Physically-based Hydrological Model Research Articles

Water yield response to forest treatment patterns in a sierra nevada watershed

Study regionSierra Nevada Study focusSnow dominated forests serve as source water supplies to much of the western United States. In recent years, these forests have experienced an increase in both drought and wildfire, which threaten critical water resources. By reducing forest density, treatments offer a promising solution to reducing fuel loads and potentially increasing surface water yield. However, the amount of fuel load reduction necessary to produce a change in water yield is not well-characterized. The objectives of the current study are to: 1) calibrate a distributed parameter, fully-integrated and physically-based hydrologic model to available watershed data and evaluate predicted hydrologic changes for a range of fuel treatment scenarios in a heavily forested experimental basin in the Sierra Nevada (Sagehen Creek basin near Truckee, California, USA), and 2) determine the extent (threshold) of forest treatments necessary to produce substantial changes, i.e. a 25% increase, in surface runoff. New hydrological insights for the regionUsing DHI’s physically-distributed code, MIKESHE, to develop the model for Sagehen Basin, twenty forest treatment scenarios with varying canopy density reductions (CDRs) and areas of treatment were simulated for a five-year period. Statistical testing showed that significant change in runoff occurred for every developed scenario at the annual scale (99% confidence interval). Surface water yield is highly correlated with precipitation patterns, however, treatments have a compounding effect over the years and the basin has a more dramatic response to higher treatment intensities. Increasing treatment CDR was more effective at increasing water yield than increasing treatment area. Results suggest that as forest managers implement fuel treatments, water yield response depends on: 1) the degree to which treatments are implemented by either area or CDR, 2) future climate variability including extended periods of drought, and 3) basin storage conditions and how this might buffer disturbance response in the basin.

Using a physics-based hydrological model and storm transposition to investigate machine-learning algorithms for streamflow prediction

Machine learning (ML) algorithms have produced remarkable advances in streamflow prediction, exceeding the performance of calibrated conceptual and physics-based hydrological models that have been developed over many decades. ML algorithms seem to overcome the issue of errors known to be present in rainfall and streamflow estimates that have hindered the performance of hydrological models for decades. In this paper, we propose a methodology for testing and benchmarking ML algorithms using artificial data generated by physically-based hydrological models. Our approach makes it possible to design controlled numerical experiments that can improve our understanding of this new generation of black-box models. We conducted a diagnostics study to demonstrate our methodology in which we attempted to determine if ML algorithms can identify a function relating streamflow and rainfall. This exercise combined the implementation of the distributed hillslope-link hydrological model (HLM) on a 4,385 km2 basin driven by precipitation fields created using the stochastic storm transposition (SST) framework, and an advanced deep learning algorithm based on gated recurrent unit (GRU)-Attention neural networks. The data generated allowed us to create prediction scenarios that are equivalent to the hindcast and real-time forecast problems. We proposed a set of scale-independent performance metrics to evaluate the results of our experiment and found that the GRU can correctly identify a predictive function for all analyzed locations in the river network. We concluded that under the circumstances tested in this study, deep learning can identify the transformation function when trained in Hindcast Mode, making it a powerful tool to determine the streamflow response of a basin to predetermined rainfall scenarios. However, it fails to significantly outperform the predictions of temporal persistence when tested in Forecast Mode.

Assessing spatial scales in hydrological effectiveness and economic costs of nature-based solutions within a scale-invariance framework

