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Terrestrial water storage (TWS) modulates the hydrological cycle and is a key determinant of water availability and an indicator of drought. While historical TWS variations have been increasingly studied, future changes in TWS and the linkages to droughts remain unexamined. Here, using ensemble hydrological simulations, we show that climate change could reduce TWS in many regions, especially those in the Southern Hemisphere. Strong inter-ensemble agreement indicates high confidence in the projected changes that are driven primarily by climate forcing rather than land and water management activities. Declines in TWS translate to increases in future droughts. By the late twenty-first century, the global land area and population in extreme-to-exceptional TWS drought could more than double, each increasing from 3% during 1976–2005 to 7% and 8%, respectively. Our findings highlight the importance of climate change mitigation to avoid adverse TWS impacts and increased droughts, and the need for improved water resource management and adaptation., Terrestrial water storage (TWS) modulates the hydrological cycle and is a key determinant of water availability and an indicator of drought. While historical TWS variations have been increasingly studied, future changes in TWS and the linkages to droughts remain unexamined. Here, using ensemble hydrological simulations, we show that climate change could reduce TWS in many regions, especially those in the Southern Hemisphere. Strong inter-ensemble agreement indicates high confidence in the projected changes that are driven primarily by climate forcing rather than land and water management activities. Declines in TWS translate to increases in future droughts. By the late twenty-first century, the global land area and population in extreme-to-exceptional TWS drought could more than double, each increasing from 3% during 1976–2005 to 7% and 8%, respectively. Our findings highlight the importance of climate change mitigation to avoid adverse TWS impacts and increased droughts, and the need for improved water resource management and adaptation. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.

The SPEC model (Predicted Environmental Concentrations in agricultural Soils) was developed and improved for the simulation of pesticide runoff. The model was applied to the Sakaecho upland bare soil field (Tokyo, Japan) to predict runoff water, sediment concentration in runoff water, pesticide concentrations in runoff water, and runoff sediment (clothianidin and imidacloprid) under artificial rainfall conditions. The results showed that the simulated time to first runoff agreed very well with the observed data. The simulated cumulative runoff, sediment yield, and imidacloprid concentration in sediment agreed well with the observed data (Nash–Sutcliffe Efficiency (NSE)>0.75). The simulated runoff rate agreed reasonably well with the observed data (NSE >0.5). The predicted clothianidin concentrations in sediment and in runoff water had acceptable agreement with the observed data (NSE >0). The results implied the model’s potential to predict runoff water, sediment yield, and pesticide runoff.

A low-cost, simple to use portable rainfall simulator is developed for use over a 5 m2 plot. The simulator is easy to transport and assemble in the field, thereby allowing for necessary experimental replicates to be done. It provides rainfall intensities of between 20 and 100 mm/h by changing the number and type of silicon nozzles used. The Christiansen coefficient of uniformities obtained in the field are appropriate and vary from 79 to 94% for rainfall intensities ranging from 30 to 70 mm/h. In addition, the median volumetric drop diameters measured for rainfall intensities of 30, 50, and 70 mm/h are in the lower range of that of natural rainfall and equal to 1.10±0.08, 1.69±0.21, and 1.66±0.20 mm, respectively. The velocities of the raindrops with diameters less than 1.2 mm reached terminal velocities, while raindrops less than 2.0 mm achieved velocities reasonably close to the terminal velocity of natural rainfall. Furthermore, the average time-specific kinetic energy (KET) for rainfall intensities of 30, 50, and 70 mm/h are 257.7, 760.1, and 1645.2 J/m2/h, respectively accounting for about 78.0 and 86.5% of the KET of natural rainfall for 50 and 70 mm/h rainfall intensity, respectively. The applicability of the portable rainfall simulator for herbicide transport study is investigated using two herbicides (atrazine and metolachlor); herbicide losses in runoff and sediment samples are in the ranges reported in the literature. As a percentage of the amount of herbicide applied, 5.29% of atrazine and 2.15% of metolachlor are lost due to combined water and sediment runoff. The results show that the portable rainfall simulator can be effectively used in studying processes such as pesticide runoff, infiltration mechanisms, and sediment generation and transport at a field plot scale with an emphasis on how surface characteristics such as slope and soil properties affect these processes.

