Showing papers in "Hydrology and Earth System Sciences in 2011"

The green, blue and grey water footprint of crops and derived crop products

Abstract: This study quantifies the green, blue and grey water footprint of global crop production in a spatially-explicit way for the period 1996–2005. The assessment improves upon earlier research by taking a high-resolution approach, estimating the water footprint of 126 crops at a 5 by 5 arc minute grid. We have used a grid-based dynamic water balance model to calculate crop water use over time, with a time step of one day. The model takes into account the daily soil water balance and climatic conditions for each grid cell. In addition, the water pollution associated with the use of nitrogen fertilizer in crop production is estimated for each grid cell. The crop evapotranspiration of additional 20 minor crops is calculated with the CROPWAT model. In addition, we have calculated the water footprint of more than two hundred derived crop products, including various flours, beverages, fibres and biofuels. We have used the water footprint assessment framework as in the guideline of the Water Footprint Network. Considering the water footprints of primary crops, we see that the global average water footprint per ton of crop increases from sugar crops (roughly 200 m3 ton−1), vegetables (300 m3 ton−1), roots and tubers (400 m3 ton−1), fruits (1000 m3 ton−1), cereals (1600 m3 ton−1), oil crops (2400 m3 ton−1) to pulses (4000 m3 ton−1). The water footprint varies, however, across different crops per crop category and per production region as well. Besides, if one considers the water footprint per kcal, the picture changes as well. When considered per ton of product, commodities with relatively large water footprints are: coffee, tea, cocoa, tobacco, spices, nuts, rubber and fibres. The analysis of water footprints of different biofuels shows that bio-ethanol has a lower water footprint (in m3 GJ−1) than biodiesel, which supports earlier analyses. The crop used matters significantly as well: the global average water footprint of bio-ethanol based on sugar beet amounts to 51 m3 GJ−1, while this is 121 m3 GJ−1 for maize. The global water footprint related to crop production in the period 1996–2005 was 7404 billion cubic meters per year (78 % green, 12 % blue, 10 % grey). A large total water footprint was calculated for wheat (1087 Gm3 yr−1), rice (992 Gm3 yr−1) and maize (770 Gm3 yr−1). Wheat and rice have the largest blue water footprints, together accounting for 45 % of the global blue water footprint. At country level, the total water footprint was largest for India (1047 Gm3 yr−1), China (967 Gm3 yr−1) and the USA (826 Gm3 yr−1). A relatively large total blue water footprint as a result of crop production is observed in the Indus river basin (117 Gm3 yr−1) and the Ganges river basin (108 Gm3 yr−1). The two basins together account for 25 % of the blue water footprint related to global crop production. Globally, rain-fed agriculture has a water footprint of 5173 Gm3 yr−1 (91 % green, 9 % grey); irrigated agriculture has a water footprint of 2230 Gm3 yr−1 (48 % green, 40 % blue, 12 % grey).

Global land-surface evaporation estimated from satellite-based observations

Abstract: . This paper outlines a new strategy to derive evaporation from satellite observations. The approach uses a variety of satellite-sensor products to estimate daily evaporation at a global scale and 0.25 degree spatial resolution. Central to this methodology is the use of the Priestley and Taylor (PT) evaporation model. The minimalistic PT equation combines a small number of inputs, the majority of which can be detected from space. This reduces the number of variables that need to be modelled. Key distinguishing features of the approach are the use of microwave-derived soil moisture, land surface temperature and vegetation density, as well as the detailed estimation of rainfall interception loss. The modelled evaporation is validated against one year of eddy covariance measurements from 43 stations. The estimated annual totals correlate well with the stations' annual cumulative evaporation (R=0.80, N=43) and present a low average bias (−5%). The validation of the daily time series at each individual station shows good model performance in all vegetation types and climate conditions with an average correlation coefficient of R =0.83, still lower than the R =0.90 found in the validation of the monthly time series. The first global map of annual evaporation developed through this methodology is also presented.

The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements

Abstract: . In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International Soil Moisture Network (ISMN; http://www.ipf.tuwien.ac.at/insitu ) was initiated to serve as a centralized data hosting facility where globally available in situ soil moisture measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their soil moisture datasets with the ISMN on a voluntary and no-cost basis. Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. These will assist the user in correctly interpreting the soil moisture data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status May 2011), the ISMN contains data of 19 networks and more than 500 stations located in North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that both the number of stations and the time period covered by the existing stations are still growing. Hence, it will become an increasingly important resource for validating and improving satellite-derived soil moisture products and studying climate related trends. As the ISMN is animated by the scientific community itself, we invite potential networks to enrich the collection by sharing their in situ soil moisture data.

Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

Abstract: . Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a methodology that takes advantage of the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates to produce an improved soil moisture product. First, volumetric soil water content (m3 m−3) from AMSR-E and degree of saturation (%) from ASCAT are rescaled against a reference land surface model data set using a cumulative distribution function matching approach. While this imposes any bias of the reference on the rescaled satellite products, it adjusts them to the same range and preserves the dynamics of original satellite-based products. Comparison with in situ measurements demonstrates that where the correlation coefficient between rescaled AMSR-E and ASCAT is greater than 0.65 ("transitional regions"), merging the different satellite products increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT, respectively, are used for the merged product. Therefore the merged product carries the advantages of better spatial coverage overall and increased number of observations, particularly for the transitional regions. The combination method developed has the potential to be applied to existing microwave satellites as well as to new missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

Climate change and mountain water resources: overview and recommendations for research, management and policy

Abstract: . Mountains are essential sources of freshwater for our world, but their role in global water resources could well be significantly altered by climate change. How well do we understand these potential changes today, and what are implications for water resources management, climate change adaptation, and evolving water policy? To answer above questions, we have examined 11 case study regions with the goal of providing a global overview, identifying research gaps and formulating recommendations for research, management and policy. After setting the scene regarding water stress, water management capacity and scientific capacity in our case study regions, we examine the state of knowledge in water resources from a highland-lowland viewpoint, focusing on mountain areas on the one hand and the adjacent lowland areas on the other hand. Based on this review, research priorities are identified, including precipitation, snow water equivalent, soil parameters, evapotranspiration and sublimation, groundwater as well as enhanced warming and feedback mechanisms. In addition, the importance of environmental monitoring at high altitudes is highlighted. We then make recommendations how advancements in the management of mountain water resources under climate change could be achieved in the fields of research, water resources management and policy as well as through better interaction between these fields. We conclude that effective management of mountain water resources urgently requires more detailed regional studies and more reliable scenario projections, and that research on mountain water resources must become more integrative by linking relevant disciplines. In addition, the knowledge exchange between managers and researchers must be improved and oriented towards long-term continuous interaction.

Catchment classification: Empirical analysis of hydrologic similarity based on catchment function in the eastern USA

Abstract: Hydrologic similarity between catchments, derived from similarity in how catchments respond to precipitation input, is the basis for catchment classification, for transferability of information, for generalization of our hydrologic understanding and also for understanding the potential impacts of environmental change. An important question in this context is, how far can widely available hydrologic information (precipitation-temperature-streamflow data and generally available physical descriptors) be used to create a first order grouping of hydrologically similar catchments? We utilize a heterogeneous dataset of 280 catchments located in the Eastern US to understand hydrologic similarity in a 6-dimensional signature space across a region with strong environmental gradients. Signatures are defined as hydrologic response characteristics that provide insight into the hydrologic function of catchments. A Bayesian clustering scheme is used to separate the catchments into 9 homogeneous classes, which enable us to interpret hydrologic similarity with respect to similarity in climatic and landscape attributes across this region. We finally derive several hypotheses regarding controls on individual signatures from the analysis performed here.

Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery

Abstract: . Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (e.g., air temperature, advection) are affecting plant functioning. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI) model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil + canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5 to 10-km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI) spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions on the order of 10 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe, Africa and other continents with geostationary satellite coverage.

A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem

Abstract: . We analyzed the seasonal variations of energy balance components over three different surfaces: irrigated cropland (Yingke, YK), alpine meadow (A'rou, AR), and spruce forest (Guantan, GT). The energy balance components were measured using eddy covariance (EC) systems and a large aperture scintillometer (LAS) in the Heihe River Basin, China, in 2008 and 2009. We also determined the source areas of the EC and LAS measurements with a footprint model for each site and discussed the differences between the sensible heat fluxes measured with EC and LAS at AR. The results show that the main EC source areas were within a radius of 250 m at all of the sites. The main source area for the LAS (with a path length of 2390 m) stretched along a path line approximately 2000 m long and 700 m wide. The surface characteristics in the source areas changed with the season at each site, and there were characteristic seasonal variations in the energy balance components at all of the sites. The sensible heat flux was the main term of the energy budget during the dormant season. During the growing season, however, the latent heat flux dominated the energy budget, and an obvious "oasis effect" was observed at YK. The sensible heat fluxes measured by LAS at AR were larger than those measured by EC at the same site. This difference seems to be caused by the so-called energy imbalance phenomenon, the heterogeneity of the underlying surfaces, and the difference between the source areas of the LAS and EC measurements.