This study proposed a scale-invariance framework within the fractal and Universal Multifractal (UM) framework to assess hydrological performances and economic dimensions of nature-based solutions (NBS) across various spatial scales. Firstly, a series of NBS scenarios are created by implementing NBS heterogeneously over Guyancourt city (a peri-urban catchment located in the Southwest of Paris). Then, the spatial heterogeneity and the implementation levels of NBS in the NBS scenarios are quantified by a scale-invariance indicator (fractal dimension; DF) across various spatial scales. The X-band radar rainfall data with high space-time resolution was obtained from École des Ponts ParisTech, which is used as the rainfall forcing for numerical modelling experiments. Then, the hydrological responses of the NBS scenarios are simulated by using the fully distributed and physically-based hydrological model (Multi-Hydro) under the selected spatially variable rainfall event. The renormalised maximum probable singularity indicator (RI) is developed based on the UM framework, and it is employed to quantify the hydrological effectiveness in terms of efficiency in runoff reduction of the NBS scenarios. The economic indicator is represented by the life cycle costs (LCC), which are used to estimate the economic costs of NBS scenarios. Finally, the economic dimensions of NBS across various spatial scales are quantified by integrating DF and the LCC of NBS scenarios. The results show that the permeable pavement scenarios 3 and 4 perform better than the other NBS scenarios in mitigating overland flow. The assessment of the economic dimensions of NBS suggests that a higher implementation level of NBS measures in the small-scale range is necessary. The economic dimensions of NBS at the large-scale range vary between 225 m2 and 600 m2. Overall, this study will potentially provide valuable strategies for better managing stormwater runoff in urban catchments and support the decision-making processes of implementing NBS on multiple spatial scales.

Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment

Streamflow forecasting is crucial in water planning and management. Physically-based hydrological models have been used for a long time in these fields, but improving forecast quality is still an active area of research. Recently, some artificial neural networks have been found to be effective in simulating and predicting short-term streamflow. In this study, we examine the reliability of Long Short-Term Memory (LSTM) deep learning model in predicting streamflow for lead times of up to ten days over a Canadian catchment. The performance of the LSTM model is compared to that of a process-based distributed hydrological model, with both models using the same weather ensemble forecasts. Furthermore, the LSTM’s ability to integrate observed streamflow on the forecast issue date is compared to the data assimilation process required for the hydrological model to reduce initial state biases. Results indicate that the LSTM model forecasted streamflows are more reliable and accurate for lead-times up to 7 and 9 days, respectively. Additionally, it is shown that the LSTM model using recent observed flows as a predictor can forecast flows with smaller errors in the first forecasting days without requiring an explicit data assimilation step, with the LSTM model generating a median value of mean absolute error (MAE) for the first day of lead-time across all forecast issue dates of 25 m3/s compared to 115 m3/s for the assimilated hydrological model.

Assessing effect of best management practices in unmonitored watersheds using the coupled SWAT-BiLSTM approach

In order to enhance the simulation of BMPs (Best Management Practices) reduction effects in unmonitored watersheds, in this study, we combined the physically-based hydrological model Soil & Water Assessment Tool (SWAT) and the data-driven model Bi-directional Long Short-Term Memory (Bi-LSTM), using the very-high-resolution (VHR) Land Use and Land Cover (LULC) dataset SinoLC-1 as data input, to evaluate the feasibility of constructing a water environment model for the Ba-River Basin (BRB) in central China and improving streamflow prediction performance. In the SWAT-BiLSTM model, we calibrated the top five SWAT parameters sorted by P-Value, allowing SWAT to act as a transfer function to convert meteorological data into base flow and storm flow, serving as the data input for the Bi-LSTM model. This optimization improved the Bi-LSTM's learning process for the relationship between the target and explanatory variables. The daily streamflow prediction results showed that the hybrid model had 9 regions rated as "Very good," 2 as "Good," 2 as "Satisfactory," and 1 as "Unsatisfactory" among the 14 regions. The model achieved an NSE of 0.86, R2 of 0.85, and PBIAS of −2.71% for the overall daily streamflow prediction performance during the verification period of the BRB. This indicates that the hybrid model has high predictive accuracy and no significant systematic bias, providing a sound hydrodynamic environment for water quality simulation. The simulation results of different BMPs scenarios showed that in the scenarios with only one BMP measure, stubble mulch had the best reduction effect, with average reductions of 17.83% for TN and 36.17% for TP. In the scenarios with a combination of multiple BMP measures, the combination of stubble mulch, soil testing and formula fertilization, and vegetative filter strip performed the best, achieving average reductions of 42.71% for TN and 50.40% for TP. The hybrid model provides a novel approach to simulate BMPs' reduction effects in regions without measured hydrological data and has the potential for wide application in BMP-related decision-making.

Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin

Against the backdrop of accelerated global climate change and urbanization, the frequency and severity of flood disasters have been increasing. In recent years, influenced by climate change, the Hai-River Basin (HRB) has experienced multiple large-scale flood disasters. During the widespread extraordinary flood event from July 28th to August 1st, 2023, eight rivers witnessed their largest floods on record. These events caused significant damage and impact on economic and social development. The development of hydrological models with better performance can help researchers understand the impacts of climate change, provide risk information on different disaster events within watersheds, support decision-makers in formulating adaptive measures, urban planning, and improve flood defense mechanisms to address the ever-changing climate environment. This study examines the potential for enhancing streamflow simulation accuracy in the HRB located in Northeast China by combining the physically-based hydrological model with the data-driven model. Three hybrid models, SWAT-D-BiLSTM, SWAT-C-BiLSTM and SWAT-C-BiLSTM with SinoLC-1, were constructed in this study, in which SWAT was used as a transfer function to simulate the base flow and quick flow generation process based on weather data and spatial features, and BiLSTM was used to directly predict the streamflow according to the base flow and quick flow. In the SWAT-C-BiLSTM model, SWAT parameters with P values less than 0.4 in each hydrological station-controlled watershed were calibrated, while the SWAT-D-BiLSTM model did not undergo calibration. Additionally, this study utilizes both 30 m resolution land use and land cover (LULC) map and the first 1 m resolution LULC map SinoLC-1 as input data for the models to explore the impact on streamflow simulation performance. Among five models, the NSE of SWAT-C-BiLSTM with SinoLC-1 reached 0.93 and the R2 reached 0.95 during the calibration period, and both of them stayed at 0.92 even in the validation period, while the NSE and R2 of the other four models were all below 0.90 in the validation period. The potential impact of climate change on streamflow in the HRB was evaluated by using predicted data from five global climate models from CMIP6 as input for the best-performing SWAT-C-BiLSTM with SinoLC-1. The results indicate that climate change exacerbates the uneven distribution of streamflow in the HRB, particularly during the concentrated heavy rainfall months of July and August. It is projected that the monthly streamflow in these two months will increase by 34% and 49% respectively in the middle of this century. Furthermore, it is expected that the annual streamflow will increase by 5.6% to 9.1% during the mid-century and by 6.7% to 9.3% by the end of the century. Both average streamflow and peak streamflow are likely to significantly increase, raising concerns about more frequent urban flooding in the capital economic region within the HRB.

Hydrological models weighting for hydrological projections: The impacts on future peak flows

Reliable hydrological projections are of great importance for climate change impact studies, especially knowing that these analyses can allow identifying regional adaptation and mitigation strategies for the future. However, the literature has highlighted that hydrological models used under climate conditions that are contrasting to those used during their calibrations can lower their performance and reliability, an issue that lowers the confidence in hydrological projections and adds uncertainty to their analyses. More studies are needed to explore this issue and evaluate potential strategies that might improve hydrological models’ reliability in climate change impact studies. Thus, the present study evaluates the robustness of hydrological models under contrasting climatic conditions and investigates the use of weighting techniques to improve their combined performance and reliability. The robustness of five lumped hydrological models is analysed using a Differential Split-Sample Testing (DSST) that evaluates their performance under cold, warm, humid and dry historical contrasting conditions over 77 basins covering different hydroclimatic conditions (two domains, one located in Quebec, Canada, and one in Mexico). Additionally, four basins were selected from the study area to evaluate the robustness of a more complex semi-distributed and more physically-based hydrological model and compare its simulations against the simpler lumped hydrological models. Based on the resulting performance of each hydrological model, five different weighting methods were applied to evaluate the potential improvements in the multi-model ensemble performance and quantify their effects on hydrological projections, particularly on future peak flows. For each basin, these streamflow projections were produced using two regional climate simulations (one per studied domain) issued from the Canadian Regional Climate Model version 5 (CRCM5) under the Representative Concentration Pathway (RCP) 8.5 for the 1976–2005, 2041–2070 and 2070–2099 periods. The results showed that weighting hydrological models, even with the most simplistic methods, showed better performances over historical contrasting conditions than the best-performing lumped hydrological model. Between the different weighting methods, the Granger-Ramanathan type A showed the overall best performance among the different basins and climate conditions, particularly in peak streamflows. Over the CRCM5-driven peak flow projections, the weighting methods Granger-Ramanathan types A and B produced the largest impacts on the projected floods magnitudes and climate change signals. On the other hand, the additional tests using the semi-distributed and more physically-based hydrological model revealed that this model showed more robust simulations than the weighted lumped hydrological models on low flows over the four selected basins. Additionally, more robust high-flow simulations were observed over a small snow-dominated basin, suggesting a potential added value in adding more complex hydrological models to simulate conditions under a changed climate.

