Water Availability Project Research Articles

Improving soil moisture and runoff simulations at 3 km over Europe using land surface data assimilation

Abstract. Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of large-scale observations, model simulation uncertainties and biases related to errors in model structure and uncertain inputs (e.g., hydrologic parameters and atmospheric forcings). The availability of long-term and global remotely sensed soil moisture offers the opportunity to improve model estimates through data assimilation with complete spatiotemporal coverage. In this study, we assimilated the European Space Agency (ESA) Climate Change Initiative (CCI) derived soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period 2000–2006 with the Community Land Model, version 3.5 (CLM3.5), integrated with the Parallel Data Assimilation Framework (PDAF) at a spatial resolution of 0.0275∘ (∼3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans Ertel Centre for Weather Research (HErZ). The performance of assimilation was assessed against open-loop model simulations and cross-validated with independent ESA CCI-derived soil moisture (CCI-SM) and gridded runoff observations. Our results showed improved estimates of soil moisture, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. The assimilation experiment results also showed overall improvements in runoff, although some regions were degraded, especially in central Europe. The results demonstrated the potential of assimilating satellite soil moisture observations to produce downscaled and improved high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.

Sensitivity of the orographic precipitation across the Australian Snowy Mountains to regional climate indices

The wintertime (May–October) precipitation across south-eastern Australia, and the Snowy Mountains, was studied for 22 years (1995–2016) to explore the sensitivity of the relationships between six established climate indices and the precipitation to the orography, both regionally and locally in high-elevation areas. The high-elevation (above 1100 m) precipitation records were provided by an independent network of rain gauges maintained by Snowy Hydro Ltd. These observations were compared with the Australian Water Availability Project (AWAP) precipitation analysis, a commonly used gridded nationwide product. As the AWAP analysis does not incorporate any high-elevation sites, it is unable to capture local orographic precipitation processes. The analysis demonstrates that the alpine precipitation over the Snowy Mountains responds differently to the indices than the AWAP precipitation. In particular, the alpine precipitation is found to be most sensitive to the position of the subtropical ridge and less sensitive to a number of other climate indices tested. This sensitivity is less evident in the AWAP representation of the high-elevation precipitation. Regionally, the analysis demonstrates that the precipitation to the east of the Snowy Mountains (the downwind precipitation) is weakly correlated with the upwind and peak precipitation. This is consistent with previous works that found that the precipitation in this downwind region commonly occurs from mechanisms other than storm systems passing over the mountains.

A simple tool for refining GCM water availability projections, applied to Chinese catchments

Abstract. There is a growing desire for reliable 21st-century projections of water availability at the regional scale. Global climate models (GCMs) are typically used together with global hydrological models (GHMs) to generate such projections. GCMs alone are unsuitable, especially if they have biased representations of aridity. The Budyko framework represents how water availability varies as a non-linear function of aridity and is used here to constrain projections of runoff from GCMs, without the need for computationally expensive GHMs. Considering a Chinese case study, we first apply the framework to observations to show that the contribution of direct human impacts (water consumption) to the significant decline in Yellow River runoff was greater than the contribution of aridity change by a factor of approximately 2, although we are unable to rule out a significant contribution from the net effect of all other factors. We then show that the Budyko framework can be used to narrow the range of Yellow River runoff projections by 34 %, using a multi-model ensemble and the high-end Representative Concentration Pathway (RCP8.5) emissions scenario. This increases confidence that the Yellow River will see an increase in runoff due to aridity change by the end of the 21st century. Yangtze River runoff projections change little, since aridity biases in GCMs are less substantial. Our approach serves as a quick and inexpensive tool to rapidly update and correct projections from GCMs alone. This could serve as a valuable resource when determining the water management policies required to alleviate water stress for future generations.