Rapid urbanization, tourism, and climate change (CC) threaten water resource management in developing countries. Conventional water-planning tools cannot account for the changing effects of water disparity, climate risks, and environmental flow (EF) requirements. This paper proposes an alternative approach that applies stylized water-demand forecasting and predicting water availability from the perspectives of CC, changing society, and EF, thereby providing managers with future scenarios of surface water sufficiency/deficiency in an active ecotourism area, namely, Puerto Princesa City, Philippines. We considered (1) scenarios of seasonal droughts to prepare for climate risks in the future and (2) scenarios of water availability that do not depend on groundwater supply, in which the projected water deficiency is frequent both annually and seasonally. The results of this case study showed that an additional water supply from the Montible Watershed to the city was projected to secure sufficient amounts of water to achieve surface-water sufficiency, which is consistent with the goals of both the municipality and the water company to reduce the dependency on groundwater. Moreover, significant infrastructure investment costs must be anticipated in Scenario 3. Our approach proves efficient in modeling water demand in regions with active tourism and hydrology and therefore has the potential for further analyses and application.

Globally, flood risk is projected to increase in the future due to climate change and population growth. Here, we quantify the role of dams in flood mitigation, previously unaccounted for in global flood studies, by simulating the floodplain dynamics and flow regulation by dams. We show that, ignoring flow regulation by dams, the average number of people exposed to flooding below dams amount to 9.1 and 15.3 million per year, by the end of the 21st century (holding population constant), for the representative concentration pathway (RCP) 2.6 and 6.0, respectively. Accounting for dams reduces the number of people exposed to floods by 20.6 and 12.9% (for RCP2.6 and RCP6.0, respectively). While environmental problems caused by dams warrant further investigations, our results indicate that consideration of dams significantly affect the estimation of future population exposure to flood, emphasizing the need to integrate them in model-based impact analysis of climate change., Global flood risk is assessed in this study; in particular, the authors describe, based on a modeling approach, the positive effect of river dams on mitigating flood hazards to people.

BACKGROUND The Soil and Water Assessment Tool combined with Pesticide Concentration in Paddy Field (PCPF-1@SWAT) model was previously developed to simulate the fate and transport of rice pesticides in watersheds. However, the current model is deficient in characterizing the rice paddy area and is incompatible with the ArcSWAT2012 program. In this study, we modified the original PCPF-1@SWAT model to develop a new PCPF-1@SWAT2012 model to address the deficiency in the rice paddy area and utilizing the ArcSWAT2012 program. Next, the new model was applied to the Sakura River watershed, Ibaraki, Japan in order to simulate the transport of four herbicides: mefenacet, pretilachlor, bensulfuron-methyl and imazosulfuron. RESULTS The results showed that the water flow rate simulated by PCPF1@SWAT2012 was similar with the observed data. The calculated Nash-Sutcliffe efficiency coefficient (NSE) (0.73) and percent bias (PBIAS) (-20.38) suggested satisfactory performance of the model. In addition, the concentrations of herbicides simulated by the PCPF-1@SWAT2012 model were in good agreement with the observed data. The statistical indices NSE and root mean square error (RMSE) estimated for mefenacet (0.69 and 0.18, respectively), pretilachlor (0.86 and 0.18, respectively), bensulfuronmethyl (0.46 and 0.21, respectively) and imazosulfuron (0.64 and 0.28, respectively) indicated satisfactory predictions. CONCLUSION The PCPF-1@SWAT2012 model is capable of simulating well the water flow rate and transport of herbicides in this watershed, comprising different land use types, including a rice paddy area. © 2018 Society of Chemical Industry.