Land use and climate change impacts on the hydrology of the upper Mara River Basin, Kenya: results of a modeling study to support better resource management

Abstract: . Some of the most valued natural and cultural landscapes on Earth lie in river basins that are poorly gauged and have incomplete historical climate and runoff records. The Mara River Basin of East Africa is such a basin. It hosts the internationally renowned Mara-Serengeti landscape as well as a rich mixture of indigenous cultures. The Mara River is the sole source of surface water to the landscape during the dry season and periods of drought. During recent years, the flow of the Mara River has become increasingly erratic, especially in the upper reaches, and resource managers are hampered by a lack of understanding of the relative influence of different sources of flow alteration. Uncertainties about the impacts of future climate change compound the challenges. We applied the Soil Water Assessment Tool (SWAT) to investigate the response of the headwater hydrology of the Mara River to scenarios of continued land use change and projected climate change. Under the data-scarce conditions of the basin, model performance was improved using satellite-based estimated rainfall data, which may also improve the usefulness of runoff models in other parts of East Africa. The results of the analysis indicate that any further conversion of forests to agriculture and grassland in the basin headwaters is likely to reduce dry season flows and increase peak flows, leading to greater water scarcity at critical times of the year and exacerbating erosion on hillslopes. Most climate change projections for the region call for modest and seasonally variable increases in precipitation (5–10 %) accompanied by increases in temperature (2.5–3.5 °C). Simulated runoff responses to climate change scenarios were non-linear and suggest the basin is highly vulnerable under low (−3 %) and high (+25 %) extremes of projected precipitation changes, but under median projections (+7 %) there is little impact on annual water yields or mean discharge. Modest increases in precipitation are partitioned largely to increased evapotranspiration. Overall, model results support the existing efforts of Mara water resource managers to protect headwater forests and indicate that additional emphasis should be placed on improving land management practices that enhance infiltration and aquifer recharge as part of a wider program of climate change adaptation.

The influence of soil moisture on threshold runoff generation processes in an alpine headwater catchment

Abstract: . This study investigates the role of soil moisture on the threshold runoff response in a small headwater catchment in the Italian Alps that is characterised by steep hillslopes and a distinct riparian zone. This study focuses on: (i) the threshold soil moisture-runoff relationship and the influence of catchment topography on this relation; (ii) the temporal dynamics of soil moisture, streamflow and groundwater that characterize the catchment's response to rainfall during dry and wet periods; and (iii) the combined effect of antecedent wetness conditions and rainfall amount on hillslope and riparian runoff. Our results highlight the strong control exerted by soil moisture on runoff in this catchment: a sharp threshold exists in the relationship between soil water content and runoff coefficient, streamflow, and hillslope-averaged depth to water table. Low runoff ratios were likely related to the response of the riparian zone, which was almost always close to saturation. High runoff ratios occurred during wet antecedent conditions, when the soil moisture threshold was exceeded. In these cases, subsurface flow was activated on hillslopes, which became a major contributor to runoff. Antecedent wetness conditions also controlled the catchment's response time: during dry periods, streamflow reacted and peaked prior to hillslope soil moisture whereas during wet conditions the opposite occurred. This difference resulted in a hysteretic behaviour in the soil moisture-streamflow relationship. Finally, the influence of antecedent moisture conditions on runoff was also evident in the relation between cumulative rainfall and total stormflow. Small storms during dry conditions produced low stormflow amounts, likely mainly from overland flow from the near saturated riparian zone. Conversely, for rainfall events during wet conditions, higher stormflow values were observed and hillslopes must have contributed to streamflow.

Status of satellite precipitation retrievals

Abstract: . Satellites offer an unrivalled vantage point to observe and measure Earth system processes and parameters. Precipitation (rain and snow) in particular, benefit from such observations since precipitation is spatially and temporally highly variable and with satellites overcoming some of the deficiencies of conventional gauge and radar measurements. This paper provides an overall review of quantitative precipitation estimation, covering the basis of the satellite systems used in the observation of precipitation, the dissemination and processing of this data, and the generation, availability and validation of these precipitation estimates. A selection of applications utilising these precipitation estimates are then outlined to exemplify the utility of such products.

Sediment management modelling in the Blue Nile Basin using SWAT model

Abstract: . Soil erosion/sedimentation is an immense problem that has threatened water resources development in the Nile river basin, particularly in the Eastern Nile (Ethiopia, Sudan and Egypt). An insight into soil erosion/sedimentation mechanisms and mitigation methods plays an imperative role for the sustainable water resources development in the region. This paper presents daily sediment yield simulations in the Upper Blue Nile under different Best Management Practice (BMP) scenarios. Scenarios applied in this paper are (i) maintaining existing conditions, (ii) introducing filter strips, (iii) applying stone bunds (parallel terraces), and (iv) reforestation. The Soil and Water Assessment Tool (SWAT) was used to model soil erosion, identify soil erosion prone areas and assess the impact of BMPs on sediment reduction. For the existing conditions scenario, the model results showed a satisfactory agreement between daily observed and simulated sediment concentrations as indicated by Nash-Sutcliffe efficiency greater than 0.83. The simulation results showed that applying filter strips, stone bunds and reforestation scenarios reduced the current sediment yields both at the subbasins and the basin outlets. However, a precise interpretation of the quantitative results may not be appropriate because some physical processes are not well represented in the SWAT model.