The Effect of Global Warming on Future Water Availability: CMIP5 Synthesis

AbstractChanges in the spatiotemporal dynamics of the global water cycle will constitute some of the greatest challenges to socioeconomic‐environmental well‐being in a warming world. Large multimodel, multiscenario intercomparisons such as the Coupled Model Intercomparison Project Phase 5 (CMIP5) experiment support our best estimates of projected climate change and associated uncertainty thereof. It is important to continually reevaluate how this information is synthesized and communicated and at what point it becomes actionable. In this study, we demonstrate a systematic and holistic framework for synthesizing multimodel ensemble projections of water availability at large river basin scale—the scale at which water resources are both managed and monitored. We identify statistically significant shifts in mean water availability at annual and monthly scales, its interannual variations, and its relative seasonality, as computed from CMIP5 historical (1976–2005) and Representative Concentration Pathway 8.5 (2070–2099) scenario multimodel ensemble output. Water availability is addressed separately through the lens of meteorologists (precipitation), hydrologists (runoff), and agriculturalists (precipitation minus evapotranspiration). We illustrate limitations in CMIP5 model representativeness through comparisons of atmosphere‐only model (Atmospheric Model Intercomparison Project) output against observational best estimates. And we find that warming‐induced shifts in water availability projected by CMIP5 carbon‐cycling Earth system models are comparatively less substantial than those projected by traditional general circulation models. As we show, knowing the seasonality of both projected changes and of the biased model background climatology onto which they are imposed is paramount to ensuring proper interpretation and ascribing confidence.

Catchment scale simulations of soil moisture dynamics using an equivalent cross-section based hydrological modelling approach

Physically based distributed hydrological models are useful for simulating the spatial distribution of hydrologic fluxes across the catchment under various climate and land cover change scenarios. However, complexities associated with their implementation at large scales make their applications limited. Previously, an equivalent cross-section (ECS) based distributed hydrological modelling approach was developed for first order sub-basins to reduce the computational time/effort. Here, the ECS approach is modified for semi-distributed hydrological modelling at the catchment scale. The modelling approach is implemented for a 314 km2 McLaughlin catchment located in south-eastern New South Wales (NSW), Australia that consists of 822 first order sub-basins. A 26 year long streamflow record simulated using an ECS based modelling approach are compared against daily observed streamflow and four calibrated lumped conceptual hydrologic models, and found to be consistent. Further, the simulated actual evapotranspiration and soil moisture from the ECS approach are compared against the Australian Water Availability Project (AWAP) model simulations and results found to be consistent. In addition, the temporal dynamics of simulated soil moisture from the ECS approach is consistent with the satellite derived European Space Agency Climate Change Initiative (ESA CCI) surface soil moisture data. In the ECS based semi-distributed modelling, all parameters are derived from the actual topographic and physiographic information of the catchment and none of the parameters is calibrated. Therefore, this approach has the advantage of simulating streamflow in ungauged catchments compared to lumped conceptual models. The impact of spatially distributed climatic forcing and land cover on soil moisture is investigated across four landforms (upslope, midslope, footslope and alluvial-flats) and at various soil depths. Our results show increase of mean soil moisture in shallow layers of upslope toward alluvial-flats. However, mean soil moisture in deeper horizons remained almost constant across all landforms. The variability of daily soil moisture at surface soil layers is higher than the deeper soil layers for all landforms. Our results illustrated that disaggregation of a catchment to a series of ECS at the scale of first order sub-basins, captures dynamics of soil moisture and actual evapotranspiration across the landscape and results are consistent with the climatology, land cover type, topography and soil hydraulic properties. Further, the use of ECS approach in the McLaughlin catchment reduced the number of computational units by 40 times in comparison to 3-d grid based distributed modelling setup.

Plants turn on the tap

Plant transpiration is the largest continental water flux. Research now shows that climate and water availability projections are highly sensitive to the ways that plant responses to changing atmospheric conditions are represented.

