective algorithms to improve the parameters identifiability and to assess the impact of parameter equifinality on the temperature sensitivity of spring peak streamflow. The large number of equifinal parameters found during calibration underscores the importance of structural non-identifiability of the coupled GR4J-Cemaneige model. The inclusion of snow observations within a multiobjective calibration improved the simulation of SWE, the identifiability of the parameters and their correlation with basins characteristics. Parameter equifinality caused a small but non negligible uncertainty in the simulated response of spring peak flow to warming temperatures. Parameter equifinality should be considered in climate impact studies in snow-dominated basins where poorly constrained snow parameters can affect the temperature sensitivity of streamflow.

Changes in event-based streamflow magnitude and timing after suburban development with infiltration-based stormwater management.

Green stormwater infrastructure implementation in urban watersheds has outpaced our understanding of practice effectiveness on streamflow response to precipitation events. Long‐term monitoring of experimental suburban watersheds in Clarksburg, Maryland, USA, provided an opportunity to examine changes in event‐based streamflow metrics in two treatment watersheds that transitioned from agriculture to suburban development with a high density of infiltration‐focused stormwater control measures (SCMs). Urban Treatment 1 has predominantly single family detached housing with 33% impervious cover and 126 SCMs. Urban Treatment 2 has a mix of single family detached and attached housing with 44% impervious cover and 219 SCMs. Differences in streamflow‐event magnitude and timing were assessed using a before‐after‐control‐reference‐impact design to compare urban treatment watersheds with a forested control and an urban control with detention‐focused SCMs. Streamflow and precipitation events were identified from 14 years of sub‐daily monitoring data with an automated approach to characterize peak streamflow, runoff yield, runoff ratio, streamflow duration, time to peak, rise rate, and precipitation depth for each event. Results indicated that streamflow magnitude and timing were altered by urbanization in the urban treatment watersheds, even with SCMs treating 100% of the impervious area. The largest hydrologic changes were observed in streamflow magnitude metrics, with greater hydrologic change in Urban Treatment 2 compared with Urban Treatment 1. Although streamflow changes were observed in both urban treatment watersheds, SCMs were able to mitigate peak flows and runoff volumes compared with the urban control. The urban control had similar impervious cover to Urban Treatment 2, but Urban Treatment 2 had more than twice the precipitation depth needed to initiate a flow response and lower median peak flow and runoff yield for events less than 20 mm. Differences in impervious cover between the Urban Treatment watersheds appeared to be a large driver of differences in streamflow response, rather than SCM density. Overall, use of infiltration‐focused SCMs implemented at a watershed‐scale did provide enhanced attenuation of peak flow and runoff volumes compared to centralized‐detention SCMs.

Change points in annual peak streamflows: Method comparisons and historical change points in the United States

Change-point, or step-trend, detection is an active area of research in statistics and an area of great interest in hydrology because change points may be evidence of natural or anthropogenic changes in climatic, hydrologic, or landscape processes. A common change-point technique is the Pettitt test; however, many change-point methods are now available and testing of methods has been limited. This study investigated eight methods for detecting change points in the location (central tendency, seven methods) and scale (dispersion or spread, one method) of annual peak streamflows, using simulated data with and without change points, and peak-streamflow series from basins with known large additions of reservoir storage. Parametric methods tested, including a Bayesian one, did not perform well, even when transforming peak streamflows to approximate normality by using logarithms. Nonparametric methods other than the Pettitt test allow for more than one change point but have an unacceptable number of false positives. Based on the results of our methods comparisons, we used the Pettitt and the Mood tests to find change points in location and scale, respectively, in thousands of streamgage records in the conterminous United States. Change points in location (median) and scale are abundant, with the changes in median peak streamflow showing regional patterns, as well as a strong increased streamflow signal around 1970. The changes in scale of peak streamflows are dominated more by temporal than spatial patterns; more streamgages had decreases in scale in earlier decades than recent decades and more streamgages had increases in scale occurring in recent decades than earlier decades.

