Your search found 39 records
1 Habib, E.; Haile, Alemseged Tamiru; Sazib, N.; Zhang, Y.; Rientjes, T. 2014. Effect of bias correction of satellite-rainfall estimates on runoff simulations at the source of the Upper Blue Nile. Remote Sensing, 6(7):6688-6708. [doi: https://doi.org/10.3390/rs6076688]
Rain ; Runoff ; Satellites ; River basins ; Hydrology ; Simulation models ; Calibration ; Catchment areas ; Stream flow / Africa / Ethiopia / Upper Blue Nile Basin
(Location: IWMI HQ Call no: e-copy only Record No: H046873)
(608 KB)
Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product. The study area is the Gilgel Abbay catchment situated at the source basin of the Upper Blue Nile basin in Ethiopia, Eastern Africa. Rain gauge networks in such area are typically sparse. We examine different bias correction schemes applied locally to the CMORPH product. These schemes vary in the degree to which spatial and temporal variability in the CMORPH bias fields are accounted for. Three schemes are tested: space and time-invariant, time-variant and spatially invariant, and space and time variant. Bias-corrected CMORPH products were used to calibrate and drive the Hydrologiska Byråns Vattenbalansavdelning (HBV) rainfall-runoff model. Applying the space and time-fixed bias correction scheme resulted in slight improvement of the CMORPH-driven runoff simulations, but in some instances caused deterioration. Accounting for temporal variation in the bias reduced the rainfall bias by up to 50%. Additional improvements were observed when both the spatial and temporal variability in the bias was accounted for. The rainfall bias was found to have a pronounced effect on model calibration. The calibrated model parameters changed significantly when using rainfall input from gauges alone, uncorrected, and bias-corrected CMORPH estimates. Changes of up to 81% were obtained for model parameters controlling the stream flow volume.
2 Haile, Alemseged Tamiru; Yan, F.; Habib, E. 2015. Accuracy of the CMORPH satellite-rainfall product over Lake Tana Basin in eastern Africa. Atmospheric Research, 163:177-187. [doi: https://doi.org/10.1016/j.atmosres.2014.11.011]
Rain ; Satellites ; River basins ; Lakes ; Remote sensing ; Spatial distribution ; Wet season ; Dry season / Eastern Africa / Lake Tana Basin
(Location: IWMI HQ Call no: e-copy only Record No: H046880)
In this study, we assessed the accuracy of rainfall occurrence, amount and distribution over the Lake Tana basin in Ethiopia, Eastern Africa, as represented in the NOAA satellite-based Climate Prediction Center Morphing technique (CMORPH) rainfall product. This analysis is carried out at high spatial and temporal resolutions (8 × 8 km2 and daily) using observations from rain gauges as a reference for the period covering January 2003 to December 2006. Graphical comparisons and several statistical metrics such as bias, correlation coefficient, and standard deviation of rainfall differences are used to perform the evaluation analysis. Spatial maps of these statistical metrics were developed to assess the spatial dependency in the CMORPH accuracy. The bias is decomposed into different components, hit, missed, and false, in order to gain additional insight into the possible sources of systematic deviations in CMORPH. Overall, CMORPH was able to capture the seasonal and spatial patterns of rainfall over the basin, but with varying degrees of accuracy that depend on topography, latitude and lake-versus-land conditions within the basin. The results show that CMORPH captured rain occurrence relatively well in both wet and dry seasons over the southern part of the basin but it significantly overestimated those over the lake and its southern shore. The bias of CMORPH in the study area is characterized by seasonal and spatial variations (-25 to 30% in wet season and -40 to 60% in dry season). False as well as missed rains contribute significantly to the total rainfall amounts over the basin. Significant levels of the differences are observed at the daily resolution of CMORPH. The relation between CMORPH and gauge rainfall amounts is stronger (correlationmostly N0.4) in thewet season than in the dry (mostly b0.4).
3 Lebel, L.; Hoanh, Chu Thai; Krittasudthacheewa, C.; Daniel, R. (Eds.) 2014. Climate risks, regional integration and sustainability in the Mekong region. Petaling Jaya, Malaysia: Strategic Information and Research Development Centre (SIRDC); Stockholm, Sweden: Stockholm Environment Institute (SEI). 405p.
