Daily Time Step Research Articles

Anticipating Future Risks of Climate-Driven Wildfires in Boreal Forests

Extreme forest fires have historically been a significant concern in Canada, the Russian Federation, the USA, and now pose an increasing threat in boreal Europe. This paper deals with application of the wildFire cLimate impacts and Adaptation Model (FLAM) in boreal forests. FLAM operates on a daily time step and utilizes mechanistic algorithms to quantify the impact of climate, human activities, and fuel availability on wildfire probabilities, frequencies, and burned areas. In our paper, we calibrate the model using historical remote sensing data and explore future projections of burned areas under different climate change scenarios. The study consists of the following steps: (i) analysis of the historical burned areas over 2001–2020; (ii) analysis of temperature and precipitation changes in the future projections as compared to the historical period; (iii) analysis of the future burned areas projected by FLAM and driven by climate change scenarios until the year 2100; (iv) simulation of adaptation options under the worst-case scenario. The modeling results show an increase in burned areas under all Representative Concentration Pathway (RCP) scenarios. Maintaining current temperatures (RCP 2.6) will still result in an increase in burned area (total and forest), but in the worst-case scenario (RCP 8.5), projected burned forest area will more than triple by 2100. Based on FLAM calibration, we identify hotspots for wildland fires in the boreal forest and suggest adaptation options such as increasing suppression efficiency at the hotspots. We model two scenarios of improved reaction times—stopping a fire within 4 days and within 24 h—which could reduce average burned forest areas by 48.6% and 79.2%, respectively, compared to projected burned areas without adaptation from 2021–2099.

Streamflow prediction using support vector regression machine learning model for Tehri Dam

Accurate and reliable streamflow prediction is critical for optimising water resource management, reservoir flood operations, watershed management, and urban water management. Many researchers have published on streamflow prediction using techniques like Rainfall-Runoff modelling, Time series Models, Data-driven models, Artificial intelligence, etc. Still, there needs to be generalised method practise in the real world. The resolution of this issue lies in selecting different methods for a particular study area. This paper uses the Support vector regression machine learning model to predict the streamflow for the Tehri Dam, Uttarakhand, India, at the Daily and Ten Daily time steps. Two cases are considered in predicting daily and ten daily time steps. The first case includes four input variables: Discharge, Rainfall, Temperature, and Snow cover area. The second case comprises only three input variables: Rainfall, Temperature, and Snow cover area. Radial Kernel is used to overcome the space complexity in the datasets. The K-fold cross-validation is suitable for prediction as it averages the prediction error rate after evaluating the SVR model’s performance on various subsets of the training data. The streamflow data for daily and ten daily time steps have been collected from 2006 to 2020. The calibration period is from 2006 to 2016, and the validation period is from 2017 to 2020. Nash Sutcliffe Efficiency (NSE) and Coefficient of determination (R2) are used as the accuracy indicator in this manuscript. The lag has been observed in the daily prediction time series when three input variables are considered. For other scenarios, the respective model shows excellent results at both the temporal scale and the parametres, which play a vital role in prediction. The study also enhances the effect on the potential use of input parametres in the machine learning model.

Rainwater Harvesting System Analysis for Semi-Arid Climate: A Daily Linear Programming Model

Rainwater harvesting has proven to be an alternative water supply scheme for sustainable water management of regions with limited water resources. In this paper, a linear programming (LP) model with daily time steps, which minimizes a rooftop rainwater harvesting system (RWHS) cost, is developed and used to calculate the optimum RWH tank size. The developed LP model is applied to the semi-arid Northern Cyprus in the Eastern Mediterranean. The analysis is carried out for 33 sites which receive average annual rainfall ranging from 292 mm to 548 mm to evaluate the spatial effect of rainfall characteristic and the water cost on the financial feasibility and performance of the RWHS. At 29 out of 33 sites, RWHS investments are found to be financially feasible with discounted payback periods ranging from 12 to 28 years. The optimum RWH tank sizes are determined to be between 2 m3 and 6 m3 resulting in up to 20 % reliability with more than 50 m3 of average annual water savings per house. It is observed that the cost of water is a critical factor that affects the financial feasibility and water savings of a RWHS, especially in regions with limited rainfall. The comparison of the developed daily LP model with an LP model with monthly time steps demonstrates that the financial feasibility and the optimum tank size can only be assessed realistically when daily time steps are used. Finally, the sensitivity analysis shows that the discounted payback period is highly sensitive to the collector area.

