Showing papers in "Water Resources Research in 2013"
TL;DR: In this article, the authors used climate, water, economic, and remote sensing data combined with biophysical modeling to understand the drivers of the "Millennium Drought" and its impacts.
Abstract: [1] The “Millennium Drought” (2001–2009) can be described as the worst drought on record for southeast Australia. Adaptation to future severe droughts requires insight into the drivers of the drought and its impacts. These were analyzed using climate, water, economic, and remote sensing data combined with biophysical modeling. Prevailing El Nino conditions explained about two thirds of rainfall deficit in east Australia. Results for south Australia were inconclusive; a contribution from global climate change remains plausible but unproven. Natural processes changed the timing and magnitude of soil moisture, streamflow, and groundwater deficits by up to several years, and caused the amplification of rainfall declines in streamflow to be greater than in normal dry years. By design, river management avoided impacts on some categories of water users, but did so by exacerbating the impacts on annual irrigation agriculture and, in particular, river ecosystems. Relative rainfall reductions were amplified 1.5–1.7 times in dryland wheat yields, but the impact was offset by steady increases in cropping area and crop water use efficiency (perhaps partly due to CO2 fertilization). Impacts beyond the agricultural sector occurred (e.g., forestry, tourism, utilities) but were often diffuse and not well quantified. Key causative pathways from physical drought to the degradation of ecological, economic, and social health remain poorly understood and quantified. Combined with the multiple dimensions of multiyear droughts and the specter of climate change, this means future droughts may well break records in ever new ways and not necessarily be managed better than past ones.
989 citations
TL;DR: It is suggested that the topic has still not received the attention that its importance deserves, in part because of the ready availability of software packages rooted firmly in the Richards domain, albeit that there is convincing evidence that this may be predicated on the wrong experimental method for natural conditions.
Abstract: The original review of macropores and water flow in soils by Beven and Germann is now 30 years old and has become one of the most highly cited papers in hydrology. This paper attempts to review the progress in observations and theoretical reasoning about preferential soil water flows over the intervening period. It is suggested that the topic has still not received the attention that its importance deserves, in part because of the ready availability of software packages rooted firmly in the Richards domain, albeit that there is convincing evidence that this may be predicated on the wrong experimental method for natural conditions. There is still not an adequate physical theory linking all types of flow, and there are still not adequate observational techniques to support the scale dependent parameterizations that will be required at practical field and hillslope scales of application. Some thoughts on future needs to develop a more comprehensive representation of soil water flows are offered.
655 citations
TL;DR: Observations from the GRACE satellite mission are used to evaluate freshwater storage trends in the north-central Middle East, including portions of the Tigris and Euphrates River Basins and western Iran, from January 2003 to December 2009 to indicate that groundwater losses are the major source of this trend.
Abstract: In this study, we use observations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission to evaluate freshwater storage trends in the north-central Middle East, including portions of the Tigris and Euphrates River Basins and western Iran, from January 2003 to December 2009. GRACE data show an alarming rate of decrease in total water storage of approximately -27.2 plus or minus 0.6 millimeters per year equivalent water height, equal to a volume of 143.6 cubic kimometers during the course of the study period. Additional remote-sensing information and output from land surface models were used to identify that groundwater losses are the major source of this trend. The approach used in this study provides an example of ''best current capabilities'' in regions like the Middle East, where data access can be severely limited. Results indicate that the region lost 17.3 plus or minus 2.1 millimeters per year equivalent water height of groundwater during the study period, or 91.3 plus or minus 10.9 cubic kilometers in volume. Furthermore, results raise important issues regarding water use in transboundary river basins and aquifers, including the necessity of international water use treaties and resolving discrepancies in international water law, while amplifying the need for increased monitoring for core components of the water budget.
607 citations
TL;DR: In this paper, the authors estimated the groundwater depletion rate in North China based on GRACE data and ground-based measurements collected from 2003 to 2010, which is equivalent to a volume of 8.3 km3/yr.
Abstract: [1] Changes in regional groundwater storage in North China were estimated from the Gravity Recovery and Climate Experiment (GRACE) satellites data and ground-based measurements collected from 2003 to 2010. The study area (∼370,000 km2) included the Beijing and Tianjin municipality, the Hebei and Shanxi province, which is one of the largest irrigation areas in the world and is subjected to intensive groundwater-based irrigation. Groundwater depletion in North China was estimated by removing the simulated soil moisture changes from the GRACE-derived terrestrial water storage changes. The rate of groundwater depletion in North China based on GRACE was 2.2 ± 0.3 cm/yr from 2003 to 2010, which is equivalent to a volume of 8.3 ± 1.1 km3/yr. The groundwater depletion rate estimated from monitoring well stations during the same time period was between 2.0 and 2.8 cm/yr, which is consistent with the GRACE-based result. However, the estimated groundwater depletion rate in shallow plain aquifers according to the Groundwater Bulletin of China Northern Plains (GBCNP) for the same time period was only approximately 2.5 km3/yr. The difference in groundwater depletion rates estimated from GRACE and GBCNP data indicates the important contribution of groundwater depletion from deep aquifers in the plain and piedmont regions of North China.
