Die Analyse einer ökologisch und ökonomisch effizienteren Pflanzen- und Wasserbewirtschaftung bildet den Schwerpunkt dieser Arbeit. Die Effekte modifizierter technologischer-, umweltrelevanter- und institutioneller Rahmenbedingungen sollen hierbei bestimmt und ausgewertet werden. Die Liberalisierung des Baumwollsektors, die Einführung von Wasserpreisen oder die Verbesserung des Bewässerungssystems beispielsweise werden auf ihre Auswirkungen hinsichtlich regionaler Wasserverteilung, landwirtschaftlicher Anbaustruktur und ihrem ökonomischen Nutzen untersucht. Zu diesem Zwecke müssen im Vorfeld die wesentlichen hydrologischen und agronomischen Interaktionen und Eigenschaften der RegionKhorezm identifiziert werden. Um diese zu Grunde liegenden Konditionen angemessen analysieren zu können, wurde ein integriertes Wasser-Management- Modell aufgebaut. Die Kombination von interdisziplinären Aspekten in einen theoretisch konsistenten Modellierungsrahmen stellt ein Novum in dieser Arbeit dar. Wesentliche klimatologische, hydrologische, agronomische, institutionelle und ökonomische Eigenschaften und Beziehungen sind in einem kohärenten Optimierungsmodell für die RegionKhorezm integriert. Der große Vorteil dieser Modellierung liegt unter anderem auch in der Berücksichtigung von Kanal- und Grundwasser, die gerade in Bewässerungssystem von Khorezm von besonderer Wichtigkeit sind. Einen weiteren Nutzen des Modells und der darauf aufbauenden Forschungsarbeit bietet die Verwendung einer Wasser-Bilanzierungs- Methode. Im Gegensatz zu dem häufig verwendeten statischen Ansatz unter Nutzung von starren Bewässerungsnormen können durch die Bilanzierung von „Wassereinnahmen“ und „Wasserausgaben“ wesentliche Prozesse in größerer Genauigkeit dargestellt werden.
The simulation of cotton and input market liberalization policy shows that under assumed changes of factor prices, this policy will not necessarily lead to an increase in the regional production of cotton. In general, the market liberalization has a positive effect on the regional rice sector which can be cultivated on land released from the procurement quotas in case of course the land is suitable for rice cultivation. Furthermore, despite the absence of a functioning land market, the completion of the farm restructuring process may increase the efficiency of land and wateruse and allow the producers to achieve a higher income level. Concerning the policy effect with regards to the location of producers and consumers, the market liberalization and farm restructuring reforms are more beneficial for districts with abundant water, while the water scarce districts will have a higher positive effect under improved water efficiency and increased productivity of the livestock sector.
Agricultural Land and WaterUse in the KhorezmRegion
et al. (2005), in 2003 the producer price control presented the main form of agricultural taxation, and was followed by direct taxes such as income and land taxes. The implicit taxation of the agricultural sector in form of procurement prices has been particularly discussed by many authors (Rosenberg et al. 1999, Guadagni et al. 2005, Khan 2005, Spoor 1999). In order to offset the negative effects of the taxation regime, the state provides significant subsidies for irrigation, financing and other inputs to agricultural producers (Guadagni et al. 2005). However, these are not direct subsidies but rather involve indirect measures, such as price differentials for inputs and cotton byproducts, arbitrary allocation and reallocation of financial resources, maintenance and operating costs of irrigation system as well as credit postponements, debt write-offs, tax remissions and lax crediting. Most of these subsidies are not directly allocated to producers, but rather allocated to the agricultural sector as a whole. Table 2.10 shows the difference between official and informal market prices for the main nitrogen fertilizer and diesel in 2002-2003 11 . Those input price differentials are producer-oriented and crop-oriented; meaning that only those agricultural producers who have a state procurement task have access to subsidized inputs. As a result of the input price differential, which is a state controlled discrimination scheme, regarding input availability, agricultural producers select either to apply inputs under a state procurement crop, or crops with higher market values, or transfer them for activities with higher incomes (Guardani et al 2005).
