Your search found 58 records
1 Djagba, J. F.; Kouyate, A. M.; Baggie, I.; Zwart, Sander J. 2019. A geospatial dataset of inland valleys in four zones in Benin, Sierra Leone and Mali. Data in Brief, 23:103699. [doi: https://doi.org/10.1016/j.dib.2019.103699]
Spatial data ; Datasets ; Agricultural development ; Farmers ; Socioeconomic environment ; Geographical distribution ; Valleys / West Africa / Benin / Sierra Leone / Mali
(Location: IWMI HQ Call no: e-copy only Record No: H049424)
https://www.sciencedirect.com/science/article/pii/S2352340919300484/pdfft?md5=a512268a8fc2fb761b9bc45e16f7abe3&pid=1-s2.0-S2352340919300484-main.pdf
https://vlibrary.iwmi.org/pdf/H049424.pdf
https://vlibrary.iwmi.org/pdf/H049424.pdf
(0.17 MB) (172 KB)
The dataset described in this data article represents four agricultural zones in West-Africa that are located in three countries: Benin, Mali and Sierra Leone. The dataset was created through a research collaboration between the Africa Rice Center (AfricaRice), Sierra Leone Agricultural Research Institute (SLARI) and the Institute for Rural Economy (IER). The dataset was compiled to investigate the potential for rice production in inland valleys of the three countries. The results of the investigation were published in Dossou-Yovo et al. (2017) and Djagba et al. (2018). The dataset describes the biophysical and socioeconomic conditions of 499 inland valleys in the four agricultural zones. In each inland valley data were collected through a focus group interview with a minimum of three farmers. In 499 interviews a total of 7496 farmers participated. The location of each inland valley was determined with handheld GPS devices. The geographic locations were used to extract additional parameters from digital maps on soils, elevation, population density, rainfall, flow accumulation, and distances to roads, market places, rice mills, chemical input stores, and settlements. The dataset contains 65 parameters in four themes (location, biophysical characteristics, socioeconomic characteristics, and inland valley land development and use). The GPS coordinates indicate the location of an inland valley, but they do not lead to the location of individual fields of farmers that were interviewed. The dataset is publicly shared as Supplementary data to this data article.
2 Dembele, M.; Ceperley, N.; Zwart, Sander J.; Salvadore, E.; Mariethoz, G.; Schaefli, B. 2020. Potential of satellite and reanalysis evaporation datasets for hydrological modelling under various model calibration strategies. Advances in Water Resources, 143:103667. [doi: https://doi.org/10.1016/j.advwatres.2020.103667]
Hydrology ; Modelling ; Calibration ; Strategies ; Satellites ; Remote sensing ; Evaporation ; River basins ; Stream flow ; Water storage ; Soil water content ; Climatic zones ; Forecasting ; Datasets ; Performance evaluation ; Spatial distribution / West Africa / Volta River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H049804)
https://www.sciencedirect.com/science/article/pii/S030917082030230X/pdfft?md5=fe6a7ca8d66941a8fd4455b385a1dd8c&pid=1-s2.0-S030917082030230X-main.pdf
https://vlibrary.iwmi.org/pdf/H049804.pdf
https://vlibrary.iwmi.org/pdf/H049804.pdf
(4.54 MB) (4.54 MB)
Twelve actual evaporation datasets are evaluated for their ability to improve the performance of the fully distributed mesoscale Hydrologic Model (mHM). The datasets consist of satellite-based diagnostic models (MOD16A2, SSEBop, ALEXI, CMRSET, SEBS), satellite-based prognostic models (GLEAM v3.2a, GLEAM v3.3a, GLEAM v3.2b, GLEAM v3.3b), and reanalysis (ERA5, MERRA-2, JRA-55). Four distinct multivariate calibration strategies (basin-average, pixel-wise, spatial bias-accounting and spatial bias-insensitive) using actual evaporation and streamflow are implemented, resulting in 48 scenarios whose results are compared with a benchmark model calibrated solely with streamflow data. A process-diagnostic approach is adopted to evaluate the model responses with in-situ data of streamflow and independent remotely sensed data of soil moisture from ESA-CCI and terrestrial water storage from GRACE. The method is implemented in the Volta River basin, which is a data scarce region in West Africa, for the period from 2003 to 2012.
Results show that the evaporation datasets have a good potential for improving model calibration, but this is dependent on the calibration strategy. All the multivariate calibration strategies outperform the streamflow-only calibration. The highest improvement in the overall model performance is obtained with the spatial bias-accounting strategy (+29%), followed by the spatial bias-insensitive strategy (+26%) and the pixel-wise strategy (+24%), while the basin-average strategy (+20%) gives the lowest improvement. On average, using evaporation data in addition to streamflow for model calibration decreases the model performance for streamflow (-7%), which is counterbalance by the increase in the performance of the terrestrial water storage (+11%), temporal dynamics of soil moisture (+6%) and spatial patterns of soil moisture (+89%). In general, the top three best performing evaporation datasets are MERRA-2, GLEAM v3.3a and SSEBop, while the bottom three datasets are MOD16A2, SEBS and ERA5. However, performances of the evaporation products diverge according to model responses and across climatic zones. These findings open up avenues for improving process representation of hydrological models and advancing the spatiotemporal prediction of floods and droughts under climate and land use changes.
Results show that the evaporation datasets have a good potential for improving model calibration, but this is dependent on the calibration strategy. All the multivariate calibration strategies outperform the streamflow-only calibration. The highest improvement in the overall model performance is obtained with the spatial bias-accounting strategy (+29%), followed by the spatial bias-insensitive strategy (+26%) and the pixel-wise strategy (+24%), while the basin-average strategy (+20%) gives the lowest improvement. On average, using evaporation data in addition to streamflow for model calibration decreases the model performance for streamflow (-7%), which is counterbalance by the increase in the performance of the terrestrial water storage (+11%), temporal dynamics of soil moisture (+6%) and spatial patterns of soil moisture (+89%). In general, the top three best performing evaporation datasets are MERRA-2, GLEAM v3.3a and SSEBop, while the bottom three datasets are MOD16A2, SEBS and ERA5. However, performances of the evaporation products diverge according to model responses and across climatic zones. These findings open up avenues for improving process representation of hydrological models and advancing the spatiotemporal prediction of floods and droughts under climate and land use changes.
