Annual Peak Research Articles

Quantifying the historic and future response of karst spring discharge to climate variability and change at a snow-influenced temperate catchment in central Europe

Climate change is projected to threaten groundwater resources in many regions, but projections are highly uncertain. Quantifying the historic impact potentially allows for understanding of hydrologic changes and increases confidence in the predictions. In this study, the responses of karst discharge to historic and future climatic changes are quantified at Blautopf Spring in southern Germany, which is one of the largest karst springs in central Europe and belongs to a regional aquifer system relevant to the freshwater supply of millions of people. Statistical approaches are first adopted to quantify the hydrodynamic characteristics of the karst system and to analyse the historic time series (1952–2021) of climate variables and discharge. A reservoir model is then calibrated and evaluated with the observed discharge and used to simulate changes with three future climate-change scenarios. Results show that changes in the annual mean and annual low discharge were not significant, but the annual peak discharge shifted to a low state (<13.6 m3 s−1) from 1988 onwards due to decreasing precipitation, increasing air temperature, and less intense peak snowmelt. The peak discharge may decrease by 50% in this century according to the projections of all climate-change scenarios. Despite there being no significant historic changes, the base flow is projected to decrease by 35–55% by 2100 due to increasing evapotranspiration. These findings show the prolonged impact of climate change and variability on the floods and droughts at the springs in central Europe, and may imply water scarcity risks at similar climatic and geologic settings worldwide.

An Alternative Method for Estimating the Peak Flow for a Regional Catchment Considering the Uncertainty via Continuous Simulation

Estimating peak flow for a catchment is commonly undertaken using the design event method; however, this method does not allow for the understanding of uncertainty in the result. This research first presents a simplified method of fragments approach to rainfall disaggregation that ignores the need to consider seasonality, offering a greater diversity in storm patterns within the resulting sub-daily rainfall. By simulating 20 iterations of the disaggregated sub-daily rainfall within a calibrated continuous simulation hydrologic model, we were able to produce multiple long series of streamflow at the outlet of the catchment. With these data, we investigated the use of both the annual maximum and peaks over threshold approaches to flood frequency analysis and found that for a 1-in-100-year annual exceedance probability peak flow, the peaks over threshold method (333 m3/s ± 50 m3/s) was significantly less uncertain than the annual maximum method (427 m3/s ± 100 m3/s). For the 1-in-100-year annual exceedance probability, the median peak flow from the peaks over threshold method (333 m3/s) produced an outcome comparable to the design event method peak flow (328 m3/s), indicating that this research offers an alternative approach to estimating peak flow, with the additional benefit of understanding the uncertainty in the estimation. Finally, this paper highlighted the impact that length and period of streamflow has on peak flow estimation and noted that previous assumptions around the minimum length of gauged streamflow required for flood frequency analysis may not be appropriate in particular catchments.

Respiratory Syncytial Virus in Outpatient Children with Bronchiolitis: Continuous Virus Circulation During the Nonepidemic Period.

We aimed to estimate the respiratory syncytial virus positivity rate among ambulatory children with bronchiolitis according to the bronchiolitis epidemic period as defined by the French Public Health Institute. The positivity rate was 28.9% during the nonepidemic period and 50.6% during the epidemic period, which suggests continuous virus circulation between bronchiolitis annual peaks.