529 citations
TL;DR: In this paper, a particle tracking algorithm was proposed to model conservative transport in surface and subsurface hydrological systems and rigorously demonstrated that this particle method converges to the diffusion-reaction equation at the limit of infinitely small time step.
Abstract: [1] Particle tracking algorithms are very useful methods to model conservative transport in surface and subsurface hydrological systems. Recently, a novel ad hoc particle-based method was proposed to account for multicomponent reactive transport by Benson and Meerschaert (2008). This one-dimensional particle method has been shown to match theoretical predictions, but, to date, there has been no rigorous demonstration that the particle method actually matches the governing equations for chemical transport. We generalize this particle method to two-dimensional and three-dimensional systems and rigorously demonstrate that this particle method converges to the diffusion-reaction equation at the limit of infinitely small time step. We also investigate the numerical error associated with the method.
423 citations
TL;DR: In this paper, the authors compared the performance of six bias correction methods for hydrological modeling over 10 North American river basins and found that bias correction performance is location dependent and that a careful validation should always be performed, especially on studies over new regions.
Abstract: [1] This work compares the performance of six bias correction methods for hydrological modeling over 10 North American river basins. Four regional climate model (RCM) simulations driven by reanalysis data taken from the North American Regional Climate Change Assessment Program intercomparison project are used to evaluate the sensitivity of bias correction methods to climate models. The hydrological impacts of bias correction methods are assessed through the comparison of streamflows simulated by a lumped empirical hydrology model (HSAMI) using raw RCM-simulated and bias-corrected precipitation time series. The results show that RCMs are biased in the simulation of precipitation, which results in biased simulated streamflows. All six bias correction methods are capable of improving the RCM-simulated precipitation in the representation of watershed streamflows to a certain degree. However, the performance of hydrological modeling depends on the choice of a bias correction method and the location of a watershed. Moreover, distribution-based methods are consistently better than mean-based methods. A low coherence between the temporal sequences of observed and RCM-simulated (driven by reanalysis data) precipitation was observed over 5 of the 10 watersheds studied. All bias corrections methods fail over these basins due to their inability to specifically correct the temporal structure of daily precipitation occurrence, which is critical for hydrology modeling. In this study, this failure occurred on basins that were distant from the RCM model boundaries and where topography exerted little control over precipitation. These results indicate that bias correction performance is location dependent and that a careful validation should always be performed, especially on studies over new regions.
337 citations
TL;DR: In this article, the authors quantify gas and wastewater production using data from 2189 Marcellus wells located throughout Pennsylvania and find that despite producing significantly less wastewater per unit gas recovered, developing the region has increased the total wastewater generated in the region by approximately 570% since 2004, overwhelming current wastewater disposal infrastructure capacity.
Abstract: [1] Hydraulic fracturing has made vast quantities of natural gas from shale available, reshaping the energy landscape of the United States. Extracting shale gas, however, generates large, unavoidable volumes of wastewater, which to date lacks accurate quantification. For the Marcellus shale, by far the largest shale gas resource in the United States, we quantify gas and wastewater production using data from 2189 wells located throughout Pennsylvania. Contrary to current perceptions, Marcellus wells produce significantly less wastewater per unit gas recovered (approximately 35%) compared to conventional natural gas wells. Further, well operators classified only 32.3% of wastewater from Marcellus wells as flowback from hydraulic fracturing; most wastewater was classified as brine, generated over multiple years. Despite producing less wastewater per unit gas, developing the Marcellus shale has increased the total wastewater generated in the region by approximately 570% since 2004, overwhelming current wastewater disposal infrastructure capacity.
332 citations
TL;DR: In this article, a hydrologic/hydrodynamic modeling of the Amazon River basin is presented using the MGB-IPH model with a validation using remotely sensed observations.