The state order implies that a pre-determined share of agricultural land has to be used for cotton production which is sold at pre-determined prices to parastatal agencies. The paper is also providing an outline of the most important features of the three major types of agricultural producers, i.e. dehqon 3 , shirkats and private fermer, which have emerged over the past decade. The model incorporates these producers, the major production activities and has a regional breakdown to the district level (10 different sub-regions are represented in the model). The model focuses on the production side and remains stylized on the demand side. Never- theless, the base-run solution, with which the model was calibrated, and some first simulations provide interesting insights into some causal relationships of the agricultural production system. For instance, the model comes up with esti- mates of shadow prices for land and water which is very relevant for one of the most controversial internal policy debates in Uzbekistan: How and with which prices should the privatisation of land and water be implemented? Regarding water this raises further questions about the institutional design of such user fees: Should there be flat rats, or should they be crop-based and/or differ between districts etc. Finally, the results of some first and cautious simulations runs are presented. One simulation looks into the effects of introducing water user fees, a second one addresses the abolishment of the state procurement system for cotton and a third one looks into the effects of completing the farm restructuring process. The results of theses model simulations always have to be seen against the sometime restrictive features of the underlying model economy. Hence, they should be considered as complements but not substitutes of one's own mental arithmetics in assessing a vast range of policy instruments.
A high level of wateruse, 16-18 thousand m 3 ha -1 , and low wateruse efficiency in flood and furrow irrigation, are argued as the main sources of ecological degradation in the region (e.g. ZEF, 2003; Saifulin et al., 1999). Improving water application efficiency at
the field level is thus considered as one of the main mechanisms for improving environmental conditions in the region. To this end, several scenarios were tested within the framework of the study to analyze the effects of improving WUE. Overall WUE in the WUA, estimated by dividing crop beneficial wateruse by total irrigation water flow into the WUA, was equal to 65.2 % in the baseline scenario. The WUE was higher than presented by Conrad (2006) for the whole region, where WUE (the depleted fraction) was equal to 48% when the entire vegetation period was considered. Discrepancy between the numbers might therefore be explained by the difference in the time period for the estimation of the WUE—in this model only the first planning period is included. 15 Once farms have satisfied the state order (i.e. in the second half of the planting period), there is evidence of increased water demand for crops such as rice and vegetables in most areas (Veldwisch, 2008). Therefore, overall WUE would be lower than 65.2 percent in the second half of the vegetation period, and would thus fall in the same range with the findings of Conrad (2006). Data limitations did not allow for calibration and validation of the model for the second half of the vegetation period; therefore, discussion is limited only to the first part of the vegetation period. However, the influence of certain policies on CE, expected income and other model parameters in the second half of the vegetation period is assumed to be in the same direction as observed in the first part of the vegetation period.
It has to be noted that CropSyst is not a specific cotton growth model such as GOSSYM, COTONS, Cotton2K, OZCOT, or other cotton-only models. These models undoubtedly allow much more detailed simulations with respect to water and N stress on plant phenology and development once the detailed data has been collected, i.e., allocation of N to plant organs, new node and boll production, and aging of leaves (Marani 2004). Also, the indefinite end of cotton yield formation, i.e., cotton bolls open over a 3-month period, can be handled by these models, which allow calculating fiber harvest for several picking times (Marani 2004). CropSyst in turn calculates yield based on the harvest index and accumulated biomass at the termination of crop growth. Therefore, the yield predictions derived from CropSyst would actually be equal to one single pick and thus less precise than specific cotton models under conditions of multiple (manual) cotton picks like in Uzbekistan. However, in view of the fact that more than 60-70 % of raw cotton is harvested at pick 1, and the observed N uptake and the corresponding yields were reproduced satisfactorily, the model served the basic purpose.
