Integrating satellite and reanalysis precipitation products for SWAT hydrological simulation in the Jing River Basin, China

In hydrological studies, satellite and reanalysis precipitation products are increasingly being used to supplement gauge observation data. This study designed the composite simulation index (COSI), considering two factors: F1 (data accuracy assessment) and F2 (hydrological simulation performance), to compare the performance of the latest satellite-based and reanalysis-based precipitation products (IMERG, ERA5, ERA5-Land), the prior precipitation products (TRMM, CMADS), and the multi-source weighted-ensemble precipitation (MSWEP). The Soil and Water Assessment Tool (SWAT) model was then applied to compare and analyze the hydrological simulation performance of four preferred products using three data fusion methods including simple model averaging, variance-based weighted averaging, and the latest quantile-based Bayesian model averaging (QBMA). The results can be summarized as follows: (1) Reanalysis products are superior to satellite-based products in terms of F1. However, the satellite-based precipitation products exhibit less BIAS and relatively higher F2, while the MSWEP has relatively high performance on both F1 and F2. (2) Among reanalysis-based precipitation products, CMADS has the best COSI value of 0.53. Although ERA5-Land shows good performance for individual parameters, the comprehensive assessment reveals that ERA5 outperforms ERA5-Land in terms of both F1 and F2. (3) IMERG and TRMM exhibit similar spatiotemporal patterns and similar F1, but IMERG is superior in F2. (4) QBMA outperformed traditional methods in F2, improving the NS coefficient of SWAT model from 0.74 to 0.85. These findings provide a useful reference for analyzing the strengths and limitations of satellite-based and reanalysis precipitation products, and also provide valuable ideas for the combined application of multi-source precipitation products in hydrological studies.

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

This study investigates the application of the Gaussian Radial Basis Function Neural Network (GRNN), Gaussian Process Regression (GPR), and Multilayer Perceptron Optimized by Particle Swarm Optimization (MLP-PSO) models in analyzing the relationship between rainfall and runoff and in predicting runoff discharge. These models utilize autoregressive input vectors based on daily-observed TRMM rainfall and TMR inflow data. The performance evaluation of each model is conducted using statistical measures to compare their effectiveness in capturing the complex relationships between input and output variables. The results consistently demonstrate that the MLP-PSO model outperforms the GRNN and GPR models, achieving the lowest root mean square error (RMSE) across multiple input combinations. Furthermore, the study explores the application of the Empirical Mode Decomposition-Hilbert-Huang Transform (EMD-HHT) in conjunction with the GPR and MLP-PSO models. This combination yields promising results in streamflow prediction, with the MLP-PSO-EMD model exhibiting superior accuracy compared to the GPR-EMD model. The incorporation of different components into the MLP-PSO-EMD model significantly improves its accuracy. Among the presented scenarios, Model M4, which incorporates the simplest components, emerges as the most favorable choice due to its lowest RMSE values. Comparisons with other models reported in the literature further underscore the effectiveness of the MLP-PSO-EMD model in streamflow prediction. This study offers valuable insights into the selection and performance of different models for rainfall-runoff analysis and prediction.

Contrasting adaptive genetic consequences of stream insects under changing climate

Freshwater biodiversity undergoes degradation due to climate change. Researchers have inferred the effects of climate change on neutral genetic diversity, assuming the fixed spatial distributions of alleles. However, the adaptive genetic evolution of populations that may change the spatial distribution of allele frequencies along environmental gradients (i.e., evolutionary rescue) have largely been overlooked. We developed a modeling approach that projects the comparatively adaptive and neutral genetic diversities of four stream insects, using empirical neutral/ putative adaptive loci, ecological niche models (ENMs), and a distributed hydrological-thermal simulation at a temperate catchment under climate change. The hydrothermal model was used to generate hydraulic and thermal variables (e.g., annual current velocity and water temperature) at the present and the climatic change conditions, projected based on the eight general circulation models and the three representative concentration pathways scenarios for the two future periods (2031-2050, near future; 2081-2100, far future). The hydraulic and thermal variables were used for predictor variables of the ENMs and adaptive genetic modeling based on machine learning approaches. The increases in annual water temperature in the near- (+0.3-0.7 °C) and far-future (+0.4-3.2 °C) were projected. Of the studied species, with different ecologies and habitat ranges, Ephemera japonica (Ephemeroptera) was projected to lose rear-edge habitats (i.e., downstream) but retain the adaptive genetic diversity owing to evolutionary rescue. In contrast, the habitat range of the upstream-dwelling Hydropsyche albicephala (Trichoptera) was found to remarkably decline, resulting in decreases in the watershed genetic diversity. While the other two Trichoptera species expanded their habitat ranges, the genetic structures were homogenized over the watershed and experienced moderate decreases in gamma diversity. The findings emphasize the evolutionary rescue potential, depending on the extent of species-specific local adaptation.

