Water Resource Planning Research Articles

Geneticizing input selection based advanced neural network model for sediment prediction in different climate zone

The study focuses on developing an accurate prediction model for suspended sediment load (SSL) based on antecedent SSL and water discharge values. Two Artificial Intelligence (AI) models, Hybrid and Parallel, were employed to test on the Kelantan and Mississippi Rivers in different climate zones and river sizes. The parallel model showed better performance than the hybrid in most cases, with the best results based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) (432.06 and 782.15 respectively) for Kelantan and (31672.25 and 62356.60 respectively) for Mississippi. The multifunctional GA neural-network model results have proven its ability to predict SSL in tropical and semi-arid zones. In the Kelantan River, the 8-input combination set was the best prediction model, showing an improvement of more than 38% compared to traditional models. The proposed method has proven to be more accurate than traditional models, ensuring better water resource planning, agricultural management and reservoir operation.

Sustainable Adaptation Plan in Response to Climate Change and Population Growth in the Iraqi Part of Tigris River Basin

Climate change and population growth play crucial roles in the planning of future water resources management strategies. In this paper, a balancing between projected water resources and water demands in the Iraqi Part of the Tigris River Basin (TRB) was evaluated till the year 2080 based on RCPs 2.6, 4.5 and 8.5 and population growth. This paper examined a sustainable adaptation plan of water resources in the TRB considering three scenarios; (S1) as no change in the current strategy, (S2) as improved irrigation efficiency and (S3) as improved irrigation and municipal water use efficiency. The results showed a decline in streamflow will occur in the range from 5 to 18.4% under RCP 2.6 and RCP 8.5, respectively. The minimum increase in water demand is expected for RCP 2.6 (maximum increase for RCP 8.5) by 51.8 (208.2), 9.9 (42) and 1.2 (7)% for the municipal–industrial, irrigation and environmental water demands, respectively, compared with the RP. The main finding indicated that S1 is the worst scenario, with water stress in four provinces, especially on the warmest RCP. Whereas, under S2 and S3 conditions, water stress can be eliminated. Increasing ambition towards adaptation becomes obligatory for developing sustainable water sources, supporting water food securities and increasing resilience towards climate change.

Coupling the remote sensing data-enhanced SWAT model with the bidirectional long short-term memory model to improve daily streamflow simulations

Global climate change has led to an increase in the frequency and scale of extreme weather events worldwide, and there is an urgent need to develop better-performing hydrological models to improve the accuracy of streamflow simulations and to facilitate water resource planning and management. The Soil and Water Assessment Tool (SWAT) has a notable physical foundation and is widely used in hydrological research. However, it uses a simplified vegetation growth model, introducing uncertainty into the simulation results. This study focused on improving the model based on remotely sensed phenological and leaf area index (LAI) data. Phenological data were used to define vegetation dormancy, and the LAI data replaced the corresponding data simulated by the original model. This approach improved the accuracy of the model in describing vegetation dynamics. Then, the enhanced SWAT model was coupled with the bidirectional long short-term memory (BiLSTM) model to validate the simulation of hydrological processes upstream of the Hei River. During model validation, the performance of the enhanced SWAT model in simulating streamflow (R2 = 0.835, NSE = 0.819) was better than that of the original SWAT model (R2 = 0.821, NSE = 0.805). In terms of simulating evapotranspiration, the enhanced SWAT model demonstrated even greater advantages. During the verification period, compared to those of the SWAT model, the R2 and NSE values of the enhanced SWAT model for daily-scale simulations increased from 0.196 and −0.269 to 0.777 and 0.732, respectively. The R2 and NSE values for monthly-scale simulations increased from 0.782 and 0.678 to 0.906 and 0.851, respectively. Simultaneously, the performance levels of two coupling approaches in streamflow prediction were compared, i.e., direct coupling of the original SWAT and BiLSTM models (SWAT-BiLSTM) and coupling of the enhanced SWAT and BiLSTM models (enhanced SWAT-BiLSTM). The results showed that the enhanced SWAT-BiLSTM model always performed better than the SWAT-BiLSTM model during the entire simulation period, especially the enhanced SWAT-BiLSTM model, which could more accurately predict peak streamflow changes. This study demonstrated that coupling an improved physical model with deep learning models could improve the streamflow prediction accuracy.

