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Wondmagegn Yigzaw, Faisal Hossain, and Alfred Kalyanapu

Abstract

Since historical (predam) data are traditionally the sole criterion for dam design, future (postdam) meteorological and hydrological variability due to land-use and land-cover change cannot be considered for assessing design robustness. For example, postdam urbanization within a basin leads to definite and immediate increase in direct runoff and reservoir peak inflow. On the other hand, urbanization can strategically (i.e., gradually) alter the mesoscale circulation patterns leading to more extreme rainfall rates. Thus, there are two key pathways (immediate or strategic) by which the design flood magnitude can be compromised. The main objective of the study is to compare the relative contribution to increase in flood magnitudes through direct effects of land-cover change (urbanization and less infiltration) with gradual climate-based effects of land-cover change (modification in mesoscale storm systems). The comparison is cast in the form of a sensitivity study that looks into the response to the design probable maximum flood (PMF) from probable maximum precipitation (PMP). Using the American River watershed (ARW) and Folsom Dam as a case study, simulated peak floods for the 1997 (New Year's) flood event show that a 100% impervious watershed has the potential of generating a flood that is close to design PMF. On the other hand, the design PMP produces an additional 1500 m3 s−1 peak flood compared to the actual PMF when the watershed is considered 100% impervious. This study points to the radical need for consideration future land-cover changes up front during the dam design and operation formulation phase by considering not only the immediate effects but also the gradual climatic effects on PMF. A dynamic dam design procedure should be implemented that takes into account the change of land–atmospheric and hydrological processes as a result of land-cover modification rather than relying on historical records alone.

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Jason A. Hubbart and Chris Zell

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Assuming pro rata reductions in baseflow resulting from urban development may not be valid in all urbanizing watersheds. Anthropogenic offsets or compensatory contributions to baseflow (e.g., net exfiltration from sewer lines, wastewater effluents, and lawn irrigation) may mask or confound fundamental changes in hydrologic pathways. These offsets illustrate the complexities of urban flow processes and the need for improved understanding to mitigate urban development impacts. The authors used two dissimilar automated baseflow separation algorithms and Monte Carlo techniques to evaluate urban baseflow and estimation uncertainty using data from a representative urban watershed in the central United States. Three uncertainties affecting trend determinations were assessed, including algorithm structure, precipitation–runoff relationships, and baseflow algorithm parameterization. Results indicate that, despite ongoing population growth and development, annual streamflow metrics in the authors' representative watershed have not significantly increased or decreased (p > 0.05) from 1967 to 2010. However, several streamflow metrics featured shallow insignificant (p > 0.05) slopes in the direction hypothesized for an urbanizing (less pervious) watershed, including a downward slope for baseflow index (BFI) and increases in runoff volume coefficient. Median annual baseflow estimations differed by 29% between techniques (85.3 versus 118.9 mm yr−1). In the absence of direct tracer measurements, uncertainties associated with precipitation–runoff relationships, algorithm structure, and parameterization should be included in analyses evaluating alterations from baseline hydrologic conditions in urban watersheds. To advance application of separation algorithms for urban watersheds and support regulatory reductions in runoff volume, future work should include calibration of model parameters to available hydrogeologic and tracer data.

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M. Sekhar, M. Shindekar, Sat K. Tomer, and P. Goswami

Abstract

Climate change impact on a groundwater-dependent small urban town has been investigated in the semiarid hard rock aquifer in southern India. A distributed groundwater model was used to simulate the groundwater levels in the study region for the projected future rainfall (2012–32) obtained from a general circulation model (GCM) to estimate the impacts of climate change and management practices on groundwater system. Management practices were based on the human-induced changes on the urban infrastructure such as reduced recharge from the lakes, reduced recharge from water and wastewater utility due to an operational and functioning underground drainage system, and additional water extracted by the water utility for domestic purposes. An assessment of impacts on the groundwater levels was carried out by calibrating a groundwater model using comprehensive data gathered during the period 2008–11 and then simulating the future groundwater level changes using rainfall from six GCMs [Institute of Numerical Mathematics Coupled Model, version 3.0 (INM-CM.3.0); L'Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL-CM4); Model for Interdisciplinary Research on Climate, version 3.2 (MIROC3.2); ECHAM and the global Hamburg Ocean Primitive Equation (ECHO-G); Hadley Centre Coupled Model, version 3 (HadCM3); and Hadley Centre Global Environment Model, version 1 (HadGEM1)] that were found to show good correlation to the historical rainfall in the study area. The model results for the present condition indicate that the annual average discharge (sum of pumping and natural groundwater outflow) was marginally or moderately higher at various locations than the recharge and further the recharge is aided from the recharge from the lakes. Model simulations showed that groundwater levels were vulnerable to the GCM rainfall and a scenario of moderate reduction in recharge from lakes. Hence, it is important to sustain the induced recharge from lakes by ensuring that sufficient runoff water flows to these lakes.