Magnitude and variability of land evaporation and its components at the global scale

Abstract: . A process-based methodology is applied to estimate land-surface evaporation from multi-satellite information. GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) combines a wide range of remotely-sensed observations to derive daily actual evaporation and its different components. Soil water stress conditions are defined from a root-zone profile of soil moisture and used to estimate transpiration based on a Priestley and Taylor equation. The methodology also derives evaporationfrom bare soil and snow sublimation. Tall vegetation rainfall interception is independently estimated by means of the Gash analytical model. Here, GLEAM is applied daily, at global scale and a quarter degree resolution. Triple collocation is used to calculate the error structure of the evaporation estimates and test the relative merits of two different precipitation inputs. The spatial distribution of evaporation – and its different components – is analysed to understand the relative importance of each component over different ecosystems. Annual land evaporation is estimated as 67.9 × 103 km3, 80% corresponding to transpiration, 11% to interception loss, 7% to bare soil evaporation and 2% snow sublimation. Results show that rainfall interception plays an important role in the partition of precipitation into evaporation and water available for runoff at a continental scale. This study gives insights into the relative importance of precipitation and net radiation in driving evaporation, and how the seasonal influence of these controls varies over different regions. Precipitation is recognised as an important factor driving evaporation, not only in areas that have limited soil water availability, but also in areas of high rainfall interception and low available energy.

The Tibetan plateau observatory of plateau scale soil moisture and soil temperature, Tibet - Obs, for quantifying uncertainties in coarse resolution satellite and model products

Abstract: . A plateau scale soil moisture and soil temperature observatory is established on the Tibetan Plateau for quantifying uncertainties in coarse resolution satellite and model products of soil moisture and soil temperature. The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) consists of three regional scale in-situ reference networks, including the Naqu network in a cold semiarid climate, the Maqu network in a cold humid climate and the Ngari network in a cold arid climate. These networks provide a representative coverage of the different climate and land surface hydrometeorological conditions on the Tibetan plateau. In this paper the details of the Tibet-Obs are reported. To demonstrate the uniqueness of the Tibet-Obs in quantifying and explaining soil moisture uncertainties in existing coarse satellite products, an analysis is carried out to assess the reliability of several satellite products for the Naqu and the Maqu network areas. It is concluded that global coarse resolution soil moisture products are useful but exhibit till now unreported uncertainties in cold and semiarid regions – use of them would be critically enhanced if uncertainties can be quantified and reduced using in-situ measurements.

Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium

Abstract: . Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces. The first step in kriging computation is the semi-variogram modelling which usually used only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. This study leaned on 30-yr daily rainfall data of 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. We found that between the seven variogram models used, the Gaussian model was the most frequently best fit. Using seven variogram models can avoid negative daily rainfall in ordinary kriging. The negative estimates of kriging were observed for convective more than stratiform rain. The performance of the different methods varied slightly according to the density of raingages, particularly between 8 and 70 raingages but it was much different for interpolation using 4 raingages. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably the interpolation with the Thiessen polygon, commonly used in various hydrological models. Integrating elevation into Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) did not improve the interpolation accuracy for daily rainfall. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases. Care should be taken in applying UNK and KED when interpolating daily rainfall with very few neighbourhood sample points. These recommendations complement the results reported in the literature. ORK, UNK and KED using only spherical model offered a slightly better result whereas OCK using seven variogram models achieved better result.

Modelling global water stress of the recent past: on the relative importance of trends in water demand and climate variability

Abstract: . During the past decades, human water use has more than doubled, yet available freshwater resources are finite. As a result, water scarcity has been prevalent in various regions of the world. Here, we present the first global assessment of past development of water stress considering not only climate variability but also growing water demand, desalinated water use and non-renewable groundwater abstraction over the period 1960–2001 at a spatial resolution of 0.5°. Agricultural water demand is estimated based on past extents of irrigated areas and livestock densities. We approximate past economic development based on GDP, energy and household consumption and electricity production, which are subsequently used together with population numbers to estimate industrial and domestic water demand. Climate variability is expressed by simulated blue water availability defined by freshwater in rivers, lakes, wetlands and reservoirs by means of the global hydrological model PCR-GLOBWB. We thus define blue water stress by comparing blue water availability with corresponding net total blue water demand by means of the commonly used, Water Scarcity Index. The results show a drastic increase in the global population living under water-stressed conditions (i.e. moderate to high water stress) due to growing water demand, primarily for irrigation, which has more than doubled from 1708/818 to 3708/1832 km3 yr−1 (gross/net) over the period 1960–2000. We estimate that 800 million people or 27% of the global population were living under water-stressed conditions for 1960. This number is eventually increased to 2.6 billion or 43% for 2000. Our results indicate that increased water demand is a decisive factor for heightened water stress in various regions such as India and North China, enhancing the intensity of water stress up to 200%, while climate variability is often a main determinant of extreme events. However, our results also suggest that in several emerging and developing economies (e.g. India, Turkey, Romania and Cuba) some of past extreme events were anthropogenically driven due to increased water demand rather than being climate-induced.