Catchment-Wide Groundwater Budget for the Inkomati-Usuthu Water Management Area in South Africa.

In South Africa, approximately 98% of the predicted total surface water resources are already being used up. Consequently, the National Water Resource Strategy considers groundwater to be important for the future planning and management of water resources. In this case, quantifying groundwater budgets is a prerequisite because they provide a means for evaluating the availability and sustainability of a water supply. This study estimated the regional groundwater budgets for the Inkomati-Usuthu Water Management Area (Usuthu, Komati, Sabie-Sand, and Crocodile) using the classical hydrological continuity equation. The equation was used to describe prevailing feedback loops between groundwater draft, recharge, baseflow, and storage change. The results were coarser scale estimates which, beforehand, were derived from the 2006 study. In the years to follow, groundwater reliance intensified and there was also the historic 2015/2016 drought. This inevitably led to an increased draft while the rest of the components of the groundwater budgets experienced decreases. Both Crocodile and Sabie-Sand experienced groundwater storage depletion which led to reduced baseflow and groundwater availability, while groundwater recharge contrarily increased due to capture. Conversely, the other two catchments experienced relatively lower drafts with correspondingly higher groundwater availability and recharge while storage change was positive. The results highlighted the need for adaptive water management whose effectiveness relies on predictive studies. Consequently, future models should be developed to capture the spatial and temporal dynamism of the natural groundwater budget due to climate change, water demands, and population growth predictions.

Spatio-temporal analysis of river flow master recession curve characteristics over Ardabil province rivers

The Master Recession Curve (MRC) is a crucial tool for evaluating, managing, and utilizing rivers, particularly under low-flow conditions. Analyzing changes in the MRC of river flow is useful for water resource planning, surface water utilization, and estimating and allocating environmental flow. This study utilized daily flow data from rivers in Ardabil province, Iran over a 48-year statistical period (1970–2015). The Visual Basic program was utilized to estimate the equations and coefficients of recession curves. The trends of the constant and slope coefficients of the MRC equations were determined using the Mann-Kendall test and linear regression. Higher constant coefficient values were attributed to main rivers with higher discharge (e.g., Borran, Mashiran, and Samian, having 17.98, 13.79, and 5.64 cms, respectively. The slope of the MRC in most of the rivers in the central part of the study area exhibited a decreasing trend (p-value<0.05), which attributed to climate change, alterations in precipitation patterns, reduced rainfall/snowmelt, and water abstraction leading to less water input during non-flood periods. Additionally, most rivers in different climatic regions and areas of the study region exhibited an increasing trend (p-value<0.05) in the MRC constants, indicating reduced groundwater recharge or increased groundwater abstraction, as a common issue in the Ardabil province. Moreover, human water use and reservoir regulation (Yamci, Sabalan, Saghezchi, Givi, and small regulating reservoirs) can influence river flow patterns during non-flood periods. The occurrence of droughts and intensified surface water use by farmers, as well as unauthorized extraction of water from wells, have exacerbated this trend.

Fish passage across Southeast Asia: key informant insights into motivations and triggers for water resource planning and policymaking

Southeast Asia’s inland fisheries are under threat from extensive irrigation and hydropower development. Of particular concern is the Mekong River, which is currently home to the world’s most productive inland fishery. To minimise these negative impacts, fish-friendly infrastructure can be incorporated into these barriers to facilitate fish migration or “passage.” However, historically fishways have often been overlooked given cost considerations and a lack of awareness of benefits across irrigation managers and funding bodies. This study aimed to understand fishway implementation decision-making across the Mekong. Findings from 19 interviews showed that funding was central to decision-making and acted as an ability and a trigger, to both positively and negatively influence stakeholders’ motivations for fishway implementation. Masterclasses were viewed as key vehicles for building technical capacity and consent within the region through the creation of networks and social capital between irrigation engineers and fisheries staff, as well as the development of “champions.”

