ALBERT

Description: In order to improve the current understanding of the dynamics of ammonia (NH3) in a major industrial and urban area, intensive measurements of atmospheric NH3 were conducted in Houston during two sampling periods (12 February 2010–1 March 2010 and 5 August 2010–25 September 2010). The measurements were performed with a 10.4-μm external cavity quantum cascade laser (EC-QCL)-based sensor employing conventional photo-acoustic spectroscopy. The mixing ratio of NH3 ranged from 0.1 to 8.7 ppb with a mean of 2.4 ± 1.2 ppb in winter and ranged from 0.2 to 27.1 ppb with a mean of 3.1 ± 2.9 ppb in summer. The larger levels in summer probably are due to higher ambient temperature. A notable morning increase and a mid-day decrease were observed in the diurnal profile of NH3 mixing ratios. Motor vehicles were found to be major contributors to the elevated levels during morning rush hours in winter. However, changes in vehicular catalytic converter performance and other local or regional emission sources from different wind directions governed the behavior of NH3 during morning rush hours in summer. There was a large amount of variability, particularly in summer, with several episodes of elevated NH3 mixing ratios that could be linked to industrial facilities. A considerable discrepancy in NH3 mixing ratios existed between weekdays and weekends. This study suggests that NH3 mixing ratios in Houston occasionally exceeded previous modeling predictions when sporadic and substantial enhancements occurred, potentially causing profound effects on particulate matter formation and local air quality.

Description: In order to improve the current understanding of the dynamics of ammonia (NH3) in a major industrial and urban area, intensive measurements of atmospheric NH3 were conducted in Houston during two sampling periods (12 February 2010–1 March 2010 and 5 August 2010–25 September 2010). The measurements were performed with a 10.4-μm external cavity quantum cascade laser (EC-QCL)-based sensor employing conventional photo-acoustic spectroscopy. The mixing ratio of NH3 ranged from 0.1 to 8.7 ppb with a mean of 2.4 ± 1.2 ppb in winter and ranged from 0.2 to 27.1 ppb with a mean of 3.1 ± 2.9 ppb in summer. The larger levels in summer probably are due to higher ambient temperature. A notable morning increase and a mid-day decrease were observed in the diurnal profile of NH3 mixing ratios. Motor vehicles were found to be major contributors to the elevated levels during morning rush hours in winter. However, changes in vehicular catalytic converter performance and other local or regional emission sources from different wind directions governed the behavior of NH3 during morning rush hours in summer. There was a large amount of variability, particularly in summer, with several episodes of elevated NH3 mixing ratios that could be linked to industrial facilities. A considerable discrepancy in NH3 mixing ratios existed between weekdays and weekends. This study suggests that NH3 mixing ratios in Houston occasionally exceeded previous modeling predictions when sporadic and substantial enhancements occurred, potentially causing profound effects on particulate matter formation and local air quality.

Description: River flow regimes in the semi-arid region of northern China show a decreasing trend in terms of quantity. River runoff (i.e. blue water) reduction within the Laohahe catchment, the source area of the Liaohe River basin, manifests the aridity that exists widely in northern China. According to the water balance equation, during the past half-century, observed streamflow records in the Laohahe catchment show that blue water was re-directed to green water flow (i.e. evapotranspiration) over annual and decadal time scales, whereas precipitation did not vary much. Human activities and land-use/land-cover changes are the fundamental reasons for such runoff change. In the studied catchment, extensive land reclamation for agriculture, water withdrawal from streams, and abstraction from aquifers for irrigation are the direct and main causes leading to the decrease in observed blue water. These factors further demonstrate that a land-use decision is also a decision about water. Therefore, there is a need for an integrated modelling framework to intrinsically link climate, hydrological, and agricultural models with social and economic analyses.

Description: Robust and reliable water-resource mapping in ungauged basins requires estimation of the uncertainties in the hydrologic model, the regionalisation method, and the observational data. In this study we investigated the use of regionalised flow-duration curves (FDCs) for constraining model predictive uncertainty, while accounting for all these uncertainty sources. A water balance model was applied to 36 basins in Central America using regionally and globally available precipitation, climate and discharge data that were screened for inconsistencies. A rating-curve analysis for 35 Honduran discharge stations was used to estimate discharge uncertainty for the region, and the consistency of the model forcing and evaluation data was analysed using two different screening methods. FDCs with uncertainty bounds were calculated for each basin, accounting for both discharge uncertainty and, in many cases, uncertainty stemming from the use of short time series, potentially not representative for the modelling period. These uncertain FDCs were then used to regionalise a FDC for each basin, treating it as ungauged in a cross-evaluation, and this regionalised FDC was used to constrain the uncertainty in the model predictions for the basin. There was a clear relationship between the performance of the local model calibration and the degree of data set consistency – with many basins with inconsistent data lacking behavioural simulations (i.e. simulations within predefined limits around the observed FDC) and the basins with the highest data set consistency also having the highest simulation reliability. For the basins where the regionalisation of the FDCs worked best, the uncertainty bounds for the regionalised simulations were only slightly wider than those for a local model calibration. The predicted uncertainty was greater for basins where the result of the FDC regionalisation was more uncertain, but the regionalised simulations still had a high reliability compared to the locally calibrated simulations and often encompassed them. The regionalised FDCs were found to be useful on their own as a basic signature constraint; however, additional regionalised signatures could further constrain the uncertainty in the predictions and may increase the robustness to severe data inconsistencies, which are difficult to detect for ungauged basins.