Climate change ; Risks ; Sustainable development ; Ecosystem services ; Policy making ; Urbanization ; Living standards ; Rural areas ; Households ; Economic development ; Investment ; Poverty ; Energy consumption ; Carbon dioxide ; Greenhouse gases ; Emission ; International waters ; Fish industry ; Employment ; Stakeholders ; Food security ; Tourism ; Forest management ; Environmental services ; Costs ; Satellites ; Remote sensing ; GIS ; Flooding ; Farming ; Rice ; Sugar ; Farmers ; Case studies / Southeast Asia / Thailand / Cambodia / Lao People's Democratic Republic / Vietnam / Khon Kaen / Vang Vieng / Chiang Mai / Hue / Lam Dong / Mekong Region
(Location: IWMI HQ Call no: IWMI Record No: H046894)
http://www.sei-international.org/mediamanager/documents/Publications/sumernet_book_climate_risks_regional_integration_sustainability_mekong_region.pdf
https://vlibrary.iwmi.org/pdf/H046894.pdf
https://vlibrary.iwmi.org/pdf/H046894.pdf
(1.87 MB) (1.87 MB)
4 Gumindoga, W.; Rientjes, T. H. M.; Haile, Alemseged Tamiru; Makurira, H.; Reggiani, P. 2019. Performance evaluation of CMORPH satellite precipitation product in the Zambezi Basin. International Journal of Remote Sensing, 40(20):7730-7749. [doi: https://doi.org/10.1080/01431161.2019.1602791]
Rain ; Precipitation ; Satellites ; Weather forecasting ; Performance evaluation ; River basins ; Meteorological stations ; Observation ; Hydrology ; Deltas / Botswana / Mozambique / Malawi / Zimbabwe / Zambia / Zambezi River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H049388)
(2.28 MB)
For evaluation of the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product in the Zambezi Basin, daily time series (1998–2013) of 60 rain gauge stations are used. Evaluations for occurrence and rain rate are at sub-basin scale and at daily, weekly, and seasonal timescale by means of probability of detection (POD), false alarm ratio (FAR), critical success index (CSI) and frequency bias (FBS). CMORPH predicts 60% of the rainfall occurrences. Rainfall detection is better for the wet season than for the dry season. Best detection is shown for rainfall rates smaller than 2.5 mm/day. Findings on error decomposition revealed sources of Hit, Missed and False rainfall bias. CMORPH performance (detection of rainfall occurrences and estimations for rainfall depth) at sub-basin scale increases when daily estimates are accumulated to weekly estimates. Findings suggest that for the Zambezi Basin, errors in CMORPH rainfall should be corrected before the product can serve applications such as in hydrological modelling that largely rely on reliable and accurate rainfall inputs.
5 Wang, R.; Liu, Y. 2020. Recent declines in global water vapor from MODIS products: artifact or real trend? Remote Sensing of Environment, 247:111896. (Online first) [doi: https://doi.org/10.1016/j.rse.2020.111896]
Water vapour ; Moderate Resolution Imaging Spectroradiometer ; Models ; Evaluation ; Remote sensing ; Satellites ; Climate change ; Trends ; Observation
(Location: IWMI HQ Call no: e-copy only Record No: H049758)
(6.62 MB)
Atmospheric water vapor plays a key role in the global water and energy cycles. Accurate estimation of water vapor and consistent representation of its spatial-temporal variation are critical to climate analysis and model validation. This study used ground observational data from global radiosonde and sunphotometer networks to evaluate MODIS (MODerate-resolution Imaging Spectroradiometer) precipitable water vapor (PWV) products for 2000–2017. The products included the thermal-infrared (TIR) (Collection 6, C006) and its updated version (Collection 061, C061), and near-infrared (NIR) products (C061). Our results demonstrated that compared to its earlier version subject to sensor crosstalk problem, the C061_TIR data showed improved accuracy in terms of bias, standard deviation, mean absolute error, root mean square error, and coefficient of determination, regression slope and intercept. Among the PWV products, C061_NIR data achieved the best overall performance in accuracy evaluation. The C061_NIR revealed the PWV had a multi-year average of 2.50 ± 0.08 cm for the globe, 2.03 ± 0.06 cm for continents, and 2.70 ± 0.09 cm for oceans in 2000–2017. The PWV values yielded an increasing rate of 0.015 cm/year for the globe, 0.010 cm/year for continents, and 0.017 cm/year for oceans. Nearly 98.95% of the globe showed an increasing trend, 80.74% of statistical significance, mainly distributed within and around the tropical zones. The findings should be valuable for understanding of global water and energy cycles.