Modeling compound hydrologic disturbances in the Rio Grande Headwaters

AbstractIn recent decades, the western United States (U.S.) has experienced increasing magnitudes and frequencies of natural land cover disturbances that impact water budget partitioning. Post‐disturbance hydrologic response is often variable at the stream outlet and is difficult to detect and quantify with traditional before–after control–impact studies. This study uses a modified version of the U.S. Geological Survey's Monthly Water Balance Model (MWBM) to simulate and separate the hydrologic response to several forest disturbances, including (1) wildfire, (2) forest conversion (subalpine to mid‐elevation forest) and (3) a climate that is hotter and drier than present in the Rio Grande Headwaters (RGH) in Colorado, U.S. (this climate scenario was derived from an ensemble of climate scenarios from the Coupled Model Intercomparison Project phases 3 and 5, which were selected based on water stress potential in the state of Colorado). We leverage historic post‐disturbance vegetation data in the RGH to add quantitative vegetation representation to the MWBM, then modeled synthetic future (2021–2050) streamflow scenarios as both single and compound disturbances. Relative to a baseline scenario, modeled scenarios predict several changes to average annual water trends over the final simulation decade (2041–2050); (1) decreases in average annual water yield under a hot and dry climate (−14%), except during the rising limb of annual snowmelt; (2) increases in average annual water yield (+32%) and peak runoff under a fire simulation; and (3) increases in average annual water yield (+24%) along with earlier and higher peak runoff under compound (fire + hot/dry) conditions. These findings show the strengths of hydrologic models in separating compound disturbance signals at the stream outlet and a need for quantitative vegetation representation within models to adequately represent dynamic disturbance conditions.

Temporal evolution of lake level fluctuations under flood conditions and impacts on the littoral ecosystems

Lake levels fluctuations are conditioned by seasonal variability, water resources management and climate change. Recent studies have shown that global warming potentially affects the risk of flooding and that the decisive factor for flood events is not temperature, but precipitation characteristics and hydrological conditions. Flood events have numerous impacts on social, economic and environmental aspects depending on how humans have altered lands, natural rivers and lake dynamics. Flood protection measures can cause conflicts with conservation measures and with ecosystem services because natural capital is not considered able to control floods and to contribute control floods and that it can contribute to human health and safety. In this paper we analysed the flood events in Lake Maggiore for return time periods of 3 – 5 – 10 – 25 – 50 – 100 – 250 – 500 years, considering the flood frequency in the last ten years using 1868-2021 as a reference period. We discussed the probability distribution of flood peaks, the correlation and linear regression between the lake level fluctuations and macroinvertebrates occurrence. We also presented lake coasts flood hazard mapping. The probability distribution that better describes the annual peak level is the Gumbel function, while for spring and autumn flood events the better distribution is the Log-Pearson type III. One of the historical flood events in terms of magnitude was in 2000, characterized by a return time of about 50 years. The last flood event in 2020, was characterized by a return period of about 10 years. Considering the seasonal frequency of flood, the autumn magnitude was higher than the spring one, and the differences between seasonal flood events progressively increased. The results suggested a high probability of a flood event every three years and also a forecast of a flood of about 197 m asl (3.14 m above the average lake level) every 10 years. Raising the lake level will affect the reed bed area from 193 m asl, and it will be more effective at 194.5 m (up to a 10% reduction). During flood events, the whole reed bed area is submerged. As regard macroinvertebrates composition and abundance, the first results show significant negative relationships between all sampling stations altogether vs the abundance of Cladotanytarsus sp. (Chironominae) and nearly significant positive relationships between water levels at Magadino vs Pscectrocladius sordidellus (Orthocladiinae) abundances. These few results are perhaps due to the current limited data availability.

Shifting summer holidays in Spain as an adaptation measure to climate change

This paper assesses whether moving summer holidays to the warmest period of the year in Spain could be a useful climate change adaptation strategy. While the most popular period for Spanish summer holidays has traditionally been August, we illustrate that the second half of July is the hottest period of the year and when the negative effects of high temperatures are most pronounced. If the holiday period in the second fortnight of August was moved to the second fortnight of July, some of the associated impacts would be mitigated due to the reduced anthropogenic activity during non-working days. In particular, we find a significant reduction in the annual peak of labour productivity loss (~25 %) and, to a lesser extent, of electricity demand and near-surface ozone concentrations (~3–4 %). Finally, we also show that global warming could lead to enhanced differences between both fortnights (even with no change in the seasonal cycle of temperature) because of the non-linear relationships between temperature and its impacts. Therefore, the positive effect of shifting holidays would be even larger in the coming future.

