Snowmelt Flood Mapping and Land Surface Short-Term Dynamics Assessment in a “Before-During-After” Scenario Based on Radar and Optical Satellite Imagery: Case Study Around the Lewisville Lake (Dallas/Fort Worth Metropolitan, Texas, USA)

The main goal of this study has been to map flood and assess land surface short-term dynamics in relation with snowy weather. The two recent snowfall events, which happened in, February 14th and 15th, of year 2021, and February 3rd and 4th, of year 2022, were chosen. A pre-analysis correlation was assumed between, the snow events, recurrency of floods, and changes in the land surface characteristics (i.e., wetness, energy, temperature), in a “Before-During-After” scenario. Active and passive microwave satellites data such as, Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral instrument (MSI) and Landsat-9 Operation Land Imager-2/Thermal Infrared Sensors-2 (OLI-2/TIRS-2), as well as cloud databased global models for water and urban layers were used. The first step of processing was thresholding of SAR image, at 0.25 cutoff, based on bimodal histogram distribution, followed by the change analysis. The following processing consisted in the images transformation, by computing the tasseled cap transformation wetness (TCTw) and the surface albedo on MSI image. In addition, the land surface temperature (LST) was modeled from OLI-2/TIRS-2 image. Then, a 5th order polynomial regression was computed, between TCTw as dependent variable and, albedo and LST as independent variables. As a first result, an area of 5.6 km2 has been mapped as recurrently flooded from the two years assessment. The other output highlighted a constant increase of wetness (TCTw), considered most influential on land surface dynamics, comparatively to energy exchange (albedo) and temperature (LST). The “After” event dependency between the three indicators was highest, with a correlation coefficient, R2 = 0.682, confirming the persistence of wetness after-snowmelt. Validation over topographic layers confirmed that, recurrently flooded areas are mostly distributed on, lowest valley depth points, farthest distances from channel network (i.e., from perennial waters), and lowest relative slope position areas. Whereas, 88.9% of the validation sampling were confirmed in the laboratory, and 86.7% of urban validation points were assessed as recurrently flooded when combining pre-/post-field-work campaign.

Spatiotemporal variability of rain-on-snow events in the arid region of Northwest China

Rain-on-snow(ROS)events involve rainfall on snow surfaces,and the occurrence of ROS events can exacerbate water scarcity and ecosystem vulnerability in the arid region of Northwest China(ARNC).In this study,using daily snow depth data and daily meteorological data from 68 meteorological stations provided by the China Meteorological Administration National Meteorological Information Centre,we investigated the spatiotemporal variability of ROS events in the ARNC from 1978 to 2015 and examined the factors affecting these events and possible changes of future ROS events in the ARNC.The results showed that ROS events in the ARNC mainly occurred from October to May of the following year and were largely distributed in the Qilian Mountains,Tianshan Mountains,Ili River Valley,Tacheng Prefecture,and Altay Prefecture,with the Ili River Valley,Tacheng City,and Altay Mountains exhibiting the most occurrences.Based on the intensity of ROS events,the areas with the highest risk of flooding resulting from ROS events in the ARNC were the Tianshan Mountains,Ili River Valley,Tacheng City,and Altay Mountains.The number and intensity of ROS events in the ARNC largely increased from 1978 to 2015,mainly influenced by air temperature and the number of rainfall days.However,due to the snowpack abundance in areas experiencing frequent ROS events in the ARNC,snowpack changes exerted slight impact on ROS events,which is a temporary phenomenon.Furthermore,elevation imposed lesser impact on ROS events in the ARNC than other factors.In the ARNC,the start time of rainfall and the end time of snowpack gradually advanced from the spring of the current year to the winter of the previous year,while the end time of rainfall and the start time of snowpack gradually delayed from autumn to winter.This may lead to more ROS events in winter in the future.These results could provide a sound basis for managing water resources and mitigating related disasters caused by ROS events in the ARNC.

