Climate change impacts on water demand and availability using CMIP5 models in the Jaguaribe basin,semi-arid Brazil

The objective of this study was to analyze climate change impacts on irrigation water demand and availability in the Jaguaribe River basin, Brazil. For northeastern Brazil, five global circulation models were selected using a rainfall seasonal evaluation screening technique from the Intergovernmental Panel on Climate Change named Coupled Model Intercomparison Project Phase 5. The climate variables were generated for the base period of 1971–2000, as were projections for the 2025–2055 future time slice. Removal of maximum and minimum temperature and rainfall output bias was used to estimate reference evapotranspiration, irrigation water needs, and river flow using the rainfall—river flow hydrological model Soil Moisture Accounting Procedure for the baseline and future climate (Representative Concentration Pathways 4.5 and 8.5 scenarios). In addition, by applying improved irrigation efficiency, a scenario was evaluated in comparison with field observed performance. The water-deficit index was used as a water availability performance indicator. Future climate projections by all five models resulted in increases in future reference evapotranspiration (2.3–6.3%) and irrigation water needs (2.8–16.7%) for all scenarios. Regarding rainfall projections, both positive (4.8–12.5%) and negative (??2.3 to ??15.2%) signals were observed. Most models and scenarios project that annual river flow will decrease. Lower future water availability was detected by the less positive water-deficit index. Improved irrigation efficiency is a key measure for the adaptation to higher future levels of water demand, as climate change impacts could be compensated by gains in irrigation efficiency (water demand changes varying from ??1.7 to ??35.2%).     

Short-term forecasting of saltwater occurrence at La Comté River (French Guiana) using a kernel-based support vector machine

Saltwater intrusion into rivers is a major concern for freshwater exploitation and management in French Guiana (South America). To detect and analyse saltwater occurrence, a permanent station was installed on La Comté River to measure the electrical conductivity C. The objective of the present study was twofold. First, the temporal link between C, sea water level SWL and river discharge Q was explored during the dry seasons from 2009 to 2012 (total measurement duration of ~6 months). A lag of 3 h between C and SWL was evidenced (i.e. the C peaks are delayed by 3 h with high water conditions), as well as the co-occurrence of sea water intrusion with the low Q period. Second, a data-driven approach was set up through a kernel-based support vector machine SVM technique to forecast two events: (1) the forthcoming maximum value of C (for the next 3 h) exceeds 500 µS/cm; (2) C exceeds 500 µS/cm during more than 2 h. One potential drawback of such a data-driven approach is to fail to predict outside the range of calibration: this issue was thoroughly explored by means of an intensive bootstrap-based test exercise. It was showed that SVM has very high degree of predictive capability with accuracy and area under receiver operator curve above 90% in average. We additionally analyse the practical implementation of the SVM model with comparison to alternative popular classification techniques (logistic regression, random forest, linear and quadratic discriminant analysis): the SVM strength is to provide the nonlinear decision boundary without making a priori restrictive assumptions on its shape (like linear or quadratic methods) and without being too sensitive to noisy observations/outliers. Yet this strength can turn to be a weakness unless a careful examination of the shape is done from a physical perspective.     

Satellite-based rainfall estimation and discharge measurement of Middle Indus River,Pakistan

The impacts of floods and droughts are intensified by climate change, lack of preparedness, and coordination. The average rainfall in study area is ranging from 200 to 400 mm per year. Rain gauge generally provides very accurate measurement of point rain rates and the amounts of rainfall but due to scarcity of the gauge locations provides very general information of the area on regional scale. Recognizing these practical limitations, it is essential to use remote sensing techniques for measuring the quantity of rainfall in the Middle Indus. In this research, Tropical Rainfall Measuring Mission (TRMM) estimation can be used as a proxy for the magnitude of rainfall estimates from classical methods (rain gauge), quantity, and its spatial distribution for Middle Indus river basin. In order to use TRMM satellite data for discharge measurement, its accuracy is determined by statistically comparing it with in situ gauged data on daily and monthly bases. The daily R ² value (0.42) is significantly lower than monthly R ² value (0.82), probably due to the time of summation of TRMM 3-hourly precipitation data into daily estimates. Daily TRMM data from 2003 to 2012 was used as input forcing in Soil and Water Assessment Tool (SWAT) hydrological model along with other input parameters. The calibration and validation results of SWAT model give R ² = 0.72 and 0.73 and Nash-Sutcliffe coefficient of efficiency = 0.69 and 0.65, respectively. Daily and monthly comparison graphs are generated on the basis of model discharge output and observed data.     