Assessing impacts of alternative land use strategies on water partitioning, storage and ages in drought‐sensitive lowland catchments using tracer‐aided ecohydrological modelling

AbstractContinuing negative rainfall anomalies, coupled with climate change projections of increased drought severity and frequency, drive an urgent need to increase resilience and integration in land and water management strategies in many regions of the world. However, complex interactions between land cover change, ecohydrological partitioning and water availability are difficult to quantify, especially at different temporal and spatial scales. In conjunction with local stakeholders, we developed plausible, alternative land use scenarios (including forest diversification and agroforestry schemes) based on the existing four primary land use types (i.e., conifer and broadleaved forests, arable agriculture, and pasture) in a 66 km2 drought‐sensitive catchment in northern Germany. We used modelling to evaluate spatial and temporal changes in water flux partitioning, storage and ages. The spatially‐distributed, tracer‐aided ecohydrological model, EcH2O‐iso, calibrated using hydrometric, ecohydrological and isotopic data at daily time steps from 2007 to 2019 was used in this assessment. The results showed that replacing conifer forests with uneven‐aged mixed forests with younger broad‐leaved trees had the greatest potential for reducing total evapotranspiration and increasing groundwater recharge. For coniferous forests, a 50% increase in the proportion of broad‐leaved trees was projected to result in an 11% increase in groundwater recharge across the catchment. The mixed‐forest management alternatives also reduced groundwater turnover times, which would support more rapid recovery of soil moisture and groundwater stores following droughts. This study demonstrates that such an ecohydrological modelling approach has the potential to contribute useful science‐based evidence for policy makers allowing quantitative assessment of potential land use effects on water availability and effective communication with stakeholders.

Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia

Abstract. A deep learning model designed for time series predictions, the long short-term memory (LSTM) architecture, is regularly producing reliable results in local and regional rainfall–runoff applications around the world. Recent large-sample hydrology studies in North America and Europe have shown the LSTM model to successfully match conceptual model performance at a daily time step over hundreds of catchments. Here we investigate how these models perform in producing monthly runoff predictions in the relatively dry and variable conditions of the Australian continent. The monthly time step matches historic data availability and is also important for future water resources planning; however, it provides significantly smaller training datasets than daily time series. In this study, a continental-scale comparison of monthly deep learning (LSTM) predictions to conceptual rainfall–runoff (WAPABA model) predictions is performed on almost 500 catchments across Australia with performance results aggregated over a variety of catchment sizes, flow conditions, and hydrological record lengths. The study period covers a wet phase followed by a prolonged drought, introducing challenges for making predictions outside of known conditions – challenges that will intensify as climate change progresses. The results show that LSTM models matched or exceeded WAPABA prediction performance for more than two-thirds of the study catchments, the largest performance gains of LSTM versus WAPABA occurred in large catchments, the LSTMs struggled less to generalise than the WAPABA models (e.g. making predictions under new conditions), and catchments with few training observations due to the monthly time step did not demonstrate a clear benefit with either WAPABA or LSTM.

Surface and subsurface flow of a glacierised catchment in the cold-arid region of Ladakh, Trans-Himalaya

Hydrological assessments of high-altitude catchments in Trans-Himalayan Ladakh are necessary for a better understanding of water availability in the context of irrigated cultivation under conditions of insufficient quantitative information on cryospheric meltwater discharge. In this study, an integrated spatially distributed temperature index model and a coupled surface/subsurface flow model were used to simulate daily, seasonal, and annual surface and subsurface flows to assess the proportion of corresponding source contributors from the Stok catchment. Snow and glacier meltwater discharge secures irrigated agriculture of more than 300 households in this catchment. The models were forced by temperature, precipitation, ice- and snow-covered areas at daily time steps with calibration (2019; 108 days) and validation (2018; 93 days) against the observed discharge. The simulated discharge shows a good agreement with the observed discharge with R2 and RMSE of 0.8 (p < 0.01) and 0.6 m3/s, respectively. The results between 2003 and 2019 show that the snowmelt contribution to the total annual discharge is largest with 65 %, followed by glacier melt and rainfall contributions of approximately 19 % and 16 %, respectively. A reduction in glacierised areas by 4.2 % was observed while snow-covered areas showed high inter-annual variation. Simulated subsurface flow makes up 62 % (mean = 37.2 × 106 m3) of the total discharge with less inter-annual variation. The simulation suggests that while surface flow ceases during the winter period and peaks in August, the annualized mean flow amounts to ∼23.7 × 106 m3. More than 50 % of the melt occurs in the summer months of June, July and August, when the intensity of snowmelt, ice melt, and rainfall reach its maximum. The findings of this study on meltwater availability and surface/subsurface flow is important for irrigated agriculture of Stok village on a local scale, and it might also help to better understand socio-hydrological dynamics and situations of water scarcity in the wider cold-arid region of Ladakh.

GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model

Abstract. We discuss the various performance aspects of parallelizing our transient global-scale groundwater model at 30′′ resolution (30 arcsec; ∼ 1 km at the Equator) on large distributed memory parallel clusters. This model, referred to as GLOBGM, is the successor of our 5′ (5 arcmin; ∼ 10 km at the Equator) PCR-GLOBWB 2 (PCRaster Global Water Balance model) groundwater model, based on MODFLOW having two model layers. The current version of GLOBGM (v1.0) used in this study also has two model layers, is uncalibrated, and uses available 30′′ PCR-GLOBWB data. Increasing the model resolution from 5′ to 30′′ creates challenges, including increased runtime, memory usage, and data storage that exceed the capacity of a single computer. We show that our parallelization tackles these problems with relatively low parallel hardware requirements to meet the needs of users or modelers who do not have exclusive access to hundreds or thousands of nodes within a supercomputer. For our simulation, we use unstructured grids and a prototype version of MODFLOW 6 that we have parallelized using the message-passing interface. We construct independent unstructured grids with a total of 278 million active cells to cancel all redundant sea and land cells, while satisfying all necessary boundary conditions, and distribute them over three continental-scale groundwater models (168 million – Afro–Eurasia; 77 million – the Americas; 16 million – Australia) and one remaining model for the smaller islands (17 million). Each of the four groundwater models is partitioned into multiple non-overlapping submodels that are tightly coupled within the MODFLOW linear solver, where each submodel is uniquely assigned to one processor core, and associated submodel data are written in parallel during the pre-processing, using data tiles. For balancing the parallel workload in advance, we apply the widely used METIS graph partitioner in two ways: it is straightforwardly applied to all (lateral) model grid cells, and it is applied in an area-based manner to HydroBASINS catchments that are assigned to submodels for pre-sorting to a future coupling with surface water. We consider an experiment for simulating the years 1958–2015 with daily time steps and monthly input, including a 20-year spin-up, on the Dutch national supercomputer Snellius. Given that the serial simulation would require ∼ 4.5 months of runtime, we set a hypothetical target of a maximum of 16 h of simulation runtime. We show that 12 nodes (32 cores per node; 384 cores in total) are sufficient to achieve this target, resulting in a speedup of 138 for the largest Afro–Eurasia model when using 7 nodes (224 cores) in parallel. A limited evaluation of the model output using the United States Geological Survey (USGS) National Water Information System (NWIS) head observations for the contiguous United States was conducted. This showed that increasing the resolution from 5′ to 30′′ results in a significant improvement with GLOBGM for the steady-state simulation when compared to the 5′ PCR-GLOBWB groundwater model. However, results for the transient simulation are quite similar, and there is much room for improvement. Monthly and multi-year total terrestrial water storage anomalies derived from the GLOBGM and PCR-GLOBWB models, however, compared favorably with observations from the GRACE satellite. For the next versions of GLOBGM, further improvements require a more detailed (hydro)geological schematization and better information on the locations, depths, and pumping rates of abstraction wells.

Modeling the Potential Influence of Subsurface Tile Drainage Systems on Downstream Flooding in a Midwestern Agricultural Watershed

