Search

The trend analysis approach is used to estimate changing climate and its impact on the environment, agriculture and water resources. Innovative polygonal trend analyses are qualitative methods applied to detect changes in the environment. In this study, the Innovative Trend Pivot Analysis Method (ITPAM) and Trend Polygon Star Concept Method were applied for temperature trend detection in Soan River Basin (SRB), Potohar region, Pakistan. The average monthly temperature data (1995–2020) for 11 stations were used to create polygon graphics. Trend length and slope were calculated separately for arithmetic mean and standard deviation. The innovative methods produced useful scientific information, with the identification of monthly shifts and trend behaviors of temperature data at different stations. Some stations showed an increasing trend and others showed decreasing behavior. This increasing and decreasing variability is the result of climate change. The winter season temperature is increasing, and the months of December to February are getting warmer. Summer is expanding and pushing autumn towards winter, swallowing the early period of the cold season. The monthly polygonal trends with risk graphs depicted a clear picture of climate change in the Potohar region of Pakistan. The phenomena of observed average temperature changes, indicated by both qualitative methods, are interesting and have the potential to aid water managers' understanding of the cropping system of the Potohar region. [ABSTRACT FROM AUTHOR]

In this study, we evaluated the spatiotemporal variability and trends of hydro-climatic time series over the Modjo watershed, central Ethiopia using long-term records of precipitation, temperature, and river flow. In the study, the modified Mann–Kendall trend test was used to identify temporal trends, and Sen's slope estimator was employed to determine trend magnitude at multiple scales. Results of the study show that the spatial variability of annual and seasonal rainfall has decreased from north to south and west to east, whereas this was increased for the evapotranspiration (ETo) over the study watershed. Based on the trend tests, the annual and main rainy season flow exhibit significantly decreasing trends, with magnitudes of −19.2 mm/yr and −18.82 mm/yr, respectively, at a 95% confidence level. Whereas, the rainfall and ETo experienced monotonic trends during most of the wet season months and the annual total. The study highlights that the drop in river flow may be better explained by human activities, as seen by the weak association with climatic indicators. Water resources and irrigated agricultural production systems may be at risk in the area due to the unpredictability and declining trend in the river flow. Therefore, the study suggests appropriate watershed management, enhanced alternative water management techniques, and efficient use of the available water resources in the future. [ABSTRACT FROM AUTHOR]

Climate change–induced precipitation unpredictability has disastrous consequences for closed basins with sensitive hydrological equilibrium. Identifying changes and trends in precipitation characteristics in such catchments is an important step towards minimizing potential disasters. This study explores the precipitation variability of the Konya Closed Basin (Turkey), an over-exploited semi-arid basin. For this purpose, monthly precipitation data for the period 1971–2020 (50 years) of 11 meteorological observation gauges operated by the Turkish State Meteorological Service in the basin were utilized. Precipitation data were examined using two graphical innovative trend methods namely innovative polygon trend analysis (IPTA) and trend polygon star concept (TPSC) approaches. With these methods, periodic features in monthly precipitation data and trend transitions between months can be interpreted visually and linguistically. IPTA and TPSC methods were applied separately to both arithmetic mean data and standard deviation for each month's precipitation data of 11 stations. A general increasing trend in precipitation was obtained in most of the months for the stations used in the IPTA application. Most of the TPSC arrows, which were distributed throughout all four regions for both the arithmetic mean and standard deviation, were in the I and III regions. Based on the TPSC graphs, the longest transition arrows between two consecutives occurred in the first months of autumn (highly increasing) and summer (highly decreasing) in most of the stations. The results of this study are of great importance for the policies to be sustained regarding the ongoing climate change in the agricultural sector in KCB, which is both a water scarce basin and a region with intensive agricultural activities. [ABSTRACT FROM AUTHOR]