Snowmelt runoff prediction under changing climate in the Himalayan cryosphere: A case of Gilgit River Basin

There are serious concerns of rise in temperatures over snowy and glacierized Himalayan region that may eventually affect future river flows of Indus river system. It is therefore necessary to predict snow and glacier melt runoff to manage future water resource of Upper Indus Basin(UIB). The snowmelt runoff model(SRM) coupled with MODIS remote sensing data was employed in this study to predict daily discharges of Gilgit River in the Karakoram Range. The SRM was calibrated successfully and then simulation was made over four years i.e. 2007, 2008, 2009 and 2010 achieving coefficient of model efficiency of 0.96, 0.86, 0.9 and 0.94 respectively. The scenarios of precipitation and mean temperature developed from regional climate model PRECIS were used in SRM model to predict future flows of Gilgit River. The increase of 3 C in mean annual temperature by the end of 21 th century may result in increase of 35-40% in Gilgit River flows. The expected increase in the surface runoff from the snow and glacier melt demands better water conservation and management for irrigation and hydel-power generation in the Indus basin in future.

Application of snowmelt runoff model(SRM) in mountainous watersheds:A review

The snowmelt runoff model （SRM） has been widely used in simulation and forecast of streamflow in snow-dominated mountainous basins around the world. This paper presents an overall review of worldwide applications of SRM in mountainous watersheds, particularly jn data-sparse watersheds of northwestern China. Issues related to proper selection of input climate variables and parameters, and determination of the snow cover area （SCA）using remote sensing data in snowmelt runoff modeling are discussed through extensive review of literature. Preliminary applications of SRM in northwestern China have shown that the model accuracies are relatively acceptable although most of the watersheds lack measured hydro-meteorological data. Future research could explore the feasibility of modeling snowmelt runoff in data-sparse mountainous watersheds in northwestern China by utilizing snow and glacier cover remote sensing data, geographic information system （GIS） tools, field measurements, and innovative ways of model parameterization.

Snowmelt modeling using two melt-rate models in the Urumqi River watershed, Xinjiang Uyghur Autonomous Region, China

In this paper,the performance of the classic snowmelt runoff model(SRM)is evaluated in a daily discharge simulation with two different melt models,the empirical temperature-index melt model and the energy-based radiation melt model,through a case study from the data-sparse mountainous watershed of the Urumqi River basin in Xinjiang Uyghur Autonomous Region of China.The classic SRM,which uses the empirical temperature-index method,and a radiation-based SRM,incorporating shortwave solar radiation and snow albedo,were developed to simulate daily runoff for the spring and summer snowmelt seasons from 2005 to 2012,respectively.Daily meteorological and hydrological data were collected from three stations located in the watershed.Snow cover area(SCA)was extracted from satellite images.Solar radiation inputs were estimated based on a digital elevation model(DEM).The results showed that the overall accuracy of the classic SRM and radiation-based SRM for simulating snowmeltdischarge was relatively high.The classic SRM outperformed the radiation-based SRM due to the robust performance of the temperature-index model in the watershed snowmelt computation.No significant improvement was achieved by employing solar radiation and snow albedo in the snowmelt runoff simulation due to the inclusion of solar radiation as a temperature-dependent energy source and the local pattern of snowmelt behavior throughout the melting season.Our results suggest that the classic SRM simulates daily runoff with favorable accuracy and that the performance of the radiation-based SRM needs to be further improved by more ground-measured data for snowmelt energy input.

Antarctic ice-sheet near-surface snowmelt detection based on the synergy of SSM/I data and QuikSCAT data

Microwave radiometer SSM/I data and scatterometer QuikSCAT data have been widely used for the icesheet near-surface snowmelt detection based on their sensitivity to liquid water present in snow. In order to improve the Antarctic ice-sheet near-surface snowmelt detection accuracy, a new Antarctic icesheet near-surface snowmelt synergistic detection method was proposed based on the principle of complementary advantages of SSM/I data(high reliability) and QuikSCAT data(high sensitivity) by the use of edge detection model to automatically extract the edge information to get the distribution of Antarctic snowmelt onset date, snowmelt duration and snowmelt end date. The verification result shows that the proposed snowmelt synergistic detection method improves the detection accuracy from about 75% to 86% based on AWS(Automatic Weather Stations) Butler Island and Larsen Ice Shelf. The algorithm can also be applied to other regions, which provides methodological support and supplement for the global snowmelt detection.

