Streamflow Data Research Articles

Trend Analysis of Hydrometeorological Data of Gilgel Gibe Catchment, Ethiopia

Trend analysis of hydrometeorological data is vital for proper water resources planning and management. This paper examines the trends of the hydrometeorological data in Gilgel Gibe catchment and whether the trends are significant. Daily rainfall, temperature, and streamflow data of the stations in/around (nearby) the catchment (7 stations for rainfall, 4 stations for temperatures, and 6 stations for streamflow) for a period longer than 25 years were collected and then analyzed to detect the variability and the changes in trend. Prior to conducting trend tests, the missed data were filled, and their inconsistencies were also adjusted. The nonparametric Mann-Kendall test along with Sen’s slope technique was employed to detect monotonic trends in the data series. The results showed that, on average, the rainfall exhibits an insignificant increasing tendency. It was also observed that there is, in general, an increasing trend in temperature (both maximum and minimum) in the study area. The analysis of the stream flows indicated that only one station (Bulbul Nr. Serbo) showed a positive slope at a 5% significance level. Two stations (Aweitu Nr. Babu and Gibe Nr. Seka) showed a slightly increasing trend, whereas the remaining 3 stations (Gibe Nr. Assendabo, Aweitu at Jimma, and Kitto Nr. Jimma) indicated an insignificant decreasing trend. The streamflow of the catchment generally shows a tiny decreasing tendency (0.007% per year) at its outlet. However, the results in general specified statistically insignificant trend changes of the hydrometrological data of the study catchment.

Impact of climate and NDVI changes on catchment storage–discharge dynamics in southern Taiwan

This study used streamflow recession analysis to assess the changes in the groundwater–streamflow relationships using streamflow data. The groundwater response to climate and Normalized Difference Vegetation Index (NDVI) changes was considered as a variable in the recession analysis. The integrated impact could thus be compared to assess the consistency of these effects on the catchment discharge processes. The results showed that the recession characteristics is linked to regional topography and streamflow condition. Variation of storage–discharge sensitivities in most catchments was not linked to the variation of groundwater due to the lower capacity of groundwater contribution. The quantified impacts aided in assessing the relationship between climate, land cover, groundwater storage and discharge. This study provides a simple explanation of effects of environmental coevolution on storage–discharge processes.

Identifying Thresholds, Regime Shifts, and Early Warning Signals Using Long-Term Streamflow Data in the Transboundary Rio Grande–Rio Bravo Basin

As the centerpiece of ecosystems and human societies, river basins are complex social–ecological systems (SESs) that depend on the natural flow regime and the hydrologic variability to adapt to changes and absorb disturbances. Anthropogenic and climate change disturbances destabilize river systems. Therefore, a resilience question arises: What is the carrying capacity of a river basin, i.e., how much disturbance can a river basin take until the system undergoes a regime shift? To answer this question, this study aims to identify regime shifts, thresholds, and the carrying capacity of the transboundary Rio Grande–Rio Bravo (RGB) basin using 110 years of monthly streamflow data. To address this research question, first, gauged (regulated) and naturalized streamflow data is collected; if naturalized flows are not available, they are calculated through streamflow naturalization. Second, streamflow standardization is estimated using the streamflow drought index. Third, a regime shift assessment is performed using Fisher Index, and fourth, the nonparametric Mann-Kendall test is used to assess the Sustainable Regime Hypothesis which evaluates regime shifts and alternative regimes. Results demonstrate that resilience thresholds are surpassed, and regime shifts, including early warning signals, occurred in multiple locations of a transboundary basin. The present study highlights the importance of assessing the carrying capacity of a river basin; hence, evaluating regime transitions, including identifying early warning signals and thresholds, is critical in managing for sustainability and ecological resilience of SESs. Looking ahead, the integration of ecological resilience theory into water management has the potential to recognize the sustainable carrying capacity of river basins at the local, regional, and international scale.

Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

Precise streamflow estimation plays a key role in optimal water resource use, reservoirs operations, and designing and planning future hydropower projects. Machine learning models were successfully utilized to estimate streamflow in recent years In this study, a new approach, covariance matrix adaptation evolution strategy (CMAES), was utilized to improve the accuracy of seven machine learning models, namely extreme learning machine (ELM), elastic net (EN), Gaussian processes regression (GPR), support vector regression (SVR), least square SVR (LSSVR), extreme gradient boosting (XGB), and radial basis function neural network (RBFNN), in predicting streamflow. The CMAES was used for proper tuning of control parameters of these selected machine learning models. Seven input combinations were decided to estimate streamflow based on previous lagged temperature and streamflow data values. For numerical prediction accuracy comparison of these machine learning models, six statistical indexes are used, i.e., relative root mean squared error (RRMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE), and the Kling–Gupta efficiency agreement index (KGE). In contrast, this study uses scatter plots, radar charts, and Taylor diagrams for graphically predicted accuracy comparison. Results show that SVR provided more accurate results than the other methods, especially for the temperature input cases. In contrast, in some streamflow input cases, the LSSVR and GPR were better than the SVR. The SVR tuned by CMAES with temperature and streamflow inputs produced the least RRMSE (0.266), MAE (263.44), and MAPE (12.44) in streamflow estimation. The EN method was found to be the worst model in streamflow prediction. Uncertainty analysis also endorsed the superiority of the SVR over other machine learning methods by having low uncertainty values. Overall, the SVR model based on either temperature or streamflow as inputs, tuned by CMAES, is highly recommended for streamflow estimation.

A Comparative Evaluation of Conceptual Rainfall–Runoff Models for a Catchment in Victoria Australia Using eWater Source

Hydrological modelling at a catchment scale was conducted to investigate the impact of climate change and land-use change individually and in combination with the available streamflow in the Painkalac catchment using an eWater Source hydrological model. This study compares the performance of three inbuilt conceptual models within eWater Source, such as the Australian water balance model (AWBM), Sacramento and GR4J for streamflow simulation. The three-model performance was predicted by bivariate statistics (Nash–Sutcliff efficiency) and univariate (mean, standard deviation) to evaluate the efficiency of model runoff predictions. Potential evapotranspiration (PET) data, daily rainfall data and observed streamflow measured from this catchment are the major inputs to these models. These models were calibrated and validated using eight objective functions while further comparisons of these models were made using objective functions of a Nash–Sutcliffe efficiency (NSE) log daily and an NSE log daily bias penalty. The observed streamflow data were split into three sections. Two-thirds of the data were used for calibration while the remaining one-third of the data was used for validation of the model. Based on the results, it was observed that the performance of the GR4J model is more suitable for the Painkalac catchment in respect of prediction and computational efficiency compared to the Sacramento and AWBM models. Further, the impact of climate change, land-use change and combined scenarios (land-use and climate change) were evaluated using the GR4J model. The results of this study suggest that the higher climate change for the year 2065 will result in approximately 45.67% less streamflow in the reservoir. In addition, the land-use change resulted in approximately 42.26% less flow while combined land-use and higher climate change will produce 48.06% less streamflow compared to the observed flow under the existing conditions.

Principal component analysis for river network data: Use of spatiotemporal correlation and heterogeneous covariance structure

AbstractSpatiotemporal measurements observed through river networks have two distinct characteristics: a spatiotemporal correlation under the flow‐connected structure and the existence of heterogeneous covariances, which require a careful approach to implement principal component analysis (PCA). This paper focuses on developing a PCA method to reflect the unique characteristics of river networks. We propose a novel method combining flow‐directed PCA and geographically weighted PCA for the domain of river networks. The strengths of our approach are that it can (i) reduce dimensionality for streamflow data while effectively removing correlation among them and (ii) identify the group structure of data. It is possible to find essential patterns and sources of variation that may not be disclosed due to the attributes of flow‐connected networks. We apply the proposed method to the daily monitoring records of total organic carbon in the Geum River catchment area in South Korea. The results show that the proposed method successfully adjusts for the topological structure of the network and temporal correlation among observations while considering the spatial heterogeneity, enabling a more concrete understanding of monitoring networks.

