Water

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19 pages, 6485 KiB

Open AccessArticle

Spatial and Temporal Variations of Drought in Inner Mongolia, China

by Qiang An, Huaxiang He, Qianwen Nie, Yingjie Cui, Juanjuan Gao, Chuanjiang Wei, Xinmin Xie and Jinjun You

Water 2020, 12(6), 1715; https://doi.org/10.3390/w12061715 - 16 Jun 2020

Cited by 38 | Viewed by 3500

Abstract

Drought has become an important natural disaster, affecting the development of Inner Mongolia, as an important animal husbandry region in China. In this study, the characteristics and trends of the Inner Mongolia drought are thoroughly analysed by calculating the standardised precipitation evapotranspiration index [...] Read more.

Drought has become an important natural disaster, affecting the development of Inner Mongolia, as an important animal husbandry region in China. In this study, the characteristics and trends of the Inner Mongolia drought are thoroughly analysed by calculating the standardised precipitation evapotranspiration index (SPEI) at different time scales, based on monthly precipitation and temperature data from 40 national meteorological stations in Inner Mongolia from 1958 to 2019. Subsequently, the area drought intensity (ADI), which is a comprehensive evaluation indicator for evaluating drought intensity within the region, is proposed, taking into account the effects of the persistent drought on drought intensity. The results show that drought has increased during this period, with a remarkable increase in the frequency and the area of drought. The areas with stronger drought intensity are mainly located in the west, north central, and the western area of the east. Since 2000, March to October are identified as drought-prone months and April is characterised as the month with the highest frequency of drought. The inflection points of SPEI and climate conditions both appeared in 1990s and it is speculated that the increase in drought may have been caused by excessive temperature rise. The frequency, coverage area, and continuous duration of drought have increased greatly after climate mutation in this region. According to the changes in the spatial distribution of the ADI and frequency of drought occurrence, the drought-stricken areas shifted from the southeast to the northwest after climate mutations. The findings from this study provide a theoretical basis for the drought management of Inner Mongolia. Full article

(This article belongs to the Special Issue Droughts and Floods Assessment and Monitoring Using Remote Sensing and Geospatial Techniques)

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18 pages, 3710 KiB

Open AccessArticle

Predicting River Flow Using an AI-Based Sequential Adaptive Neuro-Fuzzy Inference System

by Chiara Belvederesi, John A. Dominic, Quazi K. Hassan, Anil Gupta and Gopal Achari

Water 2020, 12(6), 1622; https://doi.org/10.3390/w12061622 - 6 Jun 2020

Cited by 26 | Viewed by 4565

Abstract

Artificial intelligence (AI) techniques have been successfully adopted in predictive modeling to capture the nonlinearity of natural systems. The high seasonal variability of rivers in cold weather regions poses a challenge to river flow forecasting, which tends to be complex and data demanding. [...] Read more.

Artificial intelligence (AI) techniques have been successfully adopted in predictive modeling to capture the nonlinearity of natural systems. The high seasonal variability of rivers in cold weather regions poses a challenge to river flow forecasting, which tends to be complex and data demanding. This study proposes a novel technique to forecast flows that use a single-input sequential adaptive neuro-fuzzy inference system (ANFIS) along the Athabasca River in Alberta, Canada. After estimating the optimal lead time between four hydrometric stations, gauging data measured near the source were used to predict river flow near the mouth, over approximately 1000 km. The performance of this technique was compared to nonsequential and multi-input ANFISs, which use gauging data measured at each of the four hydrometric stations. The results show that a sequential ANFIS can accurately predict river flow (r² = 0.99, Nash–Sutcliffe = 0.98) with a longer lead time (6 days) by using a single input, compared to nonsequential and multi-input ANFIS (2 days). This method provides accurate predictions over large distances, allowing for flow forecasts over longer periods of time. Therefore, governmental agencies and community planners could utilize this technique to improve flood prevention and planning, operations, maintenance, and the administration of water resource systems. Full article

(This article belongs to the Special Issue Droughts and Floods Assessment and Monitoring Using Remote Sensing and Geospatial Techniques)

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24 pages, 5667 KiB

Open AccessEditor’s ChoiceArticle

Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

by Romulus Costache, Phuong Thao Thi Ngo and Dieu Tien Bui

Water 2020, 12(6), 1549; https://doi.org/10.3390/w12061549 - 29 May 2020

Cited by 57 | Viewed by 4686

Abstract

This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In this regard, a geospatial database [...] Read more.