Highlights Tile flow was simulated to study the impacts of tile drainage on watershed hydrology and downstream flood days. Three tile drainage scenarios with varied extents of drained land were compared with a baseline scenario. The depth of tile flow contributing to streamflow was found to increase as the area of drained land increased. Results suggest that drainage systems decrease flood days when compared to the baseline scenario. Abstract. Subsurface tile drainage systems are common in agricultural regions of the Midwestern United States. Drainage systems remove excess water from the surface and soil profile of agricultural fields, allowing crop production in previously unsuitable locations. However, these systems impact watershed hydrology and could influence flooding events. Therefore, this study aimed to determine whether tile drainage systems influence downstream flood days in a Midwestern agricultural watershed, specifically the Skunk Creek watershed. The Soil and Water Assessment Tool (SWAT) model was used to simulate the hydrologic processes of the Skunk Creek Watershed for an 18-year period (2004–2021). The model was calibrated and validated using observed daily streamflow data with the SWAT Calibration and Uncertainty Program (SWAT-CUP) software and through the manual modification of missed precipitation data. Three tile drainage scenarios—ranging from 15% to 75% tile-drained agricultural land—were individually incorporated into the model. Results indicated that with increased tile drainage, surface runoff, groundwater flow, deep aquifer recharge, and percolation decreased, whereas lateral soil flow, tile flow, evapotranspiration, and water yield increased. A comparison of tile drainage scenarios suggests that increasing the area of tile-drained agricultural land decreases total flood days. However, the impact of tile drainage systems on downstream peak flood flows varied by event, with large precipitation events initiating flood days across all scenarios. While tile flow decreased flood days on a daily time step, flash floods might have occurred on a sub-daily time step but were not captured in this study. Future studies can replicate the approach with a sub-daily time step for simulating hourly flood events with various tile drainage scenarios. Keywords: Midwestern United States, Streamflow, SWAT, Tile drainage, Watershed hydrology.

Seamlessly combined historical and projected daily meteorological datasets for impact studies in Central Europe: The FORESEE v4.0 and the FORESEE-HUN v1.0

The FORESEE is an open access, climatological database for Central Europe containing observed and projected meteorological data for the 1951–2100 period. As a climate service, FORESEE disseminates basic meteorological variables at a daily time step with a 0.1° × 0.1° spatial resolution including maximum/minimum temperature, precipitation, incoming shortwave solar radiation and daylight vapour pressure deficit. The future climate in FORESEE v4.0 and FORESEE-HUN v1.0 is projected by 14 regional climate models from the EURO-CORDEX database using the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. Based on RCP4.5 the country-specific results indicate similar projected mean changes in annual mean temperature (1.5–1.7 °C) but considerable differences in precipitation (from −1.6 to 6.9%) in the region for 2071–2100 relative to 1991–2020. We present two case studies to demonstrate the applicability of FORESEE in climate change impact studies using the ensemble approach. Climate change induced negative weather effect (15.4% and 28.9% mean loss for 2071–2100 according to RCP4.5 and RCP8.5, respectively) might dominate the future winter wheat yields in Hungary that is superimposed to the overall trend determined by other factors. The projections provide consistent results about the mean advance in the start of the growing season for forests in Hungary up to 2100 with ensemble mean of 9.1 days (RCP4.5) and 19.8 days (RCP8.5). We also demonstrate that the representative model selection method might lead to misleading results in impact studies that should be considered. The updated FORESEE is a way forward in the dissemination of policy-relevant essential climate data in Central Europe.

Assessing the spatial distribution of odor at an urban waterfront using AERMOD coupled with sensor measurements

ABSTRACT Impressions of a place are partly formed by smell. The urban waterfronts often leave a rather poor impression due to odor pollution, resulting in recurring complaints. The nature of such complaints can be subjective and vague, so there is a growing interest in quantitative measurements of emissions to explore the causes of malodorous influence. In the present work, an air quality monitor with an H2S sensor was employed to continuously measure emissions of malodors at 1-min resolution. H2S is often considered to be the predominant odorous substance from sludge and water bodies as it is readily perceptible. The integrated means of concentration from in situ measurements were combined with the AERMOD dispersion model to reveal the spatial distribution of odor concentrations and estimate the extent of odor-prone areas at a daily time step. Year-long observations showed that the diurnal profile exhibits a positively skewed distribution. Meteorology plays a vital role in odor dispersion; the degree of dispersion was explored on a case-by-case basis. There is a greater likelihood of capturing the concentration peaks at night (21:00 to 6:00) as the air is more stable then with less tendency for vertical mixing but favors a horizontal spread. This study indicates that malodors are changeable in time and space and establishes a new approach to using H2S sensor data and resolves a long-standing question about odor in Hong Kong. Implications: this study establishes a new approach combining dispersion model with novel H2S sensor data to understand the characteristics and pattern of odor emanated from the urban waterfront in Hong Kong. The sensor has dynamic concentration range to detect the episodic level of H2S and low level at background conditions. It provides more complete information in relation to odor annoyance, as well as quantitative information useful for odor regulation

