Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

Spatiotemporal Evaluation of Simulated Evapotranspiration and Streamflow over Texas Using the WRF‐Hydro‐RAPID Modeling Framework

This study assesses a large‐scale hydrologic modeling framework (WRF‐Hydro‐RAPID) in terms of its high‐resolution simulation of evapotranspiration (ET) and streamflow over Texas (drainage area: 464,135 km²). The reference observations used include eight‐day ET data from MODIS and FLUXNET, and daily river discharge data from 271 U.S. Geological Survey gauges located across a climate gradient. A recursive digital filter is applied to decompose the river discharge into surface runoff and base flow for comparison with the model counterparts. While the routing component of the model is pre‐calibrated, the land component is uncalibrated. Results show the model performance for ET and runoff is aridity‐dependent. ET is better predicted in a wet year than in a dry year. Streamflow is better predicted in wet regions with the highest efficiency ~0.7. In comparison, streamflow is most poorly predicted in dry regions with a large positive bias. Modeled ET bias is more strongly correlated with the base flow bias than surface runoff bias. These results complement previous evaluations by incorporating more spatial details. They also help identify potential processes for future model improvements. Indeed, improving the dry region streamflow simulation would require synergistic enhancements of ET, soil moisture and groundwater parameterizations in the current model configuration. Our assessments are important preliminary steps towards accurate large‐scale hydrologic forecasts.     

Reservoir Sedimentation Rates in the Little Washita River Experimental Watershed,Oklahoma: Measurement and Controlling Factors

Forty‐five flood control reservoirs, authorized in the Watershed Protection and Flood Prevention Act 1954, were installed by United States Department of Agriculture (USDA) between 1969 and 1982 in the Little Washita River Experimental Watershed (LWREW), located in central Oklahoma. Over time, these reservoirs have lost sediment and flood storage capacity due to sedimentation, with rates dependent on upstream land use and climate variability. In this study, sedimentation rates for 12 reservoirs representing three major land use categories within LWREW were measured based on bathymetric surveys that used acoustic profiling system. Physiographic and climate attributes of drainage area of surveyed reservoirs were extracted from publicly available data sources including topographic maps, digital elevation models, USDA Natural Resource Conservation Service soils, and weather station databases. Correlation, principal component analysis, and stepwise regression were utilized to analyze the relationship between normalized reservoir sedimentation rates (ReSRa) and the drainage area characteristics to determine the major variables controlling sedimentation within the LWREW. Percent of drainage area with extreme slopes, saturated hydraulic conductivity, and maximum daily rainfall event recorded in spring explained most of the variability in ReSRa. It was also found that percent reduction in reservoir surface area can be used as a surrogate for estimating ReSRa. The implications of the results are discussed.     

Evaluation of Gauge‐Radar Merging Methods Using a Semi‐Distributed Hydrological Model in the Upper Thames River Basin,Canada

Gauge‐radar merging methods combine rainfall estimates from rain gauges and radar to capitalize on the strengths of the individual instruments. The performance of four well‐known gauge‐radar merging methods, including mean field bias correction, Brandes spatial adjustment, local bias correction using kriging, and conditional merging, are examined using Environment Canada radar and the Upper Thames River Basin in southwestern Ontario, Canada, as a case study. The analysis assesses the effect of gauge‐radar merging methods on: (1) the accuracy of predicted rainfall accumulations; and (2) the accuracy of predicted streamflows using a semi‐distributed hydrological model. In addition, several influencing factors (i.e., gauge density, storm type, basin type, proximity to the radar tower, and time‐step of adjustment) are analyzed to determine their effect on the performance of the rainfall estimation techniques. Confirming results of previous studies, the merging methods provide an increase in the accuracy of both rainfall accumulation estimations and predicted streamflows. The results also indicate specific factors such as gauge density, rainfall intensity, and time‐step of adjustment can reduce the accuracy of merging methods and play a key role in the examination of its use for operational purposes. Results provide guidance for hydrologists and engineers assessing how best to apply corrected radar products to improve rainfall estimation and hydrological modeling accuracy.     

Assessing Spatial Scale Effects on Hydrometric Network Design Using Entropy and Multi‐objective Methods

In order to facilitate water resources decisions, it is important that accurate and informative hydrometric data are collected. Combining information theory with multi‐objective optimization has led to methods of optimizing the information content provided by hydrometric networks; however, there is no available study on the effects of spatial scale and data limitation on these methods. Herein, a dual entropy multi‐objective optimization (DEMO) and a transinformation (TI) analysis were done to recommend optimal locations for additional hydrometric stations in the Madawaska Watershed. This analysis was designed to be comparative to a similar study conducted on the Ottawa River Basin which encompasses the Madawaska Watershed to allow for an investigation of the spatial scale effects in this type of network design. This study concludes that TI analysis is not adversely affected by scaling; however, the DEMO analysis is sensitive to the placement of potential station locations and the size of the study area. This study also examines the benefit of including nearby stations when the area of interest does not have a sufficient number of existing hydrometric stations for analysis. It is shown that these stations can provide useful information because their inclusion in the analysis increased the average TI in the watershed. Recommendations were made as to the ideal locations of additional stations in the Madawaska Watershed hydrometric network.     