Recovering missing data and access to a complete and accurate streamflow data is of great importance in water resources management. This article aims to comparatively investigate the application of different classical and machine learning-based methods in recovering missing streamflow data in three mountainous basins in northern Iran using 26 years of data duration extending from 1991 to 2017. These include Taleghan, Karaj, and Latyan basins that provide municipal water for the capital Tehran. Two periods of artificial gaps of data were considered to avoid possible duration-based impacts that may affect the results. For this purpose, several methods are investigated including simple and multiple linear regressions (LR & MLR), artificial neural network (ANN) with five different structures, support vector regression (SVR), M5 tree and two Adaptive Neuro-Fuzzy Inference System (ANFIS) comprising Subtractive (Sub-ANFIS) and fuzzy C-means (FCM-ANFIS) classification. Although these methods have been used in different problems in the past, but the comparison of all these methods and the application of ANFIS using two clustering methods in missing data is new. Overall, it was noticed that machine learning-based methods yield better outputs. For instance, in the Taleghan basin and in the gap during 2014–2017 period it shows that the evaluation criteria of Root Mean Square Error (RMSE), Nash–Sutcliffe Index (NSE) and Coefficient of Determination (R 2) for the Sub-ANFIS method are 1.67 m 3 / s , 0.96 and 0.97, respectively, while these values for the LR are 3.46 m 3 / s , 0.83 and 0.87 respectively. Also, in Latyan basin during the gap of 1991–1994, FCM-ANFIS was found to be the best method to recover the missing monthly flow data with RMSE, NSE and R 2 criteria as 3.17 m 3 / s , 0.88 and 0.92, respectively. In addition, results indicated that using the seasonal index in the artificial neural network model improves the estimations. Finally, a Social Choice (SC) method using the Borda count was employed to evaluate the overall performance of all methods. [ABSTRACT FROM AUTHOR]

Exploration of groundwater is an integral part of viable resource growth for society, economy, and irrigation. However, uncontrolled utilization is mainly reported in urban and industries due to the increasing demand for water in semi-arid and arid regions of the world. In the background, groundwater demarcation for potential areas is vital in meeting necessary demand. The current study applied an integrated method comprising the analytical hierarchy process (AHP), multiple influence factors (MIF), combined with a linear regression curve and observatory well data for groundwater prospects mapping. Thematic maps such as flow direction, flow accumulation, elevation map, land use land cover, slope, soil texture, hill shade, geomorphology, normalized vegetation index, and groundwater depth map were generated utilizing remote sensing techniques. The relative weight of each parameter was estimated and then assigned to major and minor parameters. Potential zones for groundwater were classified into five classes, namely very good, good, moderate, poor, and very poor, based on AHP and MIF methods. A spatially explicit sensitivity and uncertainty analysis method to a GIS-based multi-criteria groundwater potential zone model is presented in this research. The study addressed a flaw in the way groundwater potential mapping results are typically presented in GIS-based multi-criteria decision analysis studies, where discrete class outputs are used without any assessment of their certainty with respect to variations in criteria weighting, which is one of the main contributors to output uncertainty. The study region is categorized based on inferred results as very poor, poor, marginal, and very good in potential ground quality 3.04 km2 is considered extremely poor, 3.33 km2 is considered poor, 64.42 km2 is considered very good, and 85.84 km2 is considered marginal zones, which shows reliable and potential implementation. The outcomes of AHP and MIF were validated by linear regression curve and actual water table in a study area. The study results help to formulate the potential demarcation of groundwater zones for future sustainable planning and development of groundwater sources. This study may be helpful to provide a cost-effective solution to water resources crises. The current study finding may be helpful for decision-makers and administrative professionals for sustainable management of groundwater resources for present and future demands. [ABSTRACT FROM AUTHOR]

