Impacts of Changes in Precipitation Amount and Distribution on Water Resources Studied Using a Model Rainwater Harvesting System

Water supply reliability is expected to be affected by both precipitation amount and distribution changes under recent and future climate change. We compare historical (1951‐2010) changes in annual‐mean and annual‐maximum daily precipitation in the global set of station observations from Global Historical Climatology Network and climate models from the Inter‐Sectoral Impact Model Intercomparison Project (ISI‐MIP), and develop the study to 2011‐2099 for model projections under high radiative forcing scenario (RCP8.5). We develop a simple rainwater harvesting system (RWHS) model and drive it with observational and modeled precipitation. We study the changes in mean and maximum precipitation along with changes in the reliability of the model RWHS as tools to assess the impact of changes in precipitation amount and distribution on reliability of precipitation‐fed water supplies. Results show faster increase in observed maximum precipitation (10.14% per K global warming) than mean precipitation (7.64% per K), and increased reliability of the model RWHS driven by observed precipitation by an average of 0.2% per decade. The ISI‐MIP models show even faster increase in maximum precipitation compared to mean precipitation. However, they imply decreases in mean reliability, for an average 0.15% per decade. Compared to observations, climate models underestimate the increasing trends in mean and maximum precipitation and show the opposite direction of change in reliability of a model water supply system.     

Improved Weather Generator Algorithm for Multisite Simulation of Precipitation and Temperature

The KnnCAD Version 4 weather generator algorithm for nonparametric, multisite simulations of temperature and precipitation data is presented. The K‐nearest neighbor weather generator essentially reshuffles the historical data, with replacement. In KnnCAD Version 4, a block resampling scheme is introduced to preserve the temporal correlation structure in temperature data. Perturbation of the reshuffled variable data is also added to enhance the generation of extreme values. The Upper Thames River Basin in Ontario, Canada is used as a case study and the model is shown to simulate effectively the historical characteristics at the site. The KnnCAD Version 4 approach is shown to improve on the previous versions of the model and offers a major advantage over many parametric and semiparametric weather generators in that multisite use can be easily achieved without making statistical assumptions dealing with the spatial correlations and probability distributions of each variable.     

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.     

PRIESTLEY‐TAYLOR ALPHA COEFFICIENT: VARIABILITY AND RELATIONSHIP TO NDVI IN ARCTIC TUNDRA LANDSCAPES1

ABSTRACT: Average daily values of the Priestley‐Taylor coefficient (a) were calculated for two eddy covariance (flux) tower sites with contrasting vegetation, soil moisture, and temperature characteristics on the North Slope of Alaska over the 1994 and 1995 growing seasons. Because variations in a have been shown to be associated with changes in vegetation, soil moisture, and meteorological conditions in Arctic ecosystems, we hypothesized that a values would be significantly different between sites. Since variations in the normalized difference vegetation index (NDVI) follow patterns of vegetation community composition and state that are largely controlled by moisture and temperature gradients on the North Slope of Alaska, we hypothesized that temporal variations in a respond to these same conditions and thus co‐vary with NDVI. Significant differences in a values were found between the two sites in 1994 under average precipitation conditions. However, in 1995, when precipitation conditions were above average, no significant difference was found. Overall, the variations in a over the two growing seasons showed little relationship to the seasonal progression of the regional NDVI. The only significant relationship was found at the drier, upland study site.     

Analysis of Meteorological Drought Pattern During Different Climatic and Cropping Seasons in Bangladesh

Drought is one of the most frequent natural disasters in Bangladesh which severely affect agro‐based economy and people's livelihood in almost every year. Characterization of droughts in a systematic way is therefore critical in order to take necessary actions toward drought mitigation and sustainable development. In this study, standardized precipitation index is used to understand the spatial distribution of meteorological droughts during various climatic seasons such as premonsoon, monsoon, and winter seasons as well as cropping seasons such as Pre‐Kharif (March‐May), Kharif (May‐October), and Rabi (December‐February). Rainfall data collected from 29 rainfall gauge stations located in different parts of the country were used for a period of 50 years (1961‐2010). The study reveals that the spatial characteristics of droughts vary widely according to season. Premonsoon droughts are more frequent in the northwest, monsoon droughts mainly occur in the west and northwest, winter droughts in the west, and the Rabi and Kharif droughts are more frequent in the north and northwest of Bangladesh. It is expected that the findings of the study will support drought monitoring and mitigation activities in Bangladesh.     