3 Wei, Y.; Lu, M.; Wu, W.; Ru, Y. 2020. Multiple factors influence the consistency of cropland datasets in Africa. International Journal of Applied Earth Observation and Geoinformation, 89:102087. [doi: https://doi.org/10.1016/j.jag.2020.102087]
Farmland ; Datasets ; Land fragmentation ; Remote sensing ; Land cover mapping ; Moderate resolution imaging spectroradiometer ; Irrigated land ; Vegetation ; Precipitation ; Food security / Africa South of Sahara
(Location: IWMI HQ Call no: e-copy only Record No: H049971)
https://www.sciencedirect.com/science/article/pii/S0303243419310463/pdfft?md5=0684753fd3e8666ecb686aa90c95632d&pid=1-s2.0-S0303243419310463-main.pdf
https://vlibrary.iwmi.org/pdf/H049971.pdf
https://vlibrary.iwmi.org/pdf/H049971.pdf
(4.01 MB) (4.01 MB)
Accurate geo-information of cropland is critical for food security strategy development and grain production management, especially in Africa continent where most countries are food-insecure. Over the past decades, a series of African cropland maps have been derived from remotely-sensed data, existing comparison studies have shown that inconsistencies with statistics and discrepancies among these products are considerable. Yet, there is a knowledge gap about the factors that influence their consistency. The aim of this study is thus to estimate the consistency of five widely-used cropland datasets (MODIS Collection 5, GlobCover 2009, GlobeLand30, CCI-LC 2010, and Unified Cropland Layer) in Africa, and to explore the effects of several limiting factors (landscape fragmentation, climate and agricultural management) on spatial consistency. The results show that total cropland area for Africa derived from GlobeLand30 has the best fitness with FAO statistics, followed by MODIS Collection 5. GlobCover 2009, CCI-LC 2010, and Unified Cropland Layer have poor performances as indicated by larger deviations from statistics. In terms of spatial consistency, disagreement is about 37.9 % at continental scale, and the disparate proportion even exceeds 50 % in approximately 1/3 of the countries at national scale. We further found that there is a strong and significant correlation between spatial agreement and cropland fragmentation, suggesting that regions with higher landscape fragmentation generally have larger disparities. It is also noticed that places with better consistency are mainly distributed in regions with favorable natural environments and sufficient agricultural management such as well-developed irrigated technology. Proportions of complete agreement are thus located in favorable climate zones including Hot-summer Mediterranean climate (Csa), Subtropical highland climate (Cwb), and Temperate Mediterranean climate (Csb). The level of complete agreement keeps rising as the proportion of irrigated cropland increases. Spatial agreement among these datasets has the most significant relationship with cropland fragmentation, and a relatively small association with irrigation area, followed by climate conditions. These results can provide some insights into understanding how different factors influence the consistency of cropland datasets, and making an appropriate selection when using these datasets in different regions. We suggest that future cropland mapping activities should put more effort in those regions with significant disagreement in Sub-Saharan Africa.
4 Chen, Y.; Fang, G.; Hao, H.; Wang, X. 2020. Water use efficiency data from 2000 to 2019 in measuring progress towards SDGs in Central Asia. Big Earth Data, 14p. (Online first) [doi: https://doi.org/10.1080/20964471.2020.1851891]
Water use efficiency ; Sustainable Development Goals ; Agricultural water use ; Water resources ; Evapotranspiration ; Ecosystems ; Remote sensing ; Moderate resolution imaging spectroradiometer ; Datasets / Central Asia / Kazakhstan / Kyrgyzstan / Tajikistan / Turkmenistan / Uzbekistan
(Location: IWMI HQ Call no: e-copy only Record No: H050142)
https://www.tandfonline.com/doi/pdf/10.1080/20964471.2020.1851891?needAccess=true
https://vlibrary.iwmi.org/pdf/H050142.pdf
https://vlibrary.iwmi.org/pdf/H050142.pdf
(5.50 MB) (5.50 MB)
Central Asia, located in the hinterland of the Eurasian continent, is characterized with sparse rainfall, frequent droughts and low water use efficiency. Limited water resources have become a key factor restricting the sustainable development of this region. Accurately assessing the efficiency of water resources utilization is the first step to achieve the UN Sustainable Development Goals (SDGs) in Central Asia. However, since the collapse of the Soviet Union, the evaluation of water use efficiency is difficult due to low data availability and poor consistency. To fill this gap, this paper developed a Water Use Efficiency dataset (WUE) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Gross Primary Production (GPP) data and the MODIS evapotranspiration (ET) data. The WUE dataset ranges from 2000 to 2019 with a spatial resolution of 500 m. The agricultural WUE was then extracted based on the Global map of irrigated areas and MODIS land use map. As a complementary, the water use amount per GDP was estimated for each country. The present dataset could reflect changes in water use efficiency of agriculture and other sectors.