Urban Water Demand Prediction Based on Attention Mechanism Graph Convolutional Network-Long Short-Term Memory

Predicting short-term urban water demand is essential for water resource management and directly impacts urban water resource planning and supply–demand balance. As numerous factors impact the prediction of short-term urban water demand and present complex nonlinear dynamic characteristics, the current water demand prediction methods mainly focus on the time dimension characteristics of the variables, while ignoring the potential influence of spatial characteristics on the temporal characteristics of the variables. This leads to low prediction accuracy. To address this problem, a short-term urban water demand prediction model which integrates both spatial and temporal characteristics is proposed in this paper. Firstly, anomaly detection and correction are conducted using the Prophet model. Secondly, the maximum information coefficient (MIC) is used to construct an adjacency matrix among variables, which is combined with a graph convolutional neural network (GCN) to extract spatial characteristics among variables, while a multi-head attention mechanism is applied to enhance key features related to water use data, reducing the influence of unnecessary factors. Finally, the prediction of short-term urban water demand is made through a three-layer long short-term memory (LSTM) network. Compared with existing prediction models, the hybrid model proposed in this study reduces the average absolute percentage error by 1.868–2.718%, showing better prediction accuracy and prediction effectiveness. This study can assist cities in rationally allocating water resources and lay a foundation for future research.

Response of blue-green water to climate and vegetation changes in the water source region of China's South-North water Diversion Project

The characteristics of water fluctuations within the basin have remained ambiguous in recent years due to climate and vegetation changes, posing a challenge to the planning and utilization of regional water resources. This study coupled the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model, which can consider vegetation dynamics information and evapotranspiration physical mechanisms, into the Distributed Time Variant Gain Model (DTVGM) to improve the hydrological simulation accuracy under changing environments. Based on different simulation scenarios of the coupled model, the effects of climate and vegetation changes on hydrological processes were fully investigated using blue and green water (BW and GW) as assessment metrics. The upper Han River Basin (UHRB), as water source for the Central Line Project of China’s South-North Water Diversion (CSNWD), was selected as a typical basin for the study, results indicated that: (1) The DTVGM coupled with PT-JPL could better reflect the spatiotemporal patterns of major hydrological processes (runoff, evapotranspiration, and soil moisture) in UHRB from 2001 to 2019; (2) The BW in UHRB exhibited decreasing trend from 2001 to 2019, while the GW on the contrary. Surface runoff (Rs), vegetation transpiration (Et), and soil moisture (W) contributed most in the components of BW and GW; (3) Vegetation change dominated by greening in UHRB, which facilitated the transformation of BW to GW in the basin, and it increased the proportion of GW mainly by promoting Et. BW was mainly influenced by precipitation, but vegetation change altered the original correlation between climatic factors and hydrological processes. This study can support the management of water resources in the basin under changing environments.

Satellite-Derived Indicators of Drought Severity and Water Storage in Estuarine Reservoirs: A Case Study of Qingcaosha Reservoir, China

Estuarine reservoirs are critical for freshwater supply and security, especially for regions facing water scarcity challenges due to climate change and population growth. Conventional methods for assessing drought severity or monitoring reservoir water level and storage are often limited by data availability, accessibility and quality. We present an approach for monitoring estuarine reservoir water levels, storage and extreme drought via satellite remote sensing and waterline detection. Based on the CoastSat algorithm, Landsat-8 and Sentinel-2 images from 2013 to 2022 were adopted to extract the waterline of Qingcaosha Reservoir, the largest estuarine reservoir in the world and a key source of freshwater for Shanghai, China. This study confirmed the accuracy of the satellite-extracted results through two main methods: (1) calculating the angle of the central shoal slope in the reservoir using the extracted waterline data and measured water levels and (2) inverting the time series of water levels for comparison with measured data. The correlation coefficient of the estimated water level reached ~0.86, and the root mean square error (RMSE) of the estimated shoal slope was ~0.2°, indicating that the approach had high accuracy and reliability. We analyzed the temporal and spatial patterns of waterline changes and identified two dates (21 February 2014 and 15 October 2022) when the reservoir reached the lowest water levels, coinciding with periods of severe saltwater intrusions in the estuary. The extreme drought occurrences in the Qingcaosha Reservoir were firstly documented through the utilization of remote sensing data. The results also indicate a strong resilience of the Qingcaosha Reservoir and demonstrate that the feasibility and utility of using satellite remote sensing and waterline detection for estuarine reservoir storage can provide timely and accurate information for water resource assessment, management and planning.