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Brandon L. Parkes, Hannah L. Cloke, Florian Pappenberger, Jeff Neal, and David Demeritt

Abstract

Flood simulation models and hazard maps are only as good as the underlying data against which they are calibrated and tested. However, extreme flood events are by definition rare, so the observational data of flood inundation extent are limited in both quality and quantity. The relative importance of these observational uncertainties has increased now that computing power and accurate lidar scans make it possible to run high-resolution 2D models to simulate floods in urban areas. However, the value of these simulations is limited by the uncertainty in the true extent of the flood. This paper addresses that challenge by analyzing a point dataset of maximum water extent from a flood event on the River Eden at Carlisle, United Kingdom, in January 2005. The observation dataset is based on a collection of wrack and water marks from two postevent surveys. A smoothing algorithm for identifying, quantifying, and reducing localized inconsistencies in the dataset is proposed and evaluated showing positive results. The proposed smoothing algorithm can be applied in order to improve flood inundation modeling assessment and the determination of risk zones on the floodplain.

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Jinyang Du and Qiang Liu

Abstract

Knowledge of the spatial distribution and temporal changes of the land surface parameters at the Three Gorges Dam (TGD) region is essential to understanding the changes of hydrological processes and climate systems possibly brought by TGD. Based on accumulated observations for years from a spaceborne passive microwave radiometer, this study presents and analyzes the spatial and temporal distribution of soil moisture in the TGD region. Major drought and flood events are identified from the satellite-derived soil moisture products. Moreover, the areas around the largest freshwater lakes of China, the Dongting and Poyang Lakes, are frequently subjected to drought events, which might be partially related to the impoundment of TGD since the year 2006. Data analysis further reveals a statistically significant drying trend in May in the middle and lower reaches of the Yangtze River over the years 2003–11. These analyses indicate that water shortage becomes a realistic challenge for the once water abundant Yangtze River region, and more considerations on the possible consequences brought by climate changes are needed for the operation of TGD.

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M. P. Maneta and N. L. Silverman

Abstract

Studies seeking to understand the impacts of climate variability and change on the hydrology of a region need to take into account the dynamics of vegetation and its interaction with the hydrologic and energy cycles. Yet, most of the hydrologic models used for these kinds of studies assume that vegetation is static. This paper presents a dynamic, spatially explicit model that couples a vertical energy balance scheme (surface and canopy layer) to a hydrologic model and a forest growth component to capture the dynamic interactions between energy, vegetation, and hydrology at hourly to daily time scales. The model is designed to be forced with outputs from regional climate models. Lateral water transfers are simulated using a 1D kinematic wave model. Infiltration is simulated using the Green and Ampt approximation to Richard's equation. The dynamics of soil moisture and energy drives carbon assimilation and forest growth, which in turn affect the distribution of energy and water through leaf dynamics by altering light interception, shading, and enhanced transpiration. The model is demonstrated in two case studies simulating energy, water, and vegetation dynamics at two different spatial and temporal scales.

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Lunche Wang, Wei Gong, Yingying Ma, and Miao Zhang

Abstract

Net primary productivity (NPP) is an important component of the carbon cycle and a key indicator of ecosystem performance. The aim of this study is to construct a more accurate regional vegetation NPP estimation model and explore the relationship between NPP and climatic factors (air temperature, rainfall, sunshine hours, relative humidity, air pressure, global radiation, and surface net radiation). As a key variable in NPP modeling, photosynthetically active radiation (PAR) was obtained by finding a linear relationship between PAR and horizontal direct radiation, scattered radiation, and net radiation with high accuracy. The fraction of absorbed photosynthetically active radiation (FPAR) was estimated by enhanced vegetation index (EVI) instead of the widely used normalized difference vegetation index (NDVI). Stress factors of temperature/humidity for different types of vegetation were also considered in the simulation of light use efficiencies (LUE). The authors used EVI datasets of Moderate Resolution Imaging Spectroradiometer (MODIS) from 2001 to 2011 and geographic information techniques to reveal NPP variations in Wuhan. Time lagged serial correlation analysis was employed to study the delayed and continuous effects of climatic factors on NPP. The results showed that the authors’ improved model can simulate vegetation NPP in Wuhan effectively, and it may be adopted or used in other regions of the world that need to be further tested. The results indicated that air temperature and air pressure contributed significantly to the interannual changes of plant NPP while rainfall and global radiation were major climatic factors influencing seasonal NPP variations. A significant positive 32-day lagged correlation was observed between seasonal variation of NPP and rainfall (P < 0.01); the influence of changing climate on NPP lasted for 64 days. The impact of air pressure, global radiation, and net radiation on NPP persisted for 48 days, while the effects of sunshine hours and air temperature on NPP only lasted for 16 and 32 days, respectively.