6 Tessema, K. B.; Haile, Alemseged Tamiru; Amencho, N. W.; Habib, E. 2022. Effect of rainfall variability and gauge representativeness on satellite rainfall accuracy in a small upland watershed in southern Ethiopia. Hydrological Sciences Journal, 67(16):2490-2504. (Special issue: Hydrological Data: Opportunities and Barriers) [doi: https://doi.org/10.1080/02626667.2020.1770766]
Rainfall patterns ; Rain gauges ; Satellites ; Weather data ; Evaluation ; Watersheds ; Highlands ; Precipitation ; Observation ; Estimation ; Meteorological stations ; Models / Ethiopia / Southern Nations, Nationalities, and Peoples' Region (SNNPR) / Upper Gana Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H049792)
(3.30 MB)
The actual accuracy of satellite rainfall products is often unknown due to the limitation of raingauge networks. We evaluated the effect of gauge representativeness error on evaluation of rainfall estimates from the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) rainfall product. The reference data were collected using an experimental raingauge network within a small watershed of 1690 ha, which is comparable to the CHIRPS resolution. The study applied a total bias approach, decomposed into hit, missed and false biases, and an error-variance separation method to evaluate gauge representativeness error at the scale of CHIRPS pixel size, as well as modeled the spatial correlation field of daily rainfall with a three-parametric exponential model. The results indicate that the gauge representativeness error is still too large to ignore in evaluating satellite rainfall. However, it is significantly affected by sample size and caution should be exercised when the rainfall data has a small sample size.
7 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.
8 Pereira, L. S.; Paredes, P.; Melton, F.; Johnson, L.; Wang, T.; Lopez-Urrea, R.; Cancela, J. J.; Allen, R. G. 2020. Prediction of crop coefficients from fraction of ground cover and height. Background and validation using ground and remote sensing data. Agricultural Water Management, 241:106197. (Online first) [doi: https://doi.org/10.1016/j.agwat.2020.106197]
Crops ; Forecasting ; Remote sensing ; Satellites ; Evapotranspiration ; Vegetation ; Irrigation management ; Water stress ; Water management ; Energy balance ; Indicators ; Vegetable crops ; Field crops ; Soil water balance
(Location: IWMI HQ Call no: e-copy only Record No: H049857)
(2.32 MB)
The current study aims at reviewing and providing advances on methods for estimating and applying crop coefficients from observations of ground cover and vegetation height. The review first focuses on the relationships between single Kc and basal Kcb and various parameters including the fraction of ground covered by the canopy (fc), the leaf area index (LAI), the fraction of ground shaded by the canopy (fshad), the fraction of intercepted light (flight) and intercepted photosynthetic active radiation (fIPAR). These relationships were first studied in the 1970’s, for annual crops, and later, in the last decennia, for tree and vine perennials. Research has now provided a variety of methods to observe and measure fc and height (h) using both ground and remote sensing tools, which has favored the further development of Kc related functions. In the past, these relationships were not used predictively but to support the understanding of dynamics of Kc and Kcb in relation to the processes of evapotranspiration or transpiration, inclusive of the role of soil evaporation. Later, the approach proposed by Allen and Pereira (2009), the A&P approach, used fc and height (h) or LAI data to define a crop density coefficient that was used to directly estimate Kc and Kcb values for a variety of annual and perennial crops in both research and practice. It is opportune to review the A&P method in the context of a variety of studies that have derived Kc and Kcb values from field measured data with simultaneously observed ground cover fc and height. Applications used to test the approach include various tree and vine crops (olive, pear, and lemon orchards and vineyards), vegetable crops (pea, onion and tomato crops), field crops (barley, wheat, maize, sunflower, canola, cotton and soybean crops), as well as a grassland and a Bermudagrass pasture. Comparisons of Kcb values computed with the A&P method produced regression coefficients close to 1.0 and coefficients of determination = 0.90, except for orchards. Results indicate that the A&P approach can produce estimates of potential Kcb, using vegetation characteristics alone, within reasonable or acceptable error, and are useful for refining Kcb for conditions of plant spacing, size and density that differ from standard values. The comparisons provide parameters appropriate to applications for the tested crops. In addition, the A&P approach was applied with remotely sensed fc data for a variety of crops in California using the Satellite Irrigation Management Support (SIMS) framework. Daily SIMS crop ET (ETc-SIMS) produced Kcb values using the FAO56 and A&P approaches. Combination of satellite derived fc and Kcb values with ETo data from Spatial CIMIS (California Irrigation Management Information System) produced ET estimates that were compared with daily actual crop ET derived from energy balance calculations from micrometeorological instrumentation (ETc EB).Results produced coefficients of regression of 1.05 for field crops and 1.08 for woody crops, and R2 values of 0.81 and 0.91, respectively. These values suggest that daily ETc-SIMS -based ET can be accurately estimated within reasonable error and that the A&P approach is appropriate to support that estimation. It is likely that accuracy can be improved via progress in remote sensing determination of fc. Tabulated Kcb results and calculation parameters are presented in a companion paper in this Special Issue.