This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In this regard, a geospatial database of the flood with 178 flood locations and with 10 flash-flood predictors was prepared and used for this proposed approach. AHP and FR were used for processing and coding the predictors into a numeric format, whereas DNN, which is a powerful and state-of-the-art probabilistic machine leaning, was employed to build an inference flash-flood model. The reliability of the models was verified with the help of Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC), and several statistical measures. The result shows that the two proposed ensemble models, DNN-AHP and DNN-FR, are capable of predicting future flash-flood areas with accuracy higher than 92%; therefore, they are a new tool for flash-flood studies. Full article

(This article belongs to the Special Issue Droughts and Floods Assessment and Monitoring Using Remote Sensing and Geospatial Techniques)

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17 pages, 3679 KiB

Open AccessArticle

Integration of Microwave and Optical/Infrared Derived Datasets from Multi-Satellite Products for Drought Monitoring

by Zhengdong Wang, Peng Guo, Hong Wan, Fuyou Tian and Linjiang Wang

Water 2020, 12(5), 1504; https://doi.org/10.3390/w12051504 - 25 May 2020

Cited by 9 | Viewed by 3769

Abstract

Drought is among the most common natural disasters in North China. In order to monitor the drought of the typically arid areas in North China, this study proposes an innovative multi-source remote sensing drought index called the improved Temperature–Vegetation–Soil Moisture Dryness Index (iTVMDI), [...] Read more.

Drought is among the most common natural disasters in North China. In order to monitor the drought of the typically arid areas in North China, this study proposes an innovative multi-source remote sensing drought index called the improved Temperature–Vegetation–Soil Moisture Dryness Index (iTVMDI), which is based on passive microwave remote sensing data from the FengYun (FY)3B-Microwave Radiation Imager (MWRI) and optical and infrared data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and takes the Shandong Province of China as the research area. The iTVMDI integrated the advantages of microwave and optical remote sensing data to improve the original Temperature–Vegetation–Soil Moisture Dryness Index (TVMDI) model, and was constructed based on the Modified Soil-Adjusted Vegetation Index (MSAVI), land surface temperature (LST), and downscaled soil moisture (SM) as the three-dimensional axes. The global land data assimilation system (GLDAS) SM, meteorological data and surface water were used to evaluate and verify the monitoring results. The results showed that iTVMDI had a higher negative correlation with GLDAS SM (R = −0.73) than TVMDI (R = −0.55). Additionally, the iTVMDI was well correlated with both precipitation and surface water, with mean correlation coefficients (R) of 0.65 and 0.81, respectively. Overall, the accuracy of drought estimation can be significantly improved by using multi-source satellite data to measure the required surface variables, and the iTVMDI is an effective method for monitoring the spatial and temporal variations of drought. Full article

(This article belongs to the Special Issue Droughts and Floods Assessment and Monitoring Using Remote Sensing and Geospatial Techniques)

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16 pages, 6431 KiB

Open AccessArticle

Spatial and Temporal Characterization of Drought Events in China Using the Severity-Area-Duration Method

by Xiaoli Yang, Linyan Zhang, Yuqian Wang, Vijay P. Singh, Chong-Yu Xu, Liliang Ren, Mengru Zhang, Yi Liu, Shanhu Jiang and Fei Yuan

Water 2020, 12(1), 230; https://doi.org/10.3390/w12010230 - 14 Jan 2020

Cited by 19 | Viewed by 3861

Abstract

Global climate change not only affects the processes within the water cycle but also leads to the frequent occurrences of local and regional extreme drought events. In China, spatial and temporal characterizations of drought events and their future changing trends are of great [...] Read more.