Estimating streamflow permanence with the watershed Erosion Prediction Project Model: Implications for surface water presence modeling and data collection

Many data collection efforts and modeling studies have focused on providing accurate estimates of streamflow while fewer efforts have sought to identify when and where surface water is present and the duration of surface water presence in stream channels, hereafter referred to as streamflow permanence. While physically-based hydrological models are frequently used to explore how water quantity may be influenced by various climatic and basin characteristics at local, regional, national, and global extents they are less often used to explore streamflow permanence. Herein, the Watershed Erosion Prediction Project (WEPP) hydrological model is applied to watersheds in the humid H. J. Andrews Experimental Forest (HJA) and watersheds of the arid Willow and Whitehorse creeks (WW), both in Oregon, to simulate daily (WW) and annual (HJA and WW) streamflow permanence. One thousand parameter combinations were tested to calibrate WEPP to observed streamflow in the HJA watersheds and one hundred parameter combinations were tested to calibrate WEPP to observed surface water presence time series data in WW watersheds. When calibrated to observed streamflow, WEPP correctly classified annual streamflow permanence for 83 % of HJA stream reaches. In the WW, WEPP simulations correctly classified 63–93 % of daily streamflow permanence observations and 59–87 % of annual streamflow permanence classifications. Inclusion of a dry-day threshold (the maximum number of days a stream reach could be modeled ‘dry’ but still classified as permanent for the year) improved annual accuracy in three WW watersheds from 2 to 10 %. Parameter sets that produced the best daily accuracies in WW resulted in poor annual accuracies. Results highlight the importance of evaluating physically-based streamflow permanence models on both permanent and nonpermanent streams at daily and annual time scales to ensure evaluation metrics are appropriate for interpretation purposes. Additionally, results suggest that strategic collection of surface water presence observations and streamflow observations may support robust calibration of physically based models to simulate streamflow permanence moving forward.

Multicriteria land cover design via coupled hydrologic and multi-sector water management models

We investigate how hydrologic-land feedbacks and a hydrologic-water management linkage impact land cover arrangements optimized within a multiobjective land cover design framework. The framework integrates a spatially-distributed and physically-based hydrologic model, for simulating surface and subsurface flow and land processes, with a network-based multi-sector water resources management and allocation model. Both models used (Parflow, Pywr) are open-source. The framework is applied to a hillslope problem to identify land cover patterns that optimize trade-offs between water, food, energy and environment objectives. Results show trade-offs depend on land cover composition and the spatial arrangement of land covers within the catchment. Total runoff volume and peak flow of runoff was found to change 3 and 2-fold, respectively, between optimized solutions as land cover composition and spatial patterns were altered to satisfy different combinations of objectives. At the same time, up to a 15% reduction in the total runoff volume and an 8% reduction in the peak flow of runoff were observed within optimized land cover patterns having equal composition but different spatial arrangements. This emphasizes the impact on hydrologic behavior of the spatial location of land covers within a catchment. The emergence of patterns in land cover distribution for different trade-offs between objectives is driven by feedback mechanisms between subsurface hydrology and land processes, which are implicitly linked to the properties of each land cover and the interactions between neighboring land covers through lateral groundwater flow. The study demonstrates the added benefits of coupling distributed hydrologic models with water management simulation for multisector multicriteria land cover planning.

