RAINGAGE NETWORK DESIGN USING NEXRAD PRECIPITATION ESTIMATES1

ABSTRACT: A general framework is proposed for using precipitation estimates from NEXRAD weather radars in raingage network design. NEXRAD precipitation products are used to represent space time rainfall fields, which can be sampled by hypothetical raingage networks. A stochastic model is used to simulate gage observations based on the areal average precipitation for radar grid cells. The stochastic model accounts for subgrid variability of precipitation within the cell and gage measurement errors. The approach is ideally suited to raingage network design in regions with strong climatic variations in rainfall where conventional methods are sometimes lacking. A case study example involving the estimation of areal average precipitation for catchments in the Catskill Mountains illustrates the approach. The case study shows how the simulation approach can be used to quantify the effects of gage density, basin size, spatial variation of precipitation, and gage measurement error, on network estimates of areal average precipitation. Although the quality of NEXRAD precipitation products imposes limitations on their use in network design, weather radars can provide valuable information for empirical assessment of rain‐gage network estimation errors. Still, the biggest challenge in quantifying estimation errors is understanding subgrid spatial variability. The results from the case study show that the spatial correlation of precipitation at subgrid scales (4 km and less) is difficult to quantify, especially for short sampling durations. Network estimation errors for hourly precipitation are extremely sensitive to the uncertainty in subgrid spatial variability, although for storm total accumulation, they are much less sensitive.     

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.     

PREDICTING MEASUREMENT ERROR OF AREAL MEAN PRECIPITATION DURING EXTREME EVENTS1

ABSTRACT: The areal mean precipitation (AMP) over a catchment is normally calculated using point measurements at rainfall gages. Error in AMP estimates occurs when an insufficient number of gages are used to sample precipitation which is highly variable in space. AMP error is investigated using historic, severe rainfalls with a set of hypothetical catchments and raingage networks. The potential magnitude of error is estimated for typical gage network densities and arrangements. Possible sources of error are evaluated, and a method is proposed for predicting the magnitude of error using data that are commonly available for severe, historic rainfall.     

IMPACTS OF CLIMATE CHANGE ON WATER YIELD IN THE UPPER WIND RIVER BASIN1

ABSTRACT: The potential impacts of climate change on water yield are examined in the Upper Wind River Basin. This is a high‐elevation, mountain basin with a snowfall/snowmelt dominated stream‐flow hydrograph. A variety of physiographic conditions are represented in the rangeland, coniferous forests, and high‐elevation alpine regions. The Soil Water Assessment Tool (SWAT) is used to model the baseline input time series data and climate change scenarios. Five hydroclimatic variables (temperature, precipitation, CO₂, radiation, and humidity) are examined using sensitivity tests of individual and coupled variables with a constant change and coupled variables with a monthly change. Results indicate that the most influential variable on annual water yield is precipitation; and, the most influential variable on the timing of streamflow is temperature. Carbon dioxide, radiation, and humidity each noticeably impact water yield, but less significantly. The coupled variable analyses represent a more realistic climate change regime and reflect the combined response of the basin to each variable; for example, increased temperature offsets the effects of increased precipitation and magnifies the effects of decreased precipitation. This paper shows that the hydrologic response to climate change depends largely on the hydroclimatic variables examined and that each variable has a unique effect (e.g., magnitude, timing) on water yield.     

SERENDIPITY: CAPTURING A DESIGN LEVEL PRECIPITATION EVENT1

ABSTRACT: On February 23–24, 1998, a frontal system moved across the U.S. Department of Energy's 3,500 km² Nevada Test Site (NTS) and resulted in significant depths of precipitation at all recording gages on the NTS. A preliminary analysis suggested that this precipitation event was of the magnitude and duration for which many flood mitigation structures have been designed. Given the data and field observations available and the potential implications of the event on the methodologies used to size flood mitigation structures throughout the West, a detailed analysis of this event was undertaken. The goals of this study were to compare this event with the regulatory design precipitation event, compare the estimated peak flow rates from the rainfall/runoff model used to size the flood mitigation structures at a radioactive waste management site with the estimated peak flows from the precipitation event, and examine if modification of the standard source of the design depths of precipitation is warranted.     

THRESHOLD TIME SERIES MODELING OF TWO ICELANDIC RIVERFLOW SYSTEMS1

ABSTRACT: This paper reports our experience in building time series models which connect the flows in two Icelandic rivers with the meteorological variables of precipitation and temperature. Two rivers with different hydrological characteristics were studied. In areas where precipitation may be either in the form of rain or snow linear models are inadequate to describe the relationship between the river and the meteorological variables. The methodology of threshold models recently developed seems to be well suited for taking into account the sharp difference in the relationship according to whether it is freezing or not. The possibility of identifying an alternative threshold variable is also explored.     