9 Rana, V. K.; Suryanarayana, T. M. V. 2020. Performance evaluation of MLE [Maximum Likelihood Estimation], RF [Random Forest Tree] and SVM [Support Vector Machine] classification algorithms for watershed scale land use/land cover mapping using sentinel 2 bands. Remote Sensing Applications: Society and Environment, 19:100351. [doi: https://doi.org/10.1016/j.rsase.2020.100351]
Watersheds ; Land use mapping ; Land cover mapping ; Hydrology ; Models ; Performance evaluation ; Remote sensing ; Satellites ; Vegetation ; Cultivated land ; Rain ; Machine learning ; Principal component analysis ; Multivariate analysis / India / Gujarat / Vishwamitri Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H049839)
(14.80 MB)
The land use and land cover map plays a significant role in agricultural, water resources planning, management, and monitoring programs at regional and national levels and is an input to various hydrological models. Land use and land cover maps prepared using satellite remote sensing techniques in conjunction with landform-soil-vegetation relationships and ground truth are popular for locating suitable sites for the construction of water harvesting structures, soil and water conservation measures, runoff computations, irrigation planning and agricultural management, analyzing socio-ecological concerns, flood controlling, and overall watershed management. Here we use a novel approach to analyze Sentinel–2 multispectral satellite data using traditional and principal component analysis based approaches to evaluate the effectiveness of maximum likelihood estimation, random forest tree, and support vector machine classifiers to improve land use and land cover categorization for Soil Conservation Service Curve Number model. Additionally, we use stratified random sampling to evaluate the accuracies of resulted land use and land cover maps in terms of kappa coefficient, overall accuracy, producer's accuracy, and user's accuracy. The classifiers were used for classifying the data into seven major land use and land cover classes namely water, built-up, mixed forest, cultivated land, barren land, fallow land with vertisols dominance, and fallow land with inceptisols dominance for the Vishwamitri watershed. We find that principal component analysis with support vector machine is able to produce highly accurate land use and land cover classified maps. Principal component analysis extracts the useful spectral information by compressing redundant data embedded in each spectral channel. The study highlights the use of principal component analysis with support vector machine classifier to improve land use and land cover classification from which policymakers can make better decisions and extract basic information for policy amendments.
10 Evett, S. R.; O’Shaughnessy, S. A.; Andrade, M. A.; Kustas, W. P.; Anderson, M. C.; Schomberg, H. H.; Thompson, A. 2020. Precision agriculture and irrigation: current U.S. perspectives. Transactions of the ASABE, 63(1):57-67. [doi: https://doi.org/10.13031/trans.13355]
Precision agriculture ; Irrigation systems ; Crop water use ; Water productivity ; Water security ; Decision support systems ; Technology transfer ; Satellites ; Remote sensing ; Soil water content ; Fertilizer application ; Farmers ; Models / USA
(Location: IWMI HQ Call no: e-copy only Record No: H049849)
https://elibrary.asabe.org/azdez.asp?search=0&JID=3&AID=51121&CID=t2020&v=63&i=1&T=2
https://vlibrary.iwmi.org/pdf/H049849.pdf
https://vlibrary.iwmi.org/pdf/H049849.pdf
(2.96 MB) (2.96 MB)
11 Abro, M. I.; Zhu, D.; Khaskheli, M. A.; Elahi, E.; Aleem ul Hassan Ramay, M. 2020. Statistical and qualitative evaluation of multi-sources for hydrological suitability inflood-prone areas of Pakistan. Journal of Hydrology, 588:125117. [doi: https://doi.org/10.1016/j.jhydrol.2020.125117]
River basins ; Flooding ; Hydrology ; Evaluation ; Models ; Forecasting ; Watersheds ; Precipitation ; Rain ; Satellites / Pakistan / Indus River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H049961)
(1.42 MB)
In 2010, flood hampered commercial and agricultural activities and destroyed infrastructure at large scale in Pakistan. Accurate forecasting of the flood is necessary to implement right and timely strategies to mitigate its negative impacts. Therefore, the current study emphasis on evaluation of the suitability of satellite/reanalysis precipitation data products (ERA-Interim, MSWEP, PERSIANN-CDR and TRMM) in comparison with observed rain gauge data for hydrological studies especially for flash floods analysis in the Indus River Basin, Pakistan. The various statistical and categorical matrices Bias, Nash Sutcliff, MAE, RMSE, R2, CSI, FAR, POD, percentage difference, peak error percentage, peak time error and graphical methods were used to report the divergences in the precipitation and flow. Results revealed that the MSWEP was outperformed on daily rainfall and hydrological suitability analysis based on various evaluations and all sources were captured accurate patterns. The study results indicated a potential of satellite and reanalysis data in many applications especially flood forecasting and water management where rain gauge data are not available or limited.