Abstract: In this paper, a hydrologic/hydrodynamic modeling of the Amazon River basin iscpresented using the MGB-IPH model with a validation using remotely sensed observations. Moreover, the sources of model errors by means of the validation and sensitivity tests are investigated, and the physical functioning of the Amazon basin is also explored. The MGBIPH is a physically based model resolving all land hydrological processes and here using a full 1-D river hydrodynamic module with a simple floodplain storage model. Riverfloodplain geometry parameters were extracted from the SRTM digital elevation model, and the model was forced using satellite-derived rainfall from TRMM3B42. Model results agree with observed in situ daily river discharges and water levels and with three complementary satellite-based products: (1) water levels derived from ENVISAT altimetry data; (2) a global data set of monthly inundation extent; and (3) monthly terrestrial water storage (TWS) anomalies derived from the Gravity Recovery and Climate Experimental mission. However, the model is sensitive to precipitation forcing and river-floodplain parameters. Most of the errors occur in westerly regions, possibly due to the poor quality of TRMM 3B42 rainfall data set in these mountainous and/or poorly monitored areas. In addition, uncertainty in river-floodplain geometry causes errors in simulated water levels and inundation extent, suggesting the need for improvement of parameter estimation methods. Finally, analyses of Amazon hydrological processes demonstrate that surface waters govern most of the Amazon TWS changes (56%), followed by soil water (27%) and ground water (8%). Moreover, floodplains play a major role in stream flow routing, although backwater effects are also important to delay and attenuate flood waves.
305 citations
TL;DR: In this article, a simple parameterization for the Budyko curve parameter based solely on remotely sensed vegetation information is proposed, which improves predictions of annual actual evapotranspiration by reducing the root mean square error (RMSE) from 76 mm to 47 mm.
Abstract: [1] Budyko's framework has been widely used to study basin-scale water and energy balances and one of the formulations of the Budyko curve is Fu's equation. The curve shape parameter ϖ in Fu's equation controls how much of the available water will be evaporated given the available energy. Previous studies have found that land surface characteristics significantly affect variations in the parameter ϖ. In this study, we focus on the vegetation impact and examine the conditions under which vegetation plays a major role in controlling the variability of ϖ. Using data from 26 major global river basins that are larger than 300,000 km2, the basin-specific ϖ parameter is found to be linearly correlated with the long-term averaged annual vegetation coverage. A simple parameterization for the ϖ parameter based solely on remotely sensed vegetation information is proposed, which improves predictions of annual actual evapotranspiration by reducing the root mean square error (RMSE) from 76 mm to 47 mm as compared to the default ϖ value used in the Budyko curve method. The controlling impact of vegetation on the basin-specific ϖ parameter is diminished in small catchments with areas less than 50,000 km2, which suggests a scale-dependence of the role of vegetation in affecting water and energy balances. In small catchments, other key ecohydrological processes need to be taken into account in order to fully capture the variability of the ϖ parameter in Fu's equation.
294 citations
TL;DR: In this article, the authors analyzed the annual maximum daily rainfall of 15,137 records from all over the world, with lengths varying from 40 to 163 years, and analyzed the fitting results focusing on the behavior of the shape parameter.
Abstract: [1] Theoretically, if the distribution of daily rainfall is known or justifiably assumed, then one could argue, based on extreme value theory, that the distribution of the annual maxima of daily rainfall would resemble one of the three limiting types: (a) type I, known as Gumbel; (b) type II, known as Frechet; and (c) type III, known as reversed Weibull. Yet, the parent distribution usually is not known and often only records of annual maxima are available. Thus, the question that naturally arises is which one of the three types better describes the annual maxima of daily rainfall. The question is of great importance as the naive adoption of a particular type may lead to serious underestimation or overestimation of the return period assigned to specific rainfall amounts. To answer this question, we analyze the annual maximum daily rainfall of 15,137 records from all over the world, with lengths varying from 40 to 163 years. We fit the generalized extreme value (GEV) distribution, which comprises the three limiting types as special cases for specific values of its shape parameter, and analyze the fitting results focusing on the behavior of the shape parameter. The analysis reveals that (a) the record length strongly affects the estimate of the GEV shape parameter and long records are needed for reliable estimates; (b) when the effect of the record length is corrected, the shape parameter varies in a narrow range; (c) the geographical location of the globe may affect the value of the shape parameter; and (d) the winner of this battle is the Frechet law.
291 citations
TL;DR: In this paper, the authors simulate spatiotemporal variability in groundwater depletion across the entire North China Plain (NCP) and explore approaches to reduce future depletion and provide valuable insights for developing more sustainable groundwater management options for the NCP; the methods are useful for managing other depleted aquifers.