Insiders and outsiders: local government and NGO engagement in disaster response in Guimaras,Philippines

This paper examines local government and non‐governmental organisation (NGO) engagement in disaster response in the wake of the M/T Solar 1 oil spill in Guimaras, Western Visayas, Philippines, on 11 August 2006. It assesses the response activities of these two entities as well as the institutional factors that affected their interaction on the ground. Local government and NGO engagement was shaped by multi‐layered, overlapping, and oftentimes contending government‐designed response frameworks. Within these frameworks, government actors played the role of primary implementer and provider of relief, allowing them to determine who could be involved and the extent of their involvement. The absence of formal roles for NGOs in these frameworks not only undermines their ability to work in a setting where such institutional set‐ups are operational but also it reaffirms their ‘outsider’ status. This study of the Guimaras oil spill illustrates the complexity and the institutional difficulties inherent in disaster response and coordination in the Philippines.     

N_2:Ar法直接测定水体反硝化产物溶解N_2

为了深入开展水体反硝化过程及机理研究,利用HIDEN公司生产的HPR40溶解气体质谱分析仪,通过仪器配置和条件摸索,建立了基于N2:Ar原理直接测定水体反硝化产物溶解N2的分析方法,并总结了采样技术要点.实验结果表明,膜进样质谱仪(MIMS)在长时间(10h)连续测定情况下仍能保持良好的信号稳定性,盐度为0和2%的标样的N2/Ar(物质的量比)变异系数(CV)分别0.53%和0.17%;在最佳实验条件下(恒温槽温度设为18～25℃,磁搅拌气液平衡52h,仪器信号平稳),15d内绘制了5条校准工作曲线,其斜率的相对标准偏差为3.03%,说明该方法的重复性较好;平行水样的标准偏差小于2μmol·L-1.将N2:Ar法应用于福建省九龙江表层水样的连续观测,可观察到明显的反硝化过程,表明所建立的N2:Ar法在今后各种水体反硝化过程和机理研究中具有良好的应用前景.     

Prediction of Annual Streambank Erosion for Sequoia National Forest,California

Many bank erosion models have limitations that restrict their use in wildland settings. Scientists and land managers at the Sequoia National Forest would like to understand the mechanisms and rates of streambank erosion to evaluate management issues and post‐wildfire effects. This study uses bank erosion hazard index (BEHI) and near‐bank stress (NBS) methods developed in Rosgen (2006 Watershed Assessment of River Stability and Sediment Supply [WARSSS]) for predicting streambank erosion in a geographic area that is dominated by colluvium and in which streambank erosion modeling has not been previously evaluated. BEHI evaluates bank susceptibility to erosion based on bank angle, bank and bankfull height, rooting depth and density, surface protection, and stratification of material within the banks. NBS assesses energy distribution against the bank measured as a ratio of bankfull near‐bank maximum depth to mean bankfull depth. We compared BEHI classes and NBS to actual bank erosion measured from 2008 to 2012. This index predicted streambank erosion with clear separation among BEHI ratings with R² values of 0.76 for extreme, 0.37 for high/very high, 0.49 for moderate, and 0.70 for low BEHI. The relationships between measured erosion and BEHI extend the application of BEHI/NBS to a new region where they can inform management priorities, afforestation, stream/riparian restoration projects, and potentially burned area rehabilitation.     

缓释肥侧条施肥技术对水稻产量和氮素利用效率的影响

利用田间小区试验,系统研究了基于缓释肥料的侧条施肥技术对水稻产量和氮素利用效率的影响。试验结果表明：与农民常规施肥处理（FP）比较,侧条施肥技术高缓释肥处理（HF）水稻氮素投入比农民常规施肥处理（FP）降低约40%,水稻产量没有显著降低,穗粒数比农民常规施肥处理增加了8.36%。侧条施肥技术显著提高了水稻地上部吸氮量和氮肥偏生产力,降低了氮素的表观损失量。侧条施肥各处理氮肥偏生产力在39.1～67.8之间,显著高于FP处理的23.7。FP处理氮素表观损失量高达174.2 kg＆#183;hm-2,侧条施肥各处理表观损失量在23.2～61.9 kg＆#183;hm-2之间。综合考虑水稻产量和环境因素,基于缓释肥料的侧条施肥技术是一种资源节约和环境友好的施肥技术。     

A Hydrologic Data Screening Procedure for Exploring Monotonic Trends and Shifts in Rainfall and Runoff Patterns

A thorough understanding of past and present hydrologic responses to changes in precipitation patterns is crucial for predicting future conditions. The main objectives of this study were to determine temporal changes in rainfall‐runoff relationship and to identify significant trends and abrupt shifts in rainfall and runoff time series. Ninety‐year rainfall and runoff time series datasets from the Gasconade and Meramec watersheds in east‐central Missouri were used to develop data screening procedure to assess changes in the rainfall and runoff temporal patterns. A statistically significant change in mean and variance was detected in 1980 in the rainfall and runoff time series within both watersheds. In addition, both the rainfall and runoff time series indicated the presence of nonstationary attributes such as statistically significant monotonic trends and/or change in mean and variance, which should be taken into consideration when using the time series to predict future scenarios. The annual peak runoff and the annual low flow in the Meramec watershed showed significant temporal changes compared to that in the Gasconade watershed. Water loss in both watersheds was found to be significantly increasing which is potentially due to the increase in groundwater pumping for water supply purposes.