Urbanization and Its Effect On Runoff in the Whiteoak Bayou Watershed,Texas1

Abstract: The capacity of a watershed to urbanize without changing its hydrologic response and the relationship between that response and the spatial configuration of the developed areas was studied. The study was conducted in the Whiteoak Bayou watershed (223 km²), located northwest of Houston, Texas, over an analysis period from 1949 to 2000. Annual development data were derived from parcel data collected by the Harris County Appraisal District. Using these data, measures of the spatial configuration of the watershed urban areas were calculated for each year. Based on regression models, it was determined that the annual runoff depths and annual peak flows depended on the annual precipitation depth, the developed area and the maximum 12‐h precipitation depth on the day and day before the peak flow took place. It was found that, since the early 1970s, when the watershed reached a 10% impervious area, annual runoff depths and peak flows have increased by 146% and 159%, respectively. However, urbanization is responsible for only 77% and 32% of the increase, respectively, while precipitation changes are responsible for the remaining 39% and 96%, respectively. Likewise, an analysis of the development data showed that, starting in the early 1970s, urbanization in the watershed consisted more of connecting already developed areas than of creating new ones, which increases the watershed’s conveyance capacity and explains the change in its response. Before generalizing conclusions, though, further research on other urban watersheds with different urbanization models appears to be necessary.     

Long‐Term Trends of Nutrients and Sediment from the Nontidal Chesapeake Watershed: An Assessment of Progress by River and Season

To assess historical loads of nitrogen (N), phosphorus (P), and suspended sediment (SS) from the nontidal Chesapeake Bay watershed (NTCBW), we analyzed decadal seasonal trends of flow‐normalized loads at the fall‐line of nine major rivers that account for >90% of NTCBW flow. Evaluations of loads by season revealed N, P, and SS load magnitudes have been highest in January‐March and lowest in July‐September, but the temporal trends have followed similar decadal‐scale patterns in all seasons, with notable exceptions. Generally, total N (TN) load has dropped since the late 1980s, but particulate nutrients and SS have risen since the mid‐1990s. The majority of these rises were from Susquehanna River and relate to diminished net trapping at the Conowingo Reservoir. Substantial rises in SS were also observed, however, in other rivers. Moreover, the summed rise in particulate P load from other rivers is of similar magnitude as from Susquehanna. Dissolved nutrient loads have dropped in the upland (Piedmont and above) rivers, but risen in two small rivers in the Coastal Plain affected by lagged groundwater input. In addition, analysis of fractional contributions revealed consistent N trends across the upland watersheds. Finally, total N:total P ratios have declined in most rivers, suggesting the potential for changes in nutrient limitation. Overall, this integrated study of historical data highlights the value of maintaining long‐term monitoring at multiple watershed locations.     

陕北高原最高最低气温时空变化特征分析

采用气候倾向率、趋势系数、普通克里格插值、Mann—Kendall检验、小波分析等方法对陕北高原1960-2009年最高气温、最低气温的时空变化特征与突变特点进行分析。结果表明，近50年来陕北高原最高、最低气温均有明显增温趋势，分别为0．3℃／10a和0．32℃／10a；最高、最低气温增温幅度存在季节差异，最高气温在春季增温幅度最大，最低气温在冬季增温幅度最大；最高、最低气温增温的地区差异也较显著，最高气温增温趋势从北向南呈间隔分布，最低气温增温趋势从西向东逐渐递减；最高、最低气温升温突变显著，分别为1994年和1993年；最低气温突变早于最高气温，说明最低气温比最高气温敏感。     

Effects of Spatial and Temporal Data Aggregation on the Performance of the Multi‐Radar Multi‐Sensor System

The objectives of this study were to (1) evaluate the performance of the Multi‐Radar Multi‐Sensor (MRMS) system in capturing precipitation compared to gauge data, and (2) assess the effects of spatial (1–50 km) and temporal (15–120 min) data aggregation scales on the performance of the MRMS system. Point‐to‐grid comparisons were conducted between 215 rain gauges and the MRMS system. The MRMS system at 1 km spatial and 15 min temporal resolutions captured precipitation reasonably well with average R², root mean square error (RMSE), and percent bias (PBIAS) values of 0.65, 0.5 mm, and 11.9 mm; whereas Threat Score, probability of detection, and false alarm ratio were 0.57, 0.92, and 0.40, respectively. Decreasing temporal resolution from 15 min to two hours resulted in an increase in R² and a decrease in RMSE, whereas PBIAS was not affected. Reducing spatial resolution from 1 to 50 km resulted in increases in R² and PBIAS, whereas RMSE was decreased. Increasing spatial aggregation scale from 1 to 50 km resulted in an R² increase of only 0.08. Similarly, improvement in R² was only modest (0.17) compared to an eightfold reduction in temporal resolution (from 15 min to two hours). While aggregating data at coarser temporal resolutions resolved some of the under/overestimation issues of the MRMS system, it was apparent even at coarser spatial and temporal resolutions the MRMS system inherently overestimated smaller precipitation events while underestimated bigger events.     