Considering ecological flow in multi-objective operation of cascade reservoir systems under climate variability with different hydrological periods

The trade-off between ecological and socioeconomic benefits in the reservoir operation has become a focus issue in the watershed water resource management. However, finding a suitable reservoir ecological operation scheme in the multi-objective cascade reservoir systems remains unclear. At present, most ecological operation models are designed on the basis of water quantity balance, neglecting the dynamic variability of the hydrological process. This study proposed a multi-objective ecological operation system, which coupled a two-dimensional hydrodynamic model with a rainfall-runoff model, and integrated the ecological operation scheme into the hydrodynamic simulation system considering ecological flow. Moreover, the applicability of the operation scheme under climate variability with different hydrological periods was evaluated. Results indicated that multi-reservoir joint operation had the largest effect in normal years; the variation in the monthly hydrological magnitude, extreme events and their duration, temporal change and frequency of streamflow were significantly reduced after reservoir ecological operation. The SAM0-UNICON model performed better than the two other climate models, the ecological deficit (ED) under the Representative Concentration Pathway (RCP) 8.5 climate change scenario was larger than other scenarios with different operation schemes. Future climate change will have a larger impact on discharge change in the wet season than in other hydrological periods. This study emphasises the comprehensive application of the hydrological and hydrodynamic methods, which is of considerable importance for decision-making in basin water resource management and reservoir regulation.

Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH

River flow variations directly affect the hydro-climatological, environmental, and ecological characteristics of a region. Therefore, an accurate prediction of river flow can critically be important for water managers and planners. The present study aims to compare different data-driven models in predicting monthly flow. Two river catchments located in the Guilan province in Iran, where rivers play an essential role in agricultural productions (mainly rice), are studied. The monthly river flow dataset was provided by Guilan Regional Water Authority during 1986-2015. The models are derived from two different numerical types of stochastic and machine learning (ML) models. The stochastic model is seasonal autoregressive integrated moving average (SARIMA), and the MLs are least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), and group method of data handling (GMDH). The inputs were selected by autocorrelation and partial autocorrelation functions (ACF and PACF) from the flow rates of the previous months. The data was divided into 75% of training and 25% of testing phases, and then the mentioned models were implemented. Predictions were evaluated by the criteria of root mean square error (RMSE), normalized RMSE (NRMSE), and Nash Sutcliff (NS) coefficient. According to the calculated values of different criteria during the test phase, RMSE = 1.138 cms, NRMSE = 0.109, and NS = 0.826, it can be concluded that the SARIMA model was superior to its ML competitors. Among the ML models, GMDH had the best performance (by RMSE = 1.290 cms, NRMSE = 0.124, and NS = 0.777) because it has more optimization parameters and sample space for network make-up. The models were also evaluated in hydrological drought conditions of both rivers. It was resulted that the rivers' flow can be well predicted in drought conditions by using these models, especially the SARIMA stochastic model. According to the NRMSE values (ranged between 0.1 and 0.2), the accuracy of predictions is evaluated in the appropriate range, and the present study shows promising results of the current approaches. Consequently, a comparison between the performance of linear stochastic models and complex black-box MLs, reveals that linear stochastic models are more suitable for the current region's monthly river flow prediction.

Adapting water resources systems to climate change in tropical areas: Ecuadorian coast