Projected warming disrupts the synchrony of riparian seed release and snowmelt streamflow

Globally, spring phenology and abiotic processes are shifting earlier with warming. Differences in the magnitudes of these shifts may decouple the timing of plant resource requirements from resource availability. In riparian forests across the northern hemisphere, warming could decouple seed release from snowmelt peak streamflow, thus reducing moisture and safe sites for dominant tree recruitment. We combined field observations with climate, hydrology, and phenology models to simulate future change in synchrony of seed release and snowmelt peaks in the South Platte River Basin, Colorado, for three Salicaceae species that dominate western USA riparian forests. Chilling requirements for overcoming winter endodormancy were strongest in Salix exigua, moderately supported for Populus deltoides, and indiscernible in Salix amygdaloides. Ensemble mean projected warming of 3.5°C shifted snowmelt peaks 10-19d earlier relative to S.exigua and P.deltoides seed release, because decreased winter chilling combined with increased spring forcing limited change in their phenology. By contrast, warming shifted both snowmelt peaks and S.amygdaloides seed release 21d earlier, maintaining their synchrony. Decoupling of snowmelt from seed release for Salicaceae with strong chilling requirements is likely to reduce resources critical for recruitment of these foundational riparian forests, although the magnitude of future decoupling remains uncertain.

Utility of satellite-derived burn severity to study short- and long-term effects of wildfire on streamflow at the basin scale

We investigated the changes in hydrologic response in a forested catchment impacted by wildfire in Colorado U.S.A. from the storm event to the inter-annual scales. We also evaluated the utility of a remotely-sensed burn severity index to study post-fire shifts in streamflow. At the storm-scale, we evaluated hydrologic shifts through changes in the effective runoff (Q*/PTot), peak streamflow (Qpk) and response time (TR/TB) from multiple hydrographs, while at seasonal and inter-annual-scales we quantified hydrologic shifts through the runoff fraction (Q/PTot) and flow duration curves. Vegetation anomalies were monitored through comparisons of the Normalized Burn Ratio (NBR) between the burned and a hydrologically-similar, forested, neighboring, unburned catchment. We found short-term acute and long-term chronic transient streamflow shifts from the minute to the inter-annual scales. Flow duration curves indicate an order of magnitude increase in maximum flows. Event-average Q*/PTot increased by two orders of magnitude and Qpk increased by one order of magnitude relative to multiple representative pre-fire events of similar precipitation intensities. Decreases in TR/TB appear to be minimal. At the inter-annual scale, increases in the difference between simultaneous unburned and burned NBR are associated with increases in Q/PTot. A hydrologic recovery pathway is evident resembling a hysteresis effect driven by vegetation re-growth. Results illustrate the non-steady physical processes that increase flash-flooding risks post-fire in mountainous catchments and the utility of ΔNBR as a hydrologic predictor in ungauged watersheds.

Streamflow response to projected climate changes in the Northwestern Upper Indus Basin based on regional climate model (RegCM4.3) simulation

In the present research, we used bias corrected output of regional climate model (RegCM4.3) to generate streamflow for future climate change projections in the Northwestern Upper Indus Basin (NUIB) under RCP4.5 and RCP8.5 scenarios. The average annual runoff was found to be continuously increasing by the end of 21st century. The increase in precipitation as well as the streamflow for summer and winter in the NUIB indicated that RegCM4.3 simulated climate change projections were realistically transferred to HEC-HMS hydrological model. Maximum streamflow peaks, both in frequency and magnitude, were observed in July and August over three streamflow gauges (i.e. Besham, Nowshera and Khairabad) posing possible threats of flood risks during the monsoon season in Pakistan. Significant streamflow changes were projected for winter and spring seasons. The analysis of flow duration curve for three hydrometric stations indicates that the flow occurrence probability could be greater under both scenarios relative to the reference period. The increase in streamflow was projected under high (Q5) and medium (Q50) flow, which could possibly be related with the increase in monsoon precipitation events in the NUIB. Overall it is to be expected that the NUIB will confront more floods as a result of projected increase in medium and high flow, which demands better policies and management towards the water resources of the NUIB. This study is expected to contribute considerably to climate change impact assessment studies over the NUIB, Pakistan.

Projecting Wet Season Rainfall Extremes Using Regional Climate Models Ensemble and the Advanced Delta Change Model: Impact on the Streamflow Peaks in Mkurumudzi Catchment, Kenya