On the return period and design in a multivariate framework

Abstract: . Calculating return periods and design quantiles in a multivariate environment is a difficult problem: this paper tries to make the issue clear. First, we outline a possible way to introduce a consistent theoretical framework for the calculation of the return period in a multi-dimensional environment, based on Copulas and the Kendall's measure. Secondly, we introduce several approaches for the identification of suitable design events: these latter quantities are of utmost importance in practical applications, but their calculation is yet limited, due to the lack of an adequate theoretical environment where to embed the problem. Throughout the paper, a case study involving the behavior of a dam is used to illustrate the new concepts outlined in this work.

Changes in streamflow and sediment discharge and the response to human activities in the middle reaches of the Yellow River

Abstract: . The changes in streamflow and sediment discharge in the middle reaches of the Yellow River are a focus. In this paper, based on the precipitation, streamflow and sediment discharge series data (1950–2008), the streamflow and sediment discharge variation and its impact on precipitation/response to human activities have been analysis. The results show that significant decreasing trends in annual streamflow and sediment discharge have existed since the late 1950s in the middle reaches of the Yellow River (P = 0.01). Change-point analyses further revealed that transition years existed and that abrupt decline in streamflow and sediment discharge began in 1985 and 1981, respectively, in the middle reaches of the Yellow River (P = 0.05). Adoption of conservation measures in the 1980s and 1990s corroborates the identified transition years. Double-mass curves of precipitation vs. streamflow (sediment) for the periods before and after the transition year show remarkable decreases in proportionality of streamflow (sediment) generation. Compared with the period before the transition year, cumulative streamflow and cumulative sediment discharge reduced respectively by 17.8% and 28% during 1985–2008, which was caused by human intervention, in the middle reaches of the Yellow River. It is, therefore, concluded that human activities occupied a dominant position and played a major role in the streamflow and sediment discharge reduction in the middle reaches of the Yellow River.

Climate model bias correction and the role of timescales

Abstract: . It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However, the improvements to the statistical properties of the data are limited to the specific timescale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily temperature values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made on the stationarity of the bias over the largest timescales. First, we point out several conditions that have to be fulfilled by model data to make the application of a statistical bias correction meaningful. We then examine the effects of mixing fluctuations on different timescales and suggest an alternative statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.

Scaling and trends of hourly precipitation extremes in two different climate zones – Hong Kong and the Netherlands

Abstract: . Hourly precipitation extremes in very long time series from the Hong Kong Observatory and the Netherlands are investigated. Using the 2 m dew point temperature from 4 h before the rainfall event as a measure of near surface absolute humidity, hourly precipitation extremes closely follow a 14% per degree dependency – a scaling twice as large as following from the Clausius-Clapeyron relation. However, for dew point temperatures above 23 °C no significant dependency on humidity was found. Strikingly, in spite of the large difference in climate, results are almost identical in Hong Kong and the Netherlands for the dew point temperature range where both observational sets have sufficient data. Trends in hourly precipitation extremes show substantial increases over the last century for both De Bilt (the Netherlands) and Hong Kong. For De Bilt, not only the long term trend, but also variations in hourly precipitation extremes on an inter-decadal timescale of 30 yr and longer, can be linked very well to the above scaling; there is a very close resemblance between variations in dew point temperature and precipitation intensity with an inferred dependency of hourly precipitation extremes of 10 to 14% per degree. For Hong Kong there is no connection between variations in humidity and those in precipitation intensity in the wet season, May to September. This is consistent with the found zero-dependency of precipitation intensity on humidity for dew points above 23 °C. Yet, outside the wet season humidity changes do appear to explain the positive trend in hourly precipitation extremes, again following a dependency close to twice the Clausius-Clapeyron relation.