Linear regression models for estimating true subsurface resistivity from apparent resistivity data

Simple linear regression (SLR) models for rapid estimation of true subsurface resistivity from apparent resistivity measurements are developed and assessed in this study. The objective is to minimize the processing time and computer memory required to carry out inversion with conventional algorithms. The arrays considered are Wenner, Wenner–Schlumberger and dipole–dipole. The parameters investigated are apparent resistivity (\(\rho _a \)) and true resistivity (\(\rho _t\)) as independent and dependent variables, respectively. For the fact that subsurface resistivity is nonlinear, the datasets were first transformed into logarithmic scale to satisfy the basic regression assumptions. Three models, one each for the three array types, are thus developed based on simple linear relationships between the dependent and independent variables. The generated SLR coefficients were used to estimate \(\rho _t\) for different \(\rho _a\) datasets for validation. Accuracy of the models was assessed using coefficient of determination (\(R^{2})\), F-test, standard error (SE) and weighted mean absolute percentage error (wMAPE). The model calibration \(R^{2}\) and F-value are obtained as 0.75 and 2286, 0.63 and 1097, and 0.47 and 446 for the Wenner, Wenner–Schlumberger and dipole–dipole array models, respectively. The SE for calibration and validation are obtained as 0.12 and 0.13, 0.16 and 0.25, and 0.21 and 0.24 for the Wenner, Wenner–Schlumberger and dipole–dipole array models, respectively. Similarly, the wMAPE for calibration and validation are estimated as 3.27 and 3.49%, 3.88 and 5.72%, and 5.35 and 6.07% for the three array models, respectively. When compared with standard constraint least-squares (SCLS) inversion and Incomplete Gauss–Newton (IGN) algorithms, the SLR models were found to reduce about 80–96.5% of the processing time and memory space required to carry out the inversion with the SCLS algorithm. It is concluded that the SLR models can rapidly estimate \(\rho _t\) for the various arrays accurately.     

Indonesian rainfall variability during Western North Pacific and Australian monsoon phase related to convectively coupled equatorial waves

The Indonesian archipelago which has over 15,000 islands, lies in the tropics between Asia and Australia. This eventually alters the rainfall variability over the region, which was influenced by the Asian-Australian monsoon and controlled by intraseasonal variabilities such as convectively coupled equatorial waves (CCEW), i.e., Kelvin, n?=?1 equatorial Rossby (ER), mixed Rossby gravity (MRG), and n?=?1 Westward inertio gravity (WIG), including the Madden–Julian Oscillation (MJO). This study examines a 15-year 3B42 data for trapping CCEW and MJO in the region of Indonesia during both active and extreme Western North Pacific (WNP) and Australian (AU) monsoon phases, which are then compared with 30-year rainfall anomalies among 38 synoptic stations over Indonesia. The space–time spectral analysis is employed to filter each wave including the MJO in the equator, then proceeding with the empirical orthogonal function (EOF) method to seek each wave peak which then coincides with WNP and AU monsoon peaks over Indonesia. It is concluded that an extreme monsoon classification has proven to control rainfall activity related to the CCEW and MJO at 60.66% during December through February (DJF)-WNP for only the significant wave perturbation value. Meanwhile, the CCEW and MJO significantly increase/decrease precipitation at Day 0 for about 37.88% from the total of Day 1st to Day end. Although the contribution of the CCEW and MJO does not profoundly influence rainfall activity during monsoon phase over Indonesia, they still modulate weather condition for more than 50%. On the other hand, a complex topography with a number of land–sea complexities is capable of influencing the rainfall variability in the region as a negative relationship is associated with the CCEW and MJO either during DJF-WNP or July through August (JAS)-AU monsoon phase.     