5 Hu, Z.; Zhang, Z.; Sang, Y.-F.; Qian, J.; Feng, W.; Chen, X.; Zhou, Q. 2021. Temporal and spatial variations in the terrestrial water storage across Central Asia based on multiple satellite datasets and global hydrological models. Journal of Hydrology, 596:126013. [doi: https://doi.org/10.1016/j.jhydrol.2021.126013]
Water storage ; Datasets ; Satellite observation ; Hydrology ; Models ; Precipitation ; Temperature ; Evapotranspiration ; Soil moisture ; Arid regions ; Water resources ; Sustainable Development Goals ; River basins ; Lakes ; Spatial distribution ; Forecasting ; Uncertainty / Central Asia / Kazakhstan / Turkmenistan / Uzbekistan / Tajikistan / Kyrgyzstan / Aral Sea Basin / Balkhash Lake / Issyk-Kul Lake
(Location: IWMI HQ Call no: e-copy only Record No: H050341)
(7.60 MB)
Arid regions of Central Asia have sensitive ecosystems that rely heavily on terrestrial water storage which is composed of surface water storage, soil moisture storage and groundwater storage. Therefore, we employed three Gravity Recovery and Climate Experiment (GRACE) satellite datasets and five global hydrological models (GHMs) to explore the terrestrial water storage (TWS) changes over arid regions of Central Asia from 2003 to 2014. We observed significantly decreasing water storage trends in the GRACE data, which were underestimated by the GHMs. After averaging the three GRACE satellite datasets, we found that the water storage was decreasing at a rate of -4.74 mm/year. Contrary to the prevailing declining water storage trends, northeastern Kazakhstan (KAZ), and southern Xinjiang increased their water storage over the same period. The GRACE data showed that Turkmenistan (TKM), Uzbekistan (UZB) and KAZ experienced the most severe water depletions, while Tajikistan (TJK) and northwest China (NW) experienced the least significant depletions. With respect to the major river and lake basins, the Aral Sea Basin exhibited the most serious water loss (-0.60 mm/month to -0.38 mm/month). The water storage positively correlates with the precipitation; and negatively correlates, with a three-month lag, with temperature and potential evapotranspiration (PET). Partial least square regression (PLSR) had the high capability in simulating and predicting the TWS. These results provide scientific evidence and guidance for local policy makers working toward sustainable water resource management, and the resolution of international water resource disputes among Central Asian countries.
6 Brunetti, M. T.; Melillo, M.; Gariano, S. L.; Ciabatta, L.; Brocca, L.; Amarnath, Giriraj; Peruccacci, S. 2021. Satellite rainfall products outperform ground observations for landslide prediction in India. Hydrology and Earth System Sciences, 25(6):3267-3279. [doi: https://doi.org/10.5194/hess-25-3267-2021]
Landslides ; Forecasting ; Satellite observation ; Rain ; Precipitation ; Weather data ; Estimation ; Natural disasters ; Monsoons ; Datasets / India
(Location: IWMI HQ Call no: e-copy only Record No: H050491)
https://hess.copernicus.org/articles/25/3267/2021/hess-25-3267-2021.pdf
https://vlibrary.iwmi.org/pdf/H050491.pdf
https://vlibrary.iwmi.org/pdf/H050491.pdf
(5.89 MB) (5.89 MB)
Landslides are among the most dangerous natural hazards, particularly in developing countries, where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important detection and monitoring process to predict landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide prediction in reducing the impact of this hazard on the population.
We have performed a comparative study of ground and satellite-based rainfall products for landslide prediction in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides that occurred throughout India in the 13-year period between April 2007 and October 2019 has been used.
Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (0.1 vs. 0.25 ) and temporal (hourly vs. daily) resolutions. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g. overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.
We have performed a comparative study of ground and satellite-based rainfall products for landslide prediction in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides that occurred throughout India in the 13-year period between April 2007 and October 2019 has been used.
Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (0.1 vs. 0.25 ) and temporal (hourly vs. daily) resolutions. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g. overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.
7 Siavashani, N. S.; Jimenez-Martinez, J.; Vaquero, G.; Elorza, F. J.; Sheffield, J.; Candela, L.; Serrat-Capdevila, A. 2021. Assessment of CHADFDM satellite-based input dataset for the groundwater recharge estimation in arid and data scarce regions. Hydrological Processes, 35(6):e14250. [doi: https://doi.org/10.1002/hyp.14250]
Groundwater recharge ; Satellites ; Datasets ; Weather data ; Semiarid zones ; Precipitation ; Drought ; Rain ; Evapotranspiration ; Irrigated land ; Soil water balance ; Water resources ; Aquifers ; Air temperature ; Remote sensing ; Sensitivity analysis ; Uncertainty ; Models / Chad / Niger / Nigeria / Lake Chad Basin
(Location: IWMI HQ Call no: e-copy only Record No: H050431)
https://onlinelibrary.wiley.com/doi/epdf/10.1002/hyp.14250
https://vlibrary.iwmi.org/pdf/H050431.pdf
https://vlibrary.iwmi.org/pdf/H050431.pdf
(3.85 MB) (3.85 MB)
Aquifer natural recharge estimations are a prerequisite for understanding hydrologic systems and sustainable water resources management. As meteorological data series collection is difficult in arid and semiarid areas, satellite products have recently become an alternative for water resources studies. A daily groundwater recharge estimation in the NW part of the Lake Chad Basin, using a soil–plant-atmosphere model (VisualBALAN), from ground- and satellite-based meteorological input dataset for non-irrigated and irrigated land and for the 2005–2014 period is presented. Average annual values were 284 mm and 30°C for precipitation and temperature in ground-based gauge stations. For the satellite-model-based Lake Chad Basin Flood and Drought Monitor System platform (CHADFDM), average annual precipitation and temperature were 417 mm and 29°C, respectively. Uncertainties derived from satellite data measurement could account for the rainfall difference. The estimated mean annual aquifer recharge was always higher from satellite- than ground-based data, with differences up to 46% for dryland and 23% in irrigated areas. Recharge response to rainfall events was very variable and results were very sensitive to: wilting point, field capacity and curve number for runoff estimation. Obtained results provide plausible recharge values beyond the uncertainty related to data input and modelling approach. This work prevents on the important deviations in recharge estimation from weighted-ensemble satellite-based data, informing in decision making to both stakeholders and policy makers.