This study focuses on a hydro-meteorological variable, streamflow, and its vulnerability to global warming. In this study, we analyze monthly streamflow in 40 catchments across four major South Indian River basins: Cauvery, Godavari, Krishna, and Mahanadi, within the Indian Peninsular region. The chosen catchments have available streamflow series records ranging from 41 to 49 years, spanning from 1970 to 2018. To uncover trends in the streamflow datasets, an innovative technique called Innovative Polygon Trend Analysis (IPTA) is used. IPTA detects trend transitions between successive time series parts and identifies possible trend pieces within a year, crucial for agricultural and irrigation practices. Results reveal diverse polygon patterns across different catchments, reflecting non-systematic streamflow variation. Non-systematic changes occur primarily in the Cauvery basin, followed by the Godavari, Krishna, and Mahanadi basins. Varied trends—increasing and decreasing—are dispersed across different catchments. Increasing trends were generally observed in the standard deviation of streamflow, while decreasing trends were more widespread in the mean of streamflow. Notably, the Godavari basin displays more prevalent decreasing trends in the majority of catchments for different months, followed by Cauvery and Krishna. Mahanadi basin has more catchments showing an increasing trend than other basins. The results of this study provide deeper insight into the streamflow dynamics within these four basins. [ABSTRACT FROM AUTHOR]

Water is an essential entity of life, and its major sources are surface and groundwater. However, surface water is excluded long ago for drinking purposes due to high contamination through different human activities such as the common release of domestic and industrial wastewater into nearby running water sources. No doubt industries are the economic backbone of any country; however, due to the lack of proper waste disposal policies in under-developing and developing countries, their common activity is disposing of wastewater into nearby surface water sources. This is causing great harm to not only the surface water but also the groundwater resources since the major source of groundwater recharge is through these surface waters. Rawat Industrial State is the second-largest industrial zone in Pakistan. Due to poor waste disposal policies, their wastewater is released into nearby streams. To check the linkage of surface water as a recharging source of groundwater, we applied an electrical resistivity survey including 12 profiles throughout the waste release zone and collected 18 water samples to find the maximum affective depth of these contaminants. Our results have confirmed that all the major aquifers in the study area are receiving their recharge through these contaminated streams, and it is polluting the groundwater up to the sampled depth of 350 ft. [ABSTRACT FROM AUTHOR]

Groundwater quality risk assessment is vital to protect this precious resource, because increasing anthropogenic and agricultural activities combined with limited precipitation deteriorate the groundwater quality particularly in the arid regions. Therefore, the assessment of groundwater quality using hydro-chemical and spatial analysis can provide the guidelines for efficient management of groundwater resources. In present study, a total of 87 samples were collected from various pumping wells in district Multan, Pakistan. These samples were analysed for groundwater quality parameters like electrical conductivity (EC), total dissolve solids (TDS), pH, Na+, Ca2+ + Mg2+, Cl−, CO32−, HCO3−, sodium adsorption ratio (SAR), sodium percentage (Na%), total hardness (TH), residual sodium carbonate (RSC) and Kelly's ratio (KR). The Wilcox, United States Salinity Laboratory (USSL) and permeability index (PI) diagrams were drawn to classify the water into excellent, good, marginal and poor-quality groundwater for irrigation obligatory. Using the ArcGIS vs 10, an ordinary kriging method with best fit semivariogram model was applied for preparation of spatial distribution maps. According to Wilcox classification, 40% of groundwater samples fall in 'Excellent to a Good' category. USSL diagram showed that 27% of groundwater samples fall in 'Medium Salinity' and 'Low sodium hazard' (C2S1) class. The PI values were found in the range of 22 to 95 meq/L with an average value of 58.5 meq/L. Similarly, the spatial analysis showed that upper part (northeast and northwest) of the Multan District have good quality of groundwater for irrigation. Furthermore, the finding may help to make the spatial management decision for groundwater in the region. [ABSTRACT FROM AUTHOR]