Spatio-temporal rainfall variability and its impacts on the hydrological response of nature-based solutions

ABSTRACT The high spatio-temporal variability of precipitation makes the hydrological responses of nature-based solutions (NBS) indeterminate. Hence, this study investigated the hydrological responses of a semi-urban catchment and three NBS scenarios concerning different spatio-temporal rainfall data from both C-band radar and X-band radar. Multi-Hydro, the fully-distributed and physically-based hydrological model, is employed to study the spatio-temporal rainfall variability and the resulting hydrological impacts on the NBS scenarios. The scale-independent indicator of maximum probable singularity (γs ) within the Universal Multifractals (UM) framework is used to quantify the spatio-temporal variability of rainfall and catchment responses across scales. The main results show that the hydrological performances of the NBS scenarios can be largely underestimated or overestimated because of the spatio-temporal rainfall variability, with a relative error ranging from −91% to 199%, and NBS can effectively reduce surface runoff across scales, which is characterised by a lower γs .

Time variable effectiveness and cost-benefits of different nature-based solution types and design for drought and flood management

Nature Based Solutions (NBS) for water resources management have potential to mitigate climate change impacts, including more frequent flooding and droughts. Successful uptake requires more knowledge on the effects of NBS type and design on high and low flows. The cost-benefits of NBS impacts on these and water yield are also essential. Here, we used a modelling framework to explore the impacts of two common NBS types (Runoff Attenuation Features [RAFs] and tree planting), both varying in design, specifically location and scale. Data from an upland Scottish catchment (∼1km2) informed a coupled physically-based hydrological (MIKE SHE) and hydraulic (MIKE 11) modelling approach. NBS scenario effects on high and low flows, as well as groundwater recharge were compared and hydrological indices specific to the whisky industry informed the study site's 25-year cost-benefit analysis. Overall, tree planting reduced low flows and recharge of groundwater, whereas RAFs had a positive but smaller effect. Both NBS types reduced high and medium flows, although tree planting reduced high flows less than RAFs. RAF design, particularly increases in storage volume spread over greater areas, increased effects on all aspects of flows and recharge. Greater areas of planting increased effects on all but the highest flows. NBS type and design affected timing of water storage availability, retention and transfer, but this also depended on antecedent wetness, so these should all be considered for optimal performance or avoiding negative effects. The cost-benefit analysis revealed that RAFs would be a financially feasible NBS approach for enhancing low flows, whereas tree planting would not. This study highlighted that implementing a modelling framework alongside cost-benefit analysis could help optimise type and design of NBS for cost-effective management of specific local water availability issues. Critically this could inform NBS implementation for management of flood and drought impacts, likely to become more frequent in future with climate change.

Influence of Soil Layers on the Infiltration Rates and Cumulative Infiltration Using Modified Green Ampt Model in the HYDROL-INF Simulation Environment

Soil profiles are generally heterogeneous and consist of various horizontal layers due to geological processes, the formation of crusts, or other artificial or man-made activities. To quantify infiltration into these heterogeneous soil profiles, the Modified Green-Ampt Model (MGAM) is a physically-based hydrologic model that can efficiently perform under both steady and unsteady rainfall events. Based on the secondary data, this study sought to determine the effect of changing soil layers (soil textures) on infiltration rates and cumulative infiltrations in in both laboratory and field settings. Different scenarios were analyzed by rearranging soil layers and evaluating their impacts on corresponding infiltration rates and cumulative infiltrations. Simulations were run with HYDROL-INF software environment using MGAM. Three scenarios were considered for a laboratory experiment with two different types of soil texture coupled with five different soil profiles. Similarly, four scenarios were considered for the field experiments with five different types of soil texture couple with eight different soil profiles. The simulated infiltration rates and cumulative infiltrations were found to vary with soil layer change scenarios. The simulated cumulative infiltrations, ponding times, infiltrating rates at ponding, and total depth of wetting front at ponding of a five-layered laboratory soil column were identical for the three scenarios. Simulated cumulative infiltrations were 33.16, 23.65, 21.29, and 42.77 cm, respectively, for scenarios (combinations) 1, 2, 3, and 4 in the eight-layered soil profile in the field scenarios. Infiltration rates among scenarios at ponding were identical (0.46 to 0.53 cm/h) with field scenario data.