Each year, many African countries experience natural hazards such as floods and, because of their low adaptative capabilities, they hardly have the means to face the consequences, and therefore suffer huge economic losses. Extreme rainfall plays a key role in the occurrence of these hazards. Therefore, climate projection studies should focus more on extremes in order to provide a wider range of future scenarios of extremes which can aid policy decision making in African societies. Some researchers have attempted to analyze climate extremes through indices reflecting extremes in climate variables such as rainfall. However, it is difficult to assess impacts on streamflow based on these indices alone, as most hydrological models require daily data as inputs. Others have analyzed climate projections through general circulation models (GCMs) but have found their resolution too coarse for regional studies. Dynamic downscaling using regional climate models (RCMs) seem to address the limitation of GCMs, although RCMs might still lack accuracy due to the fact that they also contain biases that need to be eliminated. Given these limitations, the current study combined both dynamic and statistical downscaling methods to correct biases and improve the reproduction of high extremes by the models. This study’s aim was to analyze extreme high flows under the projection of extreme wet rainfall for the horizon of 2041 of a Kenyan South Coast catchment. The advanced delta change (ADC) method was applied on observed data (1982–2005), control (1982–2005) and near future (2018–2041) from an ensemble mean of multiple regional climate models (RCMs). The created future daily rainfall time series was introduced in the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) hydrological model and the generated future flow were compared to the baseline flow at the gaging station 3KD06, where the observed flow was available. The findings suggested that in the study area, the RCMs, bias corrected by the ADC method, projected an increase in rainfall wet extremes in the first rainy season of the year MAMJ (March–April–May–June) and a decrease in the second rainy season OND (October–November–December). The changes in rainfall extremes, induced a similar change pattern in streamflow extremes at the gaging station 3KD06, meaning that an increase/decrease in rainfall extremes generated an increase/decrease in the streamflow extremes. Due to lack of long-term good quality data, the researchers decided to perform a frequency analysis for up to a 50 year return period in order to assess the changes induced by the ADC method. After getting a longer data series, further analysis could be done to forecast the maximum flow to up to 1000 years, which could serve as design flow for different infrastructure.

Joint Probability of Extreme Streamflow and Its Day of Occurrence

AbstractThe magnitudes of peak streamflows and their return periods are important considerations in infrastructure design and hazard mitigation. Periods of high floods are usually associated with s...

Influence of changes in rainfall and soil moisture on trends in flooding

Despite the expectation that increases in rainfall with climatic change will result in increases in pluvial flooding, there is more historical evidence for decreases in flood magnitude. In Australia, as in many other parts of the world, flood magnitudes are mostly decreasing, despite increasing rainfall extremes. Here, we show how changes in soil moisture have led to decreasing flood magnitudes while rainfall extremes have been increasing.Using gauged streamflow, catchment average rainfall, and modelled soil moisture data across Australia we confirm that streamflow peaks (or floods) that occur at least once a year are strongly related to both the peak rainfall causing the flood and the antecedent soil moisture conditions preceding the storm event. Regions where the magnitude of the peak flow has decreased are visually and statistically correlated to regions of decreasing soil moisture. Flood magnitudes are more likely to increase only for the rarest events, with increases in rainfall offset by decreases in soil moisture for more frequent events. A recurrence interval dependent tipping point is identified beyond which trends in catchment rainfall outweigh those of soil moisture trends and dominate the flood response. For regions where soil moisture has decreased, this tipping point occurs at an average recurrence interval of ten years or more. As most studies investigating trends in flooding generally use annual maxima, changes in soil moisture are likely to be the dominant mechanism behind observed flood trends. Hence changes in soil moisture conditions need to be considered when predicting catchment flood response due to climatic change.

Flood Frequency Analysis of the Mahi Basin by Using Log Pearson Type III Probability Distribution

Flood frequency analysis is one of the techniques of examination of peak stream flow frequency and magnitude in the field of flood hydrology, flood geomorphology and hydraulic engineering. In the present study, Log Pearson Type III (LP-III) probability distribution has applied for flood series data of four sites on the Mahi River namely Mataji, Paderdi Badi, Wanakbori and Khanpur and of three sites on its tributaries such as Anas at Chakaliya, Som at Rangeli and Jakham at Dhariawad. The annual maximum series data for the record length of 26-51 years have been used for the present study. The time series plots of the data indicate that two largest ever recorded floods were observed in the year 1973 and 2006 on the Mahi River. The estimated discharges of 100 year return period range between 3676 m3/s and 47632 m3/s. The return period of the largest ever recorded flood on the Mahi River at Wankbori (40663 m3/s) is 127-yr. The recurrence interval of mean annual discharges (Qm) is between 2.73-yr and 3.95-yr, whereas, the return period of large floods (Qlf) range from 6.24-yr to 9.33-yr. The magnitude-frequency analysis curves represent the reliable estimates of the high floods. The fitted lines are fairly close to the most of the data points. Therefore, it can be reliably and conveniently used to read the recurrence intervals for a given magnitude and vice versa.