Integration of aspect and slope in snowmelt runoff modeling in a mountain watershed

This study assessed the performances of the traditional temperature-index snowmelt runoff model(SRM) and an SRM model with a finer zonation based on aspect and slope(SRM + AS model) in a data-scarce mountain watershed in the Urumqi River Basin,in Northwest China.The proposed SRM + AS model was used to estimate the melt rate with the degree-day factor(DDF) through the division of watershed elevation zones based on aspect and slope.The simulation results of the SRM + AS model were compared with those of the traditional SRM model to identify the improvements of the SRM + AS model's performance with consideration of topographic features of the watershed.The results show that the performance of the SRM + AS model has improved slightly compared to that of the SRM model.The coefficients of determination increased from 0.73,0.69,and 0.79 with the SRM model to 0.76,0.76,and 0.81 with the SRM + AS model during the simulation and validation periods in 2005,2006,and 2007,respectively.The proposed SRM + AS model that considers aspect and slope can improve the accuracy of snowmelt runoff simulation compared to the traditional SRM model in mountain watersheds in arid regions by proper parameterization,careful input data selection,and data preparation.

Application of hydrological models in a snowmelt region of Aksu River Basin

This study simulated and predicted the runoff of the Aksu River Basin, a typical river basin supplied by snowmelt in an arid mountain region, with a limited data set and few hydrological and meteorological stations. Two hydrological models, the snowmelt-runoff model （SRM） and the Danish NedbФr-AfstrФmnings rainfall-runoff model （NAM）, were used to simulate daily discharge processes in the Aksu River Basin. This study used the snow-covered area from MODIS remote sensing data as the SRM input. With the help of ArcGIS software, this study successfully derived the digital drainage network and elevation zones of the basin from digital elevation data. The simulation results showed that the SRM based on MODIS data was more accurate than NAM. This demonstrates that the application of remote sensing data to hydrological snowmelt models is a feasible and effective approach to runoff simulation and prediction in arid unguaged basins where snowmelt is a major runoff factor.

Antecedent snowmelt and orographic precipitation contributions to water supply of Pakistan disastrous floods,2022

In 2022,the Pakistan witnessed the hottest spring and wettest summer in history.And devastating floods inundated a large portion of Pakistan and caused enormous damages.However,the primary water source and its contributions to these unprecedented floods remain unclear.Based on the reservoir inflow measurements,Multi-Source Weighted-Ensemble Precipitation(MSWEP),the fifth generation ECMWF atmospheric reanalysis(ERA5)products,this study quantified the contributions of monsoon precipitation,antecedent snow-melts,and orographic precipitation enhancement to floods in Pakistan.We found that the Indus experienced at least four inflow up-rushes,which was mainly supplied by precipitation and snowmelt;In upper Indus,abnormally high temperature continued to influence the whole summer and lead to large amounts of snowmelts which not only was a key water supply to the flood but also provided favorable soil moisture conditions for the latter precipitation.Before July,the snowmelt has higher contributions than the precipitation to the streamflow of Indus River,with contribution value of more than 60%.Moreover,the snowmelt could still supply 20%-40%water to the lower Indus in July and August;The leading driver of 2022 mega-floods over the southern Pakistan in July and August was dominated by the precipitation,where terrain disturbance induced precipitation account to approximately 33%over the southern Pakistan.The results help to understand the mechanisms of flood formation,and to better predict future flood risks over complex terrain regions.

Intensified warming suppressed the snowmelt in the Tibetan Plateau

Understanding how hydrological factors interrelate is crucial when examining the impact of climate warming on snowmelt.However,these connections are often overlooked,leading to an unclear relationship between temperature and snowmelt.This study investigates the complex interplay between temperature and snowmelt in the Tibetan Plateau from 1961 to 2020,focusing on how extreme high-temperature events affect the frequency of extreme snowmelt.Using a structural equation model,we detected three temperature-related factors that predominantly influenced snowmelt and extreme snowmelt.The annual average temperature was found to have a significant indirect impact on snowmelt,mediated by changes in snowfall,snow depth and snow cover.By contrast,high-temperature days(daily maximum temperatures exceeding the 90th percentile)and heat waves(at least three consecutive high-temperature days)negatively affected extreme snowmelt directly or indirectly.The direct effect of increasing extreme temperature events was associated with an earlier onset of high-temperature periods,which accelerated snowmelt and shortened the duration of extreme snowmelt periods.Additionally,the reduction in snow cover owing to warming emerged as a main factor suppressing snowmelt and extreme snowmelt frequencies.We also revealed spatiotemporal variations in the temperature‒snowmelt relationship that highly depended on changes in snowmelt patterns.The study elucidated why warming suppresses snowmelt and extreme snowmelt events in the Tibetan Plateau,highlighting the mediating roles of snow-related and phenological factors.The findings will provide scientific support for climate simulation and water management policymaking in alpine regions worldwide.