Evaluation of Gridded Precipitation Data for Hydrologic Modeling in North-Central Texas

Over the past few decades, several high-resolution gridded precipitation products have been developed using multiple data sources and techniques, including measured precipitation, numerical modeling, and remote sensing. Each has its own sets of uncertainties and limitations. Therefore, evaluating these datasets is critical in assessing their applicability in various climatic regions. We used ten precipitation datasets, including measured (in situ), gauge-based, and satellite-based products, to assess their relevance for hydrologic modeling at the Bosque River Basin in North-Central Texas. Evaluated datasets include: (1) in situ station data from the Global Historical Climate Network (GHCN); (2) gauge-based dataset Daymet and the Parameter-elevation Regression on Independent Slope Model (PRISM); (3) satellite-based dataset Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Early and Late, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and PERSIANN-CCS (Cloud Classification System); (4) satellite-based gauge-corrected dataset IMERG-Final, PERSIANN-CDR (Climate Data Record), and CHIRPS (Climate Hazards Group Infrared Precipitation with Station data). Daily precipitation data (2000–2019) were used in the Soil and Water Assessment Tool (SWAT) for hydrologic simulations. Each precipitation dataset was used with measured monthly United States Geological Survey (USGS) streamflow data at three locations in the watershed for model calibration and validation. The SUFI-2 (Sequential Uncertainty Fitting) method on the SWAT-CUP (Calibration and Uncertainty Program) was used to quantify and compare the uncertainty in streamflow simulations from all precipitation datasets. The study has also analyzed the uncertainties in SWAT model parameter values under different gridded precipitation datasets. The results showed similar or better model calibration/validation statistics from gauge-based (Daymet and PRISM) and satellite-based gauge-corrected products (CHIRPS) compared with the GHCN data. However, satellite-based precipitation products such as PERSIANN-CCS and PERSIANN-CDR unveil comparatively inferior to capture in situ precipitation and simulate streamflow. The results showed that gauge-based datasets had comparable and even superior performances in some metrics compared with the GHCN data.

Seven hydrogeological terrains characteristic of southern Ontario

Groundwater is an important component of the hydrological cycle of southern Ontario, significant to water supply and discharge to surface water. To improve overall understanding of key hydrological mechanisms, a proof-of-concept framework is presented that consists of seven typical terrains or hydrogeological settings. Geologic and topographic controls influence each setting as recorded by climate, streamflow, and groundwater level data, which trace how water moves through various parts of the southern Ontario landscape. This hydrogeological framework considers the geology of surface sediment and bedrock, permeability and porosity (inferred), physiography, and topographic gradients to define seven distinctive hydrogeological terrains. The data and watershed selection process allow for concentration on patterns of hydrologic response across the landscape, with less focus on hydrologic accounting of the terrain. The analysis identifies events in climate, stream, and well-monitoring data indicative of influxes and temporal patterns of hydrological and hydrogeological response to local geology and topography, thus differentiating styles of groundwater movement in sediment/bedrock landscapes of the Paleozoic basin and Shield margin. A case study is provided for each setting: five in sediment (clay, sand, gravel, till upland, and thick till); and two in bedrock (crystalline and carbonate). The selected hydrogeological terrains characterize ∼90% of the landscape based on a simplified geological map of southern Ontario. The main hydrogeological characteristics and behaviour of these terrains are proposed as a set of conceptual models representative of near-surface groundwater regimes. Such models can inform water and land resource management as future climates change.

Flood inundation mapping under climate change scenarios in the Boyo watershed of Southern Ethiopia

Abstract This research aims to map flood inundated areas under changing climate in the Boyo watershed of Southern Ethiopia. A semi-distributed physically based Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) and Hydrologic Engineering Center-River Analysis System (HEC-RAS) were used to simulate the flood events and maps, respectively, for climate scenarios. The bias-corrected data of four climate models were used for the baseline (1976–2005), mid-term (2041–2070) and long-term (2071–2100) cycles under RCP4.5 and RCP8.5 scenarios. The 50- and 100-year return period flood events were generated from the baseline and future period streamflow data. The HEC-RAS model was used to simulate the inundation areas and depths from the flood events. The result exhibited that the average annual rainfall and maximum and minimum temperatures of the catchment will increase in the future with an increase in annual runoff. The severity of annual floods would increase in the future under RCP4.5 and RCP8.5 scenarios. Approximately, 193 ha of the study may be flooded with flood events having a return period of 100 years under the RCP8.5 scenario in the long-term period, which is an extreme case. The result is a benchmark to reduce the flood risk and management of floodplains in this watershed.