THROUGHFALL pH: EFFECT OF PRECIPITATION : TIMING AND AMOUNT1

ABSTRACT: Precipitation, throughfall, and stream pH were measured weekly over a 27-week period in 1982 on the Little Millseat watershed in eastern Kentucky. The average pH values over the study period were 4.3, 4.9, and 6.4, respectively, indicating significant buffering as water moved from the atmosphere, through the deciduous canopy, and through or over the soil to the stream. Regression analysis demonstrated that the timing and amount of precipitation were important factors influencing the pH of the throughfall. Weekly precipitation and the three-week average precipitation were statistically significant variables, explaining 53 percent of the variance in the observed through- fall pH. Precipitation pH was not a statistically significant variable for this watershed and sampling period.     

燃煤电VPM2.5及汞控制技术探讨

火电厂烟气中PM2.5控制技术,目前可分为电除尘和袋式除尘两大类。其中电除尘又分常规电除尘、移动电极电除尘、前置聚并器电除尘、湿式电除尘、低温电除尘等;袋式除尘又分为纯袋式除尘、电一袋复合式除尘、先进的混合式电一袋除尘等。文章对这些技术从捕集机理到应用效果进行了分析探讨,推荐了火电厂应用最合适的控制技术。     

PREDICTIVE MODEL FOR NITRATE LOAD IN THE BULL RUN WATERSHED,OREGON1

ABSTRACT: Predictive models for nitrate in four streams in the Bull Run Watershed in the Cascade Mountains of Oregon were developed from a record of 17 years of nitrate samples. The models are time series regression models written in terms of Log(nitrate load). The independent variables are logarithm of 14-day mean daily stream discharge, current day's precipitation, logarithm of the previous day's precipitation, total precipitation for the previous seven days, a hydrograph position variable that indicates rising or falling limb, and average maximum air temperature for the preceding 14 days. The models describe annual cycle and seasonable trends and variations in nitrate load, but are unable to describe large day to day variations like those associated with hydrograph peaks.     

ROLE OF BASEFLOW IN RAINFALL-RUNOFF RELATIONS1

ABSTRACT. The role of initial baseflow, or the baseflow at the beginning of storm precipitation, in modifying mathematical rainfall-runoff relations is analyzed by using data from 95 storms over a drainage basin in Illinois. A regression model is set up with total runoff, surface runoff, baseflow runoff, and peak flow as dependent variables, and storm precipitation, initial baseflow, effective and total storm durations, and highest and lowest temperatures during the storm as independent variables. Stepwise regression analyses show that storm precipitation and initial baseflow are the most important variables for making dependent variable estimates. The standard error estimates using only storm precipitation and initial baseflow as predictors show a seasonal trend with a peak in July, August, or September. An understanding of the role of baseflow as an indicator of average soil moisture condition over the basin can be of great help in short-term reservoir regulation and flood warning.     

Satellite Rainfall Estimates Over South America – Possible Applicability to the Water Management of Large Watersheds1

Pereira Filho, Augusto J., Richard E. Carbone, John E. Janowiak, Phillip Arkin, Robert Joyce, Ricardo Hallak, and Camila G.M. Ramos, 2010. Satellite Rainfall Estimates Over South America – Possible Applicability to the Water Management of Large Watersheds. Journal of the American Water Resources Association (JAWRA) 46(2):344-360. DOI: 10.1111/j.1752-1688.2009.00406.x Abstract: This work analyzes high-resolution precipitation data from satellite-derived rainfall estimates over South America, especially over the Amazon Basin. The goal is to examine whether satellite-derived precipitation estimates can be used in hydrology and in the management of larger watersheds of South America. High spatial-temporal resolution precipitation estimates obtained with the CMORPH method serve this purpose while providing an additional hydrometeorological perspective on the convective regime over South America and its predictability. CMORPH rainfall estimates at 8-km spatial resolution for 2003 and 2004 were compared with available rain gauge measurements at daily, monthly, and yearly accumulation time scales. The results show the correlation between satellite-derived and gauge-measured precipitation increases with accumulation period from daily to monthly, especially during the rainy season. Time-longitude diagrams of CMORPH hourly rainfall show the genesis, strength, longevity, and phase speed of convective systems. Hourly rainfall analyses indicate that convection over the Amazon region is often more organized than previously thought, thus inferring that basin scale predictions of rainfall for hydrological and water management purposes have the potential to become more skillful. Flow estimates based on CMORPH and the rain gauge network are compared to long-term observed average flow. The results suggest this satellite-based rainfall estimation technique has considerable utility. Other statistics for monthly accumulations also suggest CMORPH can be an important source of rainfall information at smaller spatial scales where in situ observations are lacking.     