An evaluation of the demand response potential of integrated dynamic window and HVAC systems

Demand response (DR) increases the flexibility and reliability of the electricity grid as use of intermittent renewable energy sources increases. HVAC and envelope DR measures present the largest aggregate energy and peak demand savings potential of all commercial building end uses because their net demand savings occur during critical peak demand periods. Controllable envelope measures include switchable electrochromic windows, operable window attachments such as outdoor louvers, roller shades, and awnings, as well as other innovative facade technologies that can modulate both solar heat gain and daylight admission over a broad solar-optical range. This study evaluated the technical potential of DR-enabled dynamic windows to reduce critical peak demand for a prototypical medium office building situated in all 16 U.S. climates. Model predictive control (MPC) algorithms were designed to minimize electricity cost in daylit perimeter office zones through control of an electrochromic window with and without HVAC thermostat setpoint control. Conventional and time-of-use rates were used to shape the degree of DR. Median annual peak demand savings with window and thermostat control across all climate zones were 24.3 kW (4.4 W/m2) per building or 15.9 W/m2 for non-north perimeter zones. Resource adequacy at the whole building level was estimated to be 13.1 to 43.4 $/kW per year over the 30-year life of the installation. Co-benefits were increased energy efficiency, and reduced electricity cost and emissions. Visual and thermal comfort requirements were met at all times. Dynamic facades controlled by MPC have substantial technical potential for DR across all U.S. climates and warrant serious consideration for inclusion in DR portfolios.

Integrated impact assessment of climate change and hydropower operation on streamflow and inundation in the lower Mekong Basin

Water resources are key to economic development of the Mekong River Basin, but are threatened by climate change and affected by hydropower development. Knowledge of these drivers’ integrated impact on future hydrological alterations is limited, especially with respect to flood inundation in the lower basin. This study assesses streamflow and flood extent alterations by reservoir operations and climate change using the latest climate projections. A distributed hydrologic model is used to generate discharge and flood extent. Our findings indicate substantial changes in seasonal and annual peak discharge due to reservoir operations. Under the future hydropower scenario, the discharge at Kratie will change by + 28% ( − 10%) during the dry (wet) season. While the effects of hydropower operations vary by season, climate change tends to increase river discharge overall. Under the high-emission scenario, the wet seasonal flow at Kratie will increase by + 7% in the near-future (2026–2050), but change by -5% under integrated impact of climate change and reservoir operations. In the far-future, the wet seasonal flow at Kratie under climate change only (integrated impact) will increase by + 33% (+ 19%). Although climate change is the dominant driver of flow alterations, hydropower development is critical for reducing discharge and flood magnitude. Nonparametric statistical testing shows significant changes in the inundated area by up to + 37% during the projected periods.

Frequency and trend analyses of annual peak discharges in the Lower Mekong Basin

The effectiveness of annual peak discharges under the anthropogenic impact and climate change has significance for disaster management and planning. Therefore, an attempt has been made to study the trend of annual maximum series (AMS) discharges and flood frequency in the Lower Mekong Basin (LMB). The AMS data of five stations in the LMB were procured from the Mekong River Commission for analyses of trends of the AMS and flood frequency. The Mann-Kendall test showed a significant decrease in the magnitude of annual peak floods for all the discharge gauging sites in the LMB. Likewise, the analysis of the annual discharge departure from the mean reveals noteworthy variations and departure (positive and negative) in the annual peak discharges. The goodness-of-fit (GoF) tests showed that Log-Pearson Type-III (LP-III) is the best distribution for AMS of the Mekong River than Gumbel Extreme Value Type-I (GEVI). Therefore, predicted discharges for different return periods and predicted recurrence intervals for average annual discharges (Qm), large floods (Qlf), and maximum annual peak discharge during the recording period (Qmax) by LP-III are trustworthy. The flood frequency curve specified that all the observed discharges were fairly on the best-fitted line and falls between upper and lower confidence limits. Inclusively, the results of the trend in annual peak discharges and flood frequency are consistent and can be used for water management, controlling flood disasters, and flood planning in the LMB.

Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas

Abstract. Information on snow depth and its spatial distribution is important for numerous applications, including natural hazard management, snow water equivalent estimation for hydropower, the study of the distribution and evolution of flora and fauna, and the validation of snow hydrological models. Due to its heterogeneity and complexity, specific remote sensing tools are required to accurately map the snow depth distribution in Alpine terrain. To cover large areas (&gt;100 km2), airborne laser scanning (ALS) or aerial photogrammetry with large-format cameras is needed. While both systems require piloted aircraft for data acquisition, ALS is typically more expensive than photogrammetry but yields better results in forested terrain. While photogrammetry is slightly cheaper, it is limited due to its dependency on favourable acquisition conditions (weather, light conditions). In this study, we present photogrammetrically processed high-spatial-resolution (0.5 m) annual snow depth maps, recorded during the peak of winter over a 5-year period under different acquisition conditions over a study area around Davos, Switzerland. Compared to previously carried out studies, using the Vexcel UltraCam Eagle Mark 3 (M3) sensor improves the average ground sampling distance to 0.1 m at similar flight altitudes above ground. This allows for very detailed snow depth maps in open areas, calculated by subtracting a snow-off digital terrain model (DTM, acquired with ALS) from the snow-on digital surface models (DSMs) processed from the airborne imagery. Despite challenging acquisition conditions during the recording of the UltraCam images (clouds, shaded areas and fresh snow), 99 % of unforested areas were successfully photogrammetrically reconstructed. We applied masks (high vegetation, settlements, water, glaciers) to increase the reliability of the snow depth calculations. An extensive accuracy assessment was carried out using check points, the comparison to DSMs derived from unpiloted aerial systems and the comparison of snow-free DSM pixels to the ALS DTM. The results show a root mean square error of approximately 0.25 m for the UltraCam X and 0.15 m for the successor, the UltraCam Eagle M3. We developed a consistent and reliable photogrammetric workflow for accurate snow depth distribution mapping over large regions, capable of analysing snow distribution in complex terrain. This enables more detailed investigations on seasonal snow dynamics and can be used for numerous applications related to snow depth distribution, as well as serving as a ground reference for new modelling approaches and satellite-based snow depth mapping.

Large‐scale human celebrations increase global light pollution

Abstract Culturally dependent human social behaviours involving artificial light usage can potentially affect light pollution patterns and thereby impact the night‐time ecology in populated areas, although to date this has not been examined globally. By analysing continuous (monthly), highly resolved, spatially explicit data on global night lights (Visible and Infrared Imaging Radiometer Suite–Day/Night Band‐VIIRS‐DNB; 2014–2019) with circular statistical techniques, we evaluated whether macro‐cultural activities involving social aggregations and the use of artificial lights shape annual lighting patterns globally. Scheduled routines associated with cultural‐specific festivities appear to be important drivers of observed seasonal patterns in urban night‐time lights. For instance, the display of Christmas lights between Christmas and Epiphany Day celebrations (December–January) coincides with the annual peak in urban night‐time light intensity in Christian countries. Analogously, night celebrations during the Holy Month of Ramadam (from May to July) or the month‐long period of Karthika Masam (from October to November) fits with annual night light peaks in Muslim and Hindu countries. Annual peaks of urban light intensity in China and Vietnam also match with Chinese and Vietnamese (Tê't) New Year celebrations (January–February). In contrast, predominantly Buddhist countries, which do not have such prominent and prolonged celebrations involving artificial lights, show a relatively uniform distribution of night light peaks throughout the annual cycle. Social behaviour and sociocultural contexts help explain how people modify the global nightscape and contribute to light pollution globally. Understanding the cultural contexts responsible for peaks in artificial light usage is an important first step if humans are to mitigate any deleterious effects associated with global increases in night‐time light pollution. Read the free Plain Language Summary for this article on the Journal blog.