Abstract: [1] The North China Plain (NCP) is one of the global hotspots of groundwater depletion. Currently, our understanding is limited on spatiotemporal variability in depletion and approaches toward more sustainable groundwater development in this region. This study was intended to simulate spatiotemporal variability in groundwater depletion across the entire NCP and explore approaches to reduce future depletion. Simulated predevelopment groundwater recharge (∼13 km3/yr) primarily discharged as base flow to rivers and evapotranspiration. Initial groundwater storage was estimated to be 1500 km3 of drainable storage in shallow aquifers and 40 km3 of compressive storage in deep aquifers. Simulated groundwater depletion from 1960s to 2008 averaged ∼4 km3/yr. Cumulative depletion was 50 km3 (∼20% of pumpage) in the piedmont district, 103 km3 (∼20%) in the central plain, and 5 km3 (12%) in the coastal plain. However, depletion varied with time: ∼2.5 km3/yr in the 1970s, ∼4.0 in the 1980s, ∼2.0 in 1990–1996; ∼7.0 in 1997–2001, and ∼4.0 in 2002–2008. Recharge also varied spatially, averaging ∼120 mm/yr and concentrated in the piedmont district (200–350 mm/yr) while lower in the central and coastal plains (50–100 mm/yr). Simulation of several alternatives, including managed aquifer recharge, increased water use efficiency, brackish water use, and interbasin water transfer, indicated that the combination of these strategies could be used to recover groundwater storage by 50 km3 over a 15-year period. This study provides valuable insights for developing more sustainable groundwater management options for the NCP; the methods are useful for managing other depleted aquifers.
TL;DR: In this article, the authors analyzed ensemble projections of hydrological changes in the Alpine Rhine (Eastern Switzerland) for the near-term and far-term scenario periods 2024-2050 and 2073-2099 with respect to 1964-1990.
Abstract: [1] The quantification of uncertainties in projections of climate impacts on river streamflow is highly important for climate adaptation purposes. In this study, we present a methodology to separate uncertainties arising from the climate model (CM), the statistical postprocessing (PP) scheme, and the hydrological model (HM). We analyzed ensemble projections of hydrological changes in the Alpine Rhine (Eastern Switzerland) for the near-term and far-term scenario periods 2024–2050 and 2073–2099 with respect to 1964–1990. For the latter scenario period, the model ensemble projects a decrease of daily mean runoff in summer (−32.2%, range [−45.5% to −8.1%]) and an increase in winter (+41.8%, range [+4.8% to +81.7%]). We applied an analysis of variance model combined with a subsampling procedure to assess the importance of different uncertainty sources. The CMs generally are the dominant source in summer and autumn, whereas, in winter and spring, the uncertainties due to the HMs and the statistical PP gain importance and even partly dominate. In addition, results show that the individual uncertainties from the three components are not additive. Rather, the associated interactions among the CM, the statistical PP scheme, and the HM account for about 5%–40% of the total ensemble uncertainty. The results indicate, in distinction to some previous studies, that none of the investigated uncertainty sources are negligible, and some of the uncertainty is not attributable to individual modeling chain components but rather depends upon interactions.
TL;DR: The whole‐stream reaction significance, Rs (dimensionless), was quantified by multiplying Daden‐hz by the proportion of stream discharge passing through the hyporheic zone, and together these two dimensionless metrics, one flow‐path scale and the other reach‐scale, quantify the whole‐ Stream denitrification significance.
Abstract: [1] Stream denitrification is thought to be enhanced by hyporheic transport but there is little direct evidence from the field. To investigate at a field site, we injected 15NO3−, Br (conservative tracer), and SF6 (gas exchange tracer) and compared measured whole-stream denitrification with in situ hyporheic denitrification in shallow and deeper flow paths of contrasting geomorphic units. Hyporheic denitrification accounted for between 1 and 200% of whole-stream denitrification. The reaction rate constant was positively related to hyporheic exchange rate (greater substrate delivery), concentrations of substrates DOC and nitrate, microbial denitrifier abundance (nirS), and measures of granular surface area and presence of anoxic microzones. The dimensionless product of the reaction rate constant and hyporheic residence time, λhzτhz define a Damkohler number, Daden-hz that was optimal in the subset of hyporheic flow paths where Daden-hz ≈ 1. Optimal conditions exclude inefficient deep pathways where substrates are used up and also exclude inefficient shallow pathways that require repeated hyporheic entries and exits to complete the reaction. The whole-stream reaction significance, Rs (dimensionless), was quantified by multiplying Daden-hz by the proportion of stream discharge passing through the hyporheic zone. Together these two dimensionless metrics, one flow-path scale and the other reach-scale, quantify the whole-stream significance of hyporheic denitrification. One consequence is that the effective zone of significant denitrification often differs from the full depth of the hyporheic zone, which is one reason why whole-stream denitrification rates have not previously been explained based on total hyporheic-zone metrics such as hyporheic-zone size or residence time.
TL;DR: The unified model is compared with various data sets from the literature and is able to explain fairly well a broad number of observations with a very small number of textural and electrochemical parameters and could be therefore used to interpret induced polarization, induction-based electromagnetic methods, and ground penetrating radar data to characterize the vadose zone.