The implications of climate change on water resources are of major concerns worldwide. Arid regions of the world need proper management for the water resources as climatic changes are bringing severe problems for their sustainability. Pothohar area of Pakistan is the most essential in this regard. A data set of 30 years of temperature, precipitation, and river discharge across the Pothohar Plateau were obtained from Pakistan Meteorological Department and Water and Power Development Authority, respectively. Hydro-meteorological data were subjected to a series of statistical methods for detecting trend and abrupt point changes. A rising trend in mean annual temperatures were noticed in all stations with most significant change in Jhelum and Attock stations (p < 0.05) during the period of 30 years while precipitation trends were found irregular, representing both scenarios of flash floods and droughts with non-significant change (p > 0.05). In addition, 2000 and 2001 are drought years in 30 years of time series, and the driest years were seen from 1999 to 2002. The discharge of River Haro and Soan were found downward significantly (p > 0.05), posing a threatening challenge to the future of water resources in this area. The correlation coefficient test revealed a substantial relationship between mean annual precipitation and river discharge, although the linear regression model demonstrated a diminishing trend with mean annual precipitation. Based on the analysis, it is concluded that modern techniques of irrigation and agriculture should be adopted to protect the future of the Pothohar area from climate change related extreme situations. [ABSTRACT FROM AUTHOR]

The use of new data-driven approaches based on the so-called expert systems to simulate runoff generation processes is a promising frontier that may allow for overcoming some modeling difficulties related to more complex traditional approaches. The present study highlights the potential of expert systems in creating regional hydrological models, for which they can benefit from the availability of large database. Different soft computing models for the reconstruction of the monthly natural runoff in river basins are explored, focusing on a new class of heuristic models, which is the Multi-Gene Genetic Programming (MGGP). The region under study is Sicily (Italy), where a regression based rainfall-runoff model, here used as benchmark model, was previously built starting from the analysis of a regional database relative to several gauged watersheds across the region. In the present study, different models are created using the same dataset, including: six MGGPs generated considering different modeling set-up; a Multi-Layer Perceptron Artificial Neural Network (ANN); two new hybrid models (ANN-MGGP), combining a Classifier ANN and two MGGPs that simulate separately low and high runoff. Results show how all the soft computing models perform similarly and outperform the benchmark model, demonstrating that MGGP can be considered as a valid alternative to the much more consolidated ANN technique. The new introduced hybrid ANN-MGGP is the only model showing at least satisfactory performance (i.e. Nash–Sutcliffe Efficiency above 0.5) over the full range of 38 watersheds explored, representing a useful regional tool for reconstructing monthly runoff series also at ungauged sites. [ABSTRACT FROM AUTHOR]

Climate change (precipitation and temperature) has significantly affected the hydrological regimes and future climate projection. Integration of climate model with physical based model is crucial for quantitative measurement of changes in surface water regime. For accurate estimation, modelling framework need finer scale resolution of climate model output. In this study, we examined the bias corrected, statistically downscale models drawn from the NASA, Earth Exchange Global Daily Downscaled Projections–Coupled Model Intercomparison Project Phase 5 (NEX-GDDP-CMIP5) over the study region. The rainfall and temperature projection output from the INMCM-4, MRI-CGCM3 and their ensemble mean performed well over the Mahi River basin (MRB), India. In this study, the climate data integrated with the SWAT model to analyse the potential impact of climate change on the discharge of MRB. The finding indicates that in the near future (2011–2040) projection of annual average streamflow increases by 76.74% based on the INMCM-4 outputs, 25% based on the MRI-CGCM3 outputs, and 24.53% based on the ensemble mean in comparison to the baseline period (1981–2010). Further, the modelling results of mean monthly streamflow in rainy season indicated that the lowest and highest streamflow changes will be ranging from about 631.07–2718.42 m3/s as observed by INMCM-4, 491.71–2938 m3/s observed by MRI-CGCM3, 513.02–2270.18 m3/s observed by ensemble mean, in the near future. Similarly, in the summer season, the lowest level of stream flow is found to be 158.27 m3/s observed by MRI-CGCM3, 193.38 m3/s (ensemble mean) and 258.53 m3/s (INMCM-4), respectively. Additionally, the streamflow trend was assessed by Mann–Kendall and Sen's slope method at the monthly, seasonal and annual scales. The future streamflow projection represented the ascending trend observed in south west and winter monsoon, while the descending trend was observed in pre-monsoon and post-monsoon under the INMCM-4, MRI-CGCM3, and ensemble mean. Results on projected precipitation, temperature and streamflow accretion would help to develop effective adaptation measures for reducing the impacts of climate change and to work out long-term water resource management plans in the river basin. [ABSTRACT FROM AUTHOR]