Today water has been a major factor in the development of many regions in the world, and conflicts in regional waterallocation have become into a very large problem in water resources planning and management. Downstream farmers may find that a upstream hydroelectric power plant does not release enough water for their irrigation use in vegetation periods; recreational agencies may complain that the industrial companies damage the recreational benefit by discharging too much wastewater into the water system. The most serious conflict happens in some international river basins, in which limited water is shared by two or more than two countries, and resolving the conflicts is an important international issue.
Various methods have been developed to estimate ET directly or indirectly such as weighing lysimeters, pan evaporation, soil water balance, atmometer, Bowen Ratio Energy Balance System (BREBS), Eddy covariance (EC), and sap flow (R. G. Allen, Pereira, Howell, & Jensen, 2011). However those methods are in situ point measurement and do not provide information at regional scale (Gowda, Chavez, et al., 2008; Knipper, Hogue, Scott, & Franz, 2017; Shoko et al., 2015) also some of them requires maintenance and are expensive (He et al., 2017; Maeda, Wiberg, & Pellikka, 2011; Xu et al., 2015). To overcome this problem, remote sensing techniques are alternative to estimate ET at regional scale in less time and with less cost (R. Allen, A. Irmak, R. Trezza, J. M. Hendrickx, et al., 2011; J Kjaersgaard, Allen, & Irmak, 2011). ET varies in both space and time. It is variable in space because of the wide spatial variability of precipitation, hydraulic properties of soil, and vegetation types. It is variable in time because of variability of climate and development or senescence of vegetation (R. Allen, Trezza, Tasumi, & Kjaersgaard, 2014). For these reasons satellite images are a useful tool for determining and mapping the spatial and temporal variability of ET (R. Allen et al., 2014).
hills and piedmont areas . Mountains constitute the largest ecosystem in the region and are highly significant for the country’s environmental balance and sustain- ability. They are an important source of land, water, bio- diversity, energy and mineral resources, and have a determining role in climate and landscape diversity. West Tien Shan and Gissar-Alay are the major mountain ranges of Uzbekistan. Uzbekistan’s climate is subtropical, sharply continental, hot and dry, with marked differences in day time - night time and summer-winter temperatures. Its climatic features are due to a combination of three major factors: solar radiations, general atmospheric cir- culation and the local terrain . Favourable climatic con- ditions, land and labour resources stipulated the develop- ment of cotton, rice, vegetable growing, gardening and vineyard which are characteristics of dry subtropical zone and require essential water consumption.
As a number of inter-basin transfer schemes have been in operation in South Africa for some time, an ex post facto evaluation to compare predicted transfers with actual transfers made, was undertaken. Two schemes were investigated, the Usutu-Vaal GWS (Second Phase) and the Tugela-Vaal Government Water Project (GWP), reported on in Chapter 4. The examination of water transfers of the Usutu-Vaal GWS and the Tugela-Vaal GWP, showed in both cases that the actual transfer quantities differed dramatically from what had been predicted; the transferred quantities were considerably less than envisaged at the time these projects were planned and the patterns of the transfers were erratic – not at all smooth as initially foreseen. In both these cases transfers are associated with significant variable costs as water had to be pumped against high static heads for delivery into their respective receiving basins. As can be expected, decision-making in the real world takes into account the hydrological conditions in the receiving basin and the risk of future water shortages by not making transfers, as opposed to incurring the pumping costs, which sometimes will lead to reduced, or no, transfers. The dependency on hydrological conditions therefore causes the transfers to be variable. This, it is proposed, was the main reason for the differences found between what had been envisaged at the planning stage, and what had been experienced in reality.