Streamflow Data Analysis for Flood Detection using Persistent Homology

Flooding is an environmental hazard that occurs almost everywhere around the world. Analysis of streamflow data can give us important climatic information for flooding events. Persistent homology (PH), a new analysis tool in topological data analysis (TDA) offers a new way to look at the information in a data set using qualitative approach. PH uses topology to extract topological features such as connected components and cycles that exist in the data set. In this paper, we present a new approach for streamflow data analysis for flood detection by using PH. An analysis was conducted at Sungai Kelantan, Malaysia. The result shows that PH gives different pattern of topological features for dry and wet periods. In particular, there are more persistent topological features in the form of connected components and cycles in the wet periods compared to the dry periods. We observed that the time series of the distance measure corresponding to the evolution of the components is consistent with the time series of the streamflow data. As a conclusion, this study suggests that the time series of the distance measure corresponding to the evolution of the components can be used for flood detection at Sungai Kelantan, Malaysia.

Climate change impact assessment on a tropical river resilience using the Streeter-Phelps dissolved oxygen model

This paper provides a technical analysis of a river’s current and future resilience in a watershed with intensive agricultural and fishing activities. The study area was the last section of the Culiacan River corresponding to the river mouth over a lagoon system. Dissolved oxygen modeling was performed using the Streeter-Phelps model to evaluate the river self-depuration capability using Biochemical Oxygen Demand, dissolved oxygen, streamflow, and water temperature data from 2013 to 2020. Fieldwork was carried out to establish the geomorphological characteristics of the river by determining stream velocity, width, and depth and the location of nine sources of pollution on the river. The modeling was performed for three groups of months with different temperatures, identified by hierarchical cluster analysis. Estimates were made for future scenarios, assessing the effect of climate change on the Culiacan River’s self-depuration capability. The results showed that most of the year, the degradation rate of the system results in rapid assimilation of organic matter. However, the modeling indicates that the river would lose its resilience capability under climate change. Thus, it is essential to implement wastewater treatment systems to reduce the environmental impact on the aquatic ecosystem in the river and the lagoon system.

Assessment of Suitable Gridded Climate Datasets for Large-Scale Hydrological Modelling over South Korea

There is a large number of grid-based climate datasets available which differ in terms of their data source, estimation procedures, and spatial and temporal resolutions. This study evaluates the performance of diverse meteorological datasets in terms of representing spatio-temporal climate variabilities based on a national-scale domain over South Korea. Eleven precipitation products, including six satellite-based data (CMORPH, MSWEP, MERRA, PERSIANN, TRMM, and TRMM-RT) and five reanalysis-based data (ERA5, JRA-55, CPC-U, NCEP-DOE, and K-Hidra) and four temperature products (MERRA, ERA5, CPC-U, and NCEP-DOE) are investigated. In addition, the hydrological performance of forty-four input combinations of climate datasets are explored by using the Variable Infiltration Capacity (VIC) macroscale model. For this analysis, the VIC model is independently calibrated for each combination of input and the response to each combination is then evaluated with in situ streamflow data. Our results show that the gridded datasets perform differently particularly in representing precipitation variability. When a diverse combination of the datasets are used to represent spatio-temporal variability of streamflow through the hydrological model, K-Hidra and CPC-U performed best for precipitation and temperature, followed by the MERRA and ERA5 datasets, respectively. Lastly, we obtain only marginal improvement in the hydrological performance when utilizing multiple climate datasets after comparing it to a single hydrological simulation with the best performing climate dataset. Overall, our results indicate that the hydrological performance may vary considerably based on the selection of climate datasets, emphasizing the importance of regional evaluation studies for meteorological datasets.