Conceptual models are common and efficient approaches to study short term and long term streamflow change in catchments. However, the possible role of groundwater dependent evapotranspiration on streamflow is not investigated well in literature. To fill this gap, our current study aims to investigate the importance of groundwater dependent evapotranspiration on streamflow by using several conceptual models (i.e., the ABCD type and the Budyko-type models) together with spatiotemporal groundwater level data. The performances of these models in streamflow estimation were assessed in the Harvey River Catchment of Western Australia. The results showed that monthly streamflow estimation was significantly improved, indicating the importance of groundwater level in short time scale. Yet, due to simplification and assumptions necessary for the conceptual models, these models were not able to accurately estimate peak-flow and base-flow variations in the study area. Hence, we further combine the conceptual models with a statistical module to form a hybrid conceptual-statistical framework. The result showed clear increase in the accuracy of the estimation and notable improvement in the models' performances, affirming that using only conceptual models may not be able to grasp all the aspects required for accurate streamflow estimation. The developed hybrid framework can be adapted to other catchments, particularly the areas with non-uniform or limited data. [ABSTRACT FROM AUTHOR]

The current study investigated spatiotemporal trends in hydro-meteorological data on seasonal and annual scale in the Kabul River Basin (KRB). Thirty years (1981–2010) precipitation and temperature gridded data of 88 stations were retrieved from National Centers for Environmental Prediction, Climate Forecast System Reanalysis (NCEP-CFSR). Streamflow data of the four hydrometric stations (Chitral, Chakdara, Warsak, and Nowshera) was provided by the Water and Power Development Authority (WAPDA), Pakistan. The Mann–Kendall and Sen's slope tests were applied at a significance level of 5% to identify trends. To avoid the serial correlation's effect on the test results, Trend Free Pre-Whitening (TFPW) was applied. Spatial maps of precipitation and mean temperature were prepared using the geographic information system-based Inverse Distance Weighting method. Non-significant trends were identified for precipitation during winter, summer, autumn, and annually at 92%, 93%, 86%, and 80% grid stations, respectively. Significant decreasing trends were observed during the spring at 63% of the grid stations. Similarly, non-significant trends were found for mean temperature during winter, spring, summer, and annually in 74%, 60%, 79%, and 60% grid stations, respectively, while 48% of the grid stations showed significant increasing trends during the autumn. Streamflow at Chitral indicated significant increasing trends during winter (ZMK = 2.37) and spring (ZMK = 2.40), whereas significant increasing trends were found at Chakdara on annual and seasonal basis. Significantly increasing trends were detected at Warsak during autumn (ZMK = 2.09). The findings can be beneficial for water resource managers, planners, researchers, and hydrologists for their professional applications. [ABSTRACT FROM AUTHOR]

Human activities and climate change are recognized as two of the most important drivers of hydrologic variability and have attracted the interest of researchers over the past decade. Changes in land use, dam construction, agricultural development, and global warming are forces that directly or indirectly impact the global and local hydrologic regime. This study examines the effects of these drivers on streamflow and sediment transport in the Gorganroud watershed, located in the north of Iran. In addition, the most sensitive land use patterns are detected using statistical approaches and a hydrologic model. The current study's principal argument is based on the variability of land use patterns during the modeling procedure (2007–2019). The Soil and Water Assessment Tool (SWAT) model is used to consider the land use dynamics during the simulation period based on the hydrological regime of the reference period. The Simple Differential Method (SDM) and Climate Elasticity Method (CEM) are utilized to estimate the contribution rates of land use and climate change in streamflow and sediment transport changes. The results indicate that changes in land use have contributed more than 60% to streamflow and sediment regime changes in all subbasins. A sensitivity analysis of land uses and the spatial distribution of the Human Contribution Rate (HCR) over the study area reveal that an increase in orchard land use (8.7% during the computational period) is primarily responsible for these significant changes. [ABSTRACT FROM AUTHOR]

