Global water scarcity including surface water quality and expansions of clean water technologies

Water scarcity threatens people in various regions, and has predominantly been studied from a water quantity perspective only. Here we show that global water scarcity is driven by both water quantity and water quality issues, and quantify expansions in clean water technologies (i.e. desalination and treated wastewater reuse) to ‘reduce the number of people suffering from water scarcity’ as urgently required by UN’s Sustainable Development Goal 6. Including water quality (i.e. water temperature, salinity, organic pollution and nutrients) contributes to an increase in percentage of world’s population currently suffering from severe water scarcity from an annual average of 30% (22%–35% monthly range; water quantity only) to 40% (31%–46%; both water quantity and quality). Water quality impacts are in particular high in severe water scarcity regions, such as in eastern China and India. In these regions, excessive sectoral water withdrawals do not only contribute to water scarcity from a water quantity perspective, but polluted return flows degrade water quality, exacerbating water scarcity. We show that expanding desalination (from 2.9 to 13.6 billion m3 month−1) and treated wastewater uses (from 1.6 to 4.0 billion m3 month−1) can strongly reduce water scarcity levels and the number of people affected, especially in Asia, although the side effects (e.g. brine, energy demand, economic costs) must be considered. The presented results have potential for follow-up integrated analyses accounting for technical and economic constraints of expanding desalination and treated wastewater reuse across the world

Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment

Increasing concentrations of greenhouse gases in the atmosphere are expected to modify the global water cycle with significant consequences for terrestrial hydrology. We assess the impact of climate change on hydrological droughts in a multimodel experiment including seven global impact models (GIMs) driven by biascorrected climate from five global climate models under four representative concentration pathways (RCPs). Drought severity is defined as the fraction of land under drought conditions. Results show a likely increase in the global severity of hydrological drought at the end of the 21st century, with systematically greater increases for RCPs describing stronger radiative forcings. Under RCP8.5, droughts exceeding 40% of analyzed land area are projected by nearly half of the simulations. This increase in drought severity has a strong signal-to-noise ratio at the global scale, and Southern Europe, the Middle East, the Southeast United States, Chile, and South West Australia are identified as possible hotspots for future water security issues. The uncertainty due to GIMs is greater than that from global climate models, particularly if including a GIM that accounts for the dynamic response of plants to CO2 and climate, as this model simulates little or no increase in drought frequency. Our study demonstrates that different representations of terrestrial water-cycle processes in GIMs are responsible for a much larger uncertainty in the response of hydrological drought to climate change than previously thought. When assessing the impact of climate change on hydrology, it is therefore critical to consider a diverse range of GIMs to better capture the uncertainty

Multisectoral Climate Impact Hotspots in a Warming World

The impacts of global climate change on different aspects of humanity's diverse life-support systems are complex and often difficult to predict. To facilitate policy decisions on mitigation and adaptation strategies, it is necessary to understand, quantify, and synthesize these climate-change impacts, taking into account their uncertainties. Crucial to these decisions is an understanding of how impacts in different sectors overlap, as overlapping impacts increase exposure, lead to interactions of impacts, and are likely to raise adaptation pressure. As a first step we develop herein a framework to study coinciding impacts and identify regional exposure hotspots. This framework can then be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies. We consider impacts related to water, agriculture, ecosystems, and malaria at different levels of global warming. Multisectoral overlap starts to be seen robustly at a mean global warming of 3 degC above the 1980-2010 mean, with 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4 degC. Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models. In a low probability-high impact worst-case assessment, almost the whole inhabited world is at risk for multisectoral pressures. Hence, there is a pressing need for an increased research effort to develop a more comprehensive understanding of impacts, as well as for the development of policy measures under existing uncertainty

WaterMIP: A multi-model estimate of the terrestrial water cycle. Experimental setup and first results

The Water Model Intercomparison Project (WaterMIP) aims to compare a variety of models of the terrestrial hydrological cycle, and to produce multi-model ensemble estimates of the state of the world’s water resources for the 20th and 21st centuries. WaterMIP is a joint activity between the EU Water and Global Change (WATCH) FP6 project and the Global Water System Project (GWSP)