8 Gehring, T.; Deineko, E.; Hobus, I.; Kolisch, G.; Lubken, M.; Wichern, M. 2021. Effect of sewage sampling frequency on determination of design parameters for municipal wastewater treatment plants. Water Science and Technology, 84(2):284-292. [doi: https://doi.org/10.2166/wst.2020.588]
Municipal wastewater ; Wastewater treatment plants ; Sewage ; Pollutant load ; Temperature ; Estimation ; Datasets ; Uncertainty / Germany / Switzerland
(Location: IWMI HQ Call no: e-copy only Record No: H050515)
https://iwaponline.com/wst/article-pdf/84/2/284/914986/wst084020284.pdf
https://vlibrary.iwmi.org/pdf/H050515.pdf
https://vlibrary.iwmi.org/pdf/H050515.pdf
(0.59 MB) (600 KB)
The uncertainty associated with the determination of load parameters, which is a key step in the design of wastewater treatment plants (WWTPs), was investigated on the basis of data sets from 58 WWTPs. A further analysed aspect was the organic load variations associated with variable sewage temperatures. Data from 26 WWTPs with a high inflow sampling frequency was used to simulate scenarios to investigate the effect of lower sampling frequencies through a Monte Carlo approach. The calculation of 85-percentile values for chemical oxygen demand (COD) loadings based on only 26 samples per year is associated with a variability of up to ±18%. Approximately 90 samples per year will be necessary to reduce this uncertainty for estimation of COD loadings below 10%. Hence, a low sampling frequency can potentially lead to under- or overestimation of design parameters. Through an analogous approach, it was possible to identify uncertainties of ±11% in COD loading when weekly average data was used with four samples per week. Finally, a tendency to lower COD input loads with increasing temperatures was identified, with a reduction of about 1% of the average loading per degree Celsius.
9 Assefa, A.; Haile, Alemseged Tamiru; Dhanya, C. T.; Walker, D. W.; Gowing, J.; Parkin, G. 2021. Impact of sustainable land management on vegetation cover using remote sensing in Magera micro Watershed, Omo Gibe Basin, Ethiopia. International Journal of Applied Earth Observation and Geoinformation, 103:102495. [doi: https://doi.org/10.1016/j.jag.2021.102495]
Sustainable land management ; Normalized difference vegetation index ; Watershed management ; Remote sensing ; Satellite imagery ; Datasets ; Land cover mapping ; Hydrological factors ; Rain / Ethiopia / Omo Gibe Basin / Magera Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H050722)
https://www.sciencedirect.com/science/article/pii/S0303243421002026/pdfft?md5=adc6f5caeb7b85ee841a993c82269f8c&pid=1-s2.0-S0303243421002026-main.pdf
https://vlibrary.iwmi.org/pdf/H050722.pdf
https://vlibrary.iwmi.org/pdf/H050722.pdf
(11.20 MB) (11.2 MB)
The hydrological impact of many expensive investments on watershed interventions remains unquantified due to lack of time series data. In this study, remote sensing imagery is utilized to quantify and detect vegetation cover change in Magera micro-watershed, Ethiopia, where sustainable land management interventions have been implemented. Normalized difference vegetation index (NDVI) values were retrieved for the period 2010 to 2019, which encompasses before, during and after the interventions. Mann-Kendal trend test was used to detect temporal trends in the monthly NDVI values. In addition, multiple change-point analyses were carried out using Pettitt’s, Buishand’s and Standard Normal Homogeneity (SNH) tests to detect any abrupt changes due to the watershed interventions. The possible influence of rainfall on changes in vegetation cover was investigated. A significant increasing trend (from 1.5% to 33%) was detected for dense vegetation at the expense of a significant reduction in bare land from 40.9% to 0.6% over the analysis period. An abrupt change in vegetation cover was detected in 2015 in response to the interventions. A weak and decreasing correlation was obtained between monthly rainfall magnitude and NDVI values, which indicates that the increase in vegetation cover is not from rainfall influences. The study shows that the sustainable land management has an overall positive impact on the study area. The findings of this research support the applicability of remote sensing approaches to provide useful information on the impacts of watershed intervention investments.
10 Goshime, D. W.; Haile, Alemseged Tamiru; Absi, R.; Ledesert, B. 2021. Impact of water resource development plan on water abstraction and water balance of Lake Ziway, Ethiopia. Sustainable Water Resources Management, 7(3):36. [doi: https://doi.org/10.1007/s40899-021-00516-w]
Water resources development ; Development plans ; Water extraction ; Water balance ; Lakes ; Irrigation schemes ; Water use ; Estimation ; Datasets / Ethiopia / Lake Ziway
(Location: IWMI HQ Call no: e-copy only Record No: H050725)
(2.08 MB)
Lake Ziway is providing water for a wide variety of sectors in the central rift valley of Ethiopia. However, there is a lack of systematic study that informs the effect of water abstraction on the lake water balance. In the present study, we conducted a Water Abstraction Survey (WAS) to estimate actual water withdrawal from the lake and developed a water balance model of the lake to evaluate the associated impact on the lake water storage and outflow for three development plans. The mean error and root mean square error of the simulated lake water level as compared with observed counterparts were estimated as 0.1 and 0.2 m, respectively, which is smaller than the range of the observed fluctuation of the lake water level under natural condition. Our findings indicate that the actual storage and outflow of Lake Ziway are significantly impacted by the existing water withdrawal. When the future development plans are fully implemented, the annual amount of irrigation and domestic water withdrawal from the lake will reach 95 Mm3 . This will cause the lake water level to drop by 0.94 m, which translates to 38 km2 reductions in the lake surface area. Consequently, the lake will lose 26.5% of its actual storage volume when the future development plan (2029–2038) is implemented as compared to the observed storage between 1986 and 2000. Hence, the current impact of water resources development around the lake is substantially large and will exacerbate in the future. This indicates the need for urgent actions to monitor and manage water abstraction from the lake.