Global climate change not only affects the processes within the water cycle but also leads to the frequent occurrences of local and regional extreme drought events. In China, spatial and temporal characterizations of drought events and their future changing trends are of great importance in water resources planning and management. In this study, we employed self-calibrating Palmer drought severity index (SC-PDSI), cluster algorithm, and severity-area-duration (SAD) methods to identify drought events and analyze the spatial and temporal distributions of various drought characteristics in China using observed data and CMIP5 model outputs. Results showed that during the historical period (1961–2000), the drought event of September 1965 was the most severe, affecting 47.07% of the entire land area of China, and shorter duration drought centers (lasting less than 6 months) were distributed all over the country. In the future (2021–2060), under both representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios, drought is projected to occur less frequently, but the duration of the most severe drought event is expected to be longer than that in the historical period. Furthermore, drought centers with shorter duration are expected to occur throughout China, but the long-duration drought centers (lasting more than 24 months) are expected to mostly occur in the west of the arid region and in the northeast of the semi-arid region. Full article

(This article belongs to the Special Issue Droughts and Floods Assessment and Monitoring Using Remote Sensing and Geospatial Techniques)

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19 pages, 7829 KiB

Open AccessArticle

Hydrological/Hydraulic Modeling-Based Thresholding of Multi SAR Remote Sensing Data for Flood Monitoring in Regions of the Vietnamese Lower Mekong River Basin

by Nguyen Hong Quang, Vu Anh Tuan, Le Thi Thu Hang, Nguyen Manh Hung, Doan Thi The, Dinh Thi Dieu, Ngo Duc Anh and Christopher R. Hackney

Water 2020, 12(1), 71; https://doi.org/10.3390/w12010071 - 23 Dec 2019

Cited by 17 | Viewed by 6100

Abstract

Synthetic Aperture Radar (SAR) remote sensing data can be used as an effective alternative to detect surface water and provide useful information regarding operational flood monitoring, in particular for the improvement of rapid flood assessments. However, this application frequently requires standard and simple, [...] Read more.

Synthetic Aperture Radar (SAR) remote sensing data can be used as an effective alternative to detect surface water and provide useful information regarding operational flood monitoring, in particular for the improvement of rapid flood assessments. However, this application frequently requires standard and simple, yet robust, algorithms. Although thresholding approaches meet these requirements, limitations such as data inequality over large spatial regions and challenges in estimating optimal threshold values remain. Here, we propose a new method for SAR water extraction named Hammock Swing Thresholding (HST). We applied this HST approach to four SAR remote sensing datasets, namely, Sentinel-1, ALOS-2, TerraSAR-X, and RadarSAT-2 for flood inundation mapping for a case study focusing on the Tam Nong district in the Vietnam Mekong delta. A 2D calibrated Hydrologic Engineering Centers River Analysis System (HEC-RAS) model was coupled with the HST outputs in order to estimate the optimal thresholds (OTs) where the SAR-based water masks fitted best with HEC-RAS’s inundation patterns. Our results showed that water levels extracted from Sentinel-1 data best agreed with the HEC-RAS water extent (88.3%), following by ALOS-2 (85.9%), TerraSAR-X (77.2%). and RadarSAT-2 (72%) at OTs of −15, 68, 21, and 35 decibel (dB), respectively. Generated flood maps indicated changes in the flood extent of the flooding seasons from 2010 and 2014–2016 with variations in spatial extent appearing greater in the TerraSAR-X and RadarSAT-2 higher resolution maps. We recommend the use of OTs in applications of flood monitoring using SAR remote sensing data, such as for an open data cube (ODC). Full article

(This article belongs to the Special Issue Droughts and Floods Assessment and Monitoring Using Remote Sensing and Geospatial Techniques)

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19 pages, 5926 KiB

Open AccessArticle

Spatio-Temporal Groundwater Drought Monitoring Using Multi-Satellite Data Based on an Artificial Neural Network

by Jae Young Seo and Sang-Il Lee

Water 2019, 11(9), 1953; https://doi.org/10.3390/w11091953 - 19 Sep 2019

Cited by 22 | Viewed by 5503

Abstract

Drought is a complex phenomenon caused by lack of precipitation that affects water resources and human society. Groundwater drought is difficult to assess due to its complexity and the lack of spatio-temporal groundwater observations. In this study, we present an approach to evaluate [...] Read more.