How accurate are the machine learning models in improving monthly rainfall prediction in hyper arid environment?

Arid regions like the United Arab Emirates (UAE) face a dire challenge of scarce water resources and unpredictable climate patterns. This study investigates the efficacy of advanced Machine Learning (ML) techniques in enhancing rainfall prediction within hyper-arid environments. Leveraging an extensive 30-year dataset from 1991 to 2020, this study harnessed the power of XGBoost, LSTM, Random Forest (RF), Gradient Boost (GB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Linear Regression (LR), and ensemble methods to significantly enhance the prediction accuracy of monthly rainfall over UAE. In the initial univariate analysis, focused solely on rainfall as the predictor, the ML models displayed encouraging performance during the training phase, achieving an impressive correlation coefficient (CC) of 0.88 for both XGBoost and the ensemble models. However, their predictive efficacy witnessed a decline during the testing phase, where the maximum CC reached 0.45. In contrast, traditional models like Linear Regression and SVM, yielded subpar results in both training and testing, exhibiting correlation values lower than 0.3. To address these limitations, a multivariate analysis is conducted by incorporating additional meteorological parameters, including wind speed, temperature, humidity, and evapotranspiration. This augmentation proved highly beneficial as it substantially enhanced the models' predictive capacities during the testing period. The XGB achieves a CC of 0.76, LSTM improves from 0.21 to 0.71, and stacked models exhibit promising behavior jumping from an average of 0.44 to 0.82 during the testing periods. Additionally, we performed a sensitivity analysis utilizing LASSO regression, which revealed that wind speed and minimum temperature emerged as the most influential parameters for monthly rainfall prediction in the arid context. These two meteorological factors exerted a substantial impact on the accuracy of our predictive models, underscoring their significance in understanding and forecasting rainfall patterns in hyper-arid regions, such as the United Arab Emirates. The identification of these key drivers further strengthens the foundation for effective water resource management and climate adaptation strategies in such challenging environments. This study provides valuable insights for water resource planning, agriculture, and climate resilience strategies in hyper-arid regions. Further research can build upon these results to enhance rainfall prediction models and support sustainable development in arid regions.

Effects of multi-year droughts on the precipitation-runoff relationship: An integrated analysis of meteorological, hydrological, and compound droughts

Multi-year droughts cause severe water scarcity, ecosystem collapse, and significant socio-economic losses. Understanding the impact of these droughts on the precipitation-runoff (PR) relationship is crucial for managing water resources and mitigating drought-related damage. However, previous research focuses mainly on the influence of multi-year meteorological droughts on the PR relationship, neglecting multi-year hydrological droughts and their compound effects. Studies also generally overlook the speed and volume of precipitation-to-runoff conversion, considering only the quality of PR relationship. Furthermore, there is a lack of effective simulation methods using the intensity of multi-year droughts to understand the PR relationship. To address these gaps, this study defines multi-year compound droughts and employs three key parameters, namely, the determination of coefficient (R2), linear regression slope, and runoff coefficient, to assess the impact of multi-year droughts on changes in the PR relationship's quality, precipitation-to-runoff conversion speed, and runoff volume. A random forest model is then employed to simulate these parameters based on the intensity of multi-year droughts. Case studies conducted in three basins within the arid-semiarid region of northern China as well as in three basins in the humid region of southern China reveal that multi-year droughts result in water shortages and have a significant impact on the PR relationship. Among different types of multi-year droughts, hydrological droughts have the greatest impact on the PR relationship, followed by compound and meteorological droughts, respectively. During meteorological, hydrological, and compound drought periods, the average rainfall (runoff) is lower by 26 – 30% (33 – 58%), 18 – 28% (36 – 60%), and 18 – 26% (30 – 58%) respectively compared to non-drought periods. Furthermore, the PR relationship remains stable in the rivers of the humid region in southern China. In contrast, the Loess Plateau region in northern China exhibits a substantial decrease in both the speed and volume of precipitation-to-runoff conversion. Interestingly, this decline is not accompanied by a significant increase in the R2 of the PR relationship. The random forest approach effectively simulates key parameters of the PR relationship based on the intensity of multi-year droughts. These findings hold crucial implications for the long-term regional water resource planning.

Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method

Reliable streamflow forecasting is a determining factor for water resource planning and flood control. To better understand the strengths and weaknesses of newly proposed methods in streamflow forecasting and facilitate comparisons of different research results, we test a simple, universal, and efficient benchmark method, namely, the naïve method, for short-term streamflow prediction. Using the naïve method, we assess the streamflow forecasting performance of the long short-term memory models trained with different objective functions, including mean squared error (MSE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and mean absolute error (MAE). The experiments over 273 watersheds show that the naïve method attains good forecasting performance (NSE &gt; 0.5) in 88%, 65%, and 52% of watersheds at lead times of 1 day, 2 days, and 3 days, respectively. Through benchmarking by the naïve method, we find that the LSTM models trained with squared-error-based objective functions, i.e., MSE, RMSE, NSE, and KGE, perform poorly in low flow forecasting. This is because they are more influenced by training samples with high flows than by those with low flows during the model training process. For comprehensive short-term streamflow modeling without special demand orientation, we recommend the application of MAE instead of a squared-error-based metric as the objective function. In addition, it is also feasible to perform logarithmic transformation on the streamflow data. This work underscores the critical importance of appropriately selecting the objective functions for model training/calibration, shedding light on how to effectively evaluate the performance of streamflow forecast models.

Application of Arc-SWAT Model for Water Budgeting and Water Resource Planning at the Yeralwadi Catchment of Khatav, India

Every facet of life, including human habitation, economic development, food security, etc., depends on water as a valuable resource. Due to the burgeoning population and rapid urbanization, water availability needs to be simulated and measured using hydrologic models and trustworthy data. To fulfill this aim, the SWAT model was processed in this work. The SWAT model was formulated to estimate the hydrological parameters of Yeralwadi using meteorological data from IMD (India Meteorological Department) for the period 1995-2020. The observed discharge data was collected from the HDUG Nasik group and used in the calibration and validation of the Model. The SWAT model was corrected &amp; validated through the SUFI-II algorithm in SWAT-CUP to get a better result. The model’s sensitivity is checked by using statistical parameters like Nash-Sutcliffe Efficiency (NSE) and a coefficient of determination (R2). NSE values were 0.72 and 0.80 in calibration and validation, and R2 were 0.80 &amp; 0.76 in calibration and validation, respectively, indicating the acceptance of the model. Results show that 40.6% of the total yearly precipitation was lost by evapotranspiration. The estimated total discharge from the Yeralwadi catchment was 55.6%, out of which 41.2% was surface runoff and 14.4% was baseflow. The other 17.8% was made up of percolation into confined and unconfined aquifers, which served as soil and groundwater storages. The surface runoff is influenced by Curve number (CnII), SOL_AWC, ESCO, and base flow was influenced by ALPHA-BF and GW_REVAP. This study will be useful to water managers and researchers to develop sustainable water resource management and to alleviate the water scarcity issues in the study basin.

Susurluk Havzası Akımlarının Trend Analiz Yöntemleri Kullanılarak Değerlendirilmesi

Trend analysis of annual average flow data is very important for better water resources management, planning and operation. In this study, annual average flow data from M. Kemal Paşa, Orhaneli and Simav flow observation stations located in Susurluk Basin were tested with Mann-Kendall, Spearman Rho and Şen's Innovative trend methods and the trends of the stations were tried to be determined and the performances of these methods were evaluated. In addition, annual average flow data were submitted to regression analysis, yielding equation sets. For each station, the annual and 100-year changes in m3/s were determined. Analyzing the results, only Şen's Innovative Trend Method analysis found a general decreasing trend for all 3 stations. In the analyses of Mann-Kendall and Spearman’s Rho methods, no trend was detected in all 3 stations. In addition, 100-year percentage change values of the data of 3 stations were determined. According to these values, it is concluded that there will be a significant decrease in the flow values of the stations in the future.