This study aimed to develop and validate a Dynamic in-River Agrochemical Fate and Transport (DRAFT) model simulating one-dimensional advective and dispersive pesticide transport processes under unsteady flow regime in a riverine system. The DRAFT model was coupled with two other modeling components, the PCPF-B model and the land use based tank model, which simulated hydrological/pesticide process in paddy fields and hydrological process in other land uses such as city, agricultural field and forest, respectively. The PCPF-B/DRAFT model was fed with the spatial information of the target catchment by incorporating the Geographical Information System (GIS). For the model validation, a full catchment monitoring data of a rice herbicide, mefenacet, along the Kose River, Fukuoka, Japan was utilized. After model calibration, hourly river discharge and daily mefenacet concentration were simulated by the PCPF-B/DRAFT model at individual observed points of the Kose River and model performance was evaluated by graphical assessment and multiple statistical indices (e.g. Nash-Sutcliffe efficiencies were 0.84–0.86 for streamflow and 0.16–0.72 for herbicide, respectively). The predicted mefenacet concentrations were strongly affected by: (1) water managements practiced in rice fields and (2) intensive rainfall events. The former concentrations were characterized by broad peak while for the latter the peak concentration was sharp and narrow. We used the PCPF-B/DRAFT model to further evaluate the applications of 7 days of water holding period after herbicide application in paddy fields, which was shown to effectively reduce the total loss of mefenacet from 18.9 to 12.8% of applied mass. Consequently, the broad peak concentrations of mefenacet in the Kose River decreased remarkably while the water management practice was less effective to reduce the sudden and sharp peak concentration resulting from intensive rainfall events.

A Bayesian method involving Markov Chain Monte Carlo (MCMC) technique was implemented into a pesticide fate and transport model to estimate the best input parameter ranges while considering uncertainties included in both the observed pesticide concentrations and in the model. The methodology used for integrating the MCMC technique into a pollutant fate and transport models was detailed. The uncertainties encompassed in the dissolution rate and in the adsorption coefficient of the herbicide mefenacet were greatly reduced by the MCMC simulations. In addition, an optimal set of input parameters extracted from the MCMC simulations accurately reproduced mefenacet concentrations in paddy water and paddy soil as compared to the original published dataset. Consequently, by simultaneously optimizing multiple parameters of environmental models and conducting uncertainty analysis, MCMC technique exhibits powerful capability for improving the reliability and accuracy of computer models. The main strengths of the MCMC methodology are: (1) the consideration of uncertainties from both input parameters and observations and (2) the prior distributions of the input parameters which can be reformulate when additional knowledge is available.

BACKGROUND The PCPF-1 model was improved for forecasting the fate and transport of metabolites in addition to parent compounds in rice paddies. In the new PCPF-M model, metabolites are generated from the dissipation of pesticide applied in rice paddies through hydrolysis, photolysis and biological degradations. The methodology to parameterize the model was illustrated using two scenarios for which uncertainty and sensitivity analyses were also conducted. RESULTS In a batch degradation experiment, the hourly forecasted concentrations of fipronil and its metabolites in paddy water were very accurate. In a field-scale experiment, the hourly forecasted concentrations of fipronil in paddy water and paddy soil were accurate while the corresponding daily forecasted concentrations of metabolites were adequate. The major contributors to the variation of the forecasted metabolite concentrations in paddy water and paddy soil were the formation fractions of the metabolites. The influence of uncertainty included in input parameters on the forecasted metabolite concentration was high during the peak concentration of metabolite in paddy water. In contrast, in paddy soil, the metabolite concentrations forecasted several days after the initial pesticide application were sensitive to the uncertainty incorporated in the input parameters. CONCLUSION The PCPF-M model simultaneously forecasts the concentrations of a parent pesticide and up to three metabolites. The model was validated using fipronil and two of its metabolites in paddy water and paddy soil. The model can be used in the early stage of the pesticide registration process and in risk assessment analysis for the evaluation of pesticide exposure. © 2017 Society of Chemical Industry