High‐resolution projections of evapotranspiration and water availability for Europe under climate change

Evapotranspiration plays an essential role in estimating water balance, runoff and effective precipitation. To determine historical and projected water availability for Europe, we contribute high‐resolution (1 km) estimates of monthly and annual potential evapotranspiration (ET0) and actual evapotranspiration (AET0). In the ET0 calculation, the monthly and annual heat index I and annual α parameter were estimated following the Thornthwaite method, and AET0 was calculated using the Budyko approach. The variables were estimated for a climate normal period that largely precedes an anthropogenic warming signal (1961–1990), and for two CMIP5 multi‐model future projections (2011–2040 and 2041–2070). We project widespread and relatively uniform ET0 increases of around 50–100 mm by the 2020s and 75–125 mm by the 2050s for most of Europe. These values imply important changes that may affect runoff and groundwater recharge. AET0 was identified as important driver of water availability with more regional variability. Spatial mapping of changes relative to the normal baseline imply that all except northern parts of Europe are vulnerable to water deficits, with pronounced decrease expected in southern Europe. We provide high‐resolution maps and data as an important tool for future natural resources management and climate change mitigation planning.

Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors

Soil moisture (SM) is a key component of the global energy cycle that regulates all domains of the natural environmental and the agricultural system. In this research, the challenge is to develop a low-cost data-intelligent SM forecasting model using climate dynamics (i.e., the climate indices, atmospheric and hydro-meteorological parameters) as the model inputs. A newly designed, multi-model ensemble committee machine learning approach based on the artificial neural network (ANN-CoM) is developed to forecast monthly upper layer (∼0.2 m from the surface) and the lower layer (∼0.2–1.5 m deep) SM at four agricultural sites in Australia’s Murray-Darling Basin. ANN-CoM model is validated with respect to non-tuned second-order Volterra, M5 model tree, random forest, and an extreme learning machine (ELM) models. To construct the ANN-CoM model, the input variables comprised of the hydro-meteorological data from the Australian Water Availability Project, large-scale climate indices and atmospheric parameters derived from the Interim ERA European Centre for Medium-Range Weather Forecasting ECMWF reanalysis fields leads to a total of 60 potential predictors used for SM forecasting. To reduce the model input data dimensionality for accurate forecasts, the Neighborhood Component Analysis (NCA) based feature selection algorithm for regression purposes (fsrnca) is applied to determine the relative feature weights related to the targeted variable. The optimal predictor variables are then screened with an ELM model as the fitness function of the fsrnca algorithm to identify the set of most pertinent model variables. Extensive performance evaluation using statistical score metrics with visual and diagnostic plots show that the ensemble committee based, ANN-CoM model is able to effectively capture the nonlinear dynamics involved in the modeling of monthly upper and lower layer SM levels. Therefore, the ANN-CoM multi-model ensemble-based approach can be considered to be a superior SM forecasting tool, portraying as an amicable, integrated (or ensemble) machine learning stratagem that can be explored for soil moisture modeling and applications in agriculture and other hydro-meteorological phenomena.

Flows and sediment dynamics in the Ganga River under present and future climate scenarios

ABSTRACTIdentification of factors controlling sediment dynamics under natural flow regimes can establish a baseline for quantifying effects of present day hydrological alteration and future climate change on sediment delivery and associated flooding. The process-based INCA-Sediment model was used to simulate Ganga River sediment transport under baseline conditions and to quantify possible future changes using three contrasting climate scenarios. Construction of barrages and canals has significantly altered natural flow regimes, with profound consequences for sediment transport. Projected increases in future monsoonal precipitation will lead to higher peak flows, increasing flood frequency and greater water availability. Increased groundwater recharge during monsoon periods and greater rates of evaporation due to increased temperature complicate projections of water availability in non-monsoon periods. Rainfall and land surface interaction in high-relief areas drive uncertainties in Upper Ganga sediment loads. However, higher monsoonal peak flows will increase erosion and sediment delivery in western and lower reaches.