Field experimental study on the effect of thawed depth of frozen alpine meadow soil on rill erosion by snowmelt waterflow

Soil erosion by snow or ice melt waterflow is an important type of soil erosion in many high-altitude and high-latitude regions and is further aggravated by climate warming.The snowmelt waterflow erosion process is affected by soil freeze-thaws and is highly dynamically variable.In this study,a methodology was developed to conduct in situ field experiments to investigate the effects of the thawed depth of the frozen soil profile on snowmelt waterflow erosion.The method was implemented on an alpine meadow soil slope at an altitude of 3700 m on the northeastern Tibetan Plateau.The erosion experiments involved five thawed soil depths of 0,10,30(35),50,and 80(100)mm under two snowmelt waterflow rates(3 and 5 L/min).When the topsoil was fully frozen or shallow-thawed(≤10 mm),its hydrothermal and structural properties caused a significant lag in the initiation of runoff and delayed soil erosion in the initial stage.The runoff and sediment concentration curves for fully frozen and shallow-thawed soil showed two-stage patterns characteristic of a sediment supply limited in the early stage and subject to hydrodynamic-controlled processes in the later stage.However,this effect did not exist where the thawed soil depth was greater than 30 mm.The deep-thawed cases(≥30 mm)showed normal hydrograph and sedigraph patterns similar to those of the unfrozen soil.The findings of this study are important for understanding the erosion rates of partially thawed soil and for improving erosion simulations in cold regions.

西北山区流域融雪水资源“四预”应用系统技术研究

我国西北山区流域融雪性径流是当地宝贵水资源,为提高融雪水资源综合管理业务工作效率,就具有融雪径流预报、来水极值预警、来水情势预演模拟分析、预案查询等“四预”功能的应用系统相关技术开展研究。分析西北山区流域管理机构和水库管理部门实际工作需要,提出系统功能需求,明确设计原则要求,结合最新信息化技术应用分析,提出采用主计算控制循环模式的系统处理过程控制形式,将人机交互界面设计为基于左右屏幕互动的交互式双屏显示方式,切实强化系统功能、性能等对业务工作实际需要的反映和对接,为开发实现良好的用户应用系统打下坚实基础。

山区高速公路桥面漫排式水热融雪系统应用

为通过热流体的能量传递来达到融雪化冰的目的,水热式融雪技术在预先埋设的管道内通入作为传热载体的循环热流体。针对山区高速的特殊路段,结合地理与气温环境情况,将可循环再生资源隧道温泉水作为山区高速桥面融雪的热源。在此基础上结合工程实例确定了漫排式水热融雪桥面系统的设计与铺设方案,并铺设了试验路。结果表明,对于一定环境条件下的桥面漫排式温泉水融雪系统,融雪效果良好。研究成果可以为采用水热式融雪化冰系统的路面及桥面铺装设计与施工提供指导。

分布式融雪径流模型的设计及应用

融雪径流模型的研究对洪、旱灾害的监测与预报，对水文水资源的管理都具有重要的实用价值。本丈设计和构建了一个基于“3S”技术和DEM技术的分布式融雪径流模型，整个分布式融雪模型的计算基于能量平衡和水量平衡，由分布式栅格融雪过程到分布式橱格产流，最后是基于GIS的橱格汇流过程。融雪以及产汇流过程全部基于橱格尺度，全面实现了模型的分布式特性并具有严格的物理机制。文中提出了“单元时段”的重要概念，从而得到了“度分融雪模型”，为水文时间尺度转换难题的解决提供了新的思路；针对融雪过程中颇为复杂的冻融反复性难题提出了旨在解释积雪冻融反复性物理机制的“冻融系数”的重要概念，对于准确把握融雪过程的物理机制具有重要意义。另外，开发了B／S与C／S相结合的分布式融雪径流模拟系统，利用获取的大量实测、遥感以及气象等资料，基于橱格尺度全面实现了模型的算法，完成了对融雪径流的可视化、可控性模拟。最后。对典型研究区新疆军塘湖流域2006年春季融雪期（2006年3月6日11：00—2006年3月10日11：00的洪水过程进行了模拟．模拟结果精度较高。平均相对误差在0．18以下，达到了融雪洪水预警预报的业务需求。

