Modifications to Snow-Melting and Flooding Processes in the Hydrological Model—A Case Study in Issyk-Kul, Kyrgyzstan

Streamflow impacts water supply and flood protection. Snowmelt floods occur frequently, especially in mountainous areas, and they pose serious threats to natural and socioeconomic systems. The current forecasting method relies on basic snowmelt accumulation and has geographic limitations that restrict the accuracy and timeliness of flood simulation and prediction. In this study, we clarified the precipitation types in two selected catchments by verifying accumulated and maximum temperatures’ influences on snow melting using a separation algorithm of rain and snow that incorporates with the temperatures. The new snow-melting process utilizing the algorithm in the soil and water assessment tool model (SWAT) was also developed by considering the temperatures. The SWAT model was used to simulate flooding and snowmelt in the catchments. We found that the contributions of snowmelt to the river flow were approximately 6% and 7% higher, according to our model compared to the original model, for catchments A and B, respectively. After the model improvement, the flood peaks increased by 49.42% and 43.87% in A and B, respectively. The contributions of snowmelt to stream flow increased by 24.26% and 31% for A and B, respectively. Generally, the modifications improved the model accuracy, the accuracy of snowmelt’s contributions to runoff, the accuracy of predicting flood peaks, the time precision, and the flood frequency simulations.

Tropical cyclone flooding in the Carolinas

The climatology of tropical cyclone flooding in the Carolinas is analyzed through annual flood peak observations from 411 U.S. Geological Survey (USGS) stream gaging stations. Tropical cyclones (TCs) account for 28% of the top ten annual flood peaks, 55% of record floods, and 91% of floods with peak magnitudes at least five times greater than the 10-year floods, highlighting the prominent role of TCs for flood extremes in the Carolinas. Of all TC-related flood events, the top ten storms account for nearly 1/3 of annual flood peaks and more than 2/3 of record floods, reflecting the dominant role of a small number of storms in determining the upper tail of flood peak distributions. Analyses of the ten storms highlight both common elements and diversity in storm properties that are responsible for flood peaks. Extratropical transition and orographic enhancement are important elements of extreme TC flooding in the Carolinas. Analyses of the Great Flood of 1916 highlight the flood peak of 3115 m3 s−1 in French Broad River at Asheville, 2.6 times greater than the second-largest peak from a record of 124 years. We also examine the hydroclimatology, hydrometeorology and hydrology of flooding from Hurricanes Matthew (2016) and Florence (2018). Results point to contrasting storm properties for the two events, including tracks as well as rainfall distribution and associated physical mechanisms. Climatological analyses of vertically integrated water vapor transport (IVT) highlight the critical role of anomalous moisture transport from the Atlantic Ocean in producing extreme rainfall and flooding over the Carolinas.

Impact of detention dams on the probability distribution of floods

Detention dams are one of the most effective practices for flood mitigation. Therefore, the impact of these structures on the basin hydrological response is critical for flood management and the design of flood control structures. With the aim of providing a mathematical framework to interpret the effect of flow control systems on river basin dynamics, the functional relationship between inflows and outflows is investigated and derived in a closed form. This allowed the definition of a theoretically derived probability distribution of the peak outflows from in-line detention basins. The model has been derived assuming a rectangular hydrograph shape with a fixed duration and a random flood peak. In the present study, the undisturbed flood peaks are assumed to be Gumbel distributed, but the proposed mathematical formulation can be extended to any other flood-peak probability distribution. A sensitivity analysis of parameters highlighted the influence of detention basin capacity and rainfall event duration on flood mitigation on the probability distribution of the peak outflows. The mathematical framework has been tested using for comparison a Monte Carlo simulation where most of the simplified assumptions used to describe the dam behaviours are removed. This allowed demonstrating that the proposed formulation is reliable for small river basins characterized by an impulsive response. The new approach for the quantification of flood peaks in river basins characterized by the presence of artificial detention basins can be used to improve existing flood mitigation practices and support the design of flood control systems and flood risk analyses.