Future changes in annual runoff and hydroclimatic extremes in the upper Yangtze River Basin

The upper Yangtze River Basin (UYRB) provides almost half of the total runoff volume of the entire Yangtze River and is of great importance for the population living in the upstream and downstream regions. The recent streamflow decline and more frequent hydroclimatic extremes have raised concerns about the water sustainability and water security of the UYRB in a changing climate. Here we examine the hydrological changes in the UYRB during the historical period (1961–2020) and the future period (2021–2070) using a physically-based hydrological model. Based on the simulation results, the future changes in water resources and hydroclimatic extremes in the UYRB were analyzed. The results show that the mean annual runoff volume in the UYRB during the future period will further decrease by 20.4 (SSP1-2.6), 17.7 (SSP2-4.5) and 22.0 (SSP5-8.5) billion m3 compared with the historical mean value. The agricultural drought severity will increase in all sub-regions, which tends to be more severe than the meteorological droughts, and Wujiang is going to experience the largest increase in drought severity. Averaged across the UYRB, the unprecedented agricultural droughts and pluvials will occur more than once per decade on average in the future, indicating that the most extreme events in the historical period would become the new normal in the future. The design floods with different return periods at all major hydrological stations in the UYRB are projected to increase, and in the future there will be more frequent drought-pluvial seesaws at the major hydrological stations. These findings highlight the importance of adaptive water resources management to tackle the elevated risk of water shortage and more frequent and severe hydroclimatic extremes in the future UYRB.

Simulation of flood peak discharges and volumes for flood risk management in the ungauged urban informal settlement of Kibera, Kenya

In spite of the increasing extremity and adverse consequences of urban floods under climate change, flood characteristics in most developing countries are still largely unknown due to lack of continuous monitoring. This portentous uncertainty stances high flood risk, especially to a majority low-income urban population inhabiting the flood-prone informal settlements. Physically-based hydrologic models, whose parameters are derivable from catchment features, are often used for hydrological analysis in the ungauged regions. In this study, the Hydrologic Engineering Centre's-Hydrologic Modelling System (HEC-HMS) was used for rainfall-runoff simulation in the upper Ngong River Basin of Kenya, draining into the Kibera informal settlement, for purposes of estimating flood peak discharges (FPDs) and direct runoff volumes (DRVs), often required for flood risk management. The Soil Conservation Service-Curve Number (SCS-CN), SCS-Unit Hydrograph (SCS-UH) and Muskingum models were used for hydrological simulation from four (4) observed and fifty-four (54) hypothetical extreme rainfalls. The results obtained indicated that a 50 mm rainfall event with a duration of 2.5 h has the potential of producing FPD and DRV of about 90m3s−1 and 1.17×106m3 respectively at the Ngong River confluence at Sokomoko in Kibera. On the other hand, the non-intermittent 100-year storm event of about 172 mm in depth, occurring over a period of about 4 h, potentially generated FPD of about 460m3s−1 and DRV of about 4.89×106m3, portending calamitous impacts in the basin. These results can offer preliminary information on potential flood characteristics, that can be used as a baseline to support local-level flood risk mitigation measures in the ungauged Ngong River Basin.

Exploring urban growth–climate change–flood risk nexus in fast growing cities

This study looks at the nexus between urban growth, climate change, and flood risk in Doha, Qatar, a hot-spot, climate change region that has experienced unprecedented urban growth during the last four decades. To this end, this study overviews the main stages of Doha’s urban growth and influencing climatic factors during this period. A physically-based hydrological model was then built to simulate surface runoff and quantify flood risk. Finally, the Pearson correlation was used to verify the potential nexus between flood risk, climate change, and urban growth. Surveying showed that, between 1984 and 2020, urban areas grew by 777%, and bare lands decreased by 54.7%. In addition, Doha witnessed various climatic changes with a notable increase in air temperature (+ 8.7%), a decrease in surface wind speed (− 19.5%), and a decrease in potential evapotranspiration losses (− 33.5%). Growth in urban areas and the perturbation of climatic parameters caused runoff to increase by 422%, suggesting that urban growth contributed more than climatic parameters. Pearson correlation coefficient between flood risk and urban growth was strong (0.83) and significant at p < 0.05. Flood risk has a strong significant positive (negative) correlation with air temperature (wind speed) and a moderate positive (negative) correlation with precipitation (potential evapotranspiration). These results pave the way to integrate flood risk reduction measures in local urban development and climate change adaptation plans.