Potential storage augmentation impacts on hydropower production, irrigation water supply and environmental flows in the Indus Basin in Pakistan

Study regionIndus River basin, Pakistan Study focusThe study assesses the implications on irrigation water reliability, hydropower production and environmental flows to the Indus Delta resulting from the implementation of large new storages on the Indus River. These large dams have the potential to impact water supply security of the Indus Basin Irrigation System which operates with a priority of maximising irrigation supply via the 1991 Water Apportionment Accord. Implementation of the Accord under future scenarios involving new large dams is open to interpretation. This study uses the daily time step Indus River System Model to simulate the potential impact of these new large dams on hydropower production, irrigation water supply and flows to the Indus Delta under different Accord interpretations. New insights for the regionPakistan possesses significant potential for increasing hydropower production from the Indus River through the construction of new reservoirs, which could raise the nationwide share of electricity generated from hydropower from 29% to 44–53%. This study demonstrates that augmenting storage capacity can yield threefold benefits – improved end-of-system environmental flows, more reliable irrigation supply for the Rabi season, and increased hydropower production. Additionally, this modelling approach can be instrumental in strengthening the existing water governance arrangements when considering the construction of new dams, thereby improving the health of the Indus delta ecosystem through the construction of dam construction.

Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments

In recent years, Machine Learning (ML) techniques have gained the attention of the hydrological community for their better predictive skills. Specifically, ML models are widely applied for streamflow predictions. However, limited interpretability in the ML models indicates space for improvement. Leveraging domain knowledge from conceptual models can aid in overcoming interpretability issues in ML models. Here, we have developed the Physics Informed Machine Learning (PIML) model at daily timestep, which accounts for memory in the hydrological processes and provides an interpretable model structure. We demonstrated three model cases, including lumped model and semi-distributed model structures with and without reservoir. We evaluate the first two model structures on three catchments in India, and the applicability of the third model structure is shown on the two United States catchments. Also, we compared the result of the PIML model with the conceptual model (SIMHYD), which is used as the parent model to derive contextual cues. Our results show that the PIML model outperforms simple ML model in target variable (streamflow) prediction and SIMHYD model in predicting target variable and intermediate variables (for example, evapotranspiration, reservoir storage) while being mindful of physical constraints. The water balance and runoff coefficient analysis reveals that the PIML model provides physically consistent outputs. The PIML modeling approach can make a conceptual model more modular such that it can be applied irrespective of the region for which it is developed. The successful application of PIML in different climatic as well as geographical regions shows its generalizability.

328 Comparison of Predicted Nutritional Requirements of Two Feeding Systems for Dairy Cattle Implemented in a Monte-Carlo Simulation Model of Herd Dynamics

Abstract There is increased interest in precision-feeding systems that account for individual-animal variation and temporal dairy herd dynamics to increase feed-efficiency and reduce environmental footprints. The Ruminant Farm Systems (RuFaS; Hansen et al., 2021) model simulates individual animals on a daily timestep using a stochastic, Monte Carlo framework to represent the phenotypic diversity and population dynamics expected in a dairy herd. Within RuFaS, individual animal nutrient requirements are used to inform pen-level diet formulations and feed delivery. However, nutritional requirements for feeding dairy cattle were recently updated via consideration of both animal and diet-related factors. Our aim was to calculate individual animal predictions for dry matter intake (DMI), as well as energy and metabolizable protein requirements, and compare the methods described in the 7th (NRC, 2001) and 8th (NASEM, 2021) editions of the Nutrient Requirements for Dairy Cattle. For that purpose, we simulated dairy herds (ca. 6,000 individual Holstein animals per simulation, mean adult body weight = 671 kg, standard deviation = 79, mean daily milk production = 39.8 kg·day-1·cow-1 , standard deviation = 7.5) for 10+ year time periods with an updated version of RuFaS. The simulated herds provided a wide range of values with which to test the nutrient requirement models, and their implications. The NASEM DMI estimates were less across the board but began to converge towards NRC calculated values at the larger simulated animal body weights. The principal differences in energy requirements between the two models (NRC vs. NASEM) were: 1) predicted requirements for pregnancy were significantly greater for NASEM than NRC across all life history stages; 2) NASEM displayed slightly greater energy requirements for growth in first and second lactation cows, and; 3) NASEM predicted decreased maintenance requirements for all heifer stages but the opposite was true for mature cows. Metabolizable protein requirements were greater following NASEM calculations in heifers over 300 kg, but strikingly similar to NRC-computed values for lactating cows. Overall, results from this modeling study align with our expectations and demonstrate successful implementation of the two nutrition models within the RuFaS model. These outputs confirm the usefulness of data obtained to explore applications of the two nutrient requirement models under different precision feeding management practices and, to some extent, to quantitatively evaluate their viability of the models in edge cases that can occur within the expected variation of a herd’s population. Future work will explore downstream consequences of the nutrient requirement models within the automated, least-cost ration formulation framework using nonlinear programming and metrics related to manure output and storage. These data will guide further developments of RuFaS as whole farm simulation model and provide insights into expected variation in nutritional requirements within a dynamic set of animals.