Use of SWAT to determine the effects of climate and land use changes on streamflow and sediment concentration in the Purna River basin,India

A semi-distributed, physically based, basin-scale Soil and Water Assessment Tool (SWAT) model was developed to determine the key factors that influence streamflow and sediment concentration in Purna river basin in India and to determine the potential impacts of future climate and land use changes on these factors. A SWAT domain with a Geographical Information System (GIS) was utilized for simulating and determining monthly streamflow and sediment concentration for the period 1980–2005 with a calibration period of 1980–1994 and validation period of 1995 to 2005. Additionally, a sequential uncertainty fitting (SUFI-2) method within SWAT-CUP was used for calibration and validation purpose. The overall performance of the SWAT model was assessed using the coefficient of determination (R²) and Nash–Sutcliffe efficiency parameter (E_NS) for both calibration and validation. For the calibration period, the R² and E_NS values were determined to be 0.91 and 0.91, respectively. For the validation period, the R² and E_NS were determined to be 0.83 and 0.82, respectively. The model performed equally well with observed sediment data in the basin, with the R² and E_NS determined to be 0.80 and 0.75 for the calibration period and 0.75 and 0.65 for the validation, respectively. The projected precipitation and temperature show an increasing trend compared to the baseline condition. The study indicates that SWAT is capable of simulating long-term hydrological processes in the Purna river basin.     

Comparison of logistic regressions and snowfall intensity–duration threshold as forecasting tools for direct-action snow avalanches in the Presidential Range,New Hampshire,USA

Built environment, which includes some major investments in Oman, has been designed based on historical data and do not incorporate the climate change effects. This study estimates potential variations of the hourly annual maximum rainfall (AMR) in the future in Salalah, Oman. Of the five climate models, two were selected based on their ability to simulate local rainfall characteristics. A two-stage downscaling–disaggregation approach was applied. In the first stage, daily rainfall projections in 2040–2059 and 2080–2099 periods from MRI-CGCM3 and CNRM-CM5 models based on two Representative Concentration Pathways (RCP8.5 and RCP4.5) were downscaled to the local daily scale using a stochastic downscaling software (LARS-WG5.5). In the second stage, the stochastically downscaled daily rainfall time series were disaggregated using K-nearest neighbour technique into hourly series. The AMRs, extracted from 20 years of projections for four scenarios and two future periods were then fitted with the generalized extreme value distribution to obtain the rainfall intensity–frequency relationship. These results were compared with a similar relationship developed for the AMRs in baseline period. The results show that the reduction in number of wet days and increases in total rainfall will collectively intensify the future rainfall regime. A marked difference between future and historical intensity–frequency relationships was found with greater changes estimated for higher return periods. Furthermore, intensification of rainfall regime was projected to be stronger towards the end of the twenty-first century.     

SHIA_Landslide: a distributed conceptual and physically based model to forecast the temporal and spatial occurrence of shallow landslides triggered by rainfall in tropical and mountainous basins

Landslides are a main cause of human and economic losses worldwide. For this reason, landslide hazard assessment and the capacity to predict this phenomenon have been topics of great interest within the scientific community for the implementation of early warning systems. Although several models have been proposed to forecast shallow landslides triggered by rainfall, few models have incorporated geotechnical factors into a complete hydrological model of a basin that can simulate the storage and movement of rainwater through the soil profile. These basin and full hydrological models have adopted a physically based approach. This paper develops a conceptual and physically based model called open and distributed hydrological simulation and landslides—SHIA_Landslide (Simulación HIdrológica Abierta, or SHIA, in Spanish)—that is supported by geotechnical and hydrological features occurring on a basin-wide scale in tropical and mountainous terrains. SHIA_Landslide is an original and significant contribution that offers a new perspective with which to analyse shallow landslide processes by incorporating a comprehensive distributed hydrological tank model that includes water storage in the soil coupled with a classical analysis of infinite slope stability under saturated conditions. SHIA_Landslide can be distinguished by the following: (i) its capacity to capture surface topography and effects concerning the subsurface flow; (ii) its use of digital terrain model (DTM) to establish the relationships among cells, geomorphological parameters, slope angle, direction, etc.; (iii) its continuous simulation of rainfall data over long periods and event simulations of specific storms; (iv) its consideration of the effects of horizontal and vertical flow; and (vi) its inclusion of a hydrologically complete water process that allows for hydrological calibration. SHIA_Landslide can be combined with real-time rainfall data and implemented in early warning systems.     

Impact of river network type on the time of concentration

Time of concentration (T _c) is one of the frequently used parameters to characterize the response of a drainage basin to a rainfall event. Conceptually, it is the time runoff travels from the hydraulically most distant location in a basin to its outlet. T _c was found to vary depending on river basin characteristics such as slope, soil infiltration, and flow path. In this study, we investigate if the drainage network type information can be used as an input to hydrological models, by estimating the time of concentration separately for different network types. Sixty-eight basins which have areas ranging from 24 to 965 km² in arid and non-arid regions of the USA are compared and the effect of climate is also analyzed. It is found that the slope of the linear relationship between T _c and the maximum hydraulic length of flow path shows different correlation coefficients ranging from 0.80 to 0.98 for different network types. It is observed that the slope of the regression line between T _c and the maximum hydraulic length of flow path is the lowest for dendritic networks (slope of 0.26), while pinnate networks have the steepest slope of the regression line (slope of 0.59). This indicates that the drainage network type has a direct impact on the hydrological behavior of the basin and can represent a direct input in hydrological modeling.     