Quantifying the impacts of agricultural alteration and climate change on the water cycle dynamics in a headwater catchment of Lake Urmia Basin

The rapid shrinkage of Lake Urmia over the past two decades has raised serious environmental concerns. Several restoration plans have been proposed to reduce agricultural water consumption to supply a remarkable fraction of the lake’s environmental flow requirement. However, an accurate and realistic evaluation of the effectiveness of these plans in reducing agricultural water consumption across the basin is still poorly understood. This study assessed the water-saving potential of agricultural alteration in a snow-dominated catchment in the Lake Urmia Basin. In particular, we explored the impact of crop pattern alteration via reducing the cultivation area and employing different irrigation systems. We further studied the degree of extra stress on the agricultural water demand under the future trends of air temperature and precipitation due to climate change. To this end, we developed a three-dimensional physically-based hydrological model that simulated the main processes involved in the water and energy cycles. In this setting, a surface-groundwater model was coupled to a land-surface model that integratively computed the spatiotemporal variability of evapotranspiration, snowmelt, groundwater, and soil moisture in the root zone, among other processes. Pumping from wells and different irrigation systems were also included in the model. The results showed that a reduction in the cultivation area does not necessarily lead to a significant saving in the total water loss due to evapotranspiration, which strongly depends on the trade-off between the decrease in transpiration and the increase in soil evaporation. We further found that saving in agricultural water consumption is partly controlled by topography and the vertical distribution of soil moisture. Also, the drip irrigation system showed lower evapotranspiration rates than the spray irrigation system. Finally, the study of the future impacts of climate change revealed that the agriculture sector would be under more intensive stress, which poses a severe threat to the long-term restoration of Lake Urmia if effective agricultural management practices are not taken into consideration. The findings of this study highlight important issues for the Lake Urmia restoration plans on agricultural water management. Given the fragility of the lake ecosystem, this study suggests a reliable modeling framework for water and agricultural managers to identify the most appropriate regions in the basin that would yield the highest saving in agricultural water consumption by improving the current crop patterns and irrigation systems.

Impact of stormwater infiltration on rainfall-derived inflow and infiltration: A physically based surface–subsurface urban hydrologic model

The subsurface fate of stormwater infiltration enhanced by green infrastructure (GI) is unknown. GI redirects stormflows from the surface into the subsurface, which can be partitioned to evapotranspiration or baseflow, or be intercepted by buried infrastructure trenches. Stormwater that infiltrates sanitary sewer pipes is referred to as rainfall-derived inflow and infiltration (RDII) and represents a rapid pathway for infiltrated stormwater to reach receiving waters. To quantify the partitioning of infiltrated stormwater between slow and fast flows, a physically-based hydrologic model was developed integrating coupled surface–subsurface processes and urban sewer systems and applied to a sewershed near Milwaukee, WI. In this region with shallow groundwater table and leaky sanitary sewer systems, RDII accounted for 73–79% of the sanitary sewer flow volume and 21% of the sewershed water balance. Sensitivity analysis indicated that the surface direct inflow was controlled more by precipitation/evapotranspiration ratio and the subsurface infiltration was controlled more by groundwater table depth and sanitary sewer defect density. The largest effect of GI was to shift surface runoff to evapotranspiration and reduce peak flow in urban sewer systems. The volume of RDII was relatively less sensitive to GI infiltration because reductions in directly connected surface runoff were offset by increases in groundwater inflow. A moderate level of GI implementation (5–10% replacement of impervious area) reduced RDII volume by 10%, however, these changes were less than 1% of the overall water balance. On the other hand, higher levels of GI implementation (∼20% replacement of impervious area) resulted in no change to RDII volume as more infiltrated stormwater was routed to the sewer system through the subsurface. This study highlights the necessity of considering the full hydrologic context of GI and balancing the runoff reduction by GI and groundwater infiltration into sewers.