Drought is a complex phenomenon caused by lack of precipitation that affects water resources and human society. Groundwater drought is difficult to assess due to its complexity and the lack of spatio-temporal groundwater observations. In this study, we present an approach to evaluate groundwater drought based on relatively high spatial resolution groundwater storage change data. We developed an artificial neural network (ANN) that employed satellite data (Gravity Recovery and Climate Experiment (GRACE) and Tropical Rainfall Measuring Mission (TRMM)) as well as Global Land Data Assimilation System (GLDAS) models. The Standardized Groundwater Level Index (SGI) was calculated by normalizing ANN-predicted groundwater storage changes from 2003 to 2015 across South Korea. The ANN-predicted 25 km groundwater storage changes correlated well with both the in situ and the water balance equation (WBE)-estimated groundwater storage changes, with mean correlation coefficients of 0.87 and 0.64, respectively. The Standardized Precipitation–Evapotranspiration Index (SPEI), having an accumulation time of 1–6 months, and the Palmer Drought Severity Index (PDSI) were used to validate the SGI. The results showed that the SGI had a pattern similar to that of SPEI-1 and SPEI-2 (1- and 2-month accumulation periods, respectively), and PDSI. However, the SGI performance fluctuated slightly due to its relatively short study period (13 years) as compared to SPEI and PDSI (more than 30 years). The SGI, which was developed using a new approach in this study, captured the characteristics of groundwater drought, thus presenting a framework for the assessment of these characteristics. Full article

(This article belongs to the Special Issue Droughts and Floods Assessment and Monitoring Using Remote Sensing and Geospatial Techniques)

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14 pages, 3241 KiB

Open AccessArticle

Analysing the Relationship between Multiple-Timescale SPI and GRACE Terrestrial Water Storage in the Framework of Drought Monitoring

by Carmelo Cammalleri, Paulo Barbosa and Jürgen V. Vogt

Water 2019, 11(8), 1672; https://doi.org/10.3390/w11081672 - 13 Aug 2019

Cited by 26 | Viewed by 5641

Abstract

The operational monitoring of long-term hydrological droughts is often based on the standardised precipitation index (SPI) for long accumulation periods (i.e., 12 months or longer) as a proxy indicator. This is mainly due to the current lack of near-real-time observations of relevant hydrological [...] Read more.

The operational monitoring of long-term hydrological droughts is often based on the standardised precipitation index (SPI) for long accumulation periods (i.e., 12 months or longer) as a proxy indicator. This is mainly due to the current lack of near-real-time observations of relevant hydrological quantities, such as groundwater levels or total water storage (TWS). In this study, the correlation between multiple-timescale SPIs (between 1 and 48 months) and GRACE-derived TWS is investigated, with the goals of: (i) evaluating the benefit of including TWS data in a drought monitoring system, and (ii) testing the potential use of SPI as a robust proxy for TWS in the absence of near-real-time measurements of the latter. The main outcomes of this study highlight the good correlation between TWS anomalies (TWSA) and long-term SPI (12, 24 and 48 months), with SPI-12 representing a global-average optimal solution (R = 0.350 ± 0.250). Unfortunately, the spatial variability of the local-optimal SPI underlines the difficulty in reliably capturing the dynamics of TWSA using a single meteorological drought index, at least at the global scale. On the contrary, over a limited area, such as Europe, the SPI-12 is able to capture most of the key traits of TWSA that are relevant for drought studies, including the occurrence of dry extreme values. In the absence of actual TWS observations, the SPI-12 seems to represent a good proxy of long-term hydrological drought over Europe, whereas the wide range of meteorological conditions and complex hydrological processes involved in the transformation of precipitation into TWS seems to limit the possibility of extending this result to the global scale. Full article

(This article belongs to the Special Issue Droughts and Floods Assessment and Monitoring Using Remote Sensing and Geospatial Techniques)

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20 pages, 6709 KiB

Open AccessArticle

Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data

by Amir Hossein Salimi, Jafar Masoompour Samakosh, Ehsan Sharifi, Mohammad Reza Hassanvand, Amir Noori and Hary von Rautenkranz

Water 2019, 11(8), 1653; https://doi.org/10.3390/w11081653 - 10 Aug 2019

Cited by 29 | Viewed by 4255

Abstract

Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks [...] Read more.

Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R² = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms. Full article

(This article belongs to the Special Issue Droughts and Floods Assessment and Monitoring Using Remote Sensing and Geospatial Techniques)

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13 pages, 3453 KiB

Open AccessArticle

Estimating Real-Time Water Area of Dongting Lake Using Water Level Information

by Yuannan Long, Rong Tang, Changshan Wu, Changbo Jiang and Shixiong Hu

Water 2019, 11(6), 1240; https://doi.org/10.3390/w11061240 - 13 Jun 2019

Cited by 13 | Viewed by 3326

Abstract

Dongting Lake, the second largest freshwater lake in China, is an important water source for the Yangtze River Basin. The water area of Dongting Lake fluctuates significantly daily, which may cause flooding and other relevant disasters. Although remote sensing techniques may provide lake [...] Read more.

Dongting Lake, the second largest freshwater lake in China, is an important water source for the Yangtze River Basin. The water area of Dongting Lake fluctuates significantly daily, which may cause flooding and other relevant disasters. Although remote sensing techniques may provide lake area estimates with reasonable accuracy, they are not available in real-time and may be susceptible to weather conditions. To address this issue, this paper attempted to examine the relationship between lake area and the water levels at the hydrological stations. Multi-temporal water area data were derived through analyzing Moderate Resolution Imaging Spectroradiometer (MODIS) imagery using the Automatic Water Extraction Index (AWEI). Then we analyzed the inter- and intra-annual variations in the water area of the Dongting Lake. Corresponding water level information at hydrological stations of the Dongting Lake were obtained. Simple linear regression (SLR) models and stepwise multiple linear regression (SMLR) models were constructed using water levels and water level differences from the upstream and downstream hydrological stations. We used the data from 2004 to 2012 and 2012, respectively, to build the model, and applied the data from 2013 to 2015 to evaluate the models. Results suggest that the maximum water area of the Dongting Lake during 2000–2015 has a clear decreasing trend. The variations in the water area were characterized by hydrological seasons, with the annual minimum and maximum water areas occurring in January and September, respectively. The water level at the Chengjingji station, and water level differences between upstream stations and the Chengjingji station, play a major role in estimating the water area. Further, results also show that the SMLR established in 2012 performs the best in estimating water area of the Dongting Lake, especially with high water levels. Full article

(This article belongs to the Special Issue Droughts and Floods Assessment and Monitoring Using Remote Sensing and Geospatial Techniques)

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17 pages, 7207 KiB

Open AccessArticle

Construction of Comprehensive Drought Monitoring Model in Jing-Jin-Ji Region Based on Multisource Remote Sensing Data

by Haozhe Yu, Lijuan Li, Yang Liu and Jiuyi Li

Water 2019, 11(5), 1077; https://doi.org/10.3390/w11051077 - 23 May 2019

Cited by 12 | Viewed by 4236

Abstract

Drought is a complex hazard that has more adverse effects on agricultural production and economic development. Studying drought monitoring techniques and assessment methods can improve our ability to respond to natural disasters. Numerous drought indices deriving from meteorological or remote sensing data are [...] Read more.