Impact of rainfall intensity on the hydrological performance of erosion control geotextiles

The impact of erosion control geotextiles on the surface runoff from slopes is quite variable and depends strongly on site-specific conditions (soil characteristics, slope morphology, climate, etc.), as has been shown in several earlier studies. In addition, little is known about the proportion of runoff reduction that is caused by the geotextile and the proportion that is caused by soil characteristics. To shed more light on this issue, an experiment was carried out to test the impact of 500 g m^?2 jute nets (J500) and 400 g m^?2; 700 g m^?2 coir nets (C400, C700) on the surface runoff from simulated rainfall of four different intensities (I ₁ = 18.7; I ₂ = 27.2; I ₃ = 53.6; I ₄ = 90.5 mm h^?1). Data on runoff volume, peak discharge and time to peak discharge were collected from 40 simulated rainfall events. An impermeable “no-soil” subgrade was used to examine the impact of the geotextile on runoff without any influence of soil. All tested geotextiles significantly reduced runoff (volume, peak discharge) at all rainfall intensities, with the exception of C400 and C700 during simulated rainfall intensity I ₄. J500 seemed to have the most effective runoff reduction performance at all rainfall intensities. In general, as the rainfall intensity increased, the effectiveness of the geotextiles decreased. Interesting behaviour was observed for J500 under simulated rainfall intensity I ₄—the effectiveness of the geotextile increased with the duration of the rainfall.     

Livestock productivity as indicator of vulnerability to climate hazards: a Mongolian case study

Mongolia is subject to regular peaks of livestock winter mortality called dzuds. Several kinds of dzud exist and the ‘white dzud’, characterized by heavy stochastic snowfalls preventing livestock to access forage, is considered the most common. Droughts and high livestock densities are thought to be part of the dzud process by affecting body condition, which increases vulnerability to snowfalls. Guided by the equilibrium/nonequilibrium framework, we studied how rainfall, animal numbers and pasture health (defined as the integrity of ecological processes sustaining grass growth) impact livestock body condition in a case study of West Mongolia. We studied this parameter through livestock productivity (LP) as a proxy, defined as the annual number of newborns per breeding-age female. We found no significant impact of rainfall or livestock numbers, alone or combined. We found through the study of pasture use, defined as the ratio forage consumed/forage available, an impact of the combined effect of rainfall, animal numbers and pasture health. We observed in addition sharp LP decreases prior to dzuds, which suggests that the above-mentioned drivers interact to weaken livestock which increases its vulnerability to winter hazards. This tends to show that in our case study, dzuds are not the simple consequence of stochastic hazards striking randomly, but instead, the final stage of a chain of events that involves dry years, high livestock densities and pasture degradation. This also indicates that dzud early warning indicators could be designed based on LP monitoring.     

Contributions of multiple climate hazards and overgrazing to the 2009/2010 winter disaster in Mongolia

Mongolian pastoral husbandry is subject to various climate hazards such as dzud (Mongolian for “severe winter conditions”). Dzud in the 2009/2010 winter affected 80.9% of the country and killed more than 10 million livestock (23.4% of the total). To understand the natural and man-made mechanisms of this dzud, we examined the contributions of dzud-causing factors such as climate hazards (cold temperatures and heavy snow) and winter–spring livestock grazing (measured as overgrazing rate), which created a distinct regional pattern of high livestock mortality using a regression tree method. The regression tree model accounted for 58% of the total spatial variation of the mortality and identified various types of dzud in each region. Results showed that during the 2009/2010 winter, almost all of Mongolia experienced extreme cold temperatures, with abnormally large amounts of snow. In addition, more than half of the territory was overgrazed because of the lower pasture biomass resulting from summer drought and livestock overpopulation. At the regional scale, high livestock mortalities occurred in moderately to heavily overgrazed regions in south-central and western Mongolia, resulting from the combination of these factors. Conversely, areas with lower livestock mortalities (or non-dzud) coincided with sufficient pasture capacity in the north and east, even under extreme cold and snow. This indicates the importance of controlling the number of livestock to below the pasture carrying capacity regardless of an inter-annually varying climate. Moreover, we identified critical thresholds of each factor across which serious disasters occurred. These thresholds are practically useful for future livestock management of pasture land.     