This study utilises ground, satellite and model data to investigate the observed and future precipitation changes in Pakistan. Pakistan Meteorological Department's (PMD) monthly precipitation data set along with the Tropical Rainfall Measuring Mission (TRMM) monthly dataset TRMM_3B43 (0.25° × 0.25° resolution) and European Centre for Medium-Range Weather Forecasts's (ECMWF) monthly reanalysis product ERA5 have been used to evaluate rainfall trends over the climatic zones of Pakistan through Mann–Kendall test, Sen's slope estimator and innovative trend analysis for the time period 1978–2018. Community Climate System Model (CCSM4) projections have been employed to explore the projected changes in precipitation until 2099. Furthermore, TRMM and CCSM4 projections have been correlated and validated using root mean square error (RMSE) and mean bias error (MBE). There is a good correlation between TRMM and PMD ground observation at all stations of the country for all seasons, with correlation coefficient values ranging from 0.89 (November) to 0.97 (July and August). However, ERA5 monthly precipitation tends to overestimate rainfall in the winter months. The study shows a decreasing trend in winter precipitation in all zones of the country with a significant decrease over western mountains, i.e. zone C of the country. During 2008–2018, a sharp decrease in winter precipitation is observed as compared to the baseline value of 1978–2007 in all climatic zones. Rainy days have also shown a decrease in winter and pre-monsoon seasons. There seems to be a shift in precipitation from winter towards pre-monsoon season as pre-monsoon precipitation in the last 11 years increased in all zones except for zone C. Coherently, there is a decrease in an area affected by winter precipitation and an increase in area for pre-monsoon precipitation. Future precipitation estimates from the CCSM4 model for RCP 4.5 and RCP 8.5 overestimate precipitation in most parts of the country for the first 9 observed years (2010–2018) and predict a rise in precipitation by 2099 which is more pronounced in the northern and western Pakistan while a decrease is predicted for the plains of the country, which might have negative consequences for agriculture. [ABSTRACT FROM AUTHOR]

Streamflow simulation in a snow dominated basin is complex due to the presence of a high number of interrelated hydrological processes. This complexity is affected by the delayed responses of the catchment to snow accumulation and snow melting processes. In this study, long short-term memory (LSTM) and artificial neural network (ANN) models were utilized for rainfall–runoff simulation in a snow dominated basin, the Carson River basin in the United States (US). The input structure of the models was determined using the simulated annealing algorithm with a naïve Bayes model from a high dimensional feature space to represent the long-term impacts of historical events (i.e. the hysteresis effect) on current observations. Further, to represent the different responses of the catchment in the model structure, a base flow separation method was included in the simulation framework. The obtained performance indices, root mean square error, percentage bias, Nash–Sutcliffe and Kling–Gupta efficiencies are 0.331 m3 s−1, 13.00%, 0.848, and 0.852 for the ANN model and 0.235 m3 s−1, − 0.80%, 0.923, and 0.934 for the LSTM model, respectively. The proposed methodology was found to be promising for improving the streamflow simulation capability of LSTM and ANN models by only considering precipitation, temperature, and potential evapotranspiration as input variables. Analysing the flow duration curves indicated that the LSTM model is more efficient in representing different flow dynamics within the basin due to embedded cell states. Further, the uncertainty and reliability analyses were conducted by using expanded uncertainty ( U 95 ), reliability, and resilience indices. The obtained U 95 , reliability and resilience indices are 1.78–1.72 m3 s−1, 31.28–66.67% and 11.58–38.27% for the ANN and LSTM models, respectively, showed that the LSTM model produced less uncertainty and is more reliable. However, while lacking a memory component, the proposed methodology significantly contributes to the simulation capability of the ANN model in rainfall–runoff modelling. The results of this study indicated that the proposed methodology could enhance the learning capabilities of machine learning models in rainfall–runoff simulation. [ABSTRACT FROM AUTHOR]