Climate change is expected to increase rainfall and temperature in the tropical areas of the Ecuadorian coast. The increase in temperature will also increase evapotranspiration therefore, future water balance on Ecuadorian coast will have a slight variation. Changes in precipitation patterns and evapotranspiration will produce an increase in the water requirements for current crops, so an imbalance in the water resources systems between natural resources and water demands is expected. This study presents water resources management as an adaptation measure to climate change for reducing vulnerability in tropical areas. Twelve bias-corrected climate projections are used, from: two AR5 General Circulation Models (GCMs), two Representative Concentration Pathways, 4.5-8.5 scenarios, and three time periods, short-term (2010-2039), medium-term (2040-2069) and long-term (2070-2099). These data were incorporated into the Lumped Témez Hydrological Model. Climate change scenarios predict for the long-term period both a mean rainfall and temperature increases up to 22%-2.8 °C, respectively. Besides, the potential evapotranspiration will increase until 12% by Penman-Monteith method and 60% by Thornthwaite method. Therefore, natural water resources will finally have an increase of 19% [8-30%]. Additionally, water requirements for crops will increase around 4% and 45%. As this research shows, in tropical regions, currently viable water resources systems could become unsustainable under climate change scenarios. To guarantee the water supply in the future additional measures are required as reservoir operation rules and irrigation efficiency improvement of system from 0.43 to 0.65, which it involves improving the distribution and application system. In study area future irrigation areas have been estimated for 13,268 ha, which under climate change scenarios is unsustainable, only 11,500 ha could be expanded with a very high irrigation efficiency of 0.73. Therefore, in tropical areas the effect of climate change on expansion projects for irrigated areas should be considered to ensure the functioning systems.

Numerical quantification of current status quo and future prediction of water quality in eight Asian megacities: challenges and opportunities for sustainable water management

Finite freshwater sources are facing huge threats both for quality and quantity from uncertain global changes, namely population growth, rapid urbanization, and climate change. These threats are even more prominent in developing countries where institutional capacity of decision-makers in the field of water resources is not sufficient. Attention of scientific communities to work on adaptation barriers is increasing as the need for global change adaptation becomes apparent. This paper presents a comparative study of assessing the current water quality as well as predicting its future situation using different scenarios in eight different cities of South and Southeast Asia. The idea behind this transdisciplinary work (integrated use of hydrological science, climate science, social science, and local policies) is to provide scientific evidence to decision-makers to help them to implement right management policies at timely manner. Water Evaluation and Planning (WEAP), a numerical simulation tool, was used to model river water quality using two scenarios, namely business as usual (BAU) and scenario with measures. Water quality simulation was done along one representative river from all eight cities. Simulated results for BAU scenario shows that water quality in all the study sites will further deteriorate by year 2030 compared to the current situation and will be not suitable for fishing category as desired by the local governments. Also, simulation outcome for scenario with measures advocating improvement of water quality compared to current situation signifies the importance of existing master plans. However, different measures (suggested upgradation of wastewater handling infrastructure) and policies will not be sufficient enough to achieve desirable river water quality as evident from the gap between concentration of simulated water quality and desirable water quality concentrations. This work can prove vital as it provides timely information to the decision-makers involved in keeping inventory for attaining SDG 6.0 in their regions and it also calls for immediate and inclusive action for better water resource management.

Predicting future river health in a minimally influenced mountainous area under climate change

It has been shown that climate change impacts the overall health of a river's ecosystem. Although predicting river health under climate change would be useful for stakeholders to adapt to the change and better conserve river health, little research on this topic exists. This paper presents a methodology predicting river health under different climate change scenarios. First, a multi-source, distributed, time-variant gain hydrological model (MS-DTVGM) was used to predict the runoff from a mountainous river in eastern China using the data from three existing IPCC5 climate change models (RCP2.6, RCP4.5, and RCP8.4). Next, a model was developed to predict the river's water quality under these scenarios. Finally, a multidimensional response model utilizing hydrology, water quality, and biology was used to predict the river's biological status and ascertain the impact of climate change on its overall health. The river is in a mountainous area near Jinan City, one of China's first "pilot" cities recognized as a "healthy water ecological community." Our results predict that the overall health of the Yufu River, which is minimally influenced by human activities, will improve by 2030 due to the increased river flow due to an increase in rainfall frequency and subsequent peak runoff. However, the total nitrogen concentration is predicted to increase, which is a potential eutrophication risk. Therefore, effective control of nitrogen pollutants entering the river will be necessary. The increase in flow velocity (the annual average increase is ~0.5 m/s) is favorable for fish reproduction. Our methods and results will provide scientific guidance for policy makers and river managers and will help people to better understand how global climate change impacts river health.