12 Liu, D.; Wang, X.; Aminjafari, S.; Yang, W.; Cui, B.; Yan, S.; Zhang, Y.; Zhu, J.; Jaramillo, F. 2020. Using InSAR [Interferometric Synthetic Aperture Radar] to identify hydrological connectivity and barriers in a highly fragmented wetland. Hydrological Processes, 14p. (Online first) [doi: https://doi.org/10.1002/hyp.13899]
Wetlands ; Hydrological factors ; SAR (radar) ; Radar imagery ; Water levels ; Satellites ; Remote sensing ; Interferometry ; Barriers ; Ecosystems ; Grasslands ; Vegetation / China / Baiyangdian Wetland
(Location: IWMI HQ Call no: e-copy only Record No: H049975)
https://onlinelibrary.wiley.com/doi/epdf/10.1002/hyp.13899
https://vlibrary.iwmi.org/pdf/H049975.pdf
https://vlibrary.iwmi.org/pdf/H049975.pdf
(3.71 MB) (3.71 MB)
Hydrological connectivity is a critical determinant of wetland functions and health, especially in wetlands that have been heavily fragmented and regulated by human activities. However, investigating hydrological connectivity in these wetlands is challenging due to the costs of high-resolution and large-scale monitoring required in order to identify hydrological barriers within the wetlands. To overcome this challenge, we here propose an interferometric synthetic aperture radar (InSAR)-based methodology to map hydrologic connectivity and identify hydrological barriers in fragmented wetlands. This methodology was applied along 70 transects across the Baiyangdian, the largest freshwater wetland in northern China, using Sentinel 1A and 1B data, covering the period 2016–2019. We generated 58 interferograms providing information on relative water level changes across the transects that showed the high coherence needed for the assessment of hydrological connectivity. We mapped the permanent and conditional (temporary) barriers affecting connectivity. In total, 11% of all transects are permanently disconnected by hydrological barriers across all interferograms and 58% of the transects are conditionally disconnected. Areas covered by reed grasslands show the most undisturbed hydrological connectivity while some of these barriers are the result of ditches and channels within the wetland and low water levels during different periods of the year. This study highlights the potential of the application of Wetland InSAR to determine hydrological connectivity and location of hydrological barriers in highly fragmented wetlands, and facilitates the study of hydrological processes from large spatial scales and long-time scales using remote sensing technique.
13 Pudasainee, A.; Chaulagain, B. P. 2020. Prospects of ICT based agricultural technology in Nepal. Nepalese Journal of Agricultural Sciences, 19:223-235.
Precision agriculture ; Information and Communication Technologies ; Unmanned aerial vehicles ; Imagery ; Geographical information systems ; Satellites ; Plant health ; Fertilizers ; Decision making ; Agroindustrial sector ; Farmers ; Models / Nepal
(Location: IWMI HQ Call no: e-copy only Record No: H049813)
(0.41 MB)
This cloud agriculture system (CAS) combines drone assisted diagnostics and prescription agriculture (DADAPA), value addition to agriculture produce (VAAP) and cloud market system (CMS). The CAS can be an advantage to the country where lack of trained agriculture service providers, complex geographical patterns and rain fed farming hinders the crop productivity. The low-cost drone imagery and data based analysis as DADAPA in combination with VAAP and CMS will have a significant impact for the upliftment of farming community in Nepal. The CAS can supplement wet bench laboratory and skilled agriculture services. That addresses insufficient market information system and the knowledge in value addition to crops and vegetables. It provides services to farmers by prescribing solutions accurately for problems like irrigation, weed management, plant health and growth, soil nutrition and fertilizers applications, diagnosing different diseases as precision agriculture. Information on agri-products and price in the market develops confidence and income to farmers and wholesalers.