Description: World water resources have primarily been analysed by global-scale hydrological models in the last decades. Runoff generation in many of these models are based on process formulations developed at catchments scales. The division between slow runoff (baseflow) and fast runoff is primarily governed by slope and spatial distribution of effective water storage capacity, both acting at very small scales. Many hydrological models, e.g. VIC, account for the spatial storage variability in terms of statistical distributions; such models are generally proven to perform well. The statistical approaches, however, use the same runoff-generation parameters everywhere in a basin. The TOPMODEL concept, on the other hand, links the effective maximum storage capacity with real-world topography. Recent availability of global high-quality, high-resolution topographic data makes TOPMODEL attractive as a basis for a physically-based runoff-generation algorithm at large scales, even if its assumptions are not valid in flat terrain or for deep groundwater systems. We present a new runoff-generation algorithm for large-scale hydrology based on TOPMODEL concepts intended to overcome these problems. The TRG (topography-derived runoff generation) algorithm relaxes the TOPMODEL equilibrium assumption so baseflow generation is not tied to topography. TRG only uses the topographic index to distribute average storage to each topographic index class. The maximum storage capacity is proportional to the range of topographic index and is scaled by one parameter. The distribution of storage capacity within large-scale grid cells is obtained numerically through topographic analysis. The new topography-derived distribution function is then inserted into a runoff-generation framework similar VIC's. Different basin parts are parameterised by different storage capacities, and different shapes of the storage-distribution curves depend on their topographic characteristics. The TRG algorithm is driven by the HydroSHEDS dataset with a resolution of 3" (around 90 m at the equator). The TRG algorithm was validated against the VIC algorithm in a common model framework in 3 river basins in different climates. The TRG algorithm performed equally well or marginally better than the VIC algorithm with one less parameter to be calibrated. The TRG algorithm also lacked equifinality problems and offered a realistic spatial pattern for runoff generation and evaporation.

Description: Large-scale hydrological models and land surface models are so far the only tools for assessing current and future water resources. Those models estimate discharge with large uncertainties, due to the complex interaction between climate and hydrology, the limited availability and quality of data, as well as model uncertainties. A new purely data-driven scale-extrapolation method to estimate discharge for a large region solely from selected small sub-basins, which are typically 1–2 orders of magnitude smaller than the large region, is proposed. Those small sub-basins contain sufficient information, not only on climate and land surface, but also on hydrological characteristics for the large basin. In the Baltic Sea drainage basin, best discharge estimation for the gauged area was achieved with sub-basins that cover 5% of the gauged area. There exist multiple sets of sub-basins whose climate and hydrology resemble those of the gauged area equally well. Those multiple sets estimate annual discharge for the gauged area consistently well with 6 % average error. The scale-extrapolation method is completely data-driven; therefore it does not force any modelling error into the prediction. The multiple predictions are expected to bracket the inherent variations and uncertainties of the climate and hydrology of the basin.

Description: Large-scale hydrological models and land surface models are by far the only tools for accessing future water resources in climate change impact studies. Those models estimate discharge with large uncertainties, due to the complex interaction between climate and hydrology, the limited quality and availability of data, as well as model uncertainties. A new purely data-based scale-extrapolation method is proposed, to estimate water resources for a large basin solely from selected small sub-basins, which are typically two-orders-of-magnitude smaller than the large basin. Those small sub-basins contain sufficient information, not only on climate and land surface, but also on hydrological characteristics for the large basin In the Baltic Sea drainage basin, best discharge estimation for the gauged area was achieved with sub-basins that cover 2–4% of the gauged area. There exist multiple sets of sub-basins that resemble the climate and hydrology of the basin equally well. Those multiple sets estimate annual discharge for gauged area consistently well with 5% average error. The scale-extrapolation method is completely data-based; therefore it does not force any modelling error into the prediction. The multiple predictions are expected to bracket the inherent variations and uncertainties of the climate and hydrology of the basin. The method can be applied in both un-gauged basins and un-gauged periods with uncertainty estimation.