A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models

Abstract: . We present a comparative analysis of projected impacts of climate change on river runoff from two types of distributed hydrological model, a global hydrological model (GHM) and catchment-scale hydrological models (CHM). Analyses are conducted for six catchments that are global in coverage and feature strong contrasts in spatial scale as well as climatic and developmental conditions. These include the Liard (Canada), Mekong (SE Asia), Okavango (SW Africa), Rio Grande (Brazil), Xiangxi (China) and Harper's Brook (UK). A single GHM (Mac-PDM.09) is applied to all catchments whilst different CHMs are applied for each catchment. The CHMs include SLURP v. 12.2 (Liard), SLURP v. 12.7 (Mekong), Pitman (Okavango), MGB-IPH (Rio Grande), AV-SWAT-X 2005 (Xiangxi) and Cat-PDM (Harper's Brook). The CHMs typically simulate water resource impacts based on a more explicit representation of catchment water resources than that available from the GHM and the CHMs include river routing, whereas the GHM does not. Simulations of mean annual runoff, mean monthly runoff and high (Q5) and low (Q95) monthly runoff under baseline (1961–1990) and climate change scenarios are presented. We compare the simulated runoff response of each hydrological model to (1) prescribed increases in global-mean air temperature of 1.0, 2.0, 3.0, 4.0, 5.0 and 6.0 °C relative to baseline from the UKMO HadCM3 Global Climate Model (GCM) to explore response to different amounts of climate forcing, and (2) a prescribed increase in global-mean air temperature of 2.0 °C relative to baseline for seven GCMs to explore response to climate model structural uncertainty. We find that the differences in projected changes of mean annual runoff between the two types of hydrological model can be substantial for a given GCM (e.g. an absolute GHM-CHM difference in mean annual runoff percentage change for UKMO HadCM3 2 °C warming of up to 25%), and they are generally larger for indicators of high and low monthly runoff. However, they are relatively small in comparison to the range of projections across the seven GCMs. Hence, for the six catchments and seven GCMs we considered, climate model structural uncertainty is greater than the uncertainty associated with the type of hydrological model applied. Moreover, shifts in the seasonal cycle of runoff with climate change are represented similarly by both hydrological models, although for some catchments the monthly timing of high and low flows differs. This implies that for studies that seek to quantify and assess the role of climate model uncertainty on catchment-scale runoff, it may be equally as feasible to apply a GHM (Mac-PDM.09 here) as it is to apply a CHM, especially when climate modelling uncertainty across the range of available GCMs is as large as it currently is. Whilst the GHM is able to represent the broad climate change signal that is represented by the CHMs, we find however, that for some catchments there are differences between GHMs and CHMs in mean annual runoff due to differences in potential evapotranspiration estimation methods, in the representation of the seasonality of runoff, and in the magnitude of changes in extreme (Q5, Q95) monthly runoff, all of which have implications for future water management issues.

Calibration of hydrological models using flow-duration curves

Abstract: . The degree of belief we have in predictions from hydrologic models will normally depend on how well they can reproduce observations. Calibrations with traditional performance measures, such as the Nash-Sutcliffe model efficiency, are challenged by problems including: (1) uncertain discharge data, (2) variable sensitivity of different performance measures to different flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. This paper explores a calibration method using flow-duration curves (FDCs) to address these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) on the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested – based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WASMOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application, e.g. using more/less EPs at high/low flows. While the method appears less sensitive to epistemic input/output errors than previous use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow and where peak-flow timing at sub-daily time scales is of high importance. The results suggest that the calibration method can be useful when observation time periods for discharge and model input data do not overlap. The method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.

Internal and external green-blue agricultural water footprints of nations, and related water and land savings through trade

Abstract: . The need to increase food production for a growing world population makes an assessment of global agricultural water productivities and virtual water flows important. Using the hydrology and agro-biosphere model LPJmL, we quantify at 0.5° resolution the amount of blue and green water (irrigation and precipitation water) needed to produce one unit of crop yield, for 11 of the world's major crop types. Based on these, we also quantify the agricultural water footprints (WFP) of all countries, for the period 1998–2002, distinguishing internal and external WFP (virtual water imported from other countries) and their blue and green components, respectively. Moreover, we calculate water savings and losses, and for the first time also land savings and losses, through international trade with these products. The consistent separation of blue and green water flows and footprints shows that green water globally dominates both the internal and external WFP (84 % of the global WFP and 94 % of the external WFP rely on green water). While no country ranks among the top ten with respect to all water footprints calculated here, Pakistan and Iran demonstrate high absolute and per capita blue WFP, and the US and India demonstrate high absolute green and blue WFPs. The external WFPs are relatively small (6 % of the total global blue WFP, 16 % of the total global green WFP). Nevertheless, current trade of the products considered here saves significant water volumes and land areas (~263 km3 and ~41 Mha, respectively, equivalent to 5 % of the sowing area of the considered crops and 3.5 % of the annual precipitation on this area). Relating the proportions of external to internal blue/green WFP to the per capita WFPs allows recognizing that only a few countries consume more water from abroad than from their own territory and have at the same time above-average WFPs. Thus, countries with high per capita water consumption affect mainly the water availability in their own country. Finally, this study finds that flows/savings of both virtual water and virtual land need to be analysed together, since they are intrinsically related.

Impact of climate evolution and land use changes on water yield in the ebro basin

Abstract: . In this study the climatic and hydrological trends across 88 sub-basins of the Ebro River basin were analyzed for the period 1950–2006. A new database of climate information and river flows for the entire basin facilitated a spatially distributed assessment of climate-runoff relationships. It constitutes the first assessment of water yield evolution across the whole Ebro basin, a very representative example of large Mediterranean rivers. The results revealed a marked decrease in river discharges in most of the sub-basins. Moreover, a number of changes in the seasonality of the river regime was found, resulting from dam regulation and a decrease in snowpack in the headwaters. Significant and positive trends in temperature were observed across most of the basin, whereas most of the precipitation series showed negative coefficients, although the decrease in magnitude was low. The time evolution of the residuals from empirical models that relate climate and runoff in each sub-basin provided evidence that climate alone does not explain the observed decrease in river discharge. Thus, changes in water yield are associated with an increase in evapotranspiration rates in natural vegetation, growth of which has expanded as a consequence of land abandonment in areas where agricultural activities and livestock pressure have decreased. In the lowlands of the basin the decrease in water yield has been exacerbated by increased water consumption for domestic, industrial and agricultural uses. Climate projections for the end of the 21st century suggest a reduced capacity for runoff generation because of increasing temperature and less precipitation. Thus, the maintenance of water supply under conditions of increasing demand presents a challenging issue requiring appropriate coordination amongst politicians and managers.