Lack of cool, not warm, extremes distinguishes late 20th Century climate in 979-year Tasmanian summer temperature reconstruction

Very few annually resolved millennial-length temperature reconstructions exist for the Southern Hemisphere. Here we present four 979-year reconstructions for southeastern Australia for the austral summer months of December–February. Two of the reconstructions are based on the Australian Water Availability Project dataset and two on the Berkeley Earth Surface Temperature dataset. For each climate data set, one reconstruction is based solely on Lagarostrobos franklinii (restricted reconstructions) while the other is based on multiple Tasmanian conifer species (unrestricted reconstructions). Each reconstruction calibrates ~50−60% of the variance in the temperature datasets depending on the number of tree-ring records available for the reconstruction. We found little difference in the temporal variability of the reconstructions, although extremes are amplified in the restricted reconstructions relative to the unrestricted reconstructions. The reconstructions highlight the occurrence of numerous individual years, especially in the 15th−17th Centuries, for which temperatures were comparable with those of the late 20th Century. The 1950−1999 period, however, stands out as the warmest 50-year period on average for the past 979 years, with a sustained shift away from relatively low mean temperatures, the length of which is unique in the 979-year record. The reconstructions are strongly and positively related to temperatures across the southeast of the Australian continent, negatively related to temperatures in the north and northeast of the continent, and uncorrelated with temperatures in the west. The lack of a strong relationship with temperatures across the continent highlights the necessity of a sub-regional focus for Australasian temperature reconstructions.

Attributing uncertainty in streamflow simulations due to variable inputs via the Quantile Flow Deviation metric

Abstract Every model to characterise a real world process is affected by uncertainty. Selecting a suitable model is a vital aspect of engineering planning and design. Observation or input errors make the prediction of modelled responses more uncertain. By way of a recently developed attribution metric, this study is aimed at developing a method for analysing variability in model inputs together with model structure variability to quantify their relative contributions in typical hydrological modelling applications. The Quantile Flow Deviation (QFD) metric is used to assess these alternate sources of uncertainty. The Australian Water Availability Project (AWAP) precipitation data for four different Australian catchments is used to analyse the impact of spatial rainfall variability on simulated streamflow variability via the QFD. The QFD metric attributes the variability in flow ensembles to uncertainty associated with the selection of a model structure and input time series. For the case study catchments, the relative contribution of input uncertainty due to rainfall is higher than that due to potential evapotranspiration, and overall input uncertainty is significant compared to model structure and parameter uncertainty. Overall, this study investigates the propagation of input uncertainty in a daily streamflow modelling scenario and demonstrates how input errors manifest across different streamflow magnitudes.

Impact of Rain Gauge Quality Control and Interpolation on Streamflow Simulation: An Application to the Warwick Catchment, Australia

Rain gauges are widely used to obtain temporally continuous point rainfall records, which are then interpolated into spatially continuous data to force hydrological models. However, rainfall measurements and interpolation procedure are subject to various uncertainties, which can be reduced by applying quality control and selecting appropriate spatial interpolation approaches. Consequently, the integrated impact of rainfall quality control and interpolation on streamflow simulation has attracted increased attention but not been fully addressed. This study applies a quality control procedure to the hourly rainfall measurements obtained in the Warwick catchment in eastern Australia. The grid-based daily precipitation from the Australian Water Availability Project was used as a reference. The Pearson correlation coefficient between the daily accumulation of gauged rainfall and the reference data was used to eliminate gauges with significant quality issues. The unrealistic outliers were censored based on a comparison between gauged rainfall and the reference. Four interpolation methods, including the inverse distance weighting (IDW), nearest neighbors (NN), linear spline (LN), and ordinary Kriging (OK), were implemented. The four methods were firstly assessed through a cross-validation using the quality-controlled rainfall data. The impacts of the quality control and interpolation on streamflow simulation were then evaluated through a semi-distributed hydrological model. The results showed that the Nash–Sutcliffe model efficiency coefficient (NSE) and Bias of the streamflow simulations were significantly improved after quality control. In the cross-validation, the IDW and OK methods resulted in good interpolation rainfall, while the NN led to the worst result. In term of the impact on hydrological prediction, the IDW led to the most consistent streamflow predictions with the observations, according to the validation at five streamflow-gauged locations. The OK method performed second best according to streamflow predictions at the five gauges in the calibration period (01/01/2007–31/12/2011) and four gauges during the validation period (01/01/2012–30/06/2014). However, NN produced the worst prediction at the outlet of the catchment in the validation period, indicating a low robustness. While the IDW exhibited the best performance in the study catchment in terms of accuracy, robustness and efficiency, more general recommendations on the selection of rainfall interpolation methods need to be further explored.