Seasonal streamflow forecasts for Europe - Part 2 : Sources of skill

This paper uses hindcasts (1981-2010) to investigate the sources of skill in seasonal hydrological forecasts for Europe. The hindcasts were produced with WUSHP (Wageningen University Seamless Hydrological Prediction system). Skill was identified in a companion paper. In WUSHP, hydrological processes are simulated by running the Variable Infiltration Capacity (VIC) hydrological model forced with an ensemble of bias-corrected output from the seasonal forecast system 4 (S4) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We first analysed the meteorological forcing. The precipitation forecasts contain considerable skill for the first lead month but hardly any significant skill at longer lead times. Seasonal forecasts of temperature have more skill. Skill in summer temperature is related to climate change and is more or less independent of lead time. Skill in February and March is unrelated to climate change. Different sources of skill in hydro-meteorological variables were isolated with a suite of specific hydrological hindcasts akin to ensemble streamflow prediction (ESP). These hindcasts show that in Europe, initial conditions of soil moisture (SM) form the dominant source of skill in run-off. From April to July, initial conditions of snow contribute significantly to the skill. Some remarkable skill features are due to indirect effects, i.e. skill due to forcing or initial conditions of snow and soil moisture at an earlier stage is stored in the hydrological state (snow and/or soil moisture) of a later stage, which then contributes to persistence of skill. Skill in evapotranspiration (ET) originates mostly in the meteorological forcing. For run-off we also compared the full hindcasts (with S4 forcing) with two types of ESP (or ESP-like) hindcasts (with identical forcing for all years). Beyond the second lead month, the full hindcasts are less skilful than the ESP (or ESP-like) hindcasts, because inter-annual variations in the S4 forcing consist mainly of noise which enhances degradation of the skill.</p

Seasonal streamflow forecasts for Europe-Part I : Hindcast verification with pseudo- A nd real observations

Seasonal predictions of river flow can be exploited among others to optimise hydropower energy generation, navigability of rivers and irrigation management to decrease crop yield losses. This paper is the first of two papers dealing with a physical model-based system built to produce probabilistic seasonal hydrological forecasts, applied here to Europe. This paper presents the development of the system and the evaluation of its skill. The variable infiltration capacity (VIC) hydrological model is forced with bias-corrected output of ECMWF's seasonal forecast system 4. For the assessment of skill, we analysed hindcasts (1981-2010) against a reference run, in which VIC was forced by gridded meteorological observations. The reference run was also used to generate initial hydrological conditions for the hindcasts. The skill in run-off and discharge hindcasts is analysed with monthly temporal resolution, up to 7 months of lead time, for the entire annual cycle. Using the reference run output as pseudo-observations and taking the correlation coefficient as metric, hot spots of significant theoretical skill in discharge and run-off were identified in Fennoscandia (from January to October), the southern part of the Mediterranean (from June to August), Poland, northern Germany, Romania and Bulgaria (mainly from November to January), western France (from December to May) and the eastern side of Great Britain (January to April). Generally, the skill decreases with increasing lead time, except in spring in regions with snow-rich winters. In some areas some skill persists even at the longest lead times (7 months). Theoretical skill was compared to actual skill as determined with real discharge observations from 747 stations. Actual skill is generally substantially less than theoretical skill. This effect is stronger for small basins than for large basins. Qualitatively, the use of different skill metrics (correlation coefficient; relative operating characteristics, ROC, area; and ranked probability skill score, RPSS) leads to broadly similar spatiooral patterns of skill, but the level of skill decreases, and the area of skill shrinks, in the following order: Correlation coefficient; ROC area below-normal (BN) tercile; ROC area above-normal (AN) tercile; ranked probability skill score; and, finally, ROC near-normal (NN) tercile.</p

Probabilistic maize yield prediction over East Africa using dynamic ensemble seasonal climate forecasts

We tested the usefulness of seasonal climate predictions for impacts prediction in eastern Africa. In regions where these seasonal predictions showed skill we tested if the skill also translated into maize yield forecasting skills. Using European Centre for Medium-Range Weather Forecasts (ECMWF) system-4 ensemble seasonal climate hindcasts for the period 1981–2010 at different initialization dates before sowing, we generated a 15-member ensemble of yield predictions using the World Food Studies (WOFOST) crop model implemented for water-limited maize production and single season simulation. Maize yield predictions are validated against reference yield simulations using the WATCH Forcing Data ERA-Interim (WFDEI), focussing on the dominant sowing dates in the northern region (July), equatorial region (March-April) and in the southern region (December). These reference yields show good anomaly correlations compared to the official FAO and national reported statistics, but the average reference yield values are lower than those reported in Kenya and Ethiopia, but slightly higher in Tanzania. We use the ensemble mean, interannual variability, mean errors, Ranked Probability Skill Score (RPSS) and Relative Operating Curve skill Score (ROCSS) to assess regions of useful probabilistic prediction. Annual yield anomalies are predictable 2-months before sowing in most of the regions. Difference in interannual variability between the reference and predicted yields range from ±40%, but higher interannual variability in predicted yield dominates. Anomaly correlations between the reference and predicted yields are largely positive and range from +0.3 to +0.6. The ROCSS illustrate good pre-season probabilistic prediction of above-normal and below-normal yields with at least 2-months lead time. From the sample sowing dates considered, we concluded that, there is potential to use dynamical seasonal climate forecasts with a process based crop simulation model WOFOST to predict anomalous water-limited maize yields