Evaluation of TRMM Multi-satellite Precipitation Analysis (TMPA) performance in the Central Andes region and its dependency on spatial and temporal resolution

Abstract: . Climate time series are of major importance for base line studies for climate change impact and adaptation projects. However, for instance, in mountain regions and in developing countries there exist significant gaps in ground based climate records in space and time. Specifically, in the Peruvian Andes spatially and temporally coherent precipitation information is a prerequisite for ongoing climate change adaptation projects in the fields of water resources, disasters and food security. The present work aims at evaluating the ability of Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) to estimate precipitation rates at daily 0.25° × 0.25° scale in the Central Andes and the dependency of the estimate performance on changing spatial and temporal resolution. Comparison of the TMPA product with gauge measurements in the regions of Cuzco, Peru and La Paz, Bolivia were carried out and analysed statistically. Large biases are identified in both investigation areas in the estimation of daily precipitation amounts. The occurrence of strong precipitation events was well assessed, but their intensities were underestimated. TMPA estimates for La Paz show high false alarm ratio. The dependency of the TMPA estimate quality with changing resolution was analysed by comparisons of 1-, 7-, 15- and 30-day sums for Cuzco, Peru. The correlation of TMPA estimates with ground data increases strongly and almost linearly with temporal aggregation. The spatial aggregation to 0.5°, 0.75° and 1° grid box averaged precipitation and its comparison to gauge data of the same areas revealed no significant change in correlation coefficients and estimate performance. In order to profit from the TMPA combination product on a daily basis, a procedure to blend it with daily precipitation gauge measurements is proposed. Different sources of errors and uncertainties introduced by the sensors, sensor-specific algorithm aspects and the TMPA processing scheme are discussed. This study reveals the possibilities and restrictions of the use of TMPA estimates in the Central Andes and should assist other researchers in the choice of the best resolution-accuracy relationship according to requirements of their applications.

Evaluation of satellite rainfall estimates over Ethiopian river basins

Abstract: . High resolution satellite-based rainfall estimates (SREs) have enormous potential for use in hydrological applications, particularly in the developing world as an alternative to conventional rain gauges which are typically sparse. In this study, three SREs have been evaluated against collocated rain gauge measurements in Ethiopia across six river basins that represent different rainfall regimes and topography. The comparison is made using five-year (2003–2007) averages, and results are stratified by river basin, elevation and season. The SREs considered are: the Climate Prediction Center morphing method (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Neural Networks (PERSIANN) and the real-time version of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT. Overall, the microwave-based products TMPA 3B42RT and CMORPH outperform the infrared-based product PERSIANN: PERSIANN tends to underestimate rainfall by 43 %, while CMORPH tends to underestimate by 11 % and TMPA 3B42RT tends to overestimate by 5 %. The bias in the satellite rainfall estimates depends on the rainfall regime, and, in some regimes, the elevation. In the northwest region, which is characterized mainly by highland topography, a humid climate and a strong Intertropical Convergence Zone (ITCZ) effect, elevation has a strong influence on the accuracy of the SREs: TMPA 3B42RT and CMORPH tend to overestimate at low elevations but give reasonably accurate results at high elevations, whereas PERSIANN gives reasonably accurate values at low elevations but underestimates at high elevations. In the southeast region, which is characterized mainly by lowland topography, a semi-arid climate and southerly winds, elevation does not have a significant influence on the accuracy of the SREs, and all the SREs underestimate rainfall across almost all elevations.

Estimation of predictive hydrological uncertainty using quantile regression: examples from the national flood forecasting system (England and Wales)

Abstract: In this paper, a technique is presented for assessing the predictive uncertainty of rainfall-runoff and hydraulic forecasts. The technique conditions forecast uncertainty on the forecasted value itself, based on retrospective Quantile Regression of hindcasted water level forecasts and forecast errors. To test the robustness of the method, a number of retrospective forecasts for different catchments across England and Wales having different size and hydrological characteristics have been used to derive in a probabilistic sense the relation between simulated values of water levels and matching errors. From this study, we can conclude that using Quantile Regression for estimating forecast errors conditional on the forecasted water levels provides a relatively simple, efficient and robust means for estimation of predictive uncertainty.