With regard to these limitations, the objective of this study is to propose a series of modifications to the original concep- tualisation of the unsaturated and saturated flow process of MOBIDIC in order to extend its applicability for modelling of shallow water table fluctuations while retaining its compu- tational efficiency. To this aim, the conceptual saturated flow scheme of MOBIDIC was replaced with MODFLOW as a physically based three-dimensional groundwater model us- ing the sequential coupling approach (Chenjerayi Guzha and Hardy, 2010). Then, a novel methodology for revisiting the calculation of the groundwater recharge in MOBIDIC, the specific yield in MODFLOW, and the interaction between the unsaturated and saturated zones in MOBIDIC–MODFLOW was developed. The fully coupled surface–subsurface model MIKE SHE is used as a reference for comparison; hence the methodology is based on numerical benchmarking on hy- pothetical and realistic catchments. Using the WTF method (Healy and Cook, 2002) in MIKE SHE, the rises of a shal- low water table were simulated under different sets of rainfall intensity, soil property, and depth to the water table. The sim- ulated responses were then used to reformulate the ground- water recharge of MOBIDIC based on the assumption of a quasi-steady pressure profile in the unsaturated zone as the water table fluctuates. The accuracy of the proposed modifi- cations was first evaluated in a two-dimensional case (con- stant slope), where the simulated water table rises of the two models under a uniform rainfall rate were compared. In a second experiment, the approach was tested at the catch- ment scale and under unsteady rainfall conditions. Compari- son of the simulated water table responses of the MOBIDIC– MODFLOW against those of MIKE SHE allowed us to eval- uate how the unsaturated–saturated interaction scheme of the externally coupled models can be adapted for applications in shallow water table regions.
7 HYDROLOGICAL TOOL FOR SURFACE AND SUB-SURFACE IRRIGATION MANAGEMENT
During the past few decades, competition for water among different users has increased manifold in many parts of the world. The development of new resources is not economically and environmentally viable. Therefore, the increasing demands for water can only be met by using the existing resources more efficiently (FAO, 2003). The majority of irrigation networks around the world are operating at a low overall efficiency of 30 % against the minimum achievable efficiency of 56 % (Sarma and Rao, 1997). Inappropriate system design causes in a low overall efficiency, but even with appropriate design, a proper management for the effective operation and maintenance of irrigation water delivery systems is essential, and worldwide evidence shows that significant improvements can be gained through irrigation scheduling (Malano et al., 1999). Although the crop yield and seasonal evapotranspiration (ET) relationship have been widely used for the management of water resources, the effects of timing of water application are also of key importance especially for irrigation scheduling with high temporal resolution (Hanks, 1983; Vaux et al., 1983; Howell, 1990). Irrigation scheduling should answer as to when and how much to irrigate a cropped field. Given the complexity, a number of computerized simulation models (Kincaid and Heermann, 1974; Smith, 1992 and Mateos et al., 2002) to support and improve irrigation scheduling are available.
In order to quantify the contribution of knowing this in- formation provided by remote sensing, comparisons between simulations for the different cases presented in Table 5 were analysed. The results were compared with the observed to- tal biomass. In the first two cases, simulations were per- formed without remote sensing information. LAI was com- puted by STICS, from cumulated temperatures varying ac- cording to water and nitrogen stresses. Irrigation and mow- ing dates were also computed by the model as explained in Sect. 3.3. Irrigation occurred when the water stress index was below 0.8, which meant that the grasslands were glob- ally well supplied with water. Two irrigation water quantities were chosen: case 1: 20 mm, case 2: 40 mm brought at each event. For these two situations, there was no variability, all the fields had the same behaviour. The total biomass sim- ulated was then compared to the observation average of all fields surveyed. The following cases (3–4–5–6) introduced the spatial variability at different levels, only in fixing the mowing dates (3), or irrigation dates, the LAI or the combi- nation of these variables. The best results were obtained for the biomass estimation when all the variables (LAI, mowing and irrigation dates) were forced into the model from remote sensing data. It appeared also that the knowledge of the vari- ability of agricultural practices was most important than the knowledge of LAI only, which was not surprising since the agricultural practices were crucial for the vegetation devel- opment. It should be noticed also that for three times less of water, the simulations (cases 2–6) gave the same production level. Irrigation by flooding consumes generally more water than the real need. It is the traditional method used for cen- tury, with a strict water round defined at the district level. If severe droughts increase in the next years, the frequency and duration of irrigations has to be revised. Tools such those proposed here would allow to analyze different scenarios and propose suitable strategies to maintain reasonable production in saving water. These proposals must be also discussed with economists in order to take into account all the other water uses in the region.