PRECIPITATION DISTRIBUTION IN COASTAL BRITISH COLUMBIA1

ABSTRACT: The spatial distribution and the temporal and spatial variation of the annual, seasonal, and monthly precipitation in two mountainous watersheds in southwestern British Columbia, Canada, have been analyzed using a detailed database for 1971–1990 in the Capilano and Seymour watersheds. The analysis showed that the precipitation increases up to the mid-position of the watersheds, and then either levels off or decreases. Precipitation on mountain slopes and in the valley at the same distance from the beginning of the slope is similar, and the barrier height is identified as the dominant parameter which influences the precipitation distribution. The temporal variation of the precipitation is the smallest at the mid-position of the watersheds. This variability is the least in the fall and winter and largest in the summer. Correlation between the precipitation accumulations at various stations is large, ranging from 0.80 for the wet period of October-March to 0.65 for the dry period of April-September for distances less than 32 km. Comparison with other studies and the analyses of precipitation and runoff data from coastal British Columbia showed that the results of this study are perhaps general and thus transferable to similar areas in the coastal Pacific Northwest.     

基于SML指数的环境回弹效应分析：模型与测算

本文通过搭建环境回弹效应理论框架,采用熵权法构造各地区综合环境污染指数,利用Sequential Malmquist-Luenberger指数全要素生产率模型测度技术进步对经济增长的贡献率,定量评估了1999—2017年我国30个省份的环境回弹效应,识别了环境回弹效应时序演变和区域差异,并进一步利用空间分析技术探究环境回弹效应的空间分布特征。研究表明:样本期内各省份环境回弹效应均值集中在-13.23%~29.63%,全国平均环境回弹效应为10.42%,实际减污率仅为65.74%;时序特征上,环境回弹效应与技术进步有部分相关性,但存在滞后作用,经济发展水平和技术溢出效应是主要的异质性来源;空间特征上,环境回弹效应的区域差异显著,但具有空间相关性,地理因素在解释环境回弹效应的影响因素中不容忽视;全局自相关检验表明2006—2011年各省份环境回弹效应呈显著的正自相关,空间集聚特征明显。建议进一步挖掘环境降污空间,加快经济增长与环境污染的脱钩,关注区域协同减排,是改善污染治理的有效措施。     

MODELING TRANSIENT pH DEPRESSIONS IN COASTAL STREAMS OF BRITISH COLUMBIA USING NEURAL NETWORKS1

ABSTRACT: Transient events in water chemistry in small coastal watersheds, particularly pH depressions, are largely driven by inputs of precipitation. While the response of each watershed depends upon both the nature of the precipitation event and the season of the year, how the response changes over time can provide insight into landscape changes. Neural network models for an urban watershed and a rural‐suburban watershed were developed in an attempt to detect changes in system response resulting from changes in the landscape. Separate models for describing pH depressions for wet season and dry season conditions were developed for a seven year period at each watershed. The neural network models allowed separation of the effects of precipitation variations and changes in watershed response. The ability to detect trends in pH depression magnitudes was improved by analyzing neural network residuals rather than the raw data. Examination of sensitivity plots of the models indicated how the neural networks were affected by different inputs. There were large differences in effects between seasons in the rural‐suburban watershed whereas effects in the urban watershed were consistent between seasons. During the study period, the urban watershed showed no change in pH depression response, while the rural‐suburban watershed showed a significant increase in the magnitude of pH depressions, likely the result of increased urbanization.     

Spatial and Temporal Patterns of Low Streamflow and Precipitation Changes in the Chesapeake Bay Watershed

Spatial and temporal patterns in low streamflows were investigated for 183 streamgages located in the Chesapeake Bay Watershed for the period 1939–2013. Metrics that represent different aspects of the frequency and magnitude of low streamflows were examined for trends: (1) the annual time series of seven‐day average minimum streamflow, (2) the scaled average deficit at or below the 2% mean daily streamflow value relative to a base period between 1939 and 1970, and (3) the annual number of days below the 2% threshold. Trends in these statistics showed spatial cohesion, with increasing low streamflow volume at streamgages located in the northern uplands of the Chesapeake Bay Watershed and decreasing low streamflow volume at streamgages in the southern part of the watershed. For a small subset of streamgages (12%), conflicting trend patterns were observed between the seven‐day average minimum streamflow and the below‐threshold time series and these appear to be related to upstream diversions or the influence of reservoir‐influenced streamflows in their contributing watersheds. Using multivariate classification techniques, mean annual precipitation and fraction of precipitation falling as snow appear to be broad controls of increasing and decreasing low‐flow trends. Further investigation of seasonal precipitation patterns shows summer rainfall patterns, driven by the Atlantic Multidecadal Oscillation, as the main driver of low streamflows in the Chesapeake Bay Watershed.     