14 Abolafia-Rosenzweig, R.; Pan, M.; Zeng, J. L; Livneh, B. 2021. Remotely sensed ensembles of the terrestrial water budget over major global river basins: an assessment of three closure techniques. Remote Sensing of Environment, 252:112191. [doi: https://doi.org/10.1016/j.rse.2020.112191]
Remote sensing ; Water budget ; Estimation ; Techniques ; River basins ; Hydrological cycle ; Water balance ; Water storage ; Precipitation ; Evapotranspiration ; Soil water content ; Stream flow ; Satellites ; Models ; Uncertainty
(Location: IWMI HQ Call no: e-copy only Record No: H050109)
https://www.sciencedirect.com/science/article/pii/S0034425720305642/pdfft?md5=9124755032cbc8f7578ef32174ad2423&pid=1-s2.0-S0034425720305642-main.pdf
https://vlibrary.iwmi.org/pdf/H050109.pdf
https://vlibrary.iwmi.org/pdf/H050109.pdf
(9.59 MB) (9.59 MB)
Remote sensing is a useful tool for observing the water cycle. However, combining remote sensing products over any major river basin will result in a residual error in the overall water balance. Previous studies have either quantified this error without correcting it, or have merged observations together with land surface models (LSMs) to produce a single “best” estimate of the water balance. Here, we present a new approach in which combinations of remote sensing and in situ observations are constrained to enforce water balance closure. Rather than a single estimate, this produces an ensemble of unique water balance estimates intended to characterize uncertainty and to avoid biases implicit in LSMs. We evaluate three techniques of varying complexity to enforce water balance closure for individual ensemble members over 24 global basins from Oct. 2002 - Dec. 2014, resulting in as many as 60 realizations of the monthly water budget, contingent upon data availability. Compared with a published climate data record, the ensemble shows strong agreement for precipitation, evapotranspiration and changes in storage (R2: 0.91–0.95), with less agreement for streamflow (R2: 0.42–0.47), which may be indicative of LSM biases in the climate data record. Water balance residual errors resulting from combinations of raw products vary significantly (p < 0.001) with latitude, with a tendency for positive biases for low- and mid-latitude basins, and negative biases elsewhere. Overall, residual errors are equivalent to 15% of total precipitation when averaged across all data products and basins. This study shows that closure constraints provide additional value outside of closing the water budget, including reduction of uncertainty and transfer of closure constraints in time to provide skillful estimates of mean annual basin discharge. We also showed that a simpler closure technique, proportional redistribution, performed better than more complex ones in decreasing uncertainty and for transfer through time to estimate basin discharge when a rigorous analysis of errors for each data product is not accounted for. This observation-based dataset is distinct from modeled estimates and therefore has the potential to preserve important information of anthropogenic effects on the water balance.
15 Talchabhadel, R.; Aryal, A.; Kawaike, K.; Yamanoi, K.; Nakagawa, H.; Bhatta, B.; Karki, S.; Thapa, B. R. 2021. Evaluation of precipitation elasticity using precipitation data from ground and satellite-based estimates and watershed modeling in western Nepal. Journal of Hydrology: Regional Studies, 33:100768. [doi: https://doi.org/10.1016/j.ejrh.2020.100768]
Watersheds ; Modelling ; Precipitation ; Elasticity ; Evaluation ; River basins ; Discharge ; Estimation ; Land use ; Land cover ; Hydrology ; Meteorological stations ; Satellites / Nepal / West Rapti River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H050208)
https://www.sciencedirect.com/science/article/pii/S2214581820302421/pdfft?md5=cfbce264fad6c6680322f609976ab7e1&pid=1-s2.0-S2214581820302421-main.pdf
https://vlibrary.iwmi.org/pdf/H050208.pdf
https://vlibrary.iwmi.org/pdf/H050208.pdf
(9.81 MB) (9.81 MB)
Study Region: West Rapti River (WRR) basin, Western Nepal.
Study Focus: Hydrologic modeling requires an accurate precipitation data at a high spatial resolution, which is often limited in many regions of the globe. As a complement to the ground (gauge) precipitation data, satellite-based precipitation estimates (SPEs) appear useful. At first, this study evaluated performance of three different SPEs, namely i) CHIRPS, ii) PERSIANN-CCS, and iii) IMERG, with respect to gauge data using different event detection and quantification indices. Soil Water Assessment Tool (SWAT), a semi-distributed hydrologic model, was used to simulate the river discharge. We then analysed precipitation elasticity, as a first kind of such study in Nepalese river basin, by scaling the precipitation input in both positive and negative directions (ranging from -20 % to +20 %) in order to explore basin response on likely alteration of precipitation. A non-parametric precipitation elasticity was finally computed for three different cases: 1) observed river discharge, 2) gauge-based simulated river discharge, and 3) SPEs-based simulated river discharge.
New Hydrologic Insights for the Region:
IMERG proved to be superior among three SPEs. All SPEs showed improved results after implementation of different levels of bias-correction where daily precipitation data were corrected using linear correction factors computed at a mean monthly scale. Computed correction factors are replicable to nearby basins. Precipitation elasticity of the study area ranged from +1.3 to +2.0 (approximately +1.5) which indicates that a 1.0 % change in precipitation will result in 1.5 % change in river discharge.