Hydro-climatology deals with the interactions of climate with hydrology. One of the main focuses of the hydro- climatic study is the interactions between precipitation, evap- otranspiration, soil moisture storage, groundwater recharge, and stream flow (Shelton, 2009). The study of the water bud- get at a given location and time period essentially deals with the components of hydro-climatology. Hydrologicmodel- ing is an efficient approach for understanding the relationship between climate, hydrologic cycle, and water resources. In East Africa, the current trend and future scenarios of unsus- tainable water resource utilization demands modeling studies that provide accurate spatial and temporal information on hy- drological and climatological variables. The main obstacles for these investigations are the lack of sufficient geospatial data for distributed hydrologicmodel input and validation. Availability of observed data in regions with sparse ground based networks for hydrologic estimations is the key limi- tation in hydroclimatologic studies. However, advances in satellite remote sensing data can provide objective estimates on precipitation, evapotranspiration and land surface control- ling factors for water budget calculations. The recent avail- ability of virtually real time and uninterrupted satellite-based rainfall estimates is becoming a cost-effective source of data for hydro climatologic investigations in many un-gauged and under-gauged regions around the world. Furthermore, appli- cation of remotely sensed spatially distributed datasets has made possible the transition from lumped to distributed hy- drologic models that accounts for the spatial variability of the model parameters and inputs (Hong et al. 2007; Li et al., 2009). The question remains whether with the existing spa- tial and temporal coverage of satellite precipitation and other estimates, how can we achieve their optimal use to compute a less uncertain water budget?
W hile requirements to leave water in streams and rivers for environmental and recreational uses are expanding, competition for water to meet the needs of homes, cities, farms, and industries is also increasing. As a result, many citizens are asking “Are we running out of freshwater?” In response to an expressed concern by the U.S. Congress about the future of water availability for the Nation, the U.S. Geological Survey (USGS) was directed to prepare a report describing the scope and magnitude of the efforts needed to provide periodic assessments of the status and trends in the availability and use of the nation’s freshwater resources (U.S. Geological Survey 2002). As envisioned by the USGS, the periodic assessments would consist of two primary components: (1) the development and reporting of up-to-date, nationally consistent indicators of the status and trends in surface-water flows and storage, ground-water storage and depletion, and water withdrawals and uses nationwide; and (2) improved estimates of regional-scale water budgets and water- cycle components (streamflow, evapotranspiration, interbasin transfers, and so forth) across the country. The proposed national assessment is intended to provide the nation with an overview of the status and future of its water resources. The overarching question to be answered by the program is “What is the availability of water resources in the nation and how does this availability relate to demand, source, and geographic location?” Water availability and use depend on a number of factors that affect both the
An airborne image was obtained from MODIS/ASTER airborne simulator (MASTER). This simulator has the characteristics of both the EOS Terra Advanced Space borne Thermal Emission Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors (Hook et al., 2000). This sensor has 50 spectral bands over the spectral range 0.4 to 12 μm (visible through thermal infrared) at a variety of spatial resolutions. An image was taken on July 24, 2009. Field data was also available for calibrating land surface heat fluxes in the study area. SEBAL model was applied to MASTER level 1-B dataset of visible, near infrared and thermal infrared radiation channels of airborne MASTER instrument onboard the NASA DC-8 aircraft. Meteorological data such as incoming solar radiation, relative humidity, air temperature, and wind speed are available from the California Irrigation Management Information System (CIMIS) located in Belridge, California (station no. 146). CIMIS is a program governed by the Department of Water Resources (DWR) in California. DWR manages a network of 120 weather stations to collect, store and process weather data. These data are useful for irrigator to manage water resources efficiently (www.cimis.water.ca.gov).