Projections of flood regime changes over the upper-middle Huaihe River Basin in China based on CMIP6 models

Introduction: Climate change alters the hydrological cycle to different extents, in particular the intensification of extreme precipitation and floods, which has garnered more attention as a significant scientific issue in the last few decades. The last Coupled Model Inter-comparison Project 6 (CMIP6) was designed with new shared socioeconomic pathways (SSPs) to combine socioeconomic development with greenhouse gas emissions to project future climate.Method: In this study, we used 22 global climate models (GCMs) from CMIP6 to investigate future variations in extreme precipitation and temperature under SSP2-4.5 and SSP5-8.5 scenarios over the upper-middle Huaihe River Basin (UMHRB). Then, the modified Xinanjiang model integrating the flood control module was driven to obtain projections of the daily streamflow and to evaluate the future variations in flood regimes.Results: The results show that 1) the characteristics of future extreme precipitation, such as the average intensity and amount of annual precipitation and extreme precipitation, are projected to increase, and the average, maximum, and minimum temperature values also display substantial increasing trends in the future over the UMHRB; 2) warmer climate will lead to a more severe flood magnitude under the SSP5-8.5 scenario in the far future (2071–2100) over the UMHRB. The results of the multi-model ensemble show that the annual maximum flood peaks (15-day flood volumes) of Wangjiaba and Wujiadu stations are projected to increase by 46.4% (43.1%) and 45.4% (51.1%), respectively, in the far future (2071–2100) under the SSP5-8.5 scenario; and 3) variations in the flood frequency tend to resemble variations in flood magnitude, and the return period of the design flood will obviously decrease under future climate scenarios. For instance, in the far future, under SSP5-8.5 scenarios, the return period of the design flood with a 100-year return period will become 38 years and 31 years for Wangjiaba and Wujiadu stations, respectively.Discussion: The study enhances a more realistic understanding that the occurrence of future extreme precipitation and floods is projected to be more frequent and severe, thereby resulting in an urgent imperative to develop pertinent adaptation strategies to enhance social resilience toward the warming climate.

Shifting seasonality of annual growth through ontogeny for red deer at northern latitudes

AbstractLarge mammals at northern latitudes show annual cycles of body mass gain in summer and body mass loss in winter. The amplitude and seasonal timing of these cycles may vary through ontogeny depending on trade‐offs toward investments in structural growth versus fat storage, reproduction, and future survival. Despite this knowledge, there is no detailed model of how the seasonality of body mass develops through ontogeny. Here, we define a new seasonal growth model that accounts for shifting seasonality through two sine components: one included in the growth coefficient and the other on the asymptotic size. We fitted 12 candidate models to longitudinal data on body mass of captive male and female red deer (Cervus elaphus) in Norway, with different baseline growth structures (von Bertalanffy, Gompertz, and Brody) and including zero, one, or both of the seasonal components. The best fitting model was the Brody model with both seasonal components included, allowing the annual peak to shift through ontogeny: The annual peak occurred in December for calves, shifting to November in yearlings, and October in 2‐ and 3‐year‐olds, ending with September for adults. All age classes showed an annual minimum at the end of winter around March. Males and females showed similar seasonal peaks through ontogeny, although males grow bigger and have larger seasonal amplitudes. Our new growth model provides a flexible framework to model seasonal growth changing through ontogeny, applicable to different species.

Effects of forest disturbance on water yield and peak flow in low‐relief glaciated catchments assessed with Bayesian parameter estimation

AbstractEmpirical assessment of how forest disturbance affects streamflow has been traditionally limited to small, experimental catchments. However, larger catchments where landscape management occurs have emergent drivers of streamflow at scale, and thus may exhibit novel responses to land cover disturbance. We used statistical models of water yield and annual maximum peak streamflow for multiple large (&gt; 50 km2) forested catchments in the low‐relief glaciated region of central North America to investigate how forest disturbance and climatic variability affect water yield and peak flows in similar landscapes. We used linear models, linear mixed effects models, and probabilistic flood‐frequency analysis with Bayesian parameter estimation in two case studies. These included: (1) a wildfire that burned ~30% of the 650 km2 wilderness Upper Kawishiwi catchment, and (2) 11 catchments within the St. Louis River Basin ranging from 56 to 8880 km2 with a patchwork disturbance regime wherein ~0.25% to 1% of the catchment is harvested or converted to non‐forest land use each year. We also assessed the most likely hydrological recovery year after forest disturbance, and the relative importance of stationary and nonstationary drivers of streamflow. We found forest disturbance correlated with declines in water yield for low‐level disturbance regimes in some catchments, but that water yield increased in response to the large‐scale wildfire. Positive and negative associations of forest disturbance with peak flows were observed. Hydrologic recovery time ranged from 5 to 13 years for water yield and peak flows following disturbance. Despite these effects of forest disturbance on streamflow, effects of climatic variability and stationary catchment size factors were more prominent streamflow drivers. Basins larger than ~50 km2 in low‐relief glaciated regions can be impacted by forest cover change even on &lt;30% of basin area, but climatic variability and catchment spatial scale has a larger effect than forest disturbance.