Effects of climate and land use change on runoff of the Second Songhua River Basin guided by SWAT model

Abstract The driving effect of global climate change (CC) on the ecohydrological process was quantitatively evaluated. Based on Geographic Information System technology, a Soil and Water Assessment Tool model suitable for watershed hydrological simulation was constructed to study the impact of climate and land-use change on runoff in the Second Songhua River (SSR) basin. Within the base period (1965–2010), the annual average temperature (AAT) of the SSR basin is 4.2 °C. Under the CC scenario representing concentration pathway (RCP) 4.5, the AAT of the watershed increased to 5.4 °C between 2020 and 2049. Under the CC scenarios of RCP 8.5 and RCP 4.5, the temperature in the watershed increased by 1.1 and 0.2 °C in June, respectively. The research results indicate that (1) there is a positive correlation between runoff and precipitation in the SSR watershed, and a negative correlation with temperature; (2) when the precipitation remains unchanged, the temperature increases by 1 °C and the runoff decreases by 7.2%; and (3) when the temperature remains constant, for every 10% increase in precipitation, the runoff increases by 30.5%. This study provides the scientific basis for water resource planning and sustainable development in the Northeast region and has important practical significance.

Assessment of the impact of land cover changes on sediment yield: a case study

Abstract Land use is one of the most important variables impacting river basin water and land resource planning and management. Sediment transport in streams is an important issue in the assessment of sediment deposition in dam reservoirs and its impact on operational activities. Evaluation of sediment yield contributes to decision-makers for effective water management and protection of the dam body and its elements. In this study, land use cover changes, in particular in forest areas, agricultural areas, arable lands, permanent crops, heterogeneous agricultural areas, and pastures in the Çakıt and Körkün sub-basins of the Seyhan River basin were examined between 1990 and 2018 using CORINE land use cover (CLC) data. The effect of land use/land cover (LULC) modifications on sediment yield was studied using suspended sediment observation data and CLC data. It was determined that the forest area in the Çakıt sub-basin drainage area increased between 1990 and 2018, and decreased in the Körkün sub-basin. It was determined that the increase in forest area in the Çakıt sub-basin drainage region until 2018 helped to reduce sediment movement. The regression values between forest area and suspended sediment concentration in Çakıt and Körkün sub-basins were found to be 0.9521 and 0.4431, respectively.

Optimal Operation Model of Ecological Flow of Hydropower Station Based on Genetic Algorithm and Neural Network

In view of the contradiction between the ecological flow regulation of the reservoir River and the power generation flow regulation of the reservoir with the largest power generation, this paper takes the maximum power generation and the maximum average ecological protection degree as the objective function, and uses the genetic algorithm back propagation neural networks (GA-BP) algorithm to establish the reservoir flow regulation model under the constraints of water balance, reservoir capacity, unit output and unit overflow, The optimal solution of the dispatching scheme is carried out, and the Shulehe hydropower station is taken as an example to verify the optimal scheme. The results show that the optimized dispatching model can ensure the demand of ecological flow, increase 32680 kW·h power generation and 23.85% power generation benefit, so as to provide scientific reference for the rational planning, dispatching and utilization of reservoir water resources.

Evaluation of Historic Trends for Monthly, Seasonal, and Annual Rainfall Series of Tenkasi, Tamil Nadu, India

Analyzing the variability and trends of rainfall plays a major role in water resource planning and management. Changes in rainfall patterns significantly influence the water availability in the irrigation structures and agronomical practices of crops. This study aimed to investigate the trends and estimate the magnitudes of monthly, seasonal, and annual rainfall series in Tenkasi, Tamil Nadu, India, using 49 years of rainfall data from 1971 to 2019. Sen’s Innovative trend analysis (ITA) method, Mann-Kendall (MK), and Simple Linear Regression (SLR) test were applied to assess the trends at 5 and 10 % significance levels. Trend magnitudes were estimated by Sen’s slope estimator (SSE) and SLR method. Changes in rainfall magnitude with mean values in percentage were estimated for all three slope estimation methods. The results revealed that the ITA method detected more significant trends of rainfall series over other methods. Significant downward trends were exhibited by October and north east monsoon (NEM) rainfall series had a trend magnitude of -0.43 mm/year and -0.04 mm/year. The percentage change in magnitude of the trend from mean values for October and the NEM series was -11.5% and -0.39%. Sub-trends within the October and NEM rainfall series showed that the low rainfall sub-series exhibited no trend, medium and high rainfall sub-series showed a downward trend. This study concluded that, in comparison to traditional trend analysis methods, the ITA method demonstrated a more rigorous investigation of trends. The significant decrease in rainfall during the NEM necessitates greater attention to water resources planning and management, serving as valuable scientific information for both crop planning and water resource management.

