A modelling framework for evaluation of the hydrological impacts of nature‐based approaches to flood risk management,with application to in‐channel interventions across a 29‐km2 scale catchment in the United Kingdom

Nature‐based approaches to flood risk management are increasing in popularity. Evidence for the effectiveness at the catchment scale of such spatially distributed upstream measures is inconclusive. However, it also remains an open question whether, under certain conditions, the individual impacts of a collection of flood mitigation interventions could combine to produce a detrimental effect on runoff response. A modelling framework is presented for evaluation of the impacts of hillslope and in‐channel natural flood management interventions. It couples an existing semidistributed hydrological model with a new, spatially explicit, hydraulic channel network routing model. The model is applied to assess a potential flood mitigation scheme in an agricultural catchment in North Yorkshire, United Kingdom, comprising various configurations of a single variety of in‐channel feature. The hydrological model is used to generate subsurface and surface fluxes for a flood event in 2012. The network routing model is then applied to evaluate the response to the addition of up to 59 features. Additional channel and floodplain storage of approximately 70,000 m³ is seen with a reduction of around 11% in peak discharge. Although this might be sufficient to reduce flooding in moderate events, it is inadequate to prevent flooding in the double‐peaked storm of the magnitude that caused damage within the catchment in 2012. Some strategies using features specific to this catchment are suggested in order to improve the attenuation that could be achieved by applying a nature‐based approach.     

Large‐scale poroelastic fractured reservoirs modeling using the fast multipole displacement discontinuity method

An effective approach to modeling the geomechanical behavior of the network and its permeability variation is to use a poroelastic displacement discontinuity method (DDM). However, the approach becomes rather computationally intensive for an extensive system of cracks, particularly when considering coupled diffusion/deformation processes. This is because of additional unknowns and the need for time‐marching schemes for the numerical integration. The Fast Multipole Method (FMM) is a technique that can accelerate the solution of large fracture problems with linear complexity with the number of unknowns both in memory and CPU time. Previous works combining DDM and FMM for large‐scale problems have accounted only for elastic rocks, neglecting the fluid leak‐off from the fractures into the matrix and its influence on pore pressure and stress field. In this work we develop an efficient geomechanical model for large‐scale natural fracture networks in poroelastic reservoirs with fracture flow in response to injection and production operations. Accuracy and computational performance of the proposed method with those of conventional poroelastic DDM are compared through several case studies involving up to several tens of thousands of boundary elements. The results show the effectiveness of the FMM approach to successfully evaluate field‐scale problems for the design of exploitation strategies in unconventional geothermal and petroleum reservoirs. An example considering faults reveals the impact of reservoir compartmentalization because of sealing faults for both geomechanical and flow variables under elastic and poroelastic rocks. Copyright © 2015 John Wiley & Sons, Ltd.     

Understanding the relative impacts of natural processes and human activities on the hydrology of the Central Rift Valley lakes,East Africa

Significant changes have been observed in the hydrology of Central Rift Valley (CRV) lakes in Ethiopia, East Africa as a result of both natural processes and human activities during the past three decades. This study applied an integrated approach (remote sensing, hydrologic modelling, and statistical analysis) to understand the relative effects of natural processes and human activities over a sparsely gauged CRV basin. Lake storage estimates were calculated from a hydrologic model constructed without inputs from human impacts such as water abstraction and compared with satellite‐based (observed) lake storage measurements to characterize the magnitude of human‐induced impacts. A non‐parametric Mann–Kendall test was used to detect the presence of climatic trends (e.g. a decreasing or increasing trends in precipitation), while the Standard Precipitation Index (SPI) analysis was used to assess the long‐term, inter‐annual climate variability within the basin. Results indicate human activities (e.g. abstraction) significantly contributed to the changes in the hydrology of the lakes, while no statistically significant climatic trend was seen in the basin, however inter‐annual natural climate variability, extreme dryness, and prolonged drought has negatively affected the lakes. The relative contributions of natural and human‐induced impacts on the lakes were quantified and evaluated by comparing hydrographs of the CRV lakes. Lake Abiyata has lost ~6.5 m in total lake height between 1985 and 2006, 70% (~4.5 m) of the loss has been attributed to human‐induced causes, whereas the remaining 30% is related to natural climate variability. The relative impact analysis utilized in this study could potentially be used to better plan and create effective water‐management practices in the basin and demonstrates the utility of this integrated methodology for similar studies assessing the relative natural and human‐induced impacts on lakes in data sparse areas. Copyright © 2015 John Wiley & Sons, Ltd.     

Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.     