Abstract: A model combining low-frequency complex conductivity and high-frequency permittivity is developed in the frequency range from 1 mHz to 1 GHz. The low-frequency conductivity depends on pore water and surface conductivities. Surface conductivity is controlled by the electrical diffuse layer, the outer component of the electrical double layer coating the surface of the minerals. The frequency dependence of the effective quadrature conductivity shows three domains. Below a critical frequency fp, which depends on the dynamic pore throat size Λ, the quadrature conductivity is frequency dependent. Between fp and a second critical frequency fd, the quadrature conductivity is generally well described by a plateau when clay minerals are present in the material. Clay-free porous materials with a narrow grain size distribution are described by a Cole-Cole model. The characteristic frequency fd controls the transition between double layer polarization and the effect of the high-frequency permittivity of the material. The Maxwell-Wagner polarization is found to be relatively negligible. For a broad range of frequencies below 1 MHz, the effective permittivity exhibits a strong dependence with the cation exchange capacity and the specific surface area. At high frequency, above the critical frequency fd, the effective permittivity reaches a high-frequency asymptotic limit that is controlled by the two Archie's exponents m and n like the low-frequency electrical conductivity. The unified model is compared with various data sets from the literature and is able to explain fairly well a broad number of observations with a very small number of textural and electrochemical parameters. It could be therefore used to interpret induced polarization, induction-based electromagnetic methods, and ground penetrating radar data to characterize the vadose zone.
TL;DR: The Krycklan Catchment Study (KCS) as mentioned in this paper provides a unique field infrastructure for hillslope to landscape-scale research on short and long-term ecosystem dynamics in boreal landscapes.
Abstract: [1] The Krycklan Catchment Study (KCS) provides a unique field infrastructure for hillslope to landscape-scale research on short- and long-term ecosystem dynamics in boreal landscapes. The site is designed for process-based research assessing the role of external drivers including forest management, climate change, and long-range pollutant transport on forests, mires, soils, streams, lakes, and groundwater. The overarching objectives of KCS are to (1) provide a state-of-the-art infrastructure for experimental and hypothesis-driven research, (2) maintain a collection of high-quality, long-term climatic, biogeochemical, hydrological, and environmental data, and (3) support the development of models and guidelines for research, policy, and management.
TL;DR: In this article, the authors evaluated the performance of three parameterization methods: two based on the hydrological response units (HRUs) and one on the multiscale parameter regionalization (MPR) technique.
Abstract: [1] Distributed hydrologic models are increasingly used to describe the spatiotemporal dynamics of water fluxes and state variables occurring within a basin. Reliable model simulations at multiple scales and locations, however, can be obtained only if a robust parameterization method is employed. In this study, we evaluated the performance of three parameterization methods: two based on the hydrological response units (HRUs) and one on the multiscale parameter regionalization (MPR) technique. They were implemented into the distributed mesoscale hydrologic model and then applied to 45 southern German basins. A set of numerical experiments were conducted to assess the effectiveness of the transferability of free parameters to scales and locations other than those used during calibration. The performance of all three methods was comparable for daily discharge simulations when their free parameters were calibrated independently in each basin at a given scale. A significant deterioration in performance of HRU was, however, noticed when free parameters calibrated at coarser scales were shifted to finer ones (up to 60%), whereas MPR exhibited quasi scale-invariant performance with losses in modeling efficiency of less than 2%. Moreover, the latter preserves the spatiotemporal pattern of the simulated variables at a given scale, when transferred from other scales. Evaluation of the transferability of free parameters to ungauged locations further indicated the higher effectiveness and reliability of MPR compared with those of HRU. This study emphasizes the importance of a robust parameterization method in distributed hydrologic modeling.
TL;DR: In this paper, the authors report on two strategies for accelerating posterior inference of a highly parameterized and CPU-demanding groundwater flow model using generalized polynomial chaos (gPC) theory and dimensionality reduction.
Abstract: [1] This study reports on two strategies for accelerating posterior inference of a highly parameterized and CPU-demanding groundwater flow model. Our method builds on previous stochastic collocation approaches, e.g., Marzouk and Xiu (2009) and Marzouk and Najm (2009), and uses generalized polynomial chaos (gPC) theory and dimensionality reduction to emulate the output of a large-scale groundwater flow model. The resulting surrogate model is CPU efficient and serves to explore the posterior distribution at a much lower computational cost using two-stage MCMC simulation. The case study reported in this paper demonstrates a two to five times speed-up in sampling efficiency.
TL;DR: In this paper, the authors used streamflow observations from a highly heterogeneous set of 3394 catchments worldwide to construct reliable, widely applicable models based on 18 climatic and physiographic characteristics to estimate two important base flow characteristics: (1) the base flow index (BFI), defined as the ratio of long-term mean base flow to total streamflow; and (2) the rate of base flow decay.