Drought is a complex hazard that has more adverse effects on agricultural production and economic development. Studying drought monitoring techniques and assessment methods can improve our ability to respond to natural disasters. Numerous drought indices deriving from meteorological or remote sensing data are focused mainly on monitoring single drought response factors such as soil or vegetation, and the ability to reflect comprehensive information on drought was poor. This study constructed a comprehensive drought-monitoring model considering the drought factors including precipitation, vegetation growth status, and soil moisture balance during the drought process for the Jing-Jin-Ji region, China. The comprehensive drought index of remote sensing (CDIR), a drought indicator deduced by the model, was composed of the vegetation condition index (VCI), the temperature condition index (TCI), and the precipitation condition index (PCI). The PCI was obtained from the Tropical Rainfall Measuring Mission (TRMM) satellite. The VCI and TCI were obtained from a moderate-resolution imaging spectroradiometer (MODIS). In this study, a heavy drought process was accurately explored using the CDIR in the Jing-Jin-Ji region in 2016. Finally, a three-month scales standardized precipitation index (SPI-3), drought affected crop area, and standardized unit yield of wheat were used as validation to evaluate the accuracy of this model. The results showed that the CDIR is closely related to the SPI-3, as well as variations in the drought-affected crop area and standardized unit yield of crop. The correlation coefficient of the CDIR with SPI-3 was between 0.45 and 0.85. The correlation coefficient between the CDIR and drought affected crop was between −0.81 and −0.86. Moreover, the CDIR was positively correlated with the standardized unit yield of crop. It showed that the CDIR index is a decent indicator that can be used for integrated drought monitoring and that it can synthetically reflect meteorological and agricultural drought information. Full article

(This article belongs to the Special Issue Droughts and Floods Assessment and Monitoring Using Remote Sensing and Geospatial Techniques)

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19 pages, 4143 KiB

Open AccessArticle

Simulating Current and Future River-Flows in the Karakoram and Himalayan Regions of Pakistan Using Snowmelt-Runoff Model and RCP Scenarios

by Huma Hayat, Tahir Ali Akbar, Adnan Ahmad Tahir, Quazi K. Hassan, Ashraf Dewan and Muhammad Irshad

Water 2019, 11(4), 761; https://doi.org/10.3390/w11040761 - 12 Apr 2019

Cited by 48 | Viewed by 7382

Abstract

Upper Indus Basin (UIB) supplies more than 70% flow to the downstream agricultural areas during summer due to the melting of snow and glacial ice. The estimation of the stream flow under future climatic projections is a pre-requisite to manage water resources properly. [...] Read more.

Upper Indus Basin (UIB) supplies more than 70% flow to the downstream agricultural areas during summer due to the melting of snow and glacial ice. The estimation of the stream flow under future climatic projections is a pre-requisite to manage water resources properly. This study focused on the simulation of snowmelt-runoff using Snowmelt-Runoff Model (SRM) under the current and future Representative Concentration Pathways (RCP 2.6, 4.5 and 8.5) climate scenarios in the two main tributaries of the UIB namely the Astore and the Hunza River basins. Remote sensing data from Advanced Land Observation Satellite (ALOS) and Moderate Resolution Imaging Spectroradiometer (MODIS) along with in-situ hydro-climatic data was used as input to the SRM. Basin-wide and zone-wise approaches were used in the SRM. For the zone-wise approach, basin areas were sliced into five elevation zones and the mean temperature for the zones with no weather stations was estimated using a lapse rate value of −0.48 °C to −0.76 °C/100 m in both studied basins. Zonal snow cover was estimated for each zone by reclassifying the MODIS snow maps according to the zonal boundaries. SRM was calibrated over 2000–2001 and validated over the 2002–2004 data period. The results implied that the SRM simulated the river flow efficiently with Nash-Sutcliffe model efficiency coefficient of 0.90 (0.86) and 0.86 (0.86) for the basin-wide (zone-wise) approach in the Astore and Hunza River Basins, respectively, over the entire simulation period. Mean annual discharge was projected to increase by 11–58% and 14–90% in the Astore and Hunza River Basins, respectively, under all the RCP mid- and late-21st-century scenarios. Mean summer discharge was projected to increase between 10–60% under all the RCP scenarios of mid- and late-21st century in the Astore and Hunza basins. This study suggests that the water resources of Pakistan should be managed properly to lessen the damage to human lives, agriculture, and economy posed by expected future floods as indicated by the climatic projections. Full article

(This article belongs to the Special Issue Droughts and Floods Assessment and Monitoring Using Remote Sensing and Geospatial Techniques)

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