齐齐哈尔市地理国情分析与表达

以齐齐哈尔市为例,利用地理国情普查数据与统计年鉴数据,构建自然地表指数、地理区位指数、生态覆被指数,对齐齐哈尔市各县、区的地理国情进行综合分析和地图表达。结果表明,齐齐哈尔市的总体条件较好,但区域差异较大,自然地表条件与地理区位条件均以中部的齐齐哈尔市辖区为优,而生态覆被条件则以北部和西部地区为优。     

地理国情普查外业调查与核查探讨

地理国情普查主要是查清我国地形地貌、地表覆盖等自然和人文地理要素的现状和空间分布情况,建立普查成果数据库,为开展常态化的国情监测奠定基础。正确理解吉林省地理国情普查技术设计和作业指导书,以及国情普查的内容与指标,进行外业核查与遥感影像解译样本采集工作,为最终形成地理国情要素数据、地表覆盖分类数据和遥感影像解译样本数据库奠定基础。     

迁移学习支持下的高分影像山地滑坡灾害解译模型

无人机低空遥感是近年来新兴的一种快速获取灾情信息的手段,如何利用无人机高分影像构建滑坡灾害解译模型是实现快速自动解译滑坡的关键。针对该问题,对比了多种影像特征提取方法,将迁移学习(TL)特征和支持向量机(SVM)引入到构建滑坡灾害自动解译模型中,提出了一种TL支持下的高分影像滑坡灾害解译模型。选取5·12汶川地震及4·20芦山地震系列无人机影像构建了滑坡灾害样本库并进行了实验,TL特征方法整体分类准确度ACC为95%,ROC达到0.98,识别准确率达到97%。结果表明,所提方法可用于高分影像滑坡自动解译,同时可用于大面积高分影像中快速山地滑坡灾害定位及检测。     

一种简化的暴雨灾害风险及影响评估方法和应用研究 ——以京津冀“7·21”暴雨事件为例

文章综合考虑中国区域范围内降雨时空分布特征以及地理地貌等特征,将全国降雨区划分为4大类,在此基础上,得出不同降雨类区暴雨致灾因子的强度等级评定方法;同时,研究确定了与暴雨灾害密切相关的地形高程、高程标准差、河网密度、土壤类型等环境脆弱性影响要素,并对各类要素分别进行了分级评定;将各类环境脆弱性影响要素结合暴雨致灾因子要素,运用加权求和方法建立了暴雨灾害综合风险评估模型;并结合GIS技术,将城市、农村人口分布情况、用地等数据叠加到风险分布格局中,最终分析得出不同风险等级下影响的城市和农村人口数量、土地面积等内容。该评估模型相较于以往其他暴雨风险评估模型,其适用范围更广,可以适用于全国范围内的任意区域暴雨灾害风险评估;实时评估业务能力更强,将该模型结合降雨实况资料或预报资料可以对全国任意区域降雨灾害综合风险进行事后、跟踪评估或预评估;评估对象更有针对性,结合GIS技术,可以针对得出的风险分布结果分别给出不同风险等级范围内的承灾体受影响的定量评估结果。     

湖北省旱涝灾害致灾规律的初步研究

利用1960—2005年湖北省76个地区气象灾害的灾情普查数据和逐日降水量观测资料,对湖北省旱涝灾害的时空分布特征及其致灾规律进行分析。结果表明:干旱灾害的频发区呈东西走向的带状分布,而洪涝灾害的发生频次和频发区面积均明显少于干旱;干旱和洪涝灾害年平均发生站次在1996年以后出现相反的变化趋势,干旱发生站次增加,而洪涝发生站次减少,且两种灾害均主要集中发生在夏季;1996—2001年湖北省部分地区连续出现严重干旱灾害,干旱的累积增强效应导致农业经济损失出现跳跃性增长并在2001年达到最大值;洪涝的致灾强度呈准周期的起伏振荡,农作物受洪涝影响面积最大、损失最多的年份集中在20世纪90年代,农作物受害面积与农业经济损失的决定系数为0.8;受害人口与直接经济损失具有较好的相关特征,且直接经济损失随受害人口增多而增加的速度加快,但近年来人口对洪涝灾害的抵御能力也显著提高;急转干旱和急转洪涝主要发生在鄂西北和鄂东南的夏季,农作物的脆弱度增加,农业经济损失随受害面积增大而增加的速度加快,但所造成的农业经济损失远小于仅发生干旱和洪涝时的数值。     

四川烤烟主产区冰雹灾害风险评估

以四川烤烟主产区冰雹灾害为研究对象,将烤烟气候适应性特征和自然灾害系统论相结合,利用冰雹频率、DEM高程数据、下垫面类型以及农业经济数据等资料,构建冰雹灾害风险评估指标体系,从致灾因子危险性、孕灾环境敏感性、承灾体易损性和抗灾能力4个方面进行分析,最终建立四川烤烟主产区冰雹灾害精细化风险评估模型并计算风险分布指数,借此将攀西烟区划分为由高至低的5个风险区域和1个不适宜种植区。结果表明,安宁河源头河谷地区、雅砻江下游流域以及攀西地区南部的河谷低山区和丘陵区冰雹灾害风险处于较高水平。评估结果为区域烤烟种植结构调整及冰雹灾害防御工作提供了科学指导。