11 Ahmed, M.; Mumtaz, R.; Zaidi, S. M. H. 2021. Analysis of water quality indices and machine learning techniques for rating water pollution: a case study of Rawal Dam, Pakistan. Water Supply, 21(6):3225-3250. [doi: https://doi.org/10.2166/ws.2021.082]
Water quality ; Water pollution ; Machine learning ; Techniques ; Monitoring ; Datasets ; Geographical information systems ; Chemicophysical properties ; Models ; Case studies / Pakistan / Islamabad / Rawal Dam
(Location: IWMI HQ Call no: e-copy only Record No: H050698)
https://iwaponline.com/ws/article-pdf/21/6/3225/933536/ws021063225.pdf
https://vlibrary.iwmi.org/pdf/H050698.pdf
https://vlibrary.iwmi.org/pdf/H050698.pdf
(0.99 MB) (0.99 MB)
Water Quality Index (WQI) is a unique and effective rating technique for assessing the quality of water. Nevertheless, most of the indices are not applicable to all water types as these are dependent on core physico-chemical water parameters that can make them biased and sensitive towards specific attributes including: (i) time, location and frequency for data sampling; (ii) number, variety and weights allocation of parameters. Therefore, there is a need to evaluate these indices to eliminate uncertainties that make them unpredictable and which may lead to manipulation of the water quality classes. The present study calculated five WQIs for two temporal periods: (i) June to December 2019 obtained in real time (using the Internet of Things (IoT) nodes) at inlet and outlet streams of Rawal Dam; (ii) 2012–2019 obtained from the Rawal Dam Water Filtration Plant, collected through GIS-based grab sampling. The computed WQIs categorized the collected datasets as ‘Very Poor’, primarily owing to the uneven distribution of the water samples that has led to class imbalance in the data. Additionally, this study investigates the classification of water quality using machine learning algorithms namely: Decision Tree (DT), k-Nearest Neighbor (KNN), Logistic Regression (LogR), Multilayer Perceptron (MLP) and Naive Bayes (NB); based on the parameters including: pH, dissolved oxygen, conductivity, turbidity, fecal coliform and temperature. The classification results showed that the DT algorithm outperformed other models with a classification accuracy of 99%. Although WQI is a popular method used to assess the water quality, there is a need to address the uncertainties and biases introduced by the limitations of data acquisition (such as specific location/area, type and number of parameters or water type) leading to class imbalance. This can be achieved by developing a more refined index that considers various other factors such as topographical and hydrological parameters with spatial temporal variations combined machine learning techniques to effectively contribute in estimation of water quality for all regions.
12 Guan, T.; Xu, Q.; Chen, X.; Cai, J. 2021. A novel remote sensing method to determine reservoir characteristic curves using high-resolution data. Hydrology Research, 52(5):1066-1082. [doi: https://doi.org/10.2166/nh.2021.035]
Water reservoirs ; Water levels ; Surface water ; Remote sensing ; Satellite imagery ; Landsat ; Datasets / China / Zhejiang / Jinshuitan Reservoir / Shitang Reservoir / Ou River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H050701)
https://iwaponline.com/hr/article-pdf/52/5/1066/950733/nh0521066.pdf
https://vlibrary.iwmi.org/pdf/H050701.pdf
https://vlibrary.iwmi.org/pdf/H050701.pdf
(1.20 MB) (1.20 MB)
A novel method of determining reservoir characteristic curves based on high-resolution resource satellite data was proposed in this paper, using remote sensing processing and analysis technology. According to the physical characteristics of absorption, radiation and reflection of surface water on ultraviolet, visible, near-infrared bands, etc., the satellite images at different reservoir water level and different periods were processed to analyze the relationship of measured water level corresponding to the water area. Based on the relationship, the relevance among reservoir water level, water surface area, and reservoir capacity was established, so as to determine the reservoir characteristic curve. The method was applied and validated at Jinshuitan Reservoir and Shitang Reservoir in the Ou River Basin. The results show that this method has high accuracy, and the maximum relative error between calculating values and measured values at different water level are -2.33% and -2.11% in Jinshuitan Reservoir and Shitang Reservoir, respectively. The method improves the convenience of determining the reservoir characteristic curve greatly, and the storage capacity of the reservoir can be calculated rapidly by this method.
13 Malik, Ravinder Paul Singh; Amarnath, Giriraj. 2021. Economics of Index-based Flood Insurance (IBFI): scenario analysis and stakeholder perspectives from South Asia. Colombo, Sri Lanka: International Water Management Institute (IWMI). 34p. (IWMI Working Paper 199) [doi: https://doi.org/10.5337/2021.228]
Flooding ; Agricultural insurance ; Crop insurance ; Economic analysis ; Stakeholders ; Disaster risk management ; Farmers ; State intervention ; Flood damage ; Crop losses ; Compensation ; Subsidies ; Insurance premiums ; Cost benefit analysis ; Economic viability ; Sustainability ; Villages ; Remote sensing ; Datasets ; Models ; Developing countries ; Case studies / South Asia / India / Bihar / Katihar
(Location: IWMI HQ Call no: IWMI Record No: H050736)
(1.32 MB)
The International Water Management Institute (IWMI) has recently developed an innovative Index-based Flood Insurance (IBFI) product to facilitate the scaling of flood insurance particularly in vulnerable economies, to provide risk cover to poor farmers against crop losses that occur due to floods. While the product developed is technically very sound, the economics of such an intervention is important to ensure the large-scale acceptance and adoption of the product by different stakeholders and for its sustenance in the long term. This paper attempts at conducting an ex ante assessment of the economics of IBFI from the perspectives of the three main stakeholders: farmers, the insurance company and the government. The paper discusses the methodological challenges and data issues encountered in undertaking an economic analysis of such a product. The issues and processes involved have been empirically demonstrated using a theoretical case study based on a synthesis of information drawn from a host of sources and certain assumptions. Field-based data are now being collected and analyzed from the locations where IBFI has recently been piloted by IWMI. This will help in further refining the process of economic evaluation and identifying the experiences of different stakeholders.