Machine learning method is an alternative for the hydrological model in an alpine catchment in the Tianshan region, Central Asia

Study regionKaidu River catchment in the Tianshan Mountain, northwestern China. Study focusThis paper compared the applicability and accuracy of four machine learning models and two hydrological ones to simulate the daily streamflow and extreme streamflow of the Kaidu River catchment. The machine learning models are Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), Random Forests (RF), and Long Short-Term Memory (LSTM), while the hydrological models are the Soil and Water Assessment Tool (SWAT) and the extended SWAT with a glacier dynamic module (SWAT-Glacier). New hydrological insights for the regionLSTM achieved better model performance in simulating daily streamflow than SWAT and SWAT-Glacier, with Kling-Gupta efficiency of 0.92, 0.82, and 0.80, respectively. Meanwhile, SVR, XGBoost, and RF showed satisfactory performance, with KGE of 0.67, 0.71, and 0.70, respectively. LSTM, SWAT and SWAT-Glacier could well simulate the annual peak flow (i.e., annual maximum 1-day streamflow and 5-day average streamflow) but failed to mimic the annual minimum 7-day average streamflow, with PBIAS exceeding 28%. Furthermore, all the models failed to reproduce the dates of hydrological extremes. Nevertheless, using the quantile loss function in the LSTM model resulted in significantly improved model performance in the low streamflow indices, compared to that using mean squared error as the loss function. Overall, LSTM could be a good alternative for simulating daily streamflow and extreme streamflow in data-scarce catchments.

Quantifying patterns of streamflow peaks over the southeastern United States using a long‐term retrospective data set

AbstractUtilizing simulated daily flow values for 61 948 stream nodes from the National Water Model (NWM) v.2.1 retrospective data set, which covers the period 1979–2020, streamflow peaks (SFP) were determined using an objective threshold method based on the Fisher‐Jenks breakpoint algorithm. This approach provides estimates of low probability, high in‐channel water level conditions. Strong spatial relations in temporal streamflow peak distribution were observed, with four main spatial clusters covering: (1) the central parts of Mississippi to South Carolina; (2) the south and north parts of Mississippi to South Carolina; (3) Florida, the Gulf Coast, and parts of North Carolina and Virginia; and (4) the Atlantic Coast and parts of the Gulf Coast. The first area is characterized by high multiannual SFP occurrence with long total durations, high contribution to total discharge, and occurrence throughout the year with peaks in February and March. Increasing annual peak trends are present locally. The second area is characterized by similar parameters with increased flash flooding potential. In the third area, lower occurrence and share in total discharge are observed, with two peaks of occurrence in the September–October and March periods. In the fourth region, the lowest occurrences and shortest durations are present, along with strong seasonality manifested in September–October occurrences that constitute over 80% of all SFP volumes. Decreasing trends in maximal monthly flows for the June–October period are also observed here. The results provide valuable regionalized information about historical high flow characteristics across the southeast United States, which can be used as a basis for improved long‐term flood prediction based on seasonality models and constitute a baseline for defining regional changes in high flow frequency and distribution.