A novel conceptual flood inundation model for large scale data-scarce regions

We propose a novel conceptual flood inundation model that can be coupled with any hydrological model to generate the probabilistic flood extent maps for a given time step. We generate the runoff rasters using the dynamic Budyko hydrological model that uses the regional IMD/CPC precipitation datasets and the global JAXA GSMaP gauge-precipitation (V7) product over Kerala/Louisiana flood events during August 2018/2016. A freely available global dataset is used for obtaining river geometry information and topographic information is obtained from ASTER/SRTM DEM and localised LiDAR DEM. We evaluated the proposed model using the ground observed flood points from the state disaster management agency for Kerala and flood maps published by USGS for Louisiana. The proposed model captured ∼ 45% of flooding during peak flood days while taking ∼ 15 min for each run on a daily time step on a desktop computer.

Hydrological Modeling of Large River Basin Using Soil Moisture Accounting Model and Monte Carlo Simulation

This description outlines a Geographic Information System (GIS)-based rainfall-runoff model that simulates the flow of water in a river basin. The model operates on a daily time step and consists of four non-linear storage components: interception, soil moisture, channel, and groundwater. It employs (SCS) Unit Hydrograph model to determine unit hydrograph ordinates. The model replicates the movement and storage of water in various parts of the basin, including vegetation, the soil surface, the soil profile, and groundwater layers. To address uncertainty, a Monte Carlo simulation feature is integrated into the model. This feature generates required number of sample sets with random parameter values. The model is run for all these realizations during a calibration period, and performance metrics like NSE are calculated for each calibration yearTo assess prediction uncertainty, model parameter weights are computed by normalizing the corresponding likelihood values. These weights sum up to one and represent the probabilistic distribution of predicted variables, illustrating the impact of structural and parameter errors on model predictions. A sensitivity analysis reveals that the Muskingum constants K and X have the greatest influence on model performance, while parameters ?GW, ?SW, ?fc, and ?pc have a minimal effect on the model's performance.

Modeling in-stream biogeochemical processes at catchment scale: Coupling SWAT and RIVE models

Representation of biogeochemical in-stream processes is complex, and requires use of models to understand factors controling the transfer of contaminants on watershed scale. The biogeochemical model RIVE was integrated into the SWAT model and was used, instead of the current QUAL2E subroutine, to calculate biogeochemical in-stream processes. The coupled model was applied to an 89-km section of the Garonne watershed, downstream of Toulouse, for the 2000–2010 period, with a daily time-step. An epilithic biofilm equation was added to represent the river's algae communities, dominated by drifted benthic diatoms. The model matched observed data for major variables of water quality. Dominant processes for nitrogen, phosphorus, silica and organic suspended matter were assessed. The aim of coupling RIVE to the SWAT model was to improve the biogeochemical representation of the river's in-stream ecological processes linked to C, N, P and Si elements and dissolved oxygen, with minimal calibration.

Planning and Evaluating Nature-Based Solutions for Watershed Investment Programs with a SMART Perspective Using a Distributed Modeling Tool

Watershed Investment Programs (WIPs) face many challenges in implementing strategies aimed at restoring and preserving ecosystem services using Nature-based Solutions (NbS). A key challenge lies in defining SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) objectives, which involve addressing questions such as which NbS interventions to apply, where, and in what amounts. Effectively achieving WIPs’ objectives requires strategic implementation of NbS. In response to this challenge, we present SIGA-CALv1.0, a daily time-step and distributed modeling conceptual framework that enables the design and evaluation of the impact of NbS portfolios on water quantity and quality. To validate our framework, we applied it to the Arma river basin in Colombia. Our findings indicate that NbS can lead to substantial benefits, including reductions of up to 47% in sediment, 62% in nitrogen, 8% in phosphorus, and 15% in pathogen indicators (total coliforms). The proposed methodological framework offers decision-makers robust technical support for defining strategic NbS implementation plans, guided by SMART objectives. This approach strengthens the effectiveness of ecosystem services restoration and conservation strategies in watersheds, enabling more efficient resource allocation and improved environmental outcomes.