The Sensitivity of Urban Meteorology to Soil Moisture Boundary Conditions: A Case Study in Melbourne, Australia

AbstractThe sensitivity of near-surface urban meteorological conditions to three different soil moisture initialization experiments under heat-wave conditions is investigated for the city of Melbourne, Australia. The Weather Research and Forecasting Model is used to simulate a domain over Melbourne and its surrounding rural areas. The experiments employ three suites of simulations. Two suites initialize the model with soil moisture from the top layer of the ERA-Interim soil moisture data with a 3-month and 24-h coupled spinup period, respectively. The third suite initializes the model with the arguably more realistic soil moistures from the Australian Water Availability Project (AWAP), which are an order of magnitude drier than the ERA-Interim data, again using a 24-h spinup period. The simulations employing the AWAP data are found to have smaller errors when compared with observations, with biases in urban maximum temperature reduced by 4.1°C and biases in the skin temperature reduced by 3.0°C relative to the biases of the 3-month-spinup experiment. Despite urban areas only having a small proportion of soil-covered surfaces, the results show that urban soils have a greater influence on urban near-surface temperatures at night, whereas rural soils have a greater influence on urban near-surface temperatures during the daytime.

Copula-statistical precipitation forecasting model in Australia’s agro-ecological zones

Vine copulas are employed to explore the influence of multi-synoptic-scale climate drivers – El Niño Southern Oscillation (ENSO) and Inter-decadal Pacific Oscillation (IPO) Tripole Index (TPI) – on spring precipitation forecasting at Agro-ecological Zones (AEZs) of the Australia’s wheat belt. To forecast spring precipitation, significant seasonal lagged correlation of ENSO and TPI with precipitation anomalies in AEZs using data from Australian Water Availability Project (1900–2013) was established. Most of the AEZs exhibit statistically significant dependence of precipitation and climate indices, except for the western AEZs. Bivariate and trivariate copula models were applied to capture single (ENSO) and dual predictor (ENSO & TPI) influence, respectively, on seasonal forecasting. To perform a comprehensive evaluation of the developed copula-statistical models, a total of ten one- and two-parameter bivariate copulas ranging from elliptical to Archimedean families were examined. Stronger upper tail dependence is visible in the bivariate model, suggesting that the influence of ENSO on precipitation forecasting during a La Niña event is more evident than during an El Niño event. In general, while the inclusion of TPI as a synoptic-scale driver into the models leads to a notable reduction in the mean simulated precipitation, it depicts a general improvement in the median values. The forecasting results showed that the trivariate forecasting model can yield a better accuracy than the bivariate model for the east and southeast AEZs. The trivariate forecasting model was found to improve the forecasting during the La Niña and negative TPI. This study ascertains the success of copula-statistical models for investigating the joint behaviour of seasonal precipitation modelled with multiple climate indices. The forecasting information and respective models have significant implications for water resources and crop health management including better ways to adapt and implement viable agricultural solutions in the face of climatic challenges in major agricultural hubs, such as Australia’s wheat belt.

Water Balance and Level Change of Lake Babati, Tanzania: Sensitivity to Hydroclimatic Forcings

We develop and present a novel integrated water balance model that accounts for lake water—groundwater interactions, and apply it to the semi-closed freshwater Lake Babati system, Northern Tanzania, East Africa. The model was calibrated and used to evaluate the lake level sensitivity to changes in key hydro-climatic variables such as temperature, precipitation, humidity and cloudiness. The lake response to the Coupled Model Intercomparison Project, Phase 5 (CMIP5) output on possible future climate outcomes was evaluated, an essential basis in understanding future water security and flooding risk in the region. Results show high lake level sensitivity to cloudiness. Increased focus on cloud fraction measurement and interpretation could likely improve projections of lake levels and surface water availability. Modelled divergent results on the future (21st century) development of Lake Babati can be explained by the precipitation output variability of CMIP5 models being comparable to the precipitation change needed to drive the water balance model from lake dry-out to overflow; this condition is likely shared with many other East African lake systems. The developed methodology could be useful in investigations on change-driving processes in complex climate—drainage basin—lake systems, which are needed to support sustainable water resource planning in data scarce tropical Africa.

Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia

AbstractRemote sensing of soil moisture has reached a level of maturity and accuracy for which the retrieved products can be used to improve hydrological and meteorological applications. In this study, the soil moisture product from the Soil Moisture and Ocean Salinity (SMOS) satellite is used for improving satellite rainfall estimates obtained from the Tropical Rainfall Measuring Mission multisatellite precipitation analysis product (TMPA) using three different “bottom up” techniques: SM2RAIN, Soil Moisture Analysis Rainfall Tool, and Antecedent Precipitation Index Modification. The implementation of these techniques aims at improving the well‐known “top down” rainfall estimate derived from TMPA products (version 7) available in near real time. Ground observations provided by the Australian Water Availability Project are considered as a separate validation data set. The three algorithms are calibrated against the gauge‐corrected TMPA reanalysis product, 3B42, and used for adjusting the TMPA real‐time product, 3B42RT, using SMOS soil moisture data. The study area covers the entire Australian continent, and the analysis period ranges from January 2010 to November 2013. Results show that all the SMOS‐based rainfall products improve the performance of 3B42RT, even at daily time scale (differently from previous investigations). The major improvements are obtained in terms of estimation of accumulated rainfall with a reduction of the root‐mean‐square error of more than 25%. Also, in terms of temporal dynamic (correlation) and rainfall detection (categorical scores) the SMOS‐based products provide slightly better results with respect to 3B42RT, even though the relative performance between the methods is not always the same. The strengths and weaknesses of each algorithm and the spatial variability of their performances are identified in order to indicate the ways forward for this promising research activity. Results show that the integration of bottom up and top down approaches has the potential to improve the quality of near‐real‐time rainfall estimates from remote sensing in the near future.

A bias corrected WRF mesoscale fire weather dataset for Victoria, Australia 1972-2012

Climatology data of fire weather across the landscape can provide science-based evidence for informing strategic decisions to ameliorate the impacts (at times extreme) of bushfires on community socio-economic wellbeing and to sustain ecosystem health and functions. A long-term climatology requires spatial and temporal data that are consistent to represent the landscape in sufficient detail to be useful for fire weather studies and management purposes. To address this inhomogeneity problem for analyses of a variety of fire weather interests and to provide a dataset for management decision-support, a homogeneous 41-year (1972-2012), hourly interval, 4 km gridded climate dataset for Victoria has been generated using a combination of mesoscale modelling, global reanalysis data, surface observations, and historic observed rainfall analyses. Hourly near-surface forecast fields were combined with Drought Factor (DF) fields calculated from the Australian Water Availability Project (AWAP) rainfall analyses to generate fields of hourly fire danger indices for each hour of the 41-year period. A quantile mapping (QM) bias correction technique utilizing available observations during 1996-2012 was used to ameliorate any model biases in wind speed, temperature and relative humidity. Extensive evaluation was undertaken including both quantitative and case study qualitative assessments. The final dataset includes 4-km surface hourly temperature, relative humidity, wind speed, wind direction, Forest Fire Danger Index (FFDI), and daily DF and Keetch-Byram Drought Index (KBDI), and a 32-level full three-dimensional volume atmosphere.