Description: The evolution routes and development stages in hydrological sciences are summarised from the following three dimensions: research object, discipline and methodology, by means of the descriptive-explanatory-humanistic ideology. Modern technical breakthroughs and socioeconomic developments have promoted hydrology from geographical hydrology, engineering or applied hydrology to water resources hydrology in terms of the focus of research objectives or problems. It has been observed from the point of view of methodology that hydrological sciences go through deterministic hydrology, stochastic hydrology, isotope hydrology, digital hydrology, etc. Hydrological sciences in the context of discipline dimensions can be divided into three main categories: physical hydrology, chemical/environmental hydrology, biological hydrology including eco-hydrology and socio-hydrology, although there are overlaps between these due to the complex and intertwined water-related challenges facing the hydrological community and other geosciences. Humans have played a significant role in changing land uses throughout the world and therefore, socio-hydrology is an increasingly significant branch of the hydrological sciences. It can be seen from analyses that biological hydrology is a new approach to cope with global change issues, and a new frontier direction in the field of hydrological sciences. It can also be seen from dialectical analysis of the different stages in the evolution of hydrology, that new frontiers or directions requiring investigation, scientific recognition and technical innovation will continue to be generated in the field of hydrology. The 3-dimensional diagram of evolution routes of hydrological sciences may provide some ideas for Panta Rhei, the new IAHS Science Initiative 2013–2022 for hydrological research under changing human and environmental systems in the real world.

Description: World water resources have primarily been analysed by global-scale hydrological models in the last decades. Runoff generation in many of these models are based on process formulations developed at catchments scales. The division between slow runoff (baseflow) and fast runoff is primarily governed by slope and spatial distribution of effective water storage capacity, both acting a very small scales. Many hydrological models, e.g. VIC, account for the spatial storage variability in terms of statistical distributions; such models are generally proven to perform well. The statistical approaches, however, use the same runoff-generation parameters everywhere in a basin. The TOPMODEL concept, on the other hand, links the effective maximum storage capacity with real-world topography. Recent availability of global high-quality, high-resolution topographic data makes TOPMODEL attractive as a basis for a physically-based runoff-generation algorithm at large scales, even if its assumptions are not valid in flat terrain or for deep groundwater systems. We present a new runoff-generation algorithm for large-scale hydrology based on TOPMODEL concepts intended to overcome these problems. The TRG (topography-derived runoff generation) algorithm relaxes the TOPMODEL equilibrium assumption so baseflow generation is not tied to topography. TGR only uses the topographic index to distribute average storage to each topographic index class. The maximum storage capacity is proportional to the range of topographic index and is scaled by one parameter. The distribution of storage capacity within large-scale grid cells is obtained numerically through topographic analysis. The new topography-derived distribution function is then inserted into a runoff-generation framework similar VIC's. Different basin parts are parameterised by different storage capacities, and different shapes of the storage-distribution curves depend on their topographic characteristics. The TRG algorithm is driven by the HydroSHEDS dataset with a resolution of 3'' (around 90 m at the equator). The TRG algorithm was validated against the VIC algorithm in a common model framework in 3 river basins in different climates. The TRG algorithm performed equally well or marginally better than the VIC algorithm with one less parameter to be calibrated. The TRG algorithm also lacked equifinality problems and offered a realistic spatial pattern for runoff generation and evaporation.

Description: Robust and reliable water-resources mapping in ungauged basins requires estimation of the uncertainties in the hydrologic model, the regionalisation method, and the observational data. In this study we investigated the use of regionalised flow-duration curves (FDCs) for constraining model predictive uncertainty, while accounting for all these uncertainty sources. A water-balance model was applied to 36 basins in Central America using regionally and globally available precipitation, climate and discharge data that were screened for inconsistencies. A rating-curve analysis for 35 Honduran discharge stations was used to estimate discharge uncertainty for the region, and the consistency of the model forcing and evaluation data was analysed using two different screening methods. FDCs with uncertainty bounds were calculated for each basin, accounting for both discharge uncertainty and, in many cases, uncertainty stemming from the use of short time series, potentially not representative for the modelling period. These uncertain FDCs were then used to regionalise a FDC for each basin, treating it as ungauged in a cross-evaluation, and this regionalised FDC was used to constrain the uncertainty in the model predictions for the basin. There was a clear relationship between the performance of the local model calibration and the degree of dataset consistency – with many basins with inconsistent data lacking behavioural simulations and the basins with the highest dataset consistency also having the highest simulation reliability. For the basins where the regionalisation of the FDCs worked best, the uncertainty bounds for the regionalised simulations were only slightly wider than those for a local model calibration. The predicted uncertainty was greater for basins where the result of the FDC-regionalisation was more uncertain, but the regionalised simulations still had a high reliability compared to the locally-calibrated simulations and often encompassed them. The regionalised FDCs were found to be useful on their own as a basic signature constraint; however, additional regionalised signatures could further constrain the uncertainty in the predictions and may increase the robustness to severe data inconsistencies, which are difficult to detect in ungauged basins.