Examining the open-source datasets for water quantity and quality using the soil and water assessment tool (SWAT).

Water quality modeling is very important for the management of water resources. In this study, the upper part of the Porsuk Basin in Türkiye is analyzed using SWAT. In the analysis, in addition to the data provided by the General Directorate of State Hydraulic Works (DSI), the freely available flow and water quality data from the GEMStat data portal were used. This study presents a discussion of the practicality of GEMStat data for a water quality model. For this purpose, firstly, the SWAT model was constructed with freely available global data sources on elevation, land use/land cover, and soil type. Then, the model flow outputs were calibrated and validated for both DSI and GEMStat data in three different time periods. As a result, the flow calibration and validation success in the daily time step is 0.64 and 0.44 according to the Nash-Sutcliffe efficiency (NSE). The model was also validated using GEMStat flow data and calibrated using GEMStat water quality data such as nitrate (NO3), total suspended solids (TSS), and dissolved oxygen (DO) with a reasonable value. Hence, the results showed that GEMStat flow and water quality data can be used as auxiliary open-source data in the modeling process.

A comprehensive assessment of RCP4.5 projections and bias-correction techniques in a complex coastal karstic aquifer in the Mediterranean

This study aims to assess the projected precipitation and temperature changes at the coastal karstic aquifer of Salento (Apulia, Southern Italy) under the Representative Concentration Pathway RCP4.5. For this purpose, an ensemble of twelve Regional Climate Models (RCMs) driven by several General Circulation Models (GCMs) were collected. Eight bias-correction (BC) methods were applied at daily time steps, and their results were assessed on monthly and annual time steps, using daily records from 19 and 11 precipitation and temperature (minimum and maximum) stations, respectively, for the period 1960–2005. Missing data in the observed dataset were filled-in applying the best performing techniques out of the 5 that were employed and tested. The Linear Scaling and the Power Transformation were found to be the most effective methods for precipitation BC at the case study, while all methods performed equally well in correcting air temperature datasets. Results of future climate projections show a decrease in precipitation of about 6% and an increase in temperature of 2°C until the end of this century, compared to the historical period (1971–2005). This study forms the first comprehensive attempt to test the scientific literature’s most widely used bias-correction methods over the study area. The case study may be considered a benchmark for circum-Mediterranean regions because of its high geomorphological and structural complexity, regional size, surface water scarcity, and significant water withdrawals for human activities.

Simple Bayesian reconstruction and forecasting of stream water temperature for ecologists-A tool using air temperature, optionally flow, in a time series decomposition approach.

Mitigating the impacts of global warming on wildlife entails four practical steps. First, we need to study how processes of interest vary with temperature. Second, we need to build good temperature scenarios. Third, processes can be forecast accordingly. Only then can we perform the fourth step, testing mitigating measures. While having good temperature data is essential, this is not straightforward for stream ecologists and managers. Water temperature (WT) data are often short and incomplete and future projections are currently not routinely available. There is a need for generic models which address this data gap with good resolution and current models are partly lacking. Here, we expand a previously published hierarchical Bayesian model that was driven by air temperature (AT) and flow (Q) as a second covariate. The new model can hindcast and forecast WT time series at a daily time step. It also allows a better appraisal of real uncertainties in the warming of water temperatures in rivers compared to the previous version, stemming from its hybrid structure between time series decomposition and regression. This model decomposes all-time series using seasonal sinusoidal periodic signals and time varying means and amplitudes. It then links the contrasted frequency signals of WT (daily and six month) through regressions to that of AT and optionally Q for better resolution. We apply this model to two contrasting case study rivers. For one case study, AT only is available as a covariate. This expanded model further improves the already good fitting and predictive capabilities of its earlier version while additionally highlighting warming uncertainties. The code is available online and can easily be run for other temperate rivers.