The Pesticide Concentration in Paddy Field (PCPF-1) model has been successfully used to predict the fate and transport of granular pesticides applied to the paddy fields. However, it is not applicable for pesticides in foliar formulation while previous studies have reported that foliar application may increase the risks of rice pesticide contamination to the aquatic environment due to pesticide wash-off from rice foliage. In this study, we developed and added a foliar application module into the PCPF-1 model to improve its versatility regarding pesticide application methods. In addition, some processes of the original model such as photodegradation were simplified. The updated model was then validated with data from previous studies. Critical parameters of the model were calibrated using the Sequential Uncertainty Fitting version 2 (SUFI-2) algorithm. The calibrated model simulated pesticide dissipation trend and concentrations with moderate accuracy in the two paddy compartments including rice foliage and paddy water. The accuracy of the predicted soil concentrations could not be evaluated since no observed data were available. Although the p-factor and r-factor obtained using the SUFI2 algorithm indicated that the uncertainty encompassed in the predicted concentrations was rather high, the daily predicted pesticide concentrations in rice foliage and paddy water were satisfactory based on the NSE values (0.36-0.89). The updated PCPF-1 model is a flexible tool for the environmental risk assessment of pesticide losses and the evaluation of agricultural management practices for mitigating pesticide pollution associated with rice production.

International audience; In this study, we examine the spatial and temporal characteristics of water stress in China for the historical (1971-2010) and the future (2021-2050) periods using a multimodel simulation approach. Three water stress indices (WSIs), that is, the ratios of water withdrawals to locally generated runoff (WSI R), to natural streamflow (WSI Q), and to natural streamflow minus upstream consumptive water withdrawals (WSI C), are used for the assessment. At the basin level, WSI R estimates generally match the reported data and indicate severe water stress in most northern basins. At the grid cell level, the WSIs show distinct spatial patterns of water stress wherein WSI R (WSI Q) estimates higher (lower) water stress compared to WSI C. Based on the WSI C estimates, 368 million people (nearly one third of the total population) are affected by severe water stress annually during the historical period, while WSI R and WSI Q suggest 595 and 340 million, respectively. Future projections of WSI C indicate that more than 600 million people (43% of the total) might be affected by severe water stress, and half of China's land area would be exposed to stress. The found aggravating water stress conditions could be partly attributed to the elevated future water withdrawals. This study emphasizes the necessity of considering explicit upstream and downstream relations with respect to both water availability and water use in water stress assessment and calls for more attention to increasing levels of water stress in China in the coming decades. Plain Language Summary Severe water stress in China has been widely reported, but its time evolution and spatial patterns are rarely assessed. We examine the spatial and temporal change patterns of water stress in China by using multimodel simulations and three different water stress indices (WSIs). Results suggest that different WSIs imply distinct spatial patterns of water stress over China. The WSIs indicate that water stress conditions in northern China are quite distinct from that in southern China. During the past decades (1971-2010), severe water stress is found in northern areas while little is found in southern areas. In the future (2021-2050), however, water-stressed areas might expand in southern China and water stress levels might aggravate in urban areas, putting considerably more people exposed to severe water stress. This assessment provides useful information for regional water planning/management within the context of future climate change and socioeconomic development in China.

Model applications for delivering reliable information on soil water content (θv) and soil temperature (Tsoil) specific to Andosols (Kuroboku) are still limited despite their large area coverage (0.84% of the global terrestrial surface) and great potentials for improving agricultural production. The performance of the HYDRUS-1D model was therefore evaluated by comparing the predicted θv and Tsoil with field observations gathered from the vadose zone of a volcanic ash soil located in Fuchu (western suburb of Tokyo) representative of a temperate monsoon climate. The necessary soil properties to operate the model were obtained from both field and laboratory experiments while climatic data used for the meteorological submodels were extracted from a nearby weather station. A sensitivity and uncertainty analysis leveraging a Monte Carlo method was conducted to identify the soil hydraulic parameters critical to precise and reliable θv, vapor flow, and heat flow simulations. The temporal dynamics of simulated θv and Tsoil in the vadose zone of Andosol during the 365-day period were consistent with the values monitored under field condition demonstrating overall great performance of HYDRUS-1D. Specifying soil layer-specific hydraulic parameters significantly improved the goodness of fits between predicted and measured θv compared to the default simulation in which the surface soil layer was employed for the entire depth assuming a homogeneous profile. Water vapor influence on the total water flux and Tsoil dynamics was negligible during the whole period. Although the magnitudes of sensitivity and the contributions of soil hydraulic parameters to the water flow varied with soil profile and soil water regime, the foremost proportions of uncertainties were from the parameters w2, α2 and n2. While all remaining soil hydraulic parameters significantly contributed to substantial change in the predicted θv, their overall influence was relatively small. The HYDRUS-1D model can be used as effective tool for predicting θv and Tsoil in the vadose zone of Andosols in temperate monsoon environments for decision supporting in agriculture and other sectors such as to optimize water, crop yield and quality. The performance of the model can be greatly increased by setting soil layer-specific hydraulic parameters and focusing on the calibration of w2, α2 and n2.