Choice of the parameters
The ﬁrst step of the calibration procedure consists in parameter speciﬁcation. As SWAT partitions the water- shed into sub-basins and smaller HRUs, some parameters have a uniform value over the entire watershed and others depend on soil type, land use and/or topographic features. To select the most sensitive parameters and deﬁne their reasonable bounds, literature related to SWAT (Muleta & Nicklow 2005; Bekele & Nicklow 2007) and recent studies where it was applied to Africa (e.g. Schuol & Abbaspour 2006; Schuol et al. 2008a) were consulted. As a complement, the sensitivity analysis procedure of van Griensven et al. (2006) included in the ArcSWAT interface (Winchell et al. 2010) was used to assess the importance of diﬀerent parameters on runoﬀ generation process. The
Rainfall is the most important climatic parameters influencing agriculture in Pune district of Maharashtra. Rainfall of this region is highly variable with respect to space and time and about 80-90% of precipitation falls in monsoon period from June to October resulting in drought and flood situation in the upper Bhima basin of Maharashtra. Therefore, for efficient water resources management, optimal crop planning and also for better understanding of rainfall behavior (i.e., distribution and minimum expected amount during crop growing period) probability analysis of rainfall was conducted. Probability analysis (at 50% and 80%) of monthly rainfall data of 13 raingauge stations of the left bank canal of upper Bhima basin viz., Urali, Loni Karbol, Kasurdi, Tajuproject, Yewat, Dahitane, Bhigwan, Madanwadi, Pondewadi, Kedgaon, Patas, Pimplegaon and Daund for the period from 1975 to 2002 was conducted. Reference evapotranspiration (ETo) has been calculated using climatic parameters like sun shine hour, wind speed, maximum & minimum temperature and rainfall humidity for the period from years 1993-2005 by CROPWAT model. It was found that ETo is maximum (7.72 mm/day) during April and low in December (3.10 mm/day). Effective rainfall of existing rain gauge stations falling in different sub-basins, BM48, BM49, BM50, BM51 and BM68 have been estimated using the CROPWAT model. Finally net irrigation requirement of crops Kharif Cotton, Summer Cotton, Sugarcane and Rabi Sorghum have been find out for all the sub-basin. From this study it has been concluded that, the crop planning in the area, represented by Pimplegoan and Urali stations should be done keeping in mind maximum deficit of 187 mm and 113 mm of water respectively during July. Similarly in other stations maximum deficit of water was observed during September which indicate that while selection of crops for the areas represented by these stations the crops requiring less water during September should be selected.
To better diagnose model processes, model inputs are compared with model simulation outputs over example re- gions chosen to isolate the impact of topographic slope, forc- ing, and hydraulic conductivity on subsurface–surface-water hydrodynamics. We do this as a check to see if and how this numerical experiment compares to real observations. It is im- portant to use a range of measures of success that might be different from that used in a model calibration where inad- equacies in model parameters and process might be muted while tuning the model to better match observations. Figure 9 juxtaposes slope, potential recharge, surface flow, water ta- ble depth, hydraulic conductivity, and a satellite image com- posite also at 1 km resolution (the NASA Blue Marble im- age, Justice et al., 2002) and facilitates a visual diagnosis of control by the three primary model inputs. While the model was run to a steady state and ultimately all the potential recharge has to exit the domain as discharge, the distribution and partitioning between groundwater and streams depends on the slope and hydraulic conductivity. Likewise, while to- pographic lows create the potential for flow convergence, it is not a model requirement that these will develop into stream loci. Figure 9 demonstrates some of these relation- ships quite clearly over a portion of the model that transitions from semi-arid to more humid conditions as the northern and southern Platte River systems join the Missouri River. As expected changes in slope yield flow convergence; how- ever, this figure also shows that as recharge increases from west to east (X > 1700 km, panel c), the model generally pre- dicts shallower water tables and greater stream density (pan- els d and e, respectively). Conversely, in localized areas of decreased P –E (e.g., 700 < Y < 900 km specifically south of the Platte River) water tables increase and stream densi- ties decrease. The satellite image (panel f) shows increases in vegetation that correspond to shallower water tables and increased stream density.