In water scarce and non-irrigated regions, groundwater is considered as one of the vital sources to fulfil the demand of fresh water for the survival of life and to meet with the crop water requirements. However, overexploitation and increased urbanization of groundwater in Rawalpindi City of Pakistan is causing a significant disturbance to cope with the water requirements. Therefore, a comprehensive study for assessment of subsurface lithology and groundwater depth is required to cope with increased water demands and to minimize the groundwater imbalance. In this study, electrical resistivity survey method was adopted for the assessment and identification of subsurface lithology and aquifer potential sites in the study area. ABEM SAS (signal averaging system) instrument was used for the collection of field data on which Schlumberger configuration was applied. The maximum current electrode spacing (AB) was 100–200 m based on the feasibility of the sites selected for the execution of the survey. Moreover, the field data was analyzed with the help of computer iteration software, i.e., IX1D, and data interpretation was made from the resistivity values and results were compared with the borehole log data obtained from Rawalpindi Development Authority (RDA) and previous studies. The results indicated that the deepest water table in Rawalpindi City is at municipal stadium with a depth of 51 m while the shallowest water table was found at Liaqat Bagh with a depth of 35 m. Furthermore, the analysis also demonstrated that the dominant strata of the study area were comprised of sandy clay, gravel and boulder sand, and gravel and average depth to the water table was 45.5 m. It is believed that the outcomes of this research work will help the relevant authorities in the selection of Managed Artificial Recharge (MAR) sites for Rawalpindi City, Pakistan. [ABSTRACT FROM AUTHOR]

Runoff forecasting is essential for the reasonable use of regional water resources, flood prevention, and mitigation, as well as the development of ecological civilization. Runoff is influenced by the intersection of many factors, and the change process is extremely complex, showing significant stochasticity, nonlinearity, and heterogeneity, making traditional prediction models less adaptable. The Hodrick–Prescott filter (HP filter) is a well-established signal separation method. The traditional GM(1,1) model is capable of portraying the growth trend of the time series but cannot capture the periodic characteristics of the time series. Therefore, a novel coupled prediction model-HPF-GM(1,1) model is proposed in this study and applied to the runoff prediction of the Zhuzhou section of Xiangjiang River in Hunan Province. This model enables to separate seasonal factors from non-seasonal factors in the runoff time series, and introduce seasonal factors based on the traditional GM(1,1) model, which solves the challenge that the traditional GM(1,1) model is unable to predict seasonal time series. The results show that the HPF-GM(1,1) model has a mean relative error of 4.82%, a root mean square error of 7.44, and a Nash efficiency coefficient of 0.93, which is better than the traditional GM(1,1) model, the DGGM(1,1) model and the SGM(1,1) model of prediction accuracy. Obviously, the HP filter provides a new approach to data pre-processing of runoff series and the proposed HPF-GM(1,1)-coupled model extends new ideas for nonlinear runoff prediction. [ABSTRACT FROM AUTHOR]