Abstract: [1] Numerous previous studies have constructed models to estimate base flow characteristics from climatic and physiographic characteristics of catchments and applied these to ungauged regions. However, these studies generally used streamflow observations from a relatively small number of catchments (<200) located in small, homogeneous study areas, which may have led to less reliable models with limited applicability elsewhere. Here, we use streamflow observations from a highly heterogeneous set of 3394 catchments (<10,000 km2) worldwide to construct reliable, widely applicable models based on 18 climatic and physiographic characteristics to estimate two important base flow characteristics: (1) the base flow index (BFI), defined as the ratio of long-term mean base flow to total streamflow; and (2) the base flow recession constant (k), defined as the rate of base flow decay. Regression analysis results revealed that BFI and k were related to several climatic and physiographic characteristics, notably mean annual potential evaporation, mean snow water equivalent depth, and abundance of surface water bodies. Ensembles of artificial neural networks (ANNs; obtained by subsampling the original set of catchments) were trained to estimate the base flow characteristics from climatic and physiographic data. The catchment-scale estimation of the base flow characteristics demonstrated encouraging performance with R2 values of 0.82 for BFI and 0.72 for k. The connection weights of the trained ANNs indicated that climatic characteristics were more important for estimating k than BFI. Global maps of estimated BFI and k were obtained using global climatic and physiographic data as input to the derived models. The resulting global maps are available for free download at http://www.hydrology-amsterdam.nl.
TL;DR: In this paper, an observation-modeling framework is proposed to separate natural (drought) and human (water scarcity) effects on the hydrological system using simulation of the situation that would have occurred without human influence.
Abstract: [1] Drought and water scarcity are keywords for river basin management in water-stressed regions. “Drought” is a natural hazard, caused by large-scale climatic variability, and cannot be prevented by local water management. “Water scarcity” refers to the long-term unsustainable use of water resources, which water managers can influence. Making the distinction between drought and water scarcity is not trivial, because they often occur simultaneously. In this paper, we propose an observation-modeling framework to separate natural (drought) and human (water scarcity) effects on the hydrological system. The basis of the framework is simulation of the situation that would have occurred without human influence, the “naturalized” situation, using a hydrological model. The resulting time series of naturalized state variables and fluxes are then compared to observed time series. As second, more important and novel step, anomalies (i.e., deviations from a threshold) are determined from both time series and compared. We demonstrate the use of the proposed observation-modeling framework in the Upper-Guadiana catchment in Spain. Application of the framework to the period 1980–2000 shows that the impact of groundwater abstraction on the hydrological system was, on average, four times as high as the impact of drought. Water scarcity resulted in disappearance of the winter high-flow period, even in relatively wet years, and a nonlinear response of groundwater. The proposed observation-modeling framework helps water managers in water-stressed regions to quantify the relative impact of drought and water scarcity on a transient basis and, consequently, to make decisions regarding adaptation to drought and combating water scarcity.
TL;DR: In this paper, a meta-analysis of observational studies across the globe with modeling was conducted to show that in regions with average December-January-February (DJF) temperatures greater than −1°C, forest cover reduces snow duration by 1-2 weeks compared to adjacent open areas.
Abstract: [1] Many regions of the world are dependent on snow cover for frost protection and summer water supplies. These same regions are predominantly forested, with forests highly vulnerable to change. Here we combine a meta-analysis of observational studies across the globe with modeling to show that in regions with average December-January-February (DJF) temperatures greater than −1°C, forest cover reduces snow duration by 1–2 weeks compared to adjacent open areas. This occurs because the dominant effect of forest cover shifts from slowing snowmelt by shading the snow and blocking the wind to accelerating snowmelt from increasing longwave radiation. In many locations, midwinter melt removes forest snow before solar radiation is great enough for forest shading to matter, and with warming temperatures, midwinter melt is likely to become more widespread. This temperature-effect in forest-snow-climate interactions must be considered in representations of the combined ecohydrological system and can be used advantageously in forest management strategies.
TL;DR: In this article, the authors investigate cumulative biophysical effects of small and large hydropower dams in China's Nu River basin, and compare effects normalized per megawatt of power produced.
Abstract: [1] Support for low-carbon energy and opposition to new large dams encourages global development of small hydropower facilities. This support is manifested in national and international energy and development policies designed to incentivize growth in the small hydropower sector while curtailing large dam construction. However, the preference of small to large dams assumes, without justification, that small hydropower dams entail fewer and less severe environmental and social externalities than large hydropower dams. With the objective to evaluate the validity of this assumption, we investigate cumulative biophysical effects of small (<50 MW) and large hydropower dams in China's Nu River basin, and compare effects normalized per megawatt of power produced. Results reveal that biophysical impacts of small hydropower may exceed those of large hydropower, particularly with regard to habitat and hydrologic change. These results indicate that more comprehensive standards for impact assessment and governance of small hydropower projects may be necessary to encourage low-impact energy development.