Simulating human impacts on global water resources using VIC-5

Questions related to historical and future water resources and scarcity have been addressed by several macroscale hydrological models. One of these models is the Variable Infiltration Capacity (VIC) model. However, further model developments were needed to holistically assess anthropogenic impacts on global water resources using VIC. Our study developed VIC-WUR, which extends the VIC model using (1) integrated routing, (2) surface and groundwater use for various sectors (irrigation, domestic, industrial, energy, and livestock), (3) environmental flow requirements for both surface and groundwater systems, and (4) dam operation. Global gridded datasets on sectoral demands were developed separately and used as an input for the VIC-WUR model. Simulated national water withdrawals were in line with reported Food and Agriculture Organization (FAO) national annual withdrawals (adjusted R2 > 0.8), both per sector and per source. However, trends in time for domestic and industrial water withdrawal were mixed compared with previous studies. Gravity Recovery and Climate Experiment (GRACE) monthly terrestrial water storage anomalies were well represented (global mean root-mean-squared error, RMSE, values of 1.9 and 3.5 mm for annual and interannual anomalies respectively), whereas groundwater depletion trends were overestimated. The implemented anthropogenic impact modules increased simulated streamflow performance for 370 of the 462 anthropogenically impacted Global Runoff Data Centre (GRDC) monitoring stations, mostly due to the effects of reservoir operation. An assessment of environmental flow requirements indicates that global water withdrawals have to be severely limited (by 39 %) to protect aquatic ecosystems, especially with respect to groundwater withdrawals. VIC-WUR has potential for studying the impacts of climate change and anthropogenic developments on current and future water resources and sector-specific water scarcity. The additions presented here make the VIC model more suited for fully integrated worldwide water resource assessments. </p