Study Focus: Hydrologic modeling requires an accurate precipitation data at a high spatial resolution, which is often limited in many regions of the globe. As a complement to the ground (gauge) precipitation data, satellite-based precipitation estimates (SPEs) appear useful. At first, this study evaluated performance of three different SPEs, namely i) CHIRPS, ii) PERSIANN-CCS, and iii) IMERG, with respect to gauge data using different event detection and quantification indices. Soil Water Assessment Tool (SWAT), a semi-distributed hydrologic model, was used to simulate the river discharge. We then analysed precipitation elasticity, as a first kind of such study in Nepalese river basin, by scaling the precipitation input in both positive and negative directions (ranging from -20 % to +20 %) in order to explore basin response on likely alteration of precipitation. A non-parametric precipitation elasticity was finally computed for three different cases: 1) observed river discharge, 2) gauge-based simulated river discharge, and 3) SPEs-based simulated river discharge.
New Hydrologic Insights for the Region:
IMERG proved to be superior among three SPEs. All SPEs showed improved results after implementation of different levels of bias-correction where daily precipitation data were corrected using linear correction factors computed at a mean monthly scale. Computed correction factors are replicable to nearby basins. Precipitation elasticity of the study area ranged from +1.3 to +2.0 (approximately +1.5) which indicates that a 1.0 % change in precipitation will result in 1.5 % change in river discharge.
16 Zhou, X.; Zhang, Y.; Sheng, Z.; Manevski, K.; Andersen, M. N.; Han, S.; Li, H.; Yang, Y. 2021. Did water-saving irrigation protect water resources over the past 40 years? a global analysis based on water accounting framework. Agricultural Water Management, 249:106793. [doi: https://doi.org/10.1016/j.agwat.2021.106793]
Water conservation ; Irrigation water ; Water accounting ; Irrigation efficiency ; Water use efficiency ; Technology ; Estimation ; Water resources ; Water extraction ; Irrigated land ; Evapotranspiration ; Satellites
(Location: IWMI HQ Call no: e-copy only Record No: H050288)
(11.70 MB)
Water-saving technologies have long been seen as an effective method to reduce irrigation water use and alleviate regional water shortage. However, growing reports of more severe water shortage and increasing application of water-saving technologies across the world have necessitated reassessment of agricultural water-saving. This study develops a simple method based on satellite-based ET partitions to estimate water withdrawal, water consumption and return flow from the 1980s to 2010s, and quantifies water-savings across globe and four hot-spot irrigated areas at both field and regional scales based on water accounting framework. The results show that global irrigation water flows keep increasing from the 1980s to 2010s, with over 50% increase from the expansion in irrigated lands. While water-saving technologies are found mainly applied in originally old irrigated lands, traditional flooding irrigation is still dominant in newly-developed irrigated lands. Non-beneficial water consumption (soil evaporation) is effectively reduced by water-saving technologies, but return flow has increased at the same time. At field scale, water-saving technologies fail to save water because the accumulated increased return flow is more than the accumulated decreased non-beneficial water consumption. At regional scale, however, water is saved because the return flow percolated to fresh aquifers is seen as beneficial rather than loss. At the same time, the accumulated increase of beneficial water consumption (crop transpiration) exceeds regional water savings, which explains the paradox between wide application of water-saving technologies and more severe regional water shortage. This study provides key new evidence for the paradox of irrigation efficiency and helps reconsidering water-saving technologies and their impacts on regional water resources.