SEASONAL WATER TABLE DYNAMICS OF A SOUTHERN APPALACHIAN FLOODPLAIN AND ASSOCIATED FEN1

ABSTRACT: Appalachian mountain alluvial wetlands include floodplain forests interspersed with fens or bogs. This study evaluates the water table dynamics of an Appalachian mountain flood‐plain which includes a depressional fen. Water table wells and piezometers documented seasonal patterns of the water table and the vertical hydraulic gradient (VHG) in the floodplain and fen areas. Additional water table wells determined the potential sources of water from adjacent hillslopes to the fen area. The water table of the floodplain and the fen exhibited distinct regular seasonal fluctuations. The water table remained near the surface of the fen from late winter through late spring and dropped 20 to 80 cm during the summer between precipitation events. The water table of the floodplain fluctuated more but followed similar patterns and was typically within 40 cm of the surface during late winter and early spring months and greater than 60 cm during the summer months. The water table of the floodplain was more often correlated to precipitation than the water table of the fen. The VHG in the floodplain was highly variable although seasonal patterns of upwelling of water in fall and downwelling in winter were common. The VHG of the fen showed a consistent downwelling of water and suggested that the fen serves as a recharge area for an aquifer. Principal sources of water for the fen appeared to be precipitation, inflow from a shallow aquifer on an adjacent slope plus increased interflow associated with precipitation events from another adjacent slope. The influence of soil texture on water dynamics of the fen or floodplain was not fully ascertained but it appeared to influence horizontal flow from hillslopes and the depth of the water table in the fen.     

HYDROLOGIC BEHAVIOR OF VEGETATED ROOFS1

ABSTRACT: Control of stormwater runoff from impervious surfaces is an important national goal because of disruptions to downstream ecosystems, water users, and property owners caused by increased flows and degraded quality. One method for reducing stormwater is the use of vegetated (green) roofs, which efficiently detain and retain stormwater when compared to conventional (black) roofs. A paired green roof‐black roof test plot was constructed at the University of Georgia and monitored between November 2003 and November 2004 for the green roof's effectiveness in reducing stormwater flows. Stormwater mitigation performance was monitored for 31 precipitation events, which ranged in depth from 0.28 to 8.43 cm. Green roof precipitation retention decreased with precipitation depth; ranging from just under 90 percent for small storms (< 2.54 cm) to slightly less than 50 percent for larger storms (> 7.62 cm). Runoff from the green roof was delayed; average runoff lag times increased from 17.0 minutes for the black roof to 34.9 minutes for the green roof, an average increase of 17.9 minutes. Precipitation and runoff data were used to estimate the green roof curve number, CN = 86. This information can be used in hydrologic models for developing stormwater mitigation programs.     

PRECIPITATION RETENTION AND SOIL EROSION UNDER VARYING CLIMATE,LAND USE,AND TILLAGE AND CROPPING SYSTEMS1

ABSTRACT: Nonirrigated crop yields and forage production are limited by low and variable precipitation in the southern Great Plains. Precipitation variation involves production risks, which can be reduced by considering probability of precipitation, precipitation retention, and soil erosion under various production systems. The objective of this study was to probabilistically quantify the impact of precipitation variations, land use, cropping, and tillage systems on precipitation retention and soil erosion. Five 1.6 ha watersheds that had 3 to 4 percent slopes, and similar silt loam soils were selected. One was kept in native grass, and the others were planted into winter wheat (Triticum aestivum L.) under different cropping and tillage systems. Daily runoff and soil erosion were measured at the outlet of each watershed. Precipitation distributions exhibited great seasonal and interannual variations, and precipitation retention distributions resembled those of precipitation. Cropping and tillage systems affected precipitation retention but much less than did precipitation variations. Available soil water storage, which was largely controlled by ET, played an important role in retaining precipitation. This indicates that cropping systems should be adjusted to precipitation patterns, if predictable, for better soil water use. Land use and cropping and tillage systems had a much greater impact on soil erosion than on precipitation retention. Soil erosion risks, which were proportional to the levels of tillage disturbance, were mainly caused by a few large storms in summer, when surface cover was low. This study explored a novel approach for evaluating production risks associated with insufficient precipitation retention and excessive soil erosion for certain crops or cropping systems under assumed precipitation conditions.     