Evaluation of the Global Precipitation Measurement (GPM) Satellite Rainfall Products over the Lower Colorado River Basin,Texas

Quality of precipitation products from the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement mission (IMERG) was evaluated over the Lower Colorado River Basin of Texas. Observations of several rainfall events of a wide range of magnitudes during May 2015 by a very dense network of 241 rain gauges over the basin were used as a reference. The impact of temporal and spatial downscaling of different satellite products (near/post‐real‐time) on their accuracy was studied. Generally, all IMERG products perform better when the temporal and spatial resolutions are downscaled. The Final product shows relatively better performance compared to the near‐real‐time products in terms of basic performance measures; however, regarding rainfall detection, all products show nearly similar performance. When considering rainfall detection, IMERG adequately captures the precipitation events; however, in terms of spatial patterns and accuracy, more improvements are needed. IMERG products analysis results may help developers gain insight into the regional performance of the product, improve the product algorithms, and provide information to end users on the products’ suitability for potential hydrometeorological applications. Overall, the IMERG products, even the uncalibrated product at its finest resolution, showed reasonable performance indicating their great potential for applications such as water resources management, prevention of natural disasters, and flood forecasting.     

Daily Precipitation Extremes in Iran: Decadal Anomalies and Possible Drivers

This study focuses on the empirical statistical analysis of the anomalies in daily precipitation extremes by applying the quantile perturbation method (QPM) to data from 31 Iranian weather stations during the period between 1961 and 2005. The possible causes behind the anomalies in precipitation extremes are identified by analyzing their relationship with the anomalies in eight atmospheric indices (i.e., NAO, SOI, PDO, AMO, NCP, DMI, WeMO, SSN). In terms of decadal oscillations, the country was generally wet in the 1960s and 1970s with most stations exhibiting periods of higher quantile perturbations, whereas lower quantile perturbations were dominant in the 1980s and 1990s. The highest perturbation in extreme precipitation quantiles prevails in Central Iran during the early 1980s, in which the quantiles are about 50% higher than the ones based on the full time series. The frequency of significant precipitation anomalies for winter season was greater than that for spring and autumn seasons. For the summer season, the humid region in North Iran demonstrates strong positive anomalies. The results highlight the noticeable role of large‐scale climatic factors in the anomalous behavior of precipitation extremes in Iran. The atmospheric drivers of the quantile anomalies in extreme precipitation were found to differ from one season to another.     

Annual Estimates of Recharge,Quick‐Flow Runoff,and Evapotranspiration for the Contiguous U.S. Using Empirical Regression Equations

This study presents new data‐driven, annual estimates of the division of precipitation into the recharge, quick‐flow runoff, and evapotranspiration (ET) water budget components for 2000‐2013 for the contiguous United States (CONUS). The algorithms used to produce these maps ensure water budget consistency over this broad spatial scale, with contributions from precipitation influx attributed to each component at 800 m resolution. The quick‐flow runoff estimates for the contribution to the rapidly varying portion of the hydrograph are produced using data from 1,434 gaged watersheds, and depend on precipitation, soil saturated hydraulic conductivity, and surficial geology type. Evapotranspiration estimates are produced from a regression using water balance data from 679 gaged watersheds and depend on land cover, temperature, and precipitation. The quick‐flow and ET estimates are combined to calculate recharge as the remainder of precipitation. The ET and recharge estimates are checked against independent field data, and the results show good agreement. Comparisons of recharge estimates with groundwater extraction data show that in 15% of the country, groundwater is being extracted at rates higher than the local recharge. These maps of the internally consistent water budget components of recharge, quick‐flow runoff, and ET, being derived from and tested against data, are expected to provide reliable first‐order estimates of these quantities across the CONUS, even where field measurements are sparse.     