Observed trends in timing and severity of streamflow droughts across global tropics

Drought is a recurrent climatic hazard impacting natural and built environmental systems, including human lives. Although several studies have assessed streamflow droughts and their multivariate characterization, very few studies have focused on understanding spatiotemporal changes in drought attributes, such as drought seasonality, severity and duration across global tropics. Further, the nonlinear response between onset time and severity of streamflow droughts at a large scale are unknown. Leveraging ground-based streamflow observations, this study for the first time investigate changes in streamflow drought characteristics across global tropics using two 30 year climate normal periods: 1961–1990 and 1991–2020. Our analyses of changes in probability distributions of onset time and severity (deficit volume) of streamflow droughts over the two time windows show significant shifts towards higher values for Northeast and South American Monsoon region, Western Africa, eastern South Africa, north and eastern Australia. Around 55% of the sites show an increase in drought frequency in recent times. We found that in the recent times, only 27% of sites depict an increase in deficit volume accompanied by delayed onset. Further, we identify a few regional hotspots, such as Northeast and South American monsoon region, and eastern coast of Australia show an increased frequency of droughts with an upward trend in deficit volume in recent years. As expected, the individual changes in drought attributes have translated into changes in joint occurrences of their interdependent attributes, assuming the correlation between onset time and deficit volume. Our analyses show robust dependence strengths between onset time and deficit volume, which strengthen further in the recent time window over 50% of catchments. The nonstationary changes identified here in individual drought attributes and their joint dependence can alter the hazard potential of extreme droughts, which has consequences in risk management, climate adaptation and water resources planning.

Integrating conceptual and machine learning models to enhance daily-Scale streamflow simulation and assessing climate change impact in the watersheds of the Godavari basin, India

This study examined and addressed climate change's effects on hydrological patterns, particularly in critical places like the Godavari River basin. This study used daily gridded rainfall and temperature datasets from the Indian Meteorological Department (IMD) for model training and testing, 70% and 30%, respectively. To anticipate future hydrological shifts, the study harnessed the EC-Earth3 data, presenting an innovative methodology tailored to the unique hydrological dynamics of the Godavari River basin. The Sacramento model provided initial streamflow estimates for Kanhargaon, Nowrangpur, and Wairagarh. This approach melded traditional hydrological modeling with advanced multi-layer perceptron (MLP) capabilities. When combined with parameters like lagged rainfall, lagged streamflow, potential evapotranspiration (PET), and temperature variations, these initial outputs were further refined using the Sac-MLP model. A comparison with Sacramento revealed the superior performance of the Sac-MLP model. For instance, during training, the Nash Sutcliffe efficiency (NSE) values for the Sac-MLP witnessed an improvement from 0.610 to 0.810 in Kanhargaon, 0.580 to 0.692 in Nowrangpur, and 0.675 to 0.849 in Wairagarh. The results of the testing further corroborated these findings, as evidenced by the increase in the NSE for Kanhargaon from 0.890 to 0.910. Additionally, Nowrangpur and Wairagarh experienced notable improvements, with their NSE values rising from 0.629 to 0.785 and 0.725 to 0.902, respectively. Projections based on EC-Earth3 data across various scenarios highlighted significant shifts in rainfall and temperature patterns, especially in the far future (2071–2100). Regarding the relative change in annual streamflow, Kanhargaon projections under SSP370 and SSP585 for the far future indicate increases of 584.38% and 662.74%. Similarly, Nowrangpur and Wairagarh are projected to see increases of 98.27% and 114.98%, and 81.68% and 108.08%, respectively. This study uses EC-Earth3 estimates to demonstrate the Sac-MLP model's accuracy and importance in climate change water resource planning. The unique method for region-specific hydrological analysis provides vital insights for sustainable water resource management. This research provides a deeper understanding of climate-induced hydrological changes and a robust modeling approach for accurate predictions in changing environmental conditions.