Projection of invertebrate populations in the headwater streams of a temperate catchment under a changing climate

Climate change places considerable stress on riverine ecosystems by altering flow regimes and increasing water temperature. This study evaluated how water temperature increases under climate change scenarios will affect stream invertebrates in pristine headwater streams. The studied headwater-stream sites were distributed within a temperate catchment of Japan and had similar hydraulic-geographical conditions, but were subject to varying temperature conditions due to altitudinal differences (100 to 850 m). We adopted eight general circulation models (GCMs) to project air temperature under conservative (RCP2.6), intermediate (RCP4.5), and extreme climate scenarios (RCP8.5) during the near (2031-2050) and far (2081-2100) future. Using the water temperature of headwater streams computed by a distributed hydrological-thermal model as a predictor variable, we projected the population density of stream invertebrates in the future scenarios based on generalized linear models. The mean decrease in the temporally averaged population density of Plecoptera was 61.3% among the GCMs, even under RCP2.6 in the near future, whereas density deteriorated even further (90.7%) under RCP8.5 in the far future. Trichoptera density was also projected to greatly deteriorate under RCP8.5 in the far future. We defined taxa that exhibited temperature-sensitive declines under climate change as cold stenotherms and found that most Plecoptera taxa were cold stenotherms in comparison to other orders. Specifically, the taxonomic families that only distribute in Palearctic realm (e.g., Megarcys ochracea and Scopura longa) were selectively assigned, suggesting that Plecoptera family with its restricted distribution in the Palearctic might be a sensitive indicator of climate change. Plecoptera and Trichoptera populations in the headwaters are expected/anticipated to decrease over the considerable geographical range of the catchment, even under the RCP2.6 in the near future. Given headwater invertebrates play important roles in streams, such as contributing to watershed productivity, our results provide useful information for managing streams at the catchment-level.

Effects of sea level rise, land subsidence, bathymetric change and typhoon tracks on storm flooding in the coastal areas of Shanghai

We compared the effects of three key environmental factors of coastal flooding: sea level rise (SLR), land subsidence (LS) and bathymetric change (BC) in the coastal areas of Shanghai. We use the hydrological simulation model MIKE 21 to simulate flood magnitudes under multiple scenarios created from combinations of the key environmental factors projected to year 2030 and 2050. Historical typhoons (TC9711, TC8114, TC0012, TC0205 and TC1109), which caused extremely high surges and considerable losses, were selected as reference tracks to generate potential typhoon events that would make landfalls in Shanghai (SHLD), in the north of Zhejiang (ZNLD) and moving northwards in the offshore area of Shanghai (MNS) under those scenarios. The model results provided assessment of impact of single and compound effects of the three factors (SLR, LS and BC) on coastal flooding in Shanghai for the next few decades. Model simulation showed that by the year 2030, the magnitude of storm flooding will increase due to the environmental changes defined by SLR, LS, and BC. Particularly, the compound scenario of the three factors will generate coastal floods that are 3.1, 2.7, and 1.9 times greater than the single factor change scenarios by, respectively, SLR, LS, and BC. Even more drastically, in 2050, the compound impact of the three factors would be 8.5, 7.5, and 23.4 times of the single factors. It indicates that the impact of environmental changes is not simple addition of the effects from individual factors, but rather multiple times greater of that when the projection time is longer. We also found for short-term scenarios, the bathymetry change is the most important factor for the changes in coastal flooding; and for long-term scenarios, sea level rise and land subsidence are the major factors that coastal flood prevention and management should address.

Changing hydrological conditions in the Po basin under global warming

The Po River is a crucial resource for the Italian economy, since 40% of the gross domestic product comes from this area. It is thus crucial to quantify the impact of climate change on this water resource in order to plan for future water use. In this paper a mini ensemble of 8 hydrological simulations is completed from 1960 to 2050 under the A1B emission scenario, by using the output of two regional climate models as input (REMO and RegCM) at two different resolutions (25 km-10 km and 25 km-3 km). The river discharge at the outlet point of the basin shows a change in the spring peak of the annual cycle, with a one month shift from May to April. This shift is entirely due to the change in snowmelt timing which drives most of the discharge during this period. Two other important changes are an increase of discharge in the wintertime and a decrease in the fall from September to November. The uncertainty associated with the winter change is larger compared to that in the fall. The spring shift and the fall decrease of discharge imply an extension of the hydrological dry season and thus an increase in water stress over the basin. The spatial distributions of the discharge changes are in agreement with what is observed at the outlet point and the uncertainty associated with these changes is proportional to the amplitude of the signal. The analysis of the changes in the anomaly distribution of discharge shows that both the increases and decreases in seasonal discharge are tied to the changes in the tails of the distribution, i.e. to the increase or decrease of extreme events.