TL;DR: In this article, a simple near-global database of bankfull widths and depths along with confidence intervals was developed based on hydraulic geometry equations and the HydroSHEDS hydrography data set.
Abstract: [1] Hydraulic and hydrologic modeling has been moving to larger spatial scales with increased spatial resolution, and such models require a global database of river widths and depths to facilitate accurate river flow routing. Hydraulic geometry relationships have a long history in estimating river channel characteristics as a function of discharge. A simple near-global database of bankfull widths and depths (along with confidence intervals) was developed based on hydraulic geometry equations and the HydroSHEDS hydrography data set. The bankfull width estimates were evaluated with widths derived from Landsat imagery for reaches of nine major rivers, showing errors ranging from 8 to 62% (correlation of 0.88), although it was difficult to verify whether the satellite observations corresponded to bankfull conditions. Bankfull depth estimates were compared with in situ measurements at sites in the Ohio and Willamette rivers, producing a mean error of 24%. The uncertainties in the derivation approach and a number of caveats are identified, and ways to improve the database in the future are discussed. Despite these limitations, this is the first global database that can be used directly in hydraulic models or as a set of constraints in model calibration.
TL;DR: In this article, a hybrid wavelet-bootstrap-neural network (WBNN) model was proposed for short term (1, 3, and 5 day; 1 and 2 week; and 1 and two month) urban water demand forecasting.
Abstract: [1] A new hybrid wavelet-bootstrap-neural network (WBNN) model is proposed in this study for short term (1, 3, and 5 day; 1 and 2 week; and 1 and 2 month) urban water demand forecasting. The new method was tested using data from the city of Montreal in Canada. The performance of the WBNN method was compared with the autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average model with exogenous input variables (ARIMAX), traditional NNs, wavelet analysis-based NNs (WNN), bootstrap-based NNs (BNN), and a simple naive persistence index model. The WBNN model was developed as an ensemble of several NNs built using bootstrap resamples of wavelet subtime series instead of raw data sets. The results demonstrated that the hybrid WBNN and WNN models produced significantly more accurate forecasting results than the traditional NN, BNN, ARIMA, and ARIMAX models. It was also found that the WBNN model reduces the uncertainty associated with the forecasts, and the performance of WBNN forecasted confidence bands was found to be more accurate and reliable than BNN forecasted confidence bands. It was found in this study that maximum temperature and total precipitation improved the accuracy of water demand forecasts using wavelet analysis. The performance of WBNN models was also compared for different numbers of bootstrap resamples (i.e., 25, 50, 100, 200, and 500) and it was found that WBNN models produced optimum results with different numbers of bootstrap resamples for different lead time forecasts with considerable variability.
TL;DR: In this paper, a large-scale flood inundation ensemble forecasting model using best available data and modeling approaches in data scarce areas was built for the Lower Zambezi River to demonstrate current flooding forecasting capabilities in large data-scarce regions.
Abstract: [1] At present continental to global scale flood forecasting predicts at a point discharge, with little attention to detail and accuracy of local scale inundation predictions. Yet, inundation variables are of interest and all flood impacts are inherently local in nature. This paper proposes a large-scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas. The model was built for the Lower Zambezi River to demonstrate current flood inundation forecasting capabilities in large data-scarce regions. ECMWF ensemble forecast (ENS) data were used to force the VIC (Variable Infiltration Capacity) hydrologic model, which simulated and routed daily flows to the input boundary locations of a 2-D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of channels that play a key role in flood wave propagation. We therefore employed a novel subgrid channel scheme to describe the river network in detail while representing the floodplain at an appropriate scale. The modeling system was calibrated using channel water levels from satellite laser altimetry and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of between one and two model resolutions compared to an observed flood edge and inundation area agreement was on average 86%. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km2 and at model grid resolutions up to several km2.
TL;DR: In this paper, the accuracy of the cosmic-ray probe (CRP) measurements for soil water content determination in a humid forest ecosystem has been investigated by using a wireless sensor network (SoilNet).
Abstract: [1] Soil water content is one of the key state variables in the soil-vegetation-atmosphere continuum due to its important role in the exchange of water and energy at the soil surface. A new promising method to measure integral soil water content at the field or small catchment scale is the cosmic-ray probe (CRP). Recent studies of CRP measurements have mainly presented results from test sites located in very dry areas and from agricultural fields with sandy soils. In this study, distributed continuous soil water content measurements from a wireless sensor network (SoilNet) were used to investigate the accuracy of CRP measurements for soil water content determination in a humid forest ecosystem. Such ecosystems are less favorable for CRP applications due to the presence of a litter layer. In addition, lattice water and carbohydrates of soil organic matter and belowground biomass reduce the effective sensor depth and thus were accounted for in the calibration of the CRP. The hydrogen located in the biomass decreased the level of neutron count rates and thus also decreased the sensitivity of the cosmic-ray probe, which in turn resulted in an increase of the measurement uncertainty. This uncertainty was compensated by using longer integration times (e.g., 24 h). For the Wustebach forest site, the cosmic-ray probe enabled the assessment of integral daily soil water content dynamics with a RMSE of about 0.03 cm3/cm3 without explicitly considering the litter layer. By including simulated water contents of the litter layer in the calibration, a better accuracy could be achieved.