Data-driven nonlinear modeling studies on removal of Acid Yellow 59 using Si-doped multi-walled carbon nanotubes

Data-driven modeling of removal of color index name of Acid Yellow 59 from aqueous solutions using multi-walled carbon nanotubes by multiple (non)linear regression and artificial neural networks (ANN) models based on leave-one-out cross-validation to predict the adsorbed dye amount per unit mass of adsorbent (mg g^?1) and performance evaluation of the proposed multiple (non)linear regression and ANN models is the main novel contributor of the present study. Initial dye concentration, adsorbent concentration, reaction time, and temperature were determined as explanatory variables and input neurons for multiple (non)linear regression and ANN models, respectively. The total number of experiments was determined as 1280 statistically. The results showed that multilayer perception ANN model (\(R^{2}_{\text{training}}\) = 0.9997, \(R^{2}_{\text{testing}}\) = 0.9993, RMSE = 0.7678, MAE of 0.0007) predicted q _t better than multiple (non)linear regression model (\(R^{2}_{\text{adj}}\) = 0.9645, \(R^{2}_{\text{pred}}\) = 0.9633, SE = 9.55) and MLR (R ² = 0.9543, SE = 10.87) models. The results justified the accuracy of ANN in prediction of q _t , significantly.     

Effect of Soil–Water Characteristic Parameters on Saturation Line and Stability of Slope

Rainfall infiltration is the main factor that causes slope instability. To study the effect of hydraulic parameters on the final saturation line and stability of slopes, a numerical slope model is established with a saturated–unsaturated seepage analysis method. Analysis results show the following, (1) When parameter a increases, the effective rainfall duration decreases linearly, and the ultimate safety factor increases gradually; when parameter m increases, the effective rainfall duration increases linearly, and the ultimate safety factor decreases linearly; when parameter n increases, both the effective rainfall duration and the ultimate safety factor decrease first and then remain stable. (2) When the saturated permeability coefficient decreases, the effective rainfall duration presents a crescent trend, and the ultimate safety factor decreases first and then remains the same after rainfall intensity exceeds the saturated permeability coefficient of soil. (3) When rainfall intensity is less than the saturated permeability coefficient of soil, the location of the final saturation line rises as the saturated permeability coefficient decreases and is thus independent of parameters a, m, and n.     

Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots

The Soil Conservation Service curve number (SCS-CN) method, also known as the Natural Resources Conservation Service curve number (NRCS-CN) method, is popular for computing the volume of direct surface runoff for a given rainfall event. The performance of the SCS-CN method, based on large rainfall (P) and runoff (Q) datasets of United States watersheds, is evaluated using a large dataset of natural storm events from 27 agricultural plots in India. On the whole, the CN estimates from the National Engineering Handbook (chapter 4) tables do not match those derived from the observed P and Q datasets. As a result, the runoff prediction using former CNs was poor for the data of 22 (out of 24) plots. However, the match was little better for higher CN values, consistent with the general notion that the existing SCS-CN method performs better for high rainfall–runoff (high CN) events. Infiltration capacity (fc) was the main explanatory variable for runoff (or CN) production in study plots as it exhibited the expected inverse relationship between CN and fc. The plot-data optimization yielded initial abstraction coefficient (λ) values from 0 to 0.659 for the ordered dataset and 0 to 0.208 for the natural dataset (with 0 as the most frequent value). Mean and median λ values were, respectively, 0.030 and 0 for the natural rainfall–runoff dataset and 0.108 and 0 for the ordered rainfall–runoff dataset. Runoff estimation was very sensitive to λ and it improved consistently as λ changed from 0.2 to 0.03.     