Projected changes in annual maximum discharge for Iowa communities

Understanding the projected changes in annual maximum peak discharge is important to improve resiliency in water resource planning and design at the community level. Currently, much of the literature on climate change impacts focuses on analyses at the regional scale. In this study we use a hydrologic model to evaluate the projected changes in annual maximum peak discharge at the community-level across Iowa under two emission scenarios, Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5). We utilize climate forcings from global climate models part of the Coupled Model Intercomparison Projected Phase 5 (CMIP5) from 1950 to 2100. Our simulations show a detectable increase in annual maximum discharge for 27% of Iowa’s communities under RCP8.5. However, under RCP4.5 none of the communities are projected to face an increase in annual maximum discharge by the end of the 21st century; furthermore, precipitation intensity under this scenario is not projected to increase in the latter half of the 21st century. Our results point to a larger increase in precipitation intensity and annual maximum discharge under RCP8.5 compared to RCP4.5, especially in the second half of this century. The projected flood peak distributions tend to become statistically different from the historical ones later in the 21st century under RCP8.5 than under RCP4.5. This study provides a basis for analyzing climate change impacts at a local decision-making scale.

Population Fluctuations of the Deer Mouse (Peromyscus maniculatus) in Old-Field and Bunchgrass–Sagebrush Habitats: The Role of Agricultural Setting and Optimum Habitat

In semiarid regions, the deer mouse (Peromyscus maniculatus) is a major small mammal species occupying perennial grassland habitats that include old-fields, native bunchgrass–sagebrush, and some agricultural settings. We investigated population changes in deer mouse populations in perennial grasslands, both natural and old-field, from 1982 to 2003 in southern British Columbia, Canada. Hypotheses (H) predicted that P. maniculatus populations will have (H1) multiannual fluctuations in abundance driven by population increases from extended breeding in summer and winter; (H2) relaxed spring reorganization events in some years leading to higher overall recruitment and survival; and (H3) interspecific competition with montane voles that causes deer mice to be lower in density when voles are higher. P. maniculatus populations in old-field and grass–sagebrush sites had clearly defined periods of high “peak” mean numbers (32–52/ha) and other times of low mean numbers (20–22/ha). Based on mean annual peak density in autumn, deer mouse populations exhibited fluctuations of 3–4 years in both habitats, but this pattern was not always present. The greater numbers of P. maniculatus in high than low years was directly related to population increases from extended breeding seasons and an increased number of lactating females, thereby supporting H1. Spring breeding season declines occurred but were similar or less in high than low years of mean abundance and were relaxed in comparison to forest populations of deer mice in other studies. Thus, H2 was supported for recruitment with high numbers of young-of-the-year breeding and total number of juvenile recruits but for survival was equivocal. Total summer survival was consistently higher in high than low population years but juvenile productivity in all years was poor. Mean abundance of P. maniculatus and M. montanus in old-field sites were highly correlated, and hence H3 was not supported. This latter result is the first, to our knowledge, of P. maniculatus coexisting in a similar pattern of population fluctuations with a Microtus species in a mainland grassland habitat. Higher than average precipitation in the year preceding a peak population of deer mice may have enhanced herbaceous vegetation and contributed to population increases in both habitats. We conclude that the old-field habitat associated with this agricultural setting provides optimum habitat for P. maniculatus and facilitates multiannual population fluctuations in this species.

Assessment of first-order-moment-based sample reconstruction method for design flood estimation in changing environment

Estimating the design flood under nonstationary conditions is challenging. In this study, a sample reconstruction approach was developed to transform a nonstationary series into a stationary one in a future time window (FTW). In this approach, the first-order moment (EFTW) of an extreme flood series in the FTW was used, and two possible methods of estimating EFTW values in terms of point values and confidence intervals were developed. Three schemes were proposed to analyze the uncertainty of design flood estimation in terms of sample representativeness, uncertainty from EFTW estimation, and both factors, respectively. To investigate the performance of the sample reconstruction approach, synthesis experiments were designed based on the annual peak series of the Little Sugar Creek in the United States. The results showed that the sample reconstruction approach performed well when the high-order moment of the series did not change significantly in the specified FTW. Otherwise, its performance deteriorated. In addition, the uncertainty of design flood estimation caused by sample representativeness was greater than that caused by EFTW estimation.

Assessment of severe flood events by the use of recent and long-term annual peak discharge data of Chenab River, Pakistan

Assessment of severe flood events by the use of recent and long-term annual peak discharge data of Chenab River, Pakistan