Daily vs. hourly simulation for estimating future flood peaks in mesoscale catchments

Daily hydrological models are commonly used to study changes in flood peaks due to climate change. Although they often lead to an underestimation of absolute floods, it is assumed that future flood peaks in smaller mesoscale catchments are less underestimated when examining the relative change signal of floods. In this study, the applicability of this hypothesis is investigated by comparing the results of a daily hydrological model set, calibrated on runoff hydrographs, with an hourly model set calibrated on flood peak distributions. For analysis, a daily RCP8.5 climate model ensemble is disaggregated to hourly values and the runoff is simulated on a daily and hourly basis for six mesoscale catchments in Central Germany. Absolute floods and relative flood changes are compared between both model sets. The results show significant differences between the absolute floods of both model sets, in most cases caused by underestimations due to the daily modeling process. In contrast, the differences between the two model sets are not significant for the relative change signal of the floods, especially for higher return periods. To improve results in climate studies with coarse modeling time step, the use of relative change signal of floods instead of absolute values is recommended.

ỨNG DỤNG DỮ LIỆU MƯA CHIRPS VÀ MÔ HÌNH THỦY VĂN HEC-HMS MÔ PHỎNG DÒNG CHẢY LŨ Ở LƯU VỰC SÔNG LẠI GIANG

Bài báo xác định lưu lượng dòng chảy theo thời đoạn dựa vào mô hình HEC-HMS, số liệu mưa từ ảnh vệ tinh CHIRPS của NASA và Hệ thống thông tin địa lý (GIS) trong mô phỏng dòng chảy lũ tháng 12 năm 2016 tại lưu vực sông Lại Giang, lưu vực lớn thứ hai của tỉnh Bình Định (sau lưu vực sông Kôn) và có vai trò quan trọng về phát triển kinh tế-xã hội ở phía Bắc của tỉnh. Kết quả mô phỏng dòng chảy lũ rất đáng tin cậy, lưu lượng dòng chảy lũ đạt đỉnh 2542,6 m3/s tương ứng với với tần suất lũ 5%. Chỉ số kiểm định mô hình NSE với giá trị là 0,93; hệ số R2 đạt 0,78 sai số PBIAS khoảng 24% và sai số đỉnh lũ PEC = 52,01. ABSTRACT The paper aimed to introduce the application of the HEC-HMS hydrological model combination with the CHIRPS (Climate Hazards Group Infrared Precipitation with Station) and GIS to restore flood flow data in the Lai Giang river basin in 2016. The Lai Giang river basin is the second largest basin of Binh Dinh province (after the Kon river basin), it plays an important role in socio-economic development in the North of Binh Dinh province. The simulation results of flood peaks reached 2542,6 m3.s-1 (P=5%). Model test indices such as NSE = 0.93, the correlation coefficient reached 0,78; the percentage of PBIAS error was about 24%, and peak error (PEC) was 52,01.

Do small and large floods have the same drivers of change? A regional attribution analysis in Europe

Recent studies have shown evidence of increasing and decreasing trends for average floods and flood quantiles across Europe. Studies attributing observed changes in flood peaks to their drivers have mostly focused on the average flood behaviour, without distinguishing small and large floods. This paper proposes a new framework for attributing flood changes to potential drivers, as a function of return period (T), in a regional context. We assume flood peaks to follow a non-stationary regional Gumbel distribution, where the median flood and the 100-year growth factor are used as parameters. They are allowed to vary in time and between catchments as a function of the drivers quantified by covariates. The elasticities of floods with respect to the drivers and the contributions of the drivers to flood changes are estimated by Bayesian inference. The prior distributions of the elasticities of flood quantiles to the drivers are estimated by hydrological reasoning and from the literature. The attribution model is applied to European flood and covariate data and aims at attributing the observed flood trend patterns to specific drivers for different return periods at the regional scale. We analyse flood discharge records from 2370 hydrometric stations in Europe over the period 1960–2010. Extreme precipitation, antecedent soil moisture and snowmelt are the potential drivers of flood change considered in this study. Results show that, in northwestern Europe, extreme precipitation mainly contributes to changes in both the median (q2) and 100-year flood (q100), while the contributions of antecedent soil moisture are of secondary importance. In southern Europe, both antecedent soil moisture and extreme precipitation contribute to flood changes, and their relative importance depends on the return period. Antecedent soil moisture is the main contributor to changes in q2, while the contributions of the two drivers to changes in larger floods (T>10 years) are comparable. In eastern Europe, snowmelt drives changes in both q2 and q100.