Development and Evaluation of a Saturated Zone Module in an Integrated Urban Hydrological Model

Shallow urban groundwater interacts with surface water and underground infrastructures. Low-impact development in urban water management by at-source infiltration should consider shallow urban groundwater in a holistic manner. Traditional hydrological models, however, rarely detail groundwater flows and their interaction with urban runoff and the water budget. In the present study, a new approach is proposed, using the integration of a flow module WTI for the saturated zone in a distributed urban hydrological model URBS-MO. This integration is carried out by paying attention to retaining the initial waterflow subsurface parameterization. The performance of the integrated model is evaluated by piezometric and runoff data in an experimental urban catchment, through a sensitivity analysis and a manual calibration of the main model parameters, as well as a validation step. The new module shows its capacity to improve groundwater flow simulation by assessing more realistic water table variations, along with a very small improvement of flowrate simulation. The bias on the average groundwater level was reduced from +14 to +7% for the one-year validation period. The modelling results show the importance of parameter calibration for distributed physically-based hydrological models. Difficulties in the calibration of parameter values due to spatial heterogeneities are also revealed, as the use of piezometric data for the calibration of a hydrological model is rather innovative.

Hydrological Modeling for Flood Adaptation under Climate Change: The Case of the Ancient Messene Archaeological Site in Greece

There is a growing global awareness about the impacts of climate change on cultural and natural heritage sites. In Greece—a homeland of important historical and cultural resources—archaeological sites are vulnerable to climate change-related flood events. In order to investigate the flood risk of the archaeological site of Ancient Messene under different climate change projections, a physically-based hydrological model was implemented and six climate change scenarios were examined. Additionally, the effectiveness of a soft structural nature-based solution adaption plan was evaluated. Based on the results, the archaeological site of Ancient Messene is shielded against small or larger flood events and only in case of extreme precipitation events is the area likely to be at risk. This flood risk can be further eliminated after implementing the soft structural nature-based solution adaptation plan proposed. Nature-based solutions provide a cost-effective alternative approach for flood risk reduction and climate change adaptation, with minimum possible disturbance, while hydrological modeling, even in case of data scarcity, constitute a valuable tool for flood risk estimation and adaptation plan management. Nature-based solutions seems to be most effective against small or medium precipitation events, and to limit the damages of extreme events. Their benefits for flood adaptation should not be underestimated.

Explaining water security indicators using hydrologic and agricultural systems models

Water security plays an important role in socio-economic development, ecosystem management, and environmental sustainability. Over the last four decades, water security assessment has attracted much political and economic attention. An improved understanding of the relationships between water demand and supply is needed to mitigate the impacts of diminishing water resources. This study provides an overview of water security assessment by focusing on the various water security indicators and the concept of water footprint (blue, green, and grey water). The water security indicators based on the water footprint concept is currently receiving more attention because it accounts for the return flow from the total water withdrawn from a watershed. We also investigate the application of different physically-based hydrological models, such as Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC), on water security assessment at a regional to continental scale. However, hydrological/agricultural system models cannot quantify evapotranspiration from irrigation and rainwater separately. Therefore, independent quantification of blue and green water footprint from an irrigated field is challenging. For illustration purposes, we apply the fully distributed Agricultural Ecosystems Services (AgES) model in the Big Dry Creek Watershed (BDCW), an intensively managed and irrigated watershed located in semiarid Colorado. The results indicate that the blue water footprint is higher than the green water footprint in the watershed. In addition, the spatial distribution of grey water footprint is highly correlated with the amount of fertilizer application. The variation of grey water footprint among the irrigated fields is higher than blue and green water footprints. We conclude that applying a physically distributed model can provide useful insight into the impact of climate and anthropogenic activities on water security at different scales.