Assessment of climate change impact on hydrological extremes in two source regions of the Nile River Basin

Abstract: . The potential impact of climate change was investigated on the hydrological extremes of Nyando River and Lake Tana catchments, which are located in two source regions of the Nile River basin. Climate change scenarios were developed for rainfall and potential evapotranspiration (ETo), considering 17 General Circulation Model (GCM) simulations to better understand the range of possible future change. They were constructed by transferring the extracted climate change signals to the observed series using a frequency perturbation downscaling approach, which accounts for the changes in rainfall extremes. Projected changes under two future SRES emission scenarios A1B and B1 for the 2050s were considered. Two conceptual hydrological models were calibrated and used for the impact assessment. Their difference in simulating the flows under future climate scenarios was also investigated. The results reveal increasing mean runoff and extreme peak flows for Nyando catchment for the 2050s while unclear trend is observed for Lake Tana catchment for mean volumes and high/low flows. The hydrological models for Lake Tana catchment, however, performed better in simulating the hydrological regimes than for Nyando, which obviously also induces a difference in the reliability of the extreme future projections for both catchments. The unclear impact result for Lake Tana catchment implies that the GCM uncertainty is more important for explaining the unclear trend than the hydrological models uncertainty. Nevertheless, to have a better understanding of future impact, hydrological models need to be verified for their credibility of simulating extreme flows.

Changes in land cover, rainfall and stream flow in Upper Gilgel Abbay catchment, Blue Nile basin – Ethiopia

Abstract: . In this study we evaluated changes in land cover and rainfall in the Upper Gilgel Abbay catchment in the Upper Blue Nile basin and how changes affected stream flow in terms of annual flow, high flows and low flows. Land cover change assessment was through classification analysis of remote sensing based land cover data while assessments on rainfall and stream flow data are by statistical analysis. Results of the supervised land cover classification analysis indicated that 50.9 % and 16.7 % of the catchment area was covered by forest in 1973 and 2001, respectively. This significant decrease in forest cover is mainly due to expansion of agricultural land. By use of a change detection procedure, three periods were identified for which changes in rainfall and stream flow were analyzed. Rainfall was analyzed at monthly base by use of the Mann-Kendall test statistic and results indicated a statistically significant, decreasing trend for most months of the year. However, for the wet season months of June, July and August rainfall has increased. In the period 1973–2005, the annual flow of the catchment decreased by 12.1 %. Low flow and high flow at daily base were analyzed by a low flow and a high flow index that is based on a 95 % and 5 % exceedance probability. Results of the low flow index indicated decreases of 18.1 % and 66.6 % for the periods 1982–2000 and 2001–2005 respectively. Results of high flows indicated an increase of 7.6 % and 46.6 % for the same periods. In this study it is concluded that over the period 1973–2005 stream flow has changed in the Gilgel Abbay catchment by changes in land cover and changes in rainfall.

WRF simulation of a precipitation event over the Tibetan Plateau, China - an assessment using remote sensing and ground observations

Abstract: . Meteorological observations over the Tibetan Plateau (TiP) are scarce, and precipitation estimations over this remote region are difficult. The constantly improving capabilities of numerical weather prediction (NWP) models offer the opportunity to reduce this problem by providing precipitation fields and other meteorological variables of high spatial and temporal resolution. Longer time periods of years to decades can be simulated by NWP models by successive model runs of shorter periods, which can be described by the term "regional atmospheric reanalysis". In this paper, we assess the Weather Research and Forecasting (WRF) models capacity in retrieving rain- and snowfall on the TiP in such a configuration using a nested approach: the simulations are conducted with three nested domains at spatial resolutions of 30, 10, and 2 km. A validation study is carried out for a one-month period with a special focus on one-week (22–28 October 2008), during which strong rain- and snowfall was observed on the TiP. The output of the model in each resolution is compared to the Tropical Rainfall Measuring Mission (TRMM) data set for precipitation and to the Moderate Resolution Imaging Spectroradiometer (MODIS) data set for snow extent. TRMM and WRF data are then compared to weather-station measurements. Our results suggest an overall improvement from WRF over TRMM with respect to weather-station measurements. Various configurations of the model with different nesting and forcing strategies, as well as physical parameterisation schemes are compared to propose a suitable design for a regional atmospheric reanalysis over the TiP. The WRF model showed good accuracy in simulating snow- and rainfall on the TiP for a one-month simulation period. Our study reveals that there is nothing like an optimal model strategy applicable for the high-altitude TiP, its fringing high-mountain areas of extremely complex topography and the low-altitude land and sea regions from which much of the precipitation on the TiP is originating. The choice of the physical parameterisation scheme will thus be always a compromise depending on the specific purpose of a model simulation. Our study demonstrates the high importance of orographic precipitation, but the problem of the orographic bias remains unsolved since reliable observational data are still missing. The results are relevant for anyone interested in carrying out a regional atmospheric reanalysis. Many hydrological analyses and applications like rainfall-runoff modelling or the analysis of flood events require precipitation rates at daily or even hourly intervals. Thus, our study offers a process-oriented alternative for retrieving precipitation fields of high spatio-temporal resolution in regions like the TiP, where other data sources are limited.