Linking fire-induced evapotranspiration shifts to streamflow magnitude and timing in the western United States

The impact of wildfire on water availability is a critical issue in the western United States. Because actual evapotranspiration (ETa) constitutes the largest loss in the terrestrial water budget, it has been suggested that fire-induced ETa reduction is a primary driver of elevated post-fire discharge. Ten gaged watersheds with burns exceeding 5% of their total contributing drainage area were selected from California, Oregon, Montana, Utah, New Mexico, and Colorado. Continuous daily stream gage data were compiled, and 30-meter ETa estimates were calculated with the Operational Simplified Surface Energy Balance (SSEBop) model. Fire-induced ETa shifts were quantified with statistical tests that compared pre and post-fire monthly ETa in burned and unburned pixels; the dampening effect of scale was also evaluated by repeating tests on all pixels from the entire basin. As streamflow data are point measurements that aggregate a large spatial area, additional statistical methods were required to isolate the effect of fire from climate on baseflow and runoff. Key findings include a) significant fire-induced ETa reductions were only distinguishable in basin-scale monthly datasets when at least 73% of the basin burned, b) the effect of wildfire disturbance on streamflow magnitude was seasonably variable, c) streamflow was modified in basins with as little as 6% burned drainage area; however, shifts only persisted beyond the fifth post-fire year where more than three-quarters of the basin was fire-impacted, and d) surplus water from ETa reduction was sufficient to account for boosted fire-induced streamflow. Where fire-induced streamflow increases were not significantly correlated with ETa anomaly, other fire-impacted landscape processes may have contributed to modified runoff generation and routing. Where fire reduced ETa but streamflow shifts were not detected, compensatory ETa pathways may have consumed the excess water before it reached the gage. Findings suggest that water providers with small source-water collection areas have higher relative risk for fire-induced hydromodification than providers with larger or more diversified supply portfolios. Results also illustrate the tendency of overarching climate signals to mask or artificially boost the apparent effect of landscape disturbance on streamflow at the basin outlet.

Parameter Uncertainty Analysis for Streamflow Simulation Using SWAT Model in Nashe Watershed, Blue Nile River Basin, Ethiopia

The hydrological model is an important tool in water resource management, allocation, and prediction. However, the hydrological models are subject to the uncertainty resulting from different sources of errors involved in the large number of parameters. The hydrological models in the management of water resources play a very significant role in quantifying uncertainty. Therefore, uncertainty analysis implementation is essential to advance confidence in modeling before performing the hydrological simulation. The purpose of the study was to assess the uncertainty parameters for the streamflow using the Soil and Water Assessment Tool (SWAT) hydrological model integrated sequential uncertainty fitting (SUFI-2) algorithm to Nashe watershed located in the Northwestern, Upper Blue Nile River Basin. The required input data for this study were digital elevation model, land use, soil map and data, meteorological data (precipitation, minimum and maximum temperature, wind speed, solar radiation, and relative humidity), and streamflow data. The calibration and validation model was computed to simulate the observed streamflow data from 1985 to 2008 including two years of warm-up periods. Model calibration, validation, and analysis of parameter uncertainty were conducted for both daily and monthly observed streamflows at the gauging stations through SUFI-2, which is one of the algorithms of the SWAT-Calibration and Uncertainty Program (SWAT_CUP). The results show that CN_2, GW_DELAY, ALPHA_BNK, CH_N2, and SOL_AWC were the most sensitive parameter for the monthly period and had a great impact on the streamflow simulation. Modeling results indicated that the method provides better results for the monthly time period than the daily time period for both calibration and validation. The result indicated that R2 and NSE were 0.89 and 0.85 and 0.82 and 0.79, respectively, monthly and daily during the calibration. The validation likewise demonstrated a good performance with R2 and NSE results of 0.88 and 0.78 and 0.85 and 0.76, respectively, for monthly and daily time periods. The results of this study provide a scientific reference based on uncertainty analysis to decision-makers to improve the decision support process in river basin management.