A pesticide fate and transport model, SPEC, was developed for assessing Soil-PEC (Predicted Environmental Concentrations in agricultural soils) for pesticide residues in upland field environments. The SPEC model was validated for predicting the water content and concentrations of atrazine and metolachlor in 5-cm deep soil. Uncertainty and sensitivity analyses were used to evaluate the robustness of the model's predictions. The predicted daily soil water contents were accurate regarding the number of observation points (n=269). The coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE ) were equal to 0.38 and 0.22, respectively. The predicted daily concentrations of atrazine and metolachlor were also satisfactory since the R2 and NSE statistics were greater than 0.91 and 0.76, respectively. The field capacity, the saturated water content of the soil and the Q10 parameter were identified as major contributors to variation in predicted soil water content or/and herbicide concentrations.

BACKGROUND The Pesticide Concentration in a Paddy Field model (PCPF-1) was modified by adding a root zone compartment to simulate nursery-box-applied (NB-applied) pesticide. The PCPF-NB model was validated for predicting the concentrations of NB-applied fipronil and imidacloprid in rice paddy fields using two treatment methods: before transplanting (BT) and at sowing (AS). Uncertainty and sensitivity analyses were used to evaluate the robustness of the concentrations predicted by the model. RESULTS The hourly predicted concentrations of imidacloprid and fipronil were accurate in both paddy water and 1 cm deep paddy soil. The coefficient of determination and Nash–Sutcliffe model efficiency were greater than 0.87 and 0.60 respectively. The 95th percentiles of the predicted concentrations of fipronil and imidacloprid indicated that the influence of input uncertainty was minor in paddy water but important in paddy soil. The pesticide deposition rate and the desorption rate from the root zone were identified to be the major contributors to the variation in the predicted concentrations in paddy water and soil. CONCLUSION The PCPF-NB model was validated for predicting the fate and transport of NB-applied fipronil and imidacloprid using the BT and AS treatment methods. © 2015 Society of Chemical Industry

Anthropogenic influence on climate has changed temperatures, precipitation, atmospheric circulation, and many other related physical processes, but has it changed river flow as well? Gudmundsson et al. analyzed thousands of time series of river flows and hydrological extremes across the globe and compared them with model simulations of the terrestrial water cycle (see the Perspective by Hall and Perdigão). They found that the observed trends can only be explained if the effects of climate change are included. Their analysis shows that human influence on climate has affected the magnitude of low, mean, and high river flows on a global scale.Science, this issue p. 1159; see also p. 1096Anthropogenic climate change is expected to affect global river flow. Here, we analyze time series of low, mean, and high river flows from 7250 observatories around the world covering the years 1971 to 2010. We identify spatially complex trend patterns, where some regions are drying and others are wetting consistently across low,mean, and high flows. Trends computed from state-of-the-art model simulations are consistent with the observations only if radiative forcing that accounts for anthropogenic climate change is considered. Simulated effects of water and land management do not suffice to reproduce the observed trend pattern. Thus, the analysis provides clear evidence for the role of externally forced climate change as a causal driver of recent trends in mean and extreme river flow at the global scale.