14 Satterthwaite, E. V.; Bax, N. J.; Miloslavich, P.; Ratnarajah, L.; Canonico, G.; Dunn, D.; Simmons, S. E.; Carini, R. J.; Evans, K.; Allain, V.; Appeltans, W.; Batten, S.; Benedetti-Cecchi, L.; Bernard, A. T. F.; Bristol, S.; Benson, A.; Buttigieg, P. L.; Gerhardinger, L. C.; Chiba, S.; Davies, T. E.; Duffy, J. E.; Giron-Nava, A.; Hsu, A. J.; Kraberg, A. C.; Kudela, R. M.; Lear, D.; Montes, E.; Muller-Karger, F. E.; O’Brien, T. D.; Obura, D.; Provoost, P.; Pruckner, S.; Rebelo, Lisa-Maria; Selig, E. R.; Kjesbu, O. S.; Starger, C.; Stuart-Smith, R. D.; Vierros, M.; Waller, J.; Weatherdon, L. V.; Wellman, T. P.; Zivian, A. 2021. Establishing the foundation for the global observing system for marine life. Frontiers in Marine Science, 8:737416. [doi: https://doi.org/10.3389/fmars.2021.737416]
Marine ecosystems ; Global observing systems ; Ocean observations ; Biodiversity ; Time series analysis ; Environmental monitoring ; Sustainability ; Climate change ; Coastal zones ; Mangroves ; Sea grasses ; Corals ; Algae ; Data management ; Metadata standard ; Datasets ; Best practices ; Access to information ; Spatial analysis ; Funding ; Capacity development ; Technology transfer ; Developing countries
(Location: IWMI HQ Call no: e-copy only Record No: H050793)
https://www.frontiersin.org/articles/10.3389/fmars.2021.737416/pdf
https://vlibrary.iwmi.org/pdf/H050793.pdf
https://vlibrary.iwmi.org/pdf/H050793.pdf
(3.69 MB) (3.69 MB)
Maintaining healthy, productive ecosystems in the face of pervasive and accelerating human impacts including climate change requires globally coordinated and sustained observations of marine biodiversity. Global coordination is predicated on an understanding of the scope and capacity of existing monitoring programs, and the extent to which they use standardized, interoperable practices for data management. Global coordination also requires identification of gaps in spatial and ecosystem coverage, and how these gaps correspond to management priorities and information needs. We undertook such an assessment by conducting an audit and gap analysis from global databases and structured surveys of experts. Of 371 survey respondents, 203 active, long-term (>5 years) observing programs systematically sampled marine life. These programs spanned about 7% of the ocean surface area, mostly concentrated in coastal regions of the United States, Canada, Europe, and Australia. Seagrasses, mangroves, hard corals, and macroalgae were sampled in 6% of the entire global coastal zone. Two-thirds of all observing programs offered accessible data, but methods and conditions for access were highly variable. Our assessment indicates that the global observing system is largely uncoordinated which results in a failure to deliver critical information required for informed decision-making such as, status and trends, for the conservation and sustainability of marine ecosystems and provision of ecosystem services. Based on our study, we suggest four key steps that can increase the sustainability, connectivity and spatial coverage of biological Essential Ocean Variables in the global ocean: (1) sustaining existing observing programs and encouraging coordination among these; (2) continuing to strive for data strategies that follow FAIR principles (findable, accessible, interoperable, and reusable); (3) utilizing existing ocean observing platforms and enhancing support to expand observing along coasts of developing countries, in deep ocean basins, and near the poles; and (4) targeting capacity building efforts. Following these suggestions could help create a coordinated marine biodiversity observing system enabling ecological forecasting and better planning for a sustainable use of ocean resources.
15 Chandrasekharan, Kiran M.; Subasinghe, Chandima; Haileslassie, Amare. 2021. Mapping irrigated and rainfed agriculture in Ethiopia (2015-2016) using remote sensing methods. Colombo, Sri Lanka: International Water Management Institute (IWMI). 31p. (IWMI Working Paper 196) [doi: https://doi.org/10.5337/2021.206]
Irrigated farming ; Rainfed agriculture ; Mapping ; Remote sensing ; Irrigated land ; Farmland ; Water management ; Biomass ; Dry season ; Moisture content ; Land cover ; Satellite imagery ; Landsat ; Weather data ; Rainfall patterns ; Datasets ; Normalized difference vegetation index ; Moderate resolution imaging spectroradiometer ; Time series analysis / Ethiopia
(Location: IWMI HQ Call no: IWMI Record No: H050838)
(5.78 MB)
Irrigation expansion is a critical development intervention to address food security challenges in Ethiopia. However, only a fraction of the country’s irrigation potential has been utilized so far. Information about the location and spatial extent of irrigated and rainfed areas is an important requirement for sustainable water resources development and agricultural planning.
Currently, considerable variations exist in the irrigated area estimates made by different government agencies. In addition, irrigated area maps created as part of global mapping efforts have a spatial resolution of anywhere between 10 kilometers and 250 meters, making them too coarse for planning and management at a subnational scale.
This study aims to develop an irrigated area map of Ethiopia using satellite images to support agricultural water management practices in the country, using multi-temporal, multi-resolution data sets from 2015 to 2016 with a spatial resolution of 30 m. The total area of croplands was estimated as 21.8 million hectares (Mha), of which only 1.11 Mha were mapped as the irrigated area. This is only around 5% of the estimated total agricultural area.
The accuracy of the results was evaluated using geographic coordinates of irrigated areas provided by the Ethiopian Ministry of Agriculture. The results confirmed that irrigated areas can be identified reasonably well by analyzing seasonal trends in vegetation and moisture levels.
Currently, considerable variations exist in the irrigated area estimates made by different government agencies. In addition, irrigated area maps created as part of global mapping efforts have a spatial resolution of anywhere between 10 kilometers and 250 meters, making them too coarse for planning and management at a subnational scale.
This study aims to develop an irrigated area map of Ethiopia using satellite images to support agricultural water management practices in the country, using multi-temporal, multi-resolution data sets from 2015 to 2016 with a spatial resolution of 30 m. The total area of croplands was estimated as 21.8 million hectares (Mha), of which only 1.11 Mha were mapped as the irrigated area. This is only around 5% of the estimated total agricultural area.
The accuracy of the results was evaluated using geographic coordinates of irrigated areas provided by the Ethiopian Ministry of Agriculture. The results confirmed that irrigated areas can be identified reasonably well by analyzing seasonal trends in vegetation and moisture levels.