Separating anthropogenic and climate contributions to streamflow variations in Rietspruit sub-basin, South Africa

Human activities and climate change continue to alter streamflow in many river basins. In this study, the influence of human activities and climate variability on streamflow changes in the Rietspruit sub-basin (RSB), South Africa, were investigated using precipitation, potential evapotranspiration, and discharge data for the period 1986 to 2018. Trends in the hydrometeorological variables were assessed using the Mann-Kendall test while the change point in the annual streamflow data was identified using the Pettitt test and the double mass curve technique. Results revealed no statistically significant change in annual precipitation, whereas annual streamflow showed a significant increasing trend while potential evapotranspiration showed a decreasing trend. It was identified that the change point occurred around 1999, thereby dividing the streamflow data into the pre-change (1986–1999) period with comparatively low urban cover (8.3%), and the post-change period (2000–2018) with urban land covering 16.6% of the study area. The results demonstrate that from 1986 to 2018, the elasticity of streamflow with respect to precipitation and potential evapotranspiration was 0.26 and −2.01, respectively. This implies that streamflow in the study area is more sensitive to potential evapotranspiration than precipitation. Nonetheless, changes in land use/land cover, particularly the urban settlement which increased by 101% between 1986 and 2018, were more influential than climate variability for the significant increase in streamflow in the RSB. The results provide necessary information for water resource management, land use planning and ecosystem development in the near future. This study emphasizes the necessity of addressing nonlinear and dynamic hydrological processes as a result of human impact and climate associated changes.

Hydrological time series forecasting via signal decomposition and twin support vector machine using cooperation search algorithm for parameter identification

Accurate hydrologic forecasting is of vital importance for water resource management and reservoir operation under the changing environments. However, it is difficult for a standalone method to track the complex hydrological time series with highly nonlinear and nonstationary features. To improve the forecasting accuracy, this study proposes a hybrid model using compete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and twin support vector machine (TSVM) optimized by metaheuristic algorithm. Firstly, the CEEMDAN tool is chosen to divide the original runoff data into several relatively stable subcomponents with various resolutions and frequencies. Then, the TSVM forecasting model is established to extract the deep features of each subseries, while the emerging cooperation search algorithm is used to search for satisfying parameter combination. The final forecasting value for the original hydrological time series is formulated by aggregating the outputs of all TSVM models. Long-term streamflow data from several hydrological stations in China’s Yangtze River is used for simulation. Experimental results reveal that the hybrid model significantly outperforms several conventional forecasting models in prediction accuracy. For instance, the proposed method improves the root mean squared error and mean absolute error value with about 36.5% and 41.6% reductions compared with the traditional support vector machine at station B. Thus, a novel and effective evolutionary artificial intelligence model is developed for hydrological forecasting.

Land Cover Change Effects on Stormflow Characteristics across Broad Hydroclimate Representative Urban Watersheds in the United States

Urban development alters stormflow characteristics and is associated with increasing flood risks. The long-term evaluation of stormflow characteristics that exacerbate floods, such as peak stormflow and time-to-peak stormflow at varying levels of urbanization across different hydroclimates, is limited. This study investigated the long-term (1980s to 2010s) effects of increasing urbanization on key stormflow characteristics using observed 15 min streamflow data across six broad hydroclimate representative urban watersheds in the conterminous United States. The results indicate upward trends in peak stormflow and downward trends in time-to-peak stormflow at four out of six watersheds. The watershed in the Great Plains region had the largest annual increasing (decreasing) percent change in peak stormflow (time-to-peak stormflow). With the current change rates, peak stormflow in the Great Plains region watershed is expected to increase by 55.4% and have a 2.71 h faster time-to-peak stormflow in the next decade.