VIC: VIC.5.0.0

VIC 5.0.0 Release date: (September 2, 2016) Source code is available here: This is a major update from VIC 4. The VIC 5.0.0 release aims to have nearly identical physics as VIC 4.2 while providing a clean, refactored code base supporting multiple drivers. There are a number of new features, bug fixes, and backward incompatible changes. See the VIC Github page for more details on the changes included in this release. New Features: "vic_run" (GH#7) Although the physics and model behavior of VIC 5.0.0 should be nearly identical to VIC 4.2, the source code has undergone a major cleanup and reorganization. We have separated the physical core ("vic_run") from the driver source code. This work has improved the extensibility and readability of the model. Classic Driver (GH#7) The Classic Driver provides similar functionality as VIC 4, including ASCII and binary I/O, and a time-before-space evaluation loop order. The Classic Driver is maintained for two main reasons: to provide some level of backward compatibility for existing VIC users that wish to continue using VIC using a traditional approach, and, to allow VIC to be run at individual grid cells, without requiring the infrastructure needed by the Image Driver. Documentation for the Classic Driver can be found here. Image Driver (GH#7) The Image Driver adds a number of features to the user interface of the VIC model. Most notably, it uses a space-before-time evaluation loop order, netCDF I/O, and parallelization using MPI. Image Driver specific documentation can be found here. Constants File (GH#192) Earlier versions of VIC included many hard-coded parameters and constants. We have consolidated these constants into a single structure and developed an input file that allows users to modify parameters at run-time. See here for more information. Logging (GH#173) A set of logging Macros have been added to all drivers and vic_run. The logging level can be set in the driver Makefile via the LOG_LVL variable. The logging Macros provide the filename and line number in the source code to aid in debugging. Additionally, when compiler support is available, a traceback is printed when VIC exits during runtime. When the LOG_DIR variable is provided in the global parameter file, VIC will write its log(s) to log files instead of printing to stdout. Sub-hourly Timestep (GH#188) Previous versions of VIC were limited to a minimum timestep of one hour. The units of the VIC timestep have been changed from hours to seconds and the minimum timestep is now one second. If you intend on running VIC at a timestep of less than one hour, we suggest extensive testing. Calendar Support (GH#188) Earlier versions of VIC used the standard Gregorian calendar. Because many modern climate models use non-standard calendars, we have implemented all CF compliant calendars. The standard Gregorian calendar remains the VIC default. See the documentation for individual drivers for how to set the calendar option (e.g. classic. Sample Datasets (GH#387) The VIC_sample_data repository contains the necessary input datasets (forcings and parameters) to run short simulations of the VIC model for both the classic and image driver. Tests Datasets (GH#79) See https://github.com/UW-Hydro/VIC/issues/79 for more information. A temporary location of the test data is here: ftp://ftp.hydro.washington.edu/pub/gergel/VIC5_test_data/ Testing and Continuous Integration (GH#190) A comprehensive testing platform has been implemented and is available for public use along with the VIC model. A small subset of the test platform is run on Travis-CI, which facilitates continuous integration of the VIC test platform. More information on the test platform is here. Run-time profiling and timing (GH#442) A timing module has been added to VIC in order to assess the computational cost and throughput of the VIC model. New output variables (OUT_TIME_VICRUN_WALL and OUT_TIME_VICRUN_CPU) document the time spent in vic_run for each variable. Additionally, a timing table is printed to LOG_DEST at the end of each simulation. Backwards Incompatible Changes: Classic Driver I/O Formatting (GH#18, GH#204, GH#227) The format of ASCII forcing and output files has changed in VIC 5. These changes were motivated by the desire to improve simulation metadata tracking and reproducibility of VIC simulations. Output files now include a header with simulation metadata and variable names. The PRT_HEADER option has been deprecated. Classic Driver Global Parameter Options A number of global parameter options have changed for the Classic Driver, relative to VIC 4. TIME_STEP (int, units: hours) has been changed to MODEL_STEPS_PER_DAY (int) SNOW_STEP (int, units: hours) has been changed to SNOW_STEPS_PER_DAY (int) OUT_DT (int, units: hours) has been changed to OUTPUT_STEPS_PER_DAY (int) FORCE_DT (int, units: hours) has been changed to FORCE_STEPS_PER_DAY (int) BINARY_STATE_FILE (TRUE or FALSE) has been changed to STATE_FORMAT (BINARY or ASCII) BINARY_OUTPUT (TRUE or FALSE) has been changed to OUT_FORMAT (BINARY or ASCII) State files now include seconds (GH#464) There is a new global parameter option, STATESEC. This specifies the time step at the end of which state will be saved, in units of seconds. In other words, if you have an hourly time step (3600 sec) and you want to save state at the end of the final time step of the day (which is 86400 seconds long), subtract 3600 from 86400 to get a STATESEC of 82800. This corresponds to the first second of the final time step. State will be saved at the end of that time step. When the state save date is appended to state filenames, STATESEC will be included so that the date will have the format YYYYMMDD_SSSSS. Classic Driver Output Variables (GH#352) Computation of potential evapotranspiration (PET) has been simplified, reducing the number of output variables from 6 to 1. The following output variables have been removed: OUT_PET_SATSOIL (potential evapotranspiration from saturated bare soil) OUT_PET_H2OSURF (potential evapotranspiration from open water) OUT_PET_SHORT (potential evapotranspiration (transpiration only) from short reference crop (grass)) OUT_PET_TALL (potential evapotranspiration (transpiration only) from tall reference crop (alfalfa)) OUT_PET_NATVEG (potential evapotranspiration (transpiration only) from current vegetation and current canopy resistance) OUT_PET_VEGNOCR (potential evapotranspiration (transpiration only) from current vegetation and 0 canopy resistance) These have been replaced by: OUT_PET (potential evapotranspiration, which = area-weighted sum of potential transpiration and potential soil evaporation; potential transpiration is computed using the Penman-Monteith equation with architectural resistance and LAI of the current veg cover) Deprecated Features: Removed unused global parameter option MEASURE_H (GH#284) Removed MTCLIM (GH#288) Previous versions of VIC used MTCLIM to generate missing forcing variables required to run VIC. This led to confusion by many users and considerably more complex code in the Classic Driver. VIC forcings are now required to be provided at the same time frequency as the model will be run at (SNOW_STEPS_PER_DAY). As part of this change, the following options have been removed from the Classic Driver: LW_TYPE LW_CLOUD MTCLIM_SWE_CORR VP_INTERP VP_ITER OUTPUT_FORCE As part of this change, the following output variables have been removed from the Classic Driver: OUT_COSZEN OUT_TSKC In the future, we would like to provide a stand-alone version of MTCLIM that produces subdaily meteorological forcings. We are looking for community support for this feature (GH#17) Removed LONGWAVE and SHORTWAVE forcing types (GH#379). Previous versions of VIC allowed users to specify either LONGWAVE or LWDOWN to denote the incoming longwave radiation flux and SHORTWAVE or SWDOWN to denote the incoming shortwave radiation flux. We have removed these duplicate options, standardizing on the more descriptive LWDOWN and SWDOWN. Similarly, output variables OUT_NET_LONG and OUT_NET_SHORT have been replaced with OUT_LWNET and OUT_SWNET, respectively. Changed the name of the variable VEGCOVER to FCANOPY, since this more accurately captures the meaning of the term (i.e., the fractional area of the plant canopy within the veg tile). Similarly changed OUT_VEGCOVER to OUT_FCANOPY. Similarly, changed the names of the following global parameter file options: VEGLIB_VEGCOVER --> VEGLIB_FCAN VEGPARAM_VEGCOVER --> VEGPARAM_FCAN VEGCOVER_SRC --> FCAN_SRC Bug Fixes: Miscellaneous fixes to lake module (GH#425) Several lake processes (aerodynamic resistance, albedo, latent/sensible heat fluxes, net radiation, etc) were reported incorrectly or not at all in output files. This has been fixed. In addition, in the absence of an initial state file, lake temperatures were initialized to unrealistic temperatures (the air temperature of the first simulation time step). To fix this, we now initialize the lake temperature to annual average soil temperature. Fix for computation of soil layer temperatures when soil thermal nodes do not reach the bottom of the soil column. (GH#467) Previously, if the soil thermal damping depth was shallower than the bottom of the deepest soil layer, and FROZEN_SOIL==TRUE, VIC would abort when estimating layer ice contents because it could not estimate a layer temperature if the thermal nodes did not completely span the layer. Now, a layer temperature is estimated even when thermal nodes do not completely span the layer, and the error no longer occurs. Fix related to exact restart (GH#481, GH#507, GH#509) Previously, VIC did not produce the same results (fluxes and states) if a simulation was separated into multiple shorter-period runs by saving the state variables and restarting. This was due to: The MTCLIM algorithm resulted in slightly different sub-daily meteorological variable values for different lengths of forcings (MTCLIM is deprecated in the current version) A few bugs resulting in inexact restart. The following bugs have been fixed: The prognostic state variable energy.Tfoliage (foliage temperature) is now saved to the state file Two flux variables energy.LongUnderOut and energy.snow_flux are now saved to the state file. !!!Note This is a temporary solution to ensure exact restart. A better way of handling these two flux variables needs to be done in the future (see GH#479) Fix for binary state file I/O (GH#487) Fixed a bug so that the binary format state file I/O works correctly. Fix for a physical constant (water heat capacity) (GH#574) Fixed a bug where volumetric heat capacity of water should be used in func_canopy_energy_bal (previously specific heat capacity was used)