新疆融雪洪水预警决策支持系统研究

提出以数据仓库、方法库、模型库、知识库一体化模式实现基于“3S”的新疆融雪洪水预警决策支持系统的研究思路。信息系统是整个系统的事务管理层,决策支持系统则是系统应用处理层,两者互为依托。信息系统是决策支持系统的信息源,决策支持系统所产生的结果进入信息系统并对其进行管理,当决策支持系统的模型、方法、知识运作成熟,数据结构化后,又将进入信息系统中,成为信息系统的组成部分。

河西地区融雪径流的灰色预测方法

通过对河西地区主要河流３～６月径流特征的研究，建立了以灰色系统理论为依据的ＧＭ（１，１）和ＧＭ（０，Ｎ）两种预测模式，分别应用于祁连山河西地区融雪径流的中长期预测，为基层生产部门的用水决策提供科学依据。

灰色系统理论的关联分析在融雪径流预报中的应用探讨

灰色系统即信息不完全的系统,关联分析是灰色系统理论中一种判断系统中各因素间相互关系密切程度的量化方法。在灰关联空间中,可辨别系统因子的权重,确定因子的序化关系,划分系统主行为。本文首次将灰关联分析用于融雪径流预报中,以了解黄河上游(指龙羊峡水库以上,下同)各种影响因素对融雪径流作用的程度,从而为建立龙羊峡水库的春季融雪期(每年3月下旬至6月上旬,下同)入库流量预报方程选取恰当的因子。

埋地热力管线腐蚀失效的形式

热力管线是城市基础设施的重要组成部分。热力管网输送高温高压水及蒸汽,均为危险介质,热力管线的突发破坏往往造成巨大经济损失,并引发安全事故。近年来由于城市建设、环境保护和节约能源的需要,管道直埋技术已然成为热力管网敷设的主流,但是随着埋入地下管道运行时间的推移,管道腐蚀失效问题日益严重,其安全问题的分析、评估和控制已经非常紧迫。本文综述了城市埋地热力管线的腐蚀失效形式,并针对当前城市特点及环境因素,对埋地热力管道的腐蚀失效形式进行了汇总和分类、分析。

新疆地区季节性融雪洪水模拟与预报研究

新疆春季积雪融化极易引发融雪性洪水,给当地的农牧业生产和人民生活都带来严重影响和财产损失.融雪中包含复杂的水-热耦合过程,融雪水产流机制受冻土影响,融雪洪水模拟与预报十分复杂,一直是水文研究的难点.新疆大学刘志辉研究团队长期开展季节性融雪洪水模拟与预报研究,在积雪特性监测、冻土融雪水产流机制、分布式融雪径流模型以及融雪洪水预警等方面进行了深入研究.首次提出冻土条件下的融雪水的三个产流机制,即冻土未融化时的超渗产流、冻土部分融化时的饱和产流以及冻融期的交替产流;基于热量平衡和水量平衡研制出分布式融雪径流模型,耦合WRF(Weather Research and Forcasting model)模型实现融雪洪水预报;研制出新疆融雪洪水预警决策支持系统,实现融雪洪水预警系统的应用.研究成果有助于融雪洪水模拟的进一步研究,也为政府部门融雪洪水预警决策提供科学依据.

Variation in water supply leads to different responses of tree growth to warming

Background:Global climate change,characterized by changes in precipitation,prolonged growing seasons,and warming-induced water deficits,is putting increased pressure on forest ecosystems globally.Understanding the impact of climate change on drought-prone forests is a key objective in assessing forest responses to climate change.Methods:In this study,we assessed tree growth trends and changes in physiological activity under climate change based on measurements of tree ring and stable isotopes.Additionally,structural equation models were used to identify the climate drivers influencing tree growth for the period 1957–2016.Results:We found that the mean basal area increment decreased first and then increased,while the water use efficiency showed a steady increase.The effects of climate warming on tree growth switched from negative to positive in the period 1957–2016.Adequate water supply,especially snowmelt water available in the early critical period,combined with an earlier arrival of the growing season,allowed to be the key to the reversal of the effects of warming on temperature forests.The analysis of structural equation models(SEM)also demonstrated that the growth response of Pinus tabuliformis to the observed temperature increase was closely related to the increase in water availability.Conclusions:Our study indicates that warming is not the direct cause of forest decline,but does indeed exacerbate droughts,which generally cause forest declines.Water availability at the beginning of the growing season might be critical in the adaptation to rising temperatures in Asia.Temperate forests may be better able to withstand rising temperatures if they have sufficient water,with boosted growth even possible during periods of rising temperatures,thus forming stronger carbon sinks.