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Nádia Gilma Beserra de Lima and Emerson Galvani

Abstract

A mangrove is a transitional coastal ecosystem between marine and terrestrial environments that is characterized by salinity and constant tidal flooding. Mangroves contain plant communities that are adapted to several physical constraints, including the climate. The purpose of this study was to analyze the variations in climatic attributes (air temperature, relative air humidity, global solar radiation, wind, and rainfall) in the mangroves located in the municipality of Iguape, on the southern coast of the state of São Paulo, Brazil. In addition, it was determined whether the existing variation is related to the presence of the canopy environment. A microclimate tower was installed with two weather stations to obtain an analysis of the variation of the climatic attributes above and below the canopy. The results indicate that global solar radiation had an average transmissivity of 26.8%. The air temperature at 10 m was higher than that at the sensor at 2 m. The average rainfall interception for the mangrove environment was 19.6%. Both the maximum gust and average wind speed decreased by approximately 63.7% at 2 m. The mangrove canopy was found to be an important control on the variation of climatic attributes. On a microclimatic scale, the climate attributes had a direct influence on the spatial distribution of vegetation. Additionally, characteristics of the canopy are the main control for this variation, especially for the distribution of rainfall and the amount of solar radiation below the canopy, which influence the distribution of plant species in the environment.

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Mohammad H. Mokhtari, Ibrahim Busu, Hossein Mokhtari, Gholamreza Zahedi, Leila Sheikhattar, and Mohammad A. Movahed

Abstract

The current Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-based broadband albedo model requires shortwave infrared bands 5 (2.145–2.185 nm), 6 (2.185–2.225 nm), 8 (2.295–2.365 nm), and 9 (2.360–2.430 nm) and visible/near-infrared bands 1 (0.52–0.60 nm) and 3 (0.78–0.86 nm). However, because of sensor irregularities at high temperatures, shortwave infrared wavelengths are not recorded in the ASTER data acquired after April 2008. Therefore, this study seeks to evaluate the performance of artificial neural networks (ANN) in estimating surface albedo using visible/near-infrared bands available in the data obtained after April 2008. It also compares the outcomes with the results of multiple linear regression (MLR) modeling. First, the most influential spectral bands used in the current model as well as band 2 (0.63–0.69 nm) (which is also available after April 2008 in the visible/near-infrared part) were determined by a primary analysis of the data acquired before April 2008. Then, multiple linear regression and ANN models were developed by using bands with a relatively high level of contribution. The results showed that bands 1 and 3 were the most important spectral ones for estimating albedo where land cover consisted of soil and vegetation. These two bands were used as the study input, and the albedo (estimated through a model that utilized bands 1, 3, 5, 6, 8, and 9) served as a target to remodel albedo. Because of its high collinearity with band 1, band 2 was identified less effectively by MLR as well as ANN. The study confirmed that a combination of bands 1 and 3, which are available in the current ASTER data, could be modeled through ANN and MLR to estimate surface albedo. However, because of its higher accuracy, ANN method was superior to MLR in developing objective functions.

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Pennan Chinnasamy, Jason A. Hubbart, and Govindasamy Agoramoorthy

Abstract

India is the greatest groundwater consumer in the world, with estimated annual withdrawals exceeding 230 km3. More than 60% of irrigated agriculture, 85% of drinking water supplies, and 50% of urban and industrial water needs are dependent on sustainable groundwater management. Regardless, groundwater overextraction is a growing problem in many regions. Predictions of groundwater resource availability in India are problematic in part because of a limited number of monitoring sites and insufficient data quality and quantity. Regional groundwater assessments are further complicated because of sporadic and low-frequency data. To help overcome these issues and more accurately quantify groundwater resource availability, scientists have begun using satellite-derived remote sensing data. In this study, the authors used seasonal and annual hydrologic signals obtained by NASA Gravity Recovery and Climate Experiment (GRACE) satellites and simulated soil moisture variations from land data assimilation systems to show groundwater depletion trends in the northwest state of Gujarat (surface area of 196 030 km2), India. Results were evaluated using direct measurement data from 935 wells. Remote sensing generated results compared favorably with well data (e.g., r 2 = 0.89 for Gandhinagar, a representative highly urbanized district in Gujarat: confidence interval (CI) = 0.05 and P = 0.002). Results show that remote sensing is an effective tool to compliment and interpolate observed regional groundwater well data and improve groundwater storage estimations in Gujarat, India. Properly implemented, the method will supply reliable science-based information to enhance management of groundwater resources in India and other geographic locations.

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