TL;DR: In this paper, the authors used 16 global climate models and three global warming scenarios to investigate changes in groundwater recharge rates for a 2050 climate relative to a 1990 climate in the U.S. High Plains region.
Abstract: [1] Considering that past climate changes have significantly impacted groundwater resources, quantitative predictions of climate change effects on groundwater recharge may be valuable for effective management of future water resources. This study used 16 global climate models (GCMs) and three global warming scenarios to investigate changes in groundwater recharge rates for a 2050 climate relative to a 1990 climate in the U.S. High Plains region. Groundwater recharge was modeled using the Soil-Vegetation-Atmosphere-Transfer model WAVES for a variety of soil and vegetation types representative of the High Plains. The median projection under a 2050 climate includes increased recharge in the Northern High Plains (+8%), a slight decrease in the Central High Plains (−3%), and a larger decrease in the Southern High Plains (−10%), amplifying the current spatial trend in recharge from north to south. There is considerable uncertainty in both the magnitude and direction of these changes in recharge projections. Predicted changes in recharge between dry and wet future climate scenarios encompass both an increase and decrease in recharge rates, with the magnitude of this range greater than 50% of current recharge. On a proportional basis, sensitivity of recharge to changes in rainfall indicates that areas with high current recharge rates are least sensitive to change in rainfall and vice versa. Sensitivity analyses indicate an amplification of change in recharge compared to change in rainfall, and this amplification is in the range of 1–6 with an average of 2.5–3.5 depending upon the global warming scenario.
TL;DR: In this article, a new set of empirical hydraulic models for an effective description of water dynamics from full saturation to complete dryness is introduced, which allow a clear partitioning between capillary and adsorptive water retention as well as between Capillary and film conductivity.
Abstract: [1] The commonly used hydraulic models only account for capillary water retention and conductivity. Adsorptive water retention and film conductivity is neglected. This leads to erroneous description of hydraulic properties in the dry range. The few existing models, which account for film conductivity and adsorptive retention are either difficult to use or physically inconsistent. A new set of empirical hydraulic models for an effective description of water dynamics from full saturation to complete dryness is introduced. The models allow a clear partitioning between capillary and adsorptive water retention as well as between capillary and film conductivity. The number of adjustable parameters for the new retention model is not increased compared to the commonly used models, whereas only one extra parameter for quantifying the contribution of film conductivity is required for the new conductivity model. Both models are mathematically simple and thus easy to use in simulation studies. The new liquid conductivity model is coupled with an existing vapor conductivity model to describe conductivity in the complete moisture range. The new models were successfully applied to literature data, which all reach the dry to very dry range and cannot be well described with the classic capillary models. The investigated soils range from pure sands to clay loams. A simulation study with steady-state water transport scenarios shows that neglecting either film or vapor conductivity or both can lead to significant underestimation of water transport at low water contents.
TL;DR: This paper proposes a refined concept, Design Life Level, which quantifies risk in a nonstationary climate and can serve as the basis for communication and relate this concept and variants of it to existing literature and illustrate how they, and some useful complementary plots, may be computed and used.
Abstract: In the past, the concepts of return levels and return periods have been standard and important tools for engineering design. However, these concepts are based on the assumption of a stationary climate and do not apply to a changing climate, whether local or global. In this paper, we propose a refined concept, Design Life Level, which quantifies risk in a nonstationary climate and can serve as the basis for communication. In current practice, typical hydrologic risk management focuses on a standard (e.g., in terms of a high quantile corresponding to the specified probability of failure for a single year). Nevertheless, the basic information needed for engineering design should consist of (i) the design life period (e.g., the next 50 years, say 2015-2064); and (ii) the probability (e.g., 5% chance) of a hazardous event (typically, in the form of the hydrologic variable exceeding a high level) occurring during the design life period. Capturing both of these design characteristics, the Design Life Level is defined as an upper quantile (e.g., 5%) of the distribution of the maximum value of the hydrologic variable (e.g., water level) over the design life period. We relate this concept and variants of it to existing literature and illustrate how they, and some useful complementary plots, may be computed and used. One practically important consideration concerns quantifying the statistical uncertainty in estimating a high quantile under nonstationarity.