Recent Ogallala Aquifer Region Drought Conditions as Observed by Terrestrial Water Storage Anomalies from GRACE

Recent severe drought events have occurred over the Ogallala Aquifer region (OAR) during the period 2011–2015, creating significant impacts on water resources and their use in regional environmental and economic systems. The changes in terrestrial water storage (TWS), as indicated by the Gravity Recovery and Climate Experiment (GRACE), reveals a detailed picture of the temporal and spatial evolution of drought events. The observations by GRACE indicate the worst drought conditions occurred in September 2012, with an average TWS deficit of ~8 cm in the northern OAR and ~11 cm in the southern OAR, consistent with precipitation data from the Global Precipitation Climatology Project. Comparing changes in TWS with precipitation shows the TWS changes can be predominantly attributable to variations in precipitation. Power spectrum and squared wavelet coherence analysis indicate a significant correlation between TWS change and the El Nino‐Southern Oscillation, and the influence of equatorial Pacific sea surface temperatures on TWS change is much stronger in the southern OAR than the northern OAR. The results of this study illustrate the value of GRACE in not just the diagnosis of significant drought events, but also in possibly improving the predictive power of remote signals that are impacted by nonregional climatic events (El Nino), ultimately leading to improved water resource management applications on a regional scale. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Total Maximum Daily Load Criteria Assessment Using Monitoring and Modeling Data

Applications of Total Maximum Daily Load (TMDL) criteria for complex estuarine systems like Chesapeake Bay have been limited by difficulties in estimating precisely how changes in input loads will impact ambient water quality. A method to deal with this limitation combines the strengths of the Chesapeake Bay's Water Quality Sediment Transport Model (WQSTM), which simulates load response, and the Chesapeake Bay Program's robust historical monitoring dataset. The method uses linear regression to apply simulated relative load responses to historical observations of water quality at a given location and time. Steps to optimize the application of regression analysis were to: (1) determine the best temporal and spatial scale for applying the WQSTM scenarios, (2) determine whether the WQSTM method remained valid with significant perturbation from calibration conditions, and (3) evaluate the need for log transformation of both dissolved oxygen (DO) and chlorophyll a (CHL) datasets. The final method used simple linear regression at the single month, single WQSTM grid cell scale to quantify changes in DO and CHL resulting from simulated load reduction scenarios. The resulting linear equations were applied to historical monitoring data to produce a set of “scenario‐modified” DO or CHL concentration estimates. The utility of the regression method was validated by its ability to estimate progressively increasing attainment in support of the 2010 Chesapeake Bay TMDL.     

VARIABILITY CHARACTERISTICS OF MONTHLY PRECIPITATION IN CENTRAL OKLAHOMA1

ABSTRACT: To fully take advantage of regional climate forecast information for agricultural applications, the relationship between divisional and station scale precipitation characteristics must be quantified. The spatial variability of monthly precipitation is assumed to consist of two components: a systematic and a random component. The systematic component is defined by differences in long-term mean precipitation between stations within a climate division, and the random component by differences between station and divisional standardized values. For the Central Climate Division of Oklahoma, the systematic component has a positive precipitation gradient from west to east with a slope ranging between 3 to 16 mm of precipitation per 100 km depending on the month of the year. On the other hand, the random component ranges between 27 to 48 percent of the mean temporal variation of the monthly precipitation. This significant random spatial variability leads to large localized departures from divisional values, and clearly demonstrates the critical influence of the random component in the utilization of divisional climate forecasts for local agricultural applications. The results of this study also provide an uncertainty range for local monthly precipitation projections that are derived from divisional climate information.     

Impacts of Climate Change on Extreme Precipitation Events Over Flamingo Tropicana Watershed1

Abstract: Climate change, particularly the projected changes to precipitation patterns, is likely to affect runoff both regionally and temporally. Extreme rainfall events are expected to become more intense in the future in arid urban areas and this will likely lead to higher streamflow. Through hydrological modeling, this article simulates an urban basin response to the most intense storm under anthropogenic climate change conditions. This study performs an event‐based simulation for shorter duration storms in the Flamingo Tropicana (FT) watershed in Las Vegas, Nevada. An extreme storm, defined as a 100‐year return period storm, is selected from historical records and perturbed to future climatic conditions with respect to multimodel multiscenario (A1B, A2, B1) bias corrected and spatially disaggregated data from the World Climate Research Programme's (WCRP's) database. The cumulative annual precipitation for each 30‐year period shows a continuous decrease from 2011 to 2099; however, the summer convective storms, which are considered as extreme storms for the study area, are expected to be more intense in future. Extreme storm events show larger changes in streamflow under different climate scenarios and time periods. The simulated peak streamflow and total runoff volume shows an increase from 40% to more than 150% (during 2041‐2099) for different climate scenarios. This type of analysis can help evaluate the vulnerability of existing flood control system and flood control policies.