16 Magidi, J.; van Koppen, Barbara; Nhamo, L.; Mpandeli, S.; Slotow, R.; Mabhaudhi, Tafadzwanashe. 2021. Informing equitable water and food policies through accurate spatial information on irrigated areas in smallholder farming systems. Water, 13(24):3627. [doi: https://doi.org/10.3390/w13243627]
Smallholders ; Farming systems ; Irrigated farming ; Water policies ; Food policies ; Food security ; Water security ; Spatial distribution ; Rainfed farming ; Irrigated land ; Cultivated land ; Catchment areas ; Crop production ; Farmers ; Sustainable development ; Datasets ; Normalized difference vegetation index / South Africa / Usuthu Sub-Catchment / Crocodile Sub-Catchment / Sabie Sub-Catchment / Komati Sub-Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H050853)
(5.03 MB) (5.03 MB)
Accurate information on irrigated areas’ spatial distribution and extent are crucial in enhancing agricultural water productivity, water resources management, and formulating strategic policies that enhance water and food security and ecologically sustainable development. However, data are typically limited for smallholder irrigated areas, which is key to achieving social equity and equal distribution of financial resources. This study addressed this gap by delineating disaggregated smallholder and commercial irrigated areas through the random forest algorithm, a non-parametric machine learning classifier. Location within or outside former apartheid “homelands” was taken as a proxy for smallholder, and commercial irrigation. Being in a medium rainfall area, the huge irrigation potential of the Inkomati-Usuthu Water Management Area (UWMA) is already well developed for commercial crop production outside former homelands. However, information about the spatial distribution and extent of irrigated areas within former homelands, which is largely informal, was missing. Therefore, we first classified cultivated lands in 2019 and 2020 as a baseline, from where the Normalised Difference Vegetation Index (NDVI) was used to distinguish irrigated from rainfed, focusing on the dry winter period when crops are predominately irrigated. The mapping accuracy of 84.9% improved the efficacy in defining the actual spatial extent of current irrigated areas at both smallholder and commercial spatial scales. The proportion of irrigated areas was high for both commercial (92.5%) and smallholder (96.2%) irrigation. Moreover, smallholder irrigation increased by over 19% between 2019 and 2020, compared to slightly over 7% in the commercial sector. Such information is critical for policy formulation regarding equitable and inclusive water allocation, irrigation expansion, land reform, and food and water security in smallholder farming systems.
17 Omonge, P.; Feigl, M.; Olang, L.; Schulz, K.; Herrnegger, M. 2022. Evaluation of satellite precipitation products for water allocation studies in the Sio-Malaba-Malakisi River Basin of East Africa. Journal of Hydrology: Regional Studies, 39:100983. [doi: https://doi.org/10.1016/j.ejrh.2021.100983]
Water allocation ; Precipitation ; Satellite observation ; River basins ; Hydrological modelling ; Datasets ; Rainfall patterns ; Estimation ; Runoff ; Water balance ; Rain gauges / East Africa / Kenya / Uganda / Sio-Malaba-Malakisi River Basin / Lake Victoria
(Location: IWMI HQ Call no: e-copy only Record No: H050869)
https://www.sciencedirect.com/science/article/pii/S2214581821002123/pdfft?md5=0175f7656f4a85555f7f4120eaccf665&pid=1-s2.0-S2214581821002123-main.pdf
https://vlibrary.iwmi.org/pdf/H050869.pdf
https://vlibrary.iwmi.org/pdf/H050869.pdf
(12.90 MB) (12.9 MB)
Study region: Sio Malaba Malakisi river basin, East Africa.
Study focus: Poor rain-gauge density is a limitation to comprehensive hydrological studies in Sub-Saharan Africa. Consequently, Satellite precipitation products (SPPs) provide an alternative source of data for possible use in hydrological modeling. However, there is need to test their reliabilities across varied hydro-climatic and physiographic conditions to understand their applicability. Using two approaches, we evaluated the performance of six SPPs against gauge observations for possible water allocation studies in the SMMRB: (i) a point to pixel comparison using different statistical measures; (ii) hydrological evaluation of simulated discharge using the Continuous SEmi-distributed Runoff (COSERO) model approach.
New hydrological insights for the region: Our results indicate that CHIRPSv2 product performed best followed by MSWEPv2.2 as they suitably detected seasonal and annual rainfall amounts throughout the basin. However, at lower altitudes, most of the products overestimated rainfall as indicated by the performance measures. In some parts of the basin, the COSERO output signify an underperformance by PERSIANN-CDR and a good performance by GPM-3IMERG6. This could be attributed to differences in temporal dynamics of the products. In overall, seasonal trends captured by the SPPs can be used to support catchment management efforts in data scarce regions.
Study focus: Poor rain-gauge density is a limitation to comprehensive hydrological studies in Sub-Saharan Africa. Consequently, Satellite precipitation products (SPPs) provide an alternative source of data for possible use in hydrological modeling. However, there is need to test their reliabilities across varied hydro-climatic and physiographic conditions to understand their applicability. Using two approaches, we evaluated the performance of six SPPs against gauge observations for possible water allocation studies in the SMMRB: (i) a point to pixel comparison using different statistical measures; (ii) hydrological evaluation of simulated discharge using the Continuous SEmi-distributed Runoff (COSERO) model approach.
New hydrological insights for the region: Our results indicate that CHIRPSv2 product performed best followed by MSWEPv2.2 as they suitably detected seasonal and annual rainfall amounts throughout the basin. However, at lower altitudes, most of the products overestimated rainfall as indicated by the performance measures. In some parts of the basin, the COSERO output signify an underperformance by PERSIANN-CDR and a good performance by GPM-3IMERG6. This could be attributed to differences in temporal dynamics of the products. In overall, seasonal trends captured by the SPPs can be used to support catchment management efforts in data scarce regions.