Rainfall thresholds for the possible landslide occurrence in Sicily (Southern Italy) based on the automatic reconstruction of rainfall events

Review of the literature on the reconstruction of the rainfall responsible for slope failures reveals that criteria for the identification of rainfall events are lacking or somewhat subjective. To overcome this problem, we developed an algorithm for the objective and reproducible reconstruction of rainfall events and of rainfall conditions responsible for landslides. The algorithm consists of three distinct modules for (i) the reconstruction of distinct rainfall events, in terms of duration (D, in h) and cumulated event rainfall (E, in mm), (ii) the identification of multiple ED rainfall conditions responsible for the documented landslides, and (iii) the definition of critical rainfall thresholds for possible landslide occurrences. The algorithm uses pre-defined parameters to account for different seasonal and climatic settings. We applied the algorithm in Sicily, southern Italy, using rainfall measurements obtained from a network of 169 rain gauges, and information on 229 rainfall-induced landslides occurred between July 2002 and December 2012. The algorithm identified 29,270 rainfall events and reconstructed 472 ED rainfall conditions as possible triggers of the observed landslides. The algorithm exploited the multiple rainfall conditions to define objective and reproducible empirical rainfall thresholds for the possible initiation of landslide in Sicily. The calculated thresholds may be implemented in an operational early warning system for shallow landslide forecasting.     

Seasonal evaluation habitat of Asian Houbara in the Central and East Iran

The Asian Houbara is vulnerable species, the population of which is dwindling. Its protection must be prioritized in conservation programs. In this study, maximum entropy models were developed in four seasons to evaluate habitat suitability and factors affecting Asian Houbara in the Center and East Iran. Environmental variables used in modeling and evaluating the niche consisted of 24 environmental and climate variables for Gharetapeh hunting prohibited region and 36 environmental and climate variables for Petregan protected area. Also, seasonal overlap area was obtained using the ENM TOOLS software. The results showed that the most important factors affecting habitat suitability of the Asian Houbara in all seasons included the ratio of distance to the type of Artemisia sieberi–Zygophyllum eurypterum, distance to the slope 2–5%, distance to the type of Seidlitzia rosmarinus in the Gharetapeh hunting prohibited region, distance to the type of Artemisia aucheri, distance to the land passion, and distance to the dry land farming in the Petregan region. In summer and fall–winter, the most suitable habitat is Gharetapeh but is Petergan during fall–winter. There is maximum overlap in fall and winter, and the least overlap in the spring in these areas.     

Setting up evaluate indicators for slope control engineering based on spatial clustering analysis

Rainfall-induced landslides (RILs) have been a source of social and economic disruption in the mountainous Baguio area in northern Philippines. Prolonged heavy rainfall usually happens during tropical cyclone and southwest monsoon activity. A pragmatic approach to RIL mitigation is to develop rainfall-based early warning. We implemented a modified regression method to derive the empirical minimum intensity (I)–duration (D) threshold I = 6.46 D ^?0.28 and a normalized ID threshold NI = 0.002 D ^?0.28 for rainfall duration ranging between 24 and 264 h. Using a separate data set to evaluate the applicability of the threshold, 93% of the landslide-triggering rainfall events fell above the derived threshold. RILs also occurred when 24-h rainfall was 0.02–28% of the mean annual precipitation or after accumulating at least 500 mm of rainfall from the onset of the rainy season. The thresholds may be further refined as more landslide data become available in the future.     

Quantifying land use influences on event-based flow frequency,timing, magnitude,and rate of change in an urbanizing watershed of the central USA

There is a need for research that advances understanding of flow alterations in contemporary watersheds where natural and anthropogenic interactions can confound mitigation efforts. Event-based flow frequency, timing, magnitude, and rate of change were quantified at five-site nested gauging sites in a representative mixed-land-use watershed of the central USA. Statistically independent storms were paired by site (n = 111 × 5 sites) to test for significant differences in event-based rainfall and flow response variables (n = 17) between gauging sites. Increased frequency of small peak flow events (i.e., 64 more events less than 4.0 m³ s^?1) was observed at the rural–urban interface of the watershed. Differences in flow response were apparent during drier periods when small rainfall events resulted in increased flow response at urban sites in the lower reaches. Relationships between rainfall and peak flow were stronger with decreased pasture/crop land use and increased urban land use by approximately 20%. Event-based total rainfall explained 40–68% of the variance in peak flow (p < 0.001). Coefficients of determination (r²) were negatively correlated with pasture/crop land use (r² = 0.92; p = 0.007; n = 5) and positively correlated with urban land use (r² = 0.90; p = 0.008; n = 5). Significant differences in flow metrics were observed between rural and urban sites (p < 0.05; n = 111) that were not explained by differences in rainfall variables and drainage area. An urban influence on flow timing was observed using median time lag to peak centroid and time of maximum precipitation to peak flow. Results highlight the need to establish manageable flow targets in rapidly urbanizing mixed-land-use watersheds.