As a complex hydrological problem, rainfall-runoff (RR) modeling is of importance in runoff studies, water supply, irrigation issues, and environmental management. Among the variety of approaches for RR modeling, conceptual approaches use physical concepts and are appropriate methods for representation of the physics of the problem while may fail in competition with their advanced alternatives. Contrarily, machine learning approaches for RR modeling provide high computation ability however, they are based on the data characteristics and the physics of the problem cannot be completely understood. For the sake of overcoming the aforementioned deficiencies, this study coupled conceptual and machine learning approaches to establish a robust and more reliable RR model. To this end, three hydrological process-based models namely: IHACRES, GR4J, and MISD are applied for runoff simulating in a snow-covered basin in Switzerland and then, conceptual models' outcomes together with more hydro-meteorological variables were incorporated into the model structure to construct multilayer perceptron (MLP) and support vector machine (SVM) models. At the final stage of the modeling procedure, the data fusion machine learning approach was implemented through using the outcomes of MLP and SVM models to develop two evolutionary models of fusion MLP and hybrid MLP-whale optimization algorithm (MLP-WOA). As a result of conceptual models, the IHACRES-based model better simulated the RR process in comparison to the GR4J, and MISD models. The effect of incorporating meteorological variables into the coupled hydrological process-based and machine learning models was also investigated where precipitation, wind speed, relative humidity, temperature and snow depth were added separately to each hydrological model. It is found that incorporating meteorological variables into the hydrological models increased the accuracy of the models in runoff simulation. Three different learning phases were successfully applied in the current study for improving runoff peak simulation accuracy. This study proved that phase one (only hydrological model) has a big error while phase three (coupling hydrological model by machine learning model) gave a minimum error in runoff estimation in a snow-covered catchment. The IHACRES-based MLP-WOA model with RMSE of 8.49 m3/s improved the performance of the ordinary IHACRES model by a factor of almost 27%. It can be considered as a satisfactory achievement in this study for runoff estimation through applying coupled conceptual-ML hydrological models. Recommended methodology in this study for RR modeling may motivate its application in alternative hydrological problems. [ABSTRACT FROM AUTHOR]

Global warming and abrupt seasonal changes are the current challenging issues across the globe. This study investigated the trends in average temperature and precipitation on monthly basis for the designated stations of Najd and Hejaz regions of Saudi Arabia, namely Qassim, Riyadh, Tabuk, Medina, Mecca, Al-Baha, Hail and Jizan. Two non-parametric techniques are used for this purpose. In the first stage, the Mann–Kendall test is applied to determine the trends and in the later stage, the Sen’s slope test is utilized to determine the extent of the trends. The Mann–Kendall test with a 5% significance level was applied to the 30-year data of eight selected stations from 1985 to 2014. The analysis showed increasing trends only in mean monthly temperature at all eight stations throughout the year, whereas Tabuk and Riyad showed increasing trends, while Medina, Qassim and Hail exhibited decreasing trends in precipitation in four different months of the year. The trend in precipitation at Mecca, Al-Baha and Jizan was insignificant throughout the year. From the results of Sen’s Slope test, it was concluded that the trend pattern is associated with the intensity of trend values. It was also found that temperature is rising in three of the four seasons on an annual basis during the study period. From the results, longer summer and shorter winter are expected in the future due to rising temperature. It can also be inferred that the increasing temperature will increase the evaporation from the sea causing a humid environment which can result in a heavy downpour in the near future especially in coastal areas. The rising temperature and changing pattern of precipitation may increase uncertainty in developing strategies for water resources management. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal characteristics of flash floods is important for reasonable and accurate identification of high-risk regions and for prediction of future hydrological regimes. Therefore, using time series datasets (1979–2015) of historical flash flood events, rainfall, and land use in the Hengduan Mountains region (HMR) of China, this study analyzed the effects of changes in precipitation and land use on spatiotemporal variation of flash floods. Analysis of the linear trend revealed that flash floods increased by 12 events/10 years and that 82% of events occurred in the flood season (June–August). The increase in flash flood events was found closely correlated with the increased frequency (rainstorm days increased by 3.5 d/10 years) and magnitude (rainstorm amount increased by 10.2 mm/10 years) of heavy rainfall as well as the expansion of the area of artificial surfaces (increase of 999 km2). Morlet wavelet analysis revealed significant periodic variation in the occurrence of flash floods on scales of 3–7, 8–15, and 21–31 years. On the basis of the standard elliptical difference, we identified displacement of the center of gravity of flash flooding from the northwest toward the southeast. Generally, more disasters were recorded in southern parts of the HMR owing to the frequent occurrence of rainstorms and the increase in area of both cultivated land and artificial surfaces with higher runoff potential. These findings improve understanding regarding the spatiotemporal dynamics of flash floods in the HMR and could support policymakers in identifying high-risk areas in mountainous watershed. [ABSTRACT FROM AUTHOR]