Hydrological contrast between peatlands and forests: Implications on extreme flow in the boreal landscape

<p> One of the most important benefits of natural and restored peatlands in boreal ecosystem is their critical role in storing water and consequently reducing flood peaks at the basin outlet. Compared to forests, peatlands have been suggested to have different hydrological behaviors altering the water transit time, flood peaks and runoff volumes, but the science underpinning such statements are largely lacking. This is problematic, as peatland restoration to regain landscape hydrological functioning has become high on the management agenda. However, if it is true that peatlands behave differently they can help mitigate the impacts of both extreme flooding and drought conditions by storing large volumes of water that will delay runoff and keeping streams and rivers flowing during low flow conditions. Accordingly, an accurate estimation of potential and available volume of catchment water storage with different physical characteristics would help us to choose the best peatland management strategies for reducing flood and drought risk in the future. However, the direct estimation of water storage requires an extensive amount of field observations. Hydrological models provide an indirect estimation of water storage and allow us to compare several catchments over a wide range of spatiotemporal scales.</p><p> Here, we tested the role of peatlands by using data from 14 nested sub-catchments within a 68 km2 boreal forest landscape in Northern Sweden and then classified them into four different groups (forest on till, forest on sediment, peatlands, and mixed land cover) based on their landscape characteristics. We focused on the “dynamic storage” of catchment which directly controls the catchment streamflow generation. The simple bucket-type hydrological model, HBV-light, with a calibration period of 7 years (2010 to 2017) was deployed to simulate catchments storage dynamics. The calibration trials were repeated 100 times to assess the uncertainty of simulated results. The evaluation of model performance carried out using the coefficient of efficiency, ranged from 0.76 to 0.87. The relationship between storage characteristics and physical catchment properties such as soil depth, peatland percentage, elevation, and area were then analyzed using Spearman rank correlation.  </p><p> The results of this study shows not only high differences in dynamic storage values among the sub-catchments but also the differences in locations of dynamic storage within the soil layers of peatland dominated catchments. The variations become even greater as we aggregate the storage amounts in  shorter temporal scales. The magnitude and variability of total storage change calculated using water balance method was much higher than the dynamic storage estimated by HBV, indicating that not all the water stored in the catchments were available for draining to the stream. We also found that the total amount of dynamic storage in peatland dominated catchments were higher than the amount stored in forest on till and mixed characteristics catchments. Moreover, in peatlands, the proportion of water stored in the upper zone reservoir was much higher than the estimated amounts in other catchments (Spearman rank correlation r=0.73, p < 0.05), which also shows the ability of HBV in capturing the hydrological function of peat soils.</p>

Detection, attribution and frequency analysis of non-stationary flood peaks in 32 big rivers worldwide

<p>Due to climate variability and reservoir regulation worldwide, it is fundamentally challenging to implement holistic assessments of detection, attribution and frequency analysis on non-stationary flood peaks. In this study, we proposed an integrated approach that combines the prewhitening Mann-Kendall test technique, Partial Mutual Information-Partial Weights (PMI-PW) method and Generalized Additive Models for Location, Scale and Shape parameters (GAMLSS) method to achieve reliable non-stationary flood frequency analysis. Firstly, the prewhitening Mann-Kendall test was employed to detect the trend change of flood peaks. Secondly, the PMI-PW was employed to attribute the contribution of climate change and reservoir regulation to non-stationarity of flood peaks. Lastly, the GAMLSS method was employed to quantify the change in flood risks under the non-stationary condition. The applicability of the proposed approach was investigated by long-term (1931-2017) flood series collected from 32 big river catchments globally. The results suggested that global flood trends varied from increasing +19.3%/decade to decreasing −31.6%/decade. Taking the stationary flood frequency analysis as the benchmark, the comparative results revealed that the flood risk in 5 rivers under the non-stationary condition in response to warming climate significantly increased over the historical period whereas the flood risk in 7 rivers in response to increasing reservoir storage largely reduced. Despite the spatiotemporal heterogeneity of observations, the changes in flood peaks evaluated here were explicitly associated with the changing climate and reservoir storage, supporting the demand for considering the non-stationarity of flood peaks in the best interest of social sustainability.</p><p><strong>Keywords:</strong> Flood peaks; Large catchments; Non-stationarity; Frequency analysis</p><p>*This work was supported by the Research Council of Norway (FRINATEK Project 274310).</p><p> </p><p> </p><p> </p>