18 Hojati, M.; Robertson, C.; Roberts, S.; Chaudhuri, C. 2022. GIScience research challenges for realizing discrete global grid systems as a digital earth. Big Earth Data, 23p. (Online first) [doi: https://doi.org/10.1080/20964471.2021.2012912]
Geographical information systems ; Research ; Spatial analysis ; Technology ; Databases ; Datasets ; Environmental factors ; Models ; Uncertainty
(Location: IWMI HQ Call no: e-copy only Record No: H050877)
https://www.tandfonline.com/doi/pdf/10.1080/20964471.2021.2012912
https://vlibrary.iwmi.org/pdf/H050877.pdf
https://vlibrary.iwmi.org/pdf/H050877.pdf
(7.02 MB) (7.02 MB)
Increasing data resources are available for documenting and detecting changes in environmental, ecological, and socioeconomic processes. Currently, data are distributed across a wide variety of sources (e.g. data silos) and published in a variety of formats, scales, and semantic representations. A key issue, therefore, in building systems that can realize a vision of earth system monitoring remains data integration. Discrete global grid systems (DGGSs) have emerged as a key technology that can provide a common multi-resolution spatial fabric in support of Digital Earth monitoring. However, DGGSs remain in their infancy with many technical, conceptual, and operational challenges. With renewed interest in DGGS brought on by a recently proposed standard, the demands of big data, and growing needs for monitoring environmental changes across a variety of scales, we seek to highlight current challenges that we see as central to moving the field(s) and technologies of DGGS forward. For each of the identified challenges, we illustrate the issue and provide a potential solution using a reference DGGS implementation. Through articulation of these challenges, we hope to identify a clear research agenda, expand the DGGS research footprint, and provide some ideas for moving forward towards a scaleable Digital Earth vision. Addressing such challenges helps the GIScience research community to achieve the real benefits of DGGS and provides DGGS an opportunity to play a role in the next generation of GIS.
19 Alahacoon, Niranga; Edirisinghe, M.; Simwanda, M.; Perera, E. N. C.; Nyirenda , V. R.; Ranagalage, M. 2022. Rainfall variability and trends over the African continent using TAMSAT data (1983-2020): towards climate change resilience and adaptation. Remote Sensing, 14(1):96. [doi: https://doi.org/10.3390/rs14010096]
Rainfall patterns ; Trends ; Climate change adaptation ; Resilience ; Weather hazards ; Climatic zones ; River basins ; Spatial distribution ; Monsoon climate ; Datasets / Africa
(Location: IWMI HQ Call no: e-copy only Record No: H050897)
(13.40 MB) (13.4 MB)
This study reveals rainfall variability and trends in the African continent using TAMSAT data from 1983 to 2020. In the study, a Mann–Kendall (MK) test and Sen’s slope estimator were used to analyze rainfall trends and their magnitude, respectively, under monthly, seasonal, and annual timeframes as an indication of climate change using different natural and geographical contexts (i.e., sub-regions, climate zones, major river basins, and countries). The study finds that the highest annual rainfall trends were recorded in Rwanda (11.97 mm/year), the Gulf of Guinea (river basin 8.71 mm/year), the tropical rainforest climate zone (8.21 mm/year), and the Central African region (6.84 mm/year), while Mozambique (-0.437 mm/year), the subtropical northern desert (0.80 mm/year), the west coast river basin of South Africa (-0.360 mm/year), and the Northern Africa region (1.07 mm/year) show the lowest annual rainfall trends. There is a statistically significant increase in the rainfall in the countries of Africa’s northern and central regions, while there is no statistically significant change in the countries of the southern and eastern regions. In terms of climate zones, in the tropical northern desert climates, tropical northern peninsulas, and tropical grasslands, there is a significant increase in rainfall over the entire timeframe of the month, season, and year. This implies that increased rainfall will have a positive effect on the food security of the countries in those climatic zones. Since a large percentage of Africa’s agriculture is based only on rainfall (i.e., rain-fed agriculture), increasing trends in rainfall can assist climate resilience and adaptation, while declining rainfall trends can badly affect it. This information can be crucial for decision-makers concerned with effective crop planning and water resource management. The rainfall variability and trend analysis of this study provide important information to decision-makers that need to effectively mitigate drought and flood risk.
20 Alam, Mohammad F.; Villholth, Karen G.; Podgorski, J. 2021. Human arsenic exposure risk via crop consumption and global trade from groundwater-irrigated areas. Environmental Research Letters, 16(12):124013. [doi: https://doi.org/10.1088/1748-9326/ac34bb]
Arsenic ; Exposure ; Human health ; Health hazards ; Groundwater irrigation ; Irrigated sites ; Crop production ; Rice ; Wheat ; Maize ; International trade ; Datasets
(Location: IWMI HQ Call no: e-copy only Record No: H050905)
https://iopscience.iop.org/article/10.1088/1748-9326/ac34bb/pdf
https://vlibrary.iwmi.org/pdf/H050905.pdf
https://vlibrary.iwmi.org/pdf/H050905.pdf
(13.00 MB) (13.0 MB)
While drinking water is known to create significant health risk in arsenic hazard areas, the role of exposure to arsenic through food intake is less well understood, including the impact of food trade. Using the best available datasets on crop production, irrigation, groundwater arsenic hazard, and international crop trade flows, we estimate that globally 17.2% of irrigated harvested area (or 45.2 million hectares) of 42 main crops are grown in arsenic hazard areas, contributing 19.7% of total irrigated crop production, or 418 million metric tons (MMT) per year of these crops by mass. Two-thirds of this area is dedicated to the major staple crops of rice, wheat, and maize (RWM) and produces 158 MMT per year of RWM, which is 8.0% of the total RWM production and 18% of irrigated production. More than 25% of RWM consumed in the South Asian countries of India, Pakistan, and Bangladesh, where both arsenic hazard and degree of groundwater irrigation are high, originate from arsenic hazard areas. Exposure to arsenic risk from crops also comes from international trade, with 10.6% of rice, 2.4% of wheat, and 4.1% of maize trade flows coming from production in hazard areas. Trade plays a critical role in redistributing risk, with the greatest exposure risk borne by countries with a high dependence on food imports, particularly in the Middle East and small island nations for which all arsenic risk in crops is imported. Intensifying climate variability and population growth may increase reliance on groundwater irrigation, including in arsenic hazard areas. Results show that RWM harvested area could increase by 54.1 million hectares (179% increase over current risk area), predominantly in South and Southeast Asia. This calls for the need to better understand the relative risk of arsenic exposure through food intake, considering the influence of growing trade and increased groundwater reliance for crop production.
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