17 Kafy, A.-A.; Faisal, A.-A.; Raikwar, V.; Al Rakib, A.; Kona, M. A.; Ferdousi, J. 2021. Geospatial approach for developing an integrated water resource management plan in Rajshahi, Bangladesh. Environmental Challenges, 4:100139. (Online first) [doi: https://doi.org/10.1016/j.envc.2021.100139]
Integrated management ; Water resources ; Water management ; Urbanization ; Sustainability ; Land use ; Land cover ; Remote sensing ; Geographical information systems ; Surface water ; Vegetation ; Farmland ; Climate change ; Biodiversity ; Landsat ; Satellites ; Developing countries / Bangladesh / Rajshahi / Padma River
(Location: IWMI HQ Call no: e-copy only Record No: H050415)
https://www.sciencedirect.com/science/article/pii/S2667010021001189/pdfft?md5=ba1de5300be32d5d03e32809c264f9d7&pid=1-s2.0-S2667010021001189-main.pdf
https://vlibrary.iwmi.org/pdf/H050415.pdf
https://vlibrary.iwmi.org/pdf/H050415.pdf
(8.70 MB) (8.70 MB)
The integrated water resource management (IWRM) plan plays a substantial role in addressing institutional problems and capacity building for the use, control, preservation, and sustainability of water systems, especially for developing countries like Bangladesh. Bangladesh is undergoing dramatic changes in land cover/land use (LC/LU) change, primarily due to rapid urbanization. Urbanization converts the natural resources (water bodies) into impervious surfaces (urban areas and roads). Rajshahi is one of the largest metropolitan cities in Bangladesh, and its urban sustainability is affected by the demolishment of water bodies influenced by rapid LC/LU change. Satellite images at different spatial resolutions are extensively used for extracting water body information at various periods. Using multi-temporal Landsat TM/OLI satellite images, the study aimed to estimate the spatiotemporal LC/LU change and identify the most influential LC/LU parameters that contributed to the reduction of surface water body from 1989 to 2019. The support vector machine, matrix union, and image difference algorithms were applied to estimate LC/LU classification, conversion of LC/LU, and water body to other LC/LU classes. Results revealed that a massive increase in the built-up area (16%) and infrastructural development were the primary cause for water body reduction, and loss of water bodies was estimated at around 8% in the last 30 years. Key informant's interviews were conducted to identify the effective management and technical strategies for developing a sustainable IWRM plan utilizing modern technology. The estimated hydro-geomorphological (modeling flow direction, stream network, flow accumulation, and surface water potential zones) information would be beneficial to any developing country.. This study will contribute to the development of effective strategies for conserving existing water bodies and ensuring ecological and environmental sustainability by increasing plant biodiversity and mitigating the effects of heatwaves.
18 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.
19 Han, Z.; Huang, S.; Huang, Q.; Leng, G.; Liu. Y.; Bai, Q.; He, P.; Liang, H.; Shi, W. 2021. GRACE-based high-resolution propagation threshold from meteorological to groundwater drought. Agricultural and Forest Meteorology, 307:108476. (Online first) [doi: https://doi.org/10.1016/j.agrformet.2021.108476]
Groundwater ; Drought ; Meteorological factors ; River basins ; Water storage ; Precipitation ; Vegetation ; Soil moisture ; Satellites ; Observation ; Models / China / Xijiang River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H050424)
(11.50 MB)
Groundwater drought could cause tremendous damage to the social-economy via land subsidence, seawater intrusion and permanent loss of aquifer storage capacity, and often show strong association with meteorological drought. To date, the threshold for meteorological drought triggering groundwater drought and its dominant factors have been not clarified, which inhibits the effective groundwater drought risk management based on preceding meteorological drought information. In this study, we used the Standardized precipitation index (SPI) and the drought severity index of groundwater storage anomalies (GWSA-DSI) to characterize meteorological and groundwater droughts in the Xijiang River Basin (XRB) of China, respectively. A probabilistic framework is proposed to identify the high-resolution propagation thresholds from meteorological to groundwater drought on 0.25° grid. Results show that GWSA-DSI can reliably identify groundwater drought events, and the propagation time from meteorological to groundwater drought ranges from 8 to 42 months. Although the XRB is located in a humid region with abundant precipitation, the probability of groundwater drought occurrence reached 43.8%, 54.8%, 61.2%, and 64.2% under a light, moderate, severe and extreme meteorological drought event, respectively. The propagation threshold triggering light groundwater drought is mainly dominated by moderate and severe meteorological droughts, which showed an increasing trend from central to southeast of XRB. Soil evaporation and watershed elevation are the main influencing factors on the propagation threshold. It is worth noting that anthropogenic overexploitation of groundwater not only destroy the dynamic balance of regional groundwater system, but also interfere with the propagation processes of meteorological to groundwater drought. The results have great implications for more reliably monitoring and predicting the dynamics of groundwater systems under drought stress, and our proposed framework can also be extended to other regions.
20 Ali, S.; Liu, D.; Fu, Q.; Cheema, M. J. M.; Pham, Q. B.; Rahaman, Md. M.; Dang, T. D.; Anh, D. T. 2021. Improving the resolution of GRACE data for spatio-temporal groundwater storage assessment. Remote Sensing, 13(17):3513. (Special Issue: Remote Sensing and Modelling of Water Storage Dynamics from Bedrock to Atmosphere) [doi: https://doi.org/10.3390/rs13173513]
Groundwater assessment ; Water storage ; Irrigation systems ; Aquifers ; Groundwater table ; Soil moisture ; Evapotranspiration ; Runoff ; Models ; Satellites ; Neural networks / Pakistan / Sindh / Punjab / Indus Basin Irrigation System
(Location: IWMI HQ Call no: e-copy only Record No: H050649)
(9.12 MB) (9.12 MB)
Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1° to a higher resolution (0.25°). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash–Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about -9.54 ± 1.27 km3 at the rate of -0.68 ± 0.09 km3/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales.
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