Global water scarcity including surface water quality and expansions of clean water technologies

Water scarcity threatens people in various regions, and has predominantly been studied from a water quantity perspective only. Here we show that global water scarcity is driven by both water quantity and water quality issues, and quantify expansions in clean water technologies (i.e. desalination and treated wastewater reuse) to 'reduce the number of people suffering from water scarcity' as urgently required by UN's Sustainable Development Goal 6. Including water quality (i.e. water temperature, salinity, organic pollution and nutrients) contributes to an increase in percentage of world's population currently suffering from severe water scarcity from an annual average of 30% (22%–35% monthly range; water quantity only) to 40% (31%–46%; both water quantity and quality). Water quality impacts are in particular high in severe water scarcity regions, such as in eastern China and India. In these regions, excessive sectoral water withdrawals do not only contribute to water scarcity from a water quantity perspective, but polluted return flows degrade water quality, exacerbating water scarcity. We show that expanding desalination (from 2.9 to 13.6 billion m3 month−1) and treated wastewater uses (from 1.6 to 4.0 billion m3 month−1) can strongly reduce water scarcity levels and the number of people affected, especially in Asia, although the side effects (e.g. brine, energy demand, economic costs) must be considered. The presented results have potential for follow-up integrated analyses accounting for technical and economic constraints of expanding desalination and treated wastewater reuse across the world