Statistical testing of dynamically downscaled rainfall data for the Upper Hunter region, New South Wales, Australia

This study tests the statistical properties of downscaled climate data, concentrating on the rainfall which is required for hydrology predictions used in water supply reservoir simulations. The datasets used in this study have been produced by the New South Wales (NSW) / Australian Capital Territory (ACT) Regional Climate Modelling (NARCliM) project which provides a dynamically downscaled climate dataset for southeast Australia at 10 km resolution. In this paper, we present an evaluation of the downscaled NARCliM National Centers for Environmental Prediction (NCEP) / National Center for Atmospheric Research (NCAR) reanalysis simulations. The validation has been performed in the Goulburn River catchment in the Upper Hunter region of New South Wales, Australia. The analysis compared time series of the downscaled NARCliM rain-fall data with ground based measurements for selected Bureau of Meteorology rainfall stations and 5 km gridded data from the Australian Water Availability Project (AWAP). The initial testing of the rainfall was focused on autocorrelations as persistence is an important factor in hydrological and water availability analysis. Additionally, a cross-correlation analysis was performed at daily, fort-nightly, monthly and annually averaged time resolutions. The spatial variability of these statistics were calculated and plotted at the catchment scale. The auto-correlation analysis shows that the seasonal cycle in the NARCliM data is stronger than the seasonal cycle present in the ground based measurements and AWAP data. The cross-correlation analysis also shows a poor agreement between NARCliM data, and AWAP and ground based measurements. The spatial variability plots show a possible link between these discrepancies and orography at the catchment scale.

Rapid Screening of Operational Freshwater Availability Using Global Models

Freshwater shortage already affects large parts of the world, and is expected to increase rapidly over the coming decades as a result of increased water demands and the impacts of climate change. Global-scale water risk or stress maps are available online, but these lack quantitative information on local freshwater availability, rendering them unsuitable for water risk assessment from an operational perspective, i.e. when comparing water availability to a specific quantified water demand (in m3s−1 rather than generic risk indicators). Therefore, our main goal was to develop a rapid screening method to estimate current and future operational freshwater availability using global-scale models. Operational Freshwater Availability (OFWA) was computed using the PCR-GLOBWB global hydrology and water resources model, coupled to a global MODFLOW groundwater model. PCR-GLOBWB was forced with rainfall and temperature fields from the IPSL-CM5A-LR climate model under the RCP6.0 climate scenario, with water demands based on the SSP2 socio-economic scenario. Unique to our study are the downscaling of the coupled PCR-GLOBWB-MODFLOW model to 90 m resolution and the provision of quantitative estimates on long term trends in operational freshwater availability. Our results showed a high, i.e. operationally relevant, accuracy for operational surface water availability, while the uncertainty about operational groundwater availability remained high due to limited availability of subsurface data. With this method, we developed a modelling capacity for rapidly generating scenario-based water availability projections with operational relevance in a rigorous, systematic way, such that it enables like-for-like comparisons. Further refinement is required for accurate estimates of operational groundwater availability.

Future Changes in Floods and Water Availability across China: Linkage with Changing Climate and Uncertainties

AbstractFuture changes in floods and water availability across China under representative concentration pathway 2.6 (RCP2.6) and RCP8.5 are studied by analyzing discharge simulations from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) with the consideration of uncertainties among global climate models (GCMs) and hydrologic models. Floods and water availability derived from ISI-MIP simulations are compared against observations. The uncertainties among models are quantified by model agreement. Only model agreement >50% is considered to generate reliable projections of floods and water availability and their relationships with climate change. The results show five major points. First, ISI-MIP simulations have acceptable ability in modeling floods and water availability. The spatial patterns of changes in floods and water availability highly depend on the outputs of GCMs. Uncertainties from GCMs/hydrologic models predominate the uncertainties in the wet/dry areas in eastern/northwestern China. Second, the magnitudes of floods throughout China increase during 2070–99 under RCP8.5 relative to those with the same return periods during 1971–2000. The increase rates of larger floods are higher than those of the smaller ones. Third, water availability decreases/increases in southern/northern China under RCP8.5, but changes negligibly under RCP2.6. Fourth, more severe floods in the future are driven by more intense precipitation extremes over China. The negligible change in mean precipitation and the increase in actual evapotranspiration reduce the water availability in southern China. Fifth, model agreements are higher in simulated floods than water availability because increasing precipitation extremes are more consistent among different GCM outputs compared to mean precipitation.