Added-value from innovative value chains by establishing nutrient cycles via struvite

The establishment of nutrient cycles has been widely proposed as a strategy for an efficient management of nutrients such as phosphorus (P). Global reserves of phosphate rocks are limited and are being increasingly depleted. At the same time, P is disposed of via various substance-streams in wastewater treatment. Establishing nutrient cycles may solve these problems and lead to innovative added-value chains with a higher added-value. The objective of this paper is to assess the added-value of P-recovery from sewage sludge via struvite precipitation and its application as fertilizer in Berlin-Brandenburg (Germany). The added-value from struvite precipitation was determined by performing a cost/benefit analysis based on data from standardized questionnaires and interviews with operators of wastewater treatment facilities. Surveys of 146 farmers were used to ascertain what crops were cultivated in the study area and to gauge the willingness of farmers to substitute struvite for conventional mineral P-fertilizer. Benefits from using struvite were found by calculating the fertilizer costs when struvite is substituted for conventional mineral fertilizer. The results indicate that the precipitation of struvite and its use as fertilizer generates added-value gains for wastewater treatment facilities (416,000 €) and for crop producers (35,000 €). In wastewater treatment, struvite precipitation reduces operating costs and yields additional revenues through struvite sales. In crop production, fertilization costs are reduced by substituting struvite for mineral P-, N- and Ca-fertilizers. The distribution of the added-value in the struvite value chain is determined by the marketing strategy of struvite. Farmers may obtain a higher share of added-value if struvite is marketed via direct sale.     

Testing and Improving Temperature Thresholds for Snow and Rain Prediction in the Western United States

The phase of precipitation at the land surface is critical to determine the timing and amount of water available for hydrological and ecological systems. However, there are few techniques to directly observe the precipitation phase and many prediction tools apply a single temperature threshold (e.g., 0°C) to determine phase. In this paper, we asked two questions: (1) what is the accuracy of default and station optimized daily temperature thresholds for predicting precipitation phase and (2) what are the regions and conditions in which typical temperature‐based precipitation phase predictions are most suited. We developed a ground truth dataset of rain vs. snow using an expert decision‐making system based on precipitation, snow depth, and snow water equivalent observations. This dataset was used to evaluate the accuracy of three temperature‐threshold‐based techniques of phase classification. Optimizing the temperature threshold improved the prediction of precipitation phase by 34% compared to using 0°C threshold. Developing a temperature threshold based on station elevation improved the error by 12% compared with using the 0°C temperature threshold. We also found the probability of snow as a function of temperature differed among ecoregions, which suggests a varied response to future climate change. These results highlight a current weakness in our ability to predict the effects of regional warming that could have uneven impacts on water and ecological resources.     

REGIONAL DETECTION OF CHANGE IN WATER QUALITY VARIABLES1

ABSTRACT: The detection of change in a hydrologic varaible, particularly water quality, is a current problem. A method is presented for testing whether there has been a shift in the mean of a hydrologic variable based on the well established bivariate normal distribution theory. In this technique, the dependent, or target, and the independent, or control, variables are formed as weighted linear combinations of the mean values at a number of locations in a selected target and control area. The weighting factors are determined based on a mathematical programming technique which minimizes the conditional coefficient of variation thereby minimizing the number of observations required to detect a change of a preselected magnitude in the mean of the target area. The result is a situation where a savings in the number of observations required to detect a change is a consequence of adding more stations: the space-time tradeoff. Two applications of the technique are presented, the first using electrical conductivity (EC) data from two sets of river basins and the second using EC data from a set of basins as the target variable and annual discharge as the control. The results indicate that a significant savings in time can be achieved by using this method.     

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.     

GROUND WATER/SURFACE WATER RESPONSES TO GLOBAL CLIMATE SIMULATIONS,SANTA CLARA‐CALLEGUAS BASIN,VENTURA, CALIFORNIA1

ABSTRACT: Climate variations can play an important, if not always crucial, role in successful conjunctive management of ground water and surface water resources. This will require accurate accounting of the links between variations in climate, recharge, and withdrawal from the resource systems, accurate projection or predictions of the climate variations, and accurate simulation of the responses of the resource systems. To assess linkages and predictability of climate influences on conjunctive management, global climate model (GCM) simulated precipitation rates were used to estimate inflows and outflows from a regional ground water model (RGWM) of the coastal aquifers of the Santa Clara‐Calleguas Basin at Ventura, California, for 1950 to 1993. Interannual to interdecadal time scales of the El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) climate variations are imparted to simulated precipitation variations in the Southern California area and are realistically imparted to the simulated ground water level variations through the climate‐driven recharge (and discharge) variations. For example, the simulated average ground water level response at a key observation well in the basin to ENSO variations of tropical Pacific sea surface temperatures is 1.2 m/°C, compared to 0.9 m/°C in observations. This close agreement shows that the GCM‐RGWM combination can translate global scale climate variations into realistic local ground water responses. Probability distributions of simulated ground water level excursions above a local water level threshold for potential seawater intrusion compare well to the corresponding distributions from observations and historical RGWM simulations, demonstrating the combination's potential usefulness for water management and planning. Thus the GCM‐RGWM combination could be used for planning purposes and — when the GCM forecast skills are adequate — for near term predictions.