Climate change impact on blue and green water resources distributions in the Beijiang River basin based on CORDEX projections

Abstract This study assessed the potential impact of climate change on spatiotemporal distribution of blue and green water resources in the Beijiang River basin, southern China, using the SWAT hydrological model and CORDEX-EAS regional climate models (RCMs). The outputs of three RCMs (namely RMOH, RMPI and RNCC) under two emission scenarios (RCP 2.6 and RCP 8.5) were bias-corrected by using the quantile delta mapping method for the control (1975–2004), near future (2021–2050) and far future (2061–2090) periods. Driven by the corrected climate variables, future blue and green water were assessed by using the SWAT model calibrated with streamflow data. The results indicated that the green water flow in the northwest basin predicted by RMOH under RCP 2.6 would increase up to about 2.7% in 2061–2090; RNCC suggests that most of the basin would experience the most significant reduction in green water storage (over 30%) under RCP 8.5 in the far future. For blue water, the ensemble mean of three RCMs indicates a slight decreasing trend in the northern part of the basin and an increasing one in the southern part under RCP 8.5 in the far future. Our findings could help watershed managers evaluate future hydrological risks and design appropriate adaptation strategies.

Assessing the Implication of Climate Change to Forecast Future Flood Using CMIP6 Climate Projections and HEC-RAS Modeling

Climate change has caused uncertainty in the hydrological pattern including weather change, precipitation fluctuations, and extreme temperature, thus triggering unforeseen natural tragedies such as hurricanes, flash flooding, heatwave and more. Because of these unanticipated events occurring all around the globe, the study of the influence of climate change on the alteration of flooding patterns has gained a lot of attention. This research study intends to provide an insight into how the future projected streamflow will affect the flooding-inundation extent by comparing the change in floodplain using both historical and future simulated scenarios. For the future projected data, the climate model Atmosphere/Ocean General Circulation Model (AOGCM) developed by Coupled Model Intercomparison Project Phase 6 (CMIP6) is used, which illustrates that the flood is increasing in considering climate models. Furthermore, a comparison of the existing flood inundation map by the Federal Emergency Management Agency (FEMA) study with the map generated by future projected streamflow data presents the entire inundation area in flood maps, implying the expansion area compared to FEMA needs to be considered in making emergency response plans. The effect of flooding in the inundation area from historical to future flow values, presented mathematically by a calculation of inundation extent percentage, infers that the considered watershed of Rock River is a flood-prone area. The goal is to provide insights on the importance of using the forecasted data for flood analysis and to offer the necessary background needed to strategize an emergency response plan for flood management.

Impacts and Implications of Land Use Land Cover Dynamics on Groundwater Recharge and Surface Runoff in East African Watershed

Assessing the spatiotemporal dynamics of land use land cover (LULC) change on water resources is vital for watershed sustainability and developing proper management strategies. Evaluating LULC scenarios synergistically with hydrologic modeling affords substantial evidence of factors that govern hydrologic processes. Hence, this study assessed the spatiotemporal effects and implications of LULC dynamics on groundwater recharge and surface runoff in Gilgel Gibe, an East African watershed, using the Soil and Water Assessment Tool (SWAT) model. Three different LULC maps (2000, 2010, and 2020) were derived from Landsat images, and the comparisons pointed out that the land-use pattern had changed significantly. The agricultural land and grassland cover increased by 3.76% and 1.36%, respectively, from 2000 to 2020. The implications acquired for 2000 show that forested land covers decreased by 5.49% in 2020. The SWAT simulation process was executed using a digital elevation model, soil, LULC, and weather data. The model was calibrated and validated using streamflow data to understand the surface runoff and groundwater recharge responses of each Hydrologic Response Units on reference simulation periods using the Calibration and Uncertainty Program (SWAT-CUP), Sequential Uncertainty Fitting (SUFI-2) algorithm. The observed and simulated streamflows were checked for performance indices of coefficient of determination (R2), Nash–Sutcliffe model efficiency (NSE), and percent bias (PBIAS) on monthly time steps. The results show that there is good agreement for all LULC simulations, both calibration and validation periods (R2 & NSE ≥ 0.84, −15 < PBIAS < +15). This reveals that for the LULC assessment of any hydrological modeling, the simulation of each reference period should be calibrated to have reasonable outputs. The study indicated that surface runoff has increased while groundwater decreased over the last two decades. The temporal variation revealed that the highest recharge and runoff occurred during the wet seasons. Thus, the study can